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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=Chen%2C+Z&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Chen%2C+Z&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Chen%2C+Z&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Chen%2C+Z&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Chen%2C+Z&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> <li> <a href="/search/?searchtype=author&query=Chen%2C+Z&start=200" class="pagination-link " aria-label="Page 5" aria-current="page">5 </a> </li> <li><span class="pagination-ellipsis">…</span></li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.14401">arXiv:2411.14401</a> <span> [<a href="https://arxiv.org/pdf/2411.14401">pdf</a>, <a href="https://arxiv.org/format/2411.14401">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"> Beyond Training: Dynamic Token Merging for Zero-Shot Video Understanding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yiming Zhang</a>, <a href="/search/?searchtype=author&query=Zhao%2C+Z">Zhuokai Zhao</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhaorun Chen</a>, <a href="/search/?searchtype=author&query=Ding%2C+Z">Zenghui Ding</a>, <a href="/search/?searchtype=author&query=Yang%2C+X">Xianjun Yang</a>, <a href="/search/?searchtype=author&query=Sun%2C+Y">Yining Sun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.14401v1-abstract-short" style="display: inline;"> Recent advancements in multimodal large language models (MLLMs) have opened new avenues for video understanding. However, achieving high fidelity in zero-shot video tasks remains challenging. Traditional video processing methods rely heavily on fine-tuning to capture nuanced spatial-temporal details, which incurs significant data and computation costs. In contrast, training-free approaches, though… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14401v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14401v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14401v1-abstract-full" style="display: none;"> Recent advancements in multimodal large language models (MLLMs) have opened new avenues for video understanding. However, achieving high fidelity in zero-shot video tasks remains challenging. Traditional video processing methods rely heavily on fine-tuning to capture nuanced spatial-temporal details, which incurs significant data and computation costs. In contrast, training-free approaches, though efficient, often lack robustness in preserving context-rich features across complex video content. To this end, we propose DYTO, a novel dynamic token merging framework for zero-shot video understanding that adaptively optimizes token efficiency while preserving crucial scene details. DYTO integrates a hierarchical frame selection and a bipartite token merging strategy to dynamically cluster key frames and selectively compress token sequences, striking a balance between computational efficiency with semantic richness. Extensive experiments across multiple benchmarks demonstrate the effectiveness of DYTO, achieving superior performance compared to both fine-tuned and training-free methods and setting a new state-of-the-art for zero-shot video understanding. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14401v1-abstract-full').style.display = 'none'; document.getElementById('2411.14401v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13873">arXiv:2411.13873</a> <span> [<a href="https://arxiv.org/pdf/2411.13873">pdf</a>, <a href="https://arxiv.org/format/2411.13873">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"> Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=An%2C+D">Delin An</a>, <a href="/search/?searchtype=author&query=Gu%2C+P">Pengfei Gu</a>, <a href="/search/?searchtype=author&query=Sonka%2C+M">Milan Sonka</a>, <a href="/search/?searchtype=author&query=Wang%2C+C">Chaoli Wang</a>, <a href="/search/?searchtype=author&query=Chen%2C+D+Z">Danny Z. 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="2411.13873v1-abstract-short" style="display: inline;"> Deep learning (DL) methods have shown remarkable successes in medical image segmentation, often using large amounts of annotated data for model training. However, acquiring a large number of diverse labeled 3D medical image datasets is highly difficult and expensive. Recently, mask propagation DL methods were developed to reduce the annotation burden on 3D medical images. For example, Sli2Vol~\cit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13873v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13873v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13873v1-abstract-full" style="display: none;"> Deep learning (DL) methods have shown remarkable successes in medical image segmentation, often using large amounts of annotated data for model training. However, acquiring a large number of diverse labeled 3D medical image datasets is highly difficult and expensive. Recently, mask propagation DL methods were developed to reduce the annotation burden on 3D medical images. For example, Sli2Vol~\cite{yeung2021sli2vol} proposed a self-supervised framework (SSF) to learn correspondences by matching neighboring slices via slice reconstruction in the training stage; the learned correspondences were then used to propagate a labeled slice to other slices in the test stage. But, these methods are still prone to error accumulation due to the inter-slice propagation of reconstruction errors. Also, they do not handle discontinuities well, which can occur between consecutive slices in 3D images, as they emphasize exploiting object continuity. To address these challenges, in this work, we propose a new SSF, called \proposed, {for segmenting any anatomical structures in 3D medical images using only a single annotated slice per training and testing volume.} Specifically, in the training stage, we first propagate an annotated 2D slice of a training volume to the other slices, generating pseudo-labels (PLs). Then, we develop a novel Object Estimation Guided Correspondence Flow Network to learn reliable correspondences between consecutive slices and corresponding PLs in a self-supervised manner. In the test stage, such correspondences are utilized to propagate a single annotated slice to the other slices of a test volume. We demonstrate the effectiveness of our method on various medical image segmentation tasks with different datasets, showing better generalizability across different organs, modalities, and modals. Code is available at \url{https://github.com/adlsn/Sli2Volplus} <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13873v1-abstract-full').style.display = 'none'; document.getElementById('2411.13873v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13692">arXiv:2411.13692</a> <span> [<a href="https://arxiv.org/pdf/2411.13692">pdf</a>, <a href="https://arxiv.org/format/2411.13692">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Methodology">stat.ME</span> </div> </div> <p class="title is-5 mathjax"> Randomized Basket Trial with an Interim Analysis (RaBIt) and Applications in Mental Health </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Patel%2C+S+S">Sahil S. Patel</a>, <a href="/search/?searchtype=author&query=Chen%2C+D+Z">Desmond Zeya Chen</a>, <a href="/search/?searchtype=author&query=Castle%2C+D">David Castle</a>, <a href="/search/?searchtype=author&query=Ma%2C+C">Clement 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="2411.13692v1-abstract-short" style="display: inline;"> Basket trials can efficiently evaluate a single treatment across multiple diseases with a common shared target. Prior methods for randomized basket trials required baskets to have the same sample and effect sizes. To that end, we developed a general randomized basket trial with an interim analysis (RaBIt) that allows for unequal sample sizes and effect sizes per basket. RaBIt is characterized by p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13692v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13692v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13692v1-abstract-full" style="display: none;"> Basket trials can efficiently evaluate a single treatment across multiple diseases with a common shared target. Prior methods for randomized basket trials required baskets to have the same sample and effect sizes. To that end, we developed a general randomized basket trial with an interim analysis (RaBIt) that allows for unequal sample sizes and effect sizes per basket. RaBIt is characterized by pruning at an interim stage and then analyzing a pooling of the remaining baskets. We derived the analytical power and type 1 error for the design. We first show that our results are consistent with the prior methods when the sample and effect sizes were the same across baskets. As we adjust the sample allocation between baskets, our threshold for the final test statistic becomes more stringent in order to maintain the same overall type 1 error. Finally, we notice that if we fix a sample size for the baskets proportional to their accrual rate, then at the cost of an almost negligible amount of power, the trial overall is expected to take substantially less time than the non-generalized version. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13692v1-abstract-full').style.display = 'none'; document.getElementById('2411.13692v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">23 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13676">arXiv:2411.13676</a> <span> [<a href="https://arxiv.org/pdf/2411.13676">pdf</a>, <a href="https://arxiv.org/format/2411.13676">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"> Hymba: A Hybrid-head Architecture for Small Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Dong%2C+X">Xin Dong</a>, <a href="/search/?searchtype=author&query=Fu%2C+Y">Yonggan Fu</a>, <a href="/search/?searchtype=author&query=Diao%2C+S">Shizhe Diao</a>, <a href="/search/?searchtype=author&query=Byeon%2C+W">Wonmin Byeon</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zijia Chen</a>, <a href="/search/?searchtype=author&query=Mahabaleshwarkar%2C+A+S">Ameya Sunil Mahabaleshwarkar</a>, <a href="/search/?searchtype=author&query=Liu%2C+S">Shih-Yang Liu</a>, <a href="/search/?searchtype=author&query=Van+Keirsbilck%2C+M">Matthijs Van Keirsbilck</a>, <a href="/search/?searchtype=author&query=Chen%2C+M">Min-Hung Chen</a>, <a href="/search/?searchtype=author&query=Suhara%2C+Y">Yoshi Suhara</a>, <a href="/search/?searchtype=author&query=Lin%2C+Y">Yingyan Lin</a>, <a href="/search/?searchtype=author&query=Kautz%2C+J">Jan Kautz</a>, <a href="/search/?searchtype=author&query=Molchanov%2C+P">Pavlo Molchanov</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.13676v1-abstract-short" style="display: inline;"> We propose Hymba, a family of small language models featuring a hybrid-head parallel architecture that integrates transformer attention mechanisms with state space models (SSMs) for enhanced efficiency. Attention heads provide high-resolution recall, while SSM heads enable efficient context summarization. Additionally, we introduce learnable meta tokens that are prepended to prompts, storing criti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13676v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13676v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13676v1-abstract-full" style="display: none;"> We propose Hymba, a family of small language models featuring a hybrid-head parallel architecture that integrates transformer attention mechanisms with state space models (SSMs) for enhanced efficiency. Attention heads provide high-resolution recall, while SSM heads enable efficient context summarization. Additionally, we introduce learnable meta tokens that are prepended to prompts, storing critical information and alleviating the "forced-to-attend" burden associated with attention mechanisms. This model is further optimized by incorporating cross-layer key-value (KV) sharing and partial sliding window attention, resulting in a compact cache size. During development, we conducted a controlled study comparing various architectures under identical settings and observed significant advantages of our proposed architecture. Notably, Hymba achieves state-of-the-art results for small LMs: Our Hymba-1.5B-Base model surpasses all sub-2B public models in performance and even outperforms Llama-3.2-3B with 1.32% higher average accuracy, an 11.67x cache size reduction, and 3.49x throughput. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13676v1-abstract-full').style.display = 'none'; document.getElementById('2411.13676v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, models are available on huggingface</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13353">arXiv:2411.13353</a> <span> [<a href="https://arxiv.org/pdf/2411.13353">pdf</a>, <a href="https://arxiv.org/format/2411.13353">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> </div> </div> <p class="title is-5 mathjax"> Miniaturized spectrometer enabled by end-to-end deep learning on large-scale radiative cavity array </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhou%2C+X">Xinyi Zhou</a>, <a href="/search/?searchtype=author&query=Zhang%2C+C">Cheng Zhang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+X">Xiaoyu Zhang</a>, <a href="/search/?searchtype=author&query=Zuo%2C+Y">Yi Zuo</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zixuan Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+F">Feifan Wang</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zihao Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Hongbin Li</a>, <a href="/search/?searchtype=author&query=Peng%2C+C">Chao Peng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.13353v1-abstract-short" style="display: inline;"> Miniaturized (mini-) spectrometers are highly desirable tools for chemical, biological, and medical diagnostics because of their potential for portable and in situ spectral detection. In this work, we propose and demonstrate a mini-spectrometer that combines a large-scale radiative cavity array with end-to-end deep learning networks. Specifically, we utilize high-Q bound states in continuum caviti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13353v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13353v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13353v1-abstract-full" style="display: none;"> Miniaturized (mini-) spectrometers are highly desirable tools for chemical, biological, and medical diagnostics because of their potential for portable and in situ spectral detection. In this work, we propose and demonstrate a mini-spectrometer that combines a large-scale radiative cavity array with end-to-end deep learning networks. Specifically, we utilize high-Q bound states in continuum cavities with distinct radiation characteristics as the fundamental units to achieve parallel spectral detection. We realize a 36 $\times$ 30 cavity array that spans a wide spectral range from 1525 to 1605 nm with quality factors above 10^4. We further train a deep network with 8000 outputs to directly map arbitrary spectra to array responses excited by the out-of-plane incident. Experimental results demonstrate that the proposed mini-spectrometer can resolve unknown spectra with a resolution of 0.048 nm in a bandwidth of 80 nm and fidelity exceeding 95%, thus offering a promising method for compact, high resolution, and broadband spectroscopy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13353v1-abstract-full').style.display = 'none'; document.getElementById('2411.13353v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">31 pages, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13219">arXiv:2411.13219</a> <span> [<a href="https://arxiv.org/pdf/2411.13219">pdf</a>, <a href="https://arxiv.org/ps/2411.13219">ps</a>, <a href="https://arxiv.org/format/2411.13219">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Backward Stochastic Control System with Entropy Regularization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Ziyue Chen</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Q">Qi Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.13219v1-abstract-short" style="display: inline;"> The entropy regularization is inspired by information entropy from machine learning and the ideas of exploration and exploitation in reinforcement learning, which appears in the control problem to design an approximating algorithm for the optimal control. This paper is concerned with the optimal exploratory control for backward stochastic system, generated by the backward stochastic differential e… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13219v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13219v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13219v1-abstract-full" style="display: none;"> The entropy regularization is inspired by information entropy from machine learning and the ideas of exploration and exploitation in reinforcement learning, which appears in the control problem to design an approximating algorithm for the optimal control. This paper is concerned with the optimal exploratory control for backward stochastic system, generated by the backward stochastic differential equation and with the entropy regularization in its cost functional. We give the theoretical depict of the optimal relaxed control so as to lay the foundation for the application of such a backward stochastic control system to mathematical finance and algorithm implementation. For this, we first establish the stochastic maximum principle by convex variation method. Then we prove sufficient condition for the optimal control and demonstrate the implicit form of optimal control. Finally, the existence and uniqueness of the optimal control for backward linear-quadratic control problem with entropy regularization is proved by decoupling techniques. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13219v1-abstract-full').style.display = 'none'; document.getElementById('2411.13219v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13154">arXiv:2411.13154</a> <span> [<a href="https://arxiv.org/pdf/2411.13154">pdf</a>, <a href="https://arxiv.org/format/2411.13154">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"> DMQR-RAG: Diverse Multi-Query Rewriting for RAG </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+Z">Zhicong Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">Jiahao Wang</a>, <a href="/search/?searchtype=author&query=Jiang%2C+Z">Zhishu Jiang</a>, <a href="/search/?searchtype=author&query=Mao%2C+H">Hangyu Mao</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhongxia Chen</a>, <a href="/search/?searchtype=author&query=Du%2C+J">Jiazhen Du</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yuanxing Zhang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+F">Fuzheng Zhang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+D">Di Zhang</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yong Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.13154v1-abstract-short" style="display: inline;"> Large language models often encounter challenges with static knowledge and hallucinations, which undermine their reliability. Retrieval-augmented generation (RAG) mitigates these issues by incorporating external information. However, user queries frequently contain noise and intent deviations, necessitating query rewriting to improve the relevance of retrieved documents. In this paper, we introduc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13154v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13154v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13154v1-abstract-full" style="display: none;"> Large language models often encounter challenges with static knowledge and hallucinations, which undermine their reliability. Retrieval-augmented generation (RAG) mitigates these issues by incorporating external information. However, user queries frequently contain noise and intent deviations, necessitating query rewriting to improve the relevance of retrieved documents. In this paper, we introduce DMQR-RAG, a Diverse Multi-Query Rewriting framework designed to improve the performance of both document retrieval and final responses in RAG. Specifically, we investigate how queries with varying information quantities can retrieve a diverse array of documents, presenting four rewriting strategies that operate at different levels of information to enhance the performance of baseline approaches. Additionally, we propose an adaptive strategy selection method that minimizes the number of rewrites while optimizing overall performance. Our methods have been rigorously validated through extensive experiments conducted in both academic and industry settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13154v1-abstract-full').style.display = 'none'; document.getElementById('2411.13154v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13076">arXiv:2411.13076</a> <span> [<a href="https://arxiv.org/pdf/2411.13076">pdf</a>, <a href="https://arxiv.org/format/2411.13076">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"> Hints of Prompt: Enhancing Visual Representation for Multimodal LLMs in Autonomous Driving </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhou%2C+H">Hao Zhou</a>, <a href="/search/?searchtype=author&query=Gao%2C+Z">Zhanning Gao</a>, <a href="/search/?searchtype=author&query=Ye%2C+M">Maosheng Ye</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhili Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+Q">Qifeng Chen</a>, <a href="/search/?searchtype=author&query=Cao%2C+T">Tongyi Cao</a>, <a href="/search/?searchtype=author&query=Qi%2C+H">Honggang Qi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.13076v1-abstract-short" style="display: inline;"> In light of the dynamic nature of autonomous driving environments and stringent safety requirements, general MLLMs combined with CLIP alone often struggle to represent driving-specific scenarios accurately, particularly in complex interactions and long-tail cases. To address this, we propose the Hints of Prompt (HoP) framework, which introduces three key enhancements: Affinity hint to emphasize in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13076v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13076v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13076v1-abstract-full" style="display: none;"> In light of the dynamic nature of autonomous driving environments and stringent safety requirements, general MLLMs combined with CLIP alone often struggle to represent driving-specific scenarios accurately, particularly in complex interactions and long-tail cases. To address this, we propose the Hints of Prompt (HoP) framework, which introduces three key enhancements: Affinity hint to emphasize instance-level structure by strengthening token-wise connections, Semantic hint to incorporate high-level information relevant to driving-specific cases, such as complex interactions among vehicles and traffic signs, and Question hint to align visual features with the query context, focusing on question-relevant regions. These hints are fused through a Hint Fusion module, enriching visual representations and enhancing multimodal reasoning for autonomous driving VQA tasks. Extensive experiments confirm the effectiveness of the HoP framework, showing it significantly outperforms previous state-of-the-art methods across all key metrics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13076v1-abstract-full').style.display = 'none'; document.getElementById('2411.13076v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12711">arXiv:2411.12711</a> <span> [<a href="https://arxiv.org/pdf/2411.12711">pdf</a>, <a href="https://arxiv.org/format/2411.12711">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> UBSoft: A Simulation Platform for Robotic Skill Learning in Unbounded Soft Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lin%2C+C">Chunru Lin</a>, <a href="/search/?searchtype=author&query=Fan%2C+J">Jugang Fan</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yian Wang</a>, <a href="/search/?searchtype=author&query=Yang%2C+Z">Zeyuan Yang</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhehuan Chen</a>, <a href="/search/?searchtype=author&query=Fang%2C+L">Lixing Fang</a>, <a href="/search/?searchtype=author&query=Wang%2C+T">Tsun-Hsuan Wang</a>, <a href="/search/?searchtype=author&query=Xian%2C+Z">Zhou Xian</a>, <a href="/search/?searchtype=author&query=Gan%2C+C">Chuang Gan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.12711v1-abstract-short" style="display: inline;"> It is desired to equip robots with the capability of interacting with various soft materials as they are ubiquitous in the real world. While physics simulations are one of the predominant methods for data collection and robot training, simulating soft materials presents considerable challenges. Specifically, it is significantly more costly than simulating rigid objects in terms of simulation speed… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12711v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12711v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12711v1-abstract-full" style="display: none;"> It is desired to equip robots with the capability of interacting with various soft materials as they are ubiquitous in the real world. While physics simulations are one of the predominant methods for data collection and robot training, simulating soft materials presents considerable challenges. Specifically, it is significantly more costly than simulating rigid objects in terms of simulation speed and storage requirements. These limitations typically restrict the scope of studies on soft materials to small and bounded areas, thereby hindering the learning of skills in broader spaces. To address this issue, we introduce UBSoft, a new simulation platform designed to support unbounded soft environments for robot skill acquisition. Our platform utilizes spatially adaptive resolution scales, where simulation resolution dynamically adjusts based on proximity to active robotic agents. Our framework markedly reduces the demand for extensive storage space and computation costs required for large-scale scenarios involving soft materials. We also establish a set of benchmark tasks in our platform, including both locomotion and manipulation tasks, and conduct experiments to evaluate the efficacy of various reinforcement learning algorithms and trajectory optimization techniques, both gradient-based and sampling-based. Preliminary results indicate that sampling-based trajectory optimization generally achieves better results for obtaining one trajectory to solve the task. Additionally, we conduct experiments in real-world environments to demonstrate that advancements made in our UBSoft simulator could translate to improved robot interactions with large-scale soft material. More videos can be found at https://vis-www.cs.umass.edu/ubsoft/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12711v1-abstract-full').style.display = 'none'; document.getElementById('2411.12711v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">CoRL 2024. The first two authors contributed equally to this paper</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12530">arXiv:2411.12530</a> <span> [<a href="https://arxiv.org/pdf/2411.12530">pdf</a>, <a href="https://arxiv.org/format/2411.12530">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"> Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zou%2C+Y">Yang Zou</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhixin Chen</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zhipeng Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+X">Xingyuan Li</a>, <a href="/search/?searchtype=author&query=Ma%2C+L">Long Ma</a>, <a href="/search/?searchtype=author&query=Liu%2C+J">Jinyuan Liu</a>, <a href="/search/?searchtype=author&query=Wang%2C+P">Peng Wang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yanning Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.12530v1-abstract-short" style="display: inline;"> Image super-resolution (SR) is a classical yet still active low-level vision problem that aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts, serving as a key technique for image enhancement. Current approaches to address SR tasks, such as transformer-based and diffusion-based methods, are either dedicated to extracting RGB image features or assuming simila… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12530v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12530v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12530v1-abstract-full" style="display: none;"> Image super-resolution (SR) is a classical yet still active low-level vision problem that aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts, serving as a key technique for image enhancement. Current approaches to address SR tasks, such as transformer-based and diffusion-based methods, are either dedicated to extracting RGB image features or assuming similar degradation patterns, neglecting the inherent modal disparities between infrared and visible images. When directly applied to infrared image SR tasks, these methods inevitably distort the infrared spectral distribution, compromising the machine perception in downstream tasks. In this work, we emphasize the infrared spectral distribution fidelity and propose a Contourlet refinement gate framework to restore infrared modal-specific features while preserving spectral distribution fidelity. Our approach captures high-pass subbands from multi-scale and multi-directional infrared spectral decomposition to recover infrared-degraded information through a gate architecture. The proposed Spectral Fidelity Loss regularizes the spectral frequency distribution during reconstruction, which ensures the preservation of both high- and low-frequency components and maintains the fidelity of infrared-specific features. We propose a two-stage prompt-learning optimization to guide the model in learning infrared HR characteristics from LR degradation. Extensive experiments demonstrate that our approach outperforms existing image SR models in both visual and perceptual tasks while notably enhancing machine perception in downstream tasks. Our code is available at https://github.com/hey-it-s-me/CoRPLE. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12530v1-abstract-full').style.display = 'none'; document.getElementById('2411.12530v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 figures, 6 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T45 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.4.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12491">arXiv:2411.12491</a> <span> [<a href="https://arxiv.org/pdf/2411.12491">pdf</a>, <a href="https://arxiv.org/format/2411.12491">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> </div> </div> <p class="title is-5 mathjax"> The Challenges of Modeling Astrophysical Reacting Flows </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zingale%2C+M">Michael Zingale</a>, <a href="/search/?searchtype=author&query=Bhargava%2C+K">Khanak Bhargava</a>, <a href="/search/?searchtype=author&query=Brady%2C+R">Ryan Brady</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhi Chen</a>, <a href="/search/?searchtype=author&query=Guichandut%2C+S">Simon Guichandut</a>, <a href="/search/?searchtype=author&query=Johnson%2C+E+T">Eric T. Johnson</a>, <a href="/search/?searchtype=author&query=Katz%2C+M">Max Katz</a>, <a href="/search/?searchtype=author&query=Clark%2C+A+S">Alexander Smith Clark</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.12491v1-abstract-short" style="display: inline;"> Stellar evolution is driven by the changing composition of a star from nuclear reactions. At the late stages of evolution and during explosive events, the timescale can be short and drive strong hydrodynamic flows, making simulations of astrophysical reacting flows challenging. Over the past decades, the standard approach to modeling reactions in simulation codes has been operator splitting, using… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12491v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12491v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12491v1-abstract-full" style="display: none;"> Stellar evolution is driven by the changing composition of a star from nuclear reactions. At the late stages of evolution and during explosive events, the timescale can be short and drive strong hydrodynamic flows, making simulations of astrophysical reacting flows challenging. Over the past decades, the standard approach to modeling reactions in simulation codes has been operator splitting, using implicit integrators for reactions. Here we explore some of the assumptions in this standard approach and describe some techniques for improving the efficiency and accuracy of astrophysical reacting flows. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12491v1-abstract-full').style.display = 'none'; document.getElementById('2411.12491v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">submitted to Proceedings of AstroNum 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12426">arXiv:2411.12426</a> <span> [<a href="https://arxiv.org/pdf/2411.12426">pdf</a>, <a href="https://arxiv.org/format/2411.12426">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"> Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation Graph </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Ziyang Chen</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yongjun Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+W">Wenting Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+B">Bingshu Wang</a>, <a href="/search/?searchtype=author&query=Zhao%2C+Y">Yong Zhao</a>, <a href="/search/?searchtype=author&query=Chen%2C+C+L+P">C. L. Philip 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="2411.12426v1-abstract-short" style="display: inline;"> Real-world applications of stereo matching, such as autonomous driving, place stringent demands on both safety and accuracy. However, learning-based stereo matching methods inherently suffer from the loss of geometric structures in certain feature channels, creating a bottleneck in achieving precise detail matching. Additionally, these methods lack interpretability due to the black-box nature of d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12426v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12426v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12426v1-abstract-full" style="display: none;"> Real-world applications of stereo matching, such as autonomous driving, place stringent demands on both safety and accuracy. However, learning-based stereo matching methods inherently suffer from the loss of geometric structures in certain feature channels, creating a bottleneck in achieving precise detail matching. Additionally, these methods lack interpretability due to the black-box nature of deep learning. In this paper, we propose MoCha-V2, a novel learning-based paradigm for stereo matching. MoCha-V2 introduces the Motif Correlation Graph (MCG) to capture recurring textures, which are referred to as ``motifs" within feature channels. These motifs reconstruct geometric structures and are learned in a more interpretable way. Subsequently, we integrate features from multiple frequency domains through wavelet inverse transformation. The resulting motif features are utilized to restore geometric structures in the stereo matching process. Experimental results demonstrate the effectiveness of MoCha-V2. MoCha-V2 achieved 1st place on the Middlebury benchmark at the time of its release. Code is available at https://github.com/ZYangChen/MoCha-Stereo. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12426v1-abstract-full').style.display = 'none'; document.getElementById('2411.12426v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12363">arXiv:2411.12363</a> <span> [<a href="https://arxiv.org/pdf/2411.12363">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> DGSNA: prompt-based Dynamic Generative Scene-based Noise Addition method </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Zihao Chen</a>, <a href="/search/?searchtype=author&query=Lin%2C+Z">Zhentao Lin</a>, <a href="/search/?searchtype=author&query=Zeng%2C+B">Bi Zeng</a>, <a href="/search/?searchtype=author&query=Huang%2C+L">Linyi Huang</a>, <a href="/search/?searchtype=author&query=Li%2C+Z">Zhi Li</a>, <a href="/search/?searchtype=author&query=Cai%2C+J">Jia Cai</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.12363v1-abstract-short" style="display: inline;"> This paper addresses the challenges of accurately enumerating and describing scenes and the labor-intensive process required to replicate acoustic environments using non-generative methods. We introduce the prompt-based Dynamic Generative Sce-ne-based Noise Addition method (DGSNA), which innovatively combines the Dynamic Generation of Scene Information (DGSI) with Scene-based Noise Addition for Au… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12363v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12363v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12363v1-abstract-full" style="display: none;"> This paper addresses the challenges of accurately enumerating and describing scenes and the labor-intensive process required to replicate acoustic environments using non-generative methods. We introduce the prompt-based Dynamic Generative Sce-ne-based Noise Addition method (DGSNA), which innovatively combines the Dynamic Generation of Scene Information (DGSI) with Scene-based Noise Addition for Audio (SNAA). Employing generative chat models structured within the Back-ground-Examples-Task (BET) prompt framework, DGSI com-ponent facilitates the dynamic synthesis of tailored Scene Infor-mation (SI) for specific acoustic environments. Additionally, the SNAA component leverages Room Impulse Response (RIR) fil-ters and Text-To-Audio (TTA) systems to generate realistic, scene-based noise that can be adapted for both indoor and out-door environments. Through comprehensive experiments, the adaptability of DGSNA across different generative chat models was demonstrated. The results, assessed through both objective and subjective evaluations, show that DGSNA provides robust performance in dynamically generating precise SI and effectively enhancing scene-based noise addition capabilities, thus offering significant improvements over traditional methods in acoustic scene simulation. Our implementation and demos are available at https://dgsna.github.io. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12363v1-abstract-full').style.display = 'none'; document.getElementById('2411.12363v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12178">arXiv:2411.12178</a> <span> [<a href="https://arxiv.org/pdf/2411.12178">pdf</a>, <a href="https://arxiv.org/format/2411.12178">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"> First evidence for direct CP violation in beauty to charmonium decays </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=LHCb+collaboration"> LHCb collaboration</a>, <a href="/search/?searchtype=author&query=Aaij%2C+R">R. Aaij</a>, <a href="/search/?searchtype=author&query=Abdelmotteleb%2C+A+S+W">A. S. W. Abdelmotteleb</a>, <a href="/search/?searchtype=author&query=Beteta%2C+C+A">C. Abellan Beteta</a>, <a href="/search/?searchtype=author&query=Abudin%C3%A9n%2C+F">F. Abudin茅n</a>, <a href="/search/?searchtype=author&query=Ackernley%2C+T">T. Ackernley</a>, <a href="/search/?searchtype=author&query=Adefisoye%2C+A+A">A. A. Adefisoye</a>, <a href="/search/?searchtype=author&query=Adeva%2C+B">B. Adeva</a>, <a href="/search/?searchtype=author&query=Adinolfi%2C+M">M. Adinolfi</a>, <a href="/search/?searchtype=author&query=Adlarson%2C+P">P. Adlarson</a>, <a href="/search/?searchtype=author&query=Agapopoulou%2C+C">C. Agapopoulou</a>, <a href="/search/?searchtype=author&query=Aidala%2C+C+A">C. A. Aidala</a>, <a href="/search/?searchtype=author&query=Ajaltouni%2C+Z">Z. Ajaltouni</a>, <a href="/search/?searchtype=author&query=Akar%2C+S">S. Akar</a>, <a href="/search/?searchtype=author&query=Akiba%2C+K">K. Akiba</a>, <a href="/search/?searchtype=author&query=Albicocco%2C+P">P. Albicocco</a>, <a href="/search/?searchtype=author&query=Albrecht%2C+J">J. Albrecht</a>, <a href="/search/?searchtype=author&query=Alessio%2C+F">F. Alessio</a>, <a href="/search/?searchtype=author&query=Alexander%2C+M">M. Alexander</a>, <a href="/search/?searchtype=author&query=Aliouche%2C+Z">Z. Aliouche</a>, <a href="/search/?searchtype=author&query=Cartelle%2C+P+A">P. Alvarez Cartelle</a>, <a href="/search/?searchtype=author&query=Amalric%2C+R">R. Amalric</a>, <a href="/search/?searchtype=author&query=Amato%2C+S">S. Amato</a>, <a href="/search/?searchtype=author&query=Amey%2C+J+L">J. L. Amey</a>, <a href="/search/?searchtype=author&query=Amhis%2C+Y">Y. Amhis</a> , et al. (1127 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.12178v1-abstract-short" style="display: inline;"> The {\ensuremath{C\!P}}\xspace asymmetry and branching fraction of the CKM-suppressed decay \mbox{\ensuremath{{B^+}\!\to {J\mskip -3mu/\mskip -2mu蠄}{蟺^+}}} are precisely measured relative to the favoured decay \mbox{\ensuremath{{B^+}\!\to {J\mskip -3mu/\mskip -2mu蠄}{{K}^+}}}, using a sample of proton-proton collision data corresponding to an integrated luminosity of $5.4 \text{\,fb}^{-1}$ recorded… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12178v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12178v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12178v1-abstract-full" style="display: none;"> The {\ensuremath{C\!P}}\xspace asymmetry and branching fraction of the CKM-suppressed decay \mbox{\ensuremath{{B^+}\!\to {J\mskip -3mu/\mskip -2mu蠄}{蟺^+}}} are precisely measured relative to the favoured decay \mbox{\ensuremath{{B^+}\!\to {J\mskip -3mu/\mskip -2mu蠄}{{K}^+}}}, using a sample of proton-proton collision data corresponding to an integrated luminosity of $5.4 \text{\,fb}^{-1}$ recorded at center-of-mass energy of $13\text{\,Te\kern -0.1em V}$ during 2016--2018. The results of the {\ensuremath{C\!P}}\xspace asymmetry difference and branching fraction ratio are \begin{align*} 螖\mathcal{A}^{C\!P} &\equiv \mathcal{A}^{C\!P}({B}^+ \to {J}\mskip -3mu/\mskip -2mu蠄\,蟺^+) - \mathcal{A}^{C\!P}({B}^+ \to {J}\mskip -3mu/\mskip -2mu蠄\,K^+) = (1.29 \pm 0.49 \pm 0.08) \times 10^{-2}, \end{align*} \begin{equation*} \mathcal{R}_{蟺/K} \equiv \frac{\mathcal{B}(B^+ \!\to J\mskip -3mu/\mskip -2mu蠄\,蟺^+)} {\mathcal{B}(B^+ \!\to J\mskip -3mu/\mskip -2mu蠄\,K^+)} = (3.852 \pm 0.022 \pm 0.018) \times 10^{-2}. \end{equation*}where the first uncertainties are statistical and the second systematic. A combination with previous LHCb results based on data collected at $7$ and $8~\text{Te\kern -0.1em V}$ in 2011 and 2012 yields {$螖\mathcal{A}^{C\!P} = (1.42 \pm 0.43 \pm 0.08) \times 10^{-2}$ and $\mathcal{R}_{蟺/K} = (3.846 \pm 0.018 \pm 0.018) \times 10^{-2}$}. The combined $螖\mathcal{A}^{C\!P}$ value deviates from zero by 3.2 standard deviations, providing the first evidence for direct {\ensuremath{C\!P}}\xspace violation in the amplitudes of beauty decays to charmonium final states. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12178v1-abstract-full').style.display = 'none'; document.getElementById('2411.12178v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 2 figures, no conference or journal information All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/1623/ (LHCb public pages)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LHCb-PAPER-2024-031 CERN-EP-2024-286 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12138">arXiv:2411.12138</a> <span> [<a href="https://arxiv.org/pdf/2411.12138">pdf</a>, <a href="https://arxiv.org/format/2411.12138">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"> COALAS III: The ATCA CO(1-0) look at the growth and death of H$伪$ emitters in the Spiderweb protocluster at z=2.16 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=P%C3%A9rez-Mart%C3%ADnez%2C+J+M">J. M. P茅rez-Mart铆nez</a>, <a href="/search/?searchtype=author&query=Dannerbauer%2C+H">H. Dannerbauer</a>, <a href="/search/?searchtype=author&query=Emonts%2C+B+H+C">B. H. C. Emonts</a>, <a href="/search/?searchtype=author&query=Allison%2C+J+R">J. R. Allison</a>, <a href="/search/?searchtype=author&query=Champagne%2C+J+B">J. B. Champagne</a>, <a href="/search/?searchtype=author&query=Indermuehle%2C+B">B. Indermuehle</a>, <a href="/search/?searchtype=author&query=Norris%2C+R+P">R. P. Norris</a>, <a href="/search/?searchtype=author&query=Serra%2C+P">P. Serra</a>, <a href="/search/?searchtype=author&query=Seymour%2C+N">N. Seymour</a>, <a href="/search/?searchtype=author&query=Thomson%2C+A+P">A. P. Thomson</a>, <a href="/search/?searchtype=author&query=Casey%2C+C+M">C. M. Casey</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Z. Chen</a>, <a href="/search/?searchtype=author&query=Daikuhara%2C+K">K. Daikuhara</a>, <a href="/search/?searchtype=author&query=De+Breuck%2C+C">C. De Breuck</a>, <a href="/search/?searchtype=author&query=D%27Eugenio%2C+C">C. D'Eugenio</a>, <a href="/search/?searchtype=author&query=Drouart%2C+G">G. Drouart</a>, <a href="/search/?searchtype=author&query=Hatch%2C+N">N. Hatch</a>, <a href="/search/?searchtype=author&query=Jin%2C+S">S. Jin</a>, <a href="/search/?searchtype=author&query=Kodama%2C+T">T. Kodama</a>, <a href="/search/?searchtype=author&query=Koyama%2C+Y">Y. Koyama</a>, <a href="/search/?searchtype=author&query=Lehnert%2C+M+D">M. D. Lehnert</a>, <a href="/search/?searchtype=author&query=Macgregor%2C+P">P. Macgregor</a>, <a href="/search/?searchtype=author&query=Miley%2C+G">G. Miley</a>, <a href="/search/?searchtype=author&query=Naufal%2C+A">A. Naufal</a>, <a href="/search/?searchtype=author&query=R%C3%B6ttgering%2C+H">H. R枚ttgering</a> , et al. (4 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.12138v1-abstract-short" style="display: inline;"> We obtain CO(1-0) molecular gas measurements with ATCA on a sample of 43 spectroscopically confirmed H$伪$ emitters in the Spiderweb protocluster at $z=2.16$ and investigate the relation between their star formation and cold gas reservoirs as a function of environment. We achieve a CO(1-0) detection rate of $\sim23\pm12\%$ with 10 dual CO(1-0) and H$伪$ detections at $10<\log M_{*}/M_\odot<11.5$. In… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12138v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12138v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12138v1-abstract-full" style="display: none;"> We obtain CO(1-0) molecular gas measurements with ATCA on a sample of 43 spectroscopically confirmed H$伪$ emitters in the Spiderweb protocluster at $z=2.16$ and investigate the relation between their star formation and cold gas reservoirs as a function of environment. We achieve a CO(1-0) detection rate of $\sim23\pm12\%$ with 10 dual CO(1-0) and H$伪$ detections at $10<\log M_{*}/M_\odot<11.5$. In addition, we obtain upper limits for the remaining sources. In terms of total gas fractions ($F_{gas}$), our sample is divided into two different regimes with a steep transition at $\log M_{*}/M_\odot\approx10.5$. Galaxies below that threshold have gas fractions that in some cases are close to unity, indicating that their gas reservoir has been replenished by inflows from the cosmic web. However, objects at $\log M_{*}/M_\odot>10.5$ display significantly lower gas fractions and are dominated by AGN (12 out of 20). Stacking results yield $F_{gas}\approx0.55$ for massive emitters excluding AGN, and $F_{gas}\approx0.35$ when examining only AGN candidates. Furthermore, depletion times show that most H$伪$ emitters may become passive by $1<z<1.6$, concurrently with the surge and dominance of the red sequence in the most massive clusters. Our analyses suggest that galaxies in the outskirts of the protocluster have larger molecular-to-stellar mass ratios and lower star formation efficiencies than in the core. However, star formation across the protocluster remains consistent with the main sequence, indicating that evolution is primarily driven by the depletion of the gas reservoir towards the inner regions. We discuss the relative importance of in-/outflow processes in regulating star formation during the early phases of cluster assembly and conclude that a combination of feedback and overconsumption may be responsible for the rapid cold gas depletion these objects endure. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12138v1-abstract-full').style.display = 'none'; document.getElementById('2411.12138v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">25 pages, 12 figures, 4 tables. Resubmitted to A&A after implementing the second round of comments by the referee</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12083">arXiv:2411.12083</a> <span> [<a href="https://arxiv.org/pdf/2411.12083">pdf</a>, <a href="https://arxiv.org/format/2411.12083">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Extended-Use Designs on Very Large Online Platforms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Y">Yixin Chen</a>, <a href="/search/?searchtype=author&query=Fu%2C+Y">Yue Fu</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zeya Chen</a>, <a href="/search/?searchtype=author&query=Radesky%2C+J">Jenny Radesky</a>, <a href="/search/?searchtype=author&query=Hiniker%2C+A">Alexis Hiniker</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.12083v1-abstract-short" style="display: inline;"> In the attention economy, online platforms are incentivized to maximize user engagement through extended-use designs (EUDs), even when such practices conflict with users' best interests. We conducted a structured content analysis of all Very Large Online Platforms (VLOPs) to identify the EUDs these influential apps and sites use. We conducted this analysis posing as a teenager to understand the EU… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12083v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12083v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12083v1-abstract-full" style="display: none;"> In the attention economy, online platforms are incentivized to maximize user engagement through extended-use designs (EUDs), even when such practices conflict with users' best interests. We conducted a structured content analysis of all Very Large Online Platforms (VLOPs) to identify the EUDs these influential apps and sites use. We conducted this analysis posing as a teenager to understand the EUDs that young people are exposed to. We find that VLOPs use four strategies to promote extended use: pressuring, enticing, trapping, and lulling users. We report on a hierarchical taxonomy organizing the 63 designs that fall under these categories. Applying this taxonomy to all 17 VLOPs, we identify 583 instances of EUDs, with social media platforms using twice as many EUDs as other VLOPs. We present three vignettes illustrating how these designs reinforce one another in practice. We further contribute a graphical dataset of videos illustrating these features in the wild. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12083v1-abstract-full').style.display = 'none'; document.getElementById('2411.12083v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">29 pages, 23 figures, open source Github page: https://extendedusedesign.github.io/ExtendedUseDesign/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11878">arXiv:2411.11878</a> <span> [<a href="https://arxiv.org/pdf/2411.11878">pdf</a>, <a href="https://arxiv.org/format/2411.11878">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Disordered Systems and Neural Networks">cond-mat.dis-nn</span> </div> </div> <p class="title is-5 mathjax"> Field theory of non-Hermitian disordered systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Ze Chen</a>, <a href="/search/?searchtype=author&query=Kawabata%2C+K">Kohei Kawabata</a>, <a href="/search/?searchtype=author&query=Kulkarni%2C+A">Anish Kulkarni</a>, <a href="/search/?searchtype=author&query=Ryu%2C+S">Shinsei Ryu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.11878v1-abstract-short" style="display: inline;"> The interplay between non-Hermiticity and disorder gives rise to unique universality classes of Anderson transitions. Here, we develop a field-theoretical description of non-Hermitian disordered systems based on fermionic replica nonlinear sigma models. We classify the target manifolds of the nonlinear sigma models across all the 38-fold symmetry classes of non-Hermitian systems and corroborate th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11878v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11878v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11878v1-abstract-full" style="display: none;"> The interplay between non-Hermiticity and disorder gives rise to unique universality classes of Anderson transitions. Here, we develop a field-theoretical description of non-Hermitian disordered systems based on fermionic replica nonlinear sigma models. We classify the target manifolds of the nonlinear sigma models across all the 38-fold symmetry classes of non-Hermitian systems and corroborate the correspondence of the universality classes of Anderson transitions between non-Hermitian systems and Hermitized systems with additional chiral symmetry. We apply the nonlinear sigma model framework to study the spectral properties of non-Hermitian random matrices with particle-hole symmetry. Furthermore, we demonstrate that the Anderson transition unique to nonreciprocal disordered systems in one dimension, including the Hatano-Nelson model, originates from the competition between the kinetic and topological terms in a one-dimensional nonlinear sigma model. We also discuss the critical phenomena of non-Hermitian disordered systems with symmetry and topology in higher dimensions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11878v1-abstract-full').style.display = 'none'; document.getElementById('2411.11878v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">24 pages, 2 figures, 7 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11694">arXiv:2411.11694</a> <span> [<a href="https://arxiv.org/pdf/2411.11694">pdf</a>, <a href="https://arxiv.org/format/2411.11694">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"> Technical Report: Enhancing LLM Reasoning with Reward-guided Tree Search </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jiang%2C+J">Jinhao Jiang</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhipeng Chen</a>, <a href="/search/?searchtype=author&query=Min%2C+Y">Yingqian Min</a>, <a href="/search/?searchtype=author&query=Chen%2C+J">Jie Chen</a>, <a href="/search/?searchtype=author&query=Cheng%2C+X">Xiaoxue Cheng</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">Jiapeng Wang</a>, <a href="/search/?searchtype=author&query=Tang%2C+Y">Yiru Tang</a>, <a href="/search/?searchtype=author&query=Sun%2C+H">Haoxiang Sun</a>, <a href="/search/?searchtype=author&query=Deng%2C+J">Jia Deng</a>, <a href="/search/?searchtype=author&query=Zhao%2C+W+X">Wayne Xin Zhao</a>, <a href="/search/?searchtype=author&query=Liu%2C+Z">Zheng Liu</a>, <a href="/search/?searchtype=author&query=Yan%2C+D">Dong Yan</a>, <a href="/search/?searchtype=author&query=Xie%2C+J">Jian Xie</a>, <a href="/search/?searchtype=author&query=Wang%2C+Z">Zhongyuan Wang</a>, <a href="/search/?searchtype=author&query=Wen%2C+J">Ji-Rong Wen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.11694v1-abstract-short" style="display: inline;"> Recently, test-time scaling has garnered significant attention from the research community, largely due to the substantial advancements of the o1 model released by OpenAI. By allocating more computational resources during the inference phase, large language models~(LLMs) can extensively explore the solution space by generating more thought tokens or diverse solutions, thereby producing more accura… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11694v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11694v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11694v1-abstract-full" style="display: none;"> Recently, test-time scaling has garnered significant attention from the research community, largely due to the substantial advancements of the o1 model released by OpenAI. By allocating more computational resources during the inference phase, large language models~(LLMs) can extensively explore the solution space by generating more thought tokens or diverse solutions, thereby producing more accurate responses. However, developing an o1-like reasoning approach is challenging, and researchers have been making various attempts to advance this open area of research. In this paper, we present a preliminary exploration into enhancing the reasoning abilities of LLMs through reward-guided tree search algorithms. This framework is implemented by integrating the policy model, reward model, and search algorithm. It is primarily constructed around a tree search algorithm, where the policy model navigates a dynamically expanding tree guided by a specially trained reward model. We thoroughly explore various design considerations necessary for implementing this framework and provide a detailed report of the technical aspects. To assess the effectiveness of our approach, we focus on mathematical reasoning tasks and conduct extensive evaluations on four challenging datasets, significantly enhancing the reasoning abilities of LLMs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11694v1-abstract-full').style.display = 'none'; document.getElementById('2411.11694v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">LLM;Complex Reasoning;Math</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11648">arXiv:2411.11648</a> <span> [<a href="https://arxiv.org/pdf/2411.11648">pdf</a>, <a href="https://arxiv.org/ps/2411.11648">ps</a>, <a href="https://arxiv.org/format/2411.11648">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> </div> </div> <p class="title is-5 mathjax"> Evidence for Two Excited $惟^{-}$ Hyperons </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=BESIII+Collaboration"> BESIII Collaboration</a>, <a href="/search/?searchtype=author&query=Ablikim%2C+M">M. Ablikim</a>, <a href="/search/?searchtype=author&query=Achasov%2C+M+N">M. N. Achasov</a>, <a href="/search/?searchtype=author&query=Adlarson%2C+P">P. Adlarson</a>, <a href="/search/?searchtype=author&query=Afedulidis%2C+O">O. Afedulidis</a>, <a href="/search/?searchtype=author&query=Ai%2C+X+C">X. C. Ai</a>, <a href="/search/?searchtype=author&query=Aliberti%2C+R">R. Aliberti</a>, <a href="/search/?searchtype=author&query=Amoroso%2C+A">A. Amoroso</a>, <a href="/search/?searchtype=author&query=Bai%2C+Y">Y. Bai</a>, <a href="/search/?searchtype=author&query=Bakina%2C+O">O. Bakina</a>, <a href="/search/?searchtype=author&query=Balossino%2C+I">I. Balossino</a>, <a href="/search/?searchtype=author&query=Ban%2C+Y">Y. Ban</a>, <a href="/search/?searchtype=author&query=Bao%2C+H+-">H. -R. Bao</a>, <a href="/search/?searchtype=author&query=Batozskaya%2C+V">V. Batozskaya</a>, <a href="/search/?searchtype=author&query=Begzsuren%2C+K">K. Begzsuren</a>, <a href="/search/?searchtype=author&query=Berger%2C+N">N. Berger</a>, <a href="/search/?searchtype=author&query=Berlowski%2C+M">M. Berlowski</a>, <a href="/search/?searchtype=author&query=Bertani%2C+M">M. Bertani</a>, <a href="/search/?searchtype=author&query=Bettoni%2C+D">D. Bettoni</a>, <a href="/search/?searchtype=author&query=Bianchi%2C+F">F. Bianchi</a>, <a href="/search/?searchtype=author&query=Bianco%2C+E">E. Bianco</a>, <a href="/search/?searchtype=author&query=Bortone%2C+A">A. Bortone</a>, <a href="/search/?searchtype=author&query=Boyko%2C+I">I. Boyko</a>, <a href="/search/?searchtype=author&query=Briere%2C+R+A">R. A. Briere</a>, <a href="/search/?searchtype=author&query=Brueggemann%2C+A">A. Brueggemann</a> , et al. (650 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.11648v1-abstract-short" style="display: inline;"> Using $e^+e^-$ collision data corresponding to an integrated luminosity of 19 fb$^{-1}$ collected by the BESIII detector at center-of-mass energies ranging from 4.13 to 4.70 GeV, we report the first evidence for a new excited $惟^{-}$ hyperon, the $惟^*(2109)^{-}$, through the process $e^+ e^- \to 惟^*(2109)^{-} \bar惟^{+} +c.c.$ with a significance of 3.7 $蟽$. The mass and width of $惟^*(2109)^{-}$ ar… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11648v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11648v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11648v1-abstract-full" style="display: none;"> Using $e^+e^-$ collision data corresponding to an integrated luminosity of 19 fb$^{-1}$ collected by the BESIII detector at center-of-mass energies ranging from 4.13 to 4.70 GeV, we report the first evidence for a new excited $惟^{-}$ hyperon, the $惟^*(2109)^{-}$, through the process $e^+ e^- \to 惟^*(2109)^{-} \bar惟^{+} +c.c.$ with a significance of 3.7 $蟽$. The mass and width of $惟^*(2109)^{-}$ are measured to be $2108.8 \pm 5.5_{\rm stat} \pm 1.5_{\rm syst} {\rm MeV}/c^{2}$ and $21.6 \pm 17.7_{\rm stat} \pm 9.4_{\rm syst} {\rm MeV}$, respectively. We also present evidence for production of the $惟^*(2012)^{-}$ in the process $e^+ e^- \to 惟^*(2012)^{-} \bar惟^{+} +c.c.$ with a significance of 3.7 $蟽$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11648v1-abstract-full').style.display = 'none'; document.getElementById('2411.11648v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 2 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11485">arXiv:2411.11485</a> <span> [<a href="https://arxiv.org/pdf/2411.11485">pdf</a>, <a href="https://arxiv.org/ps/2411.11485">ps</a>, <a href="https://arxiv.org/format/2411.11485">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> </div> <p class="title is-5 mathjax"> Quantum Coherence: A Fundamental Resource for Establishing Genuine Multipartite Correlations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+Z">Zong Wang</a>, <a href="/search/?searchtype=author&query=Guo%2C+Z">Zhihua Guo</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhihua Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+M">Ming Li</a>, <a href="/search/?searchtype=author&query=Zhou%2C+Z">Zihang Zhou</a>, <a href="/search/?searchtype=author&query=Zhang%2C+C">Chengjie Zhang</a>, <a href="/search/?searchtype=author&query=Fei%2C+S">Shao-Ming Fei</a>, <a href="/search/?searchtype=author&query=Ma%2C+Z">Zhihao 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="2411.11485v1-abstract-short" style="display: inline;"> We establish the profound equivalence between measures of genuine multipartite entanglement(GME) and their corresponding coherence measures. Initially we construct two distinct classes of measures for genuine multipartite entanglement utilizing real symmetric concave functions and the convex roof technique. We then demonstrate that all coherence measures for any qudit states, defined through the c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11485v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11485v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11485v1-abstract-full" style="display: none;"> We establish the profound equivalence between measures of genuine multipartite entanglement(GME) and their corresponding coherence measures. Initially we construct two distinct classes of measures for genuine multipartite entanglement utilizing real symmetric concave functions and the convex roof technique. We then demonstrate that all coherence measures for any qudit states, defined through the convex roof approach, are identical to our two classes of GME measures of the states combined with an incoherent ancilla under a unitary incoherent operation. This relationship implies that genuine multipartite entanglement can be generated from the coherence inherent in an initial state through the unitary incoherent operations. Furthermore, we explore the interplay between coherence and other forms of genuine quantum correlations, specifically genuine multipartite steering and genuine multipartite nonlocality. In the instance of special three-qubit X-states (only nonzero elements of X-state are diagonal or antidiagonal when written in an orthonormal basis), we find that genuine multipartite steering and nonlocality are present if and only if the coherence exists in the corresponding qubit states. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11485v1-abstract-full').style.display = 'none'; document.getElementById('2411.11485v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11243">arXiv:2411.11243</a> <span> [<a href="https://arxiv.org/pdf/2411.11243">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-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"> Electron Phase Detection in Single Molecules by Interferometry </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhixin Chen</a>, <a href="/search/?searchtype=author&query=Deng%2C+J">Jie-Ren Deng</a>, <a href="/search/?searchtype=author&query=Wang%2C+M">Mengyun Wang</a>, <a href="/search/?searchtype=author&query=Farmakidis%2C+N">Nikolaos Farmakidis</a>, <a href="/search/?searchtype=author&query=Baugh%2C+J">Jonathan Baugh</a>, <a href="/search/?searchtype=author&query=Bhaskaran%2C+H">Harish Bhaskaran</a>, <a href="/search/?searchtype=author&query=Mol%2C+J+A">Jan A. Mol</a>, <a href="/search/?searchtype=author&query=Anderson%2C+H+L">Harry L. Anderson</a>, <a href="/search/?searchtype=author&query=Bogani%2C+L">Lapo Bogani</a>, <a href="/search/?searchtype=author&query=Thomas%2C+J+O">James O. Thomas</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.11243v1-abstract-short" style="display: inline;"> Interferometry has underpinned a century of discoveries, ranging from the disproval of the ether theory to the detection of gravitational waves, offering insights into wave dynamics with unrivalled precision through the measurement of phase relationships. In electronics, phase-sensitive measurements can probe the nature of transmissive topological and quantum states, but are only possible using co… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11243v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11243v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11243v1-abstract-full" style="display: none;"> Interferometry has underpinned a century of discoveries, ranging from the disproval of the ether theory to the detection of gravitational waves, offering insights into wave dynamics with unrivalled precision through the measurement of phase relationships. In electronics, phase-sensitive measurements can probe the nature of transmissive topological and quantum states, but are only possible using complex device structures in magnetic fields. Here we demonstrate electronic interferometry in a single-molecule device through the study of non-equilibrium Fano resonances. We show the phase difference between an electronic orbital and a coupled Fabry-Perot resonance are tuneable through electric fields, and consequently it is possible to read out quantum information in the smallest devices, offering new avenues for the coherent manipulation down to single molecules. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11243v1-abstract-full').style.display = 'none'; document.getElementById('2411.11243v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11121">arXiv:2411.11121</a> <span> [<a href="https://arxiv.org/pdf/2411.11121">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> </div> </div> <p class="title is-5 mathjax"> Multi-topological phases of matter </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+Z">Ziteng Wang</a>, <a href="/search/?searchtype=author&query=Bongiovanni%2C+D">Domenico Bongiovanni</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xiangdong Wang</a>, <a href="/search/?searchtype=author&query=Hu%2C+Z">Zhichan Hu</a>, <a href="/search/?searchtype=author&query=Juki%C4%87%2C+D">Dario Juki膰</a>, <a href="/search/?searchtype=author&query=Song%2C+D">Daohong Song</a>, <a href="/search/?searchtype=author&query=Xu%2C+J">Jingjun Xu</a>, <a href="/search/?searchtype=author&query=Morandotti%2C+R">Roberto Morandotti</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhigang Chen</a>, <a href="/search/?searchtype=author&query=Buljan%2C+H">Hrvoje Buljan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.11121v1-abstract-short" style="display: inline;"> The discovery of topological phases of matter and topological boundary states had tremendous impact on condensed matter physics and photonics, where topological phases are defined via energy bands, giving rise to topological band theory. However, topological systems that cannot be described by band topology but still support non-trivial boundary states are little-known and largely unexplored. Here… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11121v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11121v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11121v1-abstract-full" style="display: none;"> The discovery of topological phases of matter and topological boundary states had tremendous impact on condensed matter physics and photonics, where topological phases are defined via energy bands, giving rise to topological band theory. However, topological systems that cannot be described by band topology but still support non-trivial boundary states are little-known and largely unexplored. Here, we uncover a new kind of topological phase of matter named "multi-topological phase" (MTP) that features multiple sets of boundary states, where each set is associated with one distinct topological invariant. Unlike conventional topological phase transitions, the MTP transitions can occur without band-gap closing. We present typical examples of MTPs in a one-dimensional topological insulator and a two-dimensional higher-order topological insulator, where the systems are otherwise trivial according to band topology. Furthermore, MTPs can exist also in indirectly gapped Chern insulators, beyond the regime where the conventional bulk-boundary correspondence predicts the existence of boundary states. Experimentally, we demonstrate the first two examples of MTPs in laser-written photonic lattices. Our findings constitute a fundamental advance in topological physics and provide a route for designing novel topological materials. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11121v1-abstract-full').style.display = 'none'; document.getElementById('2411.11121v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">are welcome</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10936">arXiv:2411.10936</a> <span> [<a href="https://arxiv.org/pdf/2411.10936">pdf</a>, <a href="https://arxiv.org/format/2411.10936">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"> Iterative Camera-LiDAR Extrinsic Optimization via Surrogate Diffusion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ou%2C+N">Ni Ou</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhuo Chen</a>, <a href="/search/?searchtype=author&query=Zhang%2C+X">Xinru Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">Junzheng Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.10936v1-abstract-short" style="display: inline;"> Cameras and LiDAR are essential sensors for autonomous vehicles. Camera-LiDAR data fusion compensate for deficiencies of stand-alone sensors but relies on precise extrinsic calibration. Many learning-based calibration methods predict extrinsic parameters in a single step. Driven by the growing demand for higher accuracy, a few approaches utilize multi-range models or integrate multiple methods to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10936v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10936v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10936v1-abstract-full" style="display: none;"> Cameras and LiDAR are essential sensors for autonomous vehicles. Camera-LiDAR data fusion compensate for deficiencies of stand-alone sensors but relies on precise extrinsic calibration. Many learning-based calibration methods predict extrinsic parameters in a single step. Driven by the growing demand for higher accuracy, a few approaches utilize multi-range models or integrate multiple methods to improve extrinsic parameter predictions, but these strategies incur extended training times and require additional storage for separate models. To address these issues, we propose a single-model iterative approach based on surrogate diffusion to significantly enhance the capacity of individual calibration methods. By applying a buffering technique proposed by us, the inference time of our surrogate diffusion is 43.7% less than that of multi-range models. Additionally, we create a calibration network as our denoiser, featuring both projection-first and encoding-first branches for effective point feature extraction. Extensive experiments demonstrate that our diffusion model outperforms other single-model iterative methods and delivers competitive results compared to multi-range models. Our denoiser exceeds state-of-the-art calibration methods, reducing the rotation error by 24.5% compared to the second-best method. Furthermore, with the proposed diffusion applied, it achieves 20.4% less rotation error and 9.6% less translation error. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10936v1-abstract-full').style.display = 'none'; document.getElementById('2411.10936v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 4 figures, 3 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10912">arXiv:2411.10912</a> <span> [<a href="https://arxiv.org/pdf/2411.10912">pdf</a>, <a href="https://arxiv.org/format/2411.10912">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"> SPICA: Retrieving Scenarios for Pluralistic In-Context Alignment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Q+Z">Quan Ze Chen</a>, <a href="/search/?searchtype=author&query=Feng%2C+K+J+K">K. J. Kevin Feng</a>, <a href="/search/?searchtype=author&query=Park%2C+C+Y">Chan Young Park</a>, <a href="/search/?searchtype=author&query=Zhang%2C+A+X">Amy X. Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.10912v1-abstract-short" style="display: inline;"> Alignment of large language models (LLMs) to societal values should account for pluralistic values from diverse groups. One technique uses in-context learning for inference-time alignment, but only considers similarity when drawing few-shot examples, not accounting for cross-group differences in value prioritization. We propose SPICA, a framework for pluralistic alignment that accounts for group-l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10912v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10912v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10912v1-abstract-full" style="display: none;"> Alignment of large language models (LLMs) to societal values should account for pluralistic values from diverse groups. One technique uses in-context learning for inference-time alignment, but only considers similarity when drawing few-shot examples, not accounting for cross-group differences in value prioritization. We propose SPICA, a framework for pluralistic alignment that accounts for group-level differences during in-context example retrieval. SPICA introduces three designs to facilitate pluralistic alignment: scenario banks, group-informed metrics, and in-context alignment prompts. From an evaluation of SPICA on an alignment task collecting inputs from four demographic groups ($n = 544$), our metrics retrieve in-context examples that more closely match observed preferences, with the best prompt configuration using multiple contrastive responses to demonstrate examples. In an end-to-end evaluation ($n = 80$), we observe that SPICA-aligned models are higher rated than a baseline similarity-only retrieval approach, with groups seeing up to a +0.16 point improvement on a 5 point scale. Additionally, gains from SPICA were more uniform, with all groups benefiting from alignment rather than only some. Finally, we find that while a group-agnostic approach can effectively align to aggregated values, it is not most suited for aligning to divergent groups. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10912v1-abstract-full').style.display = 'none'; document.getElementById('2411.10912v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10819">arXiv:2411.10819</a> <span> [<a href="https://arxiv.org/pdf/2411.10819">pdf</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"> An Oversampling-enhanced Multi-class Imbalanced Classification Framework for Patient Health Status Prediction Using Patient-reported Outcomes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yan%2C+Y">Yang Yan</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhong Chen</a>, <a href="/search/?searchtype=author&query=Xu%2C+C">Cai Xu</a>, <a href="/search/?searchtype=author&query=Shen%2C+X">Xinglei Shen</a>, <a href="/search/?searchtype=author&query=Shiao%2C+J">Jay Shiao</a>, <a href="/search/?searchtype=author&query=Einck%2C+J">John Einck</a>, <a href="/search/?searchtype=author&query=Chen%2C+R+C">Ronald C Chen</a>, <a href="/search/?searchtype=author&query=Gao%2C+H">Hao Gao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.10819v1-abstract-short" style="display: inline;"> Patient-reported outcomes (PROs) directly collected from cancer patients being treated with radiation therapy play a vital role in assisting clinicians in counseling patients regarding likely toxicities. Precise prediction and evaluation of symptoms or health status associated with PROs are fundamental to enhancing decision-making and planning for the required services and support as patients tran… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10819v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10819v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10819v1-abstract-full" style="display: none;"> Patient-reported outcomes (PROs) directly collected from cancer patients being treated with radiation therapy play a vital role in assisting clinicians in counseling patients regarding likely toxicities. Precise prediction and evaluation of symptoms or health status associated with PROs are fundamental to enhancing decision-making and planning for the required services and support as patients transition into survivorship. However, the raw PRO data collected from hospitals exhibits some intrinsic challenges such as incomplete item reports and imbalance patient toxicities. To the end, in this study, we explore various machine learning techniques to predict patient outcomes related to health status such as pain levels and sleep discomfort using PRO datasets from a cancer photon/proton therapy center. Specifically, we deploy six advanced machine learning classifiers -- Random Forest (RF), XGBoost, Gradient Boosting (GB), Support Vector Machine (SVM), Multi-Layer Perceptron with Bagging (MLP-Bagging), and Logistic Regression (LR) -- to tackle a multi-class imbalance classification problem across three prevalent cancer types: head and neck, prostate, and breast cancers. To address the class imbalance issue, we employ an oversampling strategy, adjusting the training set sample sizes through interpolations of in-class neighboring samples, thereby augmenting minority classes without deviating from the original skewed class distribution. Our experimental findings across multiple PRO datasets indicate that the RF and XGB methods achieve robust generalization performance, evidenced by weighted AUC and detailed confusion matrices, in categorizing outcomes as mild, intermediate, and severe post-radiation therapy. These results underscore the models' effectiveness and potential utility in clinical settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10819v1-abstract-full').style.display = 'none'; document.getElementById('2411.10819v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 12 figures, 4 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10786">arXiv:2411.10786</a> <span> [<a href="https://arxiv.org/pdf/2411.10786">pdf</a>, <a href="https://arxiv.org/format/2411.10786">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Superconductivity">cond-mat.supr-con</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Strongly Correlated Electrons">cond-mat.str-el</span> </div> </div> <p class="title is-5 mathjax"> Strongly Anisotropic Charge Dynamics in La3Ni2O7 with Coherent-to-Incoherent Crossover of Interlayer Charge Dynamics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Su%2C+B">Bo Su</a>, <a href="/search/?searchtype=author&query=Huang%2C+C">Chaoxin Huang</a>, <a href="/search/?searchtype=author&query=Zhao%2C+J">Jianzhou Zhao</a>, <a href="/search/?searchtype=author&query=Huo%2C+M">Mengwu Huo</a>, <a href="/search/?searchtype=author&query=Luo%2C+J">Jianlin Luo</a>, <a href="/search/?searchtype=author&query=Wang%2C+M">Meng Wang</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhi-Guo 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="2411.10786v1-abstract-short" style="display: inline;"> We report an optical spectroscopy study of the charge-dynamics anisotropy in the La3Ni2O7 single crystals with the electric field of the incident light parallel to the crystalline c-axis and ab-plane respectively. The evolution of the low-energy part of its c-axis optical conductivity spectra (蟽1c(蠅)) from a Drude component to a finite-energy peak, together with the change in the c-axis electron m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10786v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10786v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10786v1-abstract-full" style="display: none;"> We report an optical spectroscopy study of the charge-dynamics anisotropy in the La3Ni2O7 single crystals with the electric field of the incident light parallel to the crystalline c-axis and ab-plane respectively. The evolution of the low-energy part of its c-axis optical conductivity spectra (蟽1c(蠅)) from a Drude component to a finite-energy peak, together with the change in the c-axis electron mean-free-path which is distinctly longer than the c-axis lattice constant at 10 K but is shorter than the c-axis lattice constant at 300 K, demonstrates a crossover from coherent to incoherent interlayer charge dynamics in La3Ni2O7, which is associated with the variation from weak to strong dissipation within its ab-plane. In contrast, the Drude component robust in its 蟽1ab(蠅) and the long ab-plane electron mean-free-path greater than the a-axis and b-axis unit-cell lengths manifest the persistence of coherent in-plane charge dynamics from 10 to 300 K. Thus, the charge dynamics in La3Ni2O7 shows a remarkable anisotropy at high temperatures. At low temperatures, the large values of the ratio between the ab-plane and c-axis Drude weights and the ratio 蟽1ab(蠅 -> 0)/蟽1c(蠅 -> 0) indicate a strong anisotropy of the low-temperature charge dynamics in La3Ni2O7. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10786v1-abstract-full').style.display = 'none'; document.getElementById('2411.10786v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 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/2411.10534">arXiv:2411.10534</a> <span> [<a href="https://arxiv.org/pdf/2411.10534">pdf</a>, <a href="https://arxiv.org/format/2411.10534">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <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="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Chain of Alignment: Integrating Public Will with Expert Intelligence for Language Model Alignment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Konya%2C+A">Andrew Konya</a>, <a href="/search/?searchtype=author&query=Ovadya%2C+A">Aviv Ovadya</a>, <a href="/search/?searchtype=author&query=Feng%2C+K">Kevin Feng</a>, <a href="/search/?searchtype=author&query=Chen%2C+Q+Z">Quan Ze Chen</a>, <a href="/search/?searchtype=author&query=Schirch%2C+L">Lisa Schirch</a>, <a href="/search/?searchtype=author&query=Irwin%2C+C">Colin Irwin</a>, <a href="/search/?searchtype=author&query=Zhang%2C+A+X">Amy X. Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.10534v1-abstract-short" style="display: inline;"> We introduce a method to measure the alignment between public will and language model (LM) behavior that can be applied to fine-tuning, online oversight, and pre-release safety checks. Our `chain of alignment' (CoA) approach produces a rule based reward (RBR) by creating model behavior $\textit{rules}$ aligned to normative $\textit{objectives}$ aligned to $\textit{public will}$. This factoring ena… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10534v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10534v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10534v1-abstract-full" style="display: none;"> We introduce a method to measure the alignment between public will and language model (LM) behavior that can be applied to fine-tuning, online oversight, and pre-release safety checks. Our `chain of alignment' (CoA) approach produces a rule based reward (RBR) by creating model behavior $\textit{rules}$ aligned to normative $\textit{objectives}$ aligned to $\textit{public will}$. This factoring enables a nonexpert public to directly specify their will through the normative objectives, while expert intelligence is used to figure out rules entailing model behavior that best achieves those objectives. We validate our approach by applying it across three different domains of LM prompts related to mental health. We demonstrate a public input process built on collective dialogues and bridging-based ranking that reliably produces normative objectives supported by at least $96\% \pm 2\%$ of the US public. We then show that rules developed by mental health experts to achieve those objectives enable a RBR that evaluates an LM response's alignment with the objectives similarly to human experts (Pearson's $r=0.841$, $AUC=0.964$). By measuring alignment with objectives that have near unanimous public support, these CoA RBRs provide an approximate measure of alignment between LM behavior and public will. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10534v1-abstract-full').style.display = 'none'; document.getElementById('2411.10534v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Pluralistic Alignment Workshop at NeurIPS 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10442">arXiv:2411.10442</a> <span> [<a href="https://arxiv.org/pdf/2411.10442">pdf</a>, <a href="https://arxiv.org/format/2411.10442">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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+W">Weiyun Wang</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhe Chen</a>, <a href="/search/?searchtype=author&query=Wang%2C+W">Wenhai Wang</a>, <a href="/search/?searchtype=author&query=Cao%2C+Y">Yue Cao</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yangzhou Liu</a>, <a href="/search/?searchtype=author&query=Gao%2C+Z">Zhangwei Gao</a>, <a href="/search/?searchtype=author&query=Zhu%2C+J">Jinguo Zhu</a>, <a href="/search/?searchtype=author&query=Zhu%2C+X">Xizhou Zhu</a>, <a href="/search/?searchtype=author&query=Lu%2C+L">Lewei Lu</a>, <a href="/search/?searchtype=author&query=Qiao%2C+Y">Yu Qiao</a>, <a href="/search/?searchtype=author&query=Dai%2C+J">Jifeng Dai</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.10442v1-abstract-short" style="display: inline;"> Existing open-source multimodal large language models (MLLMs) generally follow a training process involving pre-training and supervised fine-tuning. However, these models suffer from distribution shifts, which limit their multimodal reasoning, particularly in the Chain-of-Thought (CoT) performance. To address this, we introduce a preference optimization (PO) process to enhance the multimodal reaso… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10442v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10442v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10442v1-abstract-full" style="display: none;"> Existing open-source multimodal large language models (MLLMs) generally follow a training process involving pre-training and supervised fine-tuning. However, these models suffer from distribution shifts, which limit their multimodal reasoning, particularly in the Chain-of-Thought (CoT) performance. To address this, we introduce a preference optimization (PO) process to enhance the multimodal reasoning capabilities of MLLMs. Specifically, (1) on the data side, we design an automated preference data construction pipeline to create MMPR, a high-quality, large-scale multimodal reasoning preference dataset. and (2) on the model side, we explore integrating PO with MLLMs, developing a simple yet effective method, termed Mixed Preference Optimization (MPO), which boosts multimodal CoT performance. Our approach demonstrates improved performance across multiple benchmarks, particularly in multimodal reasoning tasks. Notably, our model, InternVL2-8B-MPO, achieves an accuracy of 67.0 on MathVista, outperforming InternVL2-8B by 8.7 points and achieving performance comparable to the 10x larger InternVL2-76B. We hope this study could inspire further advancements in MLLMs. Code, data, and model shall be publicly released. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10442v1-abstract-full').style.display = 'none'; document.getElementById('2411.10442v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10219">arXiv:2411.10219</a> <span> [<a href="https://arxiv.org/pdf/2411.10219">pdf</a>, <a href="https://arxiv.org/format/2411.10219">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"> Constraints on the photon polarisation in $b \to s 纬$ transitions using $B_s^0 \rightarrow 蠁e^+e^-$ decays </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=LHCb+collaboration"> LHCb collaboration</a>, <a href="/search/?searchtype=author&query=Aaij%2C+R">R. Aaij</a>, <a href="/search/?searchtype=author&query=Abdelmotteleb%2C+A+S+W">A. S. W. Abdelmotteleb</a>, <a href="/search/?searchtype=author&query=Beteta%2C+C+A">C. Abellan Beteta</a>, <a href="/search/?searchtype=author&query=Abudin%C3%A9n%2C+F">F. Abudin茅n</a>, <a href="/search/?searchtype=author&query=Ackernley%2C+T">T. Ackernley</a>, <a href="/search/?searchtype=author&query=Adefisoye%2C+A+A">A. A. Adefisoye</a>, <a href="/search/?searchtype=author&query=Adeva%2C+B">B. Adeva</a>, <a href="/search/?searchtype=author&query=Adinolfi%2C+M">M. Adinolfi</a>, <a href="/search/?searchtype=author&query=Adlarson%2C+P">P. Adlarson</a>, <a href="/search/?searchtype=author&query=Agapopoulou%2C+C">C. Agapopoulou</a>, <a href="/search/?searchtype=author&query=Aidala%2C+C+A">C. A. Aidala</a>, <a href="/search/?searchtype=author&query=Ajaltouni%2C+Z">Z. Ajaltouni</a>, <a href="/search/?searchtype=author&query=Akar%2C+S">S. Akar</a>, <a href="/search/?searchtype=author&query=Akiba%2C+K">K. Akiba</a>, <a href="/search/?searchtype=author&query=Albicocco%2C+P">P. Albicocco</a>, <a href="/search/?searchtype=author&query=Albrecht%2C+J">J. Albrecht</a>, <a href="/search/?searchtype=author&query=Alessio%2C+F">F. Alessio</a>, <a href="/search/?searchtype=author&query=Alexander%2C+M">M. Alexander</a>, <a href="/search/?searchtype=author&query=Aliouche%2C+Z">Z. Aliouche</a>, <a href="/search/?searchtype=author&query=Cartelle%2C+P+A">P. Alvarez Cartelle</a>, <a href="/search/?searchtype=author&query=Amalric%2C+R">R. Amalric</a>, <a href="/search/?searchtype=author&query=Amato%2C+S">S. Amato</a>, <a href="/search/?searchtype=author&query=Amey%2C+J+L">J. L. Amey</a>, <a href="/search/?searchtype=author&query=Amhis%2C+Y">Y. Amhis</a> , et al. (1120 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.10219v2-abstract-short" style="display: inline;"> An angular analysis of the $B_s^0 \rightarrow 蠁e^+e^-$ decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of $9\,{\rm fb}^{-1}$ at centre-of-mass energies of 7, 8 and $13\,{\rm TeV}$. The analysis is performed in the very low dielectron invariant mass-squared region between $0.0009$ and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10219v2-abstract-full').style.display = 'inline'; document.getElementById('2411.10219v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10219v2-abstract-full" style="display: none;"> An angular analysis of the $B_s^0 \rightarrow 蠁e^+e^-$ decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of $9\,{\rm fb}^{-1}$ at centre-of-mass energies of 7, 8 and $13\,{\rm TeV}$. The analysis is performed in the very low dielectron invariant mass-squared region between $0.0009$ and $0.2615\,{\rm GeV}^2\!/c^4$. The longitudinal polarisation fraction of the $蠁$ meson is measured to be less than $11.5\%$ at $90\%$ confidence level. The $A_{\mathrm{T}}^{\mathcal{R}e C\!P}$ observable, which is related to the lepton forward-backward asymmetry, is measured to be $0.116 \pm 0.155 \pm 0.006$, where the first uncertainty is statistical and the second systematic. The transverse asymmetries, $A_{\mathrm{T}}^{(2)}$ and $A_{\mathrm{T}}^{\mathcal{I}m C\!P}$ , which are sensitive to the virtual photon polarisation, are found to be $-0.045 \pm 0.235 \pm 0.014$ and $0.002 \pm 0.247 \pm 0.016$, respectively. The results are consistent with Standard Model predictions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10219v2-abstract-full').style.display = 'none'; document.getElementById('2411.10219v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">21 pages, 4 figures. All figures and tables, along with any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3433/ (LHCb public pages)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LHCb-PAPER-2024-030, CERN-EP-2024-276 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10194">arXiv:2411.10194</a> <span> [<a href="https://arxiv.org/pdf/2411.10194">pdf</a>, <a href="https://arxiv.org/ps/2411.10194">ps</a>, <a href="https://arxiv.org/format/2411.10194">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Representation Theory">math.RT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Algebraic Geometry">math.AG</span> </div> </div> <p class="title is-5 mathjax"> A remark on decomposing the canonical representation of the Drinfeld curve </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhe Chen</a>, <a href="/search/?searchtype=author&query=Pan%2C+Y">Yushan Pan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.10194v1-abstract-short" style="display: inline;"> Recently, by studying an explicit basis, K枚ck and Laurent give the decomposition of the $\overline{\mathbb{F}}_q[\mathrm{SL}_2(\mathbb{F}_q)]$-module of holomorphic forms on the Drinfeld curve. We present a crystalline cohomological proof of this decomposition, without specifying a basis. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10194v1-abstract-full" style="display: none;"> Recently, by studying an explicit basis, K枚ck and Laurent give the decomposition of the $\overline{\mathbb{F}}_q[\mathrm{SL}_2(\mathbb{F}_q)]$-module of holomorphic forms on the Drinfeld curve. We present a crystalline cohomological proof of this decomposition, without specifying a basis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10194v1-abstract-full').style.display = 'none'; document.getElementById('2411.10194v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">4 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/2411.10161">arXiv:2411.10161</a> <span> [<a href="https://arxiv.org/pdf/2411.10161">pdf</a>, <a href="https://arxiv.org/format/2411.10161">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"> SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Zewen Chen</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">Juan Wang</a>, <a href="/search/?searchtype=author&query=Wang%2C+W">Wen Wang</a>, <a href="/search/?searchtype=author&query=Xu%2C+S">Sunhan Xu</a>, <a href="/search/?searchtype=author&query=Xiong%2C+H">Hang Xiong</a>, <a href="/search/?searchtype=author&query=Zeng%2C+Y">Yun Zeng</a>, <a href="/search/?searchtype=author&query=Guo%2C+J">Jian Guo</a>, <a href="/search/?searchtype=author&query=Wang%2C+S">Shuxun Wang</a>, <a href="/search/?searchtype=author&query=Yuan%2C+C">Chunfeng Yuan</a>, <a href="/search/?searchtype=author&query=Li%2C+B">Bing Li</a>, <a href="/search/?searchtype=author&query=Hu%2C+W">Weiming Hu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.10161v1-abstract-short" style="display: inline;"> Existing Image Quality Assessment (IQA) methods achieve remarkable success in analyzing quality for overall image, but few works explore quality analysis for Regions of Interest (ROIs). The quality analysis of ROIs can provide fine-grained guidance for image quality improvement and is crucial for scenarios focusing on region-level quality. This paper proposes a novel network, SEAGULL, which can SE… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10161v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10161v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10161v1-abstract-full" style="display: none;"> Existing Image Quality Assessment (IQA) methods achieve remarkable success in analyzing quality for overall image, but few works explore quality analysis for Regions of Interest (ROIs). The quality analysis of ROIs can provide fine-grained guidance for image quality improvement and is crucial for scenarios focusing on region-level quality. This paper proposes a novel network, SEAGULL, which can SEe and Assess ROIs quality with GUidance from a Large vision-Language model. SEAGULL incorporates a vision-language model (VLM), masks generated by Segment Anything Model (SAM) to specify ROIs, and a meticulously designed Mask-based Feature Extractor (MFE) to extract global and local tokens for specified ROIs, enabling accurate fine-grained IQA for ROIs. Moreover, this paper constructs two ROI-based IQA datasets, SEAGULL-100w and SEAGULL-3k, for training and evaluating ROI-based IQA. SEAGULL-100w comprises about 100w synthetic distortion images with 33 million ROIs for pre-training to improve the model's ability of regional quality perception, and SEAGULL-3k contains about 3k authentic distortion ROIs to enhance the model's ability to perceive real world distortions. After pre-training on SEAGULL-100w and fine-tuning on SEAGULL-3k, SEAGULL shows remarkable performance on fine-grained ROI quality assessment. Code and datasets are publicly available at the https://github.com/chencn2020/Seagull. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10161v1-abstract-full').style.display = 'none'; document.getElementById('2411.10161v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10136">arXiv:2411.10136</a> <span> [<a href="https://arxiv.org/pdf/2411.10136">pdf</a>, <a href="https://arxiv.org/format/2411.10136">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"> CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Fu%2C+Y">Yihang Fu</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Ziyang Chen</a>, <a href="/search/?searchtype=author&query=Ye%2C+Y">Yiwen Ye</a>, <a href="/search/?searchtype=author&query=Lei%2C+X">Xingliang Lei</a>, <a href="/search/?searchtype=author&query=Wang%2C+Z">Zhisong Wang</a>, <a href="/search/?searchtype=author&query=Xia%2C+Y">Yong Xia</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.10136v1-abstract-short" style="display: inline;"> Medical images often exhibit distribution shifts due to variations in imaging protocols and scanners across different medical centers. Domain Generalization (DG) methods aim to train models on source domains that can generalize to unseen target domains. Recently, the segment anything model (SAM) has demonstrated strong generalization capabilities due to its prompt-based design, and has gained sign… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10136v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10136v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10136v1-abstract-full" style="display: none;"> Medical images often exhibit distribution shifts due to variations in imaging protocols and scanners across different medical centers. Domain Generalization (DG) methods aim to train models on source domains that can generalize to unseen target domains. Recently, the segment anything model (SAM) has demonstrated strong generalization capabilities due to its prompt-based design, and has gained significant attention in image segmentation tasks. Existing SAM-based approaches attempt to address the need for manual prompts by introducing prompt generators that automatically generate these prompts. However, we argue that auto-generated prompts may not be sufficiently accurate under distribution shifts, potentially leading to incorrect predictions that still require manual verification and correction by clinicians. To address this challenge, we propose a method for 2D medical image segmentation called Self-Correcting SAM (CoSAM). Our approach begins by generating coarse masks using SAM in a prompt-free manner, providing prior prompts for the subsequent stages, and eliminating the need for prompt generators. To automatically refine these coarse masks, we introduce a generalized error decoder that simulates the correction process typically performed by clinicians. Furthermore, we generate diverse prompts as feedback based on the corrected masks, which are used to iteratively refine the predictions within a self-correcting loop, enhancing the generalization performance of our model. Extensive experiments on two medical image segmentation benchmarks across multiple scenarios demonstrate the superiority of CoSAM over state-of-the-art SAM-based methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10136v1-abstract-full').style.display = 'none'; document.getElementById('2411.10136v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10060">arXiv:2411.10060</a> <span> [<a href="https://arxiv.org/pdf/2411.10060">pdf</a>, <a href="https://arxiv.org/format/2411.10060">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</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"> CMATH: Cross-Modality Augmented Transformer with Hierarchical Variational Distillation for Multimodal Emotion Recognition in Conversation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhu%2C+X">Xiaofei Zhu</a>, <a href="/search/?searchtype=author&query=Cheng%2C+J">Jiawei Cheng</a>, <a href="/search/?searchtype=author&query=Yang%2C+Z">Zhou Yang</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhuo Chen</a>, <a href="/search/?searchtype=author&query=Wang%2C+Q">Qingyang Wang</a>, <a href="/search/?searchtype=author&query=Yao%2C+J">Jianfeng Yao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.10060v1-abstract-short" style="display: inline;"> Multimodal emotion recognition in conversation (MER) aims to accurately identify emotions in conversational utterances by integrating multimodal information. Previous methods usually treat multimodal information as equal quality and employ symmetric architectures to conduct multimodal fusion. However, in reality, the quality of different modalities usually varies considerably, and utilizing a symm… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10060v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10060v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10060v1-abstract-full" style="display: none;"> Multimodal emotion recognition in conversation (MER) aims to accurately identify emotions in conversational utterances by integrating multimodal information. Previous methods usually treat multimodal information as equal quality and employ symmetric architectures to conduct multimodal fusion. However, in reality, the quality of different modalities usually varies considerably, and utilizing a symmetric architecture is difficult to accurately recognize conversational emotions when dealing with uneven modal information. Furthermore, fusing multi-modality information in a single granularity may fail to adequately integrate modal information, exacerbating the inaccuracy in emotion recognition. In this paper, we propose a novel Cross-Modality Augmented Transformer with Hierarchical Variational Distillation, called CMATH, which consists of two major components, i.e., Multimodal Interaction Fusion and Hierarchical Variational Distillation. The former is comprised of two submodules, including Modality Reconstruction and Cross-Modality Augmented Transformer (CMA-Transformer), where Modality Reconstruction focuses on obtaining high-quality compressed representation of each modality, and CMA-Transformer adopts an asymmetric fusion strategy which treats one modality as the central modality and takes others as auxiliary modalities. The latter first designs a variational fusion network to fuse the fine-grained representations learned by CMA- Transformer into a coarse-grained representations. Then, it introduces a hierarchical distillation framework to maintain the consistency between modality representations with different granularities. Experiments on the IEMOCAP and MELD datasets demonstrate that our proposed model outperforms previous state-of-the-art baselines. Implementation codes can be available at https://github.com/ cjw-MER/CMATH. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10060v1-abstract-full').style.display = 'none'; document.getElementById('2411.10060v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.09823">arXiv:2411.09823</a> <span> [<a href="https://arxiv.org/pdf/2411.09823">pdf</a>, <a href="https://arxiv.org/format/2411.09823">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"> Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+Y">Yian Wang</a>, <a href="/search/?searchtype=author&query=Qiu%2C+X">Xiaowen Qiu</a>, <a href="/search/?searchtype=author&query=Liu%2C+J">Jiageng Liu</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhehuan Chen</a>, <a href="/search/?searchtype=author&query=Cai%2C+J">Jiting Cai</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yufei Wang</a>, <a href="/search/?searchtype=author&query=Wang%2C+T">Tsun-Hsuan Wang</a>, <a href="/search/?searchtype=author&query=Xian%2C+Z">Zhou Xian</a>, <a href="/search/?searchtype=author&query=Gan%2C+C">Chuang Gan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.09823v1-abstract-short" style="display: inline;"> Creating large-scale interactive 3D environments is essential for the development of Robotics and Embodied AI research. Current methods, including manual design, procedural generation, diffusion-based scene generation, and large language model (LLM) guided scene design, are hindered by limitations such as excessive human effort, reliance on predefined rules or training datasets, and limited 3D spa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09823v1-abstract-full').style.display = 'inline'; document.getElementById('2411.09823v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.09823v1-abstract-full" style="display: none;"> Creating large-scale interactive 3D environments is essential for the development of Robotics and Embodied AI research. Current methods, including manual design, procedural generation, diffusion-based scene generation, and large language model (LLM) guided scene design, are hindered by limitations such as excessive human effort, reliance on predefined rules or training datasets, and limited 3D spatial reasoning ability. Since pre-trained 2D image generative models better capture scene and object configuration than LLMs, we address these challenges by introducing Architect, a generative framework that creates complex and realistic 3D embodied environments leveraging diffusion-based 2D image inpainting. In detail, we utilize foundation visual perception models to obtain each generated object from the image and leverage pre-trained depth estimation models to lift the generated 2D image to 3D space. Our pipeline is further extended to a hierarchical and iterative inpainting process to continuously generate placement of large furniture and small objects to enrich the scene. This iterative structure brings the flexibility for our method to generate or refine scenes from various starting points, such as text, floor plans, or pre-arranged environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09823v1-abstract-full').style.display = 'none'; document.getElementById('2411.09823v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.09343">arXiv:2411.09343</a> <span> [<a href="https://arxiv.org/pdf/2411.09343">pdf</a>, <a href="https://arxiv.org/format/2411.09343">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Nuclear Experiment">nucl-ex</span> </div> </div> <p class="title is-5 mathjax"> Measurement of $蠁(1020)$ meson production in fixed-target $\textit{p}$Ne collisions at $\sqrt{s_{NN}}$ = 68.5 GeV </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=LHCb+collaboration"> LHCb collaboration</a>, <a href="/search/?searchtype=author&query=Aaij%2C+R">R. Aaij</a>, <a href="/search/?searchtype=author&query=Abdelmotteleb%2C+A+S+W">A. S. W. Abdelmotteleb</a>, <a href="/search/?searchtype=author&query=Beteta%2C+C+A">C. Abellan Beteta</a>, <a href="/search/?searchtype=author&query=Abudin%C3%A9n%2C+F">F. Abudin茅n</a>, <a href="/search/?searchtype=author&query=Ackernley%2C+T">T. Ackernley</a>, <a href="/search/?searchtype=author&query=Adefisoye%2C+A+A">A. A. Adefisoye</a>, <a href="/search/?searchtype=author&query=Adeva%2C+B">B. Adeva</a>, <a href="/search/?searchtype=author&query=Adinolfi%2C+M">M. Adinolfi</a>, <a href="/search/?searchtype=author&query=Adlarson%2C+P">P. Adlarson</a>, <a href="/search/?searchtype=author&query=Agapopoulou%2C+C">C. Agapopoulou</a>, <a href="/search/?searchtype=author&query=Aidala%2C+C+A">C. A. Aidala</a>, <a href="/search/?searchtype=author&query=Ajaltouni%2C+Z">Z. Ajaltouni</a>, <a href="/search/?searchtype=author&query=Akar%2C+S">S. Akar</a>, <a href="/search/?searchtype=author&query=Akiba%2C+K">K. Akiba</a>, <a href="/search/?searchtype=author&query=Albicocco%2C+P">P. Albicocco</a>, <a href="/search/?searchtype=author&query=Albrecht%2C+J">J. Albrecht</a>, <a href="/search/?searchtype=author&query=Alessio%2C+F">F. Alessio</a>, <a href="/search/?searchtype=author&query=Alexander%2C+M">M. Alexander</a>, <a href="/search/?searchtype=author&query=Aliouche%2C+Z">Z. Aliouche</a>, <a href="/search/?searchtype=author&query=Cartelle%2C+P+A">P. Alvarez Cartelle</a>, <a href="/search/?searchtype=author&query=Amalric%2C+R">R. Amalric</a>, <a href="/search/?searchtype=author&query=Amato%2C+S">S. Amato</a>, <a href="/search/?searchtype=author&query=Amey%2C+J+L">J. L. Amey</a>, <a href="/search/?searchtype=author&query=Amhis%2C+Y">Y. Amhis</a> , et al. (1127 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.09343v1-abstract-short" style="display: inline;"> The first measurement of $蠁(1020)$ meson production in fixed-target $p$Ne collisions at $\sqrt{s_{NN}}=68.5$ GeV is presented. The $蠁(1020)$ mesons are reconstructed in their $K^{+}K^{-}$ decay in a data sample consisting of proton collisions on neon nuclei at rest, corresponding to an integrated luminosity of $21.7 \pm 1.4$ nb$^{-1}$, collected by the LHCb detector at CERN. The $蠁(1020)$ producti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09343v1-abstract-full').style.display = 'inline'; document.getElementById('2411.09343v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.09343v1-abstract-full" style="display: none;"> The first measurement of $蠁(1020)$ meson production in fixed-target $p$Ne collisions at $\sqrt{s_{NN}}=68.5$ GeV is presented. The $蠁(1020)$ mesons are reconstructed in their $K^{+}K^{-}$ decay in a data sample consisting of proton collisions on neon nuclei at rest, corresponding to an integrated luminosity of $21.7 \pm 1.4$ nb$^{-1}$, collected by the LHCb detector at CERN. The $蠁(1020)$ production cross-section in the centre-of-mass rapidity range of $-1.8<y^*<0$ and transverse momentum range of $800<p_{T}<6500$ MeV/c is found to be $蟽=182.7\pm2.7~\text{(stat.)}\pm14.1~\text{(syst)}~渭$b/nucleon. A double-differential measurement of the cross-section is also provided in four regions of rapidity and six regions of transverse momentum of the $蠁(1020)$ meson and compared with the predictions from Pythia and EPOS4, which are found to underestimate the experimental values. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09343v1-abstract-full').style.display = 'none'; document.getElementById('2411.09343v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3673/ (LHCb public pages)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LHCb-PAPER-2024-036, CERN-EP-2024-274 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.08558">arXiv:2411.08558</a> <span> [<a href="https://arxiv.org/pdf/2411.08558">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> </div> </div> <p class="title is-5 mathjax"> Effect of Top Al$_2$O$_3$ Interlayer Thickness on Memory Window and Reliability of FeFETs With TiN/Al$_2$O$_3$/Hf$_{0.5}$Zr$_{0.5}$O$_2$/SiO$_x$/Si (MIFIS) Gate Structure </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Hu%2C+T">Tao Hu</a>, <a href="/search/?searchtype=author&query=Jia%2C+X">Xinpei Jia</a>, <a href="/search/?searchtype=author&query=Han%2C+R">Runhao Han</a>, <a href="/search/?searchtype=author&query=Yang%2C+J">Jia Yang</a>, <a href="/search/?searchtype=author&query=Bai%2C+M">Mingkai Bai</a>, <a href="/search/?searchtype=author&query=Dai%2C+S">Saifei Dai</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zeqi Chen</a>, <a href="/search/?searchtype=author&query=Ding%2C+Y">Yajing Ding</a>, <a href="/search/?searchtype=author&query=Yang%2C+S">Shuai Yang</a>, <a href="/search/?searchtype=author&query=Han%2C+K">Kai Han</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yanrong Wang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+J">Jing Zhang</a>, <a href="/search/?searchtype=author&query=Zhao%2C+Y">Yuanyuan Zhao</a>, <a href="/search/?searchtype=author&query=Ke%2C+X">Xiaoyu Ke</a>, <a href="/search/?searchtype=author&query=Sun%2C+X">Xiaoqing Sun</a>, <a href="/search/?searchtype=author&query=Chai%2C+J">Junshuai Chai</a>, <a href="/search/?searchtype=author&query=Xu%2C+H">Hao Xu</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xiaolei Wang</a>, <a href="/search/?searchtype=author&query=Wang%2C+W">Wenwu Wang</a>, <a href="/search/?searchtype=author&query=Ye%2C+T">Tianchun Ye</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.08558v1-abstract-short" style="display: inline;"> We investigate the effect of top Al2O3 interlayer thickness on the memory window (MW) of Si channel ferroelectric field-effect transistors (Si-FeFETs) with TiN/Al$_2$O$_3$/Hf$_{0.5}$Zr$_{0.5}$O$_2$/SiO$_x$/Si (MIFIS) gate structure. We find that the MW first increases and then remains almost constant with the increasing thickness of the top Al2O3. The phenomenon is attributed to the lower electric… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08558v1-abstract-full').style.display = 'inline'; document.getElementById('2411.08558v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.08558v1-abstract-full" style="display: none;"> We investigate the effect of top Al2O3 interlayer thickness on the memory window (MW) of Si channel ferroelectric field-effect transistors (Si-FeFETs) with TiN/Al$_2$O$_3$/Hf$_{0.5}$Zr$_{0.5}$O$_2$/SiO$_x$/Si (MIFIS) gate structure. We find that the MW first increases and then remains almost constant with the increasing thickness of the top Al2O3. The phenomenon is attributed to the lower electric field of the ferroelectric Hf$_{0.5}$Zr$_{0.5}$O$_2$ in the MIFIS structure with a thicker top Al2O3 after a program operation. The lower electric field makes the charges trapped at the top Al2O3/Hf0.5Zr0.5O$_2$ interface, which are injected from the metal gate, cannot be retained. Furthermore, we study the effect of the top Al$_2$O$_3$ interlayer thickness on the reliability (endurance characteristics and retention characteristics). We find that the MIFIS structure with a thicker top Al$_2$O$_3$ interlayer has poorer retention and endurance characteristics. Our work is helpful in deeply understanding the effect of top interlayer thickness on the MW and reliability of Si-FeFETs with MIFIS gate stacks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08558v1-abstract-full').style.display = 'none'; document.getElementById('2411.08558v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <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">7 pages, 12 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.08485">arXiv:2411.08485</a> <span> [<a href="https://arxiv.org/pdf/2411.08485">pdf</a>, <a href="https://arxiv.org/format/2411.08485">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"> The circumgalactic medium traced by Mg II absorption with DESI: dependence on galaxy stellar mass, star formation rate and azimuthal angle </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Zeyu Chen</a>, <a href="/search/?searchtype=author&query=Wang%2C+E">Enci Wang</a>, <a href="/search/?searchtype=author&query=Zou%2C+H">Hu Zou</a>, <a href="/search/?searchtype=author&query=Zou%2C+S">Siwei Zou</a>, <a href="/search/?searchtype=author&query=Gao%2C+Y">Yang Gao</a>, <a href="/search/?searchtype=author&query=Wang%2C+H">Huiyuan Wang</a>, <a href="/search/?searchtype=author&query=Yu%2C+H">Haoran Yu</a>, <a href="/search/?searchtype=author&query=Jia%2C+C">Cheng Jia</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Haixin Li</a>, <a href="/search/?searchtype=author&query=Ma%2C+C">Chengyu Ma</a>, <a href="/search/?searchtype=author&query=Yao%2C+Y">Yao Yao</a>, <a href="/search/?searchtype=author&query=Ding%2C+W">Weiyu Ding</a>, <a href="/search/?searchtype=author&query=Zhu%2C+R">Runyu Zhu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.08485v1-abstract-short" style="display: inline;"> Understanding the circumgalactic medium (CGM) distribution of galaxies is the key to revealing the dynamical exchange of materials between galaxies and their surroundings. In this work, we use DESI EDR dataset to investigate the cool CGM of galaxies ($0.3<z<1.7$) with stacking the spectra of background QSOs to obtain Mg II absorption of foreground galaxies. The equivalent width of Mg II absorption… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08485v1-abstract-full').style.display = 'inline'; document.getElementById('2411.08485v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.08485v1-abstract-full" style="display: none;"> Understanding the circumgalactic medium (CGM) distribution of galaxies is the key to revealing the dynamical exchange of materials between galaxies and their surroundings. In this work, we use DESI EDR dataset to investigate the cool CGM of galaxies ($0.3<z<1.7$) with stacking the spectra of background QSOs to obtain Mg II absorption of foreground galaxies. The equivalent width of Mg II absorption strongly correlates to stellar mass with EW(Mg II) $\propto M_{*}^{0.5}$ for star-forming galaxies with $\log M_{*}/M_{\odot} < 10$, but is independent with mass for galaxies above this mass. At given stellar mass, EW(Mg II) is larger for galaxies of higher star formation rate with impact parameter less than $50$ kpc, while showing little dependence on galaxy size. By studying the dependence on azimuthal angle, we find EW(Mg II) is strongest at the direction near the minor axis for star-forming galaxies with $\log M_{*}/M_{\odot} < 10.0$, while no dependence on azimuthal angle is seen for luminous red galaxies. This indicates that the outflow associated with star formation enhances the Mg II absorption. However, for galaxies with $\log M_{*}/M_{\odot} > 10.0$, the EW(Mg II) at the minor axis is largely suppressed with respect to low mass galaxies. This suggests that the competing processes, such as stellar feedback and gravity, play a key role in shaping the distribution of outflowing gas. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08485v1-abstract-full').style.display = 'none'; document.getElementById('2411.08485v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 17 figures. Submitted to ApJ. Comments are welcome</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.08403">arXiv:2411.08403</a> <span> [<a href="https://arxiv.org/pdf/2411.08403">pdf</a>, <a href="https://arxiv.org/ps/2411.08403">ps</a>, <a href="https://arxiv.org/format/2411.08403">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Representation Theory">math.RT</span> </div> </div> <p class="title is-5 mathjax"> Purity of the anisotropic affine Springer fibers for $\mathbf{GL}_{n}$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Zongbin 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="2411.08403v1-abstract-short" style="display: inline;"> For the group $\mathrm{GL}_{n}$ and the anisotropic elements, we confirm the purity hypothesis of Goresky, Kottwitz and MacPherson, which states that the affine Springer fibers are cohomologically pure in the sense of Grothendieck-Deligne. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.08403v1-abstract-full" style="display: none;"> For the group $\mathrm{GL}_{n}$ and the anisotropic elements, we confirm the purity hypothesis of Goresky, Kottwitz and MacPherson, which states that the affine Springer fibers are cohomologically pure in the sense of Grothendieck-Deligne. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08403v1-abstract-full').style.display = 'none'; document.getElementById('2411.08403v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.08340">arXiv:2411.08340</a> <span> [<a href="https://arxiv.org/pdf/2411.08340">pdf</a>, <a href="https://arxiv.org/format/2411.08340">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"> DyConfidMatch: Dynamic Thresholding and Re-sampling for 3D Semi-supervised Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhimin Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+B">Bing Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.08340v1-abstract-short" style="display: inline;"> Semi-supervised learning (SSL) leverages limited labeled and abundant unlabeled data but often faces challenges with data imbalance, especially in 3D contexts. This study investigates class-level confidence as an indicator of learning status in 3D SSL, proposing a novel method that utilizes dynamic thresholding to better use unlabeled data, particularly from underrepresented classes. A re-sampling… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08340v1-abstract-full').style.display = 'inline'; document.getElementById('2411.08340v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.08340v1-abstract-full" style="display: none;"> Semi-supervised learning (SSL) leverages limited labeled and abundant unlabeled data but often faces challenges with data imbalance, especially in 3D contexts. This study investigates class-level confidence as an indicator of learning status in 3D SSL, proposing a novel method that utilizes dynamic thresholding to better use unlabeled data, particularly from underrepresented classes. A re-sampling strategy is also introduced to mitigate bias towards well-represented classes, ensuring equitable class representation. Through extensive experiments in 3D SSL, our method surpasses state-of-the-art counterparts in classification and detection tasks, highlighting its effectiveness in tackling data imbalance. This approach presents a significant advancement in SSL for 3D datasets, providing a robust solution for data imbalance issues. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08340v1-abstract-full').style.display = 'none'; document.getElementById('2411.08340v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by Pattern Recognition Journal</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.08303">arXiv:2411.08303</a> <span> [<a href="https://arxiv.org/pdf/2411.08303">pdf</a>, <a href="https://arxiv.org/ps/2411.08303">ps</a>, <a href="https://arxiv.org/format/2411.08303">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> </div> </div> <p class="title is-5 mathjax"> The discrepancy in min-max statistics between two random matrices with finite third moments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Zijun Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yiming Chen</a>, <a href="/search/?searchtype=author&query=Wei%2C+C">Chengfu Wei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.08303v1-abstract-short" style="display: inline;"> We propose a novel coupling inequality of the min-max type for two random matrices with finite absolute third moments, which generalizes the quantitative versions of the well-known inequalities by Gordon. Previous results have calculated the quantitative bounds for pairs of Gaussian random matrices. Through integrating the methods utilized by Chatterjee-Meckes and Reinert-R枚llin in adapting Stein'… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08303v1-abstract-full').style.display = 'inline'; document.getElementById('2411.08303v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.08303v1-abstract-full" style="display: none;"> We propose a novel coupling inequality of the min-max type for two random matrices with finite absolute third moments, which generalizes the quantitative versions of the well-known inequalities by Gordon. Previous results have calculated the quantitative bounds for pairs of Gaussian random matrices. Through integrating the methods utilized by Chatterjee-Meckes and Reinert-R枚llin in adapting Stein's method of exchangeable pairs for multivariate normal approximation, this study eliminates the Gaussian restriction on random matrices, enabling us to achieve more extensive results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08303v1-abstract-full').style.display = 'none'; document.getElementById('2411.08303v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 60B20; 60E15 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> G.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07730">arXiv:2411.07730</a> <span> [<a href="https://arxiv.org/pdf/2411.07730">pdf</a>, <a href="https://arxiv.org/ps/2411.07730">ps</a>, <a href="https://arxiv.org/format/2411.07730">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"> Study of the light scalar $a_{0}(980)$ through the decay $D^{0} \to a_{0}(980)^-e^{+} 谓_{e}$ with $a_{0}(980)^- \to 畏蟺^-$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=BESIII+Collaboration"> BESIII Collaboration</a>, <a href="/search/?searchtype=author&query=Ablikim%2C+M">M. Ablikim</a>, <a href="/search/?searchtype=author&query=Achasov%2C+M+N">M. N. Achasov</a>, <a href="/search/?searchtype=author&query=Adlarson%2C+P">P. Adlarson</a>, <a href="/search/?searchtype=author&query=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. (649 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.07730v1-abstract-short" style="display: inline;"> Using 7.93 ${\rm fb^{-1}}$ of $e^+e^-$ collision data collected at a center-of-mass energy of 3.773 ${\rm GeV}$ with the BESIII detector, we present an analysis of the decay $D^{0} \to 畏蟺^- e^+ 谓_{e}$. The branching fraction of the decay $D^{0} \to a_{0}(980)^{-} e^+ 谓_{e}$ with $a_{0}(980)^{-} \to 畏蟺^{-}$ is measured to be $(0.86\pm0.17_{\text{stat}}\pm0.05_{\text{syst}})\times 10^{-4}$. The deca… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07730v1-abstract-full').style.display = 'inline'; document.getElementById('2411.07730v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07730v1-abstract-full" style="display: none;"> Using 7.93 ${\rm fb^{-1}}$ of $e^+e^-$ collision data collected at a center-of-mass energy of 3.773 ${\rm GeV}$ with the BESIII detector, we present an analysis of the decay $D^{0} \to 畏蟺^- e^+ 谓_{e}$. The branching fraction of the decay $D^{0} \to a_{0}(980)^{-} e^+ 谓_{e}$ with $a_{0}(980)^{-} \to 畏蟺^{-}$ is measured to be $(0.86\pm0.17_{\text{stat}}\pm0.05_{\text{syst}})\times 10^{-4}$. The decay dynamics of this process is studied with a single-pole parameterization of the hadronic form factor and the Flatt茅 formula describing the $a_0(980)$ line shape in the differential decay rate. The product of the form factor $f^{ a_0}_{+}(0)$ and the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cd}|$ is determined for the first time with the result $f^{ a_0}_+(0)|V_{cd}|=0.126\pm0.013_{\rm stat}\pm0.003_{\rm syst}$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07730v1-abstract-full').style.display = 'none'; document.getElementById('2411.07730v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07591">arXiv:2411.07591</a> <span> [<a href="https://arxiv.org/pdf/2411.07591">pdf</a>, <a href="https://arxiv.org/format/2411.07591">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"> Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lu%2C+C">Chenbei Lu</a>, <a href="/search/?searchtype=author&query=Shi%2C+L">Laixi Shi</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zaiwei Chen</a>, <a href="/search/?searchtype=author&query=Wu%2C+C">Chenye Wu</a>, <a href="/search/?searchtype=author&query=Wierman%2C+A">Adam Wierman</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.07591v1-abstract-short" style="display: inline;"> Reinforcement Learning (RL) algorithms are known to suffer from the curse of dimensionality, which refers to the fact that large-scale problems often lead to exponentially high sample complexity. A common solution is to use deep neural networks for function approximation; however, such approaches typically lack theoretical guarantees. To provably address the curse of dimensionality, we observe tha… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07591v1-abstract-full').style.display = 'inline'; document.getElementById('2411.07591v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07591v1-abstract-full" style="display: none;"> Reinforcement Learning (RL) algorithms are known to suffer from the curse of dimensionality, which refers to the fact that large-scale problems often lead to exponentially high sample complexity. A common solution is to use deep neural networks for function approximation; however, such approaches typically lack theoretical guarantees. To provably address the curse of dimensionality, we observe that many real-world problems exhibit task-specific model structures that, when properly leveraged, can improve the sample efficiency of RL. Building on this insight, we propose overcoming the curse of dimensionality by approximately factorizing the original Markov decision processes (MDPs) into smaller, independently evolving MDPs. This factorization enables the development of sample-efficient RL algorithms in both model-based and model-free settings, with the latter involving a variant of variance-reduced Q-learning. We provide improved sample complexity guarantees for both proposed algorithms. Notably, by leveraging model structure through the approximate factorization of the MDP, the dependence of sample complexity on the size of the state-action space can be exponentially reduced. Numerically, we demonstrate the practicality of our proposed methods through experiments on both synthetic MDP tasks and a wind farm-equipped storage control problem. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07591v1-abstract-full').style.display = 'none'; document.getElementById('2411.07591v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">61 pages, 10 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07569">arXiv:2411.07569</a> <span> [<a href="https://arxiv.org/pdf/2411.07569">pdf</a>, <a href="https://arxiv.org/format/2411.07569">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"> Towards Automated Model Design on Recommender Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+T">Tunhou Zhang</a>, <a href="/search/?searchtype=author&query=Cheng%2C+D">Dehua Cheng</a>, <a href="/search/?searchtype=author&query=He%2C+Y">Yuchen He</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhengxing Chen</a>, <a href="/search/?searchtype=author&query=Dai%2C+X">Xiaoliang Dai</a>, <a href="/search/?searchtype=author&query=Xiong%2C+L">Liang Xiong</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yudong Liu</a>, <a href="/search/?searchtype=author&query=Cheng%2C+F">Feng Cheng</a>, <a href="/search/?searchtype=author&query=Cao%2C+Y">Yufan Cao</a>, <a href="/search/?searchtype=author&query=Yan%2C+F">Feng Yan</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Hai Li</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yiran Chen</a>, <a href="/search/?searchtype=author&query=Wen%2C+W">Wei Wen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.07569v1-abstract-short" style="display: inline;"> The increasing popularity of deep learning models has created new opportunities for developing AI-based recommender systems. Designing recommender systems using deep neural networks requires careful architecture design, and further optimization demands extensive co-design efforts on jointly optimizing model architecture and hardware. Design automation, such as Automated Machine Learning (AutoML),… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07569v1-abstract-full').style.display = 'inline'; document.getElementById('2411.07569v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07569v1-abstract-full" style="display: none;"> The increasing popularity of deep learning models has created new opportunities for developing AI-based recommender systems. Designing recommender systems using deep neural networks requires careful architecture design, and further optimization demands extensive co-design efforts on jointly optimizing model architecture and hardware. Design automation, such as Automated Machine Learning (AutoML), is necessary to fully exploit the potential of recommender model design, including model choices and model-hardware co-design strategies. We introduce a novel paradigm that utilizes weight sharing to explore abundant solution spaces. Our paradigm creates a large supernet to search for optimal architectures and co-design strategies to address the challenges of data multi-modality and heterogeneity in the recommendation domain. From a model perspective, the supernet includes a variety of operators, dense connectivity, and dimension search options. From a co-design perspective, it encompasses versatile Processing-In-Memory (PIM) configurations to produce hardware-efficient models. Our solution space's scale, heterogeneity, and complexity pose several challenges, which we address by proposing various techniques for training and evaluating the supernet. Our crafted models show promising results on three Click-Through Rates (CTR) prediction benchmarks, outperforming both manually designed and AutoML-crafted models with state-of-the-art performance when focusing solely on architecture search. From a co-design perspective, we achieve 2x FLOPs efficiency, 1.8x energy efficiency, and 1.5x performance improvements in recommender models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07569v1-abstract-full').style.display = 'none'; document.getElementById('2411.07569v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in ACM Transactions on Recommender Systems. arXiv admin note: substantial text overlap with arXiv:2207.07187</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> ACM Transactions on Recommender Systems (TORS) 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07510">arXiv:2411.07510</a> <span> [<a href="https://arxiv.org/pdf/2411.07510">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> An Attack Traffic Identification Method Based on Temporal Spectrum </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xie%2C+W">Wenwei Xie</a>, <a href="/search/?searchtype=author&query=Yin%2C+J">Jie Yin</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zihao 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="2411.07510v1-abstract-short" style="display: inline;"> To address the issues of insufficient robustness, unstable features, and data noise interference in existing network attack detection and identification models, this paper proposes an attack traffic detection and identification method based on temporal spectrum. First, traffic data is segmented by a sliding window to construct a feature sequence and a corresponding label sequence for network traff… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07510v1-abstract-full').style.display = 'inline'; document.getElementById('2411.07510v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07510v1-abstract-full" style="display: none;"> To address the issues of insufficient robustness, unstable features, and data noise interference in existing network attack detection and identification models, this paper proposes an attack traffic detection and identification method based on temporal spectrum. First, traffic data is segmented by a sliding window to construct a feature sequence and a corresponding label sequence for network traffic. Next, the proposed spectral label generation methods, SSPE and COAP, are applied to transform the label sequence into spectral labels and the feature sequence into temporal features. Spectral labels and temporal features are used to capture and represent behavioral patterns of attacks. Finally, the constructed temporal features and spectral labels are used to train models, which subsequently detects and identifies network attack behaviors. Experimental results demonstrate that compared to traditional methods, models trained with the SSPE or COAP method improve identification accuracy by 10%, and exhibit strong robustness, particularly in noisy environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07510v1-abstract-full').style.display = 'none'; document.getElementById('2411.07510v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 7 figures, 7 tables, 8 formulas</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07465">arXiv:2411.07465</a> <span> [<a href="https://arxiv.org/pdf/2411.07465">pdf</a>, <a href="https://arxiv.org/format/2411.07465">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="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> </div> </div> <p class="title is-5 mathjax"> Hybrid skin-topological effect in non-Hermitian checkerboard lattices with large Chern numbers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yi-Ling Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+L">Li-Wei Wang</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yang Liu</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhao-Xian Chen</a>, <a href="/search/?searchtype=author&query=Jiang%2C+J">Jian-Hua 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="2411.07465v1-abstract-short" style="display: inline;"> Non-Hermitian topology provides a research frontier for exploring topological phenomena, revealing novel topological effects and driving the development of emergent materials and platforms. Here, we explore the non-Hermitian Chern insulator phases and the hybrid skin-topological effects in checkerboard lattices with synthetic gauge fluxes. Such lattices can be realized in integrated silicon photon… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07465v1-abstract-full').style.display = 'inline'; document.getElementById('2411.07465v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07465v1-abstract-full" style="display: none;"> Non-Hermitian topology provides a research frontier for exploring topological phenomena, revealing novel topological effects and driving the development of emergent materials and platforms. Here, we explore the non-Hermitian Chern insulator phases and the hybrid skin-topological effects in checkerboard lattices with synthetic gauge fluxes. Such lattices can be realized in integrated silicon photonic nanocircuits and microresonators as well as in arrays of evanescently coupled helical optical waveguides. With a simple and tunable design, the system is found to support non-Hermitian hybrid skin topological effects, exhibiting corner skin effects when the lattice symmetry either $C_4$ or $C_2$. An unconventional physical mechanism is revealed as the origin of such a transition which is connected to the corner-induced scattering between the multiple chiral edge channels. These properties are enabled by the large Chern number and the rich non-Hermitian topological edge states in our system, revealing the diverse non-Hermitian topological bulk-boundary correspondence. Our design offers excellent controllability and experimental feasibility, making it appealing for studying non-Hermitian topological phenomena. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07465v1-abstract-full').style.display = 'none'; document.getElementById('2411.07465v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07458">arXiv:2411.07458</a> <span> [<a href="https://arxiv.org/pdf/2411.07458">pdf</a>, <a href="https://arxiv.org/format/2411.07458">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"> Size Growth on Short Timescales of Star-Forming Galaxies: Insights from Size Variation with Rest-Frame Wavelength with JADES </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jia%2C+C">Cheng Jia</a>, <a href="/search/?searchtype=author&query=Wang%2C+E">Enci Wang</a>, <a href="/search/?searchtype=author&query=Wang%2C+H">Huiyuan Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Hui Li</a>, <a href="/search/?searchtype=author&query=Yao%2C+Y">Yao Yao</a>, <a href="/search/?searchtype=author&query=Song%2C+J">Jie Song</a>, <a href="/search/?searchtype=author&query=Zhang%2C+H">Hongxin Zhang</a>, <a href="/search/?searchtype=author&query=Rong%2C+Y">Yu Rong</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yangyao Chen</a>, <a href="/search/?searchtype=author&query=Yu%2C+H">Haoran Yu</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zeyu Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Haixin Li</a>, <a href="/search/?searchtype=author&query=Ma%2C+C">Chengyu Ma</a>, <a href="/search/?searchtype=author&query=Kong%2C+X">Xu Kong</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.07458v1-abstract-short" style="display: inline;"> We investigate size variation with rest-frame wavelength for star-forming galaxies based on the second JWST Advanced Deep Extragalactic Survey data release. Star-forming galaxies are typically smaller at longer wavelength from UV-to-NIR at $z<3.5$, especially for more massive galaxies, indicating the inside-out assembly with in-situ star formation if ignoring dust attenuation. The size variation w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07458v1-abstract-full').style.display = 'inline'; document.getElementById('2411.07458v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07458v1-abstract-full" style="display: none;"> We investigate size variation with rest-frame wavelength for star-forming galaxies based on the second JWST Advanced Deep Extragalactic Survey data release. Star-forming galaxies are typically smaller at longer wavelength from UV-to-NIR at $z<3.5$, especially for more massive galaxies, indicating the inside-out assembly with in-situ star formation if ignoring dust attenuation. The size variation with wavelength shows strong dependence on stellar mass, and shows little or no dependence on redshift, specific star formation rate and galaxy environment. This suggests that the size growth of star-forming galaxies is a self-regulated process primarily governed by stellar mass. We model size as a function of both mass and redshift simultaneously, obtaining $R_{\rm e} \propto M_*^{0.23} (1+z)^{-1.04}$ at a wavelength of 0.45 ${渭\mathrm{m}}$, and $R_{\rm e} \propto M_*^{0.20} (1+z)^{-1.08}$ at 1.0 ${渭\mathrm{m}}$. Based on this size evolution and the star formation main sequence from the literature, we obtain the locus of typical size growth for individual galaxies of different masses on the mass-size plane. The moving trend of galaxies on the mass-size plane, which indicates the slopes of their locus, strongly correlates with the size ratio between 0.45 ${渭\mathrm{m}}$ and 1.0 ${渭\mathrm{m}}$, supporting the idea that the size variation with wavelength provides important information on size growth of galaxies on short timescales. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07458v1-abstract-full').style.display = 'none'; document.getElementById('2411.07458v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication in ApJ, 19 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/2411.07228">arXiv:2411.07228</a> <span> [<a href="https://arxiv.org/pdf/2411.07228">pdf</a>, <a href="https://arxiv.org/format/2411.07228">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> </div> </div> <p class="title is-5 mathjax"> Tooling or Not Tooling? The Impact of Tools on Language Agents for Chemistry Problem Solving </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yu%2C+B">Botao Yu</a>, <a href="/search/?searchtype=author&query=Baker%2C+F+N">Frazier N. Baker</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Ziru Chen</a>, <a href="/search/?searchtype=author&query=Herb%2C+G">Garrett Herb</a>, <a href="/search/?searchtype=author&query=Gou%2C+B">Boyu Gou</a>, <a href="/search/?searchtype=author&query=Adu-Ampratwum%2C+D">Daniel Adu-Ampratwum</a>, <a href="/search/?searchtype=author&query=Ning%2C+X">Xia Ning</a>, <a href="/search/?searchtype=author&query=Sun%2C+H">Huan Sun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.07228v1-abstract-short" style="display: inline;"> To enhance large language models (LLMs) for chemistry problem solving, several LLM-based agents augmented with tools have been proposed, such as ChemCrow and Coscientist. However, their evaluations are narrow in scope, leaving a large gap in understanding the benefits of tools across diverse chemistry tasks. To bridge this gap, we develop ChemAgent, an enhanced chemistry agent over ChemCrow, and c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07228v1-abstract-full').style.display = 'inline'; document.getElementById('2411.07228v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07228v1-abstract-full" style="display: none;"> To enhance large language models (LLMs) for chemistry problem solving, several LLM-based agents augmented with tools have been proposed, such as ChemCrow and Coscientist. However, their evaluations are narrow in scope, leaving a large gap in understanding the benefits of tools across diverse chemistry tasks. To bridge this gap, we develop ChemAgent, an enhanced chemistry agent over ChemCrow, and conduct a comprehensive evaluation of its performance on both specialized chemistry tasks and general chemistry questions. Surprisingly, ChemAgent does not consistently outperform its base LLMs without tools. Our error analysis with a chemistry expert suggests that: For specialized chemistry tasks, such as synthesis prediction, we should augment agents with specialized tools; however, for general chemistry questions like those in exams, agents' ability to reason correctly with chemistry knowledge matters more, and tool augmentation does not always help. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07228v1-abstract-full').style.display = 'none'; document.getElementById('2411.07228v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07136">arXiv:2411.07136</a> <span> [<a href="https://arxiv.org/pdf/2411.07136">pdf</a>, <a href="https://arxiv.org/format/2411.07136">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> </div> </div> <p class="title is-5 mathjax"> Trap Identification in Molecular Charge Transport Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhongquan Chen</a>, <a href="/search/?searchtype=author&query=van+der+Hoorn%2C+P">Pim van der Hoorn</a>, <a href="/search/?searchtype=author&query=Baumeier%2C+B">Bj枚rn Baumeier</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.07136v1-abstract-short" style="display: inline;"> This paper introduces a method to identify traps in molecular charge transport networks as obtained by multiscale modeling of organic semiconductors. Depending on the materials, traps can be defect-like single molecules or clusters of several neighboring ones, and can have a significant impact on the dynamics of charge carriers. Our proposed method builds on the random walk model of charge dynamic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07136v1-abstract-full').style.display = 'inline'; document.getElementById('2411.07136v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07136v1-abstract-full" style="display: none;"> This paper introduces a method to identify traps in molecular charge transport networks as obtained by multiscale modeling of organic semiconductors. Depending on the materials, traps can be defect-like single molecules or clusters of several neighboring ones, and can have a significant impact on the dynamics of charge carriers. Our proposed method builds on the random walk model of charge dynamics on a directed, weighted graph, the molecular transport network. It comprises an effective heuristic to determine the number of traps or trap clusters based on the eigenvalues and eigenvectors of the random walk Laplacian matrix and uses subsequent spectral clustering techniques to identify these traps. In contrast to currently available methods, ours enables identification of trap molecules in organic semiconductors without having to explicitly simulate the charge dynamics. As a prototypical system we study an amorphous morphology of bathocuproine, a material with known high energetic disorder and charge trapping. Based on a first-principle multiscale model, we first obtain a reference charge transport network and then modify its properties to represent different trap characteristics. In contrast to currently available methods, our approach successfully identifies both single trap, multiple distributed traps, and a combination of a single-molecule trap and trap regions on an equal footing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07136v1-abstract-full').style.display = 'none'; document.getElementById('2411.07136v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> G.2.2 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07025">arXiv:2411.07025</a> <span> [<a href="https://arxiv.org/pdf/2411.07025">pdf</a>, <a href="https://arxiv.org/format/2411.07025">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Scaling Mesh Generation via Compressive Tokenization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Weng%2C+H">Haohan Weng</a>, <a href="/search/?searchtype=author&query=Zhao%2C+Z">Zibo Zhao</a>, <a href="/search/?searchtype=author&query=Lei%2C+B">Biwen Lei</a>, <a href="/search/?searchtype=author&query=Yang%2C+X">Xianghui Yang</a>, <a href="/search/?searchtype=author&query=Liu%2C+J">Jian Liu</a>, <a href="/search/?searchtype=author&query=Lai%2C+Z">Zeqiang Lai</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhuo Chen</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yuhong Liu</a>, <a href="/search/?searchtype=author&query=Jiang%2C+J">Jie Jiang</a>, <a href="/search/?searchtype=author&query=Guo%2C+C">Chunchao Guo</a>, <a href="/search/?searchtype=author&query=Zhang%2C+T">Tong Zhang</a>, <a href="/search/?searchtype=author&query=Gao%2C+S">Shenghua Gao</a>, <a href="/search/?searchtype=author&query=Chen%2C+C+L+P">C. L. Philip 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="2411.07025v1-abstract-short" style="display: inline;"> We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces. BPT compresses mesh sequences by employing block-wise indexing and patch aggregation, reducing their length by approximately 75\% compared to the original sequences. This compression milestone unlocks the potential to utilize mesh data wit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07025v1-abstract-full').style.display = 'inline'; document.getElementById('2411.07025v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07025v1-abstract-full" style="display: none;"> We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces. BPT compresses mesh sequences by employing block-wise indexing and patch aggregation, reducing their length by approximately 75\% compared to the original sequences. This compression milestone unlocks the potential to utilize mesh data with significantly more faces, thereby enhancing detail richness and improving generation robustness. Empowered with the BPT, we have built a foundation mesh generative model training on scaled mesh data to support flexible control for point clouds and images. Our model demonstrates the capability to generate meshes with intricate details and accurate topology, achieving SoTA performance on mesh generation and reaching the level for direct product usage. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07025v1-abstract-full').style.display = 'none'; document.getElementById('2411.07025v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Homepage: https://whaohan.github.io/bpt , Code: https://github.com/whaohan/bpt</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07019">arXiv:2411.07019</a> <span> [<a href="https://arxiv.org/pdf/2411.07019">pdf</a>, <a href="https://arxiv.org/format/2411.07019">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"> UniHR: Hierarchical Representation Learning for Unified Knowledge Graph Link Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liu%2C+Z">Zhiqiang Liu</a>, <a href="/search/?searchtype=author&query=Chen%2C+M">Mingyang Chen</a>, <a href="/search/?searchtype=author&query=Hua%2C+Y">Yin Hua</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhuo Chen</a>, <a href="/search/?searchtype=author&query=Liu%2C+Z">Ziqi Liu</a>, <a href="/search/?searchtype=author&query=Liang%2C+L">Lei Liang</a>, <a href="/search/?searchtype=author&query=Chen%2C+H">Huajun Chen</a>, <a href="/search/?searchtype=author&query=Zhang%2C+W">Wen Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.07019v1-abstract-short" style="display: inline;"> Beyond-triple fact representations including hyper-relational facts with auxiliary key-value pairs, temporal facts with additional timestamps, and nested facts implying relationships between facts, are gaining significant attention. However, existing link prediction models are usually designed for one specific type of facts, making it difficult to generalize to other fact representations. To overc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07019v1-abstract-full').style.display = 'inline'; document.getElementById('2411.07019v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07019v1-abstract-full" style="display: none;"> Beyond-triple fact representations including hyper-relational facts with auxiliary key-value pairs, temporal facts with additional timestamps, and nested facts implying relationships between facts, are gaining significant attention. However, existing link prediction models are usually designed for one specific type of facts, making it difficult to generalize to other fact representations. To overcome this limitation, we propose a Unified Hierarchical Representation learning framework (UniHR) for unified knowledge graph link prediction. It consists of a unified Hierarchical Data Representation (HiDR) module and a unified Hierarchical Structure Learning (HiSL) module as graph encoder. The HiDR module unifies hyper-relational KGs, temporal KGs, and nested factual KGs into triple-based representations. Then HiSL incorporates intra-fact and inter-fact message passing, focusing on enhancing the semantic information within individual facts and enriching the structural information between facts. Experimental results across 7 datasets from 3 types of KGs demonstrate that our UniHR outperforms baselines designed for one specific kind of KG, indicating strong generalization capability of HiDR form and the effectiveness of HiSL module. Code and data are available at https://github.com/Lza12a/UniHR. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07019v1-abstract-full').style.display = 'none'; document.getElementById('2411.07019v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Chen%2C+Z&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Chen%2C+Z&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a 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