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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=Yan%2C+Y&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Yan%2C+Y&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Yan%2C+Y&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Yan%2C+Y&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Yan%2C+Y&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> <li> <a href="/search/?searchtype=author&query=Yan%2C+Y&start=200" class="pagination-link " aria-label="Page 5" aria-current="page">5 </a> </li> <li><span class="pagination-ellipsis">…</span></li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.13540">arXiv:2502.13540</a> <span> [<a href="https://arxiv.org/pdf/2502.13540">pdf</a>, <a href="https://arxiv.org/format/2502.13540">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Amplitude analysis of $蠄(3686)\to 纬K_S^0 K_S^0 $ </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=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=An%2C+Q">Q. An</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=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>, <a href="/search/?searchtype=author&query=Cai%2C+H">H. Cai</a> , et al. (704 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.13540v1-abstract-short" style="display: inline;"> Using $(2712\pm14)\times10^6$ $蠄(3686)$ events collected with the BESIII detector, we perform the first amplitude analysis of the radiative decay $蠄(3686)\to 纬K_S^0 K_S^0$ within the mass region $M_{K_S^0 K_S^0 }<2.8$ GeV/$c^2$. Employing a one-channel K-matrix approach for the description of the dynamics of the $K^0_S K^0_S$ system, the data sample is well described with four poles for the $f_0$-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.13540v1-abstract-full').style.display = 'inline'; document.getElementById('2502.13540v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.13540v1-abstract-full" style="display: none;"> Using $(2712\pm14)\times10^6$ $蠄(3686)$ events collected with the BESIII detector, we perform the first amplitude analysis of the radiative decay $蠄(3686)\to 纬K_S^0 K_S^0$ within the mass region $M_{K_S^0 K_S^0 }<2.8$ GeV/$c^2$. Employing a one-channel K-matrix approach for the description of the dynamics of the $K^0_S K^0_S$ system, the data sample is well described with four poles for the $f_0$-wave and three poles for the $f_2$-wave. The determined pole positions are consistent with those of well-established resonance states. The observed $f_0$ and $f_{2}$ states are found to be qualitatively consistent with those produced in radiative $J/蠄$ decays, indicating the similarity between the two charmonium states in their radiative decays. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.13540v1-abstract-full').style.display = 'none'; document.getElementById('2502.13540v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 4 figures, submitted to JHEP</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.13447">arXiv:2502.13447</a> <span> [<a href="https://arxiv.org/pdf/2502.13447">pdf</a>, <a href="https://arxiv.org/ps/2502.13447">ps</a>, <a href="https://arxiv.org/format/2502.13447">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="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Enhancing Chest X-ray Classification through Knowledge Injection in Cross-Modality Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yan%2C+Y">Yang Yan</a>, <a href="/search/?searchtype=author&query=Yue%2C+B">Bingqing Yue</a>, <a href="/search/?searchtype=author&query=Li%2C+Q">Qiaxuan Li</a>, <a href="/search/?searchtype=author&query=Huang%2C+M">Man Huang</a>, <a href="/search/?searchtype=author&query=Chen%2C+J">Jingyu Chen</a>, <a href="/search/?searchtype=author&query=Lan%2C+Z">Zhenzhong Lan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.13447v1-abstract-short" style="display: inline;"> The integration of artificial intelligence in medical imaging has shown tremendous potential, yet the relationship between pre-trained knowledge and performance in cross-modality learning remains unclear. This study investigates how explicitly injecting medical knowledge into the learning process affects the performance of cross-modality classification, focusing on Chest X-ray (CXR) images. We int… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.13447v1-abstract-full').style.display = 'inline'; document.getElementById('2502.13447v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.13447v1-abstract-full" style="display: none;"> The integration of artificial intelligence in medical imaging has shown tremendous potential, yet the relationship between pre-trained knowledge and performance in cross-modality learning remains unclear. This study investigates how explicitly injecting medical knowledge into the learning process affects the performance of cross-modality classification, focusing on Chest X-ray (CXR) images. We introduce a novel Set Theory-based knowledge injection framework that generates captions for CXR images with controllable knowledge granularity. Using this framework, we fine-tune CLIP model on captions with varying levels of medical information. We evaluate the model's performance through zero-shot classification on the CheXpert dataset, a benchmark for CXR classification. Our results demonstrate that injecting fine-grained medical knowledge substantially improves classification accuracy, achieving 72.5\% compared to 49.9\% when using human-generated captions. This highlights the crucial role of domain-specific knowledge in medical cross-modality learning. Furthermore, we explore the influence of knowledge density and the use of domain-specific Large Language Models (LLMs) for caption generation, finding that denser knowledge and specialized LLMs contribute to enhanced performance. This research advances medical image analysis by demonstrating the effectiveness of knowledge injection for improving automated CXR classification, paving the way for more accurate and reliable diagnostic tools. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.13447v1-abstract-full').style.display = 'none'; document.getElementById('2502.13447v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by ICASSP'25</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.12974">arXiv:2502.12974</a> <span> [<a href="https://arxiv.org/pdf/2502.12974">pdf</a>, <a href="https://arxiv.org/format/2502.12974">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"> Learning More Effective Representations for Dense Retrieval through Deliberate Thinking Before Search </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ji%2C+Y">Yifan Ji</a>, <a href="/search/?searchtype=author&query=Xu%2C+Z">Zhipeng Xu</a>, <a href="/search/?searchtype=author&query=Liu%2C+Z">Zhenghao Liu</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yukun Yan</a>, <a href="/search/?searchtype=author&query=Yu%2C+S">Shi Yu</a>, <a href="/search/?searchtype=author&query=Li%2C+Y">Yishan Li</a>, <a href="/search/?searchtype=author&query=Liu%2C+Z">Zhiyuan Liu</a>, <a href="/search/?searchtype=author&query=Gu%2C+Y">Yu Gu</a>, <a href="/search/?searchtype=author&query=Yu%2C+G">Ge Yu</a>, <a href="/search/?searchtype=author&query=Sun%2C+M">Maosong Sun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.12974v1-abstract-short" style="display: inline;"> Recent dense retrievers usually thrive on the emergency capabilities of Large Language Models (LLMs), using them to encode queries and documents into an embedding space for retrieval. These LLM-based dense retrievers have shown promising performance across various retrieval scenarios. However, relying on a single embedding to represent documents proves less effective in capturing different perspec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12974v1-abstract-full').style.display = 'inline'; document.getElementById('2502.12974v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.12974v1-abstract-full" style="display: none;"> Recent dense retrievers usually thrive on the emergency capabilities of Large Language Models (LLMs), using them to encode queries and documents into an embedding space for retrieval. These LLM-based dense retrievers have shown promising performance across various retrieval scenarios. However, relying on a single embedding to represent documents proves less effective in capturing different perspectives of documents for matching. In this paper, we propose Deliberate Thinking based Dense Retriever (DEBATER), which enhances these LLM-based retrievers by enabling them to learn more effective document representations through a step-by-step thinking process. DEBATER introduces the Chain-of-Deliberation mechanism to iteratively optimize document representations using a continuous chain of thought. To consolidate information from various thinking steps, DEBATER also incorporates the Self Distillation mechanism, which identifies the most informative thinking steps and integrates them into a unified text embedding. Experimental results show that DEBATER significantly outperforms existing methods across several retrieval benchmarks, demonstrating superior accuracy and robustness. All codes are available at https://github.com/OpenBMB/DEBATER. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12974v1-abstract-full').style.display = 'none'; document.getElementById('2502.12974v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.12520">arXiv:2502.12520</a> <span> [<a href="https://arxiv.org/pdf/2502.12520">pdf</a>, <a href="https://arxiv.org/format/2502.12520">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"> SAFEERASER: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+J">Junkai Chen</a>, <a href="/search/?searchtype=author&query=Deng%2C+Z">Zhijie Deng</a>, <a href="/search/?searchtype=author&query=Zheng%2C+K">Kening Zheng</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yibo Yan</a>, <a href="/search/?searchtype=author&query=Liu%2C+S">Shuliang Liu</a>, <a href="/search/?searchtype=author&query=Wu%2C+P">PeiJun Wu</a>, <a href="/search/?searchtype=author&query=Jiang%2C+P">Peijie Jiang</a>, <a href="/search/?searchtype=author&query=Liu%2C+J">Jia Liu</a>, <a href="/search/?searchtype=author&query=Hu%2C+X">Xuming Hu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.12520v1-abstract-short" style="display: inline;"> As Multimodal Large Language Models (MLLMs) develop, their potential security issues have become increasingly prominent. Machine Unlearning (MU), as an effective strategy for forgetting specific knowledge in training data, has been widely used in privacy protection. However, MU for safety in MLLM has yet to be fully explored. To address this issue, we propose SAFEERASER, a safety unlearning benchm… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12520v1-abstract-full').style.display = 'inline'; document.getElementById('2502.12520v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.12520v1-abstract-full" style="display: none;"> As Multimodal Large Language Models (MLLMs) develop, their potential security issues have become increasingly prominent. Machine Unlearning (MU), as an effective strategy for forgetting specific knowledge in training data, has been widely used in privacy protection. However, MU for safety in MLLM has yet to be fully explored. To address this issue, we propose SAFEERASER, a safety unlearning benchmark for MLLMs, consisting of 3,000 images and 28.8K VQA pairs. We comprehensively evaluate unlearning methods from two perspectives: forget quality and model utility. Our findings show that existing MU methods struggle to maintain model performance while implementing the forget operation and often suffer from over-forgetting. Hence, we introduce Prompt Decouple (PD) Loss to alleviate over-forgetting through decouple prompt during unlearning process. To quantitatively measure over-forgetting mitigated by PD Loss, we propose a new metric called Safe Answer Refusal Rate (SARR). Experimental results demonstrate that combining PD Loss with existing unlearning methods can effectively prevent over-forgetting and achieve a decrease of 79.5% in the SARR metric of LLaVA-7B and LLaVA-13B, while maintaining forget quality and model utility. Our code and dataset will be released upon acceptance. Warning: This paper contains examples of harmful language and images, and reader discretion is recommended. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12520v1-abstract-full').style.display = 'none'; document.getElementById('2502.12520v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.12490">arXiv:2502.12490</a> <span> [<a href="https://arxiv.org/pdf/2502.12490">pdf</a>, <a href="https://arxiv.org/format/2502.12490">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"> UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Shao%2C+L">Liangying Shao</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yanfu Yan</a>, <a href="/search/?searchtype=author&query=Poshyvanyk%2C+D">Denys Poshyvanyk</a>, <a href="/search/?searchtype=author&query=Su%2C+J">Jinsong Su</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.12490v1-abstract-short" style="display: inline;"> Deep learning-based code generation has completely transformed the way developers write programs today. Existing approaches to code generation have focused either on the Sequence-to-Sequence paradigm, which generates target code as a sequence of tokens, or the Sequence-to-Tree paradigm, which outputs code as a sequence of actions. While these two paradigms are intuitively complementary, their comb… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12490v1-abstract-full').style.display = 'inline'; document.getElementById('2502.12490v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.12490v1-abstract-full" style="display: none;"> Deep learning-based code generation has completely transformed the way developers write programs today. Existing approaches to code generation have focused either on the Sequence-to-Sequence paradigm, which generates target code as a sequence of tokens, or the Sequence-to-Tree paradigm, which outputs code as a sequence of actions. While these two paradigms are intuitively complementary, their combination has not been previously explored. By comparing the code generated under these two paradigms, we find that integrating them holds significant potential. In this paper, we propose UniGenCoder for code-related generation tasks, which consists of a shared encoder, a shared decoder with a minimal set of additional parameters to unify two paradigms, and a selector that dynamically chooses optimal paradigm for each instance. Also, during the model training, we first perform the multi-task learning and distillation strategies to facilitate knowledge transfer between two paradigms, and then leverage contrastive learning to train the selector. Experimental results on the text-to-code and code-to-code generation tasks demonstrate the effectiveness of our proposed model. We release our code at https://github.com/DeepLearnXMU/UniGenCoder. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12490v1-abstract-full').style.display = 'none'; document.getElementById('2502.12490v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <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">ICSE2025 NIER track</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.12022">arXiv:2502.12022</a> <span> [<a href="https://arxiv.org/pdf/2502.12022">pdf</a>, <a href="https://arxiv.org/format/2502.12022">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"> Teaching LLMs According to Their Aptitude: Adaptive Reasoning for Mathematical Problem Solving </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xu%2C+X">Xin Xu</a>, <a href="/search/?searchtype=author&query=Xu%2C+Y">Yan Xu</a>, <a href="/search/?searchtype=author&query=Chen%2C+T">Tianhao Chen</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yuchen Yan</a>, <a href="/search/?searchtype=author&query=Liu%2C+C">Chengwu Liu</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zaoyu Chen</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yufei Wang</a>, <a href="/search/?searchtype=author&query=Yin%2C+Y">Yichun Yin</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yasheng Wang</a>, <a href="/search/?searchtype=author&query=Shang%2C+L">Lifeng Shang</a>, <a href="/search/?searchtype=author&query=Liu%2C+Q">Qun 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="2502.12022v1-abstract-short" style="display: inline;"> Existing approaches to mathematical reasoning with large language models (LLMs) rely on Chain-of-Thought (CoT) for generalizability or Tool-Integrated Reasoning (TIR) for precise computation. While efforts have been made to combine these methods, they primarily rely on post-selection or predefined strategies, leaving an open question: whether LLMs can autonomously adapt their reasoning strategy ba… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12022v1-abstract-full').style.display = 'inline'; document.getElementById('2502.12022v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.12022v1-abstract-full" style="display: none;"> Existing approaches to mathematical reasoning with large language models (LLMs) rely on Chain-of-Thought (CoT) for generalizability or Tool-Integrated Reasoning (TIR) for precise computation. While efforts have been made to combine these methods, they primarily rely on post-selection or predefined strategies, leaving an open question: whether LLMs can autonomously adapt their reasoning strategy based on their inherent capabilities. In this work, we propose TATA (Teaching LLMs According to Their Aptitude), an adaptive framework that enables LLMs to personalize their reasoning strategy spontaneously, aligning it with their intrinsic aptitude. TATA incorporates base-LLM-aware data selection during supervised fine-tuning (SFT) to tailor training data to the model's unique abilities. This approach equips LLMs to autonomously determine and apply the appropriate reasoning strategy at test time. We evaluate TATA through extensive experiments on six mathematical reasoning benchmarks, using both general-purpose and math-specialized LLMs. Empirical results demonstrate that TATA effectively combines the complementary strengths of CoT and TIR, achieving superior or comparable performance with improved inference efficiency compared to TIR alone. Further analysis underscores the critical role of aptitude-aware data selection in enabling LLMs to make effective and adaptive reasoning decisions and align reasoning strategies with model capabilities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12022v1-abstract-full').style.display = 'none'; document.getElementById('2502.12022v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <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</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.11946">arXiv:2502.11946</a> <span> [<a href="https://arxiv.org/pdf/2502.11946">pdf</a>, <a href="https://arxiv.org/format/2502.11946">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> <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="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Huang%2C+A">Ailin Huang</a>, <a href="/search/?searchtype=author&query=Wu%2C+B">Boyong Wu</a>, <a href="/search/?searchtype=author&query=Wang%2C+B">Bruce Wang</a>, <a href="/search/?searchtype=author&query=Yan%2C+C">Chao Yan</a>, <a href="/search/?searchtype=author&query=Hu%2C+C">Chen Hu</a>, <a href="/search/?searchtype=author&query=Feng%2C+C">Chengli Feng</a>, <a href="/search/?searchtype=author&query=Tian%2C+F">Fei Tian</a>, <a href="/search/?searchtype=author&query=Shen%2C+F">Feiyu Shen</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jingbei Li</a>, <a href="/search/?searchtype=author&query=Chen%2C+M">Mingrui Chen</a>, <a href="/search/?searchtype=author&query=Liu%2C+P">Peng Liu</a>, <a href="/search/?searchtype=author&query=Miao%2C+R">Ruihang Miao</a>, <a href="/search/?searchtype=author&query=You%2C+W">Wang You</a>, <a href="/search/?searchtype=author&query=Chen%2C+X">Xi Chen</a>, <a href="/search/?searchtype=author&query=Yang%2C+X">Xuerui Yang</a>, <a href="/search/?searchtype=author&query=Huang%2C+Y">Yechang Huang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yuxiang Zhang</a>, <a href="/search/?searchtype=author&query=Gong%2C+Z">Zheng Gong</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zixin Zhang</a>, <a href="/search/?searchtype=author&query=Zhou%2C+H">Hongyu Zhou</a>, <a href="/search/?searchtype=author&query=Sun%2C+J">Jianjian Sun</a>, <a href="/search/?searchtype=author&query=Li%2C+B">Brian Li</a>, <a href="/search/?searchtype=author&query=Feng%2C+C">Chengting Feng</a>, <a href="/search/?searchtype=author&query=Wan%2C+C">Changyi Wan</a>, <a href="/search/?searchtype=author&query=Hu%2C+H">Hanpeng Hu</a> , et al. (120 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.11946v2-abstract-short" style="display: inline;"> Real-time speech interaction, serving as a fundamental interface for human-machine collaboration, holds immense potential. However, current open-source models face limitations such as high costs in voice data collection, weakness in dynamic control, and limited intelligence. To address these challenges, this paper introduces Step-Audio, the first production-ready open-source solution. Key contribu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11946v2-abstract-full').style.display = 'inline'; document.getElementById('2502.11946v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.11946v2-abstract-full" style="display: none;"> Real-time speech interaction, serving as a fundamental interface for human-machine collaboration, holds immense potential. However, current open-source models face limitations such as high costs in voice data collection, weakness in dynamic control, and limited intelligence. To address these challenges, this paper introduces Step-Audio, the first production-ready open-source solution. Key contributions include: 1) a 130B-parameter unified speech-text multi-modal model that achieves unified understanding and generation, with the Step-Audio-Chat version open-sourced; 2) a generative speech data engine that establishes an affordable voice cloning framework and produces the open-sourced lightweight Step-Audio-TTS-3B model through distillation; 3) an instruction-driven fine control system enabling dynamic adjustments across dialects, emotions, singing, and RAP; 4) an enhanced cognitive architecture augmented with tool calling and role-playing abilities to manage complex tasks effectively. Based on our new StepEval-Audio-360 evaluation benchmark, Step-Audio achieves state-of-the-art performance in human evaluations, especially in terms of instruction following. On open-source benchmarks like LLaMA Question, shows 9.3% average performance improvement, demonstrating our commitment to advancing the development of open-source multi-modal language technologies. Our code and models are available at https://github.com/stepfun-ai/Step-Audio. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11946v2-abstract-full').style.display = 'none'; document.getElementById('2502.11946v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.11916">arXiv:2502.11916</a> <span> [<a href="https://arxiv.org/pdf/2502.11916">pdf</a>, <a href="https://arxiv.org/format/2502.11916">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"> EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Su%2C+J">Jiamin Su</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yibo Yan</a>, <a href="/search/?searchtype=author&query=Fu%2C+F">Fangteng Fu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+H">Han Zhang</a>, <a href="/search/?searchtype=author&query=Ye%2C+J">Jingheng Ye</a>, <a href="/search/?searchtype=author&query=Liu%2C+X">Xiang Liu</a>, <a href="/search/?searchtype=author&query=Huo%2C+J">Jiahao Huo</a>, <a href="/search/?searchtype=author&query=Zhou%2C+H">Huiyu Zhou</a>, <a href="/search/?searchtype=author&query=Hu%2C+X">Xuming Hu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.11916v1-abstract-short" style="display: inline;"> Automated Essay Scoring (AES) plays a crucial role in educational assessment by providing scalable and consistent evaluations of writing tasks. However, traditional AES systems face three major challenges: (1) reliance on handcrafted features that limit generalizability, (2) difficulty in capturing fine-grained traits like coherence and argumentation, and (3) inability to handle multimodal context… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11916v1-abstract-full').style.display = 'inline'; document.getElementById('2502.11916v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.11916v1-abstract-full" style="display: none;"> Automated Essay Scoring (AES) plays a crucial role in educational assessment by providing scalable and consistent evaluations of writing tasks. However, traditional AES systems face three major challenges: (1) reliance on handcrafted features that limit generalizability, (2) difficulty in capturing fine-grained traits like coherence and argumentation, and (3) inability to handle multimodal contexts. In the era of Multimodal Large Language Models (MLLMs), we propose EssayJudge, the first multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits. By leveraging MLLMs' strengths in trait-specific scoring and multimodal context understanding, EssayJudge aims to offer precise, context-rich evaluations without manual feature engineering, addressing longstanding AES limitations. Our experiments with 18 representative MLLMs reveal gaps in AES performance compared to human evaluation, particularly in discourse-level traits, highlighting the need for further advancements in MLLM-based AES research. Our dataset and code will be available upon acceptance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11916v1-abstract-full').style.display = 'none'; document.getElementById('2502.11916v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <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">JS and YY are co-first authors. XH is the corresponding author</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.11684">arXiv:2502.11684</a> <span> [<a href="https://arxiv.org/pdf/2502.11684">pdf</a>, <a href="https://arxiv.org/format/2502.11684">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"> MathFimer: Enhancing Mathematical Reasoning by Expanding Reasoning Steps through Fill-in-the-Middle Task </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yan%2C+Y">Yuchen Yan</a>, <a href="/search/?searchtype=author&query=Shen%2C+Y">Yongliang Shen</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yang Liu</a>, <a href="/search/?searchtype=author&query=Jiang%2C+J">Jin Jiang</a>, <a href="/search/?searchtype=author&query=Xu%2C+X">Xin Xu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+M">Mengdi Zhang</a>, <a href="/search/?searchtype=author&query=Shao%2C+J">Jian Shao</a>, <a href="/search/?searchtype=author&query=Zhuang%2C+Y">Yueting Zhuang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.11684v1-abstract-short" style="display: inline;"> Mathematical reasoning represents a critical frontier in advancing large language models (LLMs). While step-by-step approaches have emerged as the dominant paradigm for mathematical problem-solving in LLMs, the quality of reasoning steps in training data fundamentally constrains the performance of the models. Recent studies has demonstrated that more detailed intermediate steps can enhance model p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11684v1-abstract-full').style.display = 'inline'; document.getElementById('2502.11684v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.11684v1-abstract-full" style="display: none;"> Mathematical reasoning represents a critical frontier in advancing large language models (LLMs). While step-by-step approaches have emerged as the dominant paradigm for mathematical problem-solving in LLMs, the quality of reasoning steps in training data fundamentally constrains the performance of the models. Recent studies has demonstrated that more detailed intermediate steps can enhance model performance, yet existing methods for step expansion either require more powerful external models or incur substantial computational costs. In this paper, we introduce MathFimer, a novel framework for mathematical reasoning step expansion inspired by the "Fill-in-the-middle" task from code completion. By decomposing solution chains into prefix-suffix pairs and training models to reconstruct missing intermediate steps, we develop a specialized model, MathFimer-7B, on our carefully curated NuminaMath-FIM dataset. We then apply these models to enhance existing mathematical reasoning datasets by inserting detailed intermediate steps into their solution chains, creating MathFimer-expanded versions. Through comprehensive experiments on multiple mathematical reasoning datasets, including MathInstruct, MetaMathQA and etc., we demonstrate that models trained on MathFimer-expanded data consistently outperform their counterparts trained on original data across various benchmarks such as GSM8K and MATH. Our approach offers a practical, scalable solution for enhancing mathematical reasoning capabilities in LLMs without relying on powerful external models or expensive inference procedures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11684v1-abstract-full').style.display = 'none'; document.getElementById('2502.11684v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.11456">arXiv:2502.11456</a> <span> [<a href="https://arxiv.org/pdf/2502.11456">pdf</a>, <a href="https://arxiv.org/format/2502.11456">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 class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.media.2025.103461">10.1016/j.media.2025.103461 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Leveraging Labelled Data Knowledge: A Cooperative Rectification Learning Network for Semi-supervised 3D Medical Image Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+Y">Yanyan Wang</a>, <a href="/search/?searchtype=author&query=Song%2C+K">Kechen Song</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yuyuan Liu</a>, <a href="/search/?searchtype=author&query=Ma%2C+S">Shuai Ma</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yunhui Yan</a>, <a href="/search/?searchtype=author&query=Carneiro%2C+G">Gustavo Carneiro</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.11456v1-abstract-short" style="display: inline;"> Semi-supervised 3D medical image segmentation aims to achieve accurate segmentation using few labelled data and numerous unlabelled data. The main challenge in the design of semi-supervised learning methods consists in the effective use of the unlabelled data for training. A promising solution consists of ensuring consistent predictions across different views of the data, where the efficacy of thi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11456v1-abstract-full').style.display = 'inline'; document.getElementById('2502.11456v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.11456v1-abstract-full" style="display: none;"> Semi-supervised 3D medical image segmentation aims to achieve accurate segmentation using few labelled data and numerous unlabelled data. The main challenge in the design of semi-supervised learning methods consists in the effective use of the unlabelled data for training. A promising solution consists of ensuring consistent predictions across different views of the data, where the efficacy of this strategy depends on the accuracy of the pseudo-labels generated by the model for this consistency learning strategy. In this paper, we introduce a new methodology to produce high-quality pseudo-labels for a consistency learning strategy to address semi-supervised 3D medical image segmentation. The methodology has three important contributions. The first contribution is the Cooperative Rectification Learning Network (CRLN) that learns multiple prototypes per class to be used as external knowledge priors to adaptively rectify pseudo-labels at the voxel level. The second contribution consists of the Dynamic Interaction Module (DIM) to facilitate pairwise and cross-class interactions between prototypes and multi-resolution image features, enabling the production of accurate voxel-level clues for pseudo-label rectification. The third contribution is the Cooperative Positive Supervision (CPS), which optimises uncertain representations to align with unassertive representations of their class distributions, improving the model's accuracy in classifying uncertain regions. Extensive experiments on three public 3D medical segmentation datasets demonstrate the effectiveness and superiority of our semi-supervised learning method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11456v1-abstract-full').style.display = 'none'; document.getElementById('2502.11456v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <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">Medical Image Analysis</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.11390">arXiv:2502.11390</a> <span> [<a href="https://arxiv.org/pdf/2502.11390">pdf</a>, <a href="https://arxiv.org/format/2502.11390">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"> MARS: Mesh AutoRegressive Model for 3D Shape Detailization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Gao%2C+J">Jingnan Gao</a>, <a href="/search/?searchtype=author&query=Liu%2C+W">Weizhe Liu</a>, <a href="/search/?searchtype=author&query=Sun%2C+W">Weixuan Sun</a>, <a href="/search/?searchtype=author&query=Wang%2C+S">Senbo Wang</a>, <a href="/search/?searchtype=author&query=Song%2C+X">Xibin Song</a>, <a href="/search/?searchtype=author&query=Shang%2C+T">Taizhang Shang</a>, <a href="/search/?searchtype=author&query=Chen%2C+S">Shenzhou Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Hongdong Li</a>, <a href="/search/?searchtype=author&query=Yang%2C+X">Xiaokang Yang</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yichao Yan</a>, <a href="/search/?searchtype=author&query=Ji%2C+P">Pan Ji</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.11390v1-abstract-short" style="display: inline;"> State-of-the-art methods for mesh detailization predominantly utilize Generative Adversarial Networks (GANs) to generate detailed meshes from coarse ones. These methods typically learn a specific style code for each category or similar categories without enforcing geometry supervision across different Levels of Detail (LODs). Consequently, such methods often fail to generalize across a broader ran… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11390v1-abstract-full').style.display = 'inline'; document.getElementById('2502.11390v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.11390v1-abstract-full" style="display: none;"> State-of-the-art methods for mesh detailization predominantly utilize Generative Adversarial Networks (GANs) to generate detailed meshes from coarse ones. These methods typically learn a specific style code for each category or similar categories without enforcing geometry supervision across different Levels of Detail (LODs). Consequently, such methods often fail to generalize across a broader range of categories and cannot ensure shape consistency throughout the detailization process. In this paper, we introduce MARS, a novel approach for 3D shape detailization. Our method capitalizes on a novel multi-LOD, multi-category mesh representation to learn shape-consistent mesh representations in latent space across different LODs. We further propose a mesh autoregressive model capable of generating such latent representations through next-LOD token prediction. This approach significantly enhances the realism of the generated shapes. Extensive experiments conducted on the challenging 3D Shape Detailization benchmark demonstrate that our proposed MARS model achieves state-of-the-art performance, surpassing existing methods in both qualitative and quantitative assessments. Notably, the model's capability to generate fine-grained details while preserving the overall shape integrity is particularly commendable. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11390v1-abstract-full').style.display = 'none'; document.getElementById('2502.11390v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.11328">arXiv:2502.11328</a> <span> [<a href="https://arxiv.org/pdf/2502.11328">pdf</a>, <a href="https://arxiv.org/format/2502.11328">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="General Relativity and Quantum Cosmology">gr-qc</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> </div> </div> <p class="title is-5 mathjax"> Progress of the TianQin project </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Luo%2C+J">Jun Luo</a>, <a href="/search/?searchtype=author&query=Bai%2C+S">Shaojun Bai</a>, <a href="/search/?searchtype=author&query=Bai%2C+Y">Yan-Zheng Bai</a>, <a href="/search/?searchtype=author&query=Cai%2C+L">Lin Cai</a>, <a href="/search/?searchtype=author&query=Dang%2C+H">Hao Dang</a>, <a href="/search/?searchtype=author&query=Dong%2C+Q">Qijia Dong</a>, <a href="/search/?searchtype=author&query=Duan%2C+H">Hui-Zong Duan</a>, <a href="/search/?searchtype=author&query=Du%2C+Y">Yuanbo Du</a>, <a href="/search/?searchtype=author&query=Fan%2C+L">Lei Fan</a>, <a href="/search/?searchtype=author&query=Fu%2C+X">Xinju Fu</a>, <a href="/search/?searchtype=author&query=Gao%2C+Y">Yong Gao</a>, <a href="/search/?searchtype=author&query=Gou%2C+X">Xingyu Gou</a>, <a href="/search/?searchtype=author&query=Guo%2C+C">Changlei Guo</a>, <a href="/search/?searchtype=author&query=Hong%2C+W">Wei Hong</a>, <a href="/search/?searchtype=author&query=Hu%2C+B">Bin Hu</a>, <a href="/search/?searchtype=author&query=Hu%2C+H">Heran Hu</a>, <a href="/search/?searchtype=author&query=Hu%2C+M">Ming Hu</a>, <a href="/search/?searchtype=author&query=Hu%2C+Y">Yi-Ming Hu</a>, <a href="/search/?searchtype=author&query=Huang%2C+F+P">Fa Peng Huang</a>, <a href="/search/?searchtype=author&query=Gu%2C+D">Defeng Gu</a>, <a href="/search/?searchtype=author&query=Ji%2C+X">Xin Ji</a>, <a href="/search/?searchtype=author&query=Jiang%2C+Y">Yuan-Ze Jiang</a>, <a href="/search/?searchtype=author&query=Li%2C+E">En-Kun Li</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Hongyin Li</a>, <a href="/search/?searchtype=author&query=Li%2C+M">Ming Li</a> , et al. (76 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.11328v1-abstract-short" style="display: inline;"> TianQin is a future space-based gravitational wave observatory targeting the frequency window of $10^{-4}$ Hz $\sim 1$ Hz. A large variety of gravitational wave sources are expected in this frequency band, including the merger of massive black hole binaries, the inspiral of extreme/intermediate mass ratio systems, stellar-mass black hole binaries, Galactic compact binaries, and so on. TianQin will… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11328v1-abstract-full').style.display = 'inline'; document.getElementById('2502.11328v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.11328v1-abstract-full" style="display: none;"> TianQin is a future space-based gravitational wave observatory targeting the frequency window of $10^{-4}$ Hz $\sim 1$ Hz. A large variety of gravitational wave sources are expected in this frequency band, including the merger of massive black hole binaries, the inspiral of extreme/intermediate mass ratio systems, stellar-mass black hole binaries, Galactic compact binaries, and so on. TianQin will consist of three Earth orbiting satellites on nearly identical orbits with orbital radii of about $10^5$ km. The satellites will form a normal triangle constellation whose plane is nearly perpendicular to the ecliptic plane. The TianQin project has been progressing smoothly following the ``0123" technology roadmap. In step ``0", the TianQin laser ranging station has been constructed and it has successfully ranged to all the five retro-reflectors on the Moon. In step ``1", the drag-free control technology has been tested and demonstrated using the TianQin-1 satellite. In step ``2", the inter-satellite laser interferometry technology will be tested using the pair of TianQin-2 satellites. The TianQin-2 mission has been officially approved and the satellites will be launched around 2026. In step ``3", i.e., the TianQin-3 mission, three identical satellites will be launched around 2035 to form the space-based gravitational wave detector, TianQin, and to start gravitational wave detection in space. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11328v1-abstract-full').style.display = 'none'; document.getElementById('2502.11328v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">45 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/2502.11321">arXiv:2502.11321</a> <span> [<a href="https://arxiv.org/pdf/2502.11321">pdf</a>, <a href="https://arxiv.org/ps/2502.11321">ps</a>, <a href="https://arxiv.org/format/2502.11321">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation">stat.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Methodology">stat.ME</span> </div> </div> <p class="title is-5 mathjax"> Advances in Bayesian Modeling: Applications and Methods </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yan%2C+Y">Yifei Yan</a>, <a href="/search/?searchtype=author&query=Sosa%2C+J">Juan Sosa</a>, <a href="/search/?searchtype=author&query=Mart%C3%ADnez%2C+C+A">Carlos A. Mart铆nez</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.11321v1-abstract-short" style="display: inline;"> This paper explores the versatility and depth of Bayesian modeling by presenting a comprehensive range of applications and methods, combining Markov chain Monte Carlo (MCMC) techniques and variational approximations. Covering topics such as hierarchical modeling, spatial modeling, higher-order Markov chains, and Bayesian nonparametrics, the study emphasizes practical implementations across diverse… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11321v1-abstract-full').style.display = 'inline'; document.getElementById('2502.11321v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.11321v1-abstract-full" style="display: none;"> This paper explores the versatility and depth of Bayesian modeling by presenting a comprehensive range of applications and methods, combining Markov chain Monte Carlo (MCMC) techniques and variational approximations. Covering topics such as hierarchical modeling, spatial modeling, higher-order Markov chains, and Bayesian nonparametrics, the study emphasizes practical implementations across diverse fields, including oceanography, climatology, epidemiology, astronomy, and financial analysis. The aim is to bridge theoretical underpinnings with real-world applications, illustrating the formulation of Bayesian models, elicitation of priors, computational strategies, and posterior and predictive analyses. By leveraging different computational methods, this paper provides insights into model fitting, goodness-of-fit evaluation, and predictive accuracy, addressing computational efficiency and methodological challenges across various datasets and domains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11321v1-abstract-full').style.display = 'none'; document.getElementById('2502.11321v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">49 pages, 16 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.11204">arXiv:2502.11204</a> <span> [<a href="https://arxiv.org/pdf/2502.11204">pdf</a>, <a href="https://arxiv.org/format/2502.11204">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Superconductivity">cond-mat.supr-con</span> </div> </div> <p class="title is-5 mathjax"> Charge Orders in Fully Intercalated Bilayer TaSe$_2$: Dependence on Interlayer Stacking and Intercalation Sites </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yan%2C+Y">Yuhui Yan</a>, <a href="/search/?searchtype=author&query=Xiong%2C+L">Lingxiao Xiong</a>, <a href="/search/?searchtype=author&query=Zheng%2C+F">Feipeng 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="2502.11204v1-abstract-short" style="display: inline;"> Recent advancements have established self-intercalation as a powerful technique for manipulating quantum material properties, with precisely controllable intercalation concentrations. Given the inherently rich phase diagrams of transition metal dichalcogenides (TMDCs), studying the self-intercalated TMDCs can offer promising candidates for investigating the interplay between various orderings. Thi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11204v1-abstract-full').style.display = 'inline'; document.getElementById('2502.11204v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.11204v1-abstract-full" style="display: none;"> Recent advancements have established self-intercalation as a powerful technique for manipulating quantum material properties, with precisely controllable intercalation concentrations. Given the inherently rich phase diagrams of transition metal dichalcogenides (TMDCs), studying the self-intercalated TMDCs can offer promising candidates for investigating the interplay between various orderings. This work focuses on fully intercalated bilayer TaSe$_2$ (Ta$_3$Se$_4$), which has recently been fabricated experimentally. By performing first-principles calculations, we demonstrate the suppression of an intrinsic $3\times3$ charge density wave (CDW) in parent TaSe$_2$ layers, and the emergence of $2\times 2$, $\sqrt{3} \times \sqrt{3}$, or the absence of a CDW in the intercalated layers, depending on the interlayer stacking orders and intercalation sites being occupied. Particularly, the $2\times 2$ CDW shows an increase in electronic states at the Fermi level compared to its non-CDW phase. This unusual behavior contrasts with that of typical CDW materials in TMDCs. Furthermore, superconductivity is preserved in these Ta$_3$Se$_4$ structures, with superconducting transition temperatures comparable to or substantially smaller than those of TaSe$_2$. Spin-orbit coupling is found to enhance the density of states at Fermi levels while simultaneously reducing the electron-phonon coupling matrix elements. These two competing effects result in varying impacts on superconductivity across different Ta$_3$Se$_4$ structures. Moreover, our calculations indicate that magnetic order is absent. Our study deepens the understanding of underlying physics in Ta$_3$Se$_4$, and provides experimentally feasible candidates for studying CDW, superconductivity, and their interplay. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11204v1-abstract-full').style.display = 'none'; document.getElementById('2502.11204v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 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/2502.11051">arXiv:2502.11051</a> <span> [<a href="https://arxiv.org/pdf/2502.11051">pdf</a>, <a href="https://arxiv.org/format/2502.11051">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"> MMUNLEARNER: Reformulating Multimodal Machine Unlearning in the Era of Multimodal Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Huo%2C+J">Jiahao Huo</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yibo Yan</a>, <a href="/search/?searchtype=author&query=Zheng%2C+X">Xu Zheng</a>, <a href="/search/?searchtype=author&query=Lyu%2C+Y">Yuanhuiyi Lyu</a>, <a href="/search/?searchtype=author&query=Zou%2C+X">Xin Zou</a>, <a href="/search/?searchtype=author&query=Wei%2C+Z">Zhihua Wei</a>, <a href="/search/?searchtype=author&query=Hu%2C+X">Xuming Hu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.11051v1-abstract-short" style="display: inline;"> Recent progress in Machine Unlearning (MU) has introduced solutions for the selective removal of private or sensitive information encoded within deep neural networks. Nonetheless, MU for Multimodal Large Language Models (MLLMs) remains in its nascent phase. Therefore, we propose to reformulate the task of multimodal MU in the era of MLLMs, which aims to erase only the visual patterns associated wi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11051v1-abstract-full').style.display = 'inline'; document.getElementById('2502.11051v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.11051v1-abstract-full" style="display: none;"> Recent progress in Machine Unlearning (MU) has introduced solutions for the selective removal of private or sensitive information encoded within deep neural networks. Nonetheless, MU for Multimodal Large Language Models (MLLMs) remains in its nascent phase. Therefore, we propose to reformulate the task of multimodal MU in the era of MLLMs, which aims to erase only the visual patterns associated with a given entity while preserving the corresponding textual knowledge encoded within the original parameters of the language model backbone. Furthermore, we develop a novel geometry-constrained gradient descent method MMUnlearner. It updates the weights of MLLMs with a weight saliency map jointly restricted by the remaining concepts and textual knowledge during unlearning, thereby preserving parameters essential for non-target knowledge. Extensive experiments demonstrate that MMUnlearner surpasses baselines that finetuning MLLMs with VQA data directly through Gradient Ascent (GA) or Negative Preference Optimization (NPO), across all evaluation dimensions. Our code will be released upon acceptance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11051v1-abstract-full').style.display = 'none'; document.getElementById('2502.11051v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.11047">arXiv:2502.11047</a> <span> [<a href="https://arxiv.org/pdf/2502.11047">pdf</a>, <a href="https://arxiv.org/ps/2502.11047">ps</a>, <a href="https://arxiv.org/format/2502.11047">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Search for the Cabibbo-suppressed decays $螞_c^{+}\to危^0K^{+}蟺^{0}$ and $螞_c^{+}\to危^0K^{+}蟺^{+}蟺^{-}$ </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=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=An%2C+Q">Q. An</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=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>, <a href="/search/?searchtype=author&query=Cai%2C+H">H. Cai</a> , et al. (687 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.11047v1-abstract-short" style="display: inline;"> Utilizing 4.5 $fb^-$ of $e^+e^-$ annihilation data collected at center-of-mass energies ranging from 4599.53 MeV to 4698.82 MeV by the BESIII detector at the BEPCII collider, we search for the singly Cabibbo-suppressed hadronic decays $螞_{c}^{+}\to危^{0} K^{+}蟺^{0}$ and $螞_{c}^{+}\to危^{0}K^{+}蟺^+蟺^-$ with a single-tag method. No significant signals are observed for both decays. The upper limits on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11047v1-abstract-full').style.display = 'inline'; document.getElementById('2502.11047v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.11047v1-abstract-full" style="display: none;"> Utilizing 4.5 $fb^-$ of $e^+e^-$ annihilation data collected at center-of-mass energies ranging from 4599.53 MeV to 4698.82 MeV by the BESIII detector at the BEPCII collider, we search for the singly Cabibbo-suppressed hadronic decays $螞_{c}^{+}\to危^{0} K^{+}蟺^{0}$ and $螞_{c}^{+}\to危^{0}K^{+}蟺^+蟺^-$ with a single-tag method. No significant signals are observed for both decays. The upper limits on the branching fractions at the $90\%$ confidence level are determined to be $5.0\times 10^{-4}$ for $螞_{c}^{+}\to危^{0} K^{+}蟺^{0}$ and $6.5\times 10^{-4}$ for $螞_c^{+}\to危^0K^{+}蟺^{+}蟺^{-}$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11047v1-abstract-full').style.display = 'none'; document.getElementById('2502.11047v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.10891">arXiv:2502.10891</a> <span> [<a href="https://arxiv.org/pdf/2502.10891">pdf</a>, <a href="https://arxiv.org/format/2502.10891">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> AquaScope: Reliable Underwater Image Transmission on Mobile Devices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Tian%2C+B">Beitong Tian</a>, <a href="/search/?searchtype=author&query=Zhao%2C+L">Lingzhi Zhao</a>, <a href="/search/?searchtype=author&query=Chen%2C+B">Bo Chen</a>, <a href="/search/?searchtype=author&query=Wu%2C+M">Mingyuan Wu</a>, <a href="/search/?searchtype=author&query=Zheng%2C+H">Haozhen Zheng</a>, <a href="/search/?searchtype=author&query=Vasisht%2C+D">Deepak Vasisht</a>, <a href="/search/?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a>, <a href="/search/?searchtype=author&query=Nahrstedt%2C+K">Klara Nahrstedt</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.10891v1-abstract-short" style="display: inline;"> Underwater communication is essential for both recreational and scientific activities, such as scuba diving. However, existing methods remain highly constrained by environmental challenges and often require specialized hardware, driving research into more accessible underwater communication solutions. While recent acoustic-based communication systems support text messaging on mobile devices, their… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10891v1-abstract-full').style.display = 'inline'; document.getElementById('2502.10891v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.10891v1-abstract-full" style="display: none;"> Underwater communication is essential for both recreational and scientific activities, such as scuba diving. However, existing methods remain highly constrained by environmental challenges and often require specialized hardware, driving research into more accessible underwater communication solutions. While recent acoustic-based communication systems support text messaging on mobile devices, their low data rates severely limit broader applications. We present AquaScope, the first acoustic communication system capable of underwater image transmission on commodity mobile devices. To address the key challenges of underwater environments -- limited bandwidth and high transmission errors -- AquaScope employs and enhances generative image compression to improve compression efficiency, and integrates it with reliability-enhancement techniques at the physical layer to strengthen error resilience. We implemented AquaScope on the Android platform and demonstrated its feasibility for underwater image transmission. Experimental results show that AquaScope enables reliable, low-latency image transmission while preserving perceptual image quality, across various bandwidth-constrained and error-prone underwater conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10891v1-abstract-full').style.display = 'none'; document.getElementById('2502.10891v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages, 26 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.10405">arXiv:2502.10405</a> <span> [<a href="https://arxiv.org/pdf/2502.10405">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> </div> </div> <p class="title is-5 mathjax"> Crop Yield Time-Series Data Prediction Based on Multiple Hybrid Machine Learning Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yan%2C+Y">Yueru Yan</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yue Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jialin Li</a>, <a href="/search/?searchtype=author&query=Zhang%2C+J">Jingwei Zhang</a>, <a href="/search/?searchtype=author&query=Mo%2C+X">Xingye Mo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.10405v1-abstract-short" style="display: inline;"> Agriculture plays a crucial role in the global economy and social stability, and accurate crop yield prediction is essential for rational planting planning and decision-making. This study focuses on crop yield Time-Series Data prediction. Considering the crucial significance of agriculture in the global economy and social stability and the importance of accurate crop yield prediction for rational… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10405v1-abstract-full').style.display = 'inline'; document.getElementById('2502.10405v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.10405v1-abstract-full" style="display: none;"> Agriculture plays a crucial role in the global economy and social stability, and accurate crop yield prediction is essential for rational planting planning and decision-making. This study focuses on crop yield Time-Series Data prediction. Considering the crucial significance of agriculture in the global economy and social stability and the importance of accurate crop yield prediction for rational planting planning and decision-making, this research uses a dataset containing multiple crops, multiple regions, and data over many years to deeply explore the relationships between climatic factors (average rainfall, average temperature) and agricultural inputs (pesticide usage) and crop yield. Multiple hybrid machine learning models such as Linear Regression, Random Forest, Gradient Boost, XGBoost, KNN, Decision Tree, and Bagging Regressor are adopted for yield prediction. After evaluation, it is found that the Random Forest and Bagging Regressor models perform excellently in predicting crop yield with high accuracy and low error.As agricultural data becomes increasingly rich and time-series prediction techniques continue to evolve, the results of this study contribute to advancing the practical application of crop yield prediction in agricultural production management. The integration of time-series analysis allows for more dynamic, data-driven decision-making, enhancing the accuracy and reliability of crop yield forecasts over time. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10405v1-abstract-full').style.display = 'none'; document.getElementById('2502.10405v1-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 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.10124">arXiv:2502.10124</a> <span> [<a href="https://arxiv.org/pdf/2502.10124">pdf</a>, <a href="https://arxiv.org/format/2502.10124">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Modeling the Impact of Visual Stimuli on Redirection Noticeability with Gaze Behavior in Virtual Reality </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+Z">Zhipeng Li</a>, <a href="/search/?searchtype=author&query=Ji%2C+Y">Yishu Ji</a>, <a href="/search/?searchtype=author&query=Chen%2C+R">Ruijia Chen</a>, <a href="/search/?searchtype=author&query=Liu%2C+T">Tianqi Liu</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yuntao Wang</a>, <a href="/search/?searchtype=author&query=Shi%2C+Y">Yuanchun Shi</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yukang Yan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.10124v1-abstract-short" style="display: inline;"> While users could embody virtual avatars that mirror their physical movements in Virtual Reality, these avatars' motions can be redirected to enable novel interactions. Excessive redirection, however, could break the user's sense of embodiment due to perceptual conflicts between vision and proprioception. While prior work focused on avatar-related factors influencing the noticeability of redirecti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10124v1-abstract-full').style.display = 'inline'; document.getElementById('2502.10124v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.10124v1-abstract-full" style="display: none;"> While users could embody virtual avatars that mirror their physical movements in Virtual Reality, these avatars' motions can be redirected to enable novel interactions. Excessive redirection, however, could break the user's sense of embodiment due to perceptual conflicts between vision and proprioception. While prior work focused on avatar-related factors influencing the noticeability of redirection, we investigate how the visual stimuli in the surrounding virtual environment affect user behavior and, in turn, the noticeability of redirection. Given the wide variety of different types of visual stimuli and their tendency to elicit varying individual reactions, we propose to use users' gaze behavior as an indicator of their response to the stimuli and model the noticeability of redirection. We conducted two user studies to collect users' gaze behavior and noticeability, investigating the relationship between them and identifying the most effective gaze behavior features for predicting noticeability. Based on the data, we developed a regression model that takes users' gaze behavior as input and outputs the noticeability of redirection. We then conducted an evaluation study to test our model on unseen visual stimuli, achieving an accuracy of 0.012 MSE. We further implemented an adaptive redirection technique and conducted a proof-of-concept study to evaluate its effectiveness with complex visual stimuli in two applications. The results indicated that participants experienced less physical demanding and a stronger sense of body ownership when using our adaptive technique, demonstrating the potential of our model to support real-world use cases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10124v1-abstract-full').style.display = 'none'; document.getElementById('2502.10124v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, CHI'25</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09436">arXiv:2502.09436</a> <span> [<a href="https://arxiv.org/pdf/2502.09436">pdf</a>, <a href="https://arxiv.org/format/2502.09436">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Variable Stiffness for Robust Locomotion through Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Spoljaric%2C+D">Dario Spoljaric</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yashuai Yan</a>, <a href="/search/?searchtype=author&query=Lee%2C+D">Dongheui 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="2502.09436v1-abstract-short" style="display: inline;"> Reinforcement-learned locomotion enables legged robots to perform highly dynamic motions but often accompanies time-consuming manual tuning of joint stiffness. This paper introduces a novel control paradigm that integrates variable stiffness into the action space alongside joint positions, enabling grouped stiffness control such as per-joint stiffness (PJS), per-leg stiffness (PLS) and hybrid join… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09436v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09436v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09436v1-abstract-full" style="display: none;"> Reinforcement-learned locomotion enables legged robots to perform highly dynamic motions but often accompanies time-consuming manual tuning of joint stiffness. This paper introduces a novel control paradigm that integrates variable stiffness into the action space alongside joint positions, enabling grouped stiffness control such as per-joint stiffness (PJS), per-leg stiffness (PLS) and hybrid joint-leg stiffness (HJLS). We show that variable stiffness policies, with grouping in per-leg stiffness (PLS), outperform position-based control in velocity tracking and push recovery. In contrast, HJLS excels in energy efficiency. Furthermore, our method showcases robust walking behaviour on diverse outdoor terrains by sim-to-real transfer, although the policy is sorely trained on a flat floor. Our approach simplifies design by eliminating per-joint stiffness tuning while keeping competitive results with various metrics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09436v1-abstract-full').style.display = 'none'; document.getElementById('2502.09436v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <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">submitted to IFAC Joint Symposia on Mechatronics & Robotics</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09248">arXiv:2502.09248</a> <span> [<a href="https://arxiv.org/pdf/2502.09248">pdf</a>, <a href="https://arxiv.org/format/2502.09248">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> </div> </div> <p class="title is-5 mathjax"> Sequential Covariance Fitting for InSAR Phase Linking </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Hajjar%2C+D+E">Dana El Hajjar</a>, <a href="/search/?searchtype=author&query=Ginolhac%2C+G">Guillaume Ginolhac</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yajing Yan</a>, <a href="/search/?searchtype=author&query=Korso%2C+M+N+E">Mohammed Nabil El Korso</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.09248v1-abstract-short" style="display: inline;"> Traditional Phase-Linking (PL) algorithms are known for their high cost, especially with the huge volume of Synthetic Aperture Radar (SAR) images generated by Sentinel-1 SAR missions. Recently, a COvariance Fitting Interferometric Phase Linking (COFI-PL) approach has been proposed, which can be seen as a generic framework for existing PL methods. Although this method is less computationally expens… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09248v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09248v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09248v1-abstract-full" style="display: none;"> Traditional Phase-Linking (PL) algorithms are known for their high cost, especially with the huge volume of Synthetic Aperture Radar (SAR) images generated by Sentinel-1 SAR missions. Recently, a COvariance Fitting Interferometric Phase Linking (COFI-PL) approach has been proposed, which can be seen as a generic framework for existing PL methods. Although this method is less computationally expensive than traditional PL approaches, COFI-PL exploits the entire covariance matrix, which poses a challenge with the increasing time series of SAR images. However, COFI-PL, like traditional PL approaches, cannot accommodate the efficient inclusion of newly acquired SAR images. This paper overcomes this drawback by introducing a sequential integration of a block of newly acquired SAR images. Specifically, we propose a method for effectively addressing optimization problems associated with phase-only complex vectors on the torus based on the Majorization-Minimization framework. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09248v1-abstract-full').style.display = 'none'; document.getElementById('2502.09248v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <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</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09238">arXiv:2502.09238</a> <span> [<a href="https://arxiv.org/pdf/2502.09238">pdf</a>, <a href="https://arxiv.org/format/2502.09238">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"> OpenBench: A New Benchmark and Baseline for Semantic Navigation in Smart Logistics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+J">Junhui Wang</a>, <a href="/search/?searchtype=author&query=Huo%2C+D">Dongjie Huo</a>, <a href="/search/?searchtype=author&query=Xu%2C+Z">Zehui Xu</a>, <a href="/search/?searchtype=author&query=Shi%2C+Y">Yongliang Shi</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yimin Yan</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yuanxin Wang</a>, <a href="/search/?searchtype=author&query=Gao%2C+C">Chao Gao</a>, <a href="/search/?searchtype=author&query=Qiao%2C+Y">Yan Qiao</a>, <a href="/search/?searchtype=author&query=Zhou%2C+G">Guyue Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.09238v1-abstract-short" style="display: inline;"> The increasing demand for efficient last-mile delivery in smart logistics underscores the role of autonomous robots in enhancing operational efficiency and reducing costs. Traditional navigation methods, which depend on high-precision maps, are resource-intensive, while learning-based approaches often struggle with generalization in real-world scenarios. To address these challenges, this work prop… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09238v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09238v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09238v1-abstract-full" style="display: none;"> The increasing demand for efficient last-mile delivery in smart logistics underscores the role of autonomous robots in enhancing operational efficiency and reducing costs. Traditional navigation methods, which depend on high-precision maps, are resource-intensive, while learning-based approaches often struggle with generalization in real-world scenarios. To address these challenges, this work proposes the Openstreetmap-enhanced oPen-air sEmantic Navigation (OPEN) system that combines foundation models with classic algorithms for scalable outdoor navigation. The system uses off-the-shelf OpenStreetMap (OSM) for flexible map representation, thereby eliminating the need for extensive pre-mapping efforts. It also employs Large Language Models (LLMs) to comprehend delivery instructions and Vision-Language Models (VLMs) for global localization, map updates, and house number recognition. To compensate the limitations of existing benchmarks that are inadequate for assessing last-mile delivery, this work introduces a new benchmark specifically designed for outdoor navigation in residential areas, reflecting the real-world challenges faced by autonomous delivery systems. Extensive experiments in simulated and real-world environments demonstrate the proposed system's efficacy in enhancing navigation efficiency and reliability. To facilitate further research, our code and benchmark are publicly available. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09238v1-abstract-full').style.display = 'none'; document.getElementById('2502.09238v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09122">arXiv:2502.09122</a> <span> [<a href="https://arxiv.org/pdf/2502.09122">pdf</a>, <a href="https://arxiv.org/format/2502.09122">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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Improving Deep Regression with Tightness </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+S">Shihao Zhang</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yuguang Yan</a>, <a href="/search/?searchtype=author&query=Yao%2C+A">Angela Yao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.09122v1-abstract-short" style="display: inline;"> For deep regression, preserving the ordinality of the targets with respect to the feature representation improves performance across various tasks. However, a theoretical explanation for the benefits of ordinality is still lacking. This work reveals that preserving ordinality reduces the conditional entropy $H(Z|Y)$ of representation $Z$ conditional on the target $Y$. However, our findings reveal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09122v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09122v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09122v1-abstract-full" style="display: none;"> For deep regression, preserving the ordinality of the targets with respect to the feature representation improves performance across various tasks. However, a theoretical explanation for the benefits of ordinality is still lacking. This work reveals that preserving ordinality reduces the conditional entropy $H(Z|Y)$ of representation $Z$ conditional on the target $Y$. However, our findings reveal that typical regression losses do little to reduce $H(Z|Y)$, even though it is vital for generalization performance. With this motivation, we introduce an optimal transport-based regularizer to preserve the similarity relationships of targets in the feature space to reduce $H(Z|Y)$. Additionally, we introduce a simple yet efficient strategy of duplicating the regressor targets, also with the aim of reducing $H(Z|Y)$. Experiments on three real-world regression tasks verify the effectiveness of our strategies to improve deep regression. Code: https://github.com/needylove/Regression_tightness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09122v1-abstract-full').style.display = 'none'; document.getElementById('2502.09122v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ICLR 2025, Code: https://github.com/needylove/Regression_tightness</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09071">arXiv:2502.09071</a> <span> [<a href="https://arxiv.org/pdf/2502.09071">pdf</a>, <a href="https://arxiv.org/format/2502.09071">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> </div> </div> <p class="title is-5 mathjax"> Foreground Removal in Ground-Based CMB Observations Using a Transformer Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yan%2C+Y">Ye-Peng Yan</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Si-Yu Li</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yang Liu</a>, <a href="/search/?searchtype=author&query=Xia%2C+J">Jun-Qing Xia</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Hong 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="2502.09071v1-abstract-short" style="display: inline;"> We present a novel method for Cosmic Microwave Background (CMB) foreground removal based on deep learning techniques. This method employs a Transformer model, referred to as \texttt{TCMB}, which is specifically designed to effectively process HEALPix-format spherical sky maps. \texttt{TCMB} represents an innovative application in CMB data analysis, as it is an image-based technique that has rarely… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09071v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09071v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09071v1-abstract-full" style="display: none;"> We present a novel method for Cosmic Microwave Background (CMB) foreground removal based on deep learning techniques. This method employs a Transformer model, referred to as \texttt{TCMB}, which is specifically designed to effectively process HEALPix-format spherical sky maps. \texttt{TCMB} represents an innovative application in CMB data analysis, as it is an image-based technique that has rarely been utilized in this field. Using simulated data with noise levels representative of current ground-based CMB polarization observations, the \texttt{TCMB} method demonstrates robust performance in removing foreground contamination. The mean absolute variance for the reconstruction of the noisy CMB Q/U map is significantly less than the CMB polarization signal. To mitigate biases caused by instrumental noise, a cross-correlation approach using two half-mission maps was employed, successfully recovering CMB EE and BB power spectra that align closely with the true values, and these results validate the effectiveness of the \texttt{TCMB} method. Compared to the previously employed convolutional neural network (CNN)-based approach, the \texttt{TCMB} method offers two significant advantages: (1) It demonstrates superior effectiveness in reconstructing CMB polarization maps, outperforming CNN-based methods. (2) It can directly process HEALPix spherical sky maps without requiring rectangular region division, a step necessary for CNN-based approaches that often introduces uncertainties such as boundary effects. This study highlights the potential of Transformer-based models as a powerful tool for CMB data analysis, offering a substantial improvement over traditional CNN-based techniques. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09071v1-abstract-full').style.display = 'none'; document.getElementById('2502.09071v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <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, 13 figures, 1 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/2502.08915">arXiv:2502.08915</a> <span> [<a href="https://arxiv.org/pdf/2502.08915">pdf</a>, <a href="https://arxiv.org/format/2502.08915">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Superconductivity">cond-mat.supr-con</span> </div> </div> <p class="title is-5 mathjax"> Metal-to-superconductor Transition Induced by Lithium Adsorption on Monolayer 1$T$-Nb$_2$C </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xiong%2C+L">Lingxiao Xiong</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yuhui Yan</a>, <a href="/search/?searchtype=author&query=Zheng%2C+F">Feipeng 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="2502.08915v1-abstract-short" style="display: inline;"> Recently, two-dimensional Nb$_2$C has garnered increasing attention due to its functional-group-dependent superconductivity, both experimentally and theoretically. In contrast to the halogen and chalcogen additives that have been the main focus of previous studies, we study the effect of lithium adsorption, which can also be incorporated during the synthesis of Nb$_2$C. Our computational analysis… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08915v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08915v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08915v1-abstract-full" style="display: none;"> Recently, two-dimensional Nb$_2$C has garnered increasing attention due to its functional-group-dependent superconductivity, both experimentally and theoretically. In contrast to the halogen and chalcogen additives that have been the main focus of previous studies, we study the effect of lithium adsorption, which can also be incorporated during the synthesis of Nb$_2$C. Our computational analysis reveals a metal-to-superconductor transition in monolayer Nb$_2$C with a critical temperature ($T_{\mathrm{c}}$) of 22.1 K and a strong anisotropic superconducting gap distribution following the adsorption of lithium atoms. This emergent superconductivity is attributed to the increased electronic states at the Fermi energy, resulting from the contribution of Nb-$d$ orbitals and electron gas states induced by the low electronegativity of lithium. Furthermore, the application of tensile strain raises the $T_{\mathrm{c}}$ to 24 K, which is higher than that of most functional-group-modified Nb$_2$C systems. Our work deepens the understanding of electron-phonon coupling in layered Nb$_2$C, and provides new insights into achieving high critical temperature superconductivity with a strong anisotropic superconducting gap distribution in this system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08915v1-abstract-full').style.display = 'none'; document.getElementById('2502.08915v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 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/2502.08691">arXiv:2502.08691</a> <span> [<a href="https://arxiv.org/pdf/2502.08691">pdf</a>, <a href="https://arxiv.org/format/2502.08691">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</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"> AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Piao%2C+J">Jinghua Piao</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yuwei Yan</a>, <a href="/search/?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+N">Nian Li</a>, <a href="/search/?searchtype=author&query=Yan%2C+J">Junbo Yan</a>, <a href="/search/?searchtype=author&query=Lan%2C+X">Xiaochong Lan</a>, <a href="/search/?searchtype=author&query=Lu%2C+Z">Zhihong Lu</a>, <a href="/search/?searchtype=author&query=Zheng%2C+Z">Zhiheng Zheng</a>, <a href="/search/?searchtype=author&query=Wang%2C+J+Y">Jing Yi Wang</a>, <a href="/search/?searchtype=author&query=Zhou%2C+D">Di Zhou</a>, <a href="/search/?searchtype=author&query=Gao%2C+C">Chen Gao</a>, <a href="/search/?searchtype=author&query=Xu%2C+F">Fengli Xu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+F">Fang Zhang</a>, <a href="/search/?searchtype=author&query=Rong%2C+K">Ke Rong</a>, <a href="/search/?searchtype=author&query=Su%2C+J">Jun Su</a>, <a href="/search/?searchtype=author&query=Li%2C+Y">Yong 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="2502.08691v1-abstract-short" style="display: inline;"> Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift. By leveraging bottom-up simulations, it replaces costly and logistically challenging traditional experiments with scalable, replicable, and systematic computational approaches for studying complex social dynamics. Recent advances in la… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08691v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08691v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08691v1-abstract-full" style="display: none;"> Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift. By leveraging bottom-up simulations, it replaces costly and logistically challenging traditional experiments with scalable, replicable, and systematic computational approaches for studying complex social dynamics. Recent advances in large language models (LLMs) have further transformed this research paradigm, enabling the creation of human-like generative social agents and realistic simulacra of society. In this paper, we propose AgentSociety, a large-scale social simulator that integrates LLM-driven agents, a realistic societal environment, and a powerful large-scale simulation engine. Based on the proposed simulator, we generate social lives for over 10k agents, simulating their 5 million interactions both among agents and between agents and their environment. Furthermore, we explore the potential of AgentSociety as a testbed for computational social experiments, focusing on four key social issues: polarization, the spread of inflammatory messages, the effects of universal basic income policies, and the impact of external shocks such as hurricanes. These four issues serve as valuable cases for assessing AgentSociety's support for typical research methods -- such as surveys, interviews, and interventions -- as well as for investigating the patterns, causes, and underlying mechanisms of social issues. The alignment between AgentSociety's outcomes and real-world experimental results not only demonstrates its ability to capture human behaviors and their underlying mechanisms, but also underscores its potential as an important platform for social scientists and policymakers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08691v1-abstract-full').style.display = 'none'; document.getElementById('2502.08691v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.05849">arXiv:2502.05849</a> <span> [<a href="https://arxiv.org/pdf/2502.05849">pdf</a>, <a href="https://arxiv.org/format/2502.05849">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"> Fact-or-Fair: A Checklist for Behavioral Testing of AI Models on Fairness-Related Queries </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Huang%2C+J">Jen-tse Huang</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yuhang Yan</a>, <a href="/search/?searchtype=author&query=Liu%2C+L">Linqi Liu</a>, <a href="/search/?searchtype=author&query=Wan%2C+Y">Yixin Wan</a>, <a href="/search/?searchtype=author&query=Wang%2C+W">Wenxuan Wang</a>, <a href="/search/?searchtype=author&query=Chang%2C+K">Kai-Wei Chang</a>, <a href="/search/?searchtype=author&query=Lyu%2C+M+R">Michael R. Lyu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.05849v1-abstract-short" style="display: inline;"> The generation of incorrect images, such as depictions of people of color in Nazi-era uniforms by Gemini, frustrated users and harmed Google's reputation, motivating us to investigate the relationship between accurately reflecting factuality and promoting diversity and equity. In this study, we focus on 19 real-world statistics collected from authoritative sources. Using these statistics, we devel… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05849v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05849v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05849v1-abstract-full" style="display: none;"> The generation of incorrect images, such as depictions of people of color in Nazi-era uniforms by Gemini, frustrated users and harmed Google's reputation, motivating us to investigate the relationship between accurately reflecting factuality and promoting diversity and equity. In this study, we focus on 19 real-world statistics collected from authoritative sources. Using these statistics, we develop a checklist comprising objective and subjective queries to analyze behavior of large language models (LLMs) and text-to-image (T2I) models. Objective queries assess the models' ability to provide accurate world knowledge. In contrast, the design of subjective queries follows a key principle: statistical or experiential priors should not be overgeneralized to individuals, ensuring that models uphold diversity. These subjective queries are based on three common human cognitive errors that often result in social biases. We propose metrics to assess factuality and fairness, and formally prove the inherent trade-off between these two aspects. Results show that GPT-4o and DALL-E 3 perform notably well among six LLMs and four T2I models. Our code is publicly available at https://github.com/uclanlp/Fact-or-Fair. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05849v1-abstract-full').style.display = 'none'; document.getElementById('2502.05849v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages of main text; 7 pages of appendices;</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.05467">arXiv:2502.05467</a> <span> [<a href="https://arxiv.org/pdf/2502.05467">pdf</a>, <a href="https://arxiv.org/format/2502.05467">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"> Position: LLMs Can be Good Tutors in Foreign Language Education </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ye%2C+J">Jingheng Ye</a>, <a href="/search/?searchtype=author&query=Wang%2C+S">Shen Wang</a>, <a href="/search/?searchtype=author&query=Zou%2C+D">Deqing Zou</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yibo Yan</a>, <a href="/search/?searchtype=author&query=Wang%2C+K">Kun Wang</a>, <a href="/search/?searchtype=author&query=Zheng%2C+H">Hai-Tao Zheng</a>, <a href="/search/?searchtype=author&query=Xu%2C+Z">Zenglin Xu</a>, <a href="/search/?searchtype=author&query=King%2C+I">Irwin King</a>, <a href="/search/?searchtype=author&query=Yu%2C+P+S">Philip S. Yu</a>, <a href="/search/?searchtype=author&query=Wen%2C+Q">Qingsong Wen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.05467v1-abstract-short" style="display: inline;"> While recent efforts have begun integrating large language models (LLMs) into foreign language education (FLE), they often rely on traditional approaches to learning tasks without fully embracing educational methodologies, thus lacking adaptability to language learning. To address this gap, we argue that LLMs have the potential to serve as effective tutors in FLE. Specifically, LLMs can play three… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05467v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05467v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05467v1-abstract-full" style="display: none;"> While recent efforts have begun integrating large language models (LLMs) into foreign language education (FLE), they often rely on traditional approaches to learning tasks without fully embracing educational methodologies, thus lacking adaptability to language learning. To address this gap, we argue that LLMs have the potential to serve as effective tutors in FLE. Specifically, LLMs can play three critical roles: (1) as data enhancers, improving the creation of learning materials or serving as student simulations; (2) as task predictors, serving as learner assessment or optimizing learning pathway; and (3) as agents, enabling personalized and inclusive education. We encourage interdisciplinary research to explore these roles, fostering innovation while addressing challenges and risks, ultimately advancing FLE through the thoughtful integration of LLMs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05467v1-abstract-full').style.display = 'none'; document.getElementById('2502.05467v1-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> 8 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.05170">arXiv:2502.05170</a> <span> [<a href="https://arxiv.org/pdf/2502.05170">pdf</a>, <a href="https://arxiv.org/format/2502.05170">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="Disordered Systems and Neural Networks">cond-mat.dis-nn</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistical Mechanics">cond-mat.stat-mech</span> </div> </div> <p class="title is-5 mathjax"> Observation of a dynamic magneto-chiral instability in photoexcited tellurium </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Huang%2C+Y">Yijing Huang</a>, <a href="/search/?searchtype=author&query=Abboud%2C+N">Nick Abboud</a>, <a href="/search/?searchtype=author&query=Lv%2C+Y">Yinchuan Lv</a>, <a href="/search/?searchtype=author&query=Zhu%2C+P">Penghao Zhu</a>, <a href="/search/?searchtype=author&query=Murzabekova%2C+A">Azel Murzabekova</a>, <a href="/search/?searchtype=author&query=Lee%2C+C">Changjun Lee</a>, <a href="/search/?searchtype=author&query=Pappas%2C+E+A">Emma A. Pappas</a>, <a href="/search/?searchtype=author&query=Petruzzi%2C+D">Dominic Petruzzi</a>, <a href="/search/?searchtype=author&query=Yan%2C+J+Y">Jason Y. Yan</a>, <a href="/search/?searchtype=author&query=Chauduri%2C+D">Dipanjan Chauduri</a>, <a href="/search/?searchtype=author&query=Abbamonte%2C+P">Peter Abbamonte</a>, <a href="/search/?searchtype=author&query=Shoemaker%2C+D+P">Daniel P. Shoemaker</a>, <a href="/search/?searchtype=author&query=Fernandes%2C+R+M">Rafael M. Fernandes</a>, <a href="/search/?searchtype=author&query=Noronha%2C+J">Jorge Noronha</a>, <a href="/search/?searchtype=author&query=Mahmood%2C+F">Fahad Mahmood</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.05170v1-abstract-short" style="display: inline;"> In a system of charged chiral fermions driven out of equilibrium, an electric current parallel to the magnetic field can generate a dynamic instability by which electromagnetic waves become amplified. Whether a similar instability can occur in chiral solid-state systems remains an open question. Using time-domain terahertz (THz) emission spectroscopy, we detect signatures of what we dub a ``dynami… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05170v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05170v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05170v1-abstract-full" style="display: none;"> In a system of charged chiral fermions driven out of equilibrium, an electric current parallel to the magnetic field can generate a dynamic instability by which electromagnetic waves become amplified. Whether a similar instability can occur in chiral solid-state systems remains an open question. Using time-domain terahertz (THz) emission spectroscopy, we detect signatures of what we dub a ``dynamic magneto-chiral instability" in elemental tellurium, a structurally chiral crystal. Upon transient photoexcitation in a moderate external magnetic field, tellurium emits THz radiation consisting of coherent modes that amplify over time. An explanation for this amplification is proposed using a theoretical model based on a dynamic instability of electromagnetic waves interacting with infrared-active oscillators of impurity acceptor states in tellurium to form an amplifying polariton. Our work not only uncovers the presence of a magneto-chiral instability but also highlights its promise for THz-wave amplification in chiral materials. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05170v1-abstract-full').style.display = 'none'; document.getElementById('2502.05170v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <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">Supplementary Information (SI) available as a PDF in the TeX source</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.04755">arXiv:2502.04755</a> <span> [<a href="https://arxiv.org/pdf/2502.04755">pdf</a>, <a href="https://arxiv.org/format/2502.04755">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantum Gases">cond-mat.quant-gas</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> </div> </div> <p class="title is-5 mathjax"> Geometric origin of self-intersection points in non-Hermitian energy spectra </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Pi%2C+J">Jinghui Pi</a>, <a href="/search/?searchtype=author&query=Wang%2C+C">Chenyang Wang</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yong-Chun Liu</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yangqian Yan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.04755v1-abstract-short" style="display: inline;"> Unlike Hermitian systems, non-Hermitian energy spectra under periodic boundary conditions can form closed loops in the complex energy plane, a phenomenon known as point gap topology. In this paper, we investigate the self-intersection points of such non-Hermitian energy spectra and reveal their geometric origins. We rigorously demonstrate that these self-intersection points result from the interse… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04755v1-abstract-full').style.display = 'inline'; document.getElementById('2502.04755v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.04755v1-abstract-full" style="display: none;"> Unlike Hermitian systems, non-Hermitian energy spectra under periodic boundary conditions can form closed loops in the complex energy plane, a phenomenon known as point gap topology. In this paper, we investigate the self-intersection points of such non-Hermitian energy spectra and reveal their geometric origins. We rigorously demonstrate that these self-intersection points result from the intersection of the auxiliary generalized Brillouin zone and the Brillouin zone in one-band systems, as confirmed by an extended Hatano-Nelson model. This finding is further generalized to multi-band systems, illustrated through a non-Hermitian Su-Schrieffer-Heeger model. Moreover, we address multiple self-intersection points and derive the geometric conditions for general n-fold self-intersection points. Our results enhance the fundamental understanding of generic non-Hermitian quantum systems and provide theoretical support for further experimental investigations of energy self-intersection points. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04755v1-abstract-full').style.display = 'none'; document.getElementById('2502.04755v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <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/2502.04345">arXiv:2502.04345</a> <span> [<a href="https://arxiv.org/pdf/2502.04345">pdf</a>, <a href="https://arxiv.org/format/2502.04345">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> <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"> JingFang: A Traditional Chinese Medicine Large Language Model of Expert-Level Medical Diagnosis and Syndrome Differentiation-Based Treatment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yan%2C+Y">Yehan Yan</a>, <a href="/search/?searchtype=author&query=Ma%2C+T">Tianhao Ma</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruotai Li</a>, <a href="/search/?searchtype=author&query=Zheng%2C+X">Xinhan Zheng</a>, <a href="/search/?searchtype=author&query=Shan%2C+G">Guodong Shan</a>, <a href="/search/?searchtype=author&query=Li%2C+C">Chisheng 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="2502.04345v1-abstract-short" style="display: inline;"> Traditional Chinese medicine (TCM) plays a vital role in health protection and disease treatment, but its practical application requires extensive medical knowledge and clinical experience. Existing TCM Large Language Models (LLMs) exhibit critical limitations of uncomprehensive medical consultation and diagnoses, and inaccurate syndrome differentiation-based treatment. To address these issues, th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04345v1-abstract-full').style.display = 'inline'; document.getElementById('2502.04345v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.04345v1-abstract-full" style="display: none;"> Traditional Chinese medicine (TCM) plays a vital role in health protection and disease treatment, but its practical application requires extensive medical knowledge and clinical experience. Existing TCM Large Language Models (LLMs) exhibit critical limitations of uncomprehensive medical consultation and diagnoses, and inaccurate syndrome differentiation-based treatment. To address these issues, this study establishes JingFang (JF): a novel TCM Large Language Model that demonstrates the expert-level capability of medical diagnosis and syndrome differentiation-based treatment. We innovate a Multi-agent Dynamic Collaborative Chain-of-Thought Mechanism (MDCCTM) for medical consultation, enabling JF with effective and accurate diagnostic ability. In addition, a Syndrome Agent and a Dual-Stage Retrieval Scheme (DSRS) are developed to significantly enhance the capacity of JF for disease treatment based on syndrome differentiation. JingFang not only facilitates the application of LLMs but also promotes the effective practice of TCM in human health protection and disease treatment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04345v1-abstract-full').style.display = 'none'; document.getElementById('2502.04345v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.03812">arXiv:2502.03812</a> <span> [<a href="https://arxiv.org/pdf/2502.03812">pdf</a>, <a href="https://arxiv.org/format/2502.03812">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Plasma Physics">physics.plasm-ph</span> </div> </div> <p class="title is-5 mathjax"> Numerical study on wave attenuation via 2D fully kinetic electromagnetic particle-in-cell simulations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Du%2C+F">Fei Du</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yize Yan</a>, <a href="/search/?searchtype=author&query=Tang%2C+J">Jingfeng Tang</a>, <a href="/search/?searchtype=author&query=Yu%2C+D">Daren Yu</a>, <a href="/search/?searchtype=author&query=Zhao%2C+Y">Yinjian 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="2502.03812v2-abstract-short" style="display: inline;"> The propagation and absorption of electromagnetic waves in plasma is one of the fundamental issues in plasma physics. The electromagnetic particle-in-cell method with the finite-difference time-domain solver plus Monte Carlo collision model would be the most accurate method to simulate the wave-plasma interaction. However, the numerical effects of this method have not been carefully investigated e… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03812v2-abstract-full').style.display = 'inline'; document.getElementById('2502.03812v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.03812v2-abstract-full" style="display: none;"> The propagation and absorption of electromagnetic waves in plasma is one of the fundamental issues in plasma physics. The electromagnetic particle-in-cell method with the finite-difference time-domain solver plus Monte Carlo collision model would be the most accurate method to simulate the wave-plasma interaction. However, the numerical effects of this method have not been carefully investigated especially in two dimensions. In this paper, the 2D PIC method is used to study the electromagnetic wave attenuation by fluorescent lamp plasma tubes. The study finds that the number of macro-particles and the incident electromagnetic wave amplitude have minor effects on the wave attenuation within a certain appropriate parameter range. Furthermore, the effects of electromagnetic wave frequency, the plasma distribution structures, and collision types on wave attenuation are investigated. Particularly, it is found that the staggered way of arranging the plasma distribution structures can achieve better wave attenuation than the parallel way, which agrees with our recent experimental observation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03812v2-abstract-full').style.display = 'none'; document.getElementById('2502.03812v2-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> 11 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.03019">arXiv:2502.03019</a> <span> [<a href="https://arxiv.org/pdf/2502.03019">pdf</a>, <a href="https://arxiv.org/format/2502.03019">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Econometrics">econ.EM</span> </div> </div> <p class="title is-5 mathjax"> Panel Data Estimation and Inference: Homogeneity versus Heterogeneity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Gao%2C+J">Jiti Gao</a>, <a href="/search/?searchtype=author&query=Liu%2C+F">Fei Liu</a>, <a href="/search/?searchtype=author&query=Peng%2C+B">Bin Peng</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yayi Yan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.03019v1-abstract-short" style="display: inline;"> In this paper, we define an underlying data generating process that allows for different magnitudes of cross-sectional dependence, along with time series autocorrelation. This is achieved via high-dimensional moving average processes of infinite order (HDMA($\infty$)). Our setup and investigation integrates and enhances homogenous and heterogeneous panel data estimation and testing in a unified wa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03019v1-abstract-full').style.display = 'inline'; document.getElementById('2502.03019v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.03019v1-abstract-full" style="display: none;"> In this paper, we define an underlying data generating process that allows for different magnitudes of cross-sectional dependence, along with time series autocorrelation. This is achieved via high-dimensional moving average processes of infinite order (HDMA($\infty$)). Our setup and investigation integrates and enhances homogenous and heterogeneous panel data estimation and testing in a unified way. To study HDMA($\infty$), we extend the Beveridge-Nelson decomposition to a high-dimensional time series setting, and derive a complete toolkit set. We exam homogeneity versus heterogeneity using Gaussian approximation, a prevalent technique for establishing uniform inference. For post-testing inference, we derive central limit theorems through Edgeworth expansions for both homogenous and heterogeneous settings. Additionally, we showcase the practical relevance of the established asymptotic properties by revisiting the common correlated effects (CCE) estimators, and a classic nonstationary panel data process. Finally, we verify our theoretical findings via extensive numerical studies using both simulated and real datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03019v1-abstract-full').style.display = 'none'; document.getElementById('2502.03019v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.02871">arXiv:2502.02871</a> <span> [<a href="https://arxiv.org/pdf/2502.02871">pdf</a>, <a href="https://arxiv.org/format/2502.02871">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"> Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yan%2C+Y">Yibo Yan</a>, <a href="/search/?searchtype=author&query=Wang%2C+S">Shen Wang</a>, <a href="/search/?searchtype=author&query=Huo%2C+J">Jiahao Huo</a>, <a href="/search/?searchtype=author&query=Ye%2C+J">Jingheng Ye</a>, <a href="/search/?searchtype=author&query=Chu%2C+Z">Zhendong Chu</a>, <a href="/search/?searchtype=author&query=Hu%2C+X">Xuming Hu</a>, <a href="/search/?searchtype=author&query=Yu%2C+P+S">Philip S. Yu</a>, <a href="/search/?searchtype=author&query=Gomes%2C+C">Carla Gomes</a>, <a href="/search/?searchtype=author&query=Selman%2C+B">Bart Selman</a>, <a href="/search/?searchtype=author&query=Wen%2C+Q">Qingsong Wen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.02871v1-abstract-short" style="display: inline;"> Scientific reasoning, the process through which humans apply logic, evidence, and critical thinking to explore and interpret scientific phenomena, is essential in advancing knowledge reasoning across diverse fields. However, despite significant progress, current scientific reasoning models still struggle with generalization across domains and often fall short of multimodal perception. Multimodal L… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.02871v1-abstract-full').style.display = 'inline'; document.getElementById('2502.02871v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.02871v1-abstract-full" style="display: none;"> Scientific reasoning, the process through which humans apply logic, evidence, and critical thinking to explore and interpret scientific phenomena, is essential in advancing knowledge reasoning across diverse fields. However, despite significant progress, current scientific reasoning models still struggle with generalization across domains and often fall short of multimodal perception. Multimodal Large Language Models (MLLMs), which integrate text, images, and other modalities, present an exciting opportunity to overcome these limitations and enhance scientific reasoning. Therefore, this position paper argues that MLLMs can significantly advance scientific reasoning across disciplines such as mathematics, physics, chemistry, and biology. First, we propose a four-stage research roadmap of scientific reasoning capabilities, and highlight the current state of MLLM applications in scientific reasoning, noting their ability to integrate and reason over diverse data types. Second, we summarize the key challenges that remain obstacles to achieving MLLM's full potential. To address these challenges, we propose actionable insights and suggestions for the future. Overall, our work offers a novel perspective on MLLM integration with scientific reasoning, providing the LLM community with a valuable vision for achieving Artificial General Intelligence (AGI). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.02871v1-abstract-full').style.display = 'none'; document.getElementById('2502.02871v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.02741">arXiv:2502.02741</a> <span> [<a href="https://arxiv.org/pdf/2502.02741">pdf</a>, <a href="https://arxiv.org/format/2502.02741">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"> RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xie%2C+B">Bin Xie</a>, <a href="/search/?searchtype=author&query=Tang%2C+H">Hao Tang</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yan Yan</a>, <a href="/search/?searchtype=author&query=Agam%2C+G">Gady Agam</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.02741v1-abstract-short" style="display: inline;"> Segment Anything Model 2 (SAM 2), a prompt-driven foundation model extending SAM to both image and video domains, has shown superior zero-shot performance compared to its predecessor. Building on SAM's success in medical image segmentation, SAM 2 presents significant potential for further advancement. However, similar to SAM, SAM 2 is limited by its output of binary masks, inability to infer seman… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.02741v1-abstract-full').style.display = 'inline'; document.getElementById('2502.02741v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.02741v1-abstract-full" style="display: none;"> Segment Anything Model 2 (SAM 2), a prompt-driven foundation model extending SAM to both image and video domains, has shown superior zero-shot performance compared to its predecessor. Building on SAM's success in medical image segmentation, SAM 2 presents significant potential for further advancement. However, similar to SAM, SAM 2 is limited by its output of binary masks, inability to infer semantic labels, and dependence on precise prompts for the target object area. Additionally, direct application of SAM and SAM 2 to medical image segmentation tasks yields suboptimal results. In this paper, we explore the upper performance limit of SAM 2 using custom fine-tuning adapters, achieving a Dice Similarity Coefficient (DSC) of 92.30% on the BTCV dataset, surpassing the state-of-the-art nnUNet by 12%. Following this, we address the prompt dependency by investigating various prompt generators. We introduce a UNet to autonomously generate predicted masks and bounding boxes, which serve as input to SAM 2. Subsequent dual-stage refinements by SAM 2 further enhance performance. Extensive experiments show that our method achieves state-of-the-art results on the AMOS2022 dataset, with a Dice improvement of 2.9% compared to nnUNet, and outperforms nnUNet by 6.4% on the BTCV dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.02741v1-abstract-full').style.display = 'none'; document.getElementById('2502.02741v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.00848">arXiv:2502.00848</a> <span> [<a href="https://arxiv.org/pdf/2502.00848">pdf</a>, <a href="https://arxiv.org/format/2502.00848">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"> RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lyu%2C+Y">Yuanhuiyi Lyu</a>, <a href="/search/?searchtype=author&query=Zheng%2C+X">Xu Zheng</a>, <a href="/search/?searchtype=author&query=Jiang%2C+L">Lutao Jiang</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yibo Yan</a>, <a href="/search/?searchtype=author&query=Zou%2C+X">Xin Zou</a>, <a href="/search/?searchtype=author&query=Zhou%2C+H">Huiyu Zhou</a>, <a href="/search/?searchtype=author&query=Zhang%2C+L">Linfeng Zhang</a>, <a href="/search/?searchtype=author&query=Hu%2C+X">Xuming Hu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.00848v1-abstract-short" style="display: inline;"> Recent text-to-image generative models, e.g., Stable Diffusion V3 and Flux, have achieved notable progress. However, these models are strongly restricted to their limited knowledge, a.k.a., their own fixed parameters, that are trained with closed datasets. This leads to significant hallucinations or distortions when facing fine-grained and unseen novel real-world objects, e.g., the appearance of t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.00848v1-abstract-full').style.display = 'inline'; document.getElementById('2502.00848v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.00848v1-abstract-full" style="display: none;"> Recent text-to-image generative models, e.g., Stable Diffusion V3 and Flux, have achieved notable progress. However, these models are strongly restricted to their limited knowledge, a.k.a., their own fixed parameters, that are trained with closed datasets. This leads to significant hallucinations or distortions when facing fine-grained and unseen novel real-world objects, e.g., the appearance of the Tesla Cybertruck. To this end, we present the first real-object-based retrieval-augmented generation framework (RealRAG), which augments fine-grained and unseen novel object generation by learning and retrieving real-world images to overcome the knowledge gaps of generative models. Specifically, to integrate missing memory for unseen novel object generation, we train a reflective retriever by self-reflective contrastive learning, which injects the generator's knowledge into the sef-reflective negatives, ensuring that the retrieved augmented images compensate for the model's missing knowledge. Furthermore, the real-object-based framework integrates fine-grained visual knowledge for the generative models, tackling the distortion problem and improving the realism for fine-grained object generation. Our Real-RAG is superior in its modular application to all types of state-of-the-art text-to-image generative models and also delivers remarkable performance boosts with all of them, such as a gain of 16.18% FID score with the auto-regressive model on the Stanford Car benchmark. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.00848v1-abstract-full').style.display = 'none'; document.getElementById('2502.00848v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.00630">arXiv:2502.00630</a> <span> [<a href="https://arxiv.org/pdf/2502.00630">pdf</a>, <a href="https://arxiv.org/format/2502.00630">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"> Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xie%2C+B">Bin Xie</a>, <a href="/search/?searchtype=author&query=Tang%2C+H">Hao Tang</a>, <a href="/search/?searchtype=author&query=Cai%2C+D">Dawen Cai</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yan Yan</a>, <a href="/search/?searchtype=author&query=Agam%2C+G">Gady Agam</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.00630v1-abstract-short" style="display: inline;"> Segment Anything Model (SAM) has demonstrated impressive zero-shot performance and brought a range of unexplored capabilities to natural image segmentation tasks. However, as a very important branch of image segmentation, the performance of SAM remains uncertain when applied to medical image segmentation due to the significant differences between natural images and medical images. Meanwhile, it is… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.00630v1-abstract-full').style.display = 'inline'; document.getElementById('2502.00630v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.00630v1-abstract-full" style="display: none;"> Segment Anything Model (SAM) has demonstrated impressive zero-shot performance and brought a range of unexplored capabilities to natural image segmentation tasks. However, as a very important branch of image segmentation, the performance of SAM remains uncertain when applied to medical image segmentation due to the significant differences between natural images and medical images. Meanwhile, it is harsh to meet the SAM's requirements of extra prompts provided, such as points or boxes to specify medical regions. In this paper, we propose a novel self-prompt SAM adaptation framework for medical image segmentation, named Self-Prompt-SAM. We design a multi-scale prompt generator combined with the image encoder in SAM to generate auxiliary masks. Then, we use the auxiliary masks to generate bounding boxes as box prompts and use Distance Transform to select the most central points as point prompts. Meanwhile, we design a 3D depth-fused adapter (DfusedAdapter) and inject the DFusedAdapter into each transformer in the image encoder and mask decoder to enable pre-trained 2D SAM models to extract 3D information and adapt to 3D medical images. Extensive experiments demonstrate that our method achieves state-of-the-art performance and outperforms nnUNet by 2.3% on AMOS2022, 1.6% on ACDCand 0.5% on Synapse datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.00630v1-abstract-full').style.display = 'none'; document.getElementById('2502.00630v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.00338">arXiv:2502.00338</a> <span> [<a href="https://arxiv.org/pdf/2502.00338">pdf</a>, <a href="https://arxiv.org/format/2502.00338">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="Atmospheric and Oceanic Physics">physics.ao-ph</span> </div> </div> <p class="title is-5 mathjax"> OneForecast: A Universal Framework for Global and Regional Weather Forecasting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Gao%2C+Y">Yuan Gao</a>, <a href="/search/?searchtype=author&query=Wu%2C+H">Hao Wu</a>, <a href="/search/?searchtype=author&query=Shu%2C+R">Ruiqi Shu</a>, <a href="/search/?searchtype=author&query=Dong%2C+H">Huanshuo Dong</a>, <a href="/search/?searchtype=author&query=Xu%2C+F">Fan Xu</a>, <a href="/search/?searchtype=author&query=Chen%2C+R">Rui Chen</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yibo Yan</a>, <a href="/search/?searchtype=author&query=Wen%2C+Q">Qingsong Wen</a>, <a href="/search/?searchtype=author&query=Hu%2C+X">Xuming Hu</a>, <a href="/search/?searchtype=author&query=Wang%2C+K">Kun Wang</a>, <a href="/search/?searchtype=author&query=Wu%2C+J">Jiahao Wu</a>, <a href="/search/?searchtype=author&query=Li%2C+Q">Qing Li</a>, <a href="/search/?searchtype=author&query=Xiong%2C+H">Hui Xiong</a>, <a href="/search/?searchtype=author&query=Huang%2C+X">Xiaomeng Huang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.00338v1-abstract-short" style="display: inline;"> Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accuracy predictions but are computationally expensive and fail to fully leverage rapidly growing historical data. In recent years, deep learning methods have made significant progress in w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.00338v1-abstract-full').style.display = 'inline'; document.getElementById('2502.00338v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.00338v1-abstract-full" style="display: none;"> Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accuracy predictions but are computationally expensive and fail to fully leverage rapidly growing historical data. In recent years, deep learning methods have made significant progress in weather forecasting, but challenges remain, such as balancing global and regional high-resolution forecasts, excessive smoothing in extreme event predictions, and insufficient dynamic system modeling. To address these issues, this paper proposes a global-regional nested weather forecasting framework based on graph neural networks (GNNs). By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region to capture local high-frequency features. We introduce an adaptive information propagation mechanism, using dynamic gating units to deeply integrate node and edge features for more accurate extreme event forecasting. For high-resolution regional forecasts, we propose a neural nested grid method to mitigate boundary information loss. Experimental results show that the proposed method performs excellently across global to regional scales and short-term to long-term forecasts, especially in extreme event predictions (e.g., typhoons), significantly improving forecast accuracy. Our codes are available at https://github.com/YuanGao-YG/OneForecast. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.00338v1-abstract-full').style.display = 'none'; document.getElementById('2502.00338v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.00334">arXiv:2502.00334</a> <span> [<a href="https://arxiv.org/pdf/2502.00334">pdf</a>, <a href="https://arxiv.org/format/2502.00334">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"> UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xu%2C+X">Xin Xu</a>, <a href="/search/?searchtype=author&query=Xu%2C+Q">Qiyun Xu</a>, <a href="/search/?searchtype=author&query=Xiao%2C+T">Tong Xiao</a>, <a href="/search/?searchtype=author&query=Chen%2C+T">Tianhao Chen</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yuchen Yan</a>, <a href="/search/?searchtype=author&query=Zhang%2C+J">Jiaxin Zhang</a>, <a href="/search/?searchtype=author&query=Diao%2C+S">Shizhe Diao</a>, <a href="/search/?searchtype=author&query=Yang%2C+C">Can Yang</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yang 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="2502.00334v2-abstract-short" style="display: inline;"> Large language models (LLMs) have demonstrated remarkable capabilities in solving complex reasoning tasks, particularly in mathematics. However, the domain of physics reasoning presents unique challenges that have received significantly less attention. Existing benchmarks often fall short in evaluating LLMs' abilities on the breadth and depth of undergraduate-level physics, underscoring the need f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.00334v2-abstract-full').style.display = 'inline'; document.getElementById('2502.00334v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.00334v2-abstract-full" style="display: none;"> Large language models (LLMs) have demonstrated remarkable capabilities in solving complex reasoning tasks, particularly in mathematics. However, the domain of physics reasoning presents unique challenges that have received significantly less attention. Existing benchmarks often fall short in evaluating LLMs' abilities on the breadth and depth of undergraduate-level physics, underscoring the need for a comprehensive evaluation. To fill this gap, we introduce UGPhysics, a large-scale and comprehensive benchmark specifically designed to evaluate UnderGraduate-level Physics (UGPhysics) reasoning with LLMs. UGPhysics includes 5,520 undergraduate-level physics problems in both English and Chinese, covering 13 subjects with seven different answer types and four distinct physics reasoning skills, all rigorously screened for data leakage. Additionally, we develop a Model-Assistant Rule-based Judgment (MARJ) pipeline specifically tailored for assessing answer correctness of physics problems, ensuring accurate evaluation. Our evaluation of 31 leading LLMs shows that the highest overall accuracy, 49.8% (achieved by OpenAI-o1-mini), emphasizes the necessity for models with stronger physics reasoning skills, beyond math abilities. We hope UGPhysics, along with MARJ, will drive future advancements in AI for physics reasoning. Codes and data are available at https://github.com/YangLabHKUST/UGPhysics . <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.00334v2-abstract-full').style.display = 'none'; document.getElementById('2502.00334v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.18885">arXiv:2501.18885</a> <span> [<a href="https://arxiv.org/pdf/2501.18885">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Superconductivity">cond-mat.supr-con</span> <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"> Direct Visualization of an Incommensurate Unidirectional Charge Density Wave in La$_4$Ni$_3$O$_{10}$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+M">Mingzhe Li</a>, <a href="/search/?searchtype=author&query=Gong%2C+J">Jiashuo Gong</a>, <a href="/search/?searchtype=author&query=Zhu%2C+Y">Yinghao Zhu</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Ziyuan Chen</a>, <a href="/search/?searchtype=author&query=Zhang%2C+J">Jiakang Zhang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+E">Enkang Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+Y">Yuanji Li</a>, <a href="/search/?searchtype=author&query=Yin%2C+R">Ruotong Yin</a>, <a href="/search/?searchtype=author&query=Wang%2C+S">Shiyuan Wang</a>, <a href="/search/?searchtype=author&query=Zhao%2C+J">Jun Zhao</a>, <a href="/search/?searchtype=author&query=Feng%2C+D">Dong-Lai Feng</a>, <a href="/search/?searchtype=author&query=Du%2C+Z">Zengyi Du</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Ya-Jun Yan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.18885v1-abstract-short" style="display: inline;"> Superconductivity emerges in both La$_3$Ni$_2$O$_7$ and La$_4$Ni$_3$O$_{10}$ under high pressure by suppressing their density-wave transitions, but critical temperature (Tc) differs significantly between these two compounds. To gain deeper insights into the distinct superconducting states, it is essential to unravel the nature of the density-wave states at ambient pressure, a topic that remains la… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18885v1-abstract-full').style.display = 'inline'; document.getElementById('2501.18885v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.18885v1-abstract-full" style="display: none;"> Superconductivity emerges in both La$_3$Ni$_2$O$_7$ and La$_4$Ni$_3$O$_{10}$ under high pressure by suppressing their density-wave transitions, but critical temperature (Tc) differs significantly between these two compounds. To gain deeper insights into the distinct superconducting states, it is essential to unravel the nature of the density-wave states at ambient pressure, a topic that remains largely unexplored. Here, using scanning tunneling microscopy/spectroscopy (STM/STS), we report the direct visualization of an incommensurate unidirectional charge density wave (CDW) in La$_4$Ni$_3$O$_{10}$ in real space. The density of states (DOS) is strongly depleted near $E_F$, indicating the opening of a CDW gap of $2螖 \approx 71$ meV, which is unfavorable for the formation of superconductivity at ambient pressure. We propose that the CDW arises from Fermi surface nesting, and is likely a subsidiary phase of a spin density wave. Compared to La$_3$Ni$_2$O$_7$, the weaker electronic correlation in La$_4$Ni$_3$O$_{10}$ is likely one reason for the lower $T_c$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18885v1-abstract-full').style.display = 'none'; document.getElementById('2501.18885v1-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> 30 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 4 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/2501.18863">arXiv:2501.18863</a> <span> [<a href="https://arxiv.org/pdf/2501.18863">pdf</a>, <a href="https://arxiv.org/ps/2501.18863">ps</a>, <a href="https://arxiv.org/format/2501.18863">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">stat.ML</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"> Adaptivity and Convergence of Probability Flow ODEs in Diffusion Generative Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Tang%2C+J">Jiaqi Tang</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yuling Yan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.18863v1-abstract-short" style="display: inline;"> Score-based generative models, which transform noise into data by learning to reverse a diffusion process, have become a cornerstone of modern generative AI. This paper contributes to establishing theoretical guarantees for the probability flow ODE, a widely used diffusion-based sampler known for its practical efficiency. While a number of prior works address its general convergence theory, it rem… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18863v1-abstract-full').style.display = 'inline'; document.getElementById('2501.18863v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.18863v1-abstract-full" style="display: none;"> Score-based generative models, which transform noise into data by learning to reverse a diffusion process, have become a cornerstone of modern generative AI. This paper contributes to establishing theoretical guarantees for the probability flow ODE, a widely used diffusion-based sampler known for its practical efficiency. While a number of prior works address its general convergence theory, it remains unclear whether the probability flow ODE sampler can adapt to the low-dimensional structures commonly present in natural image data. We demonstrate that, with accurate score function estimation, the probability flow ODE sampler achieves a convergence rate of $O(k/T)$ in total variation distance (ignoring logarithmic factors), where $k$ is the intrinsic dimension of the target distribution and $T$ is the number of iterations. This dimension-free convergence rate improves upon existing results that scale with the typically much larger ambient dimension, highlighting the ability of the probability flow ODE sampler to exploit intrinsic low-dimensional structures in the target distribution for faster sampling. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18863v1-abstract-full').style.display = 'none'; document.getElementById('2501.18863v1-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> 30 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.17555">arXiv:2501.17555</a> <span> [<a href="https://arxiv.org/pdf/2501.17555">pdf</a>, <a href="https://arxiv.org/format/2501.17555">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"> An Exceptional Dataset For Rare Pancreatic Tumor Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+W">Wenqi Li</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yingli Chen</a>, <a href="/search/?searchtype=author&query=Zhou%2C+K">Keyang Zhou</a>, <a href="/search/?searchtype=author&query=Hu%2C+X">Xiaoxiao Hu</a>, <a href="/search/?searchtype=author&query=Zheng%2C+Z">Zilu Zheng</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yue Yan</a>, <a href="/search/?searchtype=author&query=Zhang%2C+X">Xinpeng Zhang</a>, <a href="/search/?searchtype=author&query=Tang%2C+W">Wei Tang</a>, <a href="/search/?searchtype=author&query=Qian%2C+Z">Zhenxing Qian</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.17555v1-abstract-short" style="display: inline;"> Pancreatic NEuroendocrine Tumors (pNETs) are very rare endocrine neoplasms that account for less than 5% of all pancreatic malignancies, with an incidence of only 1-1.5 cases per 100,000. Early detection of pNETs is critical for improving patient survival, but the rarity of pNETs makes segmenting them from CT a very challenging problem. So far, there has not been a dataset specifically for pNETs a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.17555v1-abstract-full').style.display = 'inline'; document.getElementById('2501.17555v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.17555v1-abstract-full" style="display: none;"> Pancreatic NEuroendocrine Tumors (pNETs) are very rare endocrine neoplasms that account for less than 5% of all pancreatic malignancies, with an incidence of only 1-1.5 cases per 100,000. Early detection of pNETs is critical for improving patient survival, but the rarity of pNETs makes segmenting them from CT a very challenging problem. So far, there has not been a dataset specifically for pNETs available to researchers. To address this issue, we propose a pNETs dataset, a well-annotated Contrast-Enhanced Computed Tomography (CECT) dataset focused exclusively on Pancreatic Neuroendocrine Tumors, containing data from 469 patients. This is the first dataset solely dedicated to pNETs, distinguishing it from previous collections. Additionally, we provide the baseline detection networks with a new slice-wise weight loss function designed for the UNet-based model, improving the overall pNET segmentation performance. We hope that our dataset can enhance the understanding and diagnosis of pNET Tumors within the medical community, facilitate the development of more accurate diagnostic tools, and ultimately improve patient outcomes and advance the field of oncology. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.17555v1-abstract-full').style.display = 'none'; document.getElementById('2501.17555v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.17547">arXiv:2501.17547</a> <span> [<a href="https://arxiv.org/pdf/2501.17547">pdf</a>, <a href="https://arxiv.org/format/2501.17547">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"> Towards Training-Free Open-World Classification with 3D Generative Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xia%2C+X">Xinzhe Xia</a>, <a href="/search/?searchtype=author&query=Zhao%2C+W">Weiguang Zhao</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yuyao Yan</a>, <a href="/search/?searchtype=author&query=Yang%2C+G">Guanyu Yang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+R">Rui Zhang</a>, <a href="/search/?searchtype=author&query=Huang%2C+K">Kaizhu Huang</a>, <a href="/search/?searchtype=author&query=Yang%2C+X">Xi Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.17547v1-abstract-short" style="display: inline;"> 3D open-world classification is a challenging yet essential task in dynamic and unstructured real-world scenarios, requiring both open-category and open-pose recognition. To address these challenges, recent wisdom often takes sophisticated 2D pre-trained models to provide enriched and stable representations. However, these methods largely rely on how 3D objects can be projected into 2D space, whic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.17547v1-abstract-full').style.display = 'inline'; document.getElementById('2501.17547v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.17547v1-abstract-full" style="display: none;"> 3D open-world classification is a challenging yet essential task in dynamic and unstructured real-world scenarios, requiring both open-category and open-pose recognition. To address these challenges, recent wisdom often takes sophisticated 2D pre-trained models to provide enriched and stable representations. However, these methods largely rely on how 3D objects can be projected into 2D space, which is unfortunately not well solved, and thus significantly limits their performance. Unlike these present efforts, in this paper we make a pioneering exploration of 3D generative models for 3D open-world classification. Drawing on abundant prior knowledge from 3D generative models, we additionally craft a rotation-invariant feature extractor. This innovative synergy endows our pipeline with the advantages of being training-free, open-category, and pose-invariant, thus well suited to 3D open-world classification. Extensive experiments on benchmark datasets demonstrate the potential of generative models in 3D open-world classification, achieving state-of-the-art performance on ModelNet10 and McGill with 32.0% and 8.7% overall accuracy improvement, respectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.17547v1-abstract-full').style.display = 'none'; document.getElementById('2501.17547v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.15711">arXiv:2501.15711</a> <span> [<a href="https://arxiv.org/pdf/2501.15711">pdf</a>, <a href="https://arxiv.org/format/2501.15711">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </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/10.1145/3706598.3713496">10.1145/3706598.3713496 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> DanmuA11y: Making Time-Synced On-Screen Video Comments (Danmu) Accessible to Blind and Low Vision Users via Multi-Viewer Audio Discussions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xu%2C+S">Shuchang Xu</a>, <a href="/search/?searchtype=author&query=Jin%2C+X">Xiaofu Jin</a>, <a href="/search/?searchtype=author&query=Qu%2C+H">Huamin Qu</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yukang Yan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.15711v1-abstract-short" style="display: inline;"> By overlaying time-synced user comments on videos, Danmu creates a co-watching experience for online viewers. However, its visual-centric design poses significant challenges for blind and low vision (BLV) viewers. Our formative study identified three primary challenges that hinder BLV viewers' engagement with Danmu: the lack of visual context, the speech interference between comments and videos, a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15711v1-abstract-full').style.display = 'inline'; document.getElementById('2501.15711v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.15711v1-abstract-full" style="display: none;"> By overlaying time-synced user comments on videos, Danmu creates a co-watching experience for online viewers. However, its visual-centric design poses significant challenges for blind and low vision (BLV) viewers. Our formative study identified three primary challenges that hinder BLV viewers' engagement with Danmu: the lack of visual context, the speech interference between comments and videos, and the disorganization of comments. To address these challenges, we present DanmuA11y, a system that makes Danmu accessible by transforming it into multi-viewer audio discussions. DanmuA11y incorporates three core features: (1) Augmenting Danmu with visual context, (2) Seamlessly integrating Danmu into videos, and (3) Presenting Danmu via multi-viewer discussions. Evaluation with twelve BLV viewers demonstrated that DanmuA11y significantly improved Danmu comprehension, provided smooth viewing experiences, and fostered social connections among viewers. We further highlight implications for enhancing commentary accessibility in video-based social media and live-streaming platforms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15711v1-abstract-full').style.display = 'none'; document.getElementById('2501.15711v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.15447">arXiv:2501.15447</a> <span> [<a href="https://arxiv.org/pdf/2501.15447">pdf</a>, <a href="https://arxiv.org/ps/2501.15447">ps</a>, <a href="https://arxiv.org/format/2501.15447">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Observation of $h_{c}$ radiative decays to multiple light hadrons and the tensor state $f_2(1270)$ </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=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=An%2C+Q">Q. An</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=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>, <a href="/search/?searchtype=author&query=Cai%2C+H">H. Cai</a> , et al. (666 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.15447v1-abstract-short" style="display: inline;"> Using $蠄(3686)\rightarrow 蟺^{0} h_{c}$ decays from a data sample of $(27.12\pm0.14)\times10^{8}$ $蠄(3686)$ events collected by the BESIII detector at the BEPCII collider, $h_c$ radiative decays to $纬蟺^{+}蟺^{-},~纬蟺^{+}蟺^{-}畏,~\gamma2(蟺^{+}蟺^{-})$, and $纬p\bar{p}$ are observed for the first time, each with a significance greater than $5蟽$. The corresponding branching fractions are measured. Furtherm… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15447v1-abstract-full').style.display = 'inline'; document.getElementById('2501.15447v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.15447v1-abstract-full" style="display: none;"> Using $蠄(3686)\rightarrow 蟺^{0} h_{c}$ decays from a data sample of $(27.12\pm0.14)\times10^{8}$ $蠄(3686)$ events collected by the BESIII detector at the BEPCII collider, $h_c$ radiative decays to $纬蟺^{+}蟺^{-},~纬蟺^{+}蟺^{-}畏,~\gamma2(蟺^{+}蟺^{-})$, and $纬p\bar{p}$ are observed for the first time, each with a significance greater than $5蟽$. The corresponding branching fractions are measured. Furthermore, intermediate states below 2.8 GeV/$c^{2}$ are investigated, leading to the first observation of the decay process of $h_c\rightarrow纬f_{2}(1270)\rightarrow纬蟺^{+}蟺^{-}$ with a significance of $5.5\,蟽$. This observation represents the first instance of $h_c$ radiative decay to a tensor state. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15447v1-abstract-full').style.display = 'none'; document.getElementById('2501.15447v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.15408">arXiv:2501.15408</a> <span> [<a href="https://arxiv.org/pdf/2501.15408">pdf</a>, <a href="https://arxiv.org/format/2501.15408">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Memory Reviver: Supporting Photo-Collection Reminiscence for People with Visual Impairment via a Proactive Chatbot </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xu%2C+S">Shuchang Xu</a>, <a href="/search/?searchtype=author&query=Chen%2C+C">Chang Chen</a>, <a href="/search/?searchtype=author&query=Liu%2C+Z">Zichen Liu</a>, <a href="/search/?searchtype=author&query=Jin%2C+X">Xiaofu Jin</a>, <a href="/search/?searchtype=author&query=Yuan%2C+L">Linping Yuan</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yukang Yan</a>, <a href="/search/?searchtype=author&query=Qu%2C+H">Huamin Qu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.15408v1-abstract-short" style="display: inline;"> Reminiscing with photo collections offers significant psychological benefits but poses challenges for people with visual impairment (PVI). Their current reliance on sighted help restricts the flexibility of this activity. In response, we explored using a chatbot in a preliminary study. We identified two primary challenges that hinder effective reminiscence with a chatbot: the scattering of informa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15408v1-abstract-full').style.display = 'inline'; document.getElementById('2501.15408v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.15408v1-abstract-full" style="display: none;"> Reminiscing with photo collections offers significant psychological benefits but poses challenges for people with visual impairment (PVI). Their current reliance on sighted help restricts the flexibility of this activity. In response, we explored using a chatbot in a preliminary study. We identified two primary challenges that hinder effective reminiscence with a chatbot: the scattering of information and a lack of proactive guidance. To address these limitations, we present Memory Reviver, a proactive chatbot that helps PVI reminisce with a photo collection through natural language communication. Memory Reviver incorporates two novel features: (1) a Memory Tree, which uses a hierarchical structure to organize the information in a photo collection; and (2) a Proactive Strategy, which actively delivers information to users at proper conversation rounds. Evaluation with twelve PVI demonstrated that Memory Reviver effectively facilitated engaging reminiscence, enhanced understanding of photo collections, and delivered natural conversational experiences. Based on our findings, we distill implications for supporting photo reminiscence and designing chatbots for PVI. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15408v1-abstract-full').style.display = 'none'; document.getElementById('2501.15408v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.15214">arXiv:2501.15214</a> <span> [<a href="https://arxiv.org/pdf/2501.15214">pdf</a>, <a href="https://arxiv.org/format/2501.15214">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Zero-shot Robotic Manipulation with Language-guided Instruction and Formal Task Planning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Tang%2C+J">Junfeng Tang</a>, <a href="/search/?searchtype=author&query=Ye%2C+Z">Zihan Ye</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yuping Yan</a>, <a href="/search/?searchtype=author&query=Zheng%2C+Z">Ziqi Zheng</a>, <a href="/search/?searchtype=author&query=Gao%2C+T">Ting Gao</a>, <a href="/search/?searchtype=author&query=Jin%2C+Y">Yaochu Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.15214v1-abstract-short" style="display: inline;"> Robotic manipulation is often challenging due to the long-horizon tasks and the complex object relationships. A common solution is to develop a task and motion planning framework that integrates planning for high-level task and low-level motion. Recently, inspired by the powerful reasoning ability of Large Language Models (LLMs), LLM-based planning approaches have achieved remarkable progress. How… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15214v1-abstract-full').style.display = 'inline'; document.getElementById('2501.15214v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.15214v1-abstract-full" style="display: none;"> Robotic manipulation is often challenging due to the long-horizon tasks and the complex object relationships. A common solution is to develop a task and motion planning framework that integrates planning for high-level task and low-level motion. Recently, inspired by the powerful reasoning ability of Large Language Models (LLMs), LLM-based planning approaches have achieved remarkable progress. However, these methods still heavily rely on expert-specific knowledge, often generating invalid plans for unseen and unfamiliar tasks. To address this issue, we propose an innovative language-guided symbolic task planning (LM-SymOpt) framework with optimization. It is the first expert-free planning framework since we combine the world knowledge from LLMs with formal reasoning, resulting in improved generalization capability to new tasks. Specifically, differ to most existing work, our LM-SymOpt employs LLMs to translate natural language instructions into symbolic representations, thereby representing actions as high-level symbols and reducing the search space for planning. Next, after evaluating the action probability of completing the task using LLMs, a weighted random sampling method is introduced to generate candidate plans. Their feasibility is assessed through symbolic reasoning and their cost efficiency is then evaluated using trajectory optimization for selecting the optimal planning. Our experimental results show that LM-SymOpt outperforms existing LLM-based planning approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15214v1-abstract-full').style.display = 'none'; document.getElementById('2501.15214v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.15082">arXiv:2501.15082</a> <span> [<a href="https://arxiv.org/pdf/2501.15082">pdf</a>, <a href="https://arxiv.org/format/2501.15082">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"> Spatially-resolved spectro-photometric SED Modeling of NGC 253's Central Molecular Zone I. Studying the star formation in extragalactic giant molecular clouds </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Humire%2C+P+K">Pedro K. Humire</a>, <a href="/search/?searchtype=author&query=Dey%2C+S">Subhrata Dey</a>, <a href="/search/?searchtype=author&query=Ronconi%2C+T">Tommaso Ronconi</a>, <a href="/search/?searchtype=author&query=Sasse%2C+V+H">Victor H. Sasse</a>, <a href="/search/?searchtype=author&query=Fernandes%2C+R+C">Roberto Cid Fernandes</a>, <a href="/search/?searchtype=author&query=Mart%C3%ADn%2C+S">Sergio Mart铆n</a>, <a href="/search/?searchtype=author&query=Donevski%2C+D">Darko Donevski</a>, <a href="/search/?searchtype=author&query=Ma%C5%82ek%2C+K">Katarzyna Ma艂ek</a>, <a href="/search/?searchtype=author&query=Fern%C3%A1ndez-Ontiveros%2C+J+A">Juan A. Fern谩ndez-Ontiveros</a>, <a href="/search/?searchtype=author&query=Song%2C+Y">Yiqing Song</a>, <a href="/search/?searchtype=author&query=Hamed%2C+M">Mahmoud Hamed</a>, <a href="/search/?searchtype=author&query=Mangum%2C+J+G">Jeffrey G. Mangum</a>, <a href="/search/?searchtype=author&query=Henkel%2C+C">Christian Henkel</a>, <a href="/search/?searchtype=author&query=Rivilla%2C+V+M">V铆ctor M. Rivilla</a>, <a href="/search/?searchtype=author&query=Colzi%2C+L">Laura Colzi</a>, <a href="/search/?searchtype=author&query=Harada%2C+N">N. Harada</a>, <a href="/search/?searchtype=author&query=Demarco%2C+R">Ricardo Demarco</a>, <a href="/search/?searchtype=author&query=Goyal%2C+A">Arti Goyal</a>, <a href="/search/?searchtype=author&query=Meier%2C+D+S">David S. Meier</a>, <a href="/search/?searchtype=author&query=Panda%2C+S">Swayamtrupta Panda</a>, <a href="/search/?searchtype=author&query=Krabbe%2C+%C3%82+C">脗ngela C. Krabbe</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yaoting Yan</a>, <a href="/search/?searchtype=author&query=Lopes%2C+A+R">Amanda R. Lopes</a>, <a href="/search/?searchtype=author&query=Sakamoto%2C+K">K. Sakamoto</a>, <a href="/search/?searchtype=author&query=Muller%2C+S">S. Muller</a> , et al. (7 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.15082v1-abstract-short" style="display: inline;"> Studying the interstellar medium in nearby starbursts is essential for understanding the physical mechanisms driving these objects, thought to resemble young star-forming galaxies. This study aims to analyze the physical properties of the first spatially-resolved multi-wavelength SED of an extragalactic source, spanning six decades in frequency (from near-UV to cm wavelengths) at an angular resolu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15082v1-abstract-full').style.display = 'inline'; document.getElementById('2501.15082v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.15082v1-abstract-full" style="display: none;"> Studying the interstellar medium in nearby starbursts is essential for understanding the physical mechanisms driving these objects, thought to resemble young star-forming galaxies. This study aims to analyze the physical properties of the first spatially-resolved multi-wavelength SED of an extragalactic source, spanning six decades in frequency (from near-UV to cm wavelengths) at an angular resolution of 3$^{\prime\prime}$ (51 pc at the distance of NGC,253). We focus on the central molecular zone (CMZ) of NGC,253, which contains giant molecular clouds (GMCs) responsible for half of the galaxy's star formation. We use archival data, spanning optical to centimeter wavelengths, to compute SEDs with the GalaPy and CIGALE codes for validation, and analyze stellar optical spectra with the \textsc{starlight} code. Our results show significant differences between central and external GMCs in terms of stellar and dust masses, star formation rates (SFRs), and bolometric luminosities. We identify the best SFR tracers as radio continuum bands at 33 GHz, radio recombination lines, and the total infrared luminosity (L$_{\rm IR}$; 8-1000$渭$m), as well as 60$渭$m IR emission. BPT and WHAN diagrams indicate shock signatures in NGC~253's nuclear region, associating it with AGN/star-forming hybrids, though the AGN fraction is negligible ($\leq$7.5%). Our findings show significant heterogeneity in the CMZ, with central GMCs exhibiting higher densities, SFRs, and dust masses compared to external GMCs. We confirm that certain centimeter photometric bands can reliably estimate global SFR at GMC scales. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15082v1-abstract-full').style.display = 'none'; document.getElementById('2501.15082v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to A&A. 33 pages (20 main text), 20 figures (13 main text)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.14539">arXiv:2501.14539</a> <span> [<a href="https://arxiv.org/pdf/2501.14539">pdf</a>, <a href="https://arxiv.org/format/2501.14539">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</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"> A Recurrent Spiking Network with Hierarchical Intrinsic Excitability Modulation for Schema Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yu%2C+Y">Yingchao Yu</a>, <a href="/search/?searchtype=author&query=Jin%2C+Y">Yaochu Jin</a>, <a href="/search/?searchtype=author&query=Xiao%2C+Y">Yuchen Xiao</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yuping Yan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.14539v1-abstract-short" style="display: inline;"> Schema, a form of structured knowledge that promotes transfer learning, is attracting growing attention in both neuroscience and artificial intelligence (AI). Current schema research in neural computation is largely constrained to a single behavioral paradigm and relies heavily on recurrent neural networks (RNNs) which lack the neural plausibility and biological interpretability. To address these… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.14539v1-abstract-full').style.display = 'inline'; document.getElementById('2501.14539v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.14539v1-abstract-full" style="display: none;"> Schema, a form of structured knowledge that promotes transfer learning, is attracting growing attention in both neuroscience and artificial intelligence (AI). Current schema research in neural computation is largely constrained to a single behavioral paradigm and relies heavily on recurrent neural networks (RNNs) which lack the neural plausibility and biological interpretability. To address these limitations, this work first constructs a generalized behavioral paradigm framework for schema learning and introduces three novel cognitive tasks, thus supporting a comprehensive schema exploration. Second, we propose a new model using recurrent spiking neural networks with hierarchical intrinsic excitability modulation (HM-RSNNs). The top level of the model selects excitability properties for task-specific demands, while the bottom level fine-tunes these properties for intra-task problems. Finally, extensive visualization analyses of HM-RSNNs are conducted to showcase their computational advantages, track the intrinsic excitability evolution during schema learning, and examine neural coordination differences across tasks. Biologically inspired lesion studies further uncover task-specific distributions of intrinsic excitability within schemas. Experimental results show that HM-RSNNs significantly outperform RSNN baselines across all tasks and exceed RNNs in three novel cognitive tasks. Additionally, HM-RSNNs offer deeper insights into neural dynamics underlying schema learning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.14539v1-abstract-full').style.display = 'none'; document.getElementById('2501.14539v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">31 pages, 9 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.13629">arXiv:2501.13629</a> <span> [<a href="https://arxiv.org/pdf/2501.13629">pdf</a>, <a href="https://arxiv.org/format/2501.13629">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"> Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lin%2C+Z">Zhenghao Lin</a>, <a href="/search/?searchtype=author&query=Tang%2C+Z">Zihao Tang</a>, <a href="/search/?searchtype=author&query=Liu%2C+X">Xiao Liu</a>, <a href="/search/?searchtype=author&query=Gong%2C+Y">Yeyun Gong</a>, <a href="/search/?searchtype=author&query=Cheng%2C+Y">Yi Cheng</a>, <a href="/search/?searchtype=author&query=Chen%2C+Q">Qi Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Hang Li</a>, <a href="/search/?searchtype=author&query=Xin%2C+Y">Ying Xin</a>, <a href="/search/?searchtype=author&query=Yang%2C+Z">Ziyue Yang</a>, <a href="/search/?searchtype=author&query=Yang%2C+K">Kailai Yang</a>, <a href="/search/?searchtype=author&query=Yan%2C+Y">Yu Yan</a>, <a href="/search/?searchtype=author&query=Liang%2C+X">Xiao Liang</a>, <a href="/search/?searchtype=author&query=Lu%2C+S">Shuai Lu</a>, <a href="/search/?searchtype=author&query=Huang%2C+Y">Yiming Huang</a>, <a href="/search/?searchtype=author&query=Luo%2C+Z">Zheheng Luo</a>, <a href="/search/?searchtype=author&query=Qu%2C+L">Lei Qu</a>, <a href="/search/?searchtype=author&query=Feng%2C+X">Xuan Feng</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yaoxiang Wang</a>, <a href="/search/?searchtype=author&query=Xia%2C+Y">Yuqing Xia</a>, <a href="/search/?searchtype=author&query=Chen%2C+F">Feiyang Chen</a>, <a href="/search/?searchtype=author&query=Jiang%2C+Y">Yuting Jiang</a>, <a href="/search/?searchtype=author&query=Hu%2C+Y">Yasen Hu</a>, <a href="/search/?searchtype=author&query=Ni%2C+H">Hao Ni</a>, <a href="/search/?searchtype=author&query=Li%2C+B">Binyang Li</a>, <a href="/search/?searchtype=author&query=Zhao%2C+G">Guoshuai Zhao</a> , et al. (9 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.13629v2-abstract-short" style="display: inline;"> We introduce Sigma, an efficient large language model specialized for the system domain, empowered by a novel architecture including DiffQKV attention, and pre-trained on our meticulously collected system domain data. DiffQKV attention significantly enhances the inference efficiency of Sigma by optimizing the Query (Q), Key (K), and Value (V) components in the attention mechanism differentially, b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.13629v2-abstract-full').style.display = 'inline'; document.getElementById('2501.13629v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.13629v2-abstract-full" style="display: none;"> We introduce Sigma, an efficient large language model specialized for the system domain, empowered by a novel architecture including DiffQKV attention, and pre-trained on our meticulously collected system domain data. DiffQKV attention significantly enhances the inference efficiency of Sigma by optimizing the Query (Q), Key (K), and Value (V) components in the attention mechanism differentially, based on their varying impacts on the model performance and efficiency indicators. Specifically, we (1) conduct extensive experiments that demonstrate the model's varying sensitivity to the compression of K and V components, leading to the development of differentially compressed KV, and (2) propose augmented Q to expand the Q head dimension, which enhances the model's representation capacity with minimal impacts on the inference speed. Rigorous theoretical and empirical analyses reveal that DiffQKV attention significantly enhances efficiency, achieving up to a 33.36% improvement in inference speed over the conventional grouped-query attention (GQA) in long-context scenarios. We pre-train Sigma on 6T tokens from various sources, including 19.5B system domain data that we carefully collect and 1T tokens of synthesized and rewritten data. In general domains, Sigma achieves comparable performance to other state-of-arts models. In the system domain, we introduce the first comprehensive benchmark AIMicius, where Sigma demonstrates remarkable performance across all tasks, significantly outperforming GPT-4 with an absolute improvement up to 52.5%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.13629v2-abstract-full').style.display = 'none'; document.getElementById('2501.13629v2-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> 10 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a 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