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breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Jin%2C+M&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Jin%2C+M&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Jin%2C+M&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Jin%2C+M&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Jin%2C+M&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> <li> <a href="/search/?searchtype=author&query=Jin%2C+M&start=200" class="pagination-link " aria-label="Page 5" aria-current="page">5 </a> </li> <li><span class="pagination-ellipsis">…</span></li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.17890">arXiv:2502.17890</a> <span> [<a href="https://arxiv.org/pdf/2502.17890">pdf</a>, <a href="https://arxiv.org/format/2502.17890">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Seasonal Variations of the Atmospheric Muon Neutrino Spectrum measured with IceCube </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Abbasi%2C+R">R. Abbasi</a>, <a href="/search/?searchtype=author&query=Ackermann%2C+M">M. Ackermann</a>, <a href="/search/?searchtype=author&query=Adams%2C+J">J. Adams</a>, <a href="/search/?searchtype=author&query=Agarwalla%2C+S+K">S. K. Agarwalla</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+J+A">J. A. Aguilar</a>, <a href="/search/?searchtype=author&query=Ahlers%2C+M">M. Ahlers</a>, <a href="/search/?searchtype=author&query=Alameddine%2C+J+M">J. M. Alameddine</a>, <a href="/search/?searchtype=author&query=Amin%2C+N+M">N. M. Amin</a>, <a href="/search/?searchtype=author&query=Andeen%2C+K">K. Andeen</a>, <a href="/search/?searchtype=author&query=Arg%C3%BCelles%2C+C">C. Arg眉elles</a>, <a href="/search/?searchtype=author&query=Ashida%2C+Y">Y. Ashida</a>, <a href="/search/?searchtype=author&query=Athanasiadou%2C+S">S. Athanasiadou</a>, <a href="/search/?searchtype=author&query=Axani%2C+S+N">S. N. Axani</a>, <a href="/search/?searchtype=author&query=Babu%2C+R">R. Babu</a>, <a href="/search/?searchtype=author&query=Bai%2C+X">X. Bai</a>, <a href="/search/?searchtype=author&query=V.%2C+A+B">A. Balagopal V.</a>, <a href="/search/?searchtype=author&query=Baricevic%2C+M">M. Baricevic</a>, <a href="/search/?searchtype=author&query=Barwick%2C+S+W">S. W. Barwick</a>, <a href="/search/?searchtype=author&query=Bash%2C+S">S. Bash</a>, <a href="/search/?searchtype=author&query=Basu%2C+V">V. Basu</a>, <a href="/search/?searchtype=author&query=Bay%2C+R">R. Bay</a>, <a href="/search/?searchtype=author&query=Beatty%2C+J+J">J. J. Beatty</a>, <a href="/search/?searchtype=author&query=Tjus%2C+J+B">J. Becker Tjus</a>, <a href="/search/?searchtype=author&query=Beise%2C+J">J. Beise</a>, <a href="/search/?searchtype=author&query=Bellenghi%2C+C">C. Bellenghi</a> , et al. (404 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.17890v1-abstract-short" style="display: inline;"> This study presents an energy-dependent analysis of seasonal variations in the atmospheric muon neutrino spectrum, using 11.3 years of data from the IceCube Neutrino Observatory. By leveraging a novel spectral unfolding method, we explore the energy range from 125 GeV to 10 TeV for zenith angles between 90掳 to 110掳, corresponding to the Antarctic atmosphere. Our findings reveal that the seasonal v… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17890v1-abstract-full').style.display = 'inline'; document.getElementById('2502.17890v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.17890v1-abstract-full" style="display: none;"> This study presents an energy-dependent analysis of seasonal variations in the atmospheric muon neutrino spectrum, using 11.3 years of data from the IceCube Neutrino Observatory. By leveraging a novel spectral unfolding method, we explore the energy range from 125 GeV to 10 TeV for zenith angles between 90掳 to 110掳, corresponding to the Antarctic atmosphere. Our findings reveal that the seasonal variation amplitude decreases with energy reaching ($-4.6 \pm 1.1$)\% during Austral winter and increases ($+3.9 \pm 1.2$)\% during Austral summer relative to the annual average at 10TeV. While the unfolded flux exceeds the model predictions by up to 30\%, the differential measurement of seasonal variations remains unaffected. The measured seasonal variations of the muon neutrino spectrum are consistent with theoretical predictions using the MCEq code and the NRLMSISE-00 atmospheric model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17890v1-abstract-full').style.display = 'none'; document.getElementById('2502.17890v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.17151">arXiv:2502.17151</a> <span> [<a href="https://arxiv.org/pdf/2502.17151">pdf</a>, <a href="https://arxiv.org/format/2502.17151">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Dynamical Systems">math.DS</span> </div> </div> <p class="title is-5 mathjax"> Dynamics near a class of nonhyperbolic fixed points </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jin%2C+M">Meihua Jin</a>, <a href="/search/?searchtype=author&query=Meng%2C+S">Shihao Meng</a>, <a href="/search/?searchtype=author&query=Zhou%2C+Y">Yunhua Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.17151v1-abstract-short" style="display: inline;"> In this paper, we investigate some dynamical properties near a nonhyperbolic fixed point. Under some conditions on the higher nonlinear terms, we establish a stable manifold theorem and a degenerate Hartman theorem. Furthermore, the finite shadowing property also be discussed. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.17151v1-abstract-full" style="display: none;"> In this paper, we investigate some dynamical properties near a nonhyperbolic fixed point. Under some conditions on the higher nonlinear terms, we establish a stable manifold theorem and a degenerate Hartman theorem. Furthermore, the finite shadowing property also be discussed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17151v1-abstract-full').style.display = 'none'; document.getElementById('2502.17151v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.15447">arXiv:2502.15447</a> <span> [<a href="https://arxiv.org/pdf/2502.15447">pdf</a>, <a href="https://arxiv.org/format/2502.15447">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.xinn.2025.100802">10.1016/j.xinn.2025.100802 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Ultra-high-energy $纬$-ray emission associated with the tail of a bow-shock pulsar wind nebula </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Cao%2C+Z">Zhen Cao</a>, <a href="/search/?searchtype=author&query=Aharonian%2C+F">F. Aharonian</a>, <a href="/search/?searchtype=author&query=Bai%2C+Y+X">Y. X. Bai</a>, <a href="/search/?searchtype=author&query=Bao%2C+Y+W">Y. W. Bao</a>, <a href="/search/?searchtype=author&query=Bastieri%2C+D">D. Bastieri</a>, <a href="/search/?searchtype=author&query=Bi%2C+X+J">X. J. Bi</a>, <a href="/search/?searchtype=author&query=Bi%2C+Y+J">Y. J. Bi</a>, <a href="/search/?searchtype=author&query=Bian%2C+W">W. Bian</a>, <a href="/search/?searchtype=author&query=Bukevich%2C+A+V">A. V. Bukevich</a>, <a href="/search/?searchtype=author&query=Cai%2C+C+M">C. M. Cai</a>, <a href="/search/?searchtype=author&query=Cao%2C+W+Y">W. Y. Cao</a>, <a href="/search/?searchtype=author&query=Cao%2C+Z">Zhe Cao</a>, <a href="/search/?searchtype=author&query=Chang%2C+J">J. Chang</a>, <a href="/search/?searchtype=author&query=Chang%2C+J+F">J. F. Chang</a>, <a href="/search/?searchtype=author&query=Chen%2C+A+M">A. M. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+E+S">E. S. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+H+X">H. X. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Liang Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Long Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+M+J">M. J. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+M+L">M. L. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+Q+H">Q. H. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S">S. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S+H">S. H. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S+Z">S. Z. Chen</a> , et al. (274 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.15447v2-abstract-short" style="display: inline;"> In this study, we present a comprehensive analysis of an unidentified point-like ultra-high-energy (UHE) $纬$-ray source, designated as 1LHAASO J1740+0948u, situated in the vicinity of the middle-aged pulsar PSR J1740+1000. The detection significance reached 17.1$蟽$ (9.4$蟽$) above 25$\,$TeV (100$\,$TeV). The source energy spectrum extended up to 300$\,$TeV, which was well fitted by a log-parabola f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.15447v2-abstract-full').style.display = 'inline'; document.getElementById('2502.15447v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.15447v2-abstract-full" style="display: none;"> In this study, we present a comprehensive analysis of an unidentified point-like ultra-high-energy (UHE) $纬$-ray source, designated as 1LHAASO J1740+0948u, situated in the vicinity of the middle-aged pulsar PSR J1740+1000. The detection significance reached 17.1$蟽$ (9.4$蟽$) above 25$\,$TeV (100$\,$TeV). The source energy spectrum extended up to 300$\,$TeV, which was well fitted by a log-parabola function with $N0 = (1.93\pm0.23) \times 10^{-16} \rm{TeV^{-1}\,cm^{-2}\,s^{-2}}$, $伪= 2.14\pm0.27$, and $尾= 1.20\pm0.41$ at E0 = 30$\,$TeV. The associated pulsar, PSR J1740+1000, resides at a high galactic latitude and powers a bow-shock pulsar wind nebula (BSPWN) with an extended X-ray tail. The best-fit position of the gamma-ray source appeared to be shifted by $0.2^{\circ}$ with respect to the pulsar position. As the (i) currently identified pulsar halos do not demonstrate such offsets, and (ii) centroid of the gamma-ray emission is approximately located at the extension of the X-ray tail, we speculate that the UHE $纬$-ray emission may originate from re-accelerated electron/positron pairs that are advected away in the bow-shock tail. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.15447v2-abstract-full').style.display = 'none'; document.getElementById('2502.15447v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Corrected spelling errors in several author names</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> The Innovation (2025), 100802 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.15016">arXiv:2502.15016</a> <span> [<a href="https://arxiv.org/pdf/2502.15016">pdf</a>, <a href="https://arxiv.org/format/2502.15016">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ni%2C+J">Juntong Ni</a>, <a href="/search/?searchtype=author&query=Liu%2C+Z">Zewen Liu</a>, <a href="/search/?searchtype=author&query=Wang%2C+S">Shiyu Wang</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a>, <a href="/search/?searchtype=author&query=Jin%2C+W">Wei Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.15016v1-abstract-short" style="display: inline;"> Transformer-based and CNN-based methods demonstrate strong performance in long-term time series forecasting. However, their high computational and storage requirements can hinder large-scale deployment. To address this limitation, we propose integrating lightweight MLP with advanced architectures using knowledge distillation (KD). Our preliminary study reveals different models can capture compleme… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.15016v1-abstract-full').style.display = 'inline'; document.getElementById('2502.15016v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.15016v1-abstract-full" style="display: none;"> Transformer-based and CNN-based methods demonstrate strong performance in long-term time series forecasting. However, their high computational and storage requirements can hinder large-scale deployment. To address this limitation, we propose integrating lightweight MLP with advanced architectures using knowledge distillation (KD). Our preliminary study reveals different models can capture complementary patterns, particularly multi-scale and multi-period patterns in the temporal and frequency domains. Based on this observation, we introduce TimeDistill, a cross-architecture KD framework that transfers these patterns from teacher models (e.g., Transformers, CNNs) to MLP. Additionally, we provide a theoretical analysis, demonstrating that our KD approach can be interpreted as a specialized form of mixup data augmentation. TimeDistill improves MLP performance by up to 18.6%, surpassing teacher models on eight datasets. It also achieves up to 7X faster inference and requires 130X fewer parameters. Furthermore, we conduct extensive evaluations to highlight the versatility and effectiveness of TimeDistill. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.15016v1-abstract-full').style.display = 'none'; document.getElementById('2502.15016v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.13299">arXiv:2502.13299</a> <span> [<a href="https://arxiv.org/pdf/2502.13299">pdf</a>, <a href="https://arxiv.org/format/2502.13299">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Measurement of the inelasticity distribution of neutrino-nucleon interactions for $\mathbf{80~GeV<E_谓<560~GeV}$ with IceCube DeepCore </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=IceCube+Collaboration"> IceCube Collaboration</a>, <a href="/search/?searchtype=author&query=Abbasi%2C+R">R. Abbasi</a>, <a href="/search/?searchtype=author&query=Ackermann%2C+M">M. Ackermann</a>, <a href="/search/?searchtype=author&query=Adams%2C+J">J. Adams</a>, <a href="/search/?searchtype=author&query=Agarwalla%2C+S+K">S. K. Agarwalla</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+J+A">J. A. Aguilar</a>, <a href="/search/?searchtype=author&query=Ahlers%2C+M">M. Ahlers</a>, <a href="/search/?searchtype=author&query=Alameddine%2C+J+M">J. M. Alameddine</a>, <a href="/search/?searchtype=author&query=Amin%2C+N+M">N. M. Amin</a>, <a href="/search/?searchtype=author&query=Andeen%2C+K">K. Andeen</a>, <a href="/search/?searchtype=author&query=Arg%C3%BCelles%2C+C">C. Arg眉elles</a>, <a href="/search/?searchtype=author&query=Ashida%2C+Y">Y. Ashida</a>, <a href="/search/?searchtype=author&query=Athanasiadou%2C+S">S. Athanasiadou</a>, <a href="/search/?searchtype=author&query=Axani%2C+S+N">S. N. Axani</a>, <a href="/search/?searchtype=author&query=Babu%2C+R">R. Babu</a>, <a href="/search/?searchtype=author&query=Bai%2C+X">X. Bai</a>, <a href="/search/?searchtype=author&query=V.%2C+A+B">A. Balagopal V.</a>, <a href="/search/?searchtype=author&query=Baricevic%2C+M">M. Baricevic</a>, <a href="/search/?searchtype=author&query=Barwick%2C+S+W">S. W. Barwick</a>, <a href="/search/?searchtype=author&query=Bash%2C+S">S. Bash</a>, <a href="/search/?searchtype=author&query=Basu%2C+V">V. Basu</a>, <a href="/search/?searchtype=author&query=Bay%2C+R">R. Bay</a>, <a href="/search/?searchtype=author&query=Beatty%2C+J+J">J. J. Beatty</a>, <a href="/search/?searchtype=author&query=Tjus%2C+J+B">J. Becker Tjus</a>, <a href="/search/?searchtype=author&query=Beise%2C+J">J. Beise</a> , et al. (404 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.13299v1-abstract-short" style="display: inline;"> We report a measurement of the inelasticity distribution in the scattering of neutrinos of energy $80-560$ GeV off nucleons, which is sensitive to the inclusive differential cross section. This analysis is based on a sample of atmospheric muon neutrinos detected in the IceCube sub-array DeepCore during 2012-2021, and is the first such measurement in this energy range. Our measurement extends to en… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.13299v1-abstract-full').style.display = 'inline'; document.getElementById('2502.13299v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.13299v1-abstract-full" style="display: none;"> We report a measurement of the inelasticity distribution in the scattering of neutrinos of energy $80-560$ GeV off nucleons, which is sensitive to the inclusive differential cross section. This analysis is based on a sample of atmospheric muon neutrinos detected in the IceCube sub-array DeepCore during 2012-2021, and is the first such measurement in this energy range. Our measurement extends to energies where accelerator data is not available, hence we compare our results to predictions from perturbative QCD calculations, finding good agreement. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.13299v1-abstract-full').style.display = 'none'; document.getElementById('2502.13299v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 19 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.12391">arXiv:2502.12391</a> <span> [<a href="https://arxiv.org/pdf/2502.12391">pdf</a>, <a href="https://arxiv.org/format/2502.12391">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Reward-Safety Balance in Offline Safe RL via Diffusion Regularization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Guo%2C+J">Junyu Guo</a>, <a href="/search/?searchtype=author&query=Zheng%2C+Z">Zhi Zheng</a>, <a href="/search/?searchtype=author&query=Ying%2C+D">Donghao Ying</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a>, <a href="/search/?searchtype=author&query=Gu%2C+S">Shangding Gu</a>, <a href="/search/?searchtype=author&query=Spanos%2C+C">Costas Spanos</a>, <a href="/search/?searchtype=author&query=Lavaei%2C+J">Javad Lavaei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.12391v1-abstract-short" style="display: inline;"> Constrained reinforcement learning (RL) seeks high-performance policies under safety constraints. We focus on an offline setting where the agent has only a fixed dataset -- common in realistic tasks to prevent unsafe exploration. To address this, we propose Diffusion-Regularized Constrained Offline Reinforcement Learning (DRCORL), which first uses a diffusion model to capture the behavioral policy… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12391v1-abstract-full').style.display = 'inline'; document.getElementById('2502.12391v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.12391v1-abstract-full" style="display: none;"> Constrained reinforcement learning (RL) seeks high-performance policies under safety constraints. We focus on an offline setting where the agent has only a fixed dataset -- common in realistic tasks to prevent unsafe exploration. To address this, we propose Diffusion-Regularized Constrained Offline Reinforcement Learning (DRCORL), which first uses a diffusion model to capture the behavioral policy from offline data and then extracts a simplified policy to enable efficient inference. We further apply gradient manipulation for safety adaptation, balancing the reward objective and constraint satisfaction. This approach leverages high-quality offline data while incorporating safety requirements. Empirical results show that DRCORL achieves reliable safety performance, fast inference, and strong reward outcomes across robot learning tasks. Compared to existing safe offline RL methods, it consistently meets cost limits and performs well with the same hyperparameters, indicating practical applicability in real-world scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12391v1-abstract-full').style.display = 'none'; document.getElementById('2502.12391v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.10123">arXiv:2502.10123</a> <span> [<a href="https://arxiv.org/pdf/2502.10123">pdf</a>, <a href="https://arxiv.org/format/2502.10123">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Solar and Stellar Astrophysics">astro-ph.SR</span> </div> </div> <p class="title is-5 mathjax"> Modelling methanol and hydride formation in the JWST Ice Age era </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jim%C3%A9nez-Serra%2C+I">Izaskun Jim茅nez-Serra</a>, <a href="/search/?searchtype=author&query=Meg%C3%ADas%2C+A">Andr茅s Meg铆as</a>, <a href="/search/?searchtype=author&query=Salaris%2C+J">Joseph Salaris</a>, <a href="/search/?searchtype=author&query=Cuppen%2C+H">Herma Cuppen</a>, <a href="/search/?searchtype=author&query=Taillard%2C+A">Ang猫le Taillard</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Miwha Jin</a>, <a href="/search/?searchtype=author&query=Wakelam%2C+V">Valentine Wakelam</a>, <a href="/search/?searchtype=author&query=Vasyunin%2C+A+I">Anton I. Vasyunin</a>, <a href="/search/?searchtype=author&query=Caselli%2C+P">Paola Caselli</a>, <a href="/search/?searchtype=author&query=Pendleton%2C+Y+J">Yvonne J. Pendleton</a>, <a href="/search/?searchtype=author&query=Dartois%2C+E">Emmanuel Dartois</a>, <a href="/search/?searchtype=author&query=Noble%2C+J+A">Jennifer A. Noble</a>, <a href="/search/?searchtype=author&query=Viti%2C+S">Serena Viti</a>, <a href="/search/?searchtype=author&query=Borshcheva%2C+K">Katerina Borshcheva</a>, <a href="/search/?searchtype=author&query=Garrod%2C+R+T">Robin T. Garrod</a>, <a href="/search/?searchtype=author&query=Lamberts%2C+T">Thanja Lamberts</a>, <a href="/search/?searchtype=author&query=Fraser%2C+H">Helen Fraser</a>, <a href="/search/?searchtype=author&query=Melnick%2C+G">Gary Melnick</a>, <a href="/search/?searchtype=author&query=McClure%2C+M">Melissa McClure</a>, <a href="/search/?searchtype=author&query=Rocha%2C+W">Will Rocha</a>, <a href="/search/?searchtype=author&query=Drozdovskaya%2C+M+N">Maria N. Drozdovskaya</a>, <a href="/search/?searchtype=author&query=Lis%2C+D+C">Dariusz C. Lis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.10123v1-abstract-short" style="display: inline;"> (Abridged) JWST observations have measured the ice composition toward two highly-extinguished field stars in the Chamaeleon I cloud. The observed extinction excess on the long-wavelength side of the H2O ice band at 3 micron has been attributed to a mixture of CH3OH with ammonia hydrates, which suggests that CH3OH ice could have formed in a water-rich environment with little CO depletion. Laborator… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10123v1-abstract-full').style.display = 'inline'; document.getElementById('2502.10123v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.10123v1-abstract-full" style="display: none;"> (Abridged) JWST observations have measured the ice composition toward two highly-extinguished field stars in the Chamaeleon I cloud. The observed extinction excess on the long-wavelength side of the H2O ice band at 3 micron has been attributed to a mixture of CH3OH with ammonia hydrates, which suggests that CH3OH ice could have formed in a water-rich environment with little CO depletion. Laboratory experiments and quantum chemical calculations suggest that CH3OH could form via the grain surface reactions CH3+OH and/or C+H2O in water-rich ices. However, no dedicated chemical modelling has been carried out thus far to test their efficiency and dependence on the astrochemical code employed. We model the ice chemistry in the Chamaeleon I cloud using a set of astrochemical codes (MAGICKAL, MONACO, Nautilus, UCLCHEM, and KMC simulations) to test the effects of the different code architectures and of the assumed ice chemistry. Our models show that the JWST ice observations are better reproduced for gas densities >1e5 cm-3 and collapse times >1e5 yr. CH3OH ice forms predominantly (>99%) via CO hydrogenation. The contribution of reactions CH3+OH and C+H2O, is negligible. The CO2 ice may form either via CO+OH or CO+O depending on the code. However, KMC simulations reveal that both mechanisms are efficient despite the low rate constant of the CO+O surface reaction. CH4 is largely underproduced for all codes except for UCLCHEM, for which a higher amount of atomic C is available during the initial translucent cloud phase. Large differences in the ice abundances are found at Tdust<12 K between diffusive and non-diffusive chemistry codes. This is due to the fact that non-diffusive chemistry takes over diffusive chemistry at such low Tdust. This could explain the rather constant ice chemical composition found in Chamaeleon I and other dense cores despite the different visual extinctions probed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10123v1-abstract-full').style.display = 'none'; document.getElementById('2502.10123v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in A&A</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09454">arXiv:2502.09454</a> <span> [<a href="https://arxiv.org/pdf/2502.09454">pdf</a>, <a href="https://arxiv.org/format/2502.09454">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Search for Heavy Neutral Leptons with IceCube DeepCore </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Abbasi%2C+R">R. Abbasi</a>, <a href="/search/?searchtype=author&query=Ackermann%2C+M">M. Ackermann</a>, <a href="/search/?searchtype=author&query=Adams%2C+J">J. Adams</a>, <a href="/search/?searchtype=author&query=Agarwalla%2C+S+K">S. K. Agarwalla</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+J+A">J. A. Aguilar</a>, <a href="/search/?searchtype=author&query=Ahlers%2C+M">M. Ahlers</a>, <a href="/search/?searchtype=author&query=Alameddine%2C+J+M">J. M. Alameddine</a>, <a href="/search/?searchtype=author&query=Amin%2C+N+M">N. M. Amin</a>, <a href="/search/?searchtype=author&query=Andeen%2C+K">K. Andeen</a>, <a href="/search/?searchtype=author&query=Arg%C3%BCelles%2C+C">C. Arg眉elles</a>, <a href="/search/?searchtype=author&query=Ashida%2C+Y">Y. Ashida</a>, <a href="/search/?searchtype=author&query=Athanasiadou%2C+S">S. Athanasiadou</a>, <a href="/search/?searchtype=author&query=Axani%2C+S+N">S. N. Axani</a>, <a href="/search/?searchtype=author&query=Babu%2C+R">R. Babu</a>, <a href="/search/?searchtype=author&query=Bai%2C+X">X. Bai</a>, <a href="/search/?searchtype=author&query=V.%2C+A+B">A. Balagopal V.</a>, <a href="/search/?searchtype=author&query=Baricevic%2C+M">M. Baricevic</a>, <a href="/search/?searchtype=author&query=Barwick%2C+S+W">S. W. Barwick</a>, <a href="/search/?searchtype=author&query=Bash%2C+S">S. Bash</a>, <a href="/search/?searchtype=author&query=Basu%2C+V">V. Basu</a>, <a href="/search/?searchtype=author&query=Bay%2C+R">R. Bay</a>, <a href="/search/?searchtype=author&query=Beatty%2C+J+J">J. J. Beatty</a>, <a href="/search/?searchtype=author&query=Tjus%2C+J+B">J. Becker Tjus</a>, <a href="/search/?searchtype=author&query=Beise%2C+J">J. Beise</a>, <a href="/search/?searchtype=author&query=Bellenghi%2C+C">C. Bellenghi</a> , et al. (400 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.09454v1-abstract-short" style="display: inline;"> The observation of neutrino oscillations has established that neutrinos have non-zero masses. This phenomenon is not explained by the Standard Model of particle physics, but one viable explanation to this dilemma involves the existence of heavy neutral leptons in the form of right-handed neutrinos. This work presents the first search for heavy neutral leptons with the IceCube Neutrino Observatory.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09454v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09454v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09454v1-abstract-full" style="display: none;"> The observation of neutrino oscillations has established that neutrinos have non-zero masses. This phenomenon is not explained by the Standard Model of particle physics, but one viable explanation to this dilemma involves the existence of heavy neutral leptons in the form of right-handed neutrinos. This work presents the first search for heavy neutral leptons with the IceCube Neutrino Observatory. The standard three flavor neutrino model is extended by adding a fourth GeV-scale mass state allowing mixing with the $蟿$ sector through the parameter $|U_{\tau4}|^2$. The analysis is performed by searching for signatures of heavy neutral leptons that are directly produced via up-scattering of atmospheric $谓_蟿$'s inside the IceCube detection volume. Three heavy neutral lepton mass values, $m_4$, of 0.3 GeV, 0.6 GeV, and 1.0 GeV are tested using ten years of data, collected between 2011 and 2021. No significant signal of heavy neutral leptons is observed for any of the tested masses. The resulting constraints for the mixing parameter are $|U_{\tau4}|^2 < 0.19$ ($m_4 = 0.3$ GeV), $|U_{\tau4}|^2 < 0.36$ ($m_4 = 0.6$ GeV), and $|U_{\tau4}|^2 < 0.40$ ($m_4 = 1.0$ GeV) at the 90% confidence level. This analysis serves as proof-of-concept for heavy neutral lepton searches in IceCube. The heavy neutral lepton event generator, developed in this work, and the analysis of the expected signatures lay the fundamental groundwork for future searches thereof. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09454v1-abstract-full').style.display = 'none'; document.getElementById('2502.09454v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.04848">arXiv:2502.04848</a> <span> [<a href="https://arxiv.org/pdf/2502.04848">pdf</a>, <a href="https://arxiv.org/format/2502.04848">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> Broadband $纬$-ray spectrum of supernova remnant Cassiopeia A </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Cao%2C+Z">Zhen Cao</a>, <a href="/search/?searchtype=author&query=Aharonian%2C+F">F. Aharonian</a>, <a href="/search/?searchtype=author&query=Bai%2C+Y+X">Y. X. Bai</a>, <a href="/search/?searchtype=author&query=Bao%2C+Y+W">Y. W. Bao</a>, <a href="/search/?searchtype=author&query=Bastieri%2C+D">D. Bastieri</a>, <a href="/search/?searchtype=author&query=Bi%2C+X+J">X. J. Bi</a>, <a href="/search/?searchtype=author&query=Bi%2C+Y+J">Y. J. Bi</a>, <a href="/search/?searchtype=author&query=Bian%2C+W">W. Bian</a>, <a href="/search/?searchtype=author&query=Bukevich%2C+A+V">A. V. Bukevich</a>, <a href="/search/?searchtype=author&query=Cai%2C+C+M">C. M. Cai</a>, <a href="/search/?searchtype=author&query=Cao%2C+W+Y">W. Y. Cao</a>, <a href="/search/?searchtype=author&query=Cao%2C+Z">Zhe Cao</a>, <a href="/search/?searchtype=author&query=Chang%2C+J">J. Chang</a>, <a href="/search/?searchtype=author&query=Chang%2C+J+F">J. F. Chang</a>, <a href="/search/?searchtype=author&query=Chen%2C+A+M">A. M. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+E+S">E. S. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+H+X">H. X. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Liang Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Long Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+M+J">M. J. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+M+L">M. L. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+Q+H">Q. H. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S">S. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S+H">S. H. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S+Z">S. Z. Chen</a> , et al. (293 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.04848v1-abstract-short" style="display: inline;"> The core-collapse supernova remnant (SNR) Cassiopeia A (Cas A) is one of the brightest galactic radio sources with an angular radius of $\sim$ 2.5 $\arcmin$. Although no extension of this source has been detected in the $纬$-ray band, using more than 1000 days of LHAASO data above $\sim 0.8$ TeV, we find that its spectrum is significantly softer than those obtained with Imaging Air Cherenkov Telesc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04848v1-abstract-full').style.display = 'inline'; document.getElementById('2502.04848v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.04848v1-abstract-full" style="display: none;"> The core-collapse supernova remnant (SNR) Cassiopeia A (Cas A) is one of the brightest galactic radio sources with an angular radius of $\sim$ 2.5 $\arcmin$. Although no extension of this source has been detected in the $纬$-ray band, using more than 1000 days of LHAASO data above $\sim 0.8$ TeV, we find that its spectrum is significantly softer than those obtained with Imaging Air Cherenkov Telescopes (IACTs) and its flux near $\sim 1$ TeV is about two times higher. In combination with analyses of more than 16 years of \textit{Fermi}-LAT data covering $0.1 \, \mathrm{GeV} - 1 \, \mathrm{TeV}$, we find that the spectrum above 30 GeV deviates significantly from a single power-law, and is best described by a smoothly broken power-law with a spectral index of $1.90 \pm 0.15_\mathrm{stat}$ ($3.41 \pm 0.19_\mathrm{stat}$) below (above) a break energy of $0.63 \pm 0.21_\mathrm{stat} \, \mathrm{TeV}$. Given differences in the angular resolution of LHAASO-WCDA and IACTs, TeV $纬$-ray emission detected with LHAASO may have a significant contribution from regions surrounding the SNR illuminated by particles accelerated earlier, which, however, are treated as background by IACTs. Detailed modelling can be used to constrain acceleration processes of TeV particles in the early stage of SNR evolution. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04848v1-abstract-full').style.display = 'none'; document.getElementById('2502.04848v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.04395">arXiv:2502.04395</a> <span> [<a href="https://arxiv.org/pdf/2502.04395">pdf</a>, <a href="https://arxiv.org/format/2502.04395">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhong%2C+S">Siru Zhong</a>, <a href="/search/?searchtype=author&query=Ruan%2C+W">Weilin Ruan</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Huan Li</a>, <a href="/search/?searchtype=author&query=Wen%2C+Q">Qingsong Wen</a>, <a href="/search/?searchtype=author&query=Liang%2C+Y">Yuxuan Liang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.04395v1-abstract-short" style="display: inline;"> Recent advancements in time series forecasting have explored augmenting models with text or vision modalities to improve accuracy. While text provides contextual understanding, it often lacks fine-grained temporal details. Conversely, vision captures intricate temporal patterns but lacks semantic context, limiting the complementary potential of these modalities. To address this, we propose Time-VL… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04395v1-abstract-full').style.display = 'inline'; document.getElementById('2502.04395v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.04395v1-abstract-full" style="display: none;"> Recent advancements in time series forecasting have explored augmenting models with text or vision modalities to improve accuracy. While text provides contextual understanding, it often lacks fine-grained temporal details. Conversely, vision captures intricate temporal patterns but lacks semantic context, limiting the complementary potential of these modalities. To address this, we propose Time-VLM, a novel multimodal framework that leverages pre-trained Vision-Language Models (VLMs) to bridge temporal, visual, and textual modalities for enhanced forecasting. Our framework comprises three key components: (1) a Retrieval-Augmented Learner, which extracts enriched temporal features through memory bank interactions; (2) a Vision-Augmented Learner, which encodes time series as informative images; and (3) a Text-Augmented Learner, which generates contextual textual descriptions. These components collaborate with frozen pre-trained VLMs to produce multimodal embeddings, which are then fused with temporal features for final prediction. Extensive experiments across diverse datasets demonstrate that Time-VLM achieves superior performance, particularly in few-shot and zero-shot scenarios, thereby establishing a new direction for multimodal time series forecasting. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04395v1-abstract-full').style.display = 'none'; document.getElementById('2502.04395v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">19 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.03853">arXiv:2502.03853</a> <span> [<a href="https://arxiv.org/pdf/2502.03853">pdf</a>, <a href="https://arxiv.org/format/2502.03853">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> VERITAS and multiwavelength observations of the Blazar B3 2247+381 in response to an IceCube neutrino alert </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharyya%2C+A">Atreya Acharyya</a>, <a href="/search/?searchtype=author&query=Adams%2C+C+B">Colin B. Adams</a>, <a href="/search/?searchtype=author&query=Bangale%2C+P">Priyadarshini Bangale</a>, <a href="/search/?searchtype=author&query=Bartkoske%2C+J+T">J. T. Bartkoske</a>, <a href="/search/?searchtype=author&query=Benbow%2C+W">Wystan Benbow</a>, <a href="/search/?searchtype=author&query=Buckley%2C+J+H">James H. Buckley</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yu Chen</a>, <a href="/search/?searchtype=author&query=Christiansen%2C+J">Jodi Christiansen</a>, <a href="/search/?searchtype=author&query=Chromey%2C+A">Alisha Chromey</a>, <a href="/search/?searchtype=author&query=Duerr%2C+A">Anne Duerr</a>, <a href="/search/?searchtype=author&query=Errando%2C+M">Manel Errando</a>, <a href="/search/?searchtype=author&query=Godoy%2C+M+E">Miguel E. Godoy</a>, <a href="/search/?searchtype=author&query=Falcone%2C+A">Abe Falcone</a>, <a href="/search/?searchtype=author&query=Feng%2C+Q">Qi Feng</a>, <a href="/search/?searchtype=author&query=Foote%2C+J">Juniper Foote</a>, <a href="/search/?searchtype=author&query=Fortson%2C+L">Lucy Fortson</a>, <a href="/search/?searchtype=author&query=Furniss%2C+A">Amy Furniss</a>, <a href="/search/?searchtype=author&query=Hanlon%2C+W">William Hanlon</a>, <a href="/search/?searchtype=author&query=Hanna%2C+D">David Hanna</a>, <a href="/search/?searchtype=author&query=Hervet%2C+O">Olivier Hervet</a>, <a href="/search/?searchtype=author&query=Hinrichs%2C+C+E">Claire E. Hinrichs</a>, <a href="/search/?searchtype=author&query=Holder%2C+J">Jamie Holder</a>, <a href="/search/?searchtype=author&query=Humensky%2C+T+B">Thomas B. Humensky</a>, <a href="/search/?searchtype=author&query=Jin%2C+W">Weidong Jin</a>, <a href="/search/?searchtype=author&query=Johnson%2C+M+N">Madalyn N. Johnson</a> , et al. (473 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.03853v1-abstract-short" style="display: inline;"> While the sources of the diffuse astrophysical neutrino flux detected by the IceCube Neutrino Observatory are still largely unknown, one of the promising methods used towards understanding this is investigating the potential temporal and spatial correlations between neutrino alerts and the electromagnetic radiation from blazars. We report on the multiwavelength target-of-opportunity observations o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03853v1-abstract-full').style.display = 'inline'; document.getElementById('2502.03853v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.03853v1-abstract-full" style="display: none;"> While the sources of the diffuse astrophysical neutrino flux detected by the IceCube Neutrino Observatory are still largely unknown, one of the promising methods used towards understanding this is investigating the potential temporal and spatial correlations between neutrino alerts and the electromagnetic radiation from blazars. We report on the multiwavelength target-of-opportunity observations of the blazar B3 2247+381, taken in response to an IceCube multiplet alert for a cluster of muon neutrino events compatible with the source location between May 20, 2022 and November 10, 2022. B3 2247+381 was not detected with VERITAS during this time period. The source was found to be in a low-flux state in the optical, ultraviolet and gamma-ray bands for the time interval corresponding to the neutrino event, but was detected in the hard X-ray band with NuSTAR during this period. We find the multiwavelength spectral energy distribution is well described using a simple one-zone leptonic synchrotron self-Compton radiation model. Moreover, assuming the neutrinos originate from hadronic processes within the jet, the neutrino flux would be accompanied by a photon flux from the cascade emission, and the integrated photon flux required in such a case would significantly exceed the total multiwavelength fluxes and the VERITAS upper limits presented here. The lack of flaring activity observed with VERITAS, combined with the low multiwavelength flux levels, and given the significance of the neutrino excess is at 3$蟽$ level (uncorrected for trials), makes B3 2247+381 an unlikely source of the IceCube multiplet. We conclude that the neutrino excess is likely a background fluctuation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03853v1-abstract-full').style.display = 'none'; document.getElementById('2502.03853v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">26 pages, 5 figures. Accepted for publication in the Astrophysical Journal (ApJ)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.01963">arXiv:2502.01963</a> <span> [<a href="https://arxiv.org/pdf/2502.01963">pdf</a>, <a href="https://arxiv.org/format/2502.01963">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> A search for extremely-high-energy neutrinos and first constraints on the ultra-high-energy cosmic-ray proton fraction with IceCube </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=IceCube+Collaboration"> IceCube Collaboration</a>, <a href="/search/?searchtype=author&query=Abbasi%2C+R">R. Abbasi</a>, <a href="/search/?searchtype=author&query=Ackermann%2C+M">M. Ackermann</a>, <a href="/search/?searchtype=author&query=Adams%2C+J">J. Adams</a>, <a href="/search/?searchtype=author&query=Agarwalla%2C+S+K">S. K. Agarwalla</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+J+A">J. A. Aguilar</a>, <a href="/search/?searchtype=author&query=Ahlers%2C+M">M. Ahlers</a>, <a href="/search/?searchtype=author&query=Alameddine%2C+J+M">J. M. Alameddine</a>, <a href="/search/?searchtype=author&query=Amin%2C+N+M">N. M. Amin</a>, <a href="/search/?searchtype=author&query=Andeen%2C+K">K. Andeen</a>, <a href="/search/?searchtype=author&query=Arg%C3%BCelles%2C+C">C. Arg眉elles</a>, <a href="/search/?searchtype=author&query=Ashida%2C+Y">Y. Ashida</a>, <a href="/search/?searchtype=author&query=Athanasiadou%2C+S">S. Athanasiadou</a>, <a href="/search/?searchtype=author&query=Axani%2C+S+N">S. N. Axani</a>, <a href="/search/?searchtype=author&query=Babu%2C+R">R. Babu</a>, <a href="/search/?searchtype=author&query=Bai%2C+X">X. Bai</a>, <a href="/search/?searchtype=author&query=V.%2C+A+B">A. Balagopal V.</a>, <a href="/search/?searchtype=author&query=Baricevic%2C+M">M. Baricevic</a>, <a href="/search/?searchtype=author&query=Barwick%2C+S+W">S. W. Barwick</a>, <a href="/search/?searchtype=author&query=Bash%2C+S">S. Bash</a>, <a href="/search/?searchtype=author&query=Basu%2C+V">V. Basu</a>, <a href="/search/?searchtype=author&query=Bay%2C+R">R. Bay</a>, <a href="/search/?searchtype=author&query=Beatty%2C+J+J">J. J. Beatty</a>, <a href="/search/?searchtype=author&query=Tjus%2C+J+B">J. Becker Tjus</a>, <a href="/search/?searchtype=author&query=Beise%2C+J">J. Beise</a> , et al. (402 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.01963v1-abstract-short" style="display: inline;"> We present a search for the diffuse extremely-high-energy neutrino flux using $12.6$ years of IceCube data. The non-observation of neutrinos with energies well above $10 \, \mathrm{PeV}$ constrains the all-flavor neutrino flux at $10^{18} \, \mathrm{eV}$ to a level of $E^2 桅_{谓_e + 谓_渭+ 谓_蟿} \simeq 10^{-8} \, \mathrm{GeV} \, \mathrm{cm}^{-2} \, \mathrm{s}^{-1} \, \mathrm{sr}^{-1}$, the most string… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.01963v1-abstract-full').style.display = 'inline'; document.getElementById('2502.01963v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.01963v1-abstract-full" style="display: none;"> We present a search for the diffuse extremely-high-energy neutrino flux using $12.6$ years of IceCube data. The non-observation of neutrinos with energies well above $10 \, \mathrm{PeV}$ constrains the all-flavor neutrino flux at $10^{18} \, \mathrm{eV}$ to a level of $E^2 桅_{谓_e + 谓_渭+ 谓_蟿} \simeq 10^{-8} \, \mathrm{GeV} \, \mathrm{cm}^{-2} \, \mathrm{s}^{-1} \, \mathrm{sr}^{-1}$, the most stringent limit to date. Using this data, we constrain the proton fraction of ultra-high-energy cosmic rays (UHECRs) above $\simeq 30 \, \mathrm{EeV}$ to be $\lesssim 70\,$% (at $90\,$% CL) if the cosmological evolution of the sources is comparable to or stronger than the star formation rate. This result complements direct air-shower measurements by being insensitive to uncertainties associated with hadronic interaction models. It is the first such result to disfavor the ``proton-only" hypothesis for UHECRs using neutrino data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.01963v1-abstract-full').style.display = 'none'; document.getElementById('2502.01963v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.01563">arXiv:2502.01563</a> <span> [<a href="https://arxiv.org/pdf/2502.01563">pdf</a>, <a href="https://arxiv.org/format/2502.01563">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jin%2C+M">Mingyu Jin</a>, <a href="/search/?searchtype=author&query=Mei%2C+K">Kai Mei</a>, <a href="/search/?searchtype=author&query=Xu%2C+W">Wujiang Xu</a>, <a href="/search/?searchtype=author&query=Sun%2C+M">Mingjie Sun</a>, <a href="/search/?searchtype=author&query=Tang%2C+R">Ruixiang Tang</a>, <a href="/search/?searchtype=author&query=Du%2C+M">Mengnan Du</a>, <a href="/search/?searchtype=author&query=Liu%2C+Z">Zirui Liu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yongfeng Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.01563v1-abstract-short" style="display: inline;"> Large language models (LLMs) have achieved remarkable success in contextual knowledge understanding. In this paper, we show that these concentrated massive values consistently emerge in specific regions of attention queries (Q) and keys (K) while not having such patterns in values (V) in various modern transformer-based LLMs (Q, K, and V mean the representations output by the query, key, and value… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.01563v1-abstract-full').style.display = 'inline'; document.getElementById('2502.01563v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.01563v1-abstract-full" style="display: none;"> Large language models (LLMs) have achieved remarkable success in contextual knowledge understanding. In this paper, we show that these concentrated massive values consistently emerge in specific regions of attention queries (Q) and keys (K) while not having such patterns in values (V) in various modern transformer-based LLMs (Q, K, and V mean the representations output by the query, key, and value layers respectively). Through extensive experiments, we further demonstrate that these massive values play a critical role in interpreting contextual knowledge (knowledge obtained from the current context window) rather than in retrieving parametric knowledge stored within the model's parameters. Our further investigation of quantization strategies reveals that ignoring these massive values leads to a pronounced drop in performance on tasks requiring rich contextual understanding, aligning with our analysis. Finally, we trace the emergence of concentrated massive values and find that such concentration is caused by Rotary Positional Encoding (RoPE), which has appeared since the first layers. These findings shed new light on how Q and K operate in LLMs and offer practical insights for model design and optimization. The Code is Available at https://github.com/MingyuJ666/Rope_with_LLM. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.01563v1-abstract-full').style.display = 'none'; document.getElementById('2502.01563v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.01477">arXiv:2502.01477</a> <span> [<a href="https://arxiv.org/pdf/2502.01477">pdf</a>, <a href="https://arxiv.org/format/2502.01477">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Position: Empowering Time Series Reasoning with Multimodal LLMs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Kong%2C+Y">Yaxuan Kong</a>, <a href="/search/?searchtype=author&query=Yang%2C+Y">Yiyuan Yang</a>, <a href="/search/?searchtype=author&query=Wang%2C+S">Shiyu Wang</a>, <a href="/search/?searchtype=author&query=Liu%2C+C">Chenghao Liu</a>, <a href="/search/?searchtype=author&query=Liang%2C+Y">Yuxuan Liang</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a>, <a href="/search/?searchtype=author&query=Zohren%2C+S">Stefan Zohren</a>, <a href="/search/?searchtype=author&query=Pei%2C+D">Dan Pei</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yan Liu</a>, <a href="/search/?searchtype=author&query=Wen%2C+Q">Qingsong Wen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.01477v1-abstract-short" style="display: inline;"> Understanding time series data is crucial for multiple real-world applications. While large language models (LLMs) show promise in time series tasks, current approaches often rely on numerical data alone, overlooking the multimodal nature of time-dependent information, such as textual descriptions, visual data, and audio signals. Moreover, these methods underutilize LLMs' reasoning capabilities, l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.01477v1-abstract-full').style.display = 'inline'; document.getElementById('2502.01477v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.01477v1-abstract-full" style="display: none;"> Understanding time series data is crucial for multiple real-world applications. While large language models (LLMs) show promise in time series tasks, current approaches often rely on numerical data alone, overlooking the multimodal nature of time-dependent information, such as textual descriptions, visual data, and audio signals. Moreover, these methods underutilize LLMs' reasoning capabilities, limiting the analysis to surface-level interpretations instead of deeper temporal and multimodal reasoning. In this position paper, we argue that multimodal LLMs (MLLMs) can enable more powerful and flexible reasoning for time series analysis, enhancing decision-making and real-world applications. We call on researchers and practitioners to leverage this potential by developing strategies that prioritize trust, interpretability, and robust reasoning in MLLMs. Lastly, we highlight key research directions, including novel reasoning paradigms, architectural innovations, and domain-specific applications, to advance time series reasoning with MLLMs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.01477v1-abstract-full').style.display = 'none'; document.getElementById('2502.01477v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.16453">arXiv:2501.16453</a> <span> [<a href="https://arxiv.org/pdf/2501.16453">pdf</a>, <a href="https://arxiv.org/format/2501.16453">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Detecting Zero-Day Attacks in Digital Substations via In-Context Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Manzoor%2C+F">Faizan Manzoor</a>, <a href="/search/?searchtype=author&query=Khattar%2C+V">Vanshaj Khattar</a>, <a href="/search/?searchtype=author&query=Herath%2C+A">Akila Herath</a>, <a href="/search/?searchtype=author&query=Black%2C+C">Clifton Black</a>, <a href="/search/?searchtype=author&query=Nielsen%2C+M+C">Matthew C Nielsen</a>, <a href="/search/?searchtype=author&query=Hong%2C+J">Junho Hong</a>, <a href="/search/?searchtype=author&query=Liu%2C+C">Chen-Ching Liu</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.16453v1-abstract-short" style="display: inline;"> The occurrences of cyber attacks on the power grids have been increasing every year, with novel attack techniques emerging every year. In this paper, we address the critical challenge of detecting novel/zero-day attacks in digital substations that employ the IEC-61850 communication protocol. While many heuristic and machine learning (ML)-based methods have been proposed for attack detection in IEC… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16453v1-abstract-full').style.display = 'inline'; document.getElementById('2501.16453v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.16453v1-abstract-full" style="display: none;"> The occurrences of cyber attacks on the power grids have been increasing every year, with novel attack techniques emerging every year. In this paper, we address the critical challenge of detecting novel/zero-day attacks in digital substations that employ the IEC-61850 communication protocol. While many heuristic and machine learning (ML)-based methods have been proposed for attack detection in IEC-61850 digital substations, generalization to novel or zero-day attacks remains challenging. We propose an approach that leverages the in-context learning (ICL) capability of the transformer architecture, the fundamental building block of large language models. The ICL approach enables the model to detect zero-day attacks and learn from a few examples of that attack without explicit retraining. Our experiments on the IEC-61850 dataset demonstrate that the proposed method achieves more than $85\%$ detection accuracy on zero-day attacks while the existing state-of-the-art baselines fail. This work paves the way for building more secure and resilient digital substations of the future. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16453v1-abstract-full').style.display = 'none'; document.getElementById('2501.16453v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.16440">arXiv:2501.16440</a> <span> [<a href="https://arxiv.org/pdf/2501.16440">pdf</a>, <a href="https://arxiv.org/format/2501.16440">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> Time-Integrated Southern-Sky Neutrino Source Searches with 10 Years of IceCube Starting-Track Events at Energies Down to 1 TeV </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Abbasi%2C+R">R. Abbasi</a>, <a href="/search/?searchtype=author&query=Ackermann%2C+M">M. Ackermann</a>, <a href="/search/?searchtype=author&query=Adams%2C+J">J. Adams</a>, <a href="/search/?searchtype=author&query=Agarwalla%2C+S+K">S. K. Agarwalla</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+J+A">J. A. Aguilar</a>, <a href="/search/?searchtype=author&query=Ahlers%2C+M">M. Ahlers</a>, <a href="/search/?searchtype=author&query=Alameddine%2C+J+M">J. M. Alameddine</a>, <a href="/search/?searchtype=author&query=Amin%2C+N+M">N. M. Amin</a>, <a href="/search/?searchtype=author&query=Andeen%2C+K">K. Andeen</a>, <a href="/search/?searchtype=author&query=Arg%C3%BCelles%2C+C">C. Arg眉elles</a>, <a href="/search/?searchtype=author&query=Ashida%2C+Y">Y. Ashida</a>, <a href="/search/?searchtype=author&query=Athanasiadou%2C+S">S. Athanasiadou</a>, <a href="/search/?searchtype=author&query=Axani%2C+S+N">S. N. Axani</a>, <a href="/search/?searchtype=author&query=Babu%2C+R">R. Babu</a>, <a href="/search/?searchtype=author&query=Bai%2C+X">X. Bai</a>, <a href="/search/?searchtype=author&query=V.%2C+A+B">A. Balagopal V.</a>, <a href="/search/?searchtype=author&query=Baricevic%2C+M">M. Baricevic</a>, <a href="/search/?searchtype=author&query=Barwick%2C+S+W">S. W. Barwick</a>, <a href="/search/?searchtype=author&query=Bash%2C+S">S. Bash</a>, <a href="/search/?searchtype=author&query=Basu%2C+V">V. Basu</a>, <a href="/search/?searchtype=author&query=Bay%2C+R">R. Bay</a>, <a href="/search/?searchtype=author&query=Beatty%2C+J+J">J. J. Beatty</a>, <a href="/search/?searchtype=author&query=Tjus%2C+J+B">J. Becker Tjus</a>, <a href="/search/?searchtype=author&query=Beise%2C+J">J. Beise</a>, <a href="/search/?searchtype=author&query=Bellenghi%2C+C">C. Bellenghi</a> , et al. (402 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.16440v1-abstract-short" style="display: inline;"> In the IceCube Neutrino Observatory, a signal of astrophysical neutrinos is obscured by backgrounds from atmospheric neutrinos and muons produced in cosmic-ray interactions. IceCube event selections used to isolate the astrophysical neutrino signal often focus on t/he morphology of the light patterns recorded by the detector. The analyses presented here use the new IceCube Enhanced Starting Track… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16440v1-abstract-full').style.display = 'inline'; document.getElementById('2501.16440v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.16440v1-abstract-full" style="display: none;"> In the IceCube Neutrino Observatory, a signal of astrophysical neutrinos is obscured by backgrounds from atmospheric neutrinos and muons produced in cosmic-ray interactions. IceCube event selections used to isolate the astrophysical neutrino signal often focus on t/he morphology of the light patterns recorded by the detector. The analyses presented here use the new IceCube Enhanced Starting Track Event Selection (ESTES), which identifies events likely generated by muon neutrino interactions within the detector geometry, focusing on neutrino energies of 1-500 TeV with a median angular resolution of 1.4掳. Selecting for starting track events filters out not only the atmospheric-muon background, but also the atmospheric-neutrino background in the southern sky. This improves IceCube's muon neutrino sensitivity to southern-sky neutrino sources, especially for Galactic sources that are not expected to produce a substantial flux of neutrinos above 100 TeV. In this work, the ESTES sample was applied for the first time to searches for astrophysical sources of neutrinos, including a search for diffuse neutrino emission from the Galactic plane. No significant excesses were identified from any of the analyses; however, constraining limits are set on the hadronic emission from TeV gamma-ray Galactic plane objects and models of the diffuse Galactic plane neutrino flux. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16440v1-abstract-full').style.display = 'none'; document.getElementById('2501.16440v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">23 pages, 8 figures, 4 tables. Submitted to ApJ</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.13545">arXiv:2501.13545</a> <span> [<a href="https://arxiv.org/pdf/2501.13545">pdf</a>, <a href="https://arxiv.org/format/2501.13545">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> LLMs Can Plan Only If We Tell Them </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Sel%2C+B">Bilgehan Sel</a>, <a href="/search/?searchtype=author&query=Jia%2C+R">Ruoxi Jia</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.13545v1-abstract-short" style="display: inline;"> Large language models (LLMs) have demonstrated significant capabilities in natural language processing and reasoning, yet their effectiveness in autonomous planning has been under debate. While existing studies have utilized LLMs with external feedback mechanisms or in controlled environments for planning, these approaches often involve substantial computational and development resources due to th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.13545v1-abstract-full').style.display = 'inline'; document.getElementById('2501.13545v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.13545v1-abstract-full" style="display: none;"> Large language models (LLMs) have demonstrated significant capabilities in natural language processing and reasoning, yet their effectiveness in autonomous planning has been under debate. While existing studies have utilized LLMs with external feedback mechanisms or in controlled environments for planning, these approaches often involve substantial computational and development resources due to the requirement for careful design and iterative backprompting. Moreover, even the most advanced LLMs like GPT-4 struggle to match human performance on standard planning benchmarks, such as the Blocksworld, without additional support. This paper investigates whether LLMs can independently generate long-horizon plans that rival human baselines. Our novel enhancements to Algorithm-of-Thoughts (AoT), which we dub AoT+, help achieve state-of-the-art results in planning benchmarks out-competing prior methods and human baselines all autonomously. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.13545v1-abstract-full').style.display = 'none'; document.getElementById('2501.13545v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ICLR 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.09276">arXiv:2501.09276</a> <span> [<a href="https://arxiv.org/pdf/2501.09276">pdf</a>, <a href="https://arxiv.org/format/2501.09276">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> Search for neutrino doublets and triplets using 11.4 years of IceCube data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Abbasi%2C+R">R. Abbasi</a>, <a href="/search/?searchtype=author&query=Ackermann%2C+M">M. Ackermann</a>, <a href="/search/?searchtype=author&query=Adams%2C+J">J. Adams</a>, <a href="/search/?searchtype=author&query=Agarwalla%2C+S+K">S. K. Agarwalla</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+J+A">J. A. Aguilar</a>, <a href="/search/?searchtype=author&query=Ahlers%2C+M">M. Ahlers</a>, <a href="/search/?searchtype=author&query=Alameddine%2C+J+M">J. M. Alameddine</a>, <a href="/search/?searchtype=author&query=Amin%2C+N+M">N. M. Amin</a>, <a href="/search/?searchtype=author&query=Andeen%2C+K">K. Andeen</a>, <a href="/search/?searchtype=author&query=Arg%C3%BCelles%2C+C">C. Arg眉elles</a>, <a href="/search/?searchtype=author&query=Ashida%2C+Y">Y. Ashida</a>, <a href="/search/?searchtype=author&query=Athanasiadou%2C+S">S. Athanasiadou</a>, <a href="/search/?searchtype=author&query=Axani%2C+S+N">S. N. Axani</a>, <a href="/search/?searchtype=author&query=Babu%2C+R">R. Babu</a>, <a href="/search/?searchtype=author&query=Bai%2C+X">X. Bai</a>, <a href="/search/?searchtype=author&query=V.%2C+A+B">A. Balagopal V.</a>, <a href="/search/?searchtype=author&query=Baricevic%2C+M">M. Baricevic</a>, <a href="/search/?searchtype=author&query=Barwick%2C+S+W">S. W. Barwick</a>, <a href="/search/?searchtype=author&query=Bash%2C+S">S. Bash</a>, <a href="/search/?searchtype=author&query=Basu%2C+V">V. Basu</a>, <a href="/search/?searchtype=author&query=Bay%2C+R">R. Bay</a>, <a href="/search/?searchtype=author&query=Beatty%2C+J+J">J. J. Beatty</a>, <a href="/search/?searchtype=author&query=Tjus%2C+J+B">J. Becker Tjus</a>, <a href="/search/?searchtype=author&query=Beise%2C+J">J. Beise</a>, <a href="/search/?searchtype=author&query=Bellenghi%2C+C">C. Bellenghi</a> , et al. (402 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.09276v1-abstract-short" style="display: inline;"> We report a search for high-energy astrophysical neutrino multiplets, detections of multiple neutrino clusters in the same direction within 30 days, based on an analysis of 11.4 years of IceCube data. A new search method optimized for transient neutrino emission with a monthly time scale is employed, providing a higher sensitivity to neutrino fluxes. This result is sensitive to neutrino transient… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09276v1-abstract-full').style.display = 'inline'; document.getElementById('2501.09276v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.09276v1-abstract-full" style="display: none;"> We report a search for high-energy astrophysical neutrino multiplets, detections of multiple neutrino clusters in the same direction within 30 days, based on an analysis of 11.4 years of IceCube data. A new search method optimized for transient neutrino emission with a monthly time scale is employed, providing a higher sensitivity to neutrino fluxes. This result is sensitive to neutrino transient emission, reaching per-flavor flux of approximately $10^{-10}\ {\rm erg}\ {\rm cm}^{-2}\ {\rm sec}^{-1}$ from the Northern sky in the energy range $E\gtrsim 50$~TeV. The number of doublets and triplets identified in this search is compatible with the atmospheric background hypothesis, which leads us to set limits on the nature of neutrino transient sources with emission timescales of one month. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09276v1-abstract-full').style.display = 'none'; document.getElementById('2501.09276v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.04600">arXiv:2501.04600</a> <span> [<a href="https://arxiv.org/pdf/2501.04600">pdf</a>, <a href="https://arxiv.org/format/2501.04600">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Do Automated Fixes Truly Mitigate Smart Contract Exploits? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bobadilla%2C+S">Sofia Bobadilla</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Monica Jin</a>, <a href="/search/?searchtype=author&query=Monperrus%2C+M">Martin Monperrus</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.04600v2-abstract-short" style="display: inline;"> Automated Program Repair (APR) for smart contract security promises to automatically mitigate smart contract vulnerabilities responsible for billions in financial losses. However, the true effectiveness of this research in addressing smart contract exploits remains uncharted territory. This paper bridges this critical gap by introducing a novel and systematic experimental framework for evaluating… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.04600v2-abstract-full').style.display = 'inline'; document.getElementById('2501.04600v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.04600v2-abstract-full" style="display: none;"> Automated Program Repair (APR) for smart contract security promises to automatically mitigate smart contract vulnerabilities responsible for billions in financial losses. However, the true effectiveness of this research in addressing smart contract exploits remains uncharted territory. This paper bridges this critical gap by introducing a novel and systematic experimental framework for evaluating exploit mitigation of program repair tools for smart contracts. We qualitatively and quantitatively analyze 20 state-of-the-art APR tools using a dataset of 143 vulnerable smart contracts, for which we manually craft 91 executable exploits. We are the very first to define and measure the essential "exploit mitigation rate", giving researchers and practitioners and real sense of effectiveness of cutting edge techniques. Our findings reveal substantial disparities in the state of the art, with an exploit mitigation rate ranging from a low of 27% to a high of 73%, a result that nobody would guess from reading the original papers. Our study identifies systemic limitations, such as inconsistent functionality preservation, that must be addressed in future research on program repair for smart contracts. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.04600v2-abstract-full').style.display = 'none'; document.getElementById('2501.04600v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.20850">arXiv:2412.20850</a> <span> [<a href="https://arxiv.org/pdf/2412.20850">pdf</a>, <a href="https://arxiv.org/format/2412.20850">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> </div> <p class="title is-5 mathjax"> Probing Light Bosonic Dark Matter with Transmon Qubits </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chao%2C+W">Wei Chao</a>, <a href="/search/?searchtype=author&query=Gao%2C+Y">Yu Gao</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming-jie Jin</a>, <a href="/search/?searchtype=author&query=Liu%2C+X">Xiao-sheng Liu</a>, <a href="/search/?searchtype=author&query=Sun%2C+X">Xi-lei Sun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.20850v2-abstract-short" style="display: inline;"> In this paper, we investigate the constraints of the transmon qubit, an improved version of the charge qubit, on bosonic light dark matters. Phonon excitations induced by the scattering or absorption of dark matter on a superconductor may destroy the Cooper pair, leading to the production of quasiparticles made by the electron. By measuring the production rate of the quasiparticle density, one may… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.20850v2-abstract-full').style.display = 'inline'; document.getElementById('2412.20850v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.20850v2-abstract-full" style="display: none;"> In this paper, we investigate the constraints of the transmon qubit, an improved version of the charge qubit, on bosonic light dark matters. Phonon excitations induced by the scattering or absorption of dark matter on a superconductor may destroy the Cooper pair, leading to the production of quasiparticles made by the electron. By measuring the production rate of the quasiparticle density, one may read out the coupling between dark matter and ordinary matter, assuming that these quasiparticles are solely induced by dark matter interactions. For the first time, we show constraints on the parameter space of the dark photon, light scalar dark matter, and axion-like particles from the measurement of quasiparticles in transmon qubit experiments. This study offers insights for the development of quantum qubit experiments aimed at the direct detection of dark matter in underground laboratories. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.20850v2-abstract-full').style.display = 'none'; document.getElementById('2412.20850v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 2 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.15843">arXiv:2412.15843</a> <span> [<a href="https://arxiv.org/pdf/2412.15843">pdf</a>, <a href="https://arxiv.org/format/2412.15843">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Rethinking Hardware Impairments in Multi-User Systems: Can FAS Make a Difference? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yao%2C+J">Junteng Yao</a>, <a href="/search/?searchtype=author&query=Wu%2C+T">Tuo Wu</a>, <a href="/search/?searchtype=author&query=Zhou%2C+L">Liaoshi Zhou</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a>, <a href="/search/?searchtype=author&query=Pan%2C+C">Cunhua Pan</a>, <a href="/search/?searchtype=author&query=Elkashlan%2C+M">Maged Elkashlan</a>, <a href="/search/?searchtype=author&query=Adachi%2C+F">Fumiyuki Adachi</a>, <a href="/search/?searchtype=author&query=Karagiannidis%2C+G+K">George K. Karagiannidis</a>, <a href="/search/?searchtype=author&query=Al-Dhahir%2C+N">Naofal Al-Dhahir</a>, <a href="/search/?searchtype=author&query=Yuen%2C+C">Chau Yuen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.15843v1-abstract-short" style="display: inline;"> In this paper, we analyze the role of fluid antenna systems (FAS) in multi-user systems with hardware impairments (HIs). Specifically, we investigate a scenario where a base station (BS) equipped with multiple fluid antennas communicates with multiple users (CUs), each equipped with a single fluid antenna. Our objective is to maximize the minimum communication rate among all users by jointly optim… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.15843v1-abstract-full').style.display = 'inline'; document.getElementById('2412.15843v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.15843v1-abstract-full" style="display: none;"> In this paper, we analyze the role of fluid antenna systems (FAS) in multi-user systems with hardware impairments (HIs). Specifically, we investigate a scenario where a base station (BS) equipped with multiple fluid antennas communicates with multiple users (CUs), each equipped with a single fluid antenna. Our objective is to maximize the minimum communication rate among all users by jointly optimizing the BS's transmit beamforming, the positions of its transmit fluid antennas, and the positions of the CUs' receive fluid antennas. To address this non-convex problem, we propose a block coordinate descent (BCD) algorithm integrating semidefinite relaxation (SDR), rank-one constraint relaxation (SRCR), successive convex approximation (SCA), and majorization-minimization (MM). Simulation results demonstrate that FAS significantly enhances system performance and robustness, with notable gains when both the BS and CUs are equipped with fluid antennas. Even under low transmit power conditions, deploying FAS at the BS alone yields substantial performance gains. However, the effectiveness of FAS depends on the availability of sufficient movement space, as space constraints may limit its benefits compared to fixed antenna strategies. Our findings highlight the potential of FAS to mitigate HIs and enhance multi-user system performance, while emphasizing the need for practical deployment considerations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.15843v1-abstract-full').style.display = 'none'; document.getElementById('2412.15843v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.14437">arXiv:2412.14437</a> <span> [<a href="https://arxiv.org/pdf/2412.14437">pdf</a>, <a href="https://arxiv.org/format/2412.14437">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> </div> <p class="title is-5 mathjax"> Evaluation of cosmogenic Ge-68 background in a high purity germanium detector via a time series fitting method </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Dai%2C+W+H">W. H. Dai</a>, <a href="/search/?searchtype=author&query=Chen%2C+J+K">J. K. Chen</a>, <a href="/search/?searchtype=author&query=Ma%2C+H">H. Ma</a>, <a href="/search/?searchtype=author&query=Zeng%2C+Z">Z. Zeng</a>, <a href="/search/?searchtype=author&query=Jin%2C+M+K">M. K. Jin</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Q+L">Q. L Zhang</a>, <a href="/search/?searchtype=author&query=Cheng%2C+J+P">J. P. Cheng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.14437v1-abstract-short" style="display: inline;"> Ge-68 is a cosmogenic isotope in germanium with a half-life of 270.9 days. Ge-68 and its decay daughter Ga-68 contribute considerable background with energy up to 3 MeV to low background $纬$ spectrometers using high purity germanium (HPGe) detectors. In this paper, we evaluated the background of Ge-68 and Ga-68 in a p-type coaxial HPGe detector operated at China Jinping underground laboratory (CJP… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.14437v1-abstract-full').style.display = 'inline'; document.getElementById('2412.14437v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.14437v1-abstract-full" style="display: none;"> Ge-68 is a cosmogenic isotope in germanium with a half-life of 270.9 days. Ge-68 and its decay daughter Ga-68 contribute considerable background with energy up to 3 MeV to low background $纬$ spectrometers using high purity germanium (HPGe) detectors. In this paper, we evaluated the background of Ge-68 and Ga-68 in a p-type coaxial HPGe detector operated at China Jinping underground laboratory (CJPL) via a time series fitting method. Under the assumption that Ge-68 and Ga-68 are in radioactive equilibrium and airborne radon daughters are uniformly distributed in the measurement chamber of the spectrometer, we fit the time series of count rate in 1-3 MeV to calculate the Ge-68 activity, radon daughter concentrations, and the time-invariant background component. Total 90 days measured data were used in analysis, a hypothesis test confirmed a significant Ge-68 signal at 99.64% confidence level. The initial activity of Ge-68 is fitted to be 477.0$\pm$112.4 $渭$Bq/kg, corresponding to an integral count rate of 55.9 count/day in 1-3 MeV range. During the measurement, Ge-68 activity decreased by about 30%, contributing about 62% of the total background in 1-3 MeV range. Our method also provides an estimation of the variation of airborne radon daughter concentrations in the measurement chamber, which could be used to monitor the performance of radon reduction measures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.14437v1-abstract-full').style.display = 'none'; document.getElementById('2412.14437v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.10748">arXiv:2412.10748</a> <span> [<a href="https://arxiv.org/pdf/2412.10748">pdf</a>, <a href="https://arxiv.org/format/2412.10748">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Fluid Dynamics">physics.flu-dyn</span> </div> </div> <p class="title is-5 mathjax"> A Pioneering Neural Network Method for Efficient and Robust Fluid Simulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Y">Yu Chen</a>, <a href="/search/?searchtype=author&query=Zheng%2C+S">Shuai Zheng</a>, <a href="/search/?searchtype=author&query=Wang%2C+N">Nianyi Wang</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Menglong Jin</a>, <a href="/search/?searchtype=author&query=Chang%2C+Y">Yan Chang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.10748v3-abstract-short" style="display: inline;"> Fluid simulation is an important research topic in computer graphics (CG) and animation in video games. Traditional methods based on Navier-Stokes equations are computationally expensive. In this paper, we treat fluid motion as point cloud transformation and propose the first neural network method specifically designed for efficient and robust fluid simulation in complex environments. This model i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.10748v3-abstract-full').style.display = 'inline'; document.getElementById('2412.10748v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.10748v3-abstract-full" style="display: none;"> Fluid simulation is an important research topic in computer graphics (CG) and animation in video games. Traditional methods based on Navier-Stokes equations are computationally expensive. In this paper, we treat fluid motion as point cloud transformation and propose the first neural network method specifically designed for efficient and robust fluid simulation in complex environments. This model is also the deep learning model that is the first to be capable of stably modeling fluid particle dynamics in such complex scenarios. Our triangle feature fusion design achieves an optimal balance among fluid dynamics modeling, momentum conservation constraints, and global stability control. We conducted comprehensive experiments on datasets. Compared to existing neural network-based fluid simulation algorithms, we significantly enhanced accuracy while maintaining high computational speed. Compared to traditional SPH methods, our speed improved approximately 10 times. Furthermore, compared to traditional fluid simulation software such as Flow3D, our computation speed increased by more than 300 times. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.10748v3-abstract-full').style.display = 'none'; document.getElementById('2412.10748v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been accepted by AAAI Conference on Artificial Intelligence (AAAI-25)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.09402">arXiv:2412.09402</a> <span> [<a href="https://arxiv.org/pdf/2412.09402">pdf</a>, <a href="https://arxiv.org/format/2412.09402">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+L">Lehan Wang</a>, <a href="/search/?searchtype=author&query=Qi%2C+C">Chongchong Qi</a>, <a href="/search/?searchtype=author&query=Ou%2C+C">Chubin Ou</a>, <a href="/search/?searchtype=author&query=An%2C+L">Lin An</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Mei Jin</a>, <a href="/search/?searchtype=author&query=Kong%2C+X">Xiangbin Kong</a>, <a href="/search/?searchtype=author&query=Li%2C+X">Xiaomeng Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.09402v1-abstract-short" style="display: inline;"> Existing multi-modal learning methods on fundus and OCT images mostly require both modalities to be available and strictly paired for training and testing, which appears less practical in clinical scenarios. To expand the scope of clinical applications, we formulate a novel setting, "OCT-enhanced disease recognition from fundus images", that allows for the use of unpaired multi-modal data during t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.09402v1-abstract-full').style.display = 'inline'; document.getElementById('2412.09402v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.09402v1-abstract-full" style="display: none;"> Existing multi-modal learning methods on fundus and OCT images mostly require both modalities to be available and strictly paired for training and testing, which appears less practical in clinical scenarios. To expand the scope of clinical applications, we formulate a novel setting, "OCT-enhanced disease recognition from fundus images", that allows for the use of unpaired multi-modal data during the training phase and relies on the widespread fundus photographs for testing. To benchmark this setting, we present the first large multi-modal multi-class dataset for eye disease diagnosis, MultiEYE, and propose an OCT-assisted Conceptual Distillation Approach (OCT-CoDA), which employs semantically rich concepts to extract disease-related knowledge from OCT images and leverage them into the fundus model. Specifically, we regard the image-concept relation as a link to distill useful knowledge from the OCT teacher model to the fundus student model, which considerably improves the diagnostic performance based on fundus images and formulates the cross-modal knowledge transfer into an explainable process. Through extensive experiments on the multi-disease classification task, our proposed OCT-CoDA demonstrates remarkable results and interpretability, showing great potential for clinical application. Our dataset and code are available at https://github.com/xmed-lab/MultiEYE. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.09402v1-abstract-full').style.display = 'none'; document.getElementById('2412.09402v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at IEEE TMI</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.07808">arXiv:2412.07808</a> <span> [<a href="https://arxiv.org/pdf/2412.07808">pdf</a>, <a href="https://arxiv.org/format/2412.07808">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Boosting Alignment for Post-Unlearning Text-to-Image Generative Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ko%2C+M">Myeongseob Ko</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Henry Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+Z">Zhun Wang</a>, <a href="/search/?searchtype=author&query=Patsenker%2C+J">Jonathan Patsenker</a>, <a href="/search/?searchtype=author&query=Wang%2C+J+T">Jiachen T. Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+Q">Qinbin Li</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a>, <a href="/search/?searchtype=author&query=Song%2C+D">Dawn Song</a>, <a href="/search/?searchtype=author&query=Jia%2C+R">Ruoxi Jia</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.07808v1-abstract-short" style="display: inline;"> Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data. However, this often inadvertently leads to the generation of harmful or inappropriate content and raises copyright concerns. Driven by these concerns, machine unlearning has become crucial to effectively purge undesirable knowledge from models. While existing literature has studied various… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.07808v1-abstract-full').style.display = 'inline'; document.getElementById('2412.07808v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.07808v1-abstract-full" style="display: none;"> Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data. However, this often inadvertently leads to the generation of harmful or inappropriate content and raises copyright concerns. Driven by these concerns, machine unlearning has become crucial to effectively purge undesirable knowledge from models. While existing literature has studied various unlearning techniques, these often suffer from either poor unlearning quality or degradation in text-image alignment after unlearning, due to the competitive nature of these objectives. To address these challenges, we propose a framework that seeks an optimal model update at each unlearning iteration, ensuring monotonic improvement on both objectives. We further derive the characterization of such an update. In addition, we design procedures to strategically diversify the unlearning and remaining datasets to boost performance improvement. Our evaluation demonstrates that our method effectively removes target classes from recent diffusion-based generative models and concepts from stable diffusion models while maintaining close alignment with the models' original trained states, thus outperforming state-of-the-art baselines. Our code will be made available at \url{https://github.com/reds-lab/Restricted_gradient_diversity_unlearning.git}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.07808v1-abstract-full').style.display = 'none'; document.getElementById('2412.07808v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages, The Thirty-Eighth Annual Conference on Neural Information Processing Systems</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.05046">arXiv:2412.05046</a> <span> [<a href="https://arxiv.org/pdf/2412.05046">pdf</a>, <a href="https://arxiv.org/format/2412.05046">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> Observation of Cosmic-Ray Anisotropy in the Southern Hemisphere with Twelve Years of Data Collected by the IceCube Neutrino Observatory </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Abbasi%2C+R">R. Abbasi</a>, <a href="/search/?searchtype=author&query=Ackermann%2C+M">M. Ackermann</a>, <a href="/search/?searchtype=author&query=Adams%2C+J">J. Adams</a>, <a href="/search/?searchtype=author&query=Agarwalla%2C+S+K">S. K. Agarwalla</a>, <a href="/search/?searchtype=author&query=Aguado%2C+T">T. Aguado</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+J+A">J. A. Aguilar</a>, <a href="/search/?searchtype=author&query=Ahlers%2C+M">M. Ahlers</a>, <a href="/search/?searchtype=author&query=Alameddine%2C+J+M">J. M. Alameddine</a>, <a href="/search/?searchtype=author&query=Amin%2C+N+M">N. M. Amin</a>, <a href="/search/?searchtype=author&query=Andeen%2C+K">K. Andeen</a>, <a href="/search/?searchtype=author&query=Arg%C3%BCelles%2C+C">C. Arg眉elles</a>, <a href="/search/?searchtype=author&query=Ashida%2C+Y">Y. Ashida</a>, <a href="/search/?searchtype=author&query=Athanasiadou%2C+S">S. Athanasiadou</a>, <a href="/search/?searchtype=author&query=Axani%2C+S+N">S. N. Axani</a>, <a href="/search/?searchtype=author&query=Babu%2C+R">R. Babu</a>, <a href="/search/?searchtype=author&query=Bai%2C+X">X. Bai</a>, <a href="/search/?searchtype=author&query=V.%2C+A+B">A. Balagopal V.</a>, <a href="/search/?searchtype=author&query=Baricevic%2C+M">M. Baricevic</a>, <a href="/search/?searchtype=author&query=Barwick%2C+S+W">S. W. Barwick</a>, <a href="/search/?searchtype=author&query=Bash%2C+S">S. Bash</a>, <a href="/search/?searchtype=author&query=Basu%2C+V">V. Basu</a>, <a href="/search/?searchtype=author&query=Bay%2C+R">R. Bay</a>, <a href="/search/?searchtype=author&query=Beatty%2C+J+J">J. J. Beatty</a>, <a href="/search/?searchtype=author&query=Tjus%2C+J+B">J. Becker Tjus</a>, <a href="/search/?searchtype=author&query=Beise%2C+J">J. Beise</a> , et al. (413 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.05046v1-abstract-short" style="display: inline;"> We analyzed the 7.92$\times 10^{11}$ cosmic-ray-induced muon events collected by the IceCube Neutrino Observatory from May 13, 2011, when the fully constructed experiment started to take data, to May 12, 2023. This dataset provides an up-to-date cosmic-ray arrival direction distribution in the Southern Hemisphere with unprecedented statistical accuracy covering more than a full period length of a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.05046v1-abstract-full').style.display = 'inline'; document.getElementById('2412.05046v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.05046v1-abstract-full" style="display: none;"> We analyzed the 7.92$\times 10^{11}$ cosmic-ray-induced muon events collected by the IceCube Neutrino Observatory from May 13, 2011, when the fully constructed experiment started to take data, to May 12, 2023. This dataset provides an up-to-date cosmic-ray arrival direction distribution in the Southern Hemisphere with unprecedented statistical accuracy covering more than a full period length of a solar cycle. Improvements in Monte Carlo event simulation and better handling of year-to-year differences in data processing significantly reduce systematic uncertainties below the level of statistical fluctuations compared to the previously published results. We confirm the observation of a change in the angular structure of the cosmic-ray anisotropy between 10 TeV and 1 PeV, more specifically in the 100-300 TeV energy range. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.05046v1-abstract-full').style.display = 'none'; document.getElementById('2412.05046v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.04046">arXiv:2412.04046</a> <span> [<a href="https://arxiv.org/pdf/2412.04046">pdf</a>, <a href="https://arxiv.org/format/2412.04046">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Hostility Detection in UK Politics: A Dataset on Online Abuse Targeting MPs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Pandya%2C+M">Mugdha Pandya</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Mali Jin</a>, <a href="/search/?searchtype=author&query=Bontcheva%2C+K">Kalina Bontcheva</a>, <a href="/search/?searchtype=author&query=Maynard%2C+D">Diana Maynard</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.04046v1-abstract-short" style="display: inline;"> Numerous politicians use social media platforms, particularly X, to engage with their constituents. This interaction allows constituents to pose questions and offer feedback but also exposes politicians to a barrage of hostile responses, especially given the anonymity afforded by social media. They are typically targeted in relation to their governmental role, but the comments also tend to attack… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.04046v1-abstract-full').style.display = 'inline'; document.getElementById('2412.04046v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.04046v1-abstract-full" style="display: none;"> Numerous politicians use social media platforms, particularly X, to engage with their constituents. This interaction allows constituents to pose questions and offer feedback but also exposes politicians to a barrage of hostile responses, especially given the anonymity afforded by social media. They are typically targeted in relation to their governmental role, but the comments also tend to attack their personal identity. This can discredit politicians and reduce public trust in the government. It can also incite anger and disrespect, leading to offline harm and violence. While numerous models exist for detecting hostility in general, they lack the specificity required for political contexts. Furthermore, addressing hostility towards politicians demands tailored approaches due to the distinct language and issues inherent to each country (e.g., Brexit for the UK). To bridge this gap, we construct a dataset of 3,320 English tweets spanning a two-year period manually annotated for hostility towards UK MPs. Our dataset also captures the targeted identity characteristics (race, gender, religion, none) in hostile tweets. We perform linguistic and topical analyses to delve into the unique content of the UK political data. Finally, we evaluate the performance of pre-trained language models and large language models on binary hostility detection and multi-class targeted identity type classification tasks. Our study offers valuable data and insights for future research on the prevalence and nature of politics-related hostility specific to the UK. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.04046v1-abstract-full').style.display = 'none'; document.getElementById('2412.04046v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.03963">arXiv:2412.03963</a> <span> [<a href="https://arxiv.org/pdf/2412.03963">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="General Economics">econ.GN</span> </div> </div> <p class="title is-5 mathjax"> Augmenting Minds or Automating Skills: The Differential Role of Human Capital in Generative AI's Impact on Creative Tasks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Huang%2C+M">Meiling Huang</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a>, <a href="/search/?searchtype=author&query=Li%2C+N">Ning Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.03963v1-abstract-short" style="display: inline;"> Generative AI is rapidly reshaping creative work, raising critical questions about its beneficiaries and societal implications. This study challenges prevailing assumptions by exploring how generative AI interacts with diverse forms of human capital in creative tasks. Through two random controlled experiments in flash fiction writing and song composition, we uncover a paradox: while AI democratize… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.03963v1-abstract-full').style.display = 'inline'; document.getElementById('2412.03963v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.03963v1-abstract-full" style="display: none;"> Generative AI is rapidly reshaping creative work, raising critical questions about its beneficiaries and societal implications. This study challenges prevailing assumptions by exploring how generative AI interacts with diverse forms of human capital in creative tasks. Through two random controlled experiments in flash fiction writing and song composition, we uncover a paradox: while AI democratizes access to creative tools, it simultaneously amplifies cognitive inequalities. Our findings reveal that AI enhances general human capital (cognitive abilities and education) by facilitating adaptability and idea integration but diminishes the value of domain-specific expertise. We introduce a novel theoretical framework that merges human capital theory with the automation-augmentation perspective, offering a nuanced understanding of human-AI collaboration. This framework elucidates how AI shifts the locus of creative advantage from specialized expertise to broader cognitive adaptability. Contrary to the notion of AI as a universal equalizer, our work highlights its potential to exacerbate disparities in skill valuation, reshaping workplace hierarchies and redefining the nature of creativity in the AI era. These insights advance theories of human capital and automation while providing actionable guidance for organizations navigating AI integration amidst workforce inequalities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.03963v1-abstract-full').style.display = 'none'; document.getElementById('2412.03963v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.01048">arXiv:2412.01048</a> <span> [<a href="https://arxiv.org/pdf/2412.01048">pdf</a>, <a href="https://arxiv.org/format/2412.01048">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.eswa.2024.125320">10.1016/j.eswa.2024.125320 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Cerberus: Attribute-based person re-identification using semantic IDs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Eom%2C+C">Chanho Eom</a>, <a href="/search/?searchtype=author&query=Lee%2C+G">Geon Lee</a>, <a href="/search/?searchtype=author&query=Cho%2C+K">Kyunghwan Cho</a>, <a href="/search/?searchtype=author&query=Jung%2C+H">Hyeonseok Jung</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Moonsub Jin</a>, <a href="/search/?searchtype=author&query=Ham%2C+B">Bumsub Ham</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.01048v1-abstract-short" style="display: inline;"> We introduce a new framework, dubbed Cerberus, for attribute-based person re-identification (reID). Our approach leverages person attribute labels to learn local and global person representations that encode specific traits, such as gender and clothing style. To achieve this, we define semantic IDs (SIDs) by combining attribute labels, and use a semantic guidance loss to align the person represent… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.01048v1-abstract-full').style.display = 'inline'; document.getElementById('2412.01048v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.01048v1-abstract-full" style="display: none;"> We introduce a new framework, dubbed Cerberus, for attribute-based person re-identification (reID). Our approach leverages person attribute labels to learn local and global person representations that encode specific traits, such as gender and clothing style. To achieve this, we define semantic IDs (SIDs) by combining attribute labels, and use a semantic guidance loss to align the person representations with the prototypical features of corresponding SIDs, encouraging the representations to encode the relevant semantics. Simultaneously, we enforce the representations of the same person to be embedded closely, enabling recognizing subtle differences in appearance to discriminate persons sharing the same attribute labels. To increase the generalization ability on unseen data, we also propose a regularization method that takes advantage of the relationships between SID prototypes. Our framework performs individual comparisons of local and global person representations between query and gallery images for attribute-based reID. By exploiting the SID prototypes aligned with the corresponding representations, it can also perform person attribute recognition (PAR) and attribute-based person search (APS) without bells and whistles. Experimental results on standard benchmarks on attribute-based person reID, Market-1501 and DukeMTMC, demonstrate the superiority of our model compared to the state of the art. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.01048v1-abstract-full').style.display = 'none'; document.getElementById('2412.01048v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Expert Systems with Applications 2025 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.00319">arXiv:2412.00319</a> <span> [<a href="https://arxiv.org/pdf/2412.00319">pdf</a>, <a href="https://arxiv.org/format/2412.00319">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Improving speaker verification robustness with synthetic emotional utterances </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Koditala%2C+N+K">Nikhil Kumar Koditala</a>, <a href="/search/?searchtype=author&query=Ju%2C+C+J">Chelsea Jui-Ting Ju</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruirui Li</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Minho Jin</a>, <a href="/search/?searchtype=author&query=Chadha%2C+A">Aman Chadha</a>, <a href="/search/?searchtype=author&query=Stolcke%2C+A">Andreas Stolcke</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.00319v1-abstract-short" style="display: inline;"> A speaker verification (SV) system offers an authentication service designed to confirm whether a given speech sample originates from a specific speaker. This technology has paved the way for various personalized applications that cater to individual preferences. A noteworthy challenge faced by SV systems is their ability to perform consistently across a range of emotional spectra. Most existing m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.00319v1-abstract-full').style.display = 'inline'; document.getElementById('2412.00319v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.00319v1-abstract-full" style="display: none;"> A speaker verification (SV) system offers an authentication service designed to confirm whether a given speech sample originates from a specific speaker. This technology has paved the way for various personalized applications that cater to individual preferences. A noteworthy challenge faced by SV systems is their ability to perform consistently across a range of emotional spectra. Most existing models exhibit high error rates when dealing with emotional utterances compared to neutral ones. Consequently, this phenomenon often leads to missing out on speech of interest. This issue primarily stems from the limited availability of labeled emotional speech data, impeding the development of robust speaker representations that encompass diverse emotional states. To address this concern, we propose a novel approach employing the CycleGAN framework to serve as a data augmentation method. This technique synthesizes emotional speech segments for each specific speaker while preserving the unique vocal identity. Our experimental findings underscore the effectiveness of incorporating synthetic emotional data into the training process. The models trained using this augmented dataset consistently outperform the baseline models on the task of verifying speakers in emotional speech scenarios, reducing equal error rate by as much as 3.64% relative. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.00319v1-abstract-full').style.display = 'none'; document.getElementById('2412.00319v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.19651">arXiv:2411.19651</a> <span> [<a href="https://arxiv.org/pdf/2411.19651">pdf</a>, <a href="https://arxiv.org/format/2411.19651">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Solar and Stellar Astrophysics">astro-ph.SR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1051/0004-6361/202451505">10.1051/0004-6361/202451505 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Ice inventory towards the protostar Ced 110 IRS4 observed with the James Webb Space Telescope. Results from the ERS Ice Age program </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Rocha%2C+W+R+M">W. R. M. Rocha</a>, <a href="/search/?searchtype=author&query=McClure%2C+M+K">M. K. McClure</a>, <a href="/search/?searchtype=author&query=Sturm%2C+J+A">J. A. Sturm</a>, <a href="/search/?searchtype=author&query=Beck%2C+T+L">T. L. Beck</a>, <a href="/search/?searchtype=author&query=Smith%2C+Z+L">Z. L. Smith</a>, <a href="/search/?searchtype=author&query=Dickinson%2C+H">H. Dickinson</a>, <a href="/search/?searchtype=author&query=Sun%2C+F">F. Sun</a>, <a href="/search/?searchtype=author&query=Egami%2C+E">E. Egami</a>, <a href="/search/?searchtype=author&query=Boogert%2C+A+C+A">A. C. A. Boogert</a>, <a href="/search/?searchtype=author&query=Fraser%2C+H+J">H. J. Fraser</a>, <a href="/search/?searchtype=author&query=Dartois%2C+E">E. Dartois</a>, <a href="/search/?searchtype=author&query=Jimenez-Serra%2C+I">I. Jimenez-Serra</a>, <a href="/search/?searchtype=author&query=Noble%2C+J+A">J. A. Noble</a>, <a href="/search/?searchtype=author&query=Bergner%2C+J">J. Bergner</a>, <a href="/search/?searchtype=author&query=Caselli%2C+P">P. Caselli</a>, <a href="/search/?searchtype=author&query=Charnley%2C+S+B">S. B. Charnley</a>, <a href="/search/?searchtype=author&query=Chiar%2C+J">J. Chiar</a>, <a href="/search/?searchtype=author&query=Chu%2C+L">L. Chu</a>, <a href="/search/?searchtype=author&query=Cooke%2C+I">I. Cooke</a>, <a href="/search/?searchtype=author&query=Crouzet%2C+N">N. Crouzet</a>, <a href="/search/?searchtype=author&query=van+Dishoeck%2C+E+F">E. F. van Dishoeck</a>, <a href="/search/?searchtype=author&query=Drozdovskaya%2C+M+N">M. N. Drozdovskaya</a>, <a href="/search/?searchtype=author&query=Garrod%2C+R">R. Garrod</a>, <a href="/search/?searchtype=author&query=Harsono%2C+D">D. Harsono</a>, <a href="/search/?searchtype=author&query=Ioppolo%2C+S">S. Ioppolo</a> , et al. (15 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.19651v1-abstract-short" style="display: inline;"> This work focuses on the ice features toward the binary protostellar system Ced 110 IRS 4A and 4B, and observed with JWST as part of the Early Release Science Ice Age collaboration. We aim to explore the JWST observations of the binary protostellar system Ced~110~IRS4A and IRS4B to unveil and quantify the ice inventories toward these sources. We compare the ice abundances with those found for the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.19651v1-abstract-full').style.display = 'inline'; document.getElementById('2411.19651v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.19651v1-abstract-full" style="display: none;"> This work focuses on the ice features toward the binary protostellar system Ced 110 IRS 4A and 4B, and observed with JWST as part of the Early Release Science Ice Age collaboration. We aim to explore the JWST observations of the binary protostellar system Ced~110~IRS4A and IRS4B to unveil and quantify the ice inventories toward these sources. We compare the ice abundances with those found for the same molecular cloud. The analysis is performed by fitting or comparing laboratory infrared spectra of ices to the observations. Spectral fits are carried out with the ENIIGMA fitting tool that searches for the best fit. For Ced~110~IRS4B, we detected the major ice species H$_2$O, CO, CO$_2$ and NH$_3$. All species are found in a mixture except for CO and CO$_2$, which have both mixed and pure ice components. In the case of Ced~110~IRS4A, we detected the same major species as in Ced~110~IRS4B, as well as the following minor species CH$_4$, SO$_2$, CH$_3$OH, OCN$^-$, NH$_4^+$ and HCOOH. Tentative detection of N$_2$O ice (7.75~$渭$m), forsterite dust (11.2~$渭$m) and CH$_3^+$ gas emission (7.18~$渭$m) in the primary source are also presented. Compared with the two lines of sight toward background stars in the Chameleon I molecular cloud, the protostar has similar ice abundances, except in the case of the ions that are higher in IRS4A. The clearest differences are the absence of the 7.2 and 7.4~$渭$m absorption features due to HCOO$^-$ and icy complex organic molecules in IRS4A and evidence of thermal processing in both IRS4A and IRS4B as probed by the CO$_2$ ice features. We conclude that the binary protostellar system Ced~110~IRS4A and IRS4B has a large inventory of icy species. The similar ice abundances in comparison to the starless regions in the same molecular cloud suggest that the chemical conditions of the protostar were set at earlier stages in the molecular cloud. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.19651v1-abstract-full').style.display = 'none'; document.getElementById('2411.19651v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">33 pages, 19 Figures. Accepted for publication in Astronomy & Astrophysics</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> A&A 693, A288 (2025) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13504">arXiv:2411.13504</a> <span> [<a href="https://arxiv.org/pdf/2411.13504">pdf</a>, <a href="https://arxiv.org/format/2411.13504">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Disentangling Memory and Reasoning Ability in Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jin%2C+M">Mingyu Jin</a>, <a href="/search/?searchtype=author&query=Luo%2C+W">Weidi Luo</a>, <a href="/search/?searchtype=author&query=Cheng%2C+S">Sitao Cheng</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xinyi Wang</a>, <a href="/search/?searchtype=author&query=Hua%2C+W">Wenyue Hua</a>, <a href="/search/?searchtype=author&query=Tang%2C+R">Ruixiang Tang</a>, <a href="/search/?searchtype=author&query=Wang%2C+W+Y">William Yang Wang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yongfeng Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.13504v2-abstract-short" style="display: inline;"> Large Language Models (LLMs) have demonstrated strong performance in handling complex tasks requiring both extensive knowledge and reasoning abilities. However, the existing LLM inference pipeline operates as an opaque process without explicit separation between knowledge retrieval and reasoning steps, making the model's decision-making process unclear and disorganized. This ambiguity can lead to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13504v2-abstract-full').style.display = 'inline'; document.getElementById('2411.13504v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13504v2-abstract-full" style="display: none;"> Large Language Models (LLMs) have demonstrated strong performance in handling complex tasks requiring both extensive knowledge and reasoning abilities. However, the existing LLM inference pipeline operates as an opaque process without explicit separation between knowledge retrieval and reasoning steps, making the model's decision-making process unclear and disorganized. This ambiguity can lead to issues such as hallucinations and knowledge forgetting, which significantly impact the reliability of LLMs in high-stakes domains. In this paper, we propose a new inference paradigm that decomposes the complex inference process into two distinct and clear actions: (1) memory recall: which retrieves relevant knowledge, and (2) reasoning: which performs logical steps based on the recalled knowledge. To facilitate this decomposition, we introduce two special tokens memory and reason, guiding the model to distinguish between steps that require knowledge retrieval and those that involve reasoning. Our experiment results show that this decomposition not only improves model performance but also enhances the interpretability of the inference process, enabling users to identify sources of error and refine model responses effectively. The code is available at https://github.com/MingyuJ666/Disentangling-Memory-and-Reasoning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13504v2-abstract-full').style.display = 'none'; document.getElementById('2411.13504v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.09235">arXiv:2411.09235</a> <span> [<a href="https://arxiv.org/pdf/2411.09235">pdf</a>, <a href="https://arxiv.org/ps/2411.09235">ps</a>, <a href="https://arxiv.org/format/2411.09235">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> FAS for Secure and Covert Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yao%2C+J">Junteng Yao</a>, <a href="/search/?searchtype=author&query=Xin%2C+L">Liangxiao Xin</a>, <a href="/search/?searchtype=author&query=Wu%2C+T">Tuo Wu</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a>, <a href="/search/?searchtype=author&query=Wong%2C+K">Kai-Kit Wong</a>, <a href="/search/?searchtype=author&query=Yuen%2C+C">Chau Yuen</a>, <a href="/search/?searchtype=author&query=Shin%2C+H">Hyundong Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.09235v1-abstract-short" style="display: inline;"> This letter considers a fluid antenna system (FAS)-aided secure and covert communication system, where the transmitter adjusts multiple fluid antennas' positions to achieve secure and covert transmission under the threat of an eavesdropper and the detection of a warden. This letter aims to maximize the secrecy rate while satisfying the covertness constraint. Unfortunately, the optimization problem… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09235v1-abstract-full').style.display = 'inline'; document.getElementById('2411.09235v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.09235v1-abstract-full" style="display: none;"> This letter considers a fluid antenna system (FAS)-aided secure and covert communication system, where the transmitter adjusts multiple fluid antennas' positions to achieve secure and covert transmission under the threat of an eavesdropper and the detection of a warden. This letter aims to maximize the secrecy rate while satisfying the covertness constraint. Unfortunately, the optimization problem is non-convex due to the coupled variables. To tackle this, we propose an alternating optimization (AO) algorithm to alternatively optimize the optimization variables in an iterative manner. In particular, we use a penalty-based method and the majorization-minimization (MM) algorithm to optimize the transmit beamforming and fluid antennas' positions, respectively. Simulation results show that FAS can significantly improve the performance of secrecy and covertness compared to the fixed-position antenna (FPA)-based schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09235v1-abstract-full').style.display = 'none'; document.getElementById('2411.09235v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.08383">arXiv:2411.08383</a> <span> [<a href="https://arxiv.org/pdf/2411.08383">pdf</a>, <a href="https://arxiv.org/format/2411.08383">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> FAS-Driven Spectrum Sensing for Cognitive Radio Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yao%2C+J">Junteng Yao</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a>, <a href="/search/?searchtype=author&query=Wu%2C+T">Tuo Wu</a>, <a href="/search/?searchtype=author&query=Elkashlan%2C+M">Maged Elkashlan</a>, <a href="/search/?searchtype=author&query=Yuen%2C+C">Chau Yuen</a>, <a href="/search/?searchtype=author&query=Wong%2C+K">Kai-Kit Wong</a>, <a href="/search/?searchtype=author&query=Karagiannidis%2C+G+K">George K. Karagiannidis</a>, <a href="/search/?searchtype=author&query=Shin%2C+H">Hyundong Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.08383v1-abstract-short" style="display: inline;"> Cognitive radio (CR) networks face significant challenges in spectrum sensing, especially under spectrum scarcity. Fluid antenna systems (FAS) can offer an unorthodox solution due to their ability to dynamically adjust antenna positions for improved channel gain. In this letter, we study a FAS-driven CR setup where a secondary user (SU) adjusts the positions of fluid antennas to detect signals fro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08383v1-abstract-full').style.display = 'inline'; document.getElementById('2411.08383v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.08383v1-abstract-full" style="display: none;"> Cognitive radio (CR) networks face significant challenges in spectrum sensing, especially under spectrum scarcity. Fluid antenna systems (FAS) can offer an unorthodox solution due to their ability to dynamically adjust antenna positions for improved channel gain. In this letter, we study a FAS-driven CR setup where a secondary user (SU) adjusts the positions of fluid antennas to detect signals from the primary user (PU). We aim to maximize the detection probability under the constraints of the false alarm probability and the received beamforming of the SU. To address this problem, we first derive a closed-form expression for the optimal detection threshold and reformulate the problem to find its solution. Then an alternating optimization (AO) scheme is proposed to decompose the problem into several sub-problems, addressing both the received beamforming and the antenna positions at the SU. The beamforming subproblem is addressed using a closed-form solution, while the fluid antenna positions are solved by successive convex approximation (SCA). Simulation results reveal that the proposed algorithm provides significant improvements over traditional fixed-position antenna (FPA) schemes in terms of spectrum sensing performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08383v1-abstract-full').style.display = 'none'; document.getElementById('2411.08383v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07268">arXiv:2411.07268</a> <span> [<a href="https://arxiv.org/pdf/2411.07268">pdf</a>, <a href="https://arxiv.org/format/2411.07268">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3233/FAIA240685">10.3233/FAIA240685 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Target-driven Attack for Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+C">Chong Zhang</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Mingyu Jin</a>, <a href="/search/?searchtype=author&query=Shu%2C+D">Dong Shu</a>, <a href="/search/?searchtype=author&query=Wang%2C+T">Taowen Wang</a>, <a href="/search/?searchtype=author&query=Liu%2C+D">Dongfang Liu</a>, <a href="/search/?searchtype=author&query=Jin%2C+X">Xiaobo Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.07268v2-abstract-short" style="display: inline;"> Current large language models (LLM) provide a strong foundation for large-scale user-oriented natural language tasks. Many users can easily inject adversarial text or instructions through the user interface, thus causing LLM model security challenges like the language model not giving the correct answer. Although there is currently a large amount of research on black-box attacks, most of these bla… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07268v2-abstract-full').style.display = 'inline'; document.getElementById('2411.07268v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07268v2-abstract-full" style="display: none;"> Current large language models (LLM) provide a strong foundation for large-scale user-oriented natural language tasks. Many users can easily inject adversarial text or instructions through the user interface, thus causing LLM model security challenges like the language model not giving the correct answer. Although there is currently a large amount of research on black-box attacks, most of these black-box attacks use random and heuristic strategies. It is unclear how these strategies relate to the success rate of attacks and thus effectively improve model robustness. To solve this problem, we propose our target-driven black-box attack method to maximize the KL divergence between the conditional probabilities of the clean text and the attack text to redefine the attack's goal. We transform the distance maximization problem into two convex optimization problems based on the attack goal to solve the attack text and estimate the covariance. Furthermore, the projected gradient descent algorithm solves the vector corresponding to the attack text. Our target-driven black-box attack approach includes two attack strategies: token manipulation and misinformation attack. Experimental results on multiple Large Language Models and datasets demonstrate the effectiveness of our attack method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07268v2-abstract-full').style.display = 'none'; document.getElementById('2411.07268v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 7 figures. This work is an extension of the arXiv:2404.07234 work. We propose new methods. 27th European Conference on Artificial Intelligence 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.05990">arXiv:2411.05990</a> <span> [<a href="https://arxiv.org/pdf/2411.05990">pdf</a>, <a href="https://arxiv.org/format/2411.05990">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Game-theoretic LLM: Agent Workflow for Negotiation Games </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Hua%2C+W">Wenyue Hua</a>, <a href="/search/?searchtype=author&query=Liu%2C+O">Ollie Liu</a>, <a href="/search/?searchtype=author&query=Li%2C+L">Lingyao Li</a>, <a href="/search/?searchtype=author&query=Amayuelas%2C+A">Alfonso Amayuelas</a>, <a href="/search/?searchtype=author&query=Chen%2C+J">Julie Chen</a>, <a href="/search/?searchtype=author&query=Jiang%2C+L">Lucas Jiang</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Mingyu Jin</a>, <a href="/search/?searchtype=author&query=Fan%2C+L">Lizhou Fan</a>, <a href="/search/?searchtype=author&query=Sun%2C+F">Fei Sun</a>, <a href="/search/?searchtype=author&query=Wang%2C+W">William Wang</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xintong Wang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yongfeng Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.05990v2-abstract-short" style="display: inline;"> This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory. We evaluate several state-of-the-art LLMs across a spectrum of complete-information and incomplete-information games. Our findings reveal that LLMs frequently deviate from rational strategies, particularly as the complexity of the game inc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05990v2-abstract-full').style.display = 'inline'; document.getElementById('2411.05990v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.05990v2-abstract-full" style="display: none;"> This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory. We evaluate several state-of-the-art LLMs across a spectrum of complete-information and incomplete-information games. Our findings reveal that LLMs frequently deviate from rational strategies, particularly as the complexity of the game increases with larger payoff matrices or deeper sequential trees. To address these limitations, we design multiple game-theoretic workflows that guide the reasoning and decision-making processes of LLMs. These workflows aim to enhance the models' ability to compute Nash Equilibria and make rational choices, even under conditions of uncertainty and incomplete information. Experimental results demonstrate that the adoption of these workflows significantly improves the rationality and robustness of LLMs in game-theoretic tasks. Specifically, with the workflow, LLMs exhibit marked improvements in identifying optimal strategies, achieving near-optimal allocations in negotiation scenarios, and reducing susceptibility to exploitation during negotiations. Furthermore, we explore the meta-strategic considerations of whether it is rational for agents to adopt such workflows, recognizing that the decision to use or forgo the workflow constitutes a game-theoretic issue in itself. Our research contributes to a deeper understanding of LLMs' decision-making capabilities in strategic contexts and provides insights into enhancing their rationality through structured workflows. The findings have implications for the development of more robust and strategically sound AI agents capable of navigating complex interactive environments. Code and data supporting this study are available at \url{https://github.com/Wenyueh/game_theory}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05990v2-abstract-full').style.display = 'none'; document.getElementById('2411.05990v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">45 pages, 12 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.04669">arXiv:2411.04669</a> <span> [<a href="https://arxiv.org/pdf/2411.04669">pdf</a>, <a href="https://arxiv.org/format/2411.04669">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> EffiCANet: Efficient Time Series Forecasting with Convolutional Attention </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhou%2C+X">Xinxing Zhou</a>, <a href="/search/?searchtype=author&query=Ye%2C+J">Jiaqi Ye</a>, <a href="/search/?searchtype=author&query=Zhao%2C+S">Shubao Zhao</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a>, <a href="/search/?searchtype=author&query=Yang%2C+C">Chengyi Yang</a>, <a href="/search/?searchtype=author&query=Wen%2C+Y">Yanlong Wen</a>, <a href="/search/?searchtype=author&query=Yuan%2C+X">Xiaojie Yuan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.04669v1-abstract-short" style="display: inline;"> The exponential growth of multivariate time series data from sensor networks in domains like industrial monitoring and smart cities requires efficient and accurate forecasting models. Current deep learning methods often fail to adequately capture long-range dependencies and complex inter-variable relationships, especially under real-time processing constraints. These limitations arise as many mode… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.04669v1-abstract-full').style.display = 'inline'; document.getElementById('2411.04669v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.04669v1-abstract-full" style="display: none;"> The exponential growth of multivariate time series data from sensor networks in domains like industrial monitoring and smart cities requires efficient and accurate forecasting models. Current deep learning methods often fail to adequately capture long-range dependencies and complex inter-variable relationships, especially under real-time processing constraints. These limitations arise as many models are optimized for either short-term forecasting with limited receptive fields or long-term accuracy at the cost of efficiency. Additionally, dynamic and intricate interactions between variables in real-world data further complicate modeling efforts. To address these limitations, we propose EffiCANet, an Efficient Convolutional Attention Network designed to enhance forecasting accuracy while maintaining computational efficiency. EffiCANet integrates three key components: (1) a Temporal Large-kernel Decomposed Convolution (TLDC) module that captures long-term temporal dependencies while reducing computational overhead; (2) an Inter-Variable Group Convolution (IVGC) module that captures complex and evolving relationships among variables; and (3) a Global Temporal-Variable Attention (GTVA) mechanism that prioritizes critical temporal and inter-variable features. Extensive evaluations across nine benchmark datasets show that EffiCANet achieves the maximum reduction of 10.02% in MAE over state-of-the-art models, while cutting computational costs by 26.2% relative to conventional large-kernel convolution methods, thanks to its efficient decomposition strategy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.04669v1-abstract-full').style.display = 'none'; document.getElementById('2411.04669v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.04407">arXiv:2411.04407</a> <span> [<a href="https://arxiv.org/pdf/2411.04407">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Superconductivity">cond-mat.supr-con</span> </div> </div> <p class="title is-5 mathjax"> Pressure-Induced Superconductivity at 18.2 K in CuIr2S4 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+B">Bijuan Chen</a>, <a href="/search/?searchtype=author&query=Gu%2C+Y">Yuhao Gu</a>, <a href="/search/?searchtype=author&query=Wang%2C+D">Dong Wang</a>, <a href="/search/?searchtype=author&query=Shao%2C+D">Dexi Shao</a>, <a href="/search/?searchtype=author&query=Deng%2C+W">Wen Deng</a>, <a href="/search/?searchtype=author&query=Han%2C+X">Xin Han</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Meiling Jin</a>, <a href="/search/?searchtype=author&query=Zeng%2C+Y">Yu Zeng</a>, <a href="/search/?searchtype=author&query=Ishii%2C+H">Hirofumi Ishii</a>, <a href="/search/?searchtype=author&query=Liao%2C+Y">Yen-Fa Liao</a>, <a href="/search/?searchtype=author&query=Zhang%2C+D">Dongzhou Zhang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+J">Jianbo Zhang</a>, <a href="/search/?searchtype=author&query=Long%2C+Y">Youwen Long</a>, <a href="/search/?searchtype=author&query=Zhu%2C+J">Jinlong Zhu</a>, <a href="/search/?searchtype=author&query=Yang%2C+L">Liuxiang Yang</a>, <a href="/search/?searchtype=author&query=Xiao%2C+H">Hong Xiao</a>, <a href="/search/?searchtype=author&query=Nei%2C+J">Jia-cai Nei</a>, <a href="/search/?searchtype=author&query=Shi%2C+Y">Youguo Shi</a>, <a href="/search/?searchtype=author&query=Jin%2C+C">Changqing Jin</a>, <a href="/search/?searchtype=author&query=Hu%2C+J">Jiangping Hu</a>, <a href="/search/?searchtype=author&query=Mao%2C+H">Ho-kwang Mao</a>, <a href="/search/?searchtype=author&query=Ding%2C+Y">Yang Ding</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.04407v2-abstract-short" style="display: inline;"> Attaining superconducting critical temperatures (Tc) beyond the limit around 14 K observed thus far in spinel compounds AB2X4 (A, B = transition metals, X = O/chalcogen) could elucidate interaction intricacies and inform materials design. This work spotlights CuIr2S4, which exhibits a distinct metal-insulator transition below 230 K, as an unconventional candidate for activation under high pressure… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.04407v2-abstract-full').style.display = 'inline'; document.getElementById('2411.04407v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.04407v2-abstract-full" style="display: none;"> Attaining superconducting critical temperatures (Tc) beyond the limit around 14 K observed thus far in spinel compounds AB2X4 (A, B = transition metals, X = O/chalcogen) could elucidate interaction intricacies and inform materials design. This work spotlights CuIr2S4, which exhibits a distinct metal-insulator transition below 230 K, as an unconventional candidate for activation under high pressure. Through transport, diffraction, and spectroscopy experiments conducted at pressures up to 224 GPa, we unveil pressure-tuning that suppressed CuIr2S4's transition, yielding two superconducting phases with an un-precedented Tc for spinels. Initially, 3.8 K onset rose monotonically, reaching 18.2 K at 133 GPa. Unexpectedly, a distinct phase with Tc = 2.2 K distinctly emerged at higher pressures, intimating unconventional couplings. Our findings suggest that both geometric frustration and electron-electron interactions play crucial roles in the superconductivity observed in CuIr2S4. The findings stretch perceived temperature limits in spinels and provide structure-property insights to guide the optimiza-tion of quantum materials interactions for tailored targeted functionalities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.04407v2-abstract-full').style.display = 'none'; document.getElementById('2411.04407v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 7 gifures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.01215">arXiv:2411.01215</a> <span> [<a href="https://arxiv.org/pdf/2411.01215">pdf</a>, <a href="https://arxiv.org/format/2411.01215">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> Detection of two TeV gamma-ray outbursts from NGC 1275 by LHAASO </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Cao%2C+Z">Zhen Cao</a>, <a href="/search/?searchtype=author&query=Aharonian%2C+F">F. Aharonian</a>, <a href="/search/?searchtype=author&query=Axikegu"> Axikegu</a>, <a href="/search/?searchtype=author&query=Bai%2C+Y+X">Y. X. Bai</a>, <a href="/search/?searchtype=author&query=Bao%2C+Y+W">Y. W. Bao</a>, <a href="/search/?searchtype=author&query=Bastieri%2C+D">D. Bastieri</a>, <a href="/search/?searchtype=author&query=Bi%2C+X+J">X. J. Bi</a>, <a href="/search/?searchtype=author&query=Bi%2C+Y+J">Y. J. Bi</a>, <a href="/search/?searchtype=author&query=Cai%2C+J+T">J. T. Cai</a>, <a href="/search/?searchtype=author&query=Cao%2C+Q">Q. Cao</a>, <a href="/search/?searchtype=author&query=Cao%2C+W+Y">W. Y. Cao</a>, <a href="/search/?searchtype=author&query=Cao%2C+Z">Zhe Cao</a>, <a href="/search/?searchtype=author&query=Chang%2C+J">J. Chang</a>, <a href="/search/?searchtype=author&query=Chang%2C+J+F">J. F. Chang</a>, <a href="/search/?searchtype=author&query=Chen%2C+A+M">A. M. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+E+S">E. S. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Liang Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Lin Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Long Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+M+J">M. J. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+M+L">M. L. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+Q+H">Q. H. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S+H">S. H. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S+Z">S. Z. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+T+L">T. L. Chen</a> , et al. (254 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.01215v2-abstract-short" style="display: inline;"> The Water Cherenkov Detector Array (WCDA) is one of the components of Large High Altitude Air Shower Observatory (LHAASO) and can monitor any sources over two-thirds of the sky for up to 7 hours per day with >98\% duty cycle. In this work, we report the detection of two outbursts of the Fanaroff-Riley I radio galaxy NGC 1275 that were detected by LHAASO-WCDA between November 2022 and January 2023… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01215v2-abstract-full').style.display = 'inline'; document.getElementById('2411.01215v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.01215v2-abstract-full" style="display: none;"> The Water Cherenkov Detector Array (WCDA) is one of the components of Large High Altitude Air Shower Observatory (LHAASO) and can monitor any sources over two-thirds of the sky for up to 7 hours per day with >98\% duty cycle. In this work, we report the detection of two outbursts of the Fanaroff-Riley I radio galaxy NGC 1275 that were detected by LHAASO-WCDA between November 2022 and January 2023 with statistical significance of 5.2~$蟽$ and 8.3~$蟽$. The observed spectral energy distribution in the range from 500 GeV to 3 TeV is fitted by a power-law with a best-fit spectral index of $伪=-3.37\pm0.52$ and $-3.35\pm0.29$, respectively. The outburst flux above 0.5~TeV was ($4.55\pm 4.21)\times~10^{-11}~\rm cm^{-2}~s^{-1}$ and ($3.45\pm 1.78)\times~10^{-11}~\rm cm^{-2}~s^{-1}$, corresponding to 60\%, 45\% of Crab Nebula flux. Variation analysis reveals the variability time-scale of days at the TeV energy band. A simple test by one-zone synchrotron self-Compton model reproduces the data in the gamma-ray band well. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01215v2-abstract-full').style.display = 'none'; document.getElementById('2411.01215v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 8 figures, 3 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.00673">arXiv:2411.00673</a> <span> [<a href="https://arxiv.org/pdf/2411.00673">pdf</a>, <a href="https://arxiv.org/format/2411.00673">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> </div> </div> <p class="title is-5 mathjax"> First-Principles Investigation of Grain Boundary Effects on Fluorine-Induced Initial Corrosion of NiCr Alloys </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Arkoub%2C+H">Hamdy Arkoub</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Miaomiao Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.00673v1-abstract-short" style="display: inline;"> Chromium depletion at grain boundaries (GBs) due to selective attack is a critical issue in the molten salt corrosion of NiCr alloys. Despite the importance of GBs in this process from numerous experimental studies, most theoretical work has predominantly focused on fluorine interactions with idealized crystalline surfaces, neglecting the complexity of GB local environments. This study aims to bri… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00673v1-abstract-full').style.display = 'inline'; document.getElementById('2411.00673v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.00673v1-abstract-full" style="display: none;"> Chromium depletion at grain boundaries (GBs) due to selective attack is a critical issue in the molten salt corrosion of NiCr alloys. Despite the importance of GBs in this process from numerous experimental studies, most theoretical work has predominantly focused on fluorine interactions with idealized crystalline surfaces, neglecting the complexity of GB local environments. This study aims to bridge that gap by employing density functional theory (DFT) to investigate the atomic interactions and Cr dissolution mechanisms at GB in NiCr alloys under molten fluoride salt environments. Specifically, a $危$5(210)/(001) symmetrical tilt GB is constructed to explore the adsorption energies of fluorine on Ni(100) and Cr-doped Ni(100) surfaces. We find that fluorine exhibits a strong preference for binding at GB sites, with Cr doping amplifying this effect, leading to higher adsorption energies compared to bulk Ni surfaces. Fluorine bonding with Cr significantly alters the interaction between Cr-F complexes and Ni substrate, and the consequent dissolution barriers for Cr atoms; the formation of CrF$_3$ largely reduces the energy barrier for Cr dissolution. This work highlights the essential role of GBs in enhancing fluorine adsorption and accelerating Cr depletion, providing new insights into the mechanisms of early-stage corrosion in NiCr alloys. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00673v1-abstract-full').style.display = 'none'; document.getElementById('2411.00673v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.20199">arXiv:2410.20199</a> <span> [<a href="https://arxiv.org/pdf/2410.20199">pdf</a>, <a href="https://arxiv.org/format/2410.20199">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Beigi%2C+M">Mohammad Beigi</a>, <a href="/search/?searchtype=author&query=Wang%2C+S">Sijia Wang</a>, <a href="/search/?searchtype=author&query=Shen%2C+Y">Ying Shen</a>, <a href="/search/?searchtype=author&query=Lin%2C+Z">Zihao Lin</a>, <a href="/search/?searchtype=author&query=Kulkarni%2C+A">Adithya Kulkarni</a>, <a href="/search/?searchtype=author&query=He%2C+J">Jianfeng He</a>, <a href="/search/?searchtype=author&query=Chen%2C+F">Feng Chen</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a>, <a href="/search/?searchtype=author&query=Cho%2C+J">Jin-Hee Cho</a>, <a href="/search/?searchtype=author&query=Zhou%2C+D">Dawei Zhou</a>, <a href="/search/?searchtype=author&query=Lu%2C+C">Chang-Tien Lu</a>, <a href="/search/?searchtype=author&query=Huang%2C+L">Lifu Huang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.20199v1-abstract-short" style="display: inline;"> In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating the uncertainty in their predictions has become crucial. Current methods often struggle to accurately identify, measure, and address the true uncertainty, with many focusing primarily on estimating model confidence. This… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20199v1-abstract-full').style.display = 'inline'; document.getElementById('2410.20199v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.20199v1-abstract-full" style="display: none;"> In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating the uncertainty in their predictions has become crucial. Current methods often struggle to accurately identify, measure, and address the true uncertainty, with many focusing primarily on estimating model confidence. This discrepancy is largely due to an incomplete understanding of where, when, and how uncertainties are injected into models. This paper introduces a comprehensive framework specifically designed to identify and understand the types and sources of uncertainty, aligned with the unique characteristics of LLMs. Our framework enhances the understanding of the diverse landscape of uncertainties by systematically categorizing and defining each type, establishing a solid foundation for developing targeted methods that can precisely quantify these uncertainties. We also provide a detailed introduction to key related concepts and examine the limitations of current methods in mission-critical and safety-sensitive applications. The paper concludes with a perspective on future directions aimed at enhancing the reliability and practical adoption of these methods in real-world scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20199v1-abstract-full').style.display = 'none'; document.getElementById('2410.20199v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.16032">arXiv:2410.16032</a> <span> [<a href="https://arxiv.org/pdf/2410.16032">pdf</a>, <a href="https://arxiv.org/format/2410.16032">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+S">Shiyu Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jiawei Li</a>, <a href="/search/?searchtype=author&query=Shi%2C+X">Xiaoming Shi</a>, <a href="/search/?searchtype=author&query=Ye%2C+Z">Zhou Ye</a>, <a href="/search/?searchtype=author&query=Mo%2C+B">Baichuan Mo</a>, <a href="/search/?searchtype=author&query=Lin%2C+W">Wenze Lin</a>, <a href="/search/?searchtype=author&query=Ju%2C+S">Shengtong Ju</a>, <a href="/search/?searchtype=author&query=Chu%2C+Z">Zhixuan Chu</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.16032v1-abstract-short" style="display: inline;"> Time series analysis plays a critical role in numerous applications, supporting tasks such as forecasting, classification, anomaly detection, and imputation. In this work, we present the time series pattern machine (TSPM), a model designed to excel in a broad range of time series tasks through powerful representation and pattern extraction capabilities. Traditional time series models often struggl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16032v1-abstract-full').style.display = 'inline'; document.getElementById('2410.16032v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16032v1-abstract-full" style="display: none;"> Time series analysis plays a critical role in numerous applications, supporting tasks such as forecasting, classification, anomaly detection, and imputation. In this work, we present the time series pattern machine (TSPM), a model designed to excel in a broad range of time series tasks through powerful representation and pattern extraction capabilities. Traditional time series models often struggle to capture universal patterns, limiting their effectiveness across diverse tasks. To address this, we define multiple scales in the time domain and various resolutions in the frequency domain, employing various mixing strategies to extract intricate, task-adaptive time series patterns. Specifically, we introduce a general-purpose TSPM that processes multi-scale time series using (1) multi-resolution time imaging (MRTI), (2) time image decomposition (TID), (3) multi-scale mixing (MCM), and (4) multi-resolution mixing (MRM) to extract comprehensive temporal patterns. MRTI transforms multi-scale time series into multi-resolution time images, capturing patterns across both temporal and frequency domains. TID leverages dual-axis attention to extract seasonal and trend patterns, while MCM hierarchically aggregates these patterns across scales. MRM adaptively integrates all representations across resolutions. This method achieves state-of-the-art performance across 8 time series analytical tasks, consistently surpassing both general-purpose and task-specific models. Our work marks a promising step toward the next generation of TSPMs, paving the way for further advancements in time series analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16032v1-abstract-full').style.display = 'none'; document.getElementById('2410.16032v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.12360">arXiv:2410.12360</a> <span> [<a href="https://arxiv.org/pdf/2410.12360">pdf</a>, <a href="https://arxiv.org/format/2410.12360">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Towards Neural Scaling Laws for Time Series Foundation Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yao%2C+Q">Qingren Yao</a>, <a href="/search/?searchtype=author&query=Yang%2C+C+H">Chao-Han Huck Yang</a>, <a href="/search/?searchtype=author&query=Jiang%2C+R">Renhe Jiang</a>, <a href="/search/?searchtype=author&query=Liang%2C+Y">Yuxuan Liang</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a>, <a href="/search/?searchtype=author&query=Pan%2C+S">Shirui Pan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.12360v2-abstract-short" style="display: inline;"> Scaling laws offer valuable insights into the design of time series foundation models (TSFMs). However, previous research has largely focused on the scaling laws of TSFMs for in-distribution (ID) data, leaving their out-of-distribution (OOD) scaling behavior and the influence of model architectures less explored. In this work, we examine two common TSFM architectures, encoder-only and decoder-only… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.12360v2-abstract-full').style.display = 'inline'; document.getElementById('2410.12360v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.12360v2-abstract-full" style="display: none;"> Scaling laws offer valuable insights into the design of time series foundation models (TSFMs). However, previous research has largely focused on the scaling laws of TSFMs for in-distribution (ID) data, leaving their out-of-distribution (OOD) scaling behavior and the influence of model architectures less explored. In this work, we examine two common TSFM architectures, encoder-only and decoder-only Transformers, and investigate their scaling behavior on both ID and OOD data. These models are trained and evaluated across varying parameter counts, compute budgets, and dataset sizes. Our experiments reveal that the log-likelihood loss of TSFMs exhibits similar scaling behavior in both OOD and ID settings. We further compare the scaling properties across different architectures, incorporating two state-of-the-art TSFMs as case studies, showing that model architecture plays a significant role in scaling. The encoder-only Transformers demonstrate better scalability than the decoder-only Transformers, while the architectural enhancements in the two advanced TSFMs primarily improve ID performance but reduce OOD scalability. While scaling up TSFMs is expected to drive performance breakthroughs, the lack of a comprehensive understanding of TSFM scaling laws has hindered the development of a robust framework to guide model scaling. We fill this gap in this work by synthesizing our findings and providing practical guidelines for designing and scaling larger TSFMs with enhanced model capabilities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.12360v2-abstract-full').style.display = 'none'; document.getElementById('2410.12360v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by the 13th International Conference on Learning Representations (ICLR 2025)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.04425">arXiv:2410.04425</a> <span> [<a href="https://arxiv.org/pdf/2410.04425">pdf</a>, <a href="https://arxiv.org/format/2410.04425">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> LHAASO detection of very-high-energy gamma-ray emission surrounding PSR J0248+6021 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Cao%2C+Z">Zhen Cao</a>, <a href="/search/?searchtype=author&query=Aharonian%2C+F">F. Aharonian</a>, <a href="/search/?searchtype=author&query=An%2C+Q">Q. An</a>, <a href="/search/?searchtype=author&query=Axikegu"> Axikegu</a>, <a href="/search/?searchtype=author&query=Bai%2C+Y+X">Y. X. Bai</a>, <a href="/search/?searchtype=author&query=Bao%2C+Y+W">Y. W. Bao</a>, <a href="/search/?searchtype=author&query=Bastieri%2C+D">D. Bastieri</a>, <a href="/search/?searchtype=author&query=Bi%2C+X+J">X. J. Bi</a>, <a href="/search/?searchtype=author&query=Bi%2C+Y+J">Y. J. Bi</a>, <a href="/search/?searchtype=author&query=Cai%2C+J+T">J. T. Cai</a>, <a href="/search/?searchtype=author&query=Cao%2C+Q">Q. Cao</a>, <a href="/search/?searchtype=author&query=Cao%2C+W+Y">W. Y. Cao</a>, <a href="/search/?searchtype=author&query=Cao%2C+Z">Zhe Cao</a>, <a href="/search/?searchtype=author&query=Chang%2C+J">J. Chang</a>, <a href="/search/?searchtype=author&query=Chang%2C+J+F">J. F. Chang</a>, <a href="/search/?searchtype=author&query=Chen%2C+A+M">A. M. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+E+S">E. S. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Liang Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Lin Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Long Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+M+J">M. J. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+M+L">M. L. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+Q+H">Q. H. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S+H">S. H. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S+Z">S. Z. Chen</a> , et al. (255 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.04425v2-abstract-short" style="display: inline;"> We report the detection of an extended very-high-energy (VHE) gamma-ray source coincident with the location of middle-aged (62.4~\rm kyr) pulsar PSR J0248+6021, by using the LHAASO-WCDA data of live 796 days and LHAASO-KM2A data of live 1216 days. A significant excess of \gray induced showers is observed both by WCDA in energy bands of 1-25~\rm TeV and KM2A in energy bands of $>$ 25~\rm TeV with 7… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.04425v2-abstract-full').style.display = 'inline'; document.getElementById('2410.04425v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.04425v2-abstract-full" style="display: none;"> We report the detection of an extended very-high-energy (VHE) gamma-ray source coincident with the location of middle-aged (62.4~\rm kyr) pulsar PSR J0248+6021, by using the LHAASO-WCDA data of live 796 days and LHAASO-KM2A data of live 1216 days. A significant excess of \gray induced showers is observed both by WCDA in energy bands of 1-25~\rm TeV and KM2A in energy bands of $>$ 25~\rm TeV with 7.3 $蟽$ and 13.5 $蟽$, respectively. The best-fit position derived through WCDA data is R.A. = 42.06$^\circ \pm$ 0.12$^\circ$ and Dec. = 60.24$^\circ \pm $ 0.13$^\circ$ with an extension of 0.69$^\circ\pm$0.15$^\circ$ and that of the KM2A data is R.A.= 42.29$^\circ \pm $ 0.13$^\circ$ and Dec. = 60.38$^\circ \pm$ 0.07$^\circ$ with an extension of 0.37$^\circ\pm$0.07$^\circ$. No clear extended multiwavelength counterpart of this LHAASO source has been found from the radio band to the GeV band. The most plausible explanation of the VHE \gray emission is the inverse Compton process of highly relativistic electrons and positrons injected by the pulsar. These electrons/positrons are hypothesized to be either confined within the pulsar wind nebula or to have already escaped into the interstellar medium, forming a pulsar halo. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.04425v2-abstract-full').style.display = 'none'; document.getElementById('2410.04425v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 10 figures, Accepted by Sci. China-Phys. Mech. Astron</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.03545">arXiv:2410.03545</a> <span> [<a href="https://arxiv.org/pdf/2410.03545">pdf</a>, <a href="https://arxiv.org/format/2410.03545">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Enhancing Data Quality through Simple De-duplication: Navigating Responsible Computational Social Science Research </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Mu%2C+Y">Yida Mu</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Mali Jin</a>, <a href="/search/?searchtype=author&query=Song%2C+X">Xingyi Song</a>, <a href="/search/?searchtype=author&query=Aletras%2C+N">Nikolaos Aletras</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.03545v1-abstract-short" style="display: inline;"> Research in natural language processing (NLP) for Computational Social Science (CSS) heavily relies on data from social media platforms. This data plays a crucial role in the development of models for analysing socio-linguistic phenomena within online communities. In this work, we conduct an in-depth examination of 20 datasets extensively used in NLP for CSS to comprehensively examine data quality… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03545v1-abstract-full').style.display = 'inline'; document.getElementById('2410.03545v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.03545v1-abstract-full" style="display: none;"> Research in natural language processing (NLP) for Computational Social Science (CSS) heavily relies on data from social media platforms. This data plays a crucial role in the development of models for analysing socio-linguistic phenomena within online communities. In this work, we conduct an in-depth examination of 20 datasets extensively used in NLP for CSS to comprehensively examine data quality. Our analysis reveals that social media datasets exhibit varying levels of data duplication. Consequently, this gives rise to challenges like label inconsistencies and data leakage, compromising the reliability of models. Our findings also suggest that data duplication has an impact on the current claims of state-of-the-art performance, potentially leading to an overestimation of model effectiveness in real-world scenarios. Finally, we propose new protocols and best practices for improving dataset development from social media data and its usage. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03545v1-abstract-full').style.display = 'none'; document.getElementById('2410.03545v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at EMNLP 2024 Main</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.18924">arXiv:2409.18924</a> <span> [<a href="https://arxiv.org/pdf/2409.18924">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yu%2C+H">Huizi Yu</a>, <a href="/search/?searchtype=author&query=Zhou%2C+J">Jiayan Zhou</a>, <a href="/search/?searchtype=author&query=Li%2C+L">Lingyao Li</a>, <a href="/search/?searchtype=author&query=Chen%2C+S">Shan Chen</a>, <a href="/search/?searchtype=author&query=Gallifant%2C+J">Jack Gallifant</a>, <a href="/search/?searchtype=author&query=Shi%2C+A">Anye Shi</a>, <a href="/search/?searchtype=author&query=Li%2C+X">Xiang Li</a>, <a href="/search/?searchtype=author&query=Hua%2C+W">Wenyue Hua</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Mingyu Jin</a>, <a href="/search/?searchtype=author&query=Chen%2C+G">Guang Chen</a>, <a href="/search/?searchtype=author&query=Zhou%2C+Y">Yang Zhou</a>, <a href="/search/?searchtype=author&query=Li%2C+Z">Zhao Li</a>, <a href="/search/?searchtype=author&query=Gupte%2C+T">Trisha Gupte</a>, <a href="/search/?searchtype=author&query=Chen%2C+M">Ming-Li Chen</a>, <a href="/search/?searchtype=author&query=Azizi%2C+Z">Zahra Azizi</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yongfeng Zhang</a>, <a href="/search/?searchtype=author&query=Assimes%2C+T+L">Themistocles L. Assimes</a>, <a href="/search/?searchtype=author&query=Ma%2C+X">Xin Ma</a>, <a href="/search/?searchtype=author&query=Bitterman%2C+D+S">Danielle S. Bitterman</a>, <a href="/search/?searchtype=author&query=Lu%2C+L">Lin Lu</a>, <a href="/search/?searchtype=author&query=Fan%2C+L">Lizhou Fan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.18924v2-abstract-short" style="display: inline;"> Simulated patient systems play a crucial role in modern medical education and research, providing safe, integrative learning environments and enabling clinical decision-making simulations. Large Language Models (LLM) could advance simulated patient systems by replicating medical conditions and patient-doctor interactions with high fidelity and low cost. However, ensuring the effectiveness and trus… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18924v2-abstract-full').style.display = 'inline'; document.getElementById('2409.18924v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.18924v2-abstract-full" style="display: none;"> Simulated patient systems play a crucial role in modern medical education and research, providing safe, integrative learning environments and enabling clinical decision-making simulations. Large Language Models (LLM) could advance simulated patient systems by replicating medical conditions and patient-doctor interactions with high fidelity and low cost. However, ensuring the effectiveness and trustworthiness of these systems remains a challenge, as they require a large, diverse, and precise patient knowledgebase, along with a robust and stable knowledge diffusion to users. Here, we developed AIPatient, an advanced simulated patient system with AIPatient Knowledge Graph (AIPatient KG) as the input and the Reasoning Retrieval-Augmented Generation (Reasoning RAG) agentic workflow as the generation backbone. AIPatient KG samples data from Electronic Health Records (EHRs) in the Medical Information Mart for Intensive Care (MIMIC)-III database, producing a clinically diverse and relevant cohort of 1,495 patients with high knowledgebase validity (F1 0.89). Reasoning RAG leverages six LLM powered agents spanning tasks including retrieval, KG query generation, abstraction, checker, rewrite, and summarization. This agentic framework reaches an overall accuracy of 94.15% in EHR-based medical Question Answering (QA), outperforming benchmarks that use either no agent or only partial agent integration. Our system also presents high readability (median Flesch Reading Ease 77.23; median Flesch Kincaid Grade 5.6), robustness (ANOVA F-value 0.6126, p>0.1), and stability (ANOVA F-value 0.782, p>0.1). The promising performance of the AIPatient system highlights its potential to support a wide range of applications, including medical education, model evaluation, and system integration. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18924v2-abstract-full').style.display = 'none'; document.getElementById('2409.18924v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">42 pages, 6 figures, 7 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.18103">arXiv:2409.18103</a> <span> [<a href="https://arxiv.org/pdf/2409.18103">pdf</a>, <a href="https://arxiv.org/format/2409.18103">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> </div> </div> <p class="title is-5 mathjax"> The Self-Organized Criticality of Dark Matter in the Early Universe </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jin%2C+M">Mingjie Jin</a>, <a href="/search/?searchtype=author&query=Li%2C+Y">Ying Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.18103v3-abstract-short" style="display: inline;"> We propose a new mechanism for dark matter freeze-out that results in the self-organized criticality in dark matter production, wherein the final relic abundance is independent of initial inputs, in analogy to scale invariance in other realms of non-equilibrium physics. The dynamic of self-organized is triggered through a semi-annihilation process $蠂蠁\to 蠁蠁$ in the premise of the instability of da… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18103v3-abstract-full').style.display = 'inline'; document.getElementById('2409.18103v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.18103v3-abstract-full" style="display: none;"> We propose a new mechanism for dark matter freeze-out that results in the self-organized criticality in dark matter production, wherein the final relic abundance is independent of initial inputs, in analogy to scale invariance in other realms of non-equilibrium physics. The dynamic of self-organized is triggered through a semi-annihilation process $蠂蠁\to 蠁蠁$ in the premise of the instability of dark partner $蠁$, where $蠂$ is the dark matter candidate. The relic abundance can be analytically ascertained when the dark partner is slightly heavier than the dark matter, which permits a substantially heavy dark matter mass without violating unitarity bounds. We demonstrate that this process provides a bridge between the freeze-in and freeze-out mechanisms, and that the intricate dynamics of self-organized criticality can be actualized in a thermal dark sector bath. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18103v3-abstract-full').style.display = 'none'; document.getElementById('2409.18103v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 2 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.18020">arXiv:2409.18020</a> <span> [<a href="https://arxiv.org/pdf/2409.18020">pdf</a>, <a href="https://arxiv.org/format/2409.18020">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Solar and Stellar Astrophysics">astro-ph.SR</span> </div> </div> <p class="title is-5 mathjax"> Exploring the Dynamics of CME-Driven Shocks by Comparing Numerical Modeling and Observations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jin%2C+M">Meng Jin</a>, <a href="/search/?searchtype=author&query=Li%2C+G">Gang Li</a>, <a href="/search/?searchtype=author&query=Nitta%2C+N">Nariaki Nitta</a>, <a href="/search/?searchtype=author&query=Liu%2C+W">Wei Liu</a>, <a href="/search/?searchtype=author&query=Petrosian%2C+V">Vahe Petrosian</a>, <a href="/search/?searchtype=author&query=Manchester%2C+W">Ward Manchester</a>, <a href="/search/?searchtype=author&query=Cohen%2C+C">Christina Cohen</a>, <a href="/search/?searchtype=author&query=Effenberger%2C+F">Frederic Effenberger</a>, <a href="/search/?searchtype=author&query=Ding%2C+Z">Zheyi Ding</a>, <a href="/search/?searchtype=author&query=Pesce-Rollins%2C+M">Melissa Pesce-Rollins</a>, <a href="/search/?searchtype=author&query=Omodei%2C+N">Nicola Omodei</a>, <a href="/search/?searchtype=author&query=Gopalswamy%2C+N">Nat Gopalswamy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.18020v1-abstract-short" style="display: inline;"> Shocks driven by coronal mass ejections (CMEs) are primary drivers of gradual solar energetic particle (SEP) events, posing significant risks to space technology and astronauts. Concurrently, particles accelerated at these shocks may also propagate back to the Sun, potentially generating gamma-ray emissions through pion decay. We incorporated advanced modeling and multi-messenger observations to e… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18020v1-abstract-full').style.display = 'inline'; document.getElementById('2409.18020v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.18020v1-abstract-full" style="display: none;"> Shocks driven by coronal mass ejections (CMEs) are primary drivers of gradual solar energetic particle (SEP) events, posing significant risks to space technology and astronauts. Concurrently, particles accelerated at these shocks may also propagate back to the Sun, potentially generating gamma-ray emissions through pion decay. We incorporated advanced modeling and multi-messenger observations to explore the role of CME-driven shocks in gamma-ray emissions and SEPs. Motivated by Fermi-LAT long-duration solar flares, we used the AWSoM MHD model to investigate the connection between the shocks and the properties of observed gamma-ray emissions. By coupling the AWSoM with iPATH model, we evaluate the impact of shock evolution complexity near the Sun on SEP intensity and spectra. Our result points to the importance of accurate background coronal and solar wind modeling, as well as detailed observations of CME source regions, in advancing our understanding of CME-driven shocks and the dynamics of associated energetic particles. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18020v1-abstract-full').style.display = 'none'; document.getElementById('2409.18020v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 6 figures, to appear in the Proceedings of IAU Symposium No. 388 - Solar and Stellar Coronal Mass Ejections</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.16040">arXiv:2409.16040</a> <span> [<a href="https://arxiv.org/pdf/2409.16040">pdf</a>, <a href="https://arxiv.org/format/2409.16040">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Shi%2C+X">Xiaoming Shi</a>, <a href="/search/?searchtype=author&query=Wang%2C+S">Shiyu Wang</a>, <a href="/search/?searchtype=author&query=Nie%2C+Y">Yuqi Nie</a>, <a href="/search/?searchtype=author&query=Li%2C+D">Dianqi Li</a>, <a href="/search/?searchtype=author&query=Ye%2C+Z">Zhou Ye</a>, <a href="/search/?searchtype=author&query=Wen%2C+Q">Qingsong Wen</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.16040v3-abstract-short" style="display: inline;"> Deep learning for time series forecasting has seen significant advancements over the past decades. However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and operate at a high cost, hindering the development of larger capable forecasting models in real-world applications. In response, we introduce Time-MoE, a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16040v3-abstract-full').style.display = 'inline'; document.getElementById('2409.16040v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.16040v3-abstract-full" style="display: none;"> Deep learning for time series forecasting has seen significant advancements over the past decades. However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and operate at a high cost, hindering the development of larger capable forecasting models in real-world applications. In response, we introduce Time-MoE, a scalable and unified architecture designed to pre-train larger, more capable forecasting foundation models while reducing inference costs. By leveraging a sparse mixture-of-experts (MoE) design, Time-MoE enhances computational efficiency by activating only a subset of networks for each prediction, reducing computational load while maintaining high model capacity. This allows Time-MoE to scale effectively without a corresponding increase in inference costs. Time-MoE comprises a family of decoder-only transformer models that operate in an auto-regressive manner and support flexible forecasting horizons with varying input context lengths. We pre-trained these models on our newly introduced large-scale data Time-300B, which spans over 9 domains and encompassing over 300 billion time points. For the first time, we scaled a time series foundation model up to 2.4 billion parameters, achieving significantly improved forecasting precision. Our results validate the applicability of scaling laws for training tokens and model size in the context of time series forecasting. Compared to dense models with the same number of activated parameters or equivalent computation budgets, our models consistently outperform them by large margin. These advancements position Time-MoE as a state-of-the-art solution for tackling real-world time series forecasting challenges with superior capability, efficiency, and flexibility. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16040v3-abstract-full').style.display = 'none'; document.getElementById('2409.16040v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by the 13th International Conference on Learning Representations (ICLR 2025)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.16020">arXiv:2409.16020</a> <span> [<a href="https://arxiv.org/pdf/2409.16020">pdf</a>, <a href="https://arxiv.org/ps/2409.16020">ps</a>, <a href="https://arxiv.org/format/2409.16020">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> BCRLB Under the Fusion Extended Kalman Filter </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lin%2C+M">Mushen Lin</a>, <a href="/search/?searchtype=author&query=Yan%2C+F">Fenggang Yan</a>, <a href="/search/?searchtype=author&query=Ren%2C+L">Lingda Ren</a>, <a href="/search/?searchtype=author&query=Meng%2C+X">Xiangtian Meng</a>, <a href="/search/?searchtype=author&query=Greco%2C+M">Maria Greco</a>, <a href="/search/?searchtype=author&query=Gini%2C+F">Fulvio Gini</a>, <a href="/search/?searchtype=author&query=Jin%2C+M">Ming Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.16020v1-abstract-short" style="display: inline;"> In the process of tracking multiple point targets in space using radar, since the targets are spatially well separated, the data between them will not be confused. Therefore, the multi-target tracking problem can be transformed into a single-target tracking problem. However, the data measured by radar nodes contains noise, clutter, and false targets, making it difficult for the fusion center to di… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16020v1-abstract-full').style.display = 'inline'; document.getElementById('2409.16020v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.16020v1-abstract-full" style="display: none;"> In the process of tracking multiple point targets in space using radar, since the targets are spatially well separated, the data between them will not be confused. Therefore, the multi-target tracking problem can be transformed into a single-target tracking problem. However, the data measured by radar nodes contains noise, clutter, and false targets, making it difficult for the fusion center to directly establish the association between radar measurements and real targets. To address this issue, the Probabilistic Data Association (PDA) algorithm is used to calculate the association probability between each radar measurement and the target, and the measurements are fused based on these probabilities. Finally, an extended Kalman filter (EKF) is used to predict the target states. Additionally, we derive the Bayesian Cram茅r-Rao Lower Bound (BCRLB) under the PDA fusion framework. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16020v1-abstract-full').style.display = 'none'; document.getElementById('2409.16020v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Jin%2C+M&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Jin%2C+M&start=0" class="pagination-link is-current" aria-label="Goto page 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