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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=Li%2C+J&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Li%2C+J&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+J&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+J&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+J&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+J&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/2411.14414">arXiv:2411.14414</a> <span> [<a href="https://arxiv.org/pdf/2411.14414">pdf</a>, <a href="https://arxiv.org/format/2411.14414">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> </div> <p class="title is-5 mathjax"> Quantum illumination advantage in quantum Doppler radar </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wei%2C+R">Rongyu Wei</a>, <a href="/search/?searchtype=author&query=Albarelli%2C+F">Francesco Albarelli</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jun Li</a>, <a href="/search/?searchtype=author&query=Giovannetti%2C+V">Vittorio Giovannetti</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="2411.14414v1-abstract-short" style="display: inline;"> A Doppler radar is a device that employs the Doppler effect to estimate the radial velocity of a moving target at a distance. Traditional radars are based on a classical description of the electromagnetic radiation, but in principle their performance can be improved employing entangled quantum probe states. For target detection, i.e. hypothesis testing, a quantum advantage exists even in the high-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14414v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14414v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14414v1-abstract-full" style="display: none;"> A Doppler radar is a device that employs the Doppler effect to estimate the radial velocity of a moving target at a distance. Traditional radars are based on a classical description of the electromagnetic radiation, but in principle their performance can be improved employing entangled quantum probe states. For target detection, i.e. hypothesis testing, a quantum advantage exists even in the high-noise regime appropriate to describe microwave fields, a protocol known as quantum illumination. In this paper, we show a similar advantage also for a quantum Doppler radar operating in presence of thermal noise, whereas so far a quantum advantage was shown in the noiseless scenario or in lidars operating at optical frequencies with negligible thermal noise. Concretely, we quantify the radar performance in terms of the quantum Fisher information, which captures the ultimate precision allowed by quantum mechanics in the asymptotic regime. We compare a classical protocol based on coherent states with a quantum one that uses multimode states obtained from spontaneous parametric downconversion. To ensure a fair comparison we match the signal energy and pulse duration. We show that a 3dB advantage is possible in the regime of small number of signal photons and high thermal noise, even for low transmissivity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14414v1-abstract-full').style.display = 'none'; document.getElementById('2411.14414v1-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">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">preliminary version of the paper</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.14355">arXiv:2411.14355</a> <span> [<a href="https://arxiv.org/pdf/2411.14355">pdf</a>, <a href="https://arxiv.org/format/2411.14355">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Nuclear Experiment">nucl-ex</span> </div> </div> <p class="title is-5 mathjax"> Measurement of two-neutrino double electron capture half-life of $^{124}$Xe with PandaX-4T </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=PandaX+Collaboration"> PandaX Collaboration</a>, <a href="/search/?searchtype=author&query=Bo%2C+Z">Zihao Bo</a>, <a href="/search/?searchtype=author&query=Chen%2C+W">Wei Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+X">Xun Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yunhua Chen</a>, <a href="/search/?searchtype=author&query=Cheng%2C+Z">Zhaokan Cheng</a>, <a href="/search/?searchtype=author&query=Cui%2C+X">Xiangyi Cui</a>, <a href="/search/?searchtype=author&query=Fan%2C+Y">Yingjie Fan</a>, <a href="/search/?searchtype=author&query=Fang%2C+D">Deqing Fang</a>, <a href="/search/?searchtype=author&query=Gao%2C+Z">Zhixing Gao</a>, <a href="/search/?searchtype=author&query=Geng%2C+L">Lisheng Geng</a>, <a href="/search/?searchtype=author&query=Giboni%2C+K">Karl Giboni</a>, <a href="/search/?searchtype=author&query=Guo%2C+X">Xunan Guo</a>, <a href="/search/?searchtype=author&query=Guo%2C+X">Xuyuan Guo</a>, <a href="/search/?searchtype=author&query=Guo%2C+Z">Zichao Guo</a>, <a href="/search/?searchtype=author&query=Han%2C+C">Chencheng Han</a>, <a href="/search/?searchtype=author&query=Han%2C+K">Ke Han</a>, <a href="/search/?searchtype=author&query=He%2C+C">Changda He</a>, <a href="/search/?searchtype=author&query=He%2C+J">Jinrong He</a>, <a href="/search/?searchtype=author&query=Huang%2C+D">Di Huang</a>, <a href="/search/?searchtype=author&query=Huang%2C+H">Houqi Huang</a>, <a href="/search/?searchtype=author&query=Huang%2C+J">Junting Huang</a>, <a href="/search/?searchtype=author&query=Hou%2C+R">Ruquan Hou</a>, <a href="/search/?searchtype=author&query=Hou%2C+Y">Yu Hou</a>, <a href="/search/?searchtype=author&query=Ji%2C+X">Xiangdong Ji</a> , et al. (77 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="2411.14355v1-abstract-short" style="display: inline;"> Detailed studies of two-neutrino double electron capture (2$谓$DEC) is a crucial step towards searching for the neutrino-less mode to explore the Majorana nature of neutrinos. We have measured precisely the half-life of the 2$谓$DEC process in $^{124}$Xe, utilizing a total exposure of 1.73 tonne$\cdot$year from the commissioning run and the first science run of the PandaX-4T experiment. A time-depen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14355v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14355v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14355v1-abstract-full" style="display: none;"> Detailed studies of two-neutrino double electron capture (2$谓$DEC) is a crucial step towards searching for the neutrino-less mode to explore the Majorana nature of neutrinos. We have measured precisely the half-life of the 2$谓$DEC process in $^{124}$Xe, utilizing a total exposure of 1.73 tonne$\cdot$year from the commissioning run and the first science run of the PandaX-4T experiment. A time-dependent background model in the $\mathcal{O}$(10 keV) energy is constructed for the first time in PandaX-4T data. With an unbinned maximum likelihood fit, we determine the half-life of the 2$谓$DEC process to be $(1.03\pm0.15_{\rm stat}\pm0.06_{\rm sys})\times 10^{22}$$\,$yr. Furthermore, we have evaluated the branching ratio for both electrons captured from the $K$ shell ($KK$) to be $(65\pm5)\%$, which aligns with the $^{124}$Xe nuclear model calculations within 1.5$\,$$蟽$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14355v1-abstract-full').style.display = 'none'; document.getElementById('2411.14355v1-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">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">18 pages, 5 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/2411.14164">arXiv:2411.14164</a> <span> [<a href="https://arxiv.org/pdf/2411.14164">pdf</a>, <a href="https://arxiv.org/format/2411.14164">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> FoPru: Focal Pruning for Efficient Large Vision-Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jiang%2C+L">Lei Jiang</a>, <a href="/search/?searchtype=author&query=Huang%2C+W">Weizhe Huang</a>, <a href="/search/?searchtype=author&query=Liu%2C+T">Tongxuan Liu</a>, <a href="/search/?searchtype=author&query=Zeng%2C+Y">Yuting Zeng</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jing Li</a>, <a href="/search/?searchtype=author&query=Cheng%2C+L">Lechao Cheng</a>, <a href="/search/?searchtype=author&query=Xu%2C+X">Xiaohua Xu</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="2411.14164v1-abstract-short" style="display: inline;"> Large Vision-Language Models (LVLMs) represent a significant advancement toward achieving superior multimodal capabilities by enabling powerful Large Language Models (LLMs) to understand visual input. Typically, LVLMs utilize visual encoders, such as CLIP, to transform images into visual tokens, which are then aligned with textual tokens through projection layers before being input into the LLM fo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14164v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14164v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14164v1-abstract-full" style="display: none;"> Large Vision-Language Models (LVLMs) represent a significant advancement toward achieving superior multimodal capabilities by enabling powerful Large Language Models (LLMs) to understand visual input. Typically, LVLMs utilize visual encoders, such as CLIP, to transform images into visual tokens, which are then aligned with textual tokens through projection layers before being input into the LLM for inference. Although existing LVLMs have achieved significant success, their inference efficiency is still limited by the substantial number of visual tokens and the potential redundancy among them. To mitigate this issue, we propose Focal Pruning (FoPru), a training-free method that prunes visual tokens based on the attention-based token significance derived from the vision encoder. Specifically, we introduce two alternative pruning strategies: 1) the rank strategy, which leverages all token significance scores to retain more critical tokens in a global view; 2) the row strategy, which focuses on preserving continuous key information in images from a local perspective. Finally, the selected tokens are reordered to maintain their original positional relationships. Extensive experiments across various LVLMs and multimodal datasets demonstrate that our method can prune a large number of redundant tokens while maintaining high accuracy, leading to significant improvements in inference efficiency. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14164v1-abstract-full').style.display = 'none'; document.getElementById('2411.14164v1-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">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, 7 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/2411.14052">arXiv:2411.14052</a> <span> [<a href="https://arxiv.org/pdf/2411.14052">pdf</a>, <a href="https://arxiv.org/ps/2411.14052">ps</a>, <a href="https://arxiv.org/format/2411.14052">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Song%2C+F">Fei Song</a>, <a href="/search/?searchtype=author&query=Wang%2C+Z">Zhe Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jun Li</a>, <a href="/search/?searchtype=author&query=Shi%2C+L">Long Shi</a>, <a href="/search/?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/?searchtype=author&query=Jin%2C+S">Shi 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="2411.14052v1-abstract-short" style="display: inline;"> In ultra-dense unmanned aerial vehicle (UAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale UAVs, for providing flexible and efficient service coverage to the ground users (GUs). In this paper, we propose a learning-based resource allocation scheme in an ultra-dense UAV communication network, where the GUs' service demands are time-v… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14052v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14052v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14052v1-abstract-full" style="display: none;"> In ultra-dense unmanned aerial vehicle (UAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale UAVs, for providing flexible and efficient service coverage to the ground users (GUs). In this paper, we propose a learning-based resource allocation scheme in an ultra-dense UAV communication network, where the GUs' service demands are time-varying with unknown distributions. We formulate the non-cooperative game among multiple co-channel UAVs as a stochastic game, where each UAV jointly optimizes its trajectory, user association, and downlink power control to maximize the expectation of its locally cumulative energy efficiency under the interference and energy constraints. To cope with the scalability issue in a large-scale network, we further formulate the problem as a mean-field game (MFG), which simplifies the interactions among the UAVs into a two-player game between a representative UAV and a mean-field. We prove the existence and uniqueness of the equilibrium for the MFG, and propose a model-free mean-field reinforcement learning algorithm named maximum entropy mean-field deep Q network (ME-MFDQN) to solve the mean-field equilibrium in both fully and partially observable scenarios. The simulation results reveal that the proposed algorithm improves the energy efficiency compared with the benchmark algorithms. Moreover, the performance can be further enhanced if the GUs' service demands exhibit higher temporal correlation or if the UAVs have wider observation capabilities over their nearby GUs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14052v1-abstract-full').style.display = 'none'; document.getElementById('2411.14052v1-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">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/2411.13821">arXiv:2411.13821</a> <span> [<a href="https://arxiv.org/pdf/2411.13821">pdf</a>, <a href="https://arxiv.org/format/2411.13821">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</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/10.1145/3701551.3703568">10.1145/3701551.3703568 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Heterophilic Graph Neural Networks Optimization with Causal Message-passing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+B">Botao Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jia Li</a>, <a href="/search/?searchtype=author&query=Chang%2C+H">Heng Chang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+K">Keli Zhang</a>, <a href="/search/?searchtype=author&query=Tsung%2C+F">Fugee Tsung</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="2411.13821v1-abstract-short" style="display: inline;"> In this work, we discover that causal inference provides a promising approach to capture heterophilic message-passing in Graph Neural Network (GNN). By leveraging cause-effect analysis, we can discern heterophilic edges based on asymmetric node dependency. The learned causal structure offers more accurate relationships among nodes. To reduce the computational complexity, we introduce intervention-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13821v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13821v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13821v1-abstract-full" style="display: none;"> In this work, we discover that causal inference provides a promising approach to capture heterophilic message-passing in Graph Neural Network (GNN). By leveraging cause-effect analysis, we can discern heterophilic edges based on asymmetric node dependency. The learned causal structure offers more accurate relationships among nodes. To reduce the computational complexity, we introduce intervention-based causal inference in graph learning. We first simplify causal analysis on graphs by formulating it as a structural learning model and define the optimization problem within the Bayesian scheme. We then present an analysis of decomposing the optimization target into a consistency penalty and a structure modification based on cause-effect relations. We then estimate this target by conditional entropy and present insights into how conditional entropy quantifies the heterophily. Accordingly, we propose CausalMP, a causal message-passing discovery network for heterophilic graph learning, that iteratively learns the explicit causal structure of input graphs. We conduct extensive experiments in both heterophilic and homophilic graph settings. The result demonstrates that the our model achieves superior link prediction performance. Training on causal structure can also enhance node representation in classification task across different base models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13821v1-abstract-full').style.display = 'none'; document.getElementById('2411.13821v1-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 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/2411.13775">arXiv:2411.13775</a> <span> [<a href="https://arxiv.org/pdf/2411.13775">pdf</a>, <a href="https://arxiv.org/format/2411.13775">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"> Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yan%2C+J">Jianhao Yan</a>, <a href="/search/?searchtype=author&query=Yan%2C+P">Pingchuan Yan</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yulong Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jing Li</a>, <a href="/search/?searchtype=author&query=Zhu%2C+X">Xianchao Zhu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yue 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="2411.13775v1-abstract-short" style="display: inline;"> This study presents a comprehensive evaluation of GPT-4's translation capabilities compared to human translators of varying expertise levels. Through systematic human evaluation using the MQM schema, we assess translations across three language pairs (Chinese$\longleftrightarrow$English, Russian$\longleftrightarrow$English, and Chinese$\longleftrightarrow$Hindi) and three domains (News, Technology… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13775v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13775v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13775v1-abstract-full" style="display: none;"> This study presents a comprehensive evaluation of GPT-4's translation capabilities compared to human translators of varying expertise levels. Through systematic human evaluation using the MQM schema, we assess translations across three language pairs (Chinese$\longleftrightarrow$English, Russian$\longleftrightarrow$English, and Chinese$\longleftrightarrow$Hindi) and three domains (News, Technology, and Biomedical). Our findings reveal that GPT-4 achieves performance comparable to junior-level translators in terms of total errors, while still lagging behind senior translators. Unlike traditional Neural Machine Translation systems, which show significant performance degradation in resource-poor language directions, GPT-4 maintains consistent translation quality across all evaluated language pairs. Through qualitative analysis, we identify distinctive patterns in translation approaches: GPT-4 tends toward overly literal translations and exhibits lexical inconsistency, while human translators sometimes over-interpret context and introduce hallucinations. This study represents the first systematic comparison between LLM and human translators across different proficiency levels, providing valuable insights into the current capabilities and limitations of LLM-based translation systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13775v1-abstract-full').style.display = 'none'; document.getElementById('2411.13775v1-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 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">Work in progress</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13731">arXiv:2411.13731</a> <span> [<a href="https://arxiv.org/pdf/2411.13731">pdf</a>, <a href="https://arxiv.org/format/2411.13731">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="Cryptography and Security">cs.CR</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"> Delta-Influence: Unlearning Poisons via Influence Functions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+W">Wenjie Li</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jiawei Li</a>, <a href="/search/?searchtype=author&query=de+Witt%2C+C+S">Christian Schroeder de Witt</a>, <a href="/search/?searchtype=author&query=Prabhu%2C+A">Ameya Prabhu</a>, <a href="/search/?searchtype=author&query=Sanyal%2C+A">Amartya Sanyal</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="2411.13731v1-abstract-short" style="display: inline;"> Addressing data integrity challenges, such as unlearning the effects of data poisoning after model training, is necessary for the reliable deployment of machine learning models. State-of-the-art influence functions, such as EK-FAC, often fail to accurately attribute abnormal model behavior to the specific poisoned training data responsible for the data poisoning attack. In addition, traditional un… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13731v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13731v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13731v1-abstract-full" style="display: none;"> Addressing data integrity challenges, such as unlearning the effects of data poisoning after model training, is necessary for the reliable deployment of machine learning models. State-of-the-art influence functions, such as EK-FAC, often fail to accurately attribute abnormal model behavior to the specific poisoned training data responsible for the data poisoning attack. In addition, traditional unlearning algorithms often struggle to effectively remove the influence of poisoned samples, particularly when only a few affected examples can be identified. To address these challenge, we introduce $螖$-Influence, a novel approach that leverages influence functions to trace abnormal model behavior back to the responsible poisoned training data using as little as just one poisoned test example. $螖$-Influence applies data transformations that sever the link between poisoned training data and compromised test points without significantly affecting clean data. This allows $螖$-Influence to detect large negative shifts in influence scores following data transformations, a phenomenon we term as influence collapse, thereby accurately identifying poisoned training data. Unlearning this subset, e.g. through retraining, effectively eliminates the data poisoning. We validate our method across three vision-based poisoning attacks and three datasets, benchmarking against four detection algorithms and five unlearning strategies. We show that $螖$-Influence consistently achieves the best unlearning across all settings, showing the promise of influence functions for corrective unlearning. Our code is publicly available at: \url{https://github.com/andyisokay/delta-influence} <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13731v1-abstract-full').style.display = 'none'; document.getElementById('2411.13731v1-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 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">Accepted at NeurIPS Workshop on Attributing Model Behavior at Scale (ATTRIB @ NeurIPS 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/2411.13719">arXiv:2411.13719</a> <span> [<a href="https://arxiv.org/pdf/2411.13719">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Geophysics">physics.geo-ph</span> </div> </div> <p class="title is-5 mathjax"> Persistent but weak magnetic field at Moon's midlife revealed by Chang'e-5 basalt </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Cai%2C+S">Shuhui Cai</a>, <a href="/search/?searchtype=author&query=Qin%2C+H">Huafeng Qin</a>, <a href="/search/?searchtype=author&query=Wang%2C+H">Huapei Wang</a>, <a href="/search/?searchtype=author&query=Deng%2C+C">Chenglong Deng</a>, <a href="/search/?searchtype=author&query=Yang%2C+S">Saihong Yang</a>, <a href="/search/?searchtype=author&query=Xu%2C+Y">Ya Xu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+C">Chi Zhang</a>, <a href="/search/?searchtype=author&query=Tang%2C+X">Xu Tang</a>, <a href="/search/?searchtype=author&query=Gu%2C+L">Lixin Gu</a>, <a href="/search/?searchtype=author&query=Li%2C+X">Xiaoguang Li</a>, <a href="/search/?searchtype=author&query=Shen%2C+Z">Zhongshan Shen</a>, <a href="/search/?searchtype=author&query=Zhang%2C+M">Min Zhang</a>, <a href="/search/?searchtype=author&query=He%2C+K">Kuang He</a>, <a href="/search/?searchtype=author&query=Qi%2C+K">Kaixian Qi</a>, <a href="/search/?searchtype=author&query=Fan%2C+Y">Yunchang Fan</a>, <a href="/search/?searchtype=author&query=Dong%2C+L">Liang Dong</a>, <a href="/search/?searchtype=author&query=Hou%2C+Y">Yifei Hou</a>, <a href="/search/?searchtype=author&query=Shi%2C+P">Pingyuan Shi</a>, <a href="/search/?searchtype=author&query=Liu%2C+S">Shuangchi Liu</a>, <a href="/search/?searchtype=author&query=Su%2C+F">Fei Su</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yi Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+Q">Qiuli Li</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jinhua Li</a>, <a href="/search/?searchtype=author&query=Mitchell%2C+R+N">Ross N. Mitchell</a>, <a href="/search/?searchtype=author&query=He%2C+H">Huaiyu He</a> , et al. (3 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="2411.13719v1-abstract-short" style="display: inline;"> The evolution of the lunar magnetic field can reveal the Moon's interior structure, thermal history, and surface environment. The mid-to-late stage evolution of the lunar magnetic field is poorly constrained, and thus the existence of a long-lived lunar dynamo remains controversial. The Chang'e-5 mission returned the heretofore youngest mare basalts from Oceanus Procellarum uniquely positioned at… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13719v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13719v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13719v1-abstract-full" style="display: none;"> The evolution of the lunar magnetic field can reveal the Moon's interior structure, thermal history, and surface environment. The mid-to-late stage evolution of the lunar magnetic field is poorly constrained, and thus the existence of a long-lived lunar dynamo remains controversial. The Chang'e-5 mission returned the heretofore youngest mare basalts from Oceanus Procellarum uniquely positioned at mid-latitude. We recovered weak paleointensities of 2-4 uT from the Chang'e-5 basalt clasts at 2 billion years ago, attestting to the longevity of a lunar dynamo until at least the Moon's midlife. This paleomagnetic result implies the existence of thermal convection in the lunar deep interior at the lunar mid-stage which may have supplied mantle heat flux for the young volcanism. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13719v1-abstract-full').style.display = 'none'; document.getElementById('2411.13719v1-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 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/2411.13577">arXiv:2411.13577</a> <span> [<a href="https://arxiv.org/pdf/2411.13577">pdf</a>, <a href="https://arxiv.org/format/2411.13577">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> WavChat: A Survey of Spoken Dialogue Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ji%2C+S">Shengpeng Ji</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yifu Chen</a>, <a href="/search/?searchtype=author&query=Fang%2C+M">Minghui Fang</a>, <a href="/search/?searchtype=author&query=Zuo%2C+J">Jialong Zuo</a>, <a href="/search/?searchtype=author&query=Lu%2C+J">Jingyu Lu</a>, <a href="/search/?searchtype=author&query=Wang%2C+H">Hanting Wang</a>, <a href="/search/?searchtype=author&query=Jiang%2C+Z">Ziyue Jiang</a>, <a href="/search/?searchtype=author&query=Zhou%2C+L">Long Zhou</a>, <a href="/search/?searchtype=author&query=Liu%2C+S">Shujie Liu</a>, <a href="/search/?searchtype=author&query=Cheng%2C+X">Xize Cheng</a>, <a href="/search/?searchtype=author&query=Yang%2C+X">Xiaoda Yang</a>, <a href="/search/?searchtype=author&query=Wang%2C+Z">Zehan Wang</a>, <a href="/search/?searchtype=author&query=Yang%2C+Q">Qian Yang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jian Li</a>, <a href="/search/?searchtype=author&query=Jiang%2C+Y">Yidi Jiang</a>, <a href="/search/?searchtype=author&query=He%2C+J">Jingzhen He</a>, <a href="/search/?searchtype=author&query=Chu%2C+Y">Yunfei Chu</a>, <a href="/search/?searchtype=author&query=Xu%2C+J">Jin Xu</a>, <a href="/search/?searchtype=author&query=Zhao%2C+Z">Zhou Zhao</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="2411.13577v1-abstract-short" style="display: inline;"> Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain. Compared to traditional three-tier cascaded spoken dialogue models that comprise speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS), modern spoken dialogue models exhibit greater intelligence. These advanced spoken dialogue model… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13577v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13577v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13577v1-abstract-full" style="display: none;"> Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain. Compared to traditional three-tier cascaded spoken dialogue models that comprise speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS), modern spoken dialogue models exhibit greater intelligence. These advanced spoken dialogue models not only comprehend audio, music, and other speech-related features, but also capture stylistic and timbral characteristics in speech. Moreover, they generate high-quality, multi-turn speech responses with low latency, enabling real-time interaction through simultaneous listening and speaking capability. Despite the progress in spoken dialogue systems, there is a lack of comprehensive surveys that systematically organize and analyze these systems and the underlying technologies. To address this, we have first compiled existing spoken dialogue systems in the chronological order and categorized them into the cascaded and end-to-end paradigms. We then provide an in-depth overview of the core technologies in spoken dialogue models, covering aspects such as speech representation, training paradigm, streaming, duplex, and interaction capabilities. Each section discusses the limitations of these technologies and outlines considerations for future research. Additionally, we present a thorough review of relevant datasets, evaluation metrics, and benchmarks from the perspectives of training and evaluating spoken dialogue systems. We hope this survey will contribute to advancing both academic research and industrial applications in the field of spoken dialogue systems. The related material is available at https://github.com/jishengpeng/WavChat. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13577v1-abstract-full').style.display = 'none'; document.getElementById('2411.13577v1-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> <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">60 papes, working in progress</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13384">arXiv:2411.13384</a> <span> [<a href="https://arxiv.org/pdf/2411.13384">pdf</a>, <a href="https://arxiv.org/format/2411.13384">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Risk Management">q-fin.RM</span> </div> </div> <p class="title is-5 mathjax"> Comparisons of multivariate contribution measures of risk contagion and their applications in cryptocurrency market </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wen%2C+L">Limin Wen</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Junxue Li</a>, <a href="/search/?searchtype=author&query=Pu%2C+T">Tong Pu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yiying 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="2411.13384v1-abstract-short" style="display: inline;"> Conditional risk measures and their associated risk contribution measures are commonly employed in finance and actuarial science for evaluating systemic risk and quantifying the effects of risk contagion. This paper introduces various types of contribution measures based on the MCoVaR, MCoES, and MMME studied in Ortega-Jim茅nez et al. (2021) and Das & Fasen-Hartmann (2018) to assess both the absolu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13384v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13384v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13384v1-abstract-full" style="display: none;"> Conditional risk measures and their associated risk contribution measures are commonly employed in finance and actuarial science for evaluating systemic risk and quantifying the effects of risk contagion. This paper introduces various types of contribution measures based on the MCoVaR, MCoES, and MMME studied in Ortega-Jim茅nez et al. (2021) and Das & Fasen-Hartmann (2018) to assess both the absolute and relative effects of a single risk when other risks in a group are in distress. The properties of these contribution risk measures are examined, and sufficient conditions for comparing these measures between two sets of random vectors are established using univariate and multivariate stochastic orders and stochastic dependence notions. Numerical examples are presented for validating the conditions. Finally, a real dataset from the cryptocurrency market is also utilized to analyze the contagion effect in terms of our proposed contribution measures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13384v1-abstract-full').style.display = 'none'; document.getElementById('2411.13384v1-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 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/2411.13281">arXiv:2411.13281</a> <span> [<a href="https://arxiv.org/pdf/2411.13281">pdf</a>, <a href="https://arxiv.org/format/2411.13281">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="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="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> VideoAutoArena: An Automated Arena for Evaluating Large Multimodal Models in Video Analysis through User Simulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Luo%2C+Z">Ziyang Luo</a>, <a href="/search/?searchtype=author&query=Wu%2C+H">Haoning Wu</a>, <a href="/search/?searchtype=author&query=Li%2C+D">Dongxu Li</a>, <a href="/search/?searchtype=author&query=Ma%2C+J">Jing Ma</a>, <a href="/search/?searchtype=author&query=Kankanhalli%2C+M">Mohan Kankanhalli</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Junnan 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="2411.13281v1-abstract-short" style="display: inline;"> Large multimodal models (LMMs) with advanced video analysis capabilities have recently garnered significant attention. However, most evaluations rely on traditional methods like multiple-choice questions in benchmarks such as VideoMME and LongVideoBench, which are prone to lack the depth needed to capture the complex demands of real-world users. To address this limitation-and due to the prohibitiv… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13281v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13281v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13281v1-abstract-full" style="display: none;"> Large multimodal models (LMMs) with advanced video analysis capabilities have recently garnered significant attention. However, most evaluations rely on traditional methods like multiple-choice questions in benchmarks such as VideoMME and LongVideoBench, which are prone to lack the depth needed to capture the complex demands of real-world users. To address this limitation-and due to the prohibitive cost and slow pace of human annotation for video tasks-we introduce VideoAutoArena, an arena-style benchmark inspired by LMSYS Chatbot Arena's framework, designed to automatically assess LMMs' video analysis abilities. VideoAutoArena utilizes user simulation to generate open-ended, adaptive questions that rigorously assess model performance in video understanding. The benchmark features an automated, scalable evaluation framework, incorporating a modified ELO Rating System for fair and continuous comparisons across multiple LMMs. To validate our automated judging system, we construct a 'gold standard' using a carefully curated subset of human annotations, demonstrating that our arena strongly aligns with human judgment while maintaining scalability. Additionally, we introduce a fault-driven evolution strategy, progressively increasing question complexity to push models toward handling more challenging video analysis scenarios. Experimental results demonstrate that VideoAutoArena effectively differentiates among state-of-the-art LMMs, providing insights into model strengths and areas for improvement. To further streamline our evaluation, we introduce VideoAutoBench as an auxiliary benchmark, where human annotators label winners in a subset of VideoAutoArena battles. We use GPT-4o as a judge to compare responses against these human-validated answers. Together, VideoAutoArena and VideoAutoBench offer a cost-effective, and scalable framework for evaluating LMMs in user-centric video analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13281v1-abstract-full').style.display = 'none'; document.getElementById('2411.13281v1-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 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">Project Page: https://videoautoarena.github.io/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13152">arXiv:2411.13152</a> <span> [<a href="https://arxiv.org/pdf/2411.13152">pdf</a>, <a href="https://arxiv.org/format/2411.13152">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Su%2C+H">Houcheng Su</a>, <a href="/search/?searchtype=author&query=Wang%2C+M">Mengzhu Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jiao Li</a>, <a href="/search/?searchtype=author&query=Yin%2C+N">Nan Yin</a>, <a href="/search/?searchtype=author&query=Shen%2C+L">Li Shen</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="2411.13152v1-abstract-short" style="display: inline;"> In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data to enhance its generalization capability for the target domain. A key advantage of SSDA is its ability to significantly reduce reliance on labeled data, thereby lowering the costs and time associated with data preparation. Most existin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13152v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13152v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13152v1-abstract-full" style="display: none;"> In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data to enhance its generalization capability for the target domain. A key advantage of SSDA is its ability to significantly reduce reliance on labeled data, thereby lowering the costs and time associated with data preparation. Most existing SSDA methods utilize information from domain labels and class labels but overlook the structural information of the data. To address this issue, this paper proposes a graph learning perspective (AGLP) for semi-supervised domain adaptation. We apply the graph convolutional network to the instance graph which allows structural information to propagate along the weighted graph edges. The proposed AGLP model has several advantages. First, to the best of our knowledge, this is the first work to model structural information in SSDA. Second, the proposed model can effectively learn domain-invariant and semantic representations, reducing domain discrepancies in SSDA. Extensive experimental results on multiple standard benchmarks demonstrate that the proposed AGLP algorithm outperforms state-of-the-art semi-supervised domain adaptation methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13152v1-abstract-full').style.display = 'none'; document.getElementById('2411.13152v1-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 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">8page</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07; 92C55; 62H35 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.6; I.4.10; J.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13147">arXiv:2411.13147</a> <span> [<a href="https://arxiv.org/pdf/2411.13147">pdf</a>, <a href="https://arxiv.org/format/2411.13147">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+M">Mengzhu Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jiao Li</a>, <a href="/search/?searchtype=author&query=Su%2C+H">Houcheng Su</a>, <a href="/search/?searchtype=author&query=Yin%2C+N">Nan Yin</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shen 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="2411.13147v1-abstract-short" style="display: inline;"> Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing data utilization efficiency. Previous methods primarily focus on complex training strategies to utilize unlabeled data but neglect the importance of graph structural information. Different from existing methods, we propose… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13147v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13147v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13147v1-abstract-full" style="display: none;"> Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing data utilization efficiency. Previous methods primarily focus on complex training strategies to utilize unlabeled data but neglect the importance of graph structural information. Different from existing methods, we propose a graph-based clustering for semi-supervised medical image segmentation (GraphCL) by jointly modeling graph data structure in a unified deep model. The proposed GraphCL model enjoys several advantages. Firstly, to the best of our knowledge, this is the first work to model the data structure information for semi-supervised medical image segmentation (SSMIS). Secondly, to get the clustered features across different graphs, we integrate both pairwise affinities between local image features and raw features as inputs. Extensive experimental results on three standard benchmarks show that the proposed GraphCL algorithm outperforms state-of-the-art semi-supervised medical image segmentation methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13147v1-abstract-full').style.display = 'none'; document.getElementById('2411.13147v1-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 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">9page</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07; 92C55; 62H35 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.6; I.4.10; J.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12924">arXiv:2411.12924</a> <span> [<a href="https://arxiv.org/pdf/2411.12924">pdf</a>, <a href="https://arxiv.org/format/2411.12924">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> <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="Human-Computer Interaction">cs.HC</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"> Human-In-the-Loop Software Development Agents </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Takerngsaksiri%2C+W">Wannita Takerngsaksiri</a>, <a href="/search/?searchtype=author&query=Pasuksmit%2C+J">Jirat Pasuksmit</a>, <a href="/search/?searchtype=author&query=Thongtanunam%2C+P">Patanamon Thongtanunam</a>, <a href="/search/?searchtype=author&query=Tantithamthavorn%2C+C">Chakkrit Tantithamthavorn</a>, <a href="/search/?searchtype=author&query=Zhang%2C+R">Ruixiong Zhang</a>, <a href="/search/?searchtype=author&query=Jiang%2C+F">Fan Jiang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jing Li</a>, <a href="/search/?searchtype=author&query=Cook%2C+E">Evan Cook</a>, <a href="/search/?searchtype=author&query=Chen%2C+K">Kun Chen</a>, <a href="/search/?searchtype=author&query=Wu%2C+M">Ming Wu</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="2411.12924v1-abstract-short" style="display: inline;"> Recently, Large Language Models (LLMs)-based multi-agent paradigms for software engineering are introduced to automatically resolve software development tasks (e.g., from a given issue to source code). However, existing work is evaluated based on historical benchmark datasets, does not consider human feedback at each stage of the automated software development process, and has not been deployed in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12924v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12924v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12924v1-abstract-full" style="display: none;"> Recently, Large Language Models (LLMs)-based multi-agent paradigms for software engineering are introduced to automatically resolve software development tasks (e.g., from a given issue to source code). However, existing work is evaluated based on historical benchmark datasets, does not consider human feedback at each stage of the automated software development process, and has not been deployed in practice. In this paper, we introduce a Human-in-the-loop LLM-based Agents framework (HULA) for software development that allows software engineers to refine and guide LLMs when generating coding plans and source code for a given task. We design, implement, and deploy the HULA framework into Atlassian JIRA for internal uses. Through a multi-stage evaluation of the HULA framework, Atlassian software engineers perceive that HULA can minimize the overall development time and effort, especially in initiating a coding plan and writing code for straightforward tasks. On the other hand, challenges around code quality are raised to be solved in some cases. We draw lessons learned and discuss opportunities for future work, which will pave the way for the advancement of LLM-based agents in software development. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12924v1-abstract-full').style.display = 'none'; document.getElementById('2411.12924v1-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> 19 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/2411.12887">arXiv:2411.12887</a> <span> [<a href="https://arxiv.org/pdf/2411.12887">pdf</a>, <a href="https://arxiv.org/format/2411.12887">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Strongly Correlated Electrons">cond-mat.str-el</span> </div> </div> <p class="title is-5 mathjax"> Investigation of magnetic excitations and charge order in a van der Waals ferromagnet Fe$_5$GeTe$_2$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bhartiya%2C+V+K">V. K. Bhartiya</a>, <a href="/search/?searchtype=author&query=Kim%2C+T">T. Kim</a>, <a href="/search/?searchtype=author&query=Li%2C+J">J. Li</a>, <a href="/search/?searchtype=author&query=Darlington%2C+T+P">T. P. Darlington</a>, <a href="/search/?searchtype=author&query=Rizzo%2C+D+J">D. J. Rizzo</a>, <a href="/search/?searchtype=author&query=Gu.%2C+Y">Y. Gu.</a>, <a href="/search/?searchtype=author&query=Fan%2C+S">S. Fan</a>, <a href="/search/?searchtype=author&query=Nelson%2C+C">C. Nelson</a>, <a href="/search/?searchtype=author&query=Freeland%2C+J+W">J. W. Freeland</a>, <a href="/search/?searchtype=author&query=Xu%2C+X">X. Xu</a>, <a href="/search/?searchtype=author&query=Basov%2C+D+N">D. N. Basov</a>, <a href="/search/?searchtype=author&query=Pelliciari%2C+J">J. Pelliciari</a>, <a href="/search/?searchtype=author&query=May%2C+A+F">A. F. May</a>, <a href="/search/?searchtype=author&query=Mazzoli%2C+C">C. Mazzoli</a>, <a href="/search/?searchtype=author&query=Bisogni%2C+V">V. Bisogni</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="2411.12887v2-abstract-short" style="display: inline;"> Understanding the complex ground state of van der Waals (vdW) magnets is essential for designing new materials and devices that leverage these platforms. Here, we investigate a two-dimensional vdW ferromagnet -- Fe$_5$GeTe$_2$-- with one of the highest reported Curie temperatures, to elucidate its magnetic excitations and charge order. Using Fe $L_3 - $edge resonant inelastic x-ray scattering, we… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12887v2-abstract-full').style.display = 'inline'; document.getElementById('2411.12887v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12887v2-abstract-full" style="display: none;"> Understanding the complex ground state of van der Waals (vdW) magnets is essential for designing new materials and devices that leverage these platforms. Here, we investigate a two-dimensional vdW ferromagnet -- Fe$_5$GeTe$_2$-- with one of the highest reported Curie temperatures, to elucidate its magnetic excitations and charge order. Using Fe $L_3 - $edge resonant inelastic x-ray scattering, we find the dual character of magnetic excitations, consisting of a coherent magnon and a continuum, similar to what is reported for its sister compound Fe$_3$GeTe$_2$. The magnon has an energy of $\approx$ 36 meV at the maximum in-plane momentum transfer ($-$0.35 r.l.u.) allowed at Fe $L_3 - $edge. A broad and non-dispersive continuum extends up to 150 meV, 50$\%$ higher energy than in Fe$_3$GeTe$_2$. Its intensity is sinusoidally modulated along the $L$ direction, with a period matching the inter-slab distance. Our findings suggest that while the unconventional dual character of magnetic excitations is generic to ternary Fe-Ge-Te vdW magnets, the correlation length of the out-of-plane magnetic interaction increases in Fe$_5$GeTe$_2$ as compared to Fe$_3$GeTe$_2$, supporting a stronger three-dimensional character for the former. Furthermore, by investigating the $\pm$(1/3, 1/3, $L$) peaks by resonant x-ray diffraction, we conclude these to have structural origin rather than charge order -- as previously reported -- and suggest doubling of the structural unit cell along the $c-$axis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12887v2-abstract-full').style.display = 'none'; document.getElementById('2411.12887v2-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 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">17 pages, 3 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/2411.12814">arXiv:2411.12814</a> <span> [<a href="https://arxiv.org/pdf/2411.12814">pdf</a>, <a href="https://arxiv.org/format/2411.12814">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"> Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Cheng%2C+J">Junlong Cheng</a>, <a href="/search/?searchtype=author&query=Fu%2C+B">Bin Fu</a>, <a href="/search/?searchtype=author&query=Ye%2C+J">Jin Ye</a>, <a href="/search/?searchtype=author&query=Wang%2C+G">Guoan Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+T">Tianbin Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+H">Haoyu Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruoyu Li</a>, <a href="/search/?searchtype=author&query=Yao%2C+H">He Yao</a>, <a href="/search/?searchtype=author&query=Chen%2C+J">Junren Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+J">JingWen Li</a>, <a href="/search/?searchtype=author&query=Su%2C+Y">Yanzhou Su</a>, <a href="/search/?searchtype=author&query=Zhu%2C+M">Min Zhu</a>, <a href="/search/?searchtype=author&query=He%2C+J">Junjun He</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="2411.12814v1-abstract-short" style="display: inline;"> Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model generalization and consistent evaluation across different models. In this paper, we introduce the IMed-361M benchmark dataset, a significant advancement in general IMIS research. First, we collect and standardize over 6.4 m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12814v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12814v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12814v1-abstract-full" style="display: none;"> Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model generalization and consistent evaluation across different models. In this paper, we introduce the IMed-361M benchmark dataset, a significant advancement in general IMIS research. First, we collect and standardize over 6.4 million medical images and their corresponding ground truth masks from multiple data sources. Then, leveraging the strong object recognition capabilities of a vision foundational model, we automatically generated dense interactive masks for each image and ensured their quality through rigorous quality control and granularity management. Unlike previous datasets, which are limited by specific modalities or sparse annotations, IMed-361M spans 14 modalities and 204 segmentation targets, totaling 361 million masks-an average of 56 masks per image. Finally, we developed an IMIS baseline network on this dataset that supports high-quality mask generation through interactive inputs, including clicks, bounding boxes, text prompts, and their combinations. We evaluate its performance on medical image segmentation tasks from multiple perspectives, demonstrating superior accuracy and scalability compared to existing interactive segmentation models. To facilitate research on foundational models in medical computer vision, we release the IMed-361M and model at https://github.com/uni-medical/IMIS-Bench. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12814v1-abstract-full').style.display = 'none'; document.getElementById('2411.12814v1-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> 19 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/2411.12564">arXiv:2411.12564</a> <span> [<a href="https://arxiv.org/pdf/2411.12564">pdf</a>, <a href="https://arxiv.org/format/2411.12564">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> </div> </div> <p class="title is-5 mathjax"> CHANG-ES XXXV: Cosmic Ray Transport and Magnetic Field Structure of NGC 3556 at 3 GHz </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xu%2C+J">Jianghui Xu</a>, <a href="/search/?searchtype=author&query=Yang%2C+Y">Yang Yang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jiang-Tao Li</a>, <a href="/search/?searchtype=author&query=Liu%2C+G">Guilin Liu</a>, <a href="/search/?searchtype=author&query=Irwin%2C+J">Judith Irwin</a>, <a href="/search/?searchtype=author&query=Dettmar%2C+R">Ralf-J眉rgen Dettmar</a>, <a href="/search/?searchtype=author&query=Stein%2C+M">Michael Stein</a>, <a href="/search/?searchtype=author&query=Wiegert%2C+T">Theresa Wiegert</a>, <a href="/search/?searchtype=author&query=Wang%2C+Q+D">Q. Daniel Wang</a>, <a href="/search/?searchtype=author&query=English%2C+J">Jayanne English</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="2411.12564v1-abstract-short" style="display: inline;"> Radio halos of edge-on galaxies are crucial for investigating cosmic ray propagation and magnetic field structures in galactic environments. We present VLA C-configuration S-band (2--4 GHz) observations of the spiral galaxy NGC 3556, a target from the Continuum Halos in Nearby Galaxies - an EVLA Survey (CHANG-ES). We estimate the thermal contribution to the radio emission from a combination of the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12564v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12564v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12564v1-abstract-full" style="display: none;"> Radio halos of edge-on galaxies are crucial for investigating cosmic ray propagation and magnetic field structures in galactic environments. We present VLA C-configuration S-band (2--4 GHz) observations of the spiral galaxy NGC 3556, a target from the Continuum Halos in Nearby Galaxies - an EVLA Survey (CHANG-ES). We estimate the thermal contribution to the radio emission from a combination of the H$伪$ and mid-IR data, and employ Rotation Measure Synthesis to reveal the magnetic field structures. In our data, NGC 3556 exhibits a box-like radio halo extending nearly 7 kpc from the galactic plane. The scale height of the total S-band intensity in the halo is $1.68\pm 0.29$ kpc, while that of the non-thermal intensity is $1.93\pm 0.28$ kpc. Fitting the data to a 1-D cosmic-ray transport model, we find advection to describe the cosmic-ray propagation within the halo better than diffusion, with advection speeds of $245 \pm 15$ km s$^{-1}$ and $205 \pm 25$ km s$^{-1}$ above and below the disk, respectively. The magnetic field is detected patchily across the galaxy, displaying a toroidal configuration in the rotation measure map. The mean equipartition magnetic field strength is approximately $8.3\ 渭$G in the disk and $4.5\ 渭$G in the halo. In addition, a bubble-like structure extends nearly 3~kpc into the southern halo, aligned with the polarized intensity and H$伪$ image, suggestive of superwinds generated by recent star formation feedback in the nuclear region. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12564v1-abstract-full').style.display = 'none'; document.getElementById('2411.12564v1-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> 19 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">17 pages, 10 figures, 4 tables, accepted for publication in 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/2411.12392">arXiv:2411.12392</a> <span> [<a href="https://arxiv.org/pdf/2411.12392">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Fluid Dynamics">physics.flu-dyn</span> </div> </div> <p class="title is-5 mathjax"> Extended Buoyancy-Drag Model for Ablative Rayleigh-Taylor Instability Seeded by Various Perturbations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liu%2C+D">Dongxue Liu</a>, <a href="/search/?searchtype=author&query=Tao%2C+T">Tao Tao</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jun Li</a>, <a href="/search/?searchtype=author&query=Jia%2C+Q">Qing Jia</a>, <a href="/search/?searchtype=author&query=Yan%2C+R">Rui Yan</a>, <a href="/search/?searchtype=author&query=Zheng%2C+J">Jian Zheng</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="2411.12392v1-abstract-short" style="display: inline;"> In inertial confinement fusion (ICF), affected by non-steady ablation and various physical mechanisms, we extend the classical buoyancy-drag (BD) model into an ablative version for evaluating and controlling nonlinear ablative Rayleigh-Taylor instability (ARTI) in real space. The application of our ablative BD model in the nonlinear phase lies in a single adjustable coefficient influenced by initi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12392v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12392v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12392v1-abstract-full" style="display: none;"> In inertial confinement fusion (ICF), affected by non-steady ablation and various physical mechanisms, we extend the classical buoyancy-drag (BD) model into an ablative version for evaluating and controlling nonlinear ablative Rayleigh-Taylor instability (ARTI) in real space. The application of our ablative BD model in the nonlinear phase lies in a single adjustable coefficient influenced by initial perturbations, linear growth rate and terminal velocity. After validating the effectiveness and sensitivity of this model through simulations, we propose a strategy to shift the dominant mode away from the "most dangerous mode", which depends on initial perturbations. Our findings suggest that the "most dangerous mode" may clarify gain differences among targets of similar qualities and provide guidance for target manufacturing and pulse optimization in proximity to the ignition cliff. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12392v1-abstract-full').style.display = 'none'; document.getElementById('2411.12392v1-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> 19 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/2411.12205">arXiv:2411.12205</a> <span> [<a href="https://arxiv.org/pdf/2411.12205">pdf</a>, <a href="https://arxiv.org/format/2411.12205">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> Sparser Training for On-Device Recommendation Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Qu%2C+Y">Yunke Qu</a>, <a href="/search/?searchtype=author&query=Qu%2C+L">Liang Qu</a>, <a href="/search/?searchtype=author&query=Chen%2C+T">Tong Chen</a>, <a href="/search/?searchtype=author&query=Zhao%2C+X">Xiangyu Zhao</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jianxin Li</a>, <a href="/search/?searchtype=author&query=Yin%2C+H">Hongzhi Yin</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="2411.12205v1-abstract-short" style="display: inline;"> Recommender systems often rely on large embedding tables that map users and items to dense vectors of uniform size, leading to substantial memory consumption and inefficiencies. This is particularly problematic in memory-constrained environments like mobile and Web of Things (WoT) applications, where scalability and real-time performance are critical. Various research efforts have sought to addres… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12205v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12205v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12205v1-abstract-full" style="display: none;"> Recommender systems often rely on large embedding tables that map users and items to dense vectors of uniform size, leading to substantial memory consumption and inefficiencies. This is particularly problematic in memory-constrained environments like mobile and Web of Things (WoT) applications, where scalability and real-time performance are critical. Various research efforts have sought to address these issues. Although embedding pruning methods utilizing Dynamic Sparse Training (DST) stand out due to their low training and inference costs, consistent sparsity, and end-to-end differentiability, they face key challenges. Firstly, they typically initializes the mask matrix, which is used to prune redundant parameters, with random uniform sparse initialization. This strategy often results in suboptimal performance as it creates unstructured and inefficient connections. Secondly, they tend to favor the users/items sampled in the single batch immediately before weight exploration when they reactivate pruned parameters with large gradient magnitudes, which does not necessarily improve the overall performance. Thirdly, while they use sparse weights during forward passes, they still need to compute dense gradients during backward passes. In this paper, we propose SparseRec, an lightweight embedding method based on DST, to address these issues. Specifically, SparseRec initializes the mask matrix using Nonnegative Matrix Factorization. It accumulates gradients to identify the inactive parameters that can better improve the model performance after activation. Furthermore, it avoids dense gradients during backpropagation by sampling a subset of important vectors. Gradients are calculated only for parameters in this subset, thus maintaining sparsity during training in both forward and backward passes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12205v1-abstract-full').style.display = 'none'; document.getElementById('2411.12205v1-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 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/2411.12201">arXiv:2411.12201</a> <span> [<a href="https://arxiv.org/pdf/2411.12201">pdf</a>, <a href="https://arxiv.org/format/2411.12201">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"> Invariant Shape Representation Learning For Image Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Hossain%2C+T">Tonmoy Hossain</a>, <a href="/search/?searchtype=author&query=Ma%2C+J">Jing Ma</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jundong Li</a>, <a href="/search/?searchtype=author&query=Zhang%2C+M">Miaomiao 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="2411.12201v1-abstract-short" style="display: inline;"> Geometric shape features have been widely used as strong predictors for image classification. Nevertheless, most existing classifiers such as deep neural networks (DNNs) directly leverage the statistical correlations between these shape features and target variables. However, these correlations can often be spurious and unstable across different environments (e.g., in different age groups, certain… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12201v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12201v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12201v1-abstract-full" style="display: none;"> Geometric shape features have been widely used as strong predictors for image classification. Nevertheless, most existing classifiers such as deep neural networks (DNNs) directly leverage the statistical correlations between these shape features and target variables. However, these correlations can often be spurious and unstable across different environments (e.g., in different age groups, certain types of brain changes have unstable relations with neurodegenerative disease); hence leading to biased or inaccurate predictions. In this paper, we introduce a novel framework that for the first time develops invariant shape representation learning (ISRL) to further strengthen the robustness of image classifiers. In contrast to existing approaches that mainly derive features in the image space, our model ISRL is designed to jointly capture invariant features in latent shape spaces parameterized by deformable transformations. To achieve this goal, we develop a new learning paradigm based on invariant risk minimization (IRM) to learn invariant representations of image and shape features across multiple training distributions/environments. By embedding the features that are invariant with regard to target variables in different environments, our model consistently offers more accurate predictions. We validate our method by performing classification tasks on both simulated 2D images, real 3D brain and cine cardiovascular magnetic resonance images (MRIs). Our code is publicly available at https://github.com/tonmoy-hossain/ISRL. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12201v1-abstract-full').style.display = 'none'; document.getElementById('2411.12201v1-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 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/2411.12197">arXiv:2411.12197</a> <span> [<a href="https://arxiv.org/pdf/2411.12197">pdf</a>, <a href="https://arxiv.org/format/2411.12197">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="Multimedia">cs.MM</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/10.1007/978-981-97-8508-7_12">10.1007/978-981-97-8508-7_12 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> MTFusion: Reconstructing Any 3D Object from Single Image Using Multi-word Textual Inversion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liu%2C+Y">Yu Liu</a>, <a href="/search/?searchtype=author&query=Wang%2C+R">Ruowei Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jiaqi Li</a>, <a href="/search/?searchtype=author&query=Xu%2C+Z">Zixiang Xu</a>, <a href="/search/?searchtype=author&query=Zhao%2C+Q">Qijun Zhao</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="2411.12197v1-abstract-short" style="display: inline;"> Reconstructing 3D models from single-view images is a long-standing problem in computer vision. The latest advances for single-image 3D reconstruction extract a textual description from the input image and further utilize it to synthesize 3D models. However, existing methods focus on capturing a single key attribute of the image (e.g., object type, artistic style) and fail to consider the multi-pe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12197v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12197v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12197v1-abstract-full" style="display: none;"> Reconstructing 3D models from single-view images is a long-standing problem in computer vision. The latest advances for single-image 3D reconstruction extract a textual description from the input image and further utilize it to synthesize 3D models. However, existing methods focus on capturing a single key attribute of the image (e.g., object type, artistic style) and fail to consider the multi-perspective information required for accurate 3D reconstruction, such as object shape and material properties. Besides, the reliance on Neural Radiance Fields hinders their ability to reconstruct intricate surfaces and texture details. In this work, we propose MTFusion, which leverages both image data and textual descriptions for high-fidelity 3D reconstruction. Our approach consists of two stages. First, we adopt a novel multi-word textual inversion technique to extract a detailed text description capturing the image's characteristics. Then, we use this description and the image to generate a 3D model with FlexiCubes. Additionally, MTFusion enhances FlexiCubes by employing a special decoder network for Signed Distance Functions, leading to faster training and finer surface representation. Extensive evaluations demonstrate that our MTFusion surpasses existing image-to-3D methods on a wide range of synthetic and real-world images. Furthermore, the ablation study proves the effectiveness of our network designs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12197v1-abstract-full').style.display = 'none'; document.getElementById('2411.12197v1-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 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">PRCV 2024</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Pattern Recognition and Computer Vision (2025), Springer Nature Singapore, pages 166-180, ISBN 978-981-97-8508-7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11933">arXiv:2411.11933</a> <span> [<a href="https://arxiv.org/pdf/2411.11933">pdf</a>, <a href="https://arxiv.org/format/2411.11933">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> METEOR: Evolutionary Journey of Large Language Models from Guidance to Self-Growth </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+J">Jiawei Li</a>, <a href="/search/?searchtype=author&query=Feng%2C+C">Chong Feng</a>, <a href="/search/?searchtype=author&query=Gao%2C+Y">Yang Gao</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="2411.11933v1-abstract-short" style="display: inline;"> Model evolution enables learning from feedback to refine experiences and update skills, transforming models from having no domain knowledge to becoming domain experts. However, there is currently no unified and effective method for guiding this evolutionary process. To address this gap, we propose the Meteor method, which includes three training phases: weak-to-strong data distillation, iterative… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11933v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11933v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11933v1-abstract-full" style="display: none;"> Model evolution enables learning from feedback to refine experiences and update skills, transforming models from having no domain knowledge to becoming domain experts. However, there is currently no unified and effective method for guiding this evolutionary process. To address this gap, we propose the Meteor method, which includes three training phases: weak-to-strong data distillation, iterative training, and self-evolution strategies. Each phase maximizes the model's inherent domain capabilities, allowing it to autonomously refine its domain knowledge and enhance performance. Experiments demonstrate that our approach significantly improves accuracy, completeness, relevance, coherence, and reliability across domain-specific tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11933v1-abstract-full').style.display = 'none'; document.getElementById('2411.11933v1-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 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/2411.11924">arXiv:2411.11924</a> <span> [<a href="https://arxiv.org/pdf/2411.11924">pdf</a>, <a href="https://arxiv.org/format/2411.11924">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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Dataset Distillers Are Good Label Denoisers In the Wild </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Cheng%2C+L">Lechao Cheng</a>, <a href="/search/?searchtype=author&query=Chen%2C+K">Kaifeng Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jiyang Li</a>, <a href="/search/?searchtype=author&query=Tang%2C+S">Shengeng Tang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+S">Shufei Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+M">Meng Wang</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="2411.11924v1-abstract-short" style="display: inline;"> Learning from noisy data has become essential for adapting deep learning models to real-world applications. Traditional methods often involve first evaluating the noise and then applying strategies such as discarding noisy samples, re-weighting, or re-labeling. However, these methods can fall into a vicious cycle when the initial noise evaluation is inaccurate, leading to suboptimal performance. T… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11924v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11924v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11924v1-abstract-full" style="display: none;"> Learning from noisy data has become essential for adapting deep learning models to real-world applications. Traditional methods often involve first evaluating the noise and then applying strategies such as discarding noisy samples, re-weighting, or re-labeling. However, these methods can fall into a vicious cycle when the initial noise evaluation is inaccurate, leading to suboptimal performance. To address this, we propose a novel approach that leverages dataset distillation for noise removal. This method avoids the feedback loop common in existing techniques and enhances training efficiency, while also providing strong privacy protection through offline processing. We rigorously evaluate three representative dataset distillation methods (DATM, DANCE, and RCIG) under various noise conditions, including symmetric noise, asymmetric noise, and real-world natural noise. Our empirical findings reveal that dataset distillation effectively serves as a denoising tool in random noise scenarios but may struggle with structured asymmetric noise patterns, which can be absorbed into the distilled samples. Additionally, clean but challenging samples, such as those from tail classes in imbalanced datasets, may undergo lossy compression during distillation. Despite these challenges, our results highlight that dataset distillation holds significant promise for robust model training, especially in high-privacy environments where noise is prevalent. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11924v1-abstract-full').style.display = 'none'; document.getElementById('2411.11924v1-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 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/2411.11863">arXiv:2411.11863</a> <span> [<a href="https://arxiv.org/pdf/2411.11863">pdf</a>, <a href="https://arxiv.org/ps/2411.11863">ps</a>, <a href="https://arxiv.org/format/2411.11863">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> <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"> Longitudinal Wrist PPG Analysis for Reliable Hypertension Risk Screening Using Deep Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lin%2C+H">Hui Lin</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jiyang Li</a>, <a href="/search/?searchtype=author&query=Hussein%2C+R">Ramy Hussein</a>, <a href="/search/?searchtype=author&query=Sui%2C+X">Xin Sui</a>, <a href="/search/?searchtype=author&query=Li%2C+X">Xiaoyu Li</a>, <a href="/search/?searchtype=author&query=Zhu%2C+G">Guangpu Zhu</a>, <a href="/search/?searchtype=author&query=Katsaggelos%2C+A+K">Aggelos K. Katsaggelos</a>, <a href="/search/?searchtype=author&query=Zeng%2C+Z">Zijing Zeng</a>, <a href="/search/?searchtype=author&query=Li%2C+Y">Yelei 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="2411.11863v1-abstract-short" style="display: inline;"> Hypertension is a leading risk factor for cardiovascular diseases. Traditional blood pressure monitoring methods are cumbersome and inadequate for continuous tracking, prompting the development of PPG-based cuffless blood pressure monitoring wearables. This study leverages deep learning models, including ResNet and Transformer, to analyze wrist PPG data collected with a smartwatch for efficient hy… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11863v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11863v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11863v1-abstract-full" style="display: none;"> Hypertension is a leading risk factor for cardiovascular diseases. Traditional blood pressure monitoring methods are cumbersome and inadequate for continuous tracking, prompting the development of PPG-based cuffless blood pressure monitoring wearables. This study leverages deep learning models, including ResNet and Transformer, to analyze wrist PPG data collected with a smartwatch for efficient hypertension risk screening, eliminating the need for handcrafted PPG features. Using the Home Blood Pressure Monitoring (HBPM) longitudinal dataset of 448 subjects and five-fold cross-validation, our model was trained on over 68k spot-check instances from 358 subjects and tested on real-world continuous recordings of 90 subjects. The compact ResNet model with 0.124M parameters performed significantly better than traditional machine learning methods, demonstrating its effectiveness in distinguishing between healthy and abnormal cases in real-world scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11863v1-abstract-full').style.display = 'none'; document.getElementById('2411.11863v1-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> 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">blood pressure, hypertension, cuffless, photoplethysmography, deep learning</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11839">arXiv:2411.11839</a> <span> [<a href="https://arxiv.org/pdf/2411.11839">pdf</a>, <a href="https://arxiv.org/format/2411.11839">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> RoboGSim: A Real2Sim2Real Robotic Gaussian Splatting Simulator </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+X">Xinhai Li</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jialin Li</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Ziheng Zhang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+R">Rui Zhang</a>, <a href="/search/?searchtype=author&query=Jia%2C+F">Fan Jia</a>, <a href="/search/?searchtype=author&query=Wang%2C+T">Tiancai Wang</a>, <a href="/search/?searchtype=author&query=Fan%2C+H">Haoqiang Fan</a>, <a href="/search/?searchtype=author&query=Tseng%2C+K">Kuo-Kun Tseng</a>, <a href="/search/?searchtype=author&query=Wang%2C+R">Ruiping Wang</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="2411.11839v1-abstract-short" style="display: inline;"> Efficient acquisition of real-world embodied data has been increasingly critical. However, large-scale demonstrations captured by remote operation tend to take extremely high costs and fail to scale up the data size in an efficient manner. Sampling the episodes under a simulated environment is a promising way for large-scale collection while existing simulators fail to high-fidelity modeling on te… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11839v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11839v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11839v1-abstract-full" style="display: none;"> Efficient acquisition of real-world embodied data has been increasingly critical. However, large-scale demonstrations captured by remote operation tend to take extremely high costs and fail to scale up the data size in an efficient manner. Sampling the episodes under a simulated environment is a promising way for large-scale collection while existing simulators fail to high-fidelity modeling on texture and physics. To address these limitations, we introduce the RoboGSim, a real2sim2real robotic simulator, powered by 3D Gaussian Splatting and the physics engine. RoboGSim mainly includes four parts: Gaussian Reconstructor, Digital Twins Builder, Scene Composer, and Interactive Engine. It can synthesize the simulated data with novel views, objects, trajectories, and scenes. RoboGSim also provides an online, reproducible, and safe evaluation for different manipulation policies. The real2sim and sim2real transfer experiments show a high consistency in the texture and physics. Moreover, the effectiveness of synthetic data is validated under the real-world manipulated tasks. We hope RoboGSim serves as a closed-loop simulator for fair comparison on policy learning. More information can be found on our project page https://robogsim.github.io/ . <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11839v1-abstract-full').style.display = 'none'; document.getElementById('2411.11839v1-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 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/2411.11712">arXiv:2411.11712</a> <span> [<a href="https://arxiv.org/pdf/2411.11712">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> </div> <p class="title is-5 mathjax"> Consensus Statement on Brillouin Light Scattering Microscopy of Biological Materials </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bouvet%2C+P">Pierre Bouvet</a>, <a href="/search/?searchtype=author&query=Bevilacqua%2C+C">Carlo Bevilacqua</a>, <a href="/search/?searchtype=author&query=Ambekar%2C+Y">Yogeshwari Ambekar</a>, <a href="/search/?searchtype=author&query=Antonacci%2C+G">Giuseppe Antonacci</a>, <a href="/search/?searchtype=author&query=Au%2C+J">Joshua Au</a>, <a href="/search/?searchtype=author&query=Caponi%2C+S">Silvia Caponi</a>, <a href="/search/?searchtype=author&query=Chagnon-Lessard%2C+S">Sophie Chagnon-Lessard</a>, <a href="/search/?searchtype=author&query=Czarske%2C+J">Juergen Czarske</a>, <a href="/search/?searchtype=author&query=Dehoux%2C+T">Thomas Dehoux</a>, <a href="/search/?searchtype=author&query=Fioretto%2C+D">Daniele Fioretto</a>, <a href="/search/?searchtype=author&query=Fu%2C+Y">Yujian Fu</a>, <a href="/search/?searchtype=author&query=Guck%2C+J">Jochen Guck</a>, <a href="/search/?searchtype=author&query=Hamann%2C+T">Thorsten Hamann</a>, <a href="/search/?searchtype=author&query=Heinemann%2C+D">Dag Heinemann</a>, <a href="/search/?searchtype=author&query=J%C3%A4hnke%2C+T">Torsten J盲hnke</a>, <a href="/search/?searchtype=author&query=Jean-Ruel%2C+H">Hubert Jean-Ruel</a>, <a href="/search/?searchtype=author&query=Kabakova%2C+I">Irina Kabakova</a>, <a href="/search/?searchtype=author&query=Koski%2C+K">Kristie Koski</a>, <a href="/search/?searchtype=author&query=Koukourakis%2C+N">Nektarios Koukourakis</a>, <a href="/search/?searchtype=author&query=Krause%2C+D">David Krause</a>, <a href="/search/?searchtype=author&query=Cavera%2C+S+L">Salvatore La Cavera III</a>, <a href="/search/?searchtype=author&query=Landes%2C+T">Timm Landes</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jinhao Li</a>, <a href="/search/?searchtype=author&query=Margueritat%2C+J">Jeremie Margueritat</a>, <a href="/search/?searchtype=author&query=Mattarelli%2C+M">Maurizio Mattarelli</a> , et al. (19 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="2411.11712v1-abstract-short" style="display: inline;"> Brillouin Light Scattering (BLS) spectroscopy is a non-invasive, non-contact, label-free optical technique that can provide information on the mechanical properties of a material on the sub-micron scale. Over the last decade it has seen increased applications in the life sciences, driven by the observed significance of mechanical properties in biological processes, the realization of more sensitiv… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11712v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11712v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11712v1-abstract-full" style="display: none;"> Brillouin Light Scattering (BLS) spectroscopy is a non-invasive, non-contact, label-free optical technique that can provide information on the mechanical properties of a material on the sub-micron scale. Over the last decade it has seen increased applications in the life sciences, driven by the observed significance of mechanical properties in biological processes, the realization of more sensitive BLS spectrometers and its extension to an imaging modality. As with other spectroscopic techniques, BLS measurements not only detect signals characteristic of the investigated sample, but also of the experimental apparatus, and can be significantly affected by measurement conditions. The aim of this consensus statement is to improve the comparability of BLS studies by providing reporting recommendations for the measured parameters and detailing common artifacts. Given that most BLS studies of biological matter are still at proof-of-concept stages and use different--often self-built--spectrometers, a consensus statement is particularly timely to assure unified advancement. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11712v1-abstract-full').style.display = 'none'; document.getElementById('2411.11712v1-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 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">Main Text & Supplementary Text: 56 pages, 3 Figures, 2 Supplementary Figures, 1 Supplementary Table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11681">arXiv:2411.11681</a> <span> [<a href="https://arxiv.org/pdf/2411.11681">pdf</a>, <a href="https://arxiv.org/format/2411.11681">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> PSPO*: An Effective Process-supervised Policy Optimization for Reasoning Alignment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+J">Jiawei Li</a>, <a href="/search/?searchtype=author&query=Liang%2C+X">Xinyue Liang</a>, <a href="/search/?searchtype=author&query=Yang%2C+Y">Yizhe Yang</a>, <a href="/search/?searchtype=author&query=Feng%2C+C">Chong Feng</a>, <a href="/search/?searchtype=author&query=Gao%2C+Y">Yang Gao</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="2411.11681v1-abstract-short" style="display: inline;"> Process supervision enhances the performance of large language models in reasoning tasks by providing feedback at each step of chain-of-thought reasoning. However, due to the lack of effective process supervision methods, even advanced large language models are prone to logical errors and redundant reasoning. We claim that the effectiveness of process supervision significantly depends on both the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11681v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11681v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11681v1-abstract-full" style="display: none;"> Process supervision enhances the performance of large language models in reasoning tasks by providing feedback at each step of chain-of-thought reasoning. However, due to the lack of effective process supervision methods, even advanced large language models are prone to logical errors and redundant reasoning. We claim that the effectiveness of process supervision significantly depends on both the accuracy and the length of reasoning chains. Moreover, we identify that these factors exhibit a nonlinear relationship with the overall reward score of the reasoning process. Inspired by these insights, we propose a novel process supervision paradigm, PSPO*, which systematically outlines the workflow from reward model training to policy optimization, and highlights the importance of nonlinear rewards in process supervision. Based on PSPO*, we develop the PSPO-WRS, which considers the number of reasoning steps in determining reward scores and utilizes an adjusted Weibull distribution for nonlinear reward shaping. Experimental results on six mathematical reasoning datasets demonstrate that PSPO-WRS consistently outperforms current mainstream models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11681v1-abstract-full').style.display = 'none'; document.getElementById('2411.11681v1-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 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/2411.11648">arXiv:2411.11648</a> <span> [<a href="https://arxiv.org/pdf/2411.11648">pdf</a>, <a href="https://arxiv.org/ps/2411.11648">ps</a>, <a href="https://arxiv.org/format/2411.11648">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> </div> </div> <p class="title is-5 mathjax"> Evidence for Two Excited $惟^{-}$ Hyperons </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=BESIII+Collaboration"> BESIII Collaboration</a>, <a href="/search/?searchtype=author&query=Ablikim%2C+M">M. Ablikim</a>, <a href="/search/?searchtype=author&query=Achasov%2C+M+N">M. N. Achasov</a>, <a href="/search/?searchtype=author&query=Adlarson%2C+P">P. Adlarson</a>, <a href="/search/?searchtype=author&query=Afedulidis%2C+O">O. Afedulidis</a>, <a href="/search/?searchtype=author&query=Ai%2C+X+C">X. C. Ai</a>, <a href="/search/?searchtype=author&query=Aliberti%2C+R">R. Aliberti</a>, <a href="/search/?searchtype=author&query=Amoroso%2C+A">A. Amoroso</a>, <a href="/search/?searchtype=author&query=Bai%2C+Y">Y. Bai</a>, <a href="/search/?searchtype=author&query=Bakina%2C+O">O. Bakina</a>, <a href="/search/?searchtype=author&query=Balossino%2C+I">I. Balossino</a>, <a href="/search/?searchtype=author&query=Ban%2C+Y">Y. Ban</a>, <a href="/search/?searchtype=author&query=Bao%2C+H+-">H. -R. Bao</a>, <a href="/search/?searchtype=author&query=Batozskaya%2C+V">V. Batozskaya</a>, <a href="/search/?searchtype=author&query=Begzsuren%2C+K">K. Begzsuren</a>, <a href="/search/?searchtype=author&query=Berger%2C+N">N. Berger</a>, <a href="/search/?searchtype=author&query=Berlowski%2C+M">M. Berlowski</a>, <a href="/search/?searchtype=author&query=Bertani%2C+M">M. Bertani</a>, <a href="/search/?searchtype=author&query=Bettoni%2C+D">D. Bettoni</a>, <a href="/search/?searchtype=author&query=Bianchi%2C+F">F. Bianchi</a>, <a href="/search/?searchtype=author&query=Bianco%2C+E">E. Bianco</a>, <a href="/search/?searchtype=author&query=Bortone%2C+A">A. Bortone</a>, <a href="/search/?searchtype=author&query=Boyko%2C+I">I. Boyko</a>, <a href="/search/?searchtype=author&query=Briere%2C+R+A">R. A. Briere</a>, <a href="/search/?searchtype=author&query=Brueggemann%2C+A">A. Brueggemann</a> , et al. (650 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="2411.11648v1-abstract-short" style="display: inline;"> Using $e^+e^-$ collision data corresponding to an integrated luminosity of 19 fb$^{-1}$ collected by the BESIII detector at center-of-mass energies ranging from 4.13 to 4.70 GeV, we report the first evidence for a new excited $惟^{-}$ hyperon, the $惟^*(2109)^{-}$, through the process $e^+ e^- \to 惟^*(2109)^{-} \bar惟^{+} +c.c.$ with a significance of 3.7 $蟽$. The mass and width of $惟^*(2109)^{-}$ ar… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11648v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11648v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11648v1-abstract-full" style="display: none;"> Using $e^+e^-$ collision data corresponding to an integrated luminosity of 19 fb$^{-1}$ collected by the BESIII detector at center-of-mass energies ranging from 4.13 to 4.70 GeV, we report the first evidence for a new excited $惟^{-}$ hyperon, the $惟^*(2109)^{-}$, through the process $e^+ e^- \to 惟^*(2109)^{-} \bar惟^{+} +c.c.$ with a significance of 3.7 $蟽$. The mass and width of $惟^*(2109)^{-}$ are measured to be $2108.8 \pm 5.5_{\rm stat} \pm 1.5_{\rm syst} {\rm MeV}/c^{2}$ and $21.6 \pm 17.7_{\rm stat} \pm 9.4_{\rm syst} {\rm MeV}$, respectively. We also present evidence for production of the $惟^*(2012)^{-}$ in the process $e^+ e^- \to 惟^*(2012)^{-} \bar惟^{+} +c.c.$ with a significance of 3.7 $蟽$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11648v1-abstract-full').style.display = 'none'; document.getElementById('2411.11648v1-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 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">8 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/2411.11613">arXiv:2411.11613</a> <span> [<a href="https://arxiv.org/pdf/2411.11613">pdf</a>, <a href="https://arxiv.org/format/2411.11613">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"> Leveraging Computational Pathology AI for Noninvasive Optical Imaging Analysis Without Retraining </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Barash%2C+D">Danny Barash</a>, <a href="/search/?searchtype=author&query=Manning%2C+E">Emilie Manning</a>, <a href="/search/?searchtype=author&query=Van+Vleck%2C+A">Aidan Van Vleck</a>, <a href="/search/?searchtype=author&query=Hirsch%2C+O">Omri Hirsch</a>, <a href="/search/?searchtype=author&query=Aye%2C+K+L">Kyi Lei Aye</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jingxi Li</a>, <a href="/search/?searchtype=author&query=Scumpia%2C+P+O">Philip O. Scumpia</a>, <a href="/search/?searchtype=author&query=Ozcan%2C+A">Aydogan Ozcan</a>, <a href="/search/?searchtype=author&query=Aasi%2C+S">Sumaira Aasi</a>, <a href="/search/?searchtype=author&query=Rieger%2C+K+E">Kerri E. Rieger</a>, <a href="/search/?searchtype=author&query=Sarin%2C+K+Y">Kavita Y. Sarin</a>, <a href="/search/?searchtype=author&query=Freifeld%2C+O">Oren Freifeld</a>, <a href="/search/?searchtype=author&query=Winetraub%2C+Y">Yonatan Winetraub</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="2411.11613v2-abstract-short" style="display: inline;"> Noninvasive optical imaging modalities can probe patient's tissue in 3D and over time generate gigabytes of clinically relevant data per sample. There is a need for AI models to analyze this data and assist clinical workflow. The lack of expert labelers and the large dataset required (>100,000 images) for model training and tuning are the main hurdles in creating foundation models. In this paper w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11613v2-abstract-full').style.display = 'inline'; document.getElementById('2411.11613v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11613v2-abstract-full" style="display: none;"> Noninvasive optical imaging modalities can probe patient's tissue in 3D and over time generate gigabytes of clinically relevant data per sample. There is a need for AI models to analyze this data and assist clinical workflow. The lack of expert labelers and the large dataset required (>100,000 images) for model training and tuning are the main hurdles in creating foundation models. In this paper we introduce FoundationShift, a method to apply any AI model from computational pathology without retraining. We show our method is more accurate than state of the art models (SAM, MedSAM, SAM-Med2D, CellProfiler, Hover-Net, PLIP, UNI and ChatGPT), with multiple imaging modalities (OCT and RCM). This is achieved without the need for model retraining or fine-tuning. Applying our method to noninvasive in vivo images could enable physicians to readily incorporate optical imaging modalities into their clinical practice, providing real time tissue analysis and improving patient care. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11613v2-abstract-full').style.display = 'none'; document.getElementById('2411.11613v2-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> 19 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 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/2411.11483">arXiv:2411.11483</a> <span> [<a href="https://arxiv.org/pdf/2411.11483">pdf</a>, <a href="https://arxiv.org/format/2411.11483">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Robust State Estimation for Legged Robots with Dual Beta Kalman Filter </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+T">Tianyi Zhang</a>, <a href="/search/?searchtype=author&query=Cao%2C+W">Wenhan Cao</a>, <a href="/search/?searchtype=author&query=Liu%2C+C">Chang Liu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+T">Tao Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jiangtao Li</a>, <a href="/search/?searchtype=author&query=Li%2C+S+E">Shengbo Eben 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="2411.11483v1-abstract-short" style="display: inline;"> Existing state estimation algorithms for legged robots that rely on proprioceptive sensors often overlook foot slippage and leg deformation in the physical world, leading to large estimation errors. To address this limitation, we propose a comprehensive measurement model that accounts for both foot slippage and variable leg length by analyzing the relative motion between foot contact points and th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11483v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11483v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11483v1-abstract-full" style="display: none;"> Existing state estimation algorithms for legged robots that rely on proprioceptive sensors often overlook foot slippage and leg deformation in the physical world, leading to large estimation errors. To address this limitation, we propose a comprehensive measurement model that accounts for both foot slippage and variable leg length by analyzing the relative motion between foot contact points and the robot's body center. We show that leg length is an observable quantity, meaning that its value can be explicitly inferred by designing an auxiliary filter. To this end, we introduce a dual estimation framework that iteratively employs a parameter filter to estimate the leg length parameters and a state filter to estimate the robot's state. To prevent error accumulation in this iterative framework, we construct a partial measurement model for the parameter filter using the leg static equation. This approach ensures that leg length estimation relies solely on joint torques and foot contact forces, avoiding the influence of state estimation errors on the parameter estimation. Unlike leg length which can be directly estimated, foot slippage cannot be measured directly with the current sensor configuration. However, since foot slippage occurs at a low frequency, it can be treated as outliers in the measurement data. To mitigate the impact of these outliers, we propose the beta Kalman filter (beta KF), which redefines the estimation loss in canonical Kalman filtering using beta divergence. This divergence can assign low weights to outliers in an adaptive manner, thereby enhancing the robustness of the estimation algorithm. These techniques together form the dual beta-Kalman filter (Dual beta KF), a novel algorithm for robust state estimation in legged robots. Experimental results on the Unitree GO2 robot demonstrate that the Dual beta KF significantly outperforms state-of-the-art methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11483v1-abstract-full').style.display = 'none'; document.getElementById('2411.11483v1-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 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/2411.11479">arXiv:2411.11479</a> <span> [<a href="https://arxiv.org/pdf/2411.11479">pdf</a>, <a href="https://arxiv.org/format/2411.11479">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"> Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media Contexts </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+J">Jingxuan Li</a>, <a href="/search/?searchtype=author&query=Yang%2C+Y">Yuning Yang</a>, <a href="/search/?searchtype=author&query=Yang%2C+S">Shengqi Yang</a>, <a href="/search/?searchtype=author&query=Zhao%2C+Y">Yizhou Zhao</a>, <a href="/search/?searchtype=author&query=Wu%2C+Y+N">Ying Nian Wu</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="2411.11479v1-abstract-short" style="display: inline;"> The rapid advancement of Vision-Language Models (VLMs) has expanded multimodal applications, yet evaluations often focus on basic tasks like object recognition, overlooking abstract aspects such as personalities and values. To address this gap, we introduce Value-Spectrum, a visual question-answering benchmark aimed at assessing VLMs based on Schwartz's value dimensions, which capture core values… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11479v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11479v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11479v1-abstract-full" style="display: none;"> The rapid advancement of Vision-Language Models (VLMs) has expanded multimodal applications, yet evaluations often focus on basic tasks like object recognition, overlooking abstract aspects such as personalities and values. To address this gap, we introduce Value-Spectrum, a visual question-answering benchmark aimed at assessing VLMs based on Schwartz's value dimensions, which capture core values guiding people's beliefs and actions across cultures. We constructed a vectorized database of over 50,000 short videos sourced from TikTok, YouTube Shorts, and Instagram Reels, covering multiple months and a wide array of topics such as family, health, hobbies, society, and technology. We also developed a VLM agent pipeline to automate video browsing and analysis. Benchmarking representative VLMs on Value-Spectrum reveals significant differences in their responses to value-oriented content, with most models exhibiting a preference for hedonistic topics. Beyond identifying natural preferences, we explored the ability of VLM agents to adopt specific personas when explicitly prompted, revealing insights into the models' adaptability in role-playing scenarios. These findings highlight the potential of Value-Spectrum as a comprehensive evaluation set for tracking VLM advancements in value-based tasks and for developing more sophisticated role-playing AI agents. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11479v1-abstract-full').style.display = 'none'; document.getElementById('2411.11479v1-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 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/2411.11365">arXiv:2411.11365</a> <span> [<a href="https://arxiv.org/pdf/2411.11365">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> </div> </div> <p class="title is-5 mathjax"> ezyMRI: How to build an MRI machine from scratch -- Experience from a four-day hackathon </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Huang%2C+S">Shaoying Huang</a>, <a href="/search/?searchtype=author&query=Algar%C3%ADn%2C+J+M">Jos茅 Miguel Algar铆n</a>, <a href="/search/?searchtype=author&query=Alonso%2C+J">Joseba Alonso</a>, <a href="/search/?searchtype=author&query=R%2C+A">Anieyrudh R</a>, <a href="/search/?searchtype=author&query=Borreguero%2C+J">Jose Borreguero</a>, <a href="/search/?searchtype=author&query=Bschorr%2C+F">Fabian Bschorr</a>, <a href="/search/?searchtype=author&query=Cassidy%2C+P">Paul Cassidy</a>, <a href="/search/?searchtype=author&query=Choo%2C+W+M">Wei Ming Choo</a>, <a href="/search/?searchtype=author&query=Corcos%2C+D">David Corcos</a>, <a href="/search/?searchtype=author&query=Guallart-Naval%2C+T">Teresa Guallart-Naval</a>, <a href="/search/?searchtype=author&query=Han%2C+H+J">Heng Jing Han</a>, <a href="/search/?searchtype=author&query=Igwe%2C+K+C">Kay Chioma Igwe</a>, <a href="/search/?searchtype=author&query=Kang%2C+J">Jacob Kang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Joe Li</a>, <a href="/search/?searchtype=author&query=Littin%2C+S">Sebastian Littin</a>, <a href="/search/?searchtype=author&query=Liu%2C+J">Jie Liu</a>, <a href="/search/?searchtype=author&query=Rodriguez%2C+G+G">Gonzalo Gabriel Rodriguez</a>, <a href="/search/?searchtype=author&query=Solomon%2C+E">Eddy Solomon</a>, <a href="/search/?searchtype=author&query=Tan%2C+L">Li-Kuo Tan</a>, <a href="/search/?searchtype=author&query=Tian%2C+R">Rui Tian</a>, <a href="/search/?searchtype=author&query=Webb%2C+A">Andrew Webb</a>, <a href="/search/?searchtype=author&query=Weber%2C+S">Susanna Weber</a>, <a href="/search/?searchtype=author&query=Xiao%2C+D">Dan Xiao</a>, <a href="/search/?searchtype=author&query=Xu%2C+M">Minxuan Xu</a>, <a href="/search/?searchtype=author&query=Yu%2C+W">Wenwei Yu</a> , et al. (3 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="2411.11365v1-abstract-short" style="display: inline;"> Nuclear magnetic resonance instruments are becoming available to the do-it-yourself community. The challenges encountered in the endeavor to build a magnetic resonance imaging instrument from scratch were confronted in a four-day hackathon at Singapore University of Technology and Design in spring 2024. One day was devoted to educational lectures and three days to system construction and testing.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11365v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11365v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11365v1-abstract-full" style="display: none;"> Nuclear magnetic resonance instruments are becoming available to the do-it-yourself community. The challenges encountered in the endeavor to build a magnetic resonance imaging instrument from scratch were confronted in a four-day hackathon at Singapore University of Technology and Design in spring 2024. One day was devoted to educational lectures and three days to system construction and testing. Seventy young researchers from all parts of the world formed six teams focusing on magnet, gradient coil, RF coil, console, system integration, and design, which together produced a working MRI instrument in three days. The different steps, encountered challenges, and their solutions are reported. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11365v1-abstract-full').style.display = 'none'; document.getElementById('2411.11365v1-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 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">49 pages, 23 figures, comments welcome (this paper is meant to be useful to people constructing their own MRI 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/2411.11356">arXiv:2411.11356</a> <span> [<a href="https://arxiv.org/pdf/2411.11356">pdf</a>, <a href="https://arxiv.org/format/2411.11356">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/10.1007/978-3-031-72627-9_15">10.1007/978-3-031-72627-9_15 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Superpixel-informed Implicit Neural Representation for Multi-Dimensional Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+J">Jiayi Li</a>, <a href="/search/?searchtype=author&query=Zhao%2C+X">Xile Zhao</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">Jianli Wang</a>, <a href="/search/?searchtype=author&query=Wang%2C+C">Chao Wang</a>, <a href="/search/?searchtype=author&query=Wang%2C+M">Min Wang</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="2411.11356v1-abstract-short" style="display: inline;"> Recently, implicit neural representations (INRs) have attracted increasing attention for multi-dimensional data recovery. However, INRs simply map coordinates via a multi-layer perception (MLP) to corresponding values, ignoring the inherent semantic information of the data. To leverage semantic priors from the data, we propose a novel Superpixel-informed INR (S-INR). Specifically, we suggest utili… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11356v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11356v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11356v1-abstract-full" style="display: none;"> Recently, implicit neural representations (INRs) have attracted increasing attention for multi-dimensional data recovery. However, INRs simply map coordinates via a multi-layer perception (MLP) to corresponding values, ignoring the inherent semantic information of the data. To leverage semantic priors from the data, we propose a novel Superpixel-informed INR (S-INR). Specifically, we suggest utilizing generalized superpixel instead of pixel as an alternative basic unit of INR for multi-dimensional data (e.g., images and weather data). The coordinates of generalized superpixels are first fed into exclusive attention-based MLPs, and then the intermediate results interact with a shared dictionary matrix. The elaborately designed modules in S-INR allow us to ingenuously exploit the semantic information within and across generalized superpixels. Extensive experiments on various applications validate the effectiveness and efficacy of our S-INR compared to state-of-the-art INR methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11356v1-abstract-full').style.display = 'none'; document.getElementById('2411.11356v1-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 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">Accepted at ECCV 2024, 18 pages, 7 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/2411.11355">arXiv:2411.11355</a> <span> [<a href="https://arxiv.org/pdf/2411.11355">pdf</a>, <a href="https://arxiv.org/ps/2411.11355">ps</a>, <a href="https://arxiv.org/format/2411.11355">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Number Theory">math.NT</span> </div> </div> <p class="title is-5 mathjax"> A two-dimensional delta symbol method and its application to pairs of quadratic forms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+J">Junxian Li</a>, <a href="/search/?searchtype=author&query=Myerson%2C+S+L+R">Simon L. Rydin Myerson</a>, <a href="/search/?searchtype=author&query=Vishe%2C+P">Pankaj Vishe</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="2411.11355v1-abstract-short" style="display: inline;"> We present a two-dimensional delta symbol method that facilitates a version of the Kloosterman refinement of the circle method, addressing a question posed by Heath-Brown. As an application, we establish the asymptotic formula for the number of integral points on a non-singular intersection of two integral quadratic forms with at least $10$ variables. Assuming the Generalized Lindel枚f Hypothesis,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11355v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11355v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11355v1-abstract-full" style="display: none;"> We present a two-dimensional delta symbol method that facilitates a version of the Kloosterman refinement of the circle method, addressing a question posed by Heath-Brown. As an application, we establish the asymptotic formula for the number of integral points on a non-singular intersection of two integral quadratic forms with at least $10$ variables. Assuming the Generalized Lindel枚f Hypothesis, we reduce the number of variables to $9$ by performing a double Kloosterman refinement. A heuristic argument suggests our two-dimensional delta symbol will typically outperform known expressions of this type by an increasing margin as the number of variables grows. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11355v1-abstract-full').style.display = 'none'; document.getElementById('2411.11355v1-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 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">53 Pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 11P55 (11D45; 14G05; 14J45; 11D09) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11354">arXiv:2411.11354</a> <span> [<a href="https://arxiv.org/pdf/2411.11354">pdf</a>, <a href="https://arxiv.org/format/2411.11354">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> A comprehensive survey of oracle character recognition: challenges, benchmarks, and beyond </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+J">Jing Li</a>, <a href="/search/?searchtype=author&query=Chi%2C+X">Xueke Chi</a>, <a href="/search/?searchtype=author&query=Wang%2C+Q">Qiufeng Wang</a>, <a href="/search/?searchtype=author&query=Wang%2C+D">Dahan Wang</a>, <a href="/search/?searchtype=author&query=Huang%2C+K">Kaizhu Huang</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yongge Liu</a>, <a href="/search/?searchtype=author&query=Liu%2C+C">Cheng-lin Liu</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="2411.11354v1-abstract-short" style="display: inline;"> Oracle character recognition-an analysis of ancient Chinese inscriptions found on oracle bones-has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. Wi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11354v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11354v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11354v1-abstract-full" style="display: none;"> Oracle character recognition-an analysis of ancient Chinese inscriptions found on oracle bones-has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11354v1-abstract-full').style.display = 'none'; document.getElementById('2411.11354v1-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 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/2411.11353">arXiv:2411.11353</a> <span> [<a href="https://arxiv.org/pdf/2411.11353">pdf</a>, <a href="https://arxiv.org/format/2411.11353">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> An Investigation of Reprogramming for Cross-Language Adaptation in Speaker Verification Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+J">Jingyu Li</a>, <a href="/search/?searchtype=author&query=Chiu%2C+A+Y+F">Aemon Yat Fei Chiu</a>, <a href="/search/?searchtype=author&query=Lee%2C+T">Tan Lee</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="2411.11353v1-abstract-short" style="display: inline;"> Language mismatch is among the most common and challenging domain mismatches in deploying speaker verification (SV) systems. Adversarial reprogramming has shown promising results in cross-language adaptation for SV. The reprogramming is implemented by padding learnable parameters on the two sides of input speech signals. In this paper, we investigate the relationship between the number of padded p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11353v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11353v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11353v1-abstract-full" style="display: none;"> Language mismatch is among the most common and challenging domain mismatches in deploying speaker verification (SV) systems. Adversarial reprogramming has shown promising results in cross-language adaptation for SV. The reprogramming is implemented by padding learnable parameters on the two sides of input speech signals. In this paper, we investigate the relationship between the number of padded parameters and the performance of the reprogrammed models. Sufficient experiments are conducted with different scales of SV models and datasets. The results demonstrate that reprogramming consistently improves the performance of cross-language SV, while the improvement is saturated or even degraded when using larger padding lengths. The performance is mainly determined by the capacity of the original SV models instead of the number of padded parameters. The SV models with larger scales have higher upper bounds in performance and can endure longer padding without performance degradation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11353v1-abstract-full').style.display = 'none'; document.getElementById('2411.11353v1-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 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">Accepted by ISCSLP 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/2411.11286">arXiv:2411.11286</a> <span> [<a href="https://arxiv.org/pdf/2411.11286">pdf</a>, <a href="https://arxiv.org/ps/2411.11286">ps</a>, <a href="https://arxiv.org/format/2411.11286">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Quasi-Newton method of Optimization is proved to be a steepest descent method under the ellipsoid norm </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+J">Jiongcheng 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="2411.11286v1-abstract-short" style="display: inline;"> Optimization problems, arise in many practical applications, from the view points of both theory and numerical methods. Especially, significant improvement in deep learning training came from the Quasi-Newton methods. Quasi-Newton search directions provide an attractive alternative to Newton's method in that they do not require computation of the Hessian and yet still attain a super linear rate of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11286v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11286v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11286v1-abstract-full" style="display: none;"> Optimization problems, arise in many practical applications, from the view points of both theory and numerical methods. Especially, significant improvement in deep learning training came from the Quasi-Newton methods. Quasi-Newton search directions provide an attractive alternative to Newton's method in that they do not require computation of the Hessian and yet still attain a super linear rate of convergence. In Quasi-Newton method, we require Hessian approximation to satisfy the secant equation. In this paper, the Classical Cauchy-Schwartz Inequality is introduced, then more generalization are proposed. And it is seriously proved that Quasi-Newton method is a steepest descent method under the ellipsoid norm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11286v1-abstract-full').style.display = 'none'; document.getElementById('2411.11286v1-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 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/2411.11221">arXiv:2411.11221</a> <span> [<a href="https://arxiv.org/pdf/2411.11221">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</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"> Data Driven Automatic Electrical Machine Preliminary Design with Artificial Intelligence Expert Guidance </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+Y">Yiwei Wang</a>, <a href="/search/?searchtype=author&query=Yang%2C+T">Tao Yang</a>, <a href="/search/?searchtype=author&query=Huang%2C+H">Hailin Huang</a>, <a href="/search/?searchtype=author&query=Zou%2C+T">Tianjie Zou</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jincai Li</a>, <a href="/search/?searchtype=author&query=Chen%2C+N">Nuo Chen</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zhuoran 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="2411.11221v1-abstract-short" style="display: inline;"> This paper presents a data-driven electrical machine design (EMD) framework using wound-rotor synchronous generator (WRSG) as a design example. Unlike traditional preliminary EMD processes that heavily rely on expertise, this framework leverages an artificial-intelligence based expert database, to provide preliminary designs directly from user specifications. Initial data is generated using 2D fin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11221v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11221v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11221v1-abstract-full" style="display: none;"> This paper presents a data-driven electrical machine design (EMD) framework using wound-rotor synchronous generator (WRSG) as a design example. Unlike traditional preliminary EMD processes that heavily rely on expertise, this framework leverages an artificial-intelligence based expert database, to provide preliminary designs directly from user specifications. Initial data is generated using 2D finite element (FE) machine models by sweeping fundamental design variables including machine length and diameter, enabling scalable machine geometry with machine performance for each design is recorded. This data trains a Metamodel of Optimal Prognosis (MOP)-based surrogate model, which maps design variables to key performance indicators (KPIs). Once trained, guided by metaheuristic algorithms, the surrogate model can generate thousands of geometric scalable designs, covering a wide power range, forming an AI expert database to guide future preliminary design. The framework is validated with a 30kVA WRSG design case. A prebuilt WRSG database, covering power from 10 to 60kVA, is validated by FE simulation. Design No.1138 is selected from database and compared with conventional design. Results show No.1138 achieves a higher power density of 2.21 kVA/kg in just 5 seconds, compared to 2.02 kVA/kg obtained using traditional method, which take several days. The developed AI expert database also serves as a high-quality data source for further developing AI models for automatic electrical machine design. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11221v1-abstract-full').style.display = 'none'; document.getElementById('2411.11221v1-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 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/2411.11156">arXiv:2411.11156</a> <span> [<a href="https://arxiv.org/pdf/2411.11156">pdf</a>, <a href="https://arxiv.org/format/2411.11156">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Strongly Correlated Electrons">cond-mat.str-el</span> </div> </div> <p class="title is-5 mathjax"> Observation of giant nonlinear Hall conductivity in Bernal bilayer graphene </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chichinadze%2C+D+V">Dmitry V. Chichinadze</a>, <a href="/search/?searchtype=author&query=Zhang%2C+N+J">Naiyuan James Zhang</a>, <a href="/search/?searchtype=author&query=Lin%2C+J">Jiang-Xiazi Lin</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xiaoyu Wang</a>, <a href="/search/?searchtype=author&query=Watanabe%2C+K">Kenji Watanabe</a>, <a href="/search/?searchtype=author&query=Taniguchi%2C+T">Takashi Taniguchi</a>, <a href="/search/?searchtype=author&query=Vafek%2C+O">Oskar Vafek</a>, <a href="/search/?searchtype=author&query=Li%2C+J+I+A">J. I. A. 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="2411.11156v1-abstract-short" style="display: inline;"> In a system of two-dimensional electrons, a combination of broken symmetry, interactions, and nontrivial topology can conspire to give rise to a nonlinear transport regime, where electric current density scales as the square of electric field. This regime has become a venue for exciting discoveries such as the nonlinear Hall effect and diode-like nonreciprocal transport. However, interpretation of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11156v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11156v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11156v1-abstract-full" style="display: none;"> In a system of two-dimensional electrons, a combination of broken symmetry, interactions, and nontrivial topology can conspire to give rise to a nonlinear transport regime, where electric current density scales as the square of electric field. This regime has become a venue for exciting discoveries such as the nonlinear Hall effect and diode-like nonreciprocal transport. However, interpretation of experimental data is challenging in the nonlinear regime as DC transport is described by a rank-3 conductivity tensor with 6 free parameters. Here, we resolve this challenge by analytically solving for the nonlinear potential distribution across the disk sample for an arbitrary linear and nonlinear conductivity tensors. This allows us to unambiguously extract all components of the nonlinear tensor from experimental measurement. Using this novel tool, we identify giant nonlinear Hall effect in Bernal bilayer graphene. Our methodology provides the first systematic framework for interpreting nonlinear transport and uncovers a new route towards understanding quasi-2D materials. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11156v1-abstract-full').style.display = 'none'; document.getElementById('2411.11156v1-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 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 pages, 4 figures + 85 pages, 16 figures in SI</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11107">arXiv:2411.11107</a> <span> [<a href="https://arxiv.org/pdf/2411.11107">pdf</a>, <a href="https://arxiv.org/format/2411.11107">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</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"> 4H-SiC microring opto-mechanical oscillator with a self-injection locked pump </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Savchenkov%2C+A">Anatoliy Savchenkov</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jingwei Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+R">Ruixuan Wang</a>, <a href="/search/?searchtype=author&query=Matsko%2C+A+B">Andrey B. Matsko</a>, <a href="/search/?searchtype=author&query=Li%2C+Q">Qing Li</a>, <a href="/search/?searchtype=author&query=Taheri%2C+H">Hossein Taheri</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="2411.11107v1-abstract-short" style="display: inline;"> We have demonstrated, for the first time to our knowledge, self-injection locking of a distributed feedback (DFB) diode laser to a multimode 4H-silicon carbide (4H-SiC) microring resonator, and observed resonant opto-mechanical oscillation in the cavity modes. While the fundamental transverse-electric mode family of the silicon carbide microring was optically pumped, Stokes light was generated in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11107v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11107v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11107v1-abstract-full" style="display: none;"> We have demonstrated, for the first time to our knowledge, self-injection locking of a distributed feedback (DFB) diode laser to a multimode 4H-silicon carbide (4H-SiC) microring resonator, and observed resonant opto-mechanical oscillation in the cavity modes. While the fundamental transverse-electric mode family of the silicon carbide microring was optically pumped, Stokes light was generated in the adjacent fundamental transverse-magnetic resonant mode. The threshold of the process did not exceed 5~mW of light entering the cavity characterized with a loaded optical quality factor of $\smash{2\times10^6}$. These results mark a significant milestone in unlocking the potential of 4H-SiC through turnkey soliton microcomb generation and empowering future advancements in areas such as cavity optomechanics using this versatile and quantum-friendly material platform. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11107v1-abstract-full').style.display = 'none'; document.getElementById('2411.11107v1-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 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/2411.11030">arXiv:2411.11030</a> <span> [<a href="https://arxiv.org/pdf/2411.11030">pdf</a>, <a href="https://arxiv.org/format/2411.11030">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"> IREE Oriented Active RIS-Assisted Green communication System with Outdated CSI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Cao%2C+K">Kai Cao</a>, <a href="/search/?searchtype=author&query=Yu%2C+T">Tao Yu</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jihong Li</a>, <a href="/search/?searchtype=author&query=Chen%2C+X">Xiaojing Chen</a>, <a href="/search/?searchtype=author&query=Sun%2C+Y">Yanzan Sun</a>, <a href="/search/?searchtype=author&query=Wu%2C+Q">Qingqing Wu</a>, <a href="/search/?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/?searchtype=author&query=Zhang%2C+S">Shunqing 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="2411.11030v1-abstract-short" style="display: inline;"> The rapid evolution of communication technologies has spurred a growing demand for energy-efficient network architectures and performance metrics. Active Reconfigurable Intelligent Surfaces (RIS) are emerging as a key component in green network architectures. Compared to passive RIS, active RIS are equipped with amplifiers on each reflecting element, allowing them to simultaneously reflect and amp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11030v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11030v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11030v1-abstract-full" style="display: none;"> The rapid evolution of communication technologies has spurred a growing demand for energy-efficient network architectures and performance metrics. Active Reconfigurable Intelligent Surfaces (RIS) are emerging as a key component in green network architectures. Compared to passive RIS, active RIS are equipped with amplifiers on each reflecting element, allowing them to simultaneously reflect and amplify signals, thereby overcoming the double multiplicative fading in the phase response, and improving both system coverage and performance. Additionally, the Integrated Relative Energy Efficiency (IREE) metric, as introduced in [1], addresses the dynamic variations in traffic and capacity over time and space, enabling more energy-efficient wireless systems. Building on these advancements, this paper investigates the problem of maximizing IREE in active RIS-assisted green communication systems. However, acquiring perfect Channel State Information (CSI) in practical systems poses significant challenges and costs. To address this, we derive the average achievable rate based on outdated CSI and formulated the corresponding IREE maximization problem, which is solved by jointly optimizing beamforming at both the base station and RIS. Given the non-convex nature of the problem, we propose an Alternating Optimization Successive Approximation (AOSO) algorithm. By applying quadratic transform and relaxation techniques, we simplify the original problem and alternately optimize the beamforming matrices at the base station and RIS. Furthermore, to handle the discrete constraints of the RIS reflection coefficients, we develop a successive approximation method. Experimental results validate our theoretical analysis of the algorithm's convergence , demonstrating the effectiveness of the proposed algorithm and highlighting the superiority of IREE in enhancing the performance of green communication networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11030v1-abstract-full').style.display = 'none'; document.getElementById('2411.11030v1-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 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/2411.10962">arXiv:2411.10962</a> <span> [<a href="https://arxiv.org/pdf/2411.10962">pdf</a>, <a href="https://arxiv.org/format/2411.10962">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"> V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yang%2C+L">Lei Yang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+X">Xinyu Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jun Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+C">Chen Wang</a>, <a href="/search/?searchtype=author&query=Song%2C+Z">Zhiying Song</a>, <a href="/search/?searchtype=author&query=Zhao%2C+T">Tong Zhao</a>, <a href="/search/?searchtype=author&query=Song%2C+Z">Ziying Song</a>, <a href="/search/?searchtype=author&query=Wang%2C+L">Li Wang</a>, <a href="/search/?searchtype=author&query=Zhou%2C+M">Mo Zhou</a>, <a href="/search/?searchtype=author&query=Shen%2C+Y">Yang Shen</a>, <a href="/search/?searchtype=author&query=Wu%2C+K">Kai Wu</a>, <a href="/search/?searchtype=author&query=Lv%2C+C">Chen Lv</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="2411.10962v1-abstract-short" style="display: inline;"> Modern autonomous vehicle perception systems often struggle with occlusions and limited perception range. Previous studies have demonstrated the effectiveness of cooperative perception in extending the perception range and overcoming occlusions, thereby improving the safety of autonomous driving. In recent years, a series of cooperative perception datasets have emerged. However, these datasets onl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10962v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10962v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10962v1-abstract-full" style="display: none;"> Modern autonomous vehicle perception systems often struggle with occlusions and limited perception range. Previous studies have demonstrated the effectiveness of cooperative perception in extending the perception range and overcoming occlusions, thereby improving the safety of autonomous driving. In recent years, a series of cooperative perception datasets have emerged. However, these datasets only focus on camera and LiDAR, overlooking 4D Radar, a sensor employed in single-vehicle autonomous driving for robust perception in adverse weather conditions. In this paper, to bridge the gap of missing 4D Radar datasets in cooperative perception, we present V2X-Radar, the first large real-world multi-modal dataset featuring 4D Radar. Our V2X-Radar dataset is collected using a connected vehicle platform and an intelligent roadside unit equipped with 4D Radar, LiDAR, and multi-view cameras. The collected data includes sunny and rainy weather conditions, spanning daytime, dusk, and nighttime, as well as typical challenging scenarios. The dataset comprises 20K LiDAR frames, 40K camera images, and 20K 4D Radar data, with 350K annotated bounding boxes across five categories. To facilitate diverse research domains, we establish V2X-Radar-C for cooperative perception, V2X-Radar-I for roadside perception, and V2X-Radar-V for single-vehicle perception. We further provide comprehensive benchmarks of recent perception algorithms on the above three sub-datasets. The dataset and benchmark codebase will be available at \url{http://openmpd.com/column/V2X-Radar}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10962v1-abstract-full').style.display = 'none'; document.getElementById('2411.10962v1-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 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, 5 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/2411.10960">arXiv:2411.10960</a> <span> [<a href="https://arxiv.org/pdf/2411.10960">pdf</a>, <a href="https://arxiv.org/format/2411.10960">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Beamforming Design and Multi-User Scheduling in Transmissive RIS Enabled Distributed Cooperative ISAC Networks with RSMA </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liu%2C+Z">Ziwei Liu</a>, <a href="/search/?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/?searchtype=author&query=Wu%2C+Q">Qingqing Wu</a>, <a href="/search/?searchtype=author&query=Li%2C+Z">Zhendong Li</a>, <a href="/search/?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/?searchtype=author&query=Cheng%2C+N">Nan Cheng</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jun 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="2411.10960v1-abstract-short" style="display: inline;"> In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-empowered distributed cooperative integrated sensing and communication (ISAC) network to enhance coverage as well as to enhance wireless environment understanding. Based on the network requirements, the users are categorized into cooperative users (CUEs) and destination users (DUEs), and the CUEs u… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10960v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10960v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10960v1-abstract-full" style="display: none;"> In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-empowered distributed cooperative integrated sensing and communication (ISAC) network to enhance coverage as well as to enhance wireless environment understanding. Based on the network requirements, the users are categorized into cooperative users (CUEs) and destination users (DUEs), and the CUEs utilize their own resources to serve the DUEs. To realize cooperation, we implement rate-splitting multiple access (RSMA) at the base station (BS), where the common stream is decoded and reencoded at the CUEs and forwarded to the DUEs, while the private stream satisfies the CUEs' own communication requirements. We construct an optimization problem with maximum minimum radar mutual information (RMI) as the objective function to optimize the BS beamforming matrix, the CUE beamforming matrices, the common stream rate vectors, and the user scheduling vectors. Due to the coupling of the optimization variables and non-convex operation, the proposed problem is a non-convex optimization problem that cannot be solved directly. To address the above challenges, we adopt a consensus alternating direction method of multipliers (ADMM) framework to decouple the optimization variables and solve it. Specifically, the problem is decoupled into multiple subproblems and solved by iterative optimization independently until overall convergence is achieved. Finally, numerical results validate the superiority of the proposed scheme in terms of improving communication sum-rate and RMI, and greatly reduce the algorithm complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10960v1-abstract-full').style.display = 'none'; document.getElementById('2411.10960v1-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 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/2411.10943">arXiv:2411.10943</a> <span> [<a href="https://arxiv.org/pdf/2411.10943">pdf</a>, <a href="https://arxiv.org/format/2411.10943">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Generalist Virtual Agents: A Survey on Autonomous Agents Across Digital Platforms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Gao%2C+M">Minghe Gao</a>, <a href="/search/?searchtype=author&query=Bu%2C+W">Wendong Bu</a>, <a href="/search/?searchtype=author&query=Miao%2C+B">Bingchen Miao</a>, <a href="/search/?searchtype=author&query=Wu%2C+Y">Yang Wu</a>, <a href="/search/?searchtype=author&query=Li%2C+Y">Yunfei Li</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Juncheng Li</a>, <a href="/search/?searchtype=author&query=Tang%2C+S">Siliang Tang</a>, <a href="/search/?searchtype=author&query=Wu%2C+Q">Qi Wu</a>, <a href="/search/?searchtype=author&query=Zhuang%2C+Y">Yueting Zhuang</a>, <a href="/search/?searchtype=author&query=Wang%2C+M">Meng Wang</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="2411.10943v1-abstract-short" style="display: inline;"> In this paper, we introduce the Generalist Virtual Agent (GVA), an autonomous entity engineered to function across diverse digital platforms and environments, assisting users by executing a variety of tasks. This survey delves into the evolution of GVAs, tracing their progress from early intelligent assistants to contemporary implementations that incorporate large-scale models. We explore both the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10943v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10943v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10943v1-abstract-full" style="display: none;"> In this paper, we introduce the Generalist Virtual Agent (GVA), an autonomous entity engineered to function across diverse digital platforms and environments, assisting users by executing a variety of tasks. This survey delves into the evolution of GVAs, tracing their progress from early intelligent assistants to contemporary implementations that incorporate large-scale models. We explore both the philosophical underpinnings and practical foundations of GVAs, addressing their developmental challenges and the methodologies currently employed in their design and operation. By presenting a detailed taxonomy of GVA environments, tasks, and capabilities, this paper aims to bridge the theoretical and practical aspects of GVAs, concluding those that operate in environments closely mirroring the real world are more likely to demonstrate human-like intelligence. We discuss potential future directions for GVA research, highlighting the necessity for realistic evaluation metrics and the enhancement of long-sequence decision-making capabilities to advance the field toward more systematic or embodied applications. This work not only synthesizes the existing body of literature but also proposes frameworks for future investigations, contributing significantly to the ongoing development of intelligent systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10943v1-abstract-full').style.display = 'none'; document.getElementById('2411.10943v1-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 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/2411.10752">arXiv:2411.10752</a> <span> [<a href="https://arxiv.org/pdf/2411.10752">pdf</a>, <a href="https://arxiv.org/format/2411.10752">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ling%2C+X">Xitong Ling</a>, <a href="/search/?searchtype=author&query=Lei%2C+Y">Yuanyuan Lei</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jiawen Li</a>, <a href="/search/?searchtype=author&query=Cheng%2C+J">Junru Cheng</a>, <a href="/search/?searchtype=author&query=Huang%2C+W">Wenting Huang</a>, <a href="/search/?searchtype=author&query=Guan%2C+T">Tian Guan</a>, <a href="/search/?searchtype=author&query=Guan%2C+J">Jian Guan</a>, <a href="/search/?searchtype=author&query=He%2C+Y">Yonghong He</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="2411.10752v1-abstract-short" style="display: inline;"> Advances in optical microscopy scanning have significantly contributed to computational pathology (CPath) by converting traditional histopathological slides into whole slide images (WSIs). This development enables comprehensive digital reviews by pathologists and accelerates AI-driven diagnostic support for WSI analysis. Recent advances in foundational pathology models have increased the need for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10752v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10752v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10752v1-abstract-full" style="display: none;"> Advances in optical microscopy scanning have significantly contributed to computational pathology (CPath) by converting traditional histopathological slides into whole slide images (WSIs). This development enables comprehensive digital reviews by pathologists and accelerates AI-driven diagnostic support for WSI analysis. Recent advances in foundational pathology models have increased the need for benchmarking tasks. The Camelyon series is one of the most widely used open-source datasets in computational pathology. However, the quality, accessibility, and clinical relevance of the labels have not been comprehensively evaluated. In this study, we reprocessed 1,399 WSIs and labels from the Camelyon-16 and Camelyon-17 datasets, removing low-quality slides, correcting erroneous labels, and providing expert pixel annotations for tumor regions in the previously unreleased test set. Based on the sizes of re-annotated tumor regions, we upgraded the binary cancer screening task to a four-class task: negative, micro-metastasis, macro-metastasis, and Isolated Tumor Cells (ITC). We reevaluated pre-trained pathology feature extractors and multiple instance learning (MIL) methods using the cleaned dataset, providing a benchmark that advances AI development in histopathology. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10752v1-abstract-full').style.display = 'none'; document.getElementById('2411.10752v1-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 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/2411.10709">arXiv:2411.10709</a> <span> [<a href="https://arxiv.org/pdf/2411.10709">pdf</a>, <a href="https://arxiv.org/format/2411.10709">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"> Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+J">Jiawen Li</a>, <a href="/search/?searchtype=author&query=Sun%2C+Q">Qiehe Sun</a>, <a href="/search/?searchtype=author&query=Yan%2C+R">Renao Yan</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yizhi Wang</a>, <a href="/search/?searchtype=author&query=Fu%2C+Y">Yuqiu Fu</a>, <a href="/search/?searchtype=author&query=Wei%2C+Y">Yani Wei</a>, <a href="/search/?searchtype=author&query=Guan%2C+T">Tian Guan</a>, <a href="/search/?searchtype=author&query=Shi%2C+H">Huijuan Shi</a>, <a href="/search/?searchtype=author&query=He%2C+Y">Yonghonghe He</a>, <a href="/search/?searchtype=author&query=Han%2C+A">Anjia Han</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="2411.10709v1-abstract-short" style="display: inline;"> With the development of digital imaging in medical microscopy, artificial intelligent-based analysis of pathological whole slide images (WSIs) provides a powerful tool for cancer diagnosis. Limited by the expensive cost of pixel-level annotation, current research primarily focuses on representation learning with slide-level labels, showing success in various downstream tasks. However, given the di… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10709v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10709v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10709v1-abstract-full" style="display: none;"> With the development of digital imaging in medical microscopy, artificial intelligent-based analysis of pathological whole slide images (WSIs) provides a powerful tool for cancer diagnosis. Limited by the expensive cost of pixel-level annotation, current research primarily focuses on representation learning with slide-level labels, showing success in various downstream tasks. However, given the diversity of lesion types and the complex relationships between each other, these techniques still deserve further exploration in addressing advanced pathology tasks. To this end, we introduce the concept of hierarchical pathological image classification and propose a representation learning called PathTree. PathTree considers the multi-classification of diseases as a binary tree structure. Each category is represented as a professional pathological text description, which messages information with a tree-like encoder. The interactive text features are then used to guide the aggregation of hierarchical multiple representations. PathTree uses slide-text similarity to obtain probability scores and introduces two extra tree specific losses to further constrain the association between texts and slides. Through extensive experiments on three challenging hierarchical classification datasets: in-house cryosectioned lung tissue lesion identification, public prostate cancer grade assessment, and public breast cancer subtyping, our proposed PathTree is consistently competitive compared to the state-of-the-art methods and provides a new perspective on the deep learning-assisted solution for more complex WSI classification. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10709v1-abstract-full').style.display = 'none'; document.getElementById('2411.10709v1-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 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">15 pages, 13 figures. Under Review</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10696">arXiv:2411.10696</a> <span> [<a href="https://arxiv.org/pdf/2411.10696">pdf</a>, <a href="https://arxiv.org/format/2411.10696">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"> HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhao%2C+H">Huaqin Zhao</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jiaxi Li</a>, <a href="/search/?searchtype=author&query=Pan%2C+Y">Yi Pan</a>, <a href="/search/?searchtype=author&query=Liang%2C+S">Shizhe Liang</a>, <a href="/search/?searchtype=author&query=Yang%2C+X">Xiaofeng Yang</a>, <a href="/search/?searchtype=author&query=Liu%2C+W">Wei Liu</a>, <a href="/search/?searchtype=author&query=Li%2C+X">Xiang Li</a>, <a href="/search/?searchtype=author&query=Dou%2C+F">Fei Dou</a>, <a href="/search/?searchtype=author&query=Liu%2C+T">Tianming Liu</a>, <a href="/search/?searchtype=author&query=Lu%2C+J">Jin Lu</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="2411.10696v1-abstract-short" style="display: inline;"> Fine-tuning large language models (LLMs) poses significant memory challenges, as the back-propagation process demands extensive resources, especially with growing model sizes. Recent work, MeZO, addresses this issue using a zeroth-order (ZO) optimization method, which reduces memory consumption by matching the usage to the inference phase. However, MeZO experiences slow convergence due to varying… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10696v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10696v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10696v1-abstract-full" style="display: none;"> Fine-tuning large language models (LLMs) poses significant memory challenges, as the back-propagation process demands extensive resources, especially with growing model sizes. Recent work, MeZO, addresses this issue using a zeroth-order (ZO) optimization method, which reduces memory consumption by matching the usage to the inference phase. However, MeZO experiences slow convergence due to varying curvatures across model parameters. To overcome this limitation, we introduce HELENE, a novel scalable and memory-efficient optimizer that integrates annealed A-GNB gradients with a diagonal Hessian estimation and layer-wise clipping, serving as a second-order pre-conditioner. This combination allows for faster and more stable convergence. Our theoretical analysis demonstrates that HELENE improves convergence rates, particularly for models with heterogeneous layer dimensions, by reducing the dependency on the total parameter space dimension. Instead, the method scales with the largest layer dimension, making it highly suitable for modern LLM architectures. Experimental results on RoBERTa-large and OPT-1.3B across multiple tasks show that HELENE achieves up to a 20x speedup compared to MeZO, with average accuracy improvements of 1.5%. Furthermore, HELENE remains compatible with both full parameter tuning and parameter-efficient fine-tuning (PEFT), outperforming several state-of-the-art optimizers. The codes will be released after reviewing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10696v1-abstract-full').style.display = 'none'; document.getElementById('2411.10696v1-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 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/2411.10667">arXiv:2411.10667</a> <span> [<a href="https://arxiv.org/pdf/2411.10667">pdf</a>, <a href="https://arxiv.org/ps/2411.10667">ps</a>, <a href="https://arxiv.org/format/2411.10667">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Pattern Formation and Solitons">nlin.PS</span> </div> </div> <p class="title is-5 mathjax"> Solitons in composite linear-nonlinear moir茅 lattices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zeng%2C+L">Liangwei Zeng</a>, <a href="/search/?searchtype=author&query=Malomed%2C+B+A">Boris A. Malomed</a>, <a href="/search/?searchtype=author&query=Mihalache%2C+D">Dumitru Mihalache</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jingzhen Li</a>, <a href="/search/?searchtype=author&query=Zhu%2C+X">Xing Zhu</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="2411.10667v1-abstract-short" style="display: inline;"> We produce families of two-dimensional gap solitons (GSs) maintained by moir茅 lattices (MLs) composed of linear and nonlinear sublattices, with the defocusing sign of the nonlinearity. Depending on the angle between the sublattices, the ML may be quasiperiodic or periodic, composed of mutually incommensurate or commensurate sublattices, respectively (in the latter case, the inter-lattice angle cor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10667v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10667v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10667v1-abstract-full" style="display: none;"> We produce families of two-dimensional gap solitons (GSs) maintained by moir茅 lattices (MLs) composed of linear and nonlinear sublattices, with the defocusing sign of the nonlinearity. Depending on the angle between the sublattices, the ML may be quasiperiodic or periodic, composed of mutually incommensurate or commensurate sublattices, respectively (in the latter case, the inter-lattice angle corresponds to Pythagorean triples). The GSs include fundamental, quadrupole, and octupole solitons, as well as quadrupoles and octupoles carrying unitary vorticity. Stability segments of the GS families are identified by means of the linearized equation for small perturbations, and confirmed by direct simulations of perturbed evolution. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10667v1-abstract-full').style.display = 'none'; document.getElementById('2411.10667v1-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 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">4 figures, to be published in Optics Letters (2024)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Optics Letters, (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10657">arXiv:2411.10657</a> <span> [<a href="https://arxiv.org/pdf/2411.10657">pdf</a>, <a href="https://arxiv.org/format/2411.10657">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"> Brain-to-Text Decoding with Context-Aware Neural Representations and Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+J">Jingyuan Li</a>, <a href="/search/?searchtype=author&query=Le%2C+T">Trung Le</a>, <a href="/search/?searchtype=author&query=Fan%2C+C">Chaofei Fan</a>, <a href="/search/?searchtype=author&query=Chen%2C+M">Mingfei Chen</a>, <a href="/search/?searchtype=author&query=Shlizerman%2C+E">Eli Shlizerman</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="2411.10657v1-abstract-short" style="display: inline;"> Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused on mapping neural activity to text using phonemes as the intermediate target. While successful, decoding neural activity directly to phonemes ignores the context dependent nature of the neural activity-to-phoneme mappin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10657v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10657v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10657v1-abstract-full" style="display: none;"> Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused on mapping neural activity to text using phonemes as the intermediate target. While successful, decoding neural activity directly to phonemes ignores the context dependent nature of the neural activity-to-phoneme mapping in the brain, leading to suboptimal decoding performance. In this work, we propose the use of diphone - an acoustic representation that captures the transitions between two phonemes - as the context-aware modeling target. We integrate diphones into existing phoneme decoding frameworks through a novel divide-and-conquer strategy in which we model the phoneme distribution by marginalizing over the diphone distribution. Our approach effectively leverages the enhanced context-aware representation of diphones while preserving the manageable class size of phonemes, a key factor in simplifying the subsequent phoneme-to-text conversion task. We demonstrate the effectiveness of our approach on the Brain-to-Text 2024 benchmark, where it achieves state-of-the-art Phoneme Error Rate (PER) of 15.34% compared to 16.62% PER of monophone-based decoding. When coupled with finetuned Large Language Models (LLMs), our method yields a Word Error Rate (WER) of 5.77%, significantly outperforming the 8.93% WER of the leading method in the benchmark. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10657v1-abstract-full').style.display = 'none'; document.getElementById('2411.10657v1-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 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/2411.10606">arXiv:2411.10606</a> <span> [<a href="https://arxiv.org/pdf/2411.10606">pdf</a>, <a href="https://arxiv.org/format/2411.10606">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"> AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Fu%2C+Y">Yonggan Fu</a>, <a href="/search/?searchtype=author&query=Yu%2C+Z">Zhongzhi Yu</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Junwei Li</a>, <a href="/search/?searchtype=author&query=Qian%2C+J">Jiayi Qian</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yongan Zhang</a>, <a href="/search/?searchtype=author&query=Yuan%2C+X">Xiangchi Yuan</a>, <a href="/search/?searchtype=author&query=Shi%2C+D">Dachuan Shi</a>, <a href="/search/?searchtype=author&query=Yakunin%2C+R">Roman Yakunin</a>, <a href="/search/?searchtype=author&query=Lin%2C+Y+C">Yingyan Celine Lin</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="2411.10606v1-abstract-short" style="display: inline;"> Motivated by the transformative capabilities of large language models (LLMs) across various natural language tasks, there has been a growing demand to deploy these models effectively across diverse real-world applications and platforms. However, the challenge of efficiently deploying LLMs has become increasingly pronounced due to the varying application-specific performance requirements and the ra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10606v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10606v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10606v1-abstract-full" style="display: none;"> Motivated by the transformative capabilities of large language models (LLMs) across various natural language tasks, there has been a growing demand to deploy these models effectively across diverse real-world applications and platforms. However, the challenge of efficiently deploying LLMs has become increasingly pronounced due to the varying application-specific performance requirements and the rapid evolution of computational platforms, which feature diverse resource constraints and deployment flows. These varying requirements necessitate LLMs that can adapt their structures (depth and width) for optimal efficiency across different platforms and application specifications. To address this critical gap, we propose AmoebaLLM, a novel framework designed to enable the instant derivation of LLM subnets of arbitrary shapes, which achieve the accuracy-efficiency frontier and can be extracted immediately after a one-time fine-tuning. In this way, AmoebaLLM significantly facilitates rapid deployment tailored to various platforms and applications. Specifically, AmoebaLLM integrates three innovative components: (1) a knowledge-preserving subnet selection strategy that features a dynamic-programming approach for depth shrinking and an importance-driven method for width shrinking; (2) a shape-aware mixture of LoRAs to mitigate gradient conflicts among subnets during fine-tuning; and (3) an in-place distillation scheme with loss-magnitude balancing as the fine-tuning objective. Extensive experiments validate that AmoebaLLM not only sets new standards in LLM adaptability but also successfully delivers subnets that achieve state-of-the-art trade-offs between accuracy and efficiency. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10606v1-abstract-full').style.display = 'none'; document.getElementById('2411.10606v1-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 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">Accepted at NeurIPS 2024</span> </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" 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