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breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Li%2C+R&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Li%2C+R&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+R&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+R&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+R&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+R&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/2502.09858">arXiv:2502.09858</a> <span> [<a href="https://arxiv.org/pdf/2502.09858">pdf</a>, <a href="https://arxiv.org/format/2502.09858">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Automated Hypothesis Validation with Agentic Sequential Falsifications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Huang%2C+K">Kexin Huang</a>, <a href="/search/?searchtype=author&query=Jin%2C+Y">Ying Jin</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ryan Li</a>, <a href="/search/?searchtype=author&query=Li%2C+M+Y">Michael Y. Li</a>, <a href="/search/?searchtype=author&query=Cand%C3%A8s%2C+E">Emmanuel Cand猫s</a>, <a href="/search/?searchtype=author&query=Leskovec%2C+J">Jure Leskovec</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="2502.09858v1-abstract-short" style="display: inline;"> Hypotheses are central to information acquisition, decision-making, and discovery. However, many real-world hypotheses are abstract, high-level statements that are difficult to validate directly. This challenge is further intensified by the rise of hypothesis generation from Large Language Models (LLMs), which are prone to hallucination and produce hypotheses in volumes that make manual validation… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09858v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09858v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09858v1-abstract-full" style="display: none;"> Hypotheses are central to information acquisition, decision-making, and discovery. However, many real-world hypotheses are abstract, high-level statements that are difficult to validate directly. This challenge is further intensified by the rise of hypothesis generation from Large Language Models (LLMs), which are prone to hallucination and produce hypotheses in volumes that make manual validation impractical. Here we propose Popper, an agentic framework for rigorous automated validation of free-form hypotheses. Guided by Karl Popper's principle of falsification, Popper validates a hypothesis using LLM agents that design and execute falsification experiments targeting its measurable implications. A novel sequential testing framework ensures strict Type-I error control while actively gathering evidence from diverse observations, whether drawn from existing data or newly conducted procedures. We demonstrate Popper on six domains including biology, economics, and sociology. Popper delivers robust error control, high power, and scalability. Furthermore, compared to human scientists, Popper achieved comparable performance in validating complex biological hypotheses while reducing time by 10 folds, providing a scalable, rigorous solution for hypothesis validation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09858v1-abstract-full').style.display = 'none'; document.getElementById('2502.09858v1-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/2502.09846">arXiv:2502.09846</a> <span> [<a href="https://arxiv.org/pdf/2502.09846">pdf</a>, <a href="https://arxiv.org/format/2502.09846">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Robust Event-Triggered Integrated Communication and Control with Graph Information Bottleneck Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+Z">Ziqiong Wang</a>, <a href="/search/?searchtype=author&query=Yu%2C+X">Xiaoxue Yu</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Rongpeng Li</a>, <a href="/search/?searchtype=author&query=Zhao%2C+Z">Zhifeng 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="2502.09846v1-abstract-short" style="display: inline;"> Integrated communication and control serves as a critical ingredient in Multi-Agent Reinforcement Learning. However, partial observability limitations will impair collaboration effectiveness, and a potential solution is to establish consensus through well-calibrated latent variables obtained from neighboring agents. Nevertheless, the rigid transmission of less informative content can still result… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09846v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09846v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09846v1-abstract-full" style="display: none;"> Integrated communication and control serves as a critical ingredient in Multi-Agent Reinforcement Learning. However, partial observability limitations will impair collaboration effectiveness, and a potential solution is to establish consensus through well-calibrated latent variables obtained from neighboring agents. Nevertheless, the rigid transmission of less informative content can still result in redundant information exchanges. Therefore, we propose a Consensus-Driven Event-Based Graph Information Bottleneck (CDE-GIB) method, which integrates the communication graph and information flow through a GIB regularizer to extract more concise message representations while avoiding the high computational complexity of inner-loop operations. To further minimize the communication volume required for establishing consensus during interactions, we also develop a variable-threshold event-triggering mechanism. By simultaneously considering historical data and current observations, this mechanism capably evaluates the importance of information to determine whether an event should be triggered. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art methods in terms of both efficiency and adaptability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09846v1-abstract-full').style.display = 'none'; document.getElementById('2502.09846v1-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/2502.09723">arXiv:2502.09723</a> <span> [<a href="https://arxiv.org/pdf/2502.09723">pdf</a>, <a href="https://arxiv.org/format/2502.09723">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Making Them a Malicious Database: Exploiting Query Code to Jailbreak Aligned Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zou%2C+Q">Qingsong Zou</a>, <a href="/search/?searchtype=author&query=Xiao%2C+J">Jingyu Xiao</a>, <a href="/search/?searchtype=author&query=Li%2C+Q">Qing Li</a>, <a href="/search/?searchtype=author&query=Yan%2C+Z">Zhi Yan</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yuhang Wang</a>, <a href="/search/?searchtype=author&query=Xu%2C+L">Li Xu</a>, <a href="/search/?searchtype=author&query=Wang%2C+W">Wenxuan Wang</a>, <a href="/search/?searchtype=author&query=Gao%2C+K">Kuofeng Gao</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruoyu Li</a>, <a href="/search/?searchtype=author&query=Jiang%2C+Y">Yong Jiang</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="2502.09723v1-abstract-short" style="display: inline;"> Recent advances in large language models (LLMs) have demonstrated remarkable potential in the field of natural language processing. Unfortunately, LLMs face significant security and ethical risks. Although techniques such as safety alignment are developed for defense, prior researches reveal the possibility of bypassing such defenses through well-designed jailbreak attacks. In this paper, we propo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09723v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09723v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09723v1-abstract-full" style="display: none;"> Recent advances in large language models (LLMs) have demonstrated remarkable potential in the field of natural language processing. Unfortunately, LLMs face significant security and ethical risks. Although techniques such as safety alignment are developed for defense, prior researches reveal the possibility of bypassing such defenses through well-designed jailbreak attacks. In this paper, we propose QueryAttack, a novel framework to systematically examine the generalizability of safety alignment. By treating LLMs as knowledge databases, we translate malicious queries in natural language into code-style structured query to bypass the safety alignment mechanisms of LLMs. We conduct extensive experiments on mainstream LLMs, ant the results show that QueryAttack achieves high attack success rates (ASRs) across LLMs with different developers and capabilities. We also evaluate QueryAttack's performance against common defenses, confirming that it is difficult to mitigate with general defensive techniques. To defend against QueryAttack, we tailor a defense method which can reduce ASR by up to 64\% on GPT-4-1106. The code of QueryAttack can be found on https://anonymous.4open.science/r/QueryAttack-334B. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09723v1-abstract-full').style.display = 'none'; document.getElementById('2502.09723v1-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, 11 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/2502.09439">arXiv:2502.09439</a> <span> [<a href="https://arxiv.org/pdf/2502.09439">pdf</a>, <a href="https://arxiv.org/format/2502.09439">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> </div> </div> <p class="title is-5 mathjax"> The Third Generation of Nanogenerators: The Irreplaceable Potential Source Enabled by the Flexoelectric Nanogenerator </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+S+R">Shang Ru Li</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Q+K">Qi Kang Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+X+X">Xiao Xiong 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="2502.09439v1-abstract-short" style="display: inline;"> The electroneutrality assumption has long been adopted by scholars; however, this assumption may lead to an oversight of certain physical effects. Using derivations from a discontinuous medium, we have obtained an expression for the potential and energy of a many-body unipolar charge system, which corresponds well to its counterpart in a continuous medium. The compressed form of this expression su… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09439v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09439v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09439v1-abstract-full" style="display: none;"> The electroneutrality assumption has long been adopted by scholars; however, this assumption may lead to an oversight of certain physical effects. Using derivations from a discontinuous medium, we have obtained an expression for the potential and energy of a many-body unipolar charge system, which corresponds well to its counterpart in a continuous medium. The compressed form of this expression suggests that compressing a macroscale charged body to the nanoscale can yield an enormous electric potential and energy, thereby establishing a concrete research framework for third-generation nanogenerators. This effect may serve as a crucial reference for understanding anomalous spatial electromagnetic distributions and divergent energy fields. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09439v1-abstract-full').style.display = 'none'; document.getElementById('2502.09439v1-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">No additional comments</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09247">arXiv:2502.09247</a> <span> [<a href="https://arxiv.org/pdf/2502.09247">pdf</a>, <a href="https://arxiv.org/format/2502.09247">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"> The Joint Entity-Relation Extraction Model Based on Span and Interactive Fusion Representation for Chinese Medical Texts with Complex Semantics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Feng%2C+D">Danni Feng</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Runzhi Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">Jing Wang</a>, <a href="/search/?searchtype=author&query=Yan%2C+S">Siyu Yan</a>, <a href="/search/?searchtype=author&query=Ma%2C+L">Lihong Ma</a>, <a href="/search/?searchtype=author&query=Xing%2C+Y">Yunli Xing</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="2502.09247v1-abstract-short" style="display: inline;"> Joint entity-relation extraction is a critical task in transforming unstructured or semi-structured text into triplets, facilitating the construction of large-scale knowledge graphs, and supporting various downstream applications. Despite its importance, research on Chinese text, particularly with complex semantics in specialized domains like medicine, remains limited. To address this gap, we intr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09247v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09247v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09247v1-abstract-full" style="display: none;"> Joint entity-relation extraction is a critical task in transforming unstructured or semi-structured text into triplets, facilitating the construction of large-scale knowledge graphs, and supporting various downstream applications. Despite its importance, research on Chinese text, particularly with complex semantics in specialized domains like medicine, remains limited. To address this gap, we introduce the CH-DDI, a Chinese drug-drug interactions dataset designed to capture the intricacies of medical text. Leveraging the strengths of attention mechanisms in capturing long-range dependencies, we propose the SEA module, which enhances the extraction of complex contextual semantic information, thereby improving entity recognition and relation extraction. Additionally, to address the inefficiencies of existing methods in facilitating information exchange between entity recognition and relation extraction, we present an interactive fusion representation module. This module employs Cross Attention for bidirectional information exchange between the tasks and further refines feature extraction through BiLSTM. Experimental results on both our CH-DDI dataset and public CoNLL04 dataset demonstrate that our model exhibits strong generalization capabilities. On the CH-DDI dataset, our model achieves an F1-score of 96.73% for entity recognition and 78.43% for relation extraction. On the CoNLL04 dataset, it attains an entity recognition precision of 89.54% and a relation extraction accuracy of 71.64%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09247v1-abstract-full').style.display = 'none'; document.getElementById('2502.09247v1-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/2502.09073">arXiv:2502.09073</a> <span> [<a href="https://arxiv.org/pdf/2502.09073">pdf</a>, <a href="https://arxiv.org/format/2502.09073">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Geng%2C+X">Xuzhao Geng</a>, <a href="/search/?searchtype=author&query=Wang%2C+H">Haozhao Wang</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">Jun Wang</a>, <a href="/search/?searchtype=author&query=Liu%2C+W">Wei Liu</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruixuan 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="2502.09073v1-abstract-short" style="display: inline;"> Retrieval-augmented generation (RAG) is a key technique for leveraging external knowledge and reducing hallucinations in large language models (LLMs). However, RAG still struggles to fully prevent hallucinated responses. To address this, it is essential to identify samples prone to hallucination or guide LLMs toward correct responses, which experts then annotate to develop high-quality datasets fo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09073v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09073v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09073v1-abstract-full" style="display: none;"> Retrieval-augmented generation (RAG) is a key technique for leveraging external knowledge and reducing hallucinations in large language models (LLMs). However, RAG still struggles to fully prevent hallucinated responses. To address this, it is essential to identify samples prone to hallucination or guide LLMs toward correct responses, which experts then annotate to develop high-quality datasets for refining LLMs. However, the growing scarcity of such datasets makes their creation challenging. This paper proposes using the vast amount of conversations from widespread LLM usage to build these datasets, training LLMs to avoid hallucination-prone questions while accurately responding to manageable ones. Given the impracticality of expert-annotating all conversation records, the paper introduces AL4RAG, which uses active learning to select the most suitable conversation samples for annotation, optimizing performance within an annotation budget. Additionally, recognizing that traditional active learning methods are not fully compatible with RAG due to unsuitable distance metrics, we develop a novel sample distance measurement for RAG active learning. Extensive experiments show that our method consistently outperforms baselines across multiple metrics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09073v1-abstract-full').style.display = 'none'; document.getElementById('2502.09073v1-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/2502.08929">arXiv:2502.08929</a> <span> [<a href="https://arxiv.org/pdf/2502.08929">pdf</a>, <a href="https://arxiv.org/ps/2502.08929">ps</a>, <a href="https://arxiv.org/format/2502.08929">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"> Precise Measurement of the $蠂_{c0}$ Resonance Parameters and Branching Fractions of $蠂_{c0,c2}\to蟺^+蟺^-/K^+K^-$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=BESIII+Collaboration"> BESIII Collaboration</a>, <a href="/search/?searchtype=author&query=Ablikim%2C+M">M. Ablikim</a>, <a href="/search/?searchtype=author&query=Achasov%2C+M+N">M. N. Achasov</a>, <a href="/search/?searchtype=author&query=Adlarson%2C+P">P. Adlarson</a>, <a href="/search/?searchtype=author&query=Afedulidis%2C+O">O. Afedulidis</a>, <a href="/search/?searchtype=author&query=Ai%2C+X+C">X. C. Ai</a>, <a href="/search/?searchtype=author&query=Aliberti%2C+R">R. Aliberti</a>, <a href="/search/?searchtype=author&query=Amoroso%2C+A">A. Amoroso</a>, <a href="/search/?searchtype=author&query=Bai%2C+Y">Y. Bai</a>, <a href="/search/?searchtype=author&query=Bakina%2C+O">O. Bakina</a>, <a href="/search/?searchtype=author&query=Balossino%2C+I">I. Balossino</a>, <a href="/search/?searchtype=author&query=Ban%2C+Y">Y. Ban</a>, <a href="/search/?searchtype=author&query=Bao%2C+H+-">H. -R. Bao</a>, <a href="/search/?searchtype=author&query=Batozskaya%2C+V">V. Batozskaya</a>, <a href="/search/?searchtype=author&query=Begzsuren%2C+K">K. Begzsuren</a>, <a href="/search/?searchtype=author&query=Berger%2C+N">N. Berger</a>, <a href="/search/?searchtype=author&query=Berlowski%2C+M">M. Berlowski</a>, <a href="/search/?searchtype=author&query=Bertani%2C+M">M. Bertani</a>, <a href="/search/?searchtype=author&query=Bettoni%2C+D">D. Bettoni</a>, <a href="/search/?searchtype=author&query=Bianchi%2C+F">F. Bianchi</a>, <a href="/search/?searchtype=author&query=Bianco%2C+E">E. Bianco</a>, <a href="/search/?searchtype=author&query=Bortone%2C+A">A. Bortone</a>, <a href="/search/?searchtype=author&query=Boyko%2C+I">I. Boyko</a>, <a href="/search/?searchtype=author&query=Briere%2C+R+A">R. A. Briere</a>, <a href="/search/?searchtype=author&query=Brueggemann%2C+A">A. Brueggemann</a> , et al. (648 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="2502.08929v1-abstract-short" style="display: inline;"> By analyzing a $蠄(3686)$ data sample containing $(107.7\pm0.6)\times10^{6}$ events taken with the BESIII detector at the BEPCII storage ring in 2009, the $蠂_{c0}$ resonance parameters are precisely measured using $蠂_{c0,c2} \to 蟺^+蟺^-/K^+K^-$ events. The mass of $蠂_{c0}$ is determined to be $M(蠂_{c0})=(3415.67\pm0.07\pm0.06\pm0.07$)~MeV/$c^2$, and its full width is… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08929v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08929v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08929v1-abstract-full" style="display: none;"> By analyzing a $蠄(3686)$ data sample containing $(107.7\pm0.6)\times10^{6}$ events taken with the BESIII detector at the BEPCII storage ring in 2009, the $蠂_{c0}$ resonance parameters are precisely measured using $蠂_{c0,c2} \to 蟺^+蟺^-/K^+K^-$ events. The mass of $蠂_{c0}$ is determined to be $M(蠂_{c0})=(3415.67\pm0.07\pm0.06\pm0.07$)~MeV/$c^2$, and its full width is $螕(蠂_{c0})=(12.44\pm0.12\pm0.12)~{\rm MeV}$, where the first uncertainty is statistical, the second systematic, and the third for mass comes from $蠂_{c2}$ mass uncertainty. These measurements improve the precision of $蠂_{c0}$ mass by a factor of four and width by one order of magnitude over the previous individual measurements, and significantly boost our knowledge about the charmonium spectrum. Together with additional $(345.4\pm2.6)\times10^{6}$ $蠄(3686)$ data events taken in 2012, the decay branching fractions of $蠂_{c0,c2}\to蟺^+蟺^-/K^+K^-$ are measured as well, with precision improved by a factor of three compared to previous measurements. These $蠂_{c0}$ decay branching fractions provide important inputs for the study of glueballs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08929v1-abstract-full').style.display = 'none'; document.getElementById('2502.08929v1-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, 1 figure</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.08863">arXiv:2502.08863</a> <span> [<a href="https://arxiv.org/pdf/2502.08863">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> </div> <p class="title is-5 mathjax"> RF phase effect in the ion-guide laser ion source (IG-LIS) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+R">Ruohong Li</a>, <a href="/search/?searchtype=author&query=Mostamand%2C+M">Maryam Mostamand</a>, <a href="/search/?searchtype=author&query=Lassen%2C+J">Jens Lassen</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="2502.08863v1-abstract-short" style="display: inline;"> The effect of the phase between the radio frequency (RF) waveform driving the ion guide and the laser pulses generating ions on the intensity of the transmitted ion beam has been studied. Experiments were conducted at TRIUMF's offline laser ion source test stand (LIS-stand) and online at the isotope separator and accelerator (ISAC) facility for radioactive ion beam delivery. In this study, a maste… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08863v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08863v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08863v1-abstract-full" style="display: none;"> The effect of the phase between the radio frequency (RF) waveform driving the ion guide and the laser pulses generating ions on the intensity of the transmitted ion beam has been studied. Experiments were conducted at TRIUMF's offline laser ion source test stand (LIS-stand) and online at the isotope separator and accelerator (ISAC) facility for radioactive ion beam delivery. In this study, a master clock is used to synchronize the laser trigger for laser ionization and the RF waveform generator driving the ion guide, so that laser ionization within the ionization volume inside the RF ion guide will occur at a specific RF phase which affects the ions' transmission through the RFQ. At optimal phase the ion extraction from the IG-LIS can be improved by 10-50%. Simulations were run considering both fringe field and RFQ phase effects. The method also provides an additional function of IG-LIS to modulate the laser-ionized ions at hundreds of kHz, allowing phase-sensitive detection for experiments downstream. In addition, modulation of the RF envelope (on and off like a gating device) in transmission mode allows for the suppression of surface-ionized species outside the laser-ion pulse, which provides an alternative to a classic fast kicker for beam purification. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08863v1-abstract-full').style.display = 'none'; document.getElementById('2502.08863v1-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">13 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/2502.08048">arXiv:2502.08048</a> <span> [<a href="https://arxiv.org/pdf/2502.08048">pdf</a>, <a href="https://arxiv.org/format/2502.08048">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Efficiently Laser Driven Terahertz Surface Plasmon Polaritons on Long Metal Wire </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Shao%2C+S">Shuoting Shao</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xiangbing Wang</a>, <a href="/search/?searchtype=author&query=Huang%2C+R">Rong Huang</a>, <a href="/search/?searchtype=author&query=Hu%2C+G">Guangyue Hu</a>, <a href="/search/?searchtype=author&query=Chen%2C+M">Min Chen</a>, <a href="/search/?searchtype=author&query=Tang%2C+H">Huibo Tang</a>, <a href="/search/?searchtype=author&query=Kuang%2C+L">Longyu Kuang</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yuxi Liu</a>, <a href="/search/?searchtype=author&query=Gu%2C+Y">Yuqiu Gu</a>, <a href="/search/?searchtype=author&query=Ding%2C+Y">Yongkun Ding</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruxin Li</a>, <a href="/search/?searchtype=author&query=Zhuo%2C+H">Hongbin Zhuo</a>, <a href="/search/?searchtype=author&query=Yu%2C+M">Mingyang Yu</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="2502.08048v1-abstract-short" style="display: inline;"> We experimentally demonstrate a novel scheme for efficiently generating intense terahertz (THz) surface plasmon polaritons (SPPs) on a sub-wavelength-diameter meter-long metal wire. Driven by a subrelativistic femtosecond laser (a0=0.3, 3 mJ) focused at the wire's midpoint, single-cycle ten-megawatt THz SPPs are excited and propagating bidirectionally along it over 25 cm. The measured laser-to-SPP… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08048v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08048v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08048v1-abstract-full" style="display: none;"> We experimentally demonstrate a novel scheme for efficiently generating intense terahertz (THz) surface plasmon polaritons (SPPs) on a sub-wavelength-diameter meter-long metal wire. Driven by a subrelativistic femtosecond laser (a0=0.3, 3 mJ) focused at the wire's midpoint, single-cycle ten-megawatt THz SPPs are excited and propagating bidirectionally along it over 25 cm. The measured laser-to-SPPs energy conversion efficiency is reaching up to ~2.4%, which is the highest value at present. It is proved that the THz SPPs are excited by coherent transition radiation of the subrelativistic laser produced escaping electrons. Particle-in-cell together with CST simulations confirm the experimental observations. Our scheme of using readily available subrelativistic laser should thus be useful to applications requiring terawatt level single-cycle THz SPPs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08048v1-abstract-full').style.display = 'none'; document.getElementById('2502.08048v1-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">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/2502.07477">arXiv:2502.07477</a> <span> [<a href="https://arxiv.org/pdf/2502.07477">pdf</a>, <a href="https://arxiv.org/format/2502.07477">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> </div> </div> <p class="title is-5 mathjax"> Robust zero modes in PbTe-Pb hybrid nanowires </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+S">Shan Zhang</a>, <a href="/search/?searchtype=author&query=Song%2C+W">Wenyu Song</a>, <a href="/search/?searchtype=author&query=Li%2C+Z">Zonglin Li</a>, <a href="/search/?searchtype=author&query=Yu%2C+Z">Zehao Yu</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruidong Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yuhao Wang</a>, <a href="/search/?searchtype=author&query=Yan%2C+Z">Zeyu Yan</a>, <a href="/search/?searchtype=author&query=Xu%2C+J">Jiaye Xu</a>, <a href="/search/?searchtype=author&query=Wang%2C+Z">Zhaoyu Wang</a>, <a href="/search/?searchtype=author&query=Gao%2C+Y">Yichun Gao</a>, <a href="/search/?searchtype=author&query=Yang%2C+S">Shuai Yang</a>, <a href="/search/?searchtype=author&query=Yang%2C+L">Lining Yang</a>, <a href="/search/?searchtype=author&query=Feng%2C+X">Xiao Feng</a>, <a href="/search/?searchtype=author&query=Wang%2C+T">Tiantian Wang</a>, <a href="/search/?searchtype=author&query=Zang%2C+Y">Yunyi Zang</a>, <a href="/search/?searchtype=author&query=Li%2C+L">Lin Li</a>, <a href="/search/?searchtype=author&query=Shang%2C+R">Runan Shang</a>, <a href="/search/?searchtype=author&query=Xue%2C+Q">Qi-Kun Xue</a>, <a href="/search/?searchtype=author&query=He%2C+K">Ke He</a>, <a href="/search/?searchtype=author&query=Zhang%2C+H">Hao Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.07477v1-abstract-short" style="display: inline;"> Majorana zero modes in tunneling conductance are expected to manifest as robust zero bias peaks (ZBPs). While ZBPs alone are not conclusive evidence of Majorana modes due to alternative explanations, robust ZBPs remain a crucial and necessary first-step indicator in the search for topological states. Here, we report the observation of robust ZBPs in PbTe-Pb hybrid nanowires. The peak height can re… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07477v1-abstract-full').style.display = 'inline'; document.getElementById('2502.07477v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.07477v1-abstract-full" style="display: none;"> Majorana zero modes in tunneling conductance are expected to manifest as robust zero bias peaks (ZBPs). While ZBPs alone are not conclusive evidence of Majorana modes due to alternative explanations, robust ZBPs remain a crucial and necessary first-step indicator in the search for topological states. Here, we report the observation of robust ZBPs in PbTe-Pb hybrid nanowires. The peak height can reach $2e^2/h$, though it does not yet form a quantized plateau. Importantly, these ZBPs can remain non-split over sizable ranges in both magnetic field and gate voltage scans, highlighting their robustness. We discuss possible interpretations based on Majorana zero modes as well as Andreev bound states. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07477v1-abstract-full').style.display = 'none'; document.getElementById('2502.07477v1-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">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/2502.07406">arXiv:2502.07406</a> <span> [<a href="https://arxiv.org/pdf/2502.07406">pdf</a>, <a href="https://arxiv.org/format/2502.07406">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 $e^+e^-\to K_S^0 K_S^0 h_c$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=BESIII+Collaboration"> BESIII Collaboration</a>, <a href="/search/?searchtype=author&query=Ablikim%2C+M">M. Ablikim</a>, <a href="/search/?searchtype=author&query=Achasov%2C+M+N">M. N. Achasov</a>, <a href="/search/?searchtype=author&query=Adlarson%2C+P">P. Adlarson</a>, <a href="/search/?searchtype=author&query=Afedulidis%2C+O">O. Afedulidis</a>, <a href="/search/?searchtype=author&query=Ai%2C+X+C">X. C. Ai</a>, <a href="/search/?searchtype=author&query=Aliberti%2C+R">R. Aliberti</a>, <a href="/search/?searchtype=author&query=Amoroso%2C+A">A. Amoroso</a>, <a href="/search/?searchtype=author&query=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=Balossino%2C+I">I. Balossino</a>, <a href="/search/?searchtype=author&query=Ban%2C+Y">Y. Ban</a>, <a href="/search/?searchtype=author&query=Bao%2C+H+-">H. -R. Bao</a>, <a href="/search/?searchtype=author&query=Batozskaya%2C+V">V. Batozskaya</a>, <a href="/search/?searchtype=author&query=Begzsuren%2C+K">K. Begzsuren</a>, <a href="/search/?searchtype=author&query=Berger%2C+N">N. Berger</a>, <a href="/search/?searchtype=author&query=Berlowski%2C+M">M. Berlowski</a>, <a href="/search/?searchtype=author&query=Bertani%2C+M">M. Bertani</a>, <a href="/search/?searchtype=author&query=Bettoni%2C+D">D. Bettoni</a>, <a href="/search/?searchtype=author&query=Bianchi%2C+F">F. Bianchi</a>, <a href="/search/?searchtype=author&query=Bianco%2C+E">E. Bianco</a>, <a href="/search/?searchtype=author&query=Bortone%2C+A">A. Bortone</a>, <a href="/search/?searchtype=author&query=Boyko%2C+I">I. Boyko</a>, <a href="/search/?searchtype=author&query=Briere%2C+R+A">R. A. Briere</a> , et al. (642 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="2502.07406v1-abstract-short" style="display: inline;"> Using $e^+e^-$ collision data at 13 center-of-mass energies ranging from 4.600 to 4.950 GeV collected with the BESIII detector, we search for the unmeasured $e^+e^-\to K_S^0 K_S^0 h_c$ process . No significant signal is observed, and the upper limits of the Born cross sections at each center-of-mass energy are presented. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.07406v1-abstract-full" style="display: none;"> Using $e^+e^-$ collision data at 13 center-of-mass energies ranging from 4.600 to 4.950 GeV collected with the BESIII detector, we search for the unmeasured $e^+e^-\to K_S^0 K_S^0 h_c$ process . No significant signal is observed, and the upper limits of the Born cross sections at each center-of-mass energy are presented. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07406v1-abstract-full').style.display = 'none'; document.getElementById('2502.07406v1-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">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/2502.06834">arXiv:2502.06834</a> <span> [<a href="https://arxiv.org/pdf/2502.06834">pdf</a>, <a href="https://arxiv.org/format/2502.06834">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> A Unified Knowledge-Distillation and Semi-Supervised Learning Framework to Improve Industrial Ads Delivery Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Eghbalzadeh%2C+H">Hamid Eghbalzadeh</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yang Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Rui Li</a>, <a href="/search/?searchtype=author&query=Mo%2C+Y">Yuji Mo</a>, <a href="/search/?searchtype=author&query=Ding%2C+Q">Qin Ding</a>, <a href="/search/?searchtype=author&query=Fu%2C+J">Jiaxiang Fu</a>, <a href="/search/?searchtype=author&query=Dai%2C+L">Liang Dai</a>, <a href="/search/?searchtype=author&query=Gu%2C+S">Shuo Gu</a>, <a href="/search/?searchtype=author&query=Noorshams%2C+N">Nima Noorshams</a>, <a href="/search/?searchtype=author&query=Park%2C+S">Sem Park</a>, <a href="/search/?searchtype=author&query=Long%2C+B">Bo Long</a>, <a href="/search/?searchtype=author&query=Feng%2C+X">Xue Feng</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="2502.06834v1-abstract-short" style="display: inline;"> Industrial ads ranking systems conventionally rely on labeled impression data, which leads to challenges such as overfitting, slower incremental gain from model scaling, and biases due to discrepancies between training and serving data. To overcome these issues, we propose a Unified framework for Knowledge-Distillation and Semi-supervised Learning (UKDSL) for ads ranking, empowering the training o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06834v1-abstract-full').style.display = 'inline'; document.getElementById('2502.06834v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.06834v1-abstract-full" style="display: none;"> Industrial ads ranking systems conventionally rely on labeled impression data, which leads to challenges such as overfitting, slower incremental gain from model scaling, and biases due to discrepancies between training and serving data. To overcome these issues, we propose a Unified framework for Knowledge-Distillation and Semi-supervised Learning (UKDSL) for ads ranking, empowering the training of models on a significantly larger and more diverse datasets, thereby reducing overfitting and mitigating training-serving data discrepancies. We provide detailed formal analysis and numerical simulations on the inherent miscalibration and prediction bias of multi-stage ranking systems, and show empirical evidence of the proposed framework's capability to mitigate those. Compared to prior work, UKDSL can enable models to learn from a much larger set of unlabeled data, hence, improving the performance while being computationally efficient. Finally, we report the successful deployment of UKDSL in an industrial setting across various ranking models, serving users at multi-billion scale, across various surfaces, geological locations, clients, and optimize for various events, which to the best of our knowledge is the first of its kind in terms of the scale and efficiency at which it operates. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06834v1-abstract-full').style.display = 'none'; document.getElementById('2502.06834v1-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/2502.06269">arXiv:2502.06269</a> <span> [<a href="https://arxiv.org/pdf/2502.06269">pdf</a>, <a href="https://arxiv.org/format/2502.06269">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"> Progressive Collaborative and Semantic Knowledge Fusion for Generative Recommendation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xiao%2C+L">Longtao Xiao</a>, <a href="/search/?searchtype=author&query=Wang%2C+H">Haozhao Wang</a>, <a href="/search/?searchtype=author&query=Wang%2C+C">Cheng Wang</a>, <a href="/search/?searchtype=author&query=Ji%2C+L">Linfei Ji</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yifan Wang</a>, <a href="/search/?searchtype=author&query=Zhu%2C+J">Jieming Zhu</a>, <a href="/search/?searchtype=author&query=Dong%2C+Z">Zhenhua Dong</a>, <a href="/search/?searchtype=author&query=Zhang%2C+R">Rui Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruixuan 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="2502.06269v1-abstract-short" style="display: inline;"> With the recent surge in interest surrounding generative paradigms, generative recommendation has increasingly attracted the attention of researchers in the recommendation community. This paradigm generally consists of two stages. In the first stage, pretrained semantic embeddings or collaborative ID embeddings are quantized to create item codes, aiming to capture and preserve rich semantic or col… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06269v1-abstract-full').style.display = 'inline'; document.getElementById('2502.06269v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.06269v1-abstract-full" style="display: none;"> With the recent surge in interest surrounding generative paradigms, generative recommendation has increasingly attracted the attention of researchers in the recommendation community. This paradigm generally consists of two stages. In the first stage, pretrained semantic embeddings or collaborative ID embeddings are quantized to create item codes, aiming to capture and preserve rich semantic or collaborative knowledge within these codes. The second stage involves utilizing these discrete codes to perform an autoregressive sequence generation task. Existing methods often either overlook collaborative or semantic knowledge, or combine the two roughly. In this paper, we observe that naively concatenating representations from semantic and collaborative modality leads to a semantic domination issue, where the resulting representation is overly influenced by semantic information, effectively overshadowing the collaborative representation. Consequently, downstream recommendation tasks fail to fully exploit the knowledge from both modalities, resulting in suboptimal performance. To address this, we propose a progressive collaborative and semantic knowledge fusion model for generative recommendation, named PRORec, which integrates semantic and collaborative knowledge with a unified code through a two-stage framework. Specifically, in the first stage, we propose a cross-modality knowledge alignment task, which integrates semantic knowledge into collaborative embeddings, enhancing their representational capability. In the second stage, we propose an in-modality knowledge distillation task, designed to effectively capture and integrate knowledge from both semantic and collaborative modalities. Extensive experiments on three widely used benchmarks validate the effectiveness of our approach, demonstrating its superiority compared to existing methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06269v1-abstract-full').style.display = 'none'; document.getElementById('2502.06269v1-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">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/2502.05845">arXiv:2502.05845</a> <span> [<a href="https://arxiv.org/pdf/2502.05845">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Exploiting the Hidden Capacity of MMC Through Accurate Quantification of Modulation Indices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Sun%2C+Q">Qianhao Sun</a>, <a href="/search/?searchtype=author&query=Meng%2C+J">Jingwei Meng</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruofan Li</a>, <a href="/search/?searchtype=author&query=Xia%2C+M">Mingchao Xia</a>, <a href="/search/?searchtype=author&query=Chen%2C+Q">Qifang Chen</a>, <a href="/search/?searchtype=author&query=Zhou%2C+J">Jiejie Zhou</a>, <a href="/search/?searchtype=author&query=Fan%2C+M">Meiqi Fan</a>, <a href="/search/?searchtype=author&query=Guo%2C+P">Peiqian Guo</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="2502.05845v1-abstract-short" style="display: inline;"> The modular multilevel converter (MMC) has become increasingly important in voltage-source converter-based high-voltage direct current (VSC-HVDC) systems. Direct and indirect modulation are widely used as mainstream modulation techniques in MMCs. However, due to the challenge of quantitatively evaluating the operation of different modulation schemes, the academic and industrial communities still h… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05845v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05845v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05845v1-abstract-full" style="display: none;"> The modular multilevel converter (MMC) has become increasingly important in voltage-source converter-based high-voltage direct current (VSC-HVDC) systems. Direct and indirect modulation are widely used as mainstream modulation techniques in MMCs. However, due to the challenge of quantitatively evaluating the operation of different modulation schemes, the academic and industrial communities still hold differing opinions on their performance. To address this controversy, this paper employs the state-of-the-art computational methods and quantitative metrics to compare the performance among different modulation schemes. The findings indicate that direct modulation offers superior modulation potential for MMCs, highlighting its higher ac voltage output capability and broader linear PQ operation region. Conversely, indirect modulation is disadvantaged in linear modulation, which indicates inferior output voltage capability. Furthermore, this paper delves into the conditions whereby direct and indirect modulation techniques become equivalent in steady-state. The study findings suggest that the modulation capability of direct modulation is the same as that of indirect modulation in steady-state when additional controls, including closed-loop capacitor voltage control and circulating current suppression control (CCSC), are simultaneously active. Simulation and experiments verify the correctness and validity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05845v1-abstract-full').style.display = 'none'; document.getElementById('2502.05845v1-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> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.05842">arXiv:2502.05842</a> <span> [<a href="https://arxiv.org/pdf/2502.05842">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> A Grid-Forming HVDC Series Tapping Converter Using Extended Techniques of Flex-LCC </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Sun%2C+Q">Qianhao Sun</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruofan Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">Jichen Wang</a>, <a href="/search/?searchtype=author&query=Xia%2C+M">Mingchao Xia</a>, <a href="/search/?searchtype=author&query=Chen%2C+Q">Qifang Chen</a>, <a href="/search/?searchtype=author&query=Fan%2C+M">Meiqi Fan</a>, <a href="/search/?searchtype=author&query=Li%2C+G">Gen Li</a>, <a href="/search/?searchtype=author&query=Qiao%2C+X">Xuebo Qiao</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="2502.05842v1-abstract-short" style="display: inline;"> This paper discusses an extension technology for the previously proposed Flexible Line-Commutated Converter (Flex LCC) [1]. The proposed extension involves modifying the arm internal-electromotive-force control, redesigning the main-circuit parameters, and integrating a low-power coordination strategy. As a result, the Flex-LCC transforms from a grid-forming (GFM) voltage source converter (VSC) ba… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05842v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05842v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05842v1-abstract-full" style="display: none;"> This paper discusses an extension technology for the previously proposed Flexible Line-Commutated Converter (Flex LCC) [1]. The proposed extension involves modifying the arm internal-electromotive-force control, redesigning the main-circuit parameters, and integrating a low-power coordination strategy. As a result, the Flex-LCC transforms from a grid-forming (GFM) voltage source converter (VSC) based on series-connected LCC and FBMMC into a novel GFM HVDC series tapping converter, referred to as the Extended Flex-LCC (EFLCC). The EFLCC provides dc characteristics resembling those of current source converters (CSCs) and ac characteristics resembling those of GFM VSCs. This makes it easier to integrate relatively small renewable energy sources (RESs) that operate in islanded or weak-grid supported conditions with an existing LCC-HVDC. Meanwhile, the EFLCC distinguishes itself by requiring fewer full-controlled switches and less energy storage, resulting in lower losses and costs compared to the FBMMC HVDC series tap solution. In particular, the reduced capacity requirement and the wide allowable range of valve-side ac voltages in the FBMMC part facilitate the matching of current-carrying capacities between full-controlled switches and thyristors. The application scenario, system-level analysis, implementation, converter-level operation, and comparison of the EFLCC are presented in detail in this paper. The theoretical analysis is confirmed by experimental and simulation results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05842v1-abstract-full').style.display = 'none'; document.getElementById('2502.05842v1-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> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.05562">arXiv:2502.05562</a> <span> [<a href="https://arxiv.org/pdf/2502.05562">pdf</a>, <a href="https://arxiv.org/format/2502.05562">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> </div> </div> <p class="title is-5 mathjax"> Can Large Language Models Be Query Optimizer for Relational Databases? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Tan%2C+J">Jie Tan</a>, <a href="/search/?searchtype=author&query=Zhao%2C+K">Kangfei Zhao</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Rui Li</a>, <a href="/search/?searchtype=author&query=Yu%2C+J+X">Jeffrey Xu Yu</a>, <a href="/search/?searchtype=author&query=Piao%2C+C">Chengzhi Piao</a>, <a href="/search/?searchtype=author&query=Cheng%2C+H">Hong Cheng</a>, <a href="/search/?searchtype=author&query=Meng%2C+H">Helen Meng</a>, <a href="/search/?searchtype=author&query=Zhao%2C+D">Deli Zhao</a>, <a href="/search/?searchtype=author&query=Rong%2C+Y">Yu Rong</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="2502.05562v1-abstract-short" style="display: inline;"> Query optimization, which finds the optimized execution plan for a given query, is a complex planning and decision-making problem within the exponentially growing plan space in database management systems (DBMS). Traditional optimizers heavily rely on a certain cost model constructed by various heuristics and empirical tuning, probably leading to generating suboptimal plans. Recent developments of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05562v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05562v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05562v1-abstract-full" style="display: none;"> Query optimization, which finds the optimized execution plan for a given query, is a complex planning and decision-making problem within the exponentially growing plan space in database management systems (DBMS). Traditional optimizers heavily rely on a certain cost model constructed by various heuristics and empirical tuning, probably leading to generating suboptimal plans. Recent developments of Large Language Models (LLMs) have demonstrated their potential in solving complex planning and decision-making problems, such as arithmetic and programmatic tasks. In this paper, we try to explore the potential of LLMs in handling query optimization and propose a tentative LLM-based query optimizer dubbed LLM-QO, established on PostgreSQL's execution engine. In LLM-QO, we formulate query optimization in an autoregressive fashion which directly generates the execution plan without explicit plan enumeration. To investigate the essential input of LLM-QO, we design a customized data recipe named QInstruct to collect the training data from various optimizers and serialize the database's meta data, queries and corresponding plans into a textual format. Based on QInstruct, we implement a two-stage fine-tuning pipeline, Query Instruction Tuning (QIT) and Query Direct Preference Optimization (QDPO), to empower the capability of general-purpose LLMs in handling query optimization. In our experiments, LLM-QO can generate valid and high-quality plans and consistently outperforms both traditional and learned optimizers on three query workloads. Our findings verify that LLMs can be derived as query optimizers where generalization, efficiency and adaptivity deserve further research efforts. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05562v1-abstract-full').style.display = 'none'; document.getElementById('2502.05562v1-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">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/2502.05416">arXiv:2502.05416</a> <span> [<a href="https://arxiv.org/pdf/2502.05416">pdf</a>, <a href="https://arxiv.org/format/2502.05416">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Deep Generative Models with Hard Linear Equality Constraints </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+R">Ruoyan Li</a>, <a href="/search/?searchtype=author&query=Sahu%2C+D+R">Dipti Ranjan Sahu</a>, <a href="/search/?searchtype=author&query=Broeck%2C+G+V+d">Guy Van den Broeck</a>, <a href="/search/?searchtype=author&query=Zeng%2C+Z">Zhe Zeng</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="2502.05416v2-abstract-short" style="display: inline;"> While deep generative models~(DGMs) have demonstrated remarkable success in capturing complex data distributions, they consistently fail to learn constraints that encode domain knowledge and thus require constraint integration. Existing solutions to this challenge have primarily relied on heuristic methods and often ignore the underlying data distribution, harming the generative performance. In th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05416v2-abstract-full').style.display = 'inline'; document.getElementById('2502.05416v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05416v2-abstract-full" style="display: none;"> While deep generative models~(DGMs) have demonstrated remarkable success in capturing complex data distributions, they consistently fail to learn constraints that encode domain knowledge and thus require constraint integration. Existing solutions to this challenge have primarily relied on heuristic methods and often ignore the underlying data distribution, harming the generative performance. In this work, we propose a probabilistically sound approach for enforcing the hard constraints into DGMs to generate constraint-compliant and realistic data. This is achieved by our proposed gradient estimators that allow the constrained distribution, the data distribution conditioned on constraints, to be differentiably learned. We carry out extensive experiments with various DGM model architectures over five image datasets and three scientific applications in which domain knowledge is governed by linear equality constraints. We validate that the standard DGMs almost surely generate data violating the constraints. Among all the constraint integration strategies, ours not only guarantees the satisfaction of constraints in generation but also archives superior generative performance than the other methods across every benchmark. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05416v2-abstract-full').style.display = 'none'; document.getElementById('2502.05416v2-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">v1</span> submitted 7 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.05073">arXiv:2502.05073</a> <span> [<a href="https://arxiv.org/pdf/2502.05073">pdf</a>, <a href="https://arxiv.org/ps/2502.05073">ps</a>, <a href="https://arxiv.org/format/2502.05073">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Complexity">cs.CC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Combinatorics">math.CO</span> </div> </div> <p class="title is-5 mathjax"> Noise Sensitivity of Hierarchical Functions and Deep Learning Lower Bounds in General Product Measures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+R">Rupert Li</a>, <a href="/search/?searchtype=author&query=Mossel%2C+E">Elchanan Mossel</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="2502.05073v1-abstract-short" style="display: inline;"> Recent works explore deep learning's success by examining functions or data with hierarchical structure. Complementarily, research on gradient descent performance for deep nets has shown that noise sensitivity of functions under independent and identically distributed (i.i.d.) Bernoulli inputs establishes learning complexity bounds. This paper aims to bridge these research streams by demonstrating… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05073v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05073v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05073v1-abstract-full" style="display: none;"> Recent works explore deep learning's success by examining functions or data with hierarchical structure. Complementarily, research on gradient descent performance for deep nets has shown that noise sensitivity of functions under independent and identically distributed (i.i.d.) Bernoulli inputs establishes learning complexity bounds. This paper aims to bridge these research streams by demonstrating that functions constructed through repeated composition of non-linear functions are noise sensitive under general product measures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05073v1-abstract-full').style.display = 'none'; document.getElementById('2502.05073v1-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">17 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/2502.04922">arXiv:2502.04922</a> <span> [<a href="https://arxiv.org/pdf/2502.04922">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> </div> </div> <p class="title is-5 mathjax"> Observation of non-Hermitian topological disclination states and charge fractionalization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+R">Ruifeng Li</a>, <a href="/search/?searchtype=author&query=Banerjee%2C+R">Rimi Banerjee</a>, <a href="/search/?searchtype=author&query=Mandal%2C+S">Subhaskar Mandal</a>, <a href="/search/?searchtype=author&query=Li%2C+D">Da Li</a>, <a href="/search/?searchtype=author&query=Long%2C+Y">Yang Long</a>, <a href="/search/?searchtype=author&query=Ma%2C+T">Tianchi Ma</a>, <a href="/search/?searchtype=author&query=Liu%2C+J">Jianwei Liu</a>, <a href="/search/?searchtype=author&query=Liu%2C+G">Gui-Geng Liu</a>, <a href="/search/?searchtype=author&query=Chong%2C+Y">Yidong Chong</a>, <a href="/search/?searchtype=author&query=Zhang%2C+B">Baile Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+E">Er-Ping 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="2502.04922v1-abstract-short" style="display: inline;"> There has been significant interest in exploring topological disclination states, which effectively probe the band topology of the host material beyond the conventional bulk-edge correspondence. While most studies in this area have primarily focused on Hermitian systems, recent theoretical work predicts that non-Hermiticity can drive topological phase transitions and host topological disclination… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04922v1-abstract-full').style.display = 'inline'; document.getElementById('2502.04922v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.04922v1-abstract-full" style="display: none;"> There has been significant interest in exploring topological disclination states, which effectively probe the band topology of the host material beyond the conventional bulk-edge correspondence. While most studies in this area have primarily focused on Hermitian systems, recent theoretical work predicts that non-Hermiticity can drive topological phase transitions and host topological disclination states associated with fractional charge. However, no experimental observations have been reported to date. Here, we report the first experimental observation of topological disclination states in electric circuits, induced solely by gain and loss. Through admittance matrix measurements and eigenstate analysis, we confirm their emergence and compute the corresponding fractional charge. Moreover, the disclination mode profile and localization effect can be directly visualized via monochromatic field excitation. Additionally, we demonstrate the emergence of degenerate zero-energy topological disclination states, devoid of fractional charge, in distinct non-Hermitian geometries. Our findings open the possibility of non-Hermiticity-induced fractional charges in two-dimensional non-Hermitian lattices, which may pave the way for advancements in active topological photonic devices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04922v1-abstract-full').style.display = 'none'; document.getElementById('2502.04922v1-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">16 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/2502.04848">arXiv:2502.04848</a> <span> [<a href="https://arxiv.org/pdf/2502.04848">pdf</a>, <a href="https://arxiv.org/format/2502.04848">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> Broadband $纬$-ray spectrum of supernova remnant Cassiopeia A </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Cao%2C+Z">Zhen Cao</a>, <a href="/search/?searchtype=author&query=Aharonian%2C+F">F. Aharonian</a>, <a href="/search/?searchtype=author&query=Bai%2C+Y+X">Y. X. Bai</a>, <a href="/search/?searchtype=author&query=Bao%2C+Y+W">Y. W. Bao</a>, <a href="/search/?searchtype=author&query=Bastieri%2C+D">D. Bastieri</a>, <a href="/search/?searchtype=author&query=Bi%2C+X+J">X. J. Bi</a>, <a href="/search/?searchtype=author&query=Bi%2C+Y+J">Y. J. Bi</a>, <a href="/search/?searchtype=author&query=Bian%2C+W">W. Bian</a>, <a href="/search/?searchtype=author&query=Bukevich%2C+A+V">A. V. Bukevich</a>, <a href="/search/?searchtype=author&query=Cai%2C+C+M">C. M. Cai</a>, <a href="/search/?searchtype=author&query=Cao%2C+W+Y">W. Y. Cao</a>, <a href="/search/?searchtype=author&query=Cao%2C+Z">Zhe Cao</a>, <a href="/search/?searchtype=author&query=Chang%2C+J">J. Chang</a>, <a href="/search/?searchtype=author&query=Chang%2C+J+F">J. F. Chang</a>, <a href="/search/?searchtype=author&query=Chen%2C+A+M">A. M. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+E+S">E. S. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+H+X">H. X. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Liang Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Long Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+M+J">M. J. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+M+L">M. L. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+Q+H">Q. H. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S">S. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S+H">S. H. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S+Z">S. Z. Chen</a> , et al. (293 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.04848v1-abstract-short" style="display: inline;"> The core-collapse supernova remnant (SNR) Cassiopeia A (Cas A) is one of the brightest galactic radio sources with an angular radius of $\sim$ 2.5 $\arcmin$. Although no extension of this source has been detected in the $纬$-ray band, using more than 1000 days of LHAASO data above $\sim 0.8$ TeV, we find that its spectrum is significantly softer than those obtained with Imaging Air Cherenkov Telesc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04848v1-abstract-full').style.display = 'inline'; document.getElementById('2502.04848v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.04848v1-abstract-full" style="display: none;"> The core-collapse supernova remnant (SNR) Cassiopeia A (Cas A) is one of the brightest galactic radio sources with an angular radius of $\sim$ 2.5 $\arcmin$. Although no extension of this source has been detected in the $纬$-ray band, using more than 1000 days of LHAASO data above $\sim 0.8$ TeV, we find that its spectrum is significantly softer than those obtained with Imaging Air Cherenkov Telescopes (IACTs) and its flux near $\sim 1$ TeV is about two times higher. In combination with analyses of more than 16 years of \textit{Fermi}-LAT data covering $0.1 \, \mathrm{GeV} - 1 \, \mathrm{TeV}$, we find that the spectrum above 30 GeV deviates significantly from a single power-law, and is best described by a smoothly broken power-law with a spectral index of $1.90 \pm 0.15_\mathrm{stat}$ ($3.41 \pm 0.19_\mathrm{stat}$) below (above) a break energy of $0.63 \pm 0.21_\mathrm{stat} \, \mathrm{TeV}$. Given differences in the angular resolution of LHAASO-WCDA and IACTs, TeV $纬$-ray emission detected with LHAASO may have a significant contribution from regions surrounding the SNR illuminated by particles accelerated earlier, which, however, are treated as background by IACTs. Detailed modelling can be used to constrain acceleration processes of TeV particles in the early stage of SNR evolution. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04848v1-abstract-full').style.display = 'none'; document.getElementById('2502.04848v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.04672">arXiv:2502.04672</a> <span> [<a href="https://arxiv.org/pdf/2502.04672">pdf</a>, <a href="https://arxiv.org/ps/2502.04672">ps</a>, <a href="https://arxiv.org/format/2502.04672">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Algebraic Geometry">math.AG</span> </div> </div> <p class="title is-5 mathjax"> The Nakai Conjecture for isolated hypersurface singularities of modality $\le 2$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+R">Rui Li</a>, <a href="/search/?searchtype=author&query=Xiao%2C+Z">Zida Xiao</a>, <a href="/search/?searchtype=author&query=Zuo%2C+H">Huaiqing Zuo</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="2502.04672v1-abstract-short" style="display: inline;"> The well-known Nakai Conjecture concerns a very natural question: For an algebra of finite type over a characteristic zero field, if the ring of its differential operators is generated by the first order derivations, is the algebra regular? And it is natural to extend the Nakai Conjecture to local domains, in this paper, we verify it for isolated hypersurface singularities of modality $\le 2$, thi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04672v1-abstract-full').style.display = 'inline'; document.getElementById('2502.04672v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.04672v1-abstract-full" style="display: none;"> The well-known Nakai Conjecture concerns a very natural question: For an algebra of finite type over a characteristic zero field, if the ring of its differential operators is generated by the first order derivations, is the algebra regular? And it is natural to extend the Nakai Conjecture to local domains, in this paper, we verify it for isolated hypersurface singularities of modality $\le 2$, this extends the existing works. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04672v1-abstract-full').style.display = 'none'; document.getElementById('2502.04672v1-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">MSC Class:</span> 14B05; 32S05 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.04387">arXiv:2502.04387</a> <span> [<a href="https://arxiv.org/pdf/2502.04387">pdf</a>, <a href="https://arxiv.org/format/2502.04387">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"> FedP$^2$EFT: Federated Learning to Personalize Parameter Efficient Fine-Tuning for Multilingual LLMs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lee%2C+R">Royson Lee</a>, <a href="/search/?searchtype=author&query=Kim%2C+M">Minyoung Kim</a>, <a href="/search/?searchtype=author&query=Rezk%2C+F">Fady Rezk</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Rui Li</a>, <a href="/search/?searchtype=author&query=Venieris%2C+S+I">Stylianos I. Venieris</a>, <a href="/search/?searchtype=author&query=Hospedales%2C+T">Timothy Hospedales</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="2502.04387v1-abstract-short" style="display: inline;"> Federated learning (FL) has enabled the training of multilingual large language models (LLMs) on diverse and decentralized multilingual data, especially on low-resource languages. To improve client-specific performance, personalization via the use of parameter-efficient fine-tuning (PEFT) modules such as LoRA is common. This involves a personalization strategy (PS), such as the design of the PEFT… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04387v1-abstract-full').style.display = 'inline'; document.getElementById('2502.04387v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.04387v1-abstract-full" style="display: none;"> Federated learning (FL) has enabled the training of multilingual large language models (LLMs) on diverse and decentralized multilingual data, especially on low-resource languages. To improve client-specific performance, personalization via the use of parameter-efficient fine-tuning (PEFT) modules such as LoRA is common. This involves a personalization strategy (PS), such as the design of the PEFT adapter structures (e.g., in which layers to add LoRAs and what ranks) and choice of hyperparameters (e.g., learning rates) for fine-tuning. Instead of manual PS configuration, we propose FedP$^2$EFT, a federated learning-to-personalize method for multilingual LLMs in cross-device FL settings. Unlike most existing PEFT structure selection methods, which are prone to overfitting low-data regimes, FedP$^2$EFT collaboratively learns the optimal personalized PEFT structure for each client via Bayesian sparse rank selection. Evaluations on both simulated and real-world multilingual FL benchmarks demonstrate that FedP$^2$EFT largely outperforms existing personalized fine-tuning methods, while complementing a range of existing FL methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04387v1-abstract-full').style.display = 'none'; document.getElementById('2502.04387v1-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> <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">Preprint</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/2502.04093">arXiv:2502.04093</a> <span> [<a href="https://arxiv.org/pdf/2502.04093">pdf</a>, <a href="https://arxiv.org/format/2502.04093">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> PSZ: Enhancing the SZ Scientific Lossy Compressor With Progressive Data Retrieval </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yang%2C+Z">Zhuoxun Yang</a>, <a href="/search/?searchtype=author&query=Di%2C+S">Sheng Di</a>, <a href="/search/?searchtype=author&query=Zhang%2C+L">Longtao Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruoyu Li</a>, <a href="/search/?searchtype=author&query=Li%2C+X">Ximiao Li</a>, <a href="/search/?searchtype=author&query=Huang%2C+J">Jiajun Huang</a>, <a href="/search/?searchtype=author&query=Liu%2C+J">Jinyang Liu</a>, <a href="/search/?searchtype=author&query=Cappello%2C+F">Franck Cappello</a>, <a href="/search/?searchtype=author&query=Zhao%2C+K">Kai 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="2502.04093v2-abstract-short" style="display: inline;"> Compression is a crucial solution for data reduction in modern scientific applications due to the exponential growth of data from simulations, experiments, and observations. Compression with progressive retrieval capability allows users to access coarse approximations of data quickly and then incrementally refine these approximations to higher fidelity. Existing progressive compression solutions s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04093v2-abstract-full').style.display = 'inline'; document.getElementById('2502.04093v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.04093v2-abstract-full" style="display: none;"> Compression is a crucial solution for data reduction in modern scientific applications due to the exponential growth of data from simulations, experiments, and observations. Compression with progressive retrieval capability allows users to access coarse approximations of data quickly and then incrementally refine these approximations to higher fidelity. Existing progressive compression solutions suffer from low reduction ratios or high operation costs, effectively undermining the approach's benefits. In this paper, we propose the first-ever interpolation-based progressive lossy compression solution that has both high reduction ratios and low operation costs. The interpolation-based algorithm has been verified as one of the best for scientific data reduction, but previously no effort exists to make it support progressive retrieval. Our contributions are three-fold: (1) We thoroughly analyze the error characteristics of the interpolation algorithm and propose our solution IPComp with multi-level bitplane and predictive coding. (2) We derive optimized strategies toward minimum data retrieval under different fidelity levels indicated by users through error bounds and bitrates. (3) We evaluate the proposed solution using six real-world datasets from four diverse domains. Experimental results demonstrate our solution archives up to $487\%$ higher compression ratios and $698\%$ faster speed than other state-of-the-art progressive compressors, and reduces the data volume for retrieval by up to $83\%$ compared to baselines under the same error bound, and reduces the error by up to $99\%$ under the same bitrate. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04093v2-abstract-full').style.display = 'none'; document.getElementById('2502.04093v2-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">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/2502.03828">arXiv:2502.03828</a> <span> [<a href="https://arxiv.org/pdf/2502.03828">pdf</a>, <a href="https://arxiv.org/ps/2502.03828">ps</a>, <a href="https://arxiv.org/format/2502.03828">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 $D^+\to \bar K_1(1270)^0渭^+谓_渭$ and $D^0\to K_1(1270)^-渭^+谓_渭$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=BESIII+Collaboration"> BESIII Collaboration</a>, <a href="/search/?searchtype=author&query=Ablikim%2C+M">M. Ablikim</a>, <a href="/search/?searchtype=author&query=Achasov%2C+M+N">M. N. Achasov</a>, <a href="/search/?searchtype=author&query=Adlarson%2C+P">P. Adlarson</a>, <a href="/search/?searchtype=author&query=Afedulidis%2C+O">O. Afedulidis</a>, <a href="/search/?searchtype=author&query=Ai%2C+X+C">X. C. Ai</a>, <a href="/search/?searchtype=author&query=Aliberti%2C+R">R. Aliberti</a>, <a href="/search/?searchtype=author&query=Amoroso%2C+A">A. Amoroso</a>, <a href="/search/?searchtype=author&query=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=Balossino%2C+I">I. Balossino</a>, <a href="/search/?searchtype=author&query=Ban%2C+Y">Y. Ban</a>, <a href="/search/?searchtype=author&query=Bao%2C+H+-">H. -R. Bao</a>, <a href="/search/?searchtype=author&query=Batozskaya%2C+V">V. Batozskaya</a>, <a href="/search/?searchtype=author&query=Begzsuren%2C+K">K. Begzsuren</a>, <a href="/search/?searchtype=author&query=Berger%2C+N">N. Berger</a>, <a href="/search/?searchtype=author&query=Berlowski%2C+M">M. Berlowski</a>, <a href="/search/?searchtype=author&query=Bertani%2C+M">M. Bertani</a>, <a href="/search/?searchtype=author&query=Bettoni%2C+D">D. Bettoni</a>, <a href="/search/?searchtype=author&query=Bianchi%2C+F">F. Bianchi</a>, <a href="/search/?searchtype=author&query=Bianco%2C+E">E. Bianco</a>, <a href="/search/?searchtype=author&query=Bortone%2C+A">A. Bortone</a>, <a href="/search/?searchtype=author&query=Boyko%2C+I">I. Boyko</a>, <a href="/search/?searchtype=author&query=Briere%2C+R+A">R. A. Briere</a> , et al. (646 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="2502.03828v1-abstract-short" style="display: inline;"> By analyzing 7.93 $\rm fb^{-1}$ of $e^+e^-$ collision data collected at the center-of-mass energy of 3.773 GeV with the BESIII detector operated at the BEPCII collider, we report the observation of the semimuonic decays of $D^+\to \bar K_1(1270)^0渭^+谓_渭$ and $D^0\to K_1(1270)^-渭^+谓_渭$ with statistical significances of $12.5蟽$ and $6.0蟽$, respectively. Their decay branching fractions are determined… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03828v1-abstract-full').style.display = 'inline'; document.getElementById('2502.03828v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.03828v1-abstract-full" style="display: none;"> By analyzing 7.93 $\rm fb^{-1}$ of $e^+e^-$ collision data collected at the center-of-mass energy of 3.773 GeV with the BESIII detector operated at the BEPCII collider, we report the observation of the semimuonic decays of $D^+\to \bar K_1(1270)^0渭^+谓_渭$ and $D^0\to K_1(1270)^-渭^+谓_渭$ with statistical significances of $12.5蟽$ and $6.0蟽$, respectively. Their decay branching fractions are determined to be ${\mathcal B}[D^{+}\to \bar{K}_1(1270)^0 渭^{+}谓_渭]=(2.36\pm0.20^{+0.18}_{-0.27}\pm 0.48)\times10^{-3}$ and ${\mathcal B}[D^{0}\to K_1(1270)^{-} 渭^{+}谓_渭]=(0.78\pm0.11^{+0.05}_{-0.09}\pm 0.15)\times10^{-3}$, where the first and second uncertainties are statistical and systematic, respectively, and the third originates from the input branching fraction of $\bar K_{1}(1270)^0\to K^- 蟺^+蟺^0$ or $K_1(1270)^-\to K^-蟺^+蟺^-$. Combining our branching fractions with the previous measurements of ${\mathcal B}[D^+\to \bar K_1(1270)^0e^+谓_{e}]$ and ${\mathcal B}[D^0\to K_1(1270)^-e^+谓_{e}]$, we determine the branching fraction ratios to be ${\mathcal B}[D^+\to \bar K_1(1270)^0渭^+谓_渭]/{\mathcal B}[D^+\to \bar K_1(1270)^0e^+谓_{e}]=1.03 \pm 0.14 \substack{+0.11\\-0.15}$ and ${\mathcal B}[D^0\to K_1(1270)^-渭^+谓_渭]/{\mathcal B}[D^0\to K_1(1270)^-e^+谓_{e}]=0.74\pm 0.13 \substack{+0.08\\-0.13}$. Using the branching fractions measured in this work and the world-average lifetimes of the $D^+$ and $D^0$ mesons, we determine the semimuonic partial decay width ratio to be $螕[D^+\to \bar K_1(1270)^0 渭^+谓_渭]/螕[D^0\to K_1(1270)^- 渭^+谓_渭]=1.22\pm 0.10\substack{+0.06\\-0.09}$, which is consistent with unity as predicted by isospin conservation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03828v1-abstract-full').style.display = 'none'; document.getElementById('2502.03828v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 2 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.03781">arXiv:2502.03781</a> <span> [<a href="https://arxiv.org/pdf/2502.03781">pdf</a>, <a href="https://arxiv.org/ps/2502.03781">ps</a>, <a href="https://arxiv.org/format/2502.03781">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="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound Image Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+R">Ruiyi Li</a>, <a href="/search/?searchtype=author&query=He%2C+Y">Yuting He</a>, <a href="/search/?searchtype=author&query=Ge%2C+R">Rongjun Ge</a>, <a href="/search/?searchtype=author&query=Wang%2C+C">Chong Wang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+D">Daoqiang Zhang</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yang Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shuo 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="2502.03781v1-abstract-short" style="display: inline;"> Domain adaptation (DA) for cardiac ultrasound image segmentation is clinically significant and valuable. However, previous domain adaptation methods are prone to be affected by the incomplete pseudo-label and low-quality target to source images. Human-centric domain adaptation has great advantages of human cognitive guidance to help model adapt to target domain and reduce reliance on labels. Docto… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03781v1-abstract-full').style.display = 'inline'; document.getElementById('2502.03781v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.03781v1-abstract-full" style="display: none;"> Domain adaptation (DA) for cardiac ultrasound image segmentation is clinically significant and valuable. However, previous domain adaptation methods are prone to be affected by the incomplete pseudo-label and low-quality target to source images. Human-centric domain adaptation has great advantages of human cognitive guidance to help model adapt to target domain and reduce reliance on labels. Doctor gaze trajectories contains a large amount of cross-domain human guidance. To leverage gaze information and human cognition for guiding domain adaptation, we propose gaze-assisted human-centric domain adaptation (GAHCDA), which reliably guides the domain adaptation of cardiac ultrasound images. GAHCDA includes following modules: (1) Gaze Augment Alignment (GAA): GAA enables the model to obtain human cognition general features to recognize segmentation target in different domain of cardiac ultrasound images like humans. (2) Gaze Balance Loss (GBL): GBL fused gaze heatmap with outputs which makes the segmentation result structurally closer to the target domain. The experimental results illustrate that our proposed framework is able to segment cardiac ultrasound images more effectively in the target domain than GAN-based methods and other self-train based methods, showing great potential in clinical application. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03781v1-abstract-full').style.display = 'none'; document.getElementById('2502.03781v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.03229">arXiv:2502.03229</a> <span> [<a href="https://arxiv.org/pdf/2502.03229">pdf</a>, <a href="https://arxiv.org/format/2502.03229">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"> A Unified Framework for Semi-Supervised Image Segmentation and Registration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+R">Ruizhe Li</a>, <a href="/search/?searchtype=author&query=Figueredo%2C+G">Grazziela Figueredo</a>, <a href="/search/?searchtype=author&query=Auer%2C+D">Dorothee Auer</a>, <a href="/search/?searchtype=author&query=Dineen%2C+R">Rob Dineen</a>, <a href="/search/?searchtype=author&query=Morgan%2C+P">Paul Morgan</a>, <a href="/search/?searchtype=author&query=Chen%2C+X">Xin Chen</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="2502.03229v1-abstract-short" style="display: inline;"> Semi-supervised learning, which leverages both annotated and unannotated data, is an efficient approach for medical image segmentation, where obtaining annotations for the whole dataset is time-consuming and costly. Traditional semi-supervised methods primarily focus on extracting features and learning data distributions from unannotated data to enhance model training. In this paper, we introduce… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03229v1-abstract-full').style.display = 'inline'; document.getElementById('2502.03229v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.03229v1-abstract-full" style="display: none;"> Semi-supervised learning, which leverages both annotated and unannotated data, is an efficient approach for medical image segmentation, where obtaining annotations for the whole dataset is time-consuming and costly. Traditional semi-supervised methods primarily focus on extracting features and learning data distributions from unannotated data to enhance model training. In this paper, we introduce a novel approach incorporating an image registration model to generate pseudo-labels for the unannotated data, producing more geometrically correct pseudo-labels to improve the model training. Our method was evaluated on a 2D brain data set, showing excellent performance even using only 1\% of the annotated data. The results show that our approach outperforms conventional semi-supervised segmentation methods (e.g. teacher-student model), particularly in a low percentage of annotation scenario. GitHub: https://github.com/ruizhe-l/UniSegReg. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03229v1-abstract-full').style.display = 'none'; document.getElementById('2502.03229v1-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> <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 for publication at IEEE International Symposium on Biomedical Imaging (ISBI) 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.02504">arXiv:2502.02504</a> <span> [<a href="https://arxiv.org/pdf/2502.02504">pdf</a>, <a href="https://arxiv.org/format/2502.02504">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"> Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+R">Ruochen Li</a>, <a href="/search/?searchtype=author&query=Qiao%2C+T">Tanqiu Qiao</a>, <a href="/search/?searchtype=author&query=Katsigiannis%2C+S">Stamos Katsigiannis</a>, <a href="/search/?searchtype=author&query=Zhu%2C+Z">Zhanxing Zhu</a>, <a href="/search/?searchtype=author&query=Shum%2C+H+P+H">Hubert P. H. Shum</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="2502.02504v1-abstract-short" style="display: inline;"> Pedestrian trajectory prediction aims to forecast future movements based on historical paths. Spatial-temporal (ST) methods often separately model spatial interactions among pedestrians and temporal dependencies of individuals. They overlook the direct impacts of interactions among different pedestrians across various time steps (i.e., high-order cross-time interactions). This limits their ability… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.02504v1-abstract-full').style.display = 'inline'; document.getElementById('2502.02504v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.02504v1-abstract-full" style="display: none;"> Pedestrian trajectory prediction aims to forecast future movements based on historical paths. Spatial-temporal (ST) methods often separately model spatial interactions among pedestrians and temporal dependencies of individuals. They overlook the direct impacts of interactions among different pedestrians across various time steps (i.e., high-order cross-time interactions). This limits their ability to capture ST inter-dependencies and hinders prediction performance. To address these limitations, we propose UniEdge with three major designs. Firstly, we introduce a unified ST graph data structure that simplifies high-order cross-time interactions into first-order relationships, enabling the learning of ST inter-dependencies in a single step. This avoids the information loss caused by multi-step aggregation. Secondly, traditional GNNs focus on aggregating pedestrian node features, neglecting the propagation of implicit interaction patterns encoded in edge features. We propose the Edge-to-Edge-Node-to-Node Graph Convolution (E2E-N2N-GCN), a novel dual-graph network that jointly models explicit N2N social interactions among pedestrians and implicit E2E influence propagation across these interaction patterns. Finally, to overcome the limited receptive fields and challenges in capturing long-range dependencies of auto-regressive architectures, we introduce a transformer encoder-based predictor that enables global modeling of temporal correlation. UniEdge outperforms state-of-the-arts on multiple datasets, including ETH, UCY, and SDD. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.02504v1-abstract-full').style.display = 'none'; document.getElementById('2502.02504v1-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/2502.02096">arXiv:2502.02096</a> <span> [<a href="https://arxiv.org/pdf/2502.02096">pdf</a>, <a href="https://arxiv.org/format/2502.02096">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"> Dual-Flow: Transferable Multi-Target, Instance-Agnostic Attacks via In-the-wild Cascading Flow Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Y">Yixiao Chen</a>, <a href="/search/?searchtype=author&query=Sun%2C+S">Shikun Sun</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jianshu Li</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruoyu Li</a>, <a href="/search/?searchtype=author&query=Li%2C+Z">Zhe Li</a>, <a href="/search/?searchtype=author&query=Xing%2C+J">Junliang Xing</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="2502.02096v2-abstract-short" style="display: inline;"> Adversarial attacks are widely used to evaluate model robustness, and in black-box scenarios, the transferability of these attacks becomes crucial. Existing generator-based attacks have excellent generalization and transferability due to their instance-agnostic nature. However, when training generators for multi-target tasks, the success rate of transfer attacks is relatively low due to the limita… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.02096v2-abstract-full').style.display = 'inline'; document.getElementById('2502.02096v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.02096v2-abstract-full" style="display: none;"> Adversarial attacks are widely used to evaluate model robustness, and in black-box scenarios, the transferability of these attacks becomes crucial. Existing generator-based attacks have excellent generalization and transferability due to their instance-agnostic nature. However, when training generators for multi-target tasks, the success rate of transfer attacks is relatively low due to the limitations of the model's capacity. To address these challenges, we propose a novel Dual-Flow framework for multi-target instance-agnostic adversarial attacks, utilizing Cascading Distribution Shift Training to develop an adversarial velocity function. Extensive experiments demonstrate that Dual-Flow significantly improves transferability over previous multi-target generative attacks. For example, it increases the success rate from Inception-v3 to ResNet-152 by 34.58%. Furthermore, our attack method shows substantially stronger robustness against defense mechanisms, such as adversarially trained models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.02096v2-abstract-full').style.display = 'none'; document.getElementById('2502.02096v2-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 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/2502.02021">arXiv:2502.02021</a> <span> [<a href="https://arxiv.org/pdf/2502.02021">pdf</a>, <a href="https://arxiv.org/format/2502.02021">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="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Multi-illuminant Color Constancy via Multi-scale Illuminant Estimation and Fusion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Luo%2C+H">Hang Luo</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Rongwei Li</a>, <a href="/search/?searchtype=author&query=Liang%2C+J">Jinxing Liang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.02021v1-abstract-short" style="display: inline;"> Multi-illuminant color constancy methods aim to eliminate local color casts within an image through pixel-wise illuminant estimation. Existing methods mainly employ deep learning to establish a direct mapping between an image and its illumination map, which neglects the impact of image scales. To alleviate this problem, we represent an illuminant map as the linear combination of components estimat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.02021v1-abstract-full').style.display = 'inline'; document.getElementById('2502.02021v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.02021v1-abstract-full" style="display: none;"> Multi-illuminant color constancy methods aim to eliminate local color casts within an image through pixel-wise illuminant estimation. Existing methods mainly employ deep learning to establish a direct mapping between an image and its illumination map, which neglects the impact of image scales. To alleviate this problem, we represent an illuminant map as the linear combination of components estimated from multi-scale images. Furthermore, we propose a tri-branch convolution networks to estimate multi-grained illuminant distribution maps from multi-scale images. These multi-grained illuminant maps are merged adaptively with an attentional illuminant fusion module. Through comprehensive experimental analysis and evaluation, the results demonstrate the effectiveness of our method, and it has achieved state-of-the-art performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.02021v1-abstract-full').style.display = 'none'; document.getElementById('2502.02021v1-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> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 4 figures, this manuscript is under the consideration of Optics Express</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.00171">arXiv:2502.00171</a> <span> [<a href="https://arxiv.org/pdf/2502.00171">pdf</a>, <a href="https://arxiv.org/format/2502.00171">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="Methodology">stat.ME</span> </div> </div> <p class="title is-5 mathjax"> Flexible Bayesian Tensor Decomposition for Verbal Autopsy Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhu%2C+Y">Yu Zhu</a>, <a href="/search/?searchtype=author&query=Li%2C+Z+R">Zehang Richard 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="2502.00171v1-abstract-short" style="display: inline;"> Cause-of-death data is fundamental for understanding population health trends and inequalities as well as designing and evaluating public health interventions. A significant proportion of global deaths, particularly in low- and middle-income countries (LMICs), do not have medically certified causes assigned. In such settings, verbal autopsy (VA) is a widely adopted approach to estimate disease bur… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.00171v1-abstract-full').style.display = 'inline'; document.getElementById('2502.00171v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.00171v1-abstract-full" style="display: none;"> Cause-of-death data is fundamental for understanding population health trends and inequalities as well as designing and evaluating public health interventions. A significant proportion of global deaths, particularly in low- and middle-income countries (LMICs), do not have medically certified causes assigned. In such settings, verbal autopsy (VA) is a widely adopted approach to estimate disease burdens by interviewing caregivers of the deceased. Recently, latent class models have been developed to model the joint distribution of symptoms and perform probabilistic cause-of-death assignment. A large number of latent classes are usually needed in order to characterize the complex dependence among symptoms, making the estimated symptom profiles challenging to summarize and interpret. In this paper, we propose a flexible Bayesian tensor decomposition framework that balances the predictive accuracy of the cause-of-death assignment task and the interpretability of the latent structures. The key to our approach is to partition symptoms into groups and model the joint distributions of group-level symptom sub-profiles. The proposed methods achieve better predictive accuracy than existing VA methods and provide a more parsimonious representation of the symptom distributions. We show our methods provide new insights into the clustering patterns of both symptoms and causes using the PHMRC gold-standard VA dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.00171v1-abstract-full').style.display = 'none'; document.getElementById('2502.00171v1-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> 31 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/2501.19272">arXiv:2501.19272</a> <span> [<a href="https://arxiv.org/pdf/2501.19272">pdf</a>, <a href="https://arxiv.org/ps/2501.19272">ps</a>, <a href="https://arxiv.org/format/2501.19272">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Combinatorics">math.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Number Theory">math.NT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantum Algebra">math.QA</span> </div> </div> <p class="title is-5 mathjax"> A MacMahon Analysis View of Cylindric Partitions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+R">Runqiao Li</a>, <a href="/search/?searchtype=author&query=Uncu%2C+A+K">Ali K. Uncu</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="2501.19272v1-abstract-short" style="display: inline;"> We study cylindric partitions with two-element profiles using MacMahon's partition analysis. We find explicit formulas for the generating functions of the number of cylindric partitions by first finding the recurrences using partition analysis and then solving them. We also note some q-series identities related to these objects that show the manifestly positive nature of some alternating series. W… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.19272v1-abstract-full').style.display = 'inline'; document.getElementById('2501.19272v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.19272v1-abstract-full" style="display: none;"> We study cylindric partitions with two-element profiles using MacMahon's partition analysis. We find explicit formulas for the generating functions of the number of cylindric partitions by first finding the recurrences using partition analysis and then solving them. We also note some q-series identities related to these objects that show the manifestly positive nature of some alternating series. We generalize the proven identities and conjecture new polynomial refinements of Andrews-Gordon and Bressoud identities, which are companions to Foda-Quano's refinements. Finally, using a variant of the Bailey lemma, we present many new infinite hierarchies of polynomial identities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.19272v1-abstract-full').style.display = 'none'; document.getElementById('2501.19272v1-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> 31 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">28 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 11B65; 33F10; 11C08; 11P81; 11P82; 11P84; 05A10; 05A15; 05A17; 05A30 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.18619">arXiv:2501.18619</a> <span> [<a href="https://arxiv.org/pdf/2501.18619">pdf</a>, <a href="https://arxiv.org/format/2501.18619">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> FAAGC: Feature Augmentation on Adaptive Geodesic Curve Based on the shape space theory </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Han%2C+Y">Yuexing Han</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruijie 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="2501.18619v1-abstract-short" style="display: inline;"> Deep learning models have been widely applied across various domains and industries. However, many fields still face challenges due to limited and insufficient data. This paper proposes a Feature Augmentation on Adaptive Geodesic Curve (FAAGC) method in the pre-shape space to increase data. In the pre-shape space, objects with identical shapes lie on a great circle. Thus, we project deep model rep… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18619v1-abstract-full').style.display = 'inline'; document.getElementById('2501.18619v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.18619v1-abstract-full" style="display: none;"> Deep learning models have been widely applied across various domains and industries. However, many fields still face challenges due to limited and insufficient data. This paper proposes a Feature Augmentation on Adaptive Geodesic Curve (FAAGC) method in the pre-shape space to increase data. In the pre-shape space, objects with identical shapes lie on a great circle. Thus, we project deep model representations into the pre-shape space and construct a geodesic curve, i.e., an arc of a great circle, for each class. Feature augmentation is then performed by sampling along these geodesic paths. Extensive experiments demonstrate that FAAGC improves classification accuracy under data-scarce conditions and generalizes well across various feature types. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18619v1-abstract-full').style.display = 'none'; document.getElementById('2501.18619v1-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">8pages, 3figures, submitted to IJCAI 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.18367">arXiv:2501.18367</a> <span> [<a href="https://arxiv.org/pdf/2501.18367">pdf</a>, <a href="https://arxiv.org/format/2501.18367">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+Y">Yifan Wang</a>, <a href="/search/?searchtype=author&query=Ai%2C+H">Hongfeng Ai</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruiqi Li</a>, <a href="/search/?searchtype=author&query=Jiang%2C+M">Maowei Jiang</a>, <a href="/search/?searchtype=author&query=Jiang%2C+C">Cheng Jiang</a>, <a href="/search/?searchtype=author&query=Li%2C+C">Chenzhong 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="2501.18367v1-abstract-short" style="display: inline;"> In medical time series disease diagnosis, two key challenges are identified.First, the high annotation cost of medical data leads to overfitting in models trained on label-limited, single-center datasets. To address this, we propose incorporating external data from related tasks and leveraging AE-GAN to extract prior knowledge,providing valuable references for downstream tasks. Second, many existi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18367v1-abstract-full').style.display = 'inline'; document.getElementById('2501.18367v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.18367v1-abstract-full" style="display: none;"> In medical time series disease diagnosis, two key challenges are identified.First, the high annotation cost of medical data leads to overfitting in models trained on label-limited, single-center datasets. To address this, we propose incorporating external data from related tasks and leveraging AE-GAN to extract prior knowledge,providing valuable references for downstream tasks. Second, many existing studies employ contrastive learning to derive more generalized medical sequence representations for diagnostic tasks, usually relying on manually designed diverse positive and negative sample pairs.However, these approaches are complex, lack generalizability, and fail to adaptively capture disease-specific features across different conditions.To overcome this, we introduce LMCF (Learnable Multi-views Contrastive Framework), a framework that integrates a multi-head attention mechanism and adaptively learns representations from different views through inter-view and intra-view contrastive learning strategies.Additionally, the pre-trained AE-GAN is used to reconstruct discrepancies in the target data as disease probabilities, which are then integrated into the contrastive learning process.Experiments on three target datasets demonstrate that our method consistently outperforms seven other baselines, highlighting its significant impact on healthcare applications such as the diagnosis of myocardial infarction, Alzheimer's disease, and Parkinson's disease. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18367v1-abstract-full').style.display = 'none'; document.getElementById('2501.18367v1-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">15 pages,6 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; K.3.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.17814">arXiv:2501.17814</a> <span> [<a href="https://arxiv.org/pdf/2501.17814">pdf</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="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> </div> </div> <p class="title is-5 mathjax"> A trilinear quantum dot architecture for semiconductor spin qubits </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+R">R. Li</a>, <a href="/search/?searchtype=author&query=Levajac%2C+V">V. Levajac</a>, <a href="/search/?searchtype=author&query=Godfrin%2C+C">C. Godfrin</a>, <a href="/search/?searchtype=author&query=Kubicek%2C+S">S. Kubicek</a>, <a href="/search/?searchtype=author&query=Simion%2C+G">G. Simion</a>, <a href="/search/?searchtype=author&query=Raes%2C+B">B. Raes</a>, <a href="/search/?searchtype=author&query=Beyne%2C+S">S. Beyne</a>, <a href="/search/?searchtype=author&query=Fattal%2C+I">I. Fattal</a>, <a href="/search/?searchtype=author&query=Loenders%2C+A">A. Loenders</a>, <a href="/search/?searchtype=author&query=De+Roeck%2C+W">W. De Roeck</a>, <a href="/search/?searchtype=author&query=Mongillo%2C+M">M. Mongillo</a>, <a href="/search/?searchtype=author&query=Wan%2C+D">D. Wan</a>, <a href="/search/?searchtype=author&query=De+Greve%2C+K">K. De Greve</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="2501.17814v1-abstract-short" style="display: inline;"> Semiconductor quantum dot spin qubits hold significant potential for scaling to millions of qubits for practical quantum computing applications, as their structure highly resembles the structure of conventional transistors. Since classical semiconductor manufacturing technology has reached an unprecedented level of maturity, reliably mass-producing CMOS chips with hundreds of billions of component… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.17814v1-abstract-full').style.display = 'inline'; document.getElementById('2501.17814v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.17814v1-abstract-full" style="display: none;"> Semiconductor quantum dot spin qubits hold significant potential for scaling to millions of qubits for practical quantum computing applications, as their structure highly resembles the structure of conventional transistors. Since classical semiconductor manufacturing technology has reached an unprecedented level of maturity, reliably mass-producing CMOS chips with hundreds of billions of components, conventional wisdom dictates that leveraging CMOS technologies for quantum dot qubits can result in upscaled quantum processors with thousands or even millions of interconnected qubits. However, the interconnect requirements for quantum circuits are very different from those for classical circuits, where for each qubit individual control and readout wiring could be needed. Although significant developments have been demonstrated on small scale systems, qubit numbers remain limited, to a large extent due to the lack of scalable qubit interconnect schemes. Here, we present a trilinear quantum dot array that is simple in physical layout while allowing individual wiring to each quantum dot. By means of electron shuttling, the trilinear architecture provides qubit connectivity that is equivalent to or even surpasses that of 2D square lattice. Assuming the current qubit fidelities of small-scale devices can be extrapolated to large-scale arrays, medium-length shuttling arrays on the order of tens of microns would allow million-scale qubit systems, while maintaining manageable overheads. We also present a scalable control scheme, where the qubit chip is 3D-integrated with a low-power switch-based cryoCMOS circuit for parallel qubit operation with limited control inputs. As our trilinear quantum dot array is fully compatible with existing semiconductor technologies, this qubit architecture represents one possible framework for future research and development of large-scale spin qubit systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.17814v1-abstract-full').style.display = 'none'; document.getElementById('2501.17814v1-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> <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, 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/2501.17065">arXiv:2501.17065</a> <span> [<a href="https://arxiv.org/pdf/2501.17065">pdf</a>, <a href="https://arxiv.org/format/2501.17065">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Combinatorics">math.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Number Theory">math.NT</span> </div> </div> <p class="title is-5 mathjax"> Distribution of Alternating Sums of Parts in Partitions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Craig%2C+W">William Craig</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Runqiao 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="2501.17065v1-abstract-short" style="display: inline;"> Recently, many authors have investigated how various partition statistics distribute as the size of the partition grows. In this work, we look at a particular statistic arising from the recent rejuvenation of MacMahon's partition analysis. More specifically, we show that the alternating sum of parts in partitions has an asymptotically normal distribution. We prove this results using the saddle poi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.17065v1-abstract-full').style.display = 'inline'; document.getElementById('2501.17065v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.17065v1-abstract-full" style="display: none;"> Recently, many authors have investigated how various partition statistics distribute as the size of the partition grows. In this work, we look at a particular statistic arising from the recent rejuvenation of MacMahon's partition analysis. More specifically, we show that the alternating sum of parts in partitions has an asymptotically normal distribution. We prove this results using the saddle point method. We also propose a general framework for studying further questions of this type that may avoid some of the complications that arise in traditional approaches to the distributions of partition statistics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.17065v1-abstract-full').style.display = 'none'; document.getElementById('2501.17065v1-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> 28 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/2501.16254">arXiv:2501.16254</a> <span> [<a href="https://arxiv.org/pdf/2501.16254">pdf</a>, <a href="https://arxiv.org/format/2501.16254">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Multi-Agent Geospatial Copilots for Remote Sensing Workflows </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lee%2C+C">Chaehong Lee</a>, <a href="/search/?searchtype=author&query=Paramanayakam%2C+V">Varatheepan Paramanayakam</a>, <a href="/search/?searchtype=author&query=Karatzas%2C+A">Andreas Karatzas</a>, <a href="/search/?searchtype=author&query=Jian%2C+Y">Yanan Jian</a>, <a href="/search/?searchtype=author&query=Fore%2C+M">Michael Fore</a>, <a href="/search/?searchtype=author&query=Liao%2C+H">Heming Liao</a>, <a href="/search/?searchtype=author&query=Yu%2C+F">Fuxun Yu</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruopu Li</a>, <a href="/search/?searchtype=author&query=Anagnostopoulos%2C+I">Iraklis Anagnostopoulos</a>, <a href="/search/?searchtype=author&query=Stamoulis%2C+D">Dimitrios Stamoulis</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="2501.16254v1-abstract-short" style="display: inline;"> We present GeoLLM-Squad, a geospatial Copilot that introduces the novel multi-agent paradigm to remote sensing (RS) workflows. Unlike existing single-agent approaches that rely on monolithic large language models (LLM), GeoLLM-Squad separates agentic orchestration from geospatial task-solving, by delegating RS tasks to specialized sub-agents. Built on the open-source AutoGen and GeoLLM-Engine fram… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16254v1-abstract-full').style.display = 'inline'; document.getElementById('2501.16254v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.16254v1-abstract-full" style="display: none;"> We present GeoLLM-Squad, a geospatial Copilot that introduces the novel multi-agent paradigm to remote sensing (RS) workflows. Unlike existing single-agent approaches that rely on monolithic large language models (LLM), GeoLLM-Squad separates agentic orchestration from geospatial task-solving, by delegating RS tasks to specialized sub-agents. Built on the open-source AutoGen and GeoLLM-Engine frameworks, our work enables the modular integration of diverse applications, spanning urban monitoring, forestry protection, climate analysis, and agriculture studies. Our results demonstrate that while single-agent systems struggle to scale with increasing RS task complexity, GeoLLM-Squad maintains robust performance, achieving a 17% improvement in agentic correctness over state-of-the-art baselines. Our findings highlight the potential of multi-agent AI in advancing RS workflows. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16254v1-abstract-full').style.display = 'none'; document.getElementById('2501.16254v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.16139">arXiv:2501.16139</a> <span> [<a href="https://arxiv.org/pdf/2501.16139">pdf</a>, <a href="https://arxiv.org/format/2501.16139">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"> Measuring the Stellar-to-Halo Mass Relation at $\sim10^{10}$ Solar masses, using space-based imaging of galaxy-galaxy strong lenses </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+K">Kaihao Wang</a>, <a href="/search/?searchtype=author&query=Cao%2C+X">Xiaoyue Cao</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ran Li</a>, <a href="/search/?searchtype=author&query=Nightingale%2C+J+W">James W. Nightingale</a>, <a href="/search/?searchtype=author&query=He%2C+Q">Qiuhan He</a>, <a href="/search/?searchtype=author&query=Amvrosiadis%2C+A">Aristeidis Amvrosiadis</a>, <a href="/search/?searchtype=author&query=Massey%2C+R">Richard Massey</a>, <a href="/search/?searchtype=author&query=von+Wietersheim-Kramsta%2C+M">Maximilian von Wietersheim-Kramsta</a>, <a href="/search/?searchtype=author&query=Fung%2C+L+W+H">Leo W. H. Fung</a>, <a href="/search/?searchtype=author&query=Frenk%2C+C+S">Carlos S. Frenk</a>, <a href="/search/?searchtype=author&query=Cole%2C+S">Shaun Cole</a>, <a href="/search/?searchtype=author&query=Robertson%2C+A">Andrew Robertson</a>, <a href="/search/?searchtype=author&query=Lange%2C+S+C">Samuel C. Lange</a>, <a href="/search/?searchtype=author&query=Ma%2C+X">Xianghao Ma</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="2501.16139v1-abstract-short" style="display: inline;"> The stellar-to-halo mass relation (SHMR) embodies the joint evolution of galaxies and their host dark matter halos. However, the relation is poorly constrained at sub-galactic masses, because the stellar emission from such objects is so faint. However, it is possible to directly detect the mass of halos along the line of sight to a strong gravitational lens, when they perturb one of its multiple i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16139v1-abstract-full').style.display = 'inline'; document.getElementById('2501.16139v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.16139v1-abstract-full" style="display: none;"> The stellar-to-halo mass relation (SHMR) embodies the joint evolution of galaxies and their host dark matter halos. However, the relation is poorly constrained at sub-galactic masses, because the stellar emission from such objects is so faint. However, it is possible to directly detect the mass of halos along the line of sight to a strong gravitational lens, when they perturb one of its multiple images. Space telescopes including Euclid, CSST, and Roman will soon discover millions of galaxy-galaxy strong lensing systems. We simulate Euclid-like imaging of a typical lens galaxy, and find that a lensing reconstruction is sensitive to $3\times10^{10}$ subhalos with various positions and concentrations, at statistical signficance $>$$3.6蟽$. The subhalo mass can be measured without bias, provided the model simultaneously fits light from both the main lens and the subhalo. A future sample of 48 subhalos with $\geqslant$$5蟽$ detection significance would constrain the SHMR at this mass range with $1蟽$ uncertainty of 0.045 dex: distinguishing between different theoretical predictions at the sub-galactic scale. Follow-up spectroscopy is needed to measure lens and source redshifts; follow-up imaging at greater spatial resolution and depth would substantially improve the measurement, and eliminate false-positives at even lower halo masses. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16139v1-abstract-full').style.display = 'none'; document.getElementById('2501.16139v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 9 figures, submitted for publication in MNRAS</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.16029">arXiv:2501.16029</a> <span> [<a href="https://arxiv.org/pdf/2501.16029">pdf</a>, <a href="https://arxiv.org/format/2501.16029">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> FDLLM: A Text Fingerprint Detection Method for LLMs in Multi-Language, Multi-Domain Black-Box Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Fu%2C+Z">Zhiyuan Fu</a>, <a href="/search/?searchtype=author&query=Chen%2C+J">Junfan Chen</a>, <a href="/search/?searchtype=author&query=Sun%2C+H">Hongyu Sun</a>, <a href="/search/?searchtype=author&query=Yang%2C+T">Ting Yang</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruidong Li</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yuqing Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.16029v1-abstract-short" style="display: inline;"> Using large language models (LLMs) integration platforms without transparency about which LLM is being invoked can lead to potential security risks. Specifically, attackers may exploit this black-box scenario to deploy malicious models and embed viruses in the code provided to users. In this context, it is increasingly urgent for users to clearly identify the LLM they are interacting with, in orde… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16029v1-abstract-full').style.display = 'inline'; document.getElementById('2501.16029v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.16029v1-abstract-full" style="display: none;"> Using large language models (LLMs) integration platforms without transparency about which LLM is being invoked can lead to potential security risks. Specifically, attackers may exploit this black-box scenario to deploy malicious models and embed viruses in the code provided to users. In this context, it is increasingly urgent for users to clearly identify the LLM they are interacting with, in order to avoid unknowingly becoming victims of malicious models. However, existing studies primarily focus on mixed classification of human and machine-generated text, with limited attention to classifying texts generated solely by different models. Current research also faces dual bottlenecks: poor quality of LLM-generated text (LLMGT) datasets and limited coverage of detectable LLMs, resulting in poor detection performance for various LLMGT in black-box scenarios. We propose the first LLMGT fingerprint detection model, \textbf{FDLLM}, based on Qwen2.5-7B and fine-tuned using LoRA to address these challenges. FDLLM can more efficiently handle detection tasks across multilingual and multi-domain scenarios. Furthermore, we constructed a dataset named \textbf{FD-Datasets}, consisting of 90,000 samples that span multiple languages and domains, covering 20 different LLMs. Experimental results demonstrate that FDLLM achieves a macro F1 score 16.7\% higher than the best baseline method, LM-D. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16029v1-abstract-full').style.display = 'none'; document.getElementById('2501.16029v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.16002">arXiv:2501.16002</a> <span> [<a href="https://arxiv.org/pdf/2501.16002">pdf</a>, <a href="https://arxiv.org/format/2501.16002">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wu%2C+X">Xiang Wu</a>, <a href="/search/?searchtype=author&query=Li%2C+X">Xunkai Li</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Rong-Hua Li</a>, <a href="/search/?searchtype=author&query=Zhao%2C+K">Kangfei Zhao</a>, <a href="/search/?searchtype=author&query=Wang%2C+G">Guoren 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="2501.16002v2-abstract-short" style="display: inline;"> Dynamic graphs (DGs), which capture time-evolving relationships between graph entities, have widespread real-world applications. To efficiently encode DGs for downstream tasks, most dynamic graph neural networks follow the traditional message-passing mechanism and extend it with time-based techniques. Despite their effectiveness, the growth of historical interactions introduces significant scalabi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16002v2-abstract-full').style.display = 'inline'; document.getElementById('2501.16002v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.16002v2-abstract-full" style="display: none;"> Dynamic graphs (DGs), which capture time-evolving relationships between graph entities, have widespread real-world applications. To efficiently encode DGs for downstream tasks, most dynamic graph neural networks follow the traditional message-passing mechanism and extend it with time-based techniques. Despite their effectiveness, the growth of historical interactions introduces significant scalability issues, particularly in industry scenarios. To address this limitation, we propose ScaDyG, with the core idea of designing a time-aware scalable learning paradigm as follows: 1) Time-aware Topology Reformulation: ScaDyG first segments historical interactions into time steps (intra and inter) based on dynamic modeling, enabling weight-free and time-aware graph propagation within pre-processing. 2) Dynamic Temporal Encoding: To further achieve fine-grained graph propagation within time steps, ScaDyG integrates temporal encoding through a combination of exponential functions in a scalable manner. 3) Hypernetwork-driven Message Aggregation: After obtaining the propagated features (i.e., messages), ScaDyG utilizes hypernetwork to analyze historical dependencies, implementing node-wise representation by an adaptive temporal fusion. Extensive experiments on 12 datasets demonstrate that ScaDyG performs comparably well or even outperforms other SOTA methods in both node and link-level downstream tasks, with fewer learnable parameters and higher efficiency. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16002v2-abstract-full').style.display = 'none'; document.getElementById('2501.16002v2-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">v1</span> submitted 27 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.15810">arXiv:2501.15810</a> <span> [<a href="https://arxiv.org/pdf/2501.15810">pdf</a>, <a href="https://arxiv.org/format/2501.15810">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Nuclear Theory">nucl-th</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Theory">hep-th</span> </div> </div> <p class="title is-5 mathjax"> Reconstruction of QCD first-order phase transition from neutron star measurements </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+R">Ronghao Li</a>, <a href="/search/?searchtype=author&query=Han%2C+S">Sophia Han</a>, <a href="/search/?searchtype=author&query=Lin%2C+Z">Zidu Lin</a>, <a href="/search/?searchtype=author&query=Wang%2C+L">Lingxiao Wang</a>, <a href="/search/?searchtype=author&query=Zhou%2C+K">Kai Zhou</a>, <a href="/search/?searchtype=author&query=Shi%2C+S">Shuzhe Shi</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="2501.15810v1-abstract-short" style="display: inline;"> The potential hadron-to-quark phase transition in neutron stars has not been fully understood as the property of cold, dense, and strongly interacting matter cannot be theoretically described by the first-principle perturbative calculations, nor have they been systematically measured through terrestrial low-to-intermediate energy heavy-ion experiments. Given the Tolman--Oppenheimer--Volkoff (TOV)… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15810v1-abstract-full').style.display = 'inline'; document.getElementById('2501.15810v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.15810v1-abstract-full" style="display: none;"> The potential hadron-to-quark phase transition in neutron stars has not been fully understood as the property of cold, dense, and strongly interacting matter cannot be theoretically described by the first-principle perturbative calculations, nor have they been systematically measured through terrestrial low-to-intermediate energy heavy-ion experiments. Given the Tolman--Oppenheimer--Volkoff (TOV) equations, the equation of state (EoS) of the neutron star (NS) matter can be constrained by the observations of NS mass, radius, and tidal deformability. However, large observational uncertainties and the limited number of observations currently make it challenging to strictly reconstruct the EoS, especially to identify interesting features such as a strong first-order phase transition. In this work, we study the dependency of reconstruction quality of the phase transition on the number of NS observations of mass and radius as well as their uncertainty, based on a fiducial EoS. We conquer this challenging problem by constructing a neural network, which allows one to parameterize the EoS with minimum model-dependency, and by devising an algorithm of parameter optimization based on the analytical linear response analysis of the TOV equations. This work may pave the way for the understanding of the phase transition features in NSs using future $X$-ray and gravitational wave measurements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15810v1-abstract-full').style.display = 'none'; document.getElementById('2501.15810v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> RIKEN-iTHEMS-Report-25 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.15465">arXiv:2501.15465</a> <span> [<a href="https://arxiv.org/pdf/2501.15465">pdf</a>, <a href="https://arxiv.org/ps/2501.15465">ps</a>, <a href="https://arxiv.org/format/2501.15465">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="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Geometry of symplectic group and optimal EAQECC codes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+R">Ruihu Li</a>, <a href="/search/?searchtype=author&query=Ren%2C+Y">Yuezhen Ren</a>, <a href="/search/?searchtype=author&query=Guan%2C+C">Chaofeng Guan</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yang 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="2501.15465v1-abstract-short" style="display: inline;"> A new type of link between geometry of symplectic group and entanglement-assisted (EA) quantum error-correcting codes (EAQECCs) is presented. Relations of symplectic subspaces and quaternary additive codes concerning parameters of EAQECCs are described. Thus, parameters of EA stabilizer codes are revealed in the nomenclature of additive codes. Our techniques enable us solve some open problems abou… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15465v1-abstract-full').style.display = 'inline'; document.getElementById('2501.15465v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.15465v1-abstract-full" style="display: none;"> A new type of link between geometry of symplectic group and entanglement-assisted (EA) quantum error-correcting codes (EAQECCs) is presented. Relations of symplectic subspaces and quaternary additive codes concerning parameters of EAQECCs are described. Thus, parameters of EA stabilizer codes are revealed in the nomenclature of additive codes. Our techniques enable us solve some open problems about optimal EAQECCs and entanglement-assisted quantum minimum distance separable (EAQMDS) codes, and are also useful for designing encoding and decoding quantum circuit of EA stabilizer codes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15465v1-abstract-full').style.display = 'none'; document.getElementById('2501.15465v1-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> <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">5 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/2501.15447">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/2501.14650">arXiv:2501.14650</a> <span> [<a href="https://arxiv.org/pdf/2501.14650">pdf</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"> Automation of finding strong gravitational lenses in the Kilo Degree Survey with U-DenseLens (DenseLens + Segmentation) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Nagam%2C+B+C">Bharath Chowdhary Nagam</a>, <a href="/search/?searchtype=author&query=Koopmans%2C+L+V+E">L茅on V E Koopmans</a>, <a href="/search/?searchtype=author&query=Valentijn%2C+E+A">Edwin A Valentijn</a>, <a href="/search/?searchtype=author&query=Kleijn%2C+G+V">Gijs Verdoes Kleijn</a>, <a href="/search/?searchtype=author&query=de+Jong%2C+J+T+A">Jelte T A de Jong</a>, <a href="/search/?searchtype=author&query=Napolitano%2C+N">Nicola Napolitano</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Rui Li</a>, <a href="/search/?searchtype=author&query=Tortora%2C+C">Crescenzo Tortora</a>, <a href="/search/?searchtype=author&query=Busillo%2C+V">Valerio Busillo</a>, <a href="/search/?searchtype=author&query=Dong%2C+Y">Yue Dong</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="2501.14650v1-abstract-short" style="display: inline;"> In the context of upcoming large-scale surveys like Euclid, the necessity for the automation of strong lens detection is essential. While existing machine learning pipelines heavily rely on the classification probability (P), this study intends to address the importance of integrating additional metrics, such as Information Content (IC) and the number of pixels above the segmentation threshold, to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.14650v1-abstract-full').style.display = 'inline'; document.getElementById('2501.14650v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.14650v1-abstract-full" style="display: none;"> In the context of upcoming large-scale surveys like Euclid, the necessity for the automation of strong lens detection is essential. While existing machine learning pipelines heavily rely on the classification probability (P), this study intends to address the importance of integrating additional metrics, such as Information Content (IC) and the number of pixels above the segmentation threshold, to alleviate the false positive rate in unbalanced data-sets. In this work, we introduce a segmentation algorithm (U-Net) as a supplementary step in the established strong gravitational lens identification pipeline (Denselens), which primarily utilizes P$_{\rm mean}$ and IC$_{\rm mean}$ parameters for the detection and ranking. The results demonstrate that the inclusion of segmentation enables significant reduction of false positives by approximately 25 per cent in the final sample extracted from DenseLens, without compromising the identification of strong lenses. The main objective of this study is to automate the strong lens detection process by integrating these three metrics. To achieve this, a decision tree-based selection process is introduced, applied to the Kilo Degree Survey (KiDS) data. This process involves rank-ordering based on classification scores, filtering based on Information Content, and segmentation score. Additionally, the study presents 14 newly discovered strong lensing candidates identified by the U-Denselens network using the KiDS DR4 data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.14650v1-abstract-full').style.display = 'none'; document.getElementById('2501.14650v1-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> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.14206">arXiv:2501.14206</a> <span> [<a href="https://arxiv.org/pdf/2501.14206">pdf</a>, <a href="https://arxiv.org/ps/2501.14206">ps</a>, <a href="https://arxiv.org/format/2501.14206">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"> Cross section measurement of $e^{+}e^{-} \to f_{1}(1285)蟺^{+}蟺^{-}$ at center-of-mass energies between $3.808$ and $4.951\rm GeV$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=BESIII+Collaboration"> BESIII Collaboration</a>, <a href="/search/?searchtype=author&query=Ablikim%2C+M">M. Ablikim</a>, <a href="/search/?searchtype=author&query=Achasov%2C+M+N">M. N. Achasov</a>, <a href="/search/?searchtype=author&query=Adlarson%2C+P">P. Adlarson</a>, <a href="/search/?searchtype=author&query=Afedulidis%2C+O">O. Afedulidis</a>, <a href="/search/?searchtype=author&query=Ai%2C+X+C">X. C. Ai</a>, <a href="/search/?searchtype=author&query=Aliberti%2C+R">R. Aliberti</a>, <a href="/search/?searchtype=author&query=Amoroso%2C+A">A. Amoroso</a>, <a href="/search/?searchtype=author&query=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=Balossino%2C+I">I. Balossino</a>, <a href="/search/?searchtype=author&query=Ban%2C+Y">Y. Ban</a>, <a href="/search/?searchtype=author&query=Bao%2C+H+-">H. -R. Bao</a>, <a href="/search/?searchtype=author&query=Batozskaya%2C+V">V. Batozskaya</a>, <a href="/search/?searchtype=author&query=Begzsuren%2C+K">K. Begzsuren</a>, <a href="/search/?searchtype=author&query=Berger%2C+N">N. Berger</a>, <a href="/search/?searchtype=author&query=Berlowski%2C+M">M. Berlowski</a>, <a href="/search/?searchtype=author&query=Bertani%2C+M">M. Bertani</a>, <a href="/search/?searchtype=author&query=Bettoni%2C+D">D. Bettoni</a>, <a href="/search/?searchtype=author&query=Bianchi%2C+F">F. Bianchi</a>, <a href="/search/?searchtype=author&query=Bianco%2C+E">E. Bianco</a>, <a href="/search/?searchtype=author&query=Bortone%2C+A">A. Bortone</a>, <a href="/search/?searchtype=author&query=Boyko%2C+I">I. Boyko</a>, <a href="/search/?searchtype=author&query=Briere%2C+R+A">R. A. Briere</a> , et al. (639 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="2501.14206v1-abstract-short" style="display: inline;"> Using data samples collected by the \mbox{BESIII} detector located at the Beijing Electron Positron Collider, the cross sections of the process $e^+e^-\to f_{1}(1285)蟺^+蟺^-$ are measured at forty-five center-of-mass energies from $3.808$ to $4.951 {\rm GeV}$. An investigation on the cross section line shape is performed, and no significant structure is observed. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.14206v1-abstract-full" style="display: none;"> Using data samples collected by the \mbox{BESIII} detector located at the Beijing Electron Positron Collider, the cross sections of the process $e^+e^-\to f_{1}(1285)蟺^+蟺^-$ are measured at forty-five center-of-mass energies from $3.808$ to $4.951 {\rm GeV}$. An investigation on the cross section line shape is performed, and no significant structure is observed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.14206v1-abstract-full').style.display = 'none'; document.getElementById('2501.14206v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.14103">arXiv:2501.14103</a> <span> [<a href="https://arxiv.org/pdf/2501.14103">pdf</a>, <a href="https://arxiv.org/format/2501.14103">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Personalized Interpolation: An Efficient Method to Tame Flexible Optimization Window Estimation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+X">Xin Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+W">Weiliang Li</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Rui Li</a>, <a href="/search/?searchtype=author&query=Fu%2C+Z">Zihang Fu</a>, <a href="/search/?searchtype=author&query=Tang%2C+T">Tongyi Tang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zhengyu Zhang</a>, <a href="/search/?searchtype=author&query=Chen%2C+W">Wen-Yen Chen</a>, <a href="/search/?searchtype=author&query=Noorshams%2C+N">Nima Noorshams</a>, <a href="/search/?searchtype=author&query=Jasapara%2C+N">Nirav Jasapara</a>, <a href="/search/?searchtype=author&query=Ding%2C+X">Xiaowen Ding</a>, <a href="/search/?searchtype=author&query=Wen%2C+E">Ellie Wen</a>, <a href="/search/?searchtype=author&query=Feng%2C+X">Xue Feng</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="2501.14103v1-abstract-short" style="display: inline;"> In the realm of online advertising, optimizing conversions is crucial for delivering relevant products to users and enhancing business outcomes. Predicting conversion events is challenging due to variable delays between user interactions, such as impressions or clicks, and the actual conversions. These delays differ significantly across various advertisers and products, necessitating distinct opti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.14103v1-abstract-full').style.display = 'inline'; document.getElementById('2501.14103v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.14103v1-abstract-full" style="display: none;"> In the realm of online advertising, optimizing conversions is crucial for delivering relevant products to users and enhancing business outcomes. Predicting conversion events is challenging due to variable delays between user interactions, such as impressions or clicks, and the actual conversions. These delays differ significantly across various advertisers and products, necessitating distinct optimization time windows for targeted conversions. To address this, we introduce a novel approach named the \textit{Personalized Interpolation} method, which innovatively builds upon existing fixed conversion window models to estimate flexible conversion windows. This method allows for the accurate estimation of conversions across a variety of delay ranges, thus meeting the diverse needs of advertisers without increasing system complexity. To validate the efficacy of our proposed method, we conducted comprehensive experiments using ads conversion model. Our experiments demonstrate that this method not only achieves high prediction accuracy but also does so more efficiently than other existing solutions. This validation underscores the potential of our Personalized Interpolation method to significantly enhance conversion optimization in real-world online advertising systems, promising improved targeting and effectiveness in advertising strategies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.14103v1-abstract-full').style.display = 'none'; document.getElementById('2501.14103v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.13919">arXiv:2501.13919</a> <span> [<a href="https://arxiv.org/pdf/2501.13919">pdf</a>, <a href="https://arxiv.org/format/2501.13919">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Temporal Preference Optimization for Long-Form Video Understanding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+R">Rui Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xiaohan Wang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yuhui Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+Z">Zeyu Wang</a>, <a href="/search/?searchtype=author&query=Yeung-Levy%2C+S">Serena Yeung-Levy</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="2501.13919v2-abstract-short" style="display: inline;"> Despite significant advancements in video large multimodal models (video-LMMs), achieving effective temporal grounding in long-form videos remains a challenge for existing models. To address this limitation, we propose Temporal Preference Optimization (TPO), a novel post-training framework designed to enhance the temporal grounding capabilities of video-LMMs through preference learning. TPO adopts… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.13919v2-abstract-full').style.display = 'inline'; document.getElementById('2501.13919v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.13919v2-abstract-full" style="display: none;"> Despite significant advancements in video large multimodal models (video-LMMs), achieving effective temporal grounding in long-form videos remains a challenge for existing models. To address this limitation, we propose Temporal Preference Optimization (TPO), a novel post-training framework designed to enhance the temporal grounding capabilities of video-LMMs through preference learning. TPO adopts a self-training approach that enables models to differentiate between well-grounded and less accurate temporal responses by leveraging curated preference datasets at two granularities: localized temporal grounding, which focuses on specific video segments, and comprehensive temporal grounding, which captures extended temporal dependencies across entire video sequences. By optimizing on these preference datasets, TPO significantly enhances temporal understanding while reducing reliance on manually annotated data. Extensive experiments on three long-form video understanding benchmarks--LongVideoBench, MLVU, and Video-MME--demonstrate the effectiveness of TPO across two state-of-the-art video-LMMs. Notably, LLaVA-Video-TPO establishes itself as the leading 7B model on the Video-MME benchmark, underscoring the potential of TPO as a scalable and efficient solution for advancing temporal reasoning in long-form video understanding. Project page: https://ruili33.github.io/tpo_website. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.13919v2-abstract-full').style.display = 'none'; document.getElementById('2501.13919v2-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">v1</span> submitted 23 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/2501.13746">arXiv:2501.13746</a> <span> [<a href="https://arxiv.org/pdf/2501.13746">pdf</a>, <a href="https://arxiv.org/format/2501.13746">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> <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"> EICopilot: Search and Explore Enterprise Information over Large-scale Knowledge Graphs with LLM-driven Agents </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yun%2C+Y">Yuhui Yun</a>, <a href="/search/?searchtype=author&query=Ye%2C+H">Huilong Ye</a>, <a href="/search/?searchtype=author&query=Li%2C+X">Xinru Li</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruojia Li</a>, <a href="/search/?searchtype=author&query=Deng%2C+J">Jingfeng Deng</a>, <a href="/search/?searchtype=author&query=Li%2C+L">Li Li</a>, <a href="/search/?searchtype=author&query=Xiong%2C+H">Haoyi Xiong</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="2501.13746v1-abstract-short" style="display: inline;"> The paper introduces EICopilot, an novel agent-based solution enhancing search and exploration of enterprise registration data within extensive online knowledge graphs like those detailing legal entities, registered capital, and major shareholders. Traditional methods necessitate text-based queries and manual subgraph explorations, often resulting in time-consuming processes. EICopilot, deployed a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.13746v1-abstract-full').style.display = 'inline'; document.getElementById('2501.13746v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.13746v1-abstract-full" style="display: none;"> The paper introduces EICopilot, an novel agent-based solution enhancing search and exploration of enterprise registration data within extensive online knowledge graphs like those detailing legal entities, registered capital, and major shareholders. Traditional methods necessitate text-based queries and manual subgraph explorations, often resulting in time-consuming processes. EICopilot, deployed as a chatbot via Baidu Enterprise Search, improves this landscape by utilizing Large Language Models (LLMs) to interpret natural language queries. This solution automatically generates and executes Gremlin scripts, providing efficient summaries of complex enterprise relationships. Distinct feature a data pre-processing pipeline that compiles and annotates representative queries into a vector database of examples for In-context learning (ICL), a comprehensive reasoning pipeline combining Chain-of-Thought with ICL to enhance Gremlin script generation for knowledge graph search and exploration, and a novel query masking strategy that improves intent recognition for heightened script accuracy. Empirical evaluations demonstrate the superior performance of EICopilot, including speed and accuracy, over baseline methods, with the \emph{Full Mask} variant achieving a syntax error rate reduction to as low as 10.00% and an execution correctness of up to 82.14%. These components collectively contribute to superior querying capabilities and summarization of intricate datasets, positioning EICopilot as a groundbreaking tool in the exploration and exploitation of large-scale knowledge graphs for enterprise information search. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.13746v1-abstract-full').style.display = 'none'; document.getElementById('2501.13746v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.13245">arXiv:2501.13245</a> <span> [<a href="https://arxiv.org/pdf/2501.13245">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> </div> </div> <p class="title is-5 mathjax"> Accelerating Discovery of Solid-State Thin-Film Metal Dealloying for 3D Nanoarchitecture Materials Design through Laser Thermal Gradient Treatment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chung%2C+C">Cheng-Chu Chung</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruipeng Li</a>, <a href="/search/?searchtype=author&query=Veith%2C+G+M">Gabriel M. Veith</a>, <a href="/search/?searchtype=author&query=Zhang%2C+H">Honghu Zhang</a>, <a href="/search/?searchtype=author&query=Camino%2C+F">Fernando Camino</a>, <a href="/search/?searchtype=author&query=Lu%2C+M">Ming Lu</a>, <a href="/search/?searchtype=author&query=Tiwale%2C+N">Nikhil Tiwale</a>, <a href="/search/?searchtype=author&query=Zhang%2C+S">Sheng Zhang</a>, <a href="/search/?searchtype=author&query=Yager%2C+K">Kevin Yager</a>, <a href="/search/?searchtype=author&query=Chen-Wiegart%2C+Y+K">Yu-chen Karen Chen-Wiegart</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="2501.13245v1-abstract-short" style="display: inline;"> Thin-film solid-state metal dealloying (thin-film SSMD) is a promising method for fabricating nanostructures with controlled morphology and efficiency, offering advantages over conventional bulk materials processing methods for integration into practical applications. Although machine learning (ML) has facilitated the design of dealloying systems, the selection of key thermal treatment parameters… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.13245v1-abstract-full').style.display = 'inline'; document.getElementById('2501.13245v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.13245v1-abstract-full" style="display: none;"> Thin-film solid-state metal dealloying (thin-film SSMD) is a promising method for fabricating nanostructures with controlled morphology and efficiency, offering advantages over conventional bulk materials processing methods for integration into practical applications. Although machine learning (ML) has facilitated the design of dealloying systems, the selection of key thermal treatment parameters for nanostructure formation remains largely unknown and dependent on experimental trial and error. To overcome this challenge, a workflow enabling high-throughput characterization of thermal treatment parameters while probing local nanostructures of thin-film samples is needed. In this work, a laser-based thermal treatment is demonstrated to create temperature gradients on single thin-film samples of Nb-Al/Sc and Nb-Al/Cu. This continuous thermal space enables observation of dealloying transitions and the resulting nanostructures of interest. Through synchrotron X-ray multimodal and high-throughput characterization, critical transitions and nanostructures can be rapidly captured and subsequently verified using electron microscopy. The key temperatures driving chemical reactions and morphological evolutions are clearly identified within this framework. While the oxidation process may contribute to nanostructure formation during thin-film treatment, the dealloying process at the dealloying front involves interactions solely between the dealloying elements, highlighting the availability and viability of the selected systems. This approach enables efficient exploration of the dealloying process and validation of ML predictions, thereby accelerating the discovery of thin-film SSMD systems with targeted nanostructures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.13245v1-abstract-full').style.display = 'none'; document.getElementById('2501.13245v1-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> 22 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">The main content contains 6 figures within 25 pages. The supporting information includes 5 figures within 5 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/2501.12683">arXiv:2501.12683</a> <span> [<a href="https://arxiv.org/pdf/2501.12683">pdf</a>, <a href="https://arxiv.org/format/2501.12683">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"> Enhanced Proton Acceleration via Petawatt Laguerre-Gaussian Lasers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+W">Wenpeng Wang</a>, <a href="/search/?searchtype=author&query=Sun%2C+X">Xinyue Sun</a>, <a href="/search/?searchtype=author&query=Sun%2C+F">Fengyu Sun</a>, <a href="/search/?searchtype=author&query=Lv%2C+Z">Zhengxing Lv</a>, <a href="/search/?searchtype=author&query=Glize%2C+K">K. Glize</a>, <a href="/search/?searchtype=author&query=Shi%2C+Z">Zhiyong Shi</a>, <a href="/search/?searchtype=author&query=Xu%2C+Y">Yi Xu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zongxin Zhang</a>, <a href="/search/?searchtype=author&query=Wu%2C+F">Fenxiang Wu</a>, <a href="/search/?searchtype=author&query=Hu%2C+J">Jiabing Hu</a>, <a href="/search/?searchtype=author&query=Qian%2C+J">Jiayi Qian</a>, <a href="/search/?searchtype=author&query=Zhu%2C+J">Jiacheng Zhu</a>, <a href="/search/?searchtype=author&query=Liang%2C+X">Xiaoyan Liang</a>, <a href="/search/?searchtype=author&query=Leng%2C+Y">Yuxin Leng</a>, <a href="/search/?searchtype=author&query=Li%2C+R">Ruxin Li</a>, <a href="/search/?searchtype=author&query=Xu%2C+Z">Zhizhan Xu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.12683v1-abstract-short" style="display: inline;"> High-energy, high-flux collimated proton beams with high repetition rates are critical for applications such as proton therapy, proton radiography, high-energy-density matter generation, and compact particle accelerators. However, achieving proton beam collimation has typically relied on complex and expensive target fabrication or precise control of auxiliary laser pulses, which poses significant… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.12683v1-abstract-full').style.display = 'inline'; document.getElementById('2501.12683v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.12683v1-abstract-full" style="display: none;"> High-energy, high-flux collimated proton beams with high repetition rates are critical for applications such as proton therapy, proton radiography, high-energy-density matter generation, and compact particle accelerators. However, achieving proton beam collimation has typically relied on complex and expensive target fabrication or precise control of auxiliary laser pulses, which poses significant limitations for high-repetition applications. Here, we demonstrate an all-optical method for collimated proton acceleration using a single femtosecond Laguerre-Gaussian (LG) laser with an intensity exceeding 1020 W/cm2 irradiating a simple planar target. Compared to conventional Gaussian laser-driven schemes, the maximum proton energy is enhanced by 60% (reaching 35 MeV) and beam divergence is much reduced. Particle-in-cell simulations reveal that a plasma jet is initially focused by the hollow electric sheath field of the LG laser, and then electrons in the jet are further collimated by self-generated magnetic fields. This process amplifies the charge-separation electric field between electrons and ions, leading to increased proton energy in the longitudinal direction and improved collimation in the transverse direction. This single-LG-laser-driven collimation mechanism offers a promising pathway for high-repetition, high-quality proton beam generation, with broad potential applications including proton therapy and fast ignition in inertial confinement fusion. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.12683v1-abstract-full').style.display = 'none'; document.getElementById('2501.12683v1-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> 22 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 href="/search/?searchtype=author&query=Li%2C+R&start=50" class="pagination-next" >Next </a> <ul 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