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is-link">Go</button> </div> </div> </form> </div> </div> <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/2411.13887">arXiv:2411.13887</a> <span> [<a href="https://arxiv.org/pdf/2411.13887">pdf</a>, <a href="https://arxiv.org/format/2411.13887">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Algebraic Topology">math.AT</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="Computational Geometry">cs.CG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Metric Geometry">math.MG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> A cohomology-based Gromov-Hausdorff metric approach for quantifying molecular similarity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wee%2C+J">JunJie Wee</a>, <a href="/search/cs?searchtype=author&query=Gong%2C+X">Xue Gong</a>, <a href="/search/cs?searchtype=author&query=Tuschmann%2C+W">Wilderich Tuschmann</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+K">Kelin 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="2411.13887v1-abstract-short" style="display: inline;"> We introduce, for the first time, a cohomology-based Gromov-Hausdorff ultrametric method to analyze 1-dimensional and higher-dimensional (co)homology groups, focusing on loops, voids, and higher-dimensional cavity structures in simplicial complexes, to address typical clustering questions arising in molecular data analysis. The Gromov-Hausdorff distance quantifies the dissimilarity between two met… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13887v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13887v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13887v1-abstract-full" style="display: none;"> We introduce, for the first time, a cohomology-based Gromov-Hausdorff ultrametric method to analyze 1-dimensional and higher-dimensional (co)homology groups, focusing on loops, voids, and higher-dimensional cavity structures in simplicial complexes, to address typical clustering questions arising in molecular data analysis. The Gromov-Hausdorff distance quantifies the dissimilarity between two metric spaces. In this framework, molecules are represented as simplicial complexes, and their cohomology vector spaces are computed to capture intrinsic topological invariants encoding loop and cavity structures. These vector spaces are equipped with a suitable distance measure, enabling the computation of the Gromov-Hausdorff ultrametric to evaluate structural dissimilarities. We demonstrate the methodology using organic-inorganic halide perovskite (OIHP) structures. The results highlight the effectiveness of this approach in clustering various molecular structures. By incorporating geometric information, our method provides deeper insights compared to traditional persistent homology techniques. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13887v1-abstract-full').style.display = 'none'; document.getElementById('2411.13887v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 3 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 55N31; 68U05; 92E10; 62H30; 55U10 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> G.2.2; I.1.1; I.5.3; J.2 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.01100">arXiv:2410.01100</a> <span> [<a href="https://arxiv.org/pdf/2410.01100">pdf</a>, <a href="https://arxiv.org/format/2410.01100">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"> Unlocking Korean Verbs: A User-Friendly Exploration into the Verb Lexicon </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Song%2C+S">Seohyun Song</a>, <a href="/search/cs?searchtype=author&query=Jo%2C+E+L">Eunkyul Leah Jo</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yige Chen</a>, <a href="/search/cs?searchtype=author&query=Hong%2C+J">Jeen-Pyo Hong</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+K">Kyuwon Kim</a>, <a href="/search/cs?searchtype=author&query=Wee%2C+J">Jin Wee</a>, <a href="/search/cs?searchtype=author&query=Kang%2C+M">Miyoung Kang</a>, <a href="/search/cs?searchtype=author&query=Lim%2C+K">KyungTae Lim</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">Jungyeul Park</a>, <a href="/search/cs?searchtype=author&query=Park%2C+C">Chulwoo Park</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.01100v1-abstract-short" style="display: inline;"> The Sejong dictionary dataset offers a valuable resource, providing extensive coverage of morphology, syntax, and semantic representation. This dataset can be utilized to explore linguistic information in greater depth. The labeled linguistic structures within this dataset form the basis for uncovering relationships between words and phrases and their associations with target verbs. This paper int… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01100v1-abstract-full').style.display = 'inline'; document.getElementById('2410.01100v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.01100v1-abstract-full" style="display: none;"> The Sejong dictionary dataset offers a valuable resource, providing extensive coverage of morphology, syntax, and semantic representation. This dataset can be utilized to explore linguistic information in greater depth. The labeled linguistic structures within this dataset form the basis for uncovering relationships between words and phrases and their associations with target verbs. This paper introduces a user-friendly web interface designed for the collection and consolidation of verb-related information, with a particular focus on subcategorization frames. Additionally, it outlines our efforts in mapping this information by aligning subcategorization frames with corresponding illustrative sentence examples. Furthermore, we provide a Python library that would simplify syntactic parsing and semantic role labeling. These tools are intended to assist individuals interested in harnessing the Sejong dictionary dataset to develop applications for Korean language processing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01100v1-abstract-full').style.display = 'none'; document.getElementById('2410.01100v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">COLING2025 System Demonstrations (Submitted)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.08974">arXiv:2407.08974</a> <span> [<a href="https://arxiv.org/pdf/2407.08974">pdf</a>, <a href="https://arxiv.org/format/2407.08974">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="General Topology">math.GN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Topology-enhanced machine learning model (Top-ML) for anticancer peptide prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tan%2C+J+Z+E">Joshua Zhi En Tan</a>, <a href="/search/cs?searchtype=author&query=Wee%2C+J">JunJie Wee</a>, <a href="/search/cs?searchtype=author&query=Gong%2C+X">Xue Gong</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+K">Kelin 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="2407.08974v1-abstract-short" style="display: inline;"> Recently, therapeutic peptides have demonstrated great promise for cancer treatment. To explore powerful anticancer peptides, artificial intelligence (AI)-based approaches have been developed to systematically screen potential candidates. However, the lack of efficient featurization of peptides has become a bottleneck for these machine-learning models. In this paper, we propose a topology-enhanced… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.08974v1-abstract-full').style.display = 'inline'; document.getElementById('2407.08974v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.08974v1-abstract-full" style="display: none;"> Recently, therapeutic peptides have demonstrated great promise for cancer treatment. To explore powerful anticancer peptides, artificial intelligence (AI)-based approaches have been developed to systematically screen potential candidates. However, the lack of efficient featurization of peptides has become a bottleneck for these machine-learning models. In this paper, we propose a topology-enhanced machine learning model (Top-ML) for anticancer peptide prediction. Our Top-ML employs peptide topological features derived from its sequence "connection" information characterized by vector and spectral descriptors. Our Top-ML model has been validated on two widely used AntiCP 2.0 benchmark datasets and has achieved state-of-the-art performance. Our results highlight the potential of leveraging novel topology-based featurization to accelerate the identification of anticancer peptides. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.08974v1-abstract-full').style.display = 'none'; document.getElementById('2407.08974v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.11202">arXiv:2401.11202</a> <span> [<a href="https://arxiv.org/pdf/2401.11202">pdf</a>, <a href="https://arxiv.org/format/2401.11202">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Programming Languages">cs.PL</span> </div> </div> <p class="title is-5 mathjax"> PartIR: Composing SPMD Partitioning Strategies for Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Alabed%2C+S">Sami Alabed</a>, <a href="/search/cs?searchtype=author&query=Belov%2C+D">Daniel Belov</a>, <a href="/search/cs?searchtype=author&query=Chrzaszcz%2C+B">Bart Chrzaszcz</a>, <a href="/search/cs?searchtype=author&query=Franco%2C+J">Juliana Franco</a>, <a href="/search/cs?searchtype=author&query=Grewe%2C+D">Dominik Grewe</a>, <a href="/search/cs?searchtype=author&query=Maclaurin%2C+D">Dougal Maclaurin</a>, <a href="/search/cs?searchtype=author&query=Molloy%2C+J">James Molloy</a>, <a href="/search/cs?searchtype=author&query=Natan%2C+T">Tom Natan</a>, <a href="/search/cs?searchtype=author&query=Norman%2C+T">Tamara Norman</a>, <a href="/search/cs?searchtype=author&query=Pan%2C+X">Xiaoyue Pan</a>, <a href="/search/cs?searchtype=author&query=Paszke%2C+A">Adam Paszke</a>, <a href="/search/cs?searchtype=author&query=Rink%2C+N+A">Norman A. Rink</a>, <a href="/search/cs?searchtype=author&query=Schaarschmidt%2C+M">Michael Schaarschmidt</a>, <a href="/search/cs?searchtype=author&query=Sitdikov%2C+T">Timur Sitdikov</a>, <a href="/search/cs?searchtype=author&query=Swietlik%2C+A">Agnieszka Swietlik</a>, <a href="/search/cs?searchtype=author&query=Vytiniotis%2C+D">Dimitrios Vytiniotis</a>, <a href="/search/cs?searchtype=author&query=Wee%2C+J">Joel Wee</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="2401.11202v4-abstract-short" style="display: inline;"> Training of modern large neural networks (NN) requires a combination of parallelization strategies encompassing data, model, or optimizer sharding. When strategies increase in complexity, it becomes necessary for partitioning tools to be 1) expressive, allowing the composition of simpler strategies, and 2) predictable to estimate performance analytically. We present PartIR, our design for a NN par… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11202v4-abstract-full').style.display = 'inline'; document.getElementById('2401.11202v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.11202v4-abstract-full" style="display: none;"> Training of modern large neural networks (NN) requires a combination of parallelization strategies encompassing data, model, or optimizer sharding. When strategies increase in complexity, it becomes necessary for partitioning tools to be 1) expressive, allowing the composition of simpler strategies, and 2) predictable to estimate performance analytically. We present PartIR, our design for a NN partitioning system. PartIR is focused on an incremental approach to rewriting and is hardware-and-runtime agnostic. We present a simple but powerful API for composing sharding strategies and a simulator to validate them. The process is driven by high-level programmer-issued partitioning tactics, which can be both manual and automatic. Importantly, the tactics are specified separately from the model code, making them easy to change. We evaluate PartIR on several different models to demonstrate its predictability, expressibility, and ability to reach peak performance.. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11202v4-abstract-full').style.display = 'none'; document.getElementById('2401.11202v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.13699">arXiv:2306.13699</a> <span> [<a href="https://arxiv.org/pdf/2306.13699">pdf</a>, <a href="https://arxiv.org/format/2306.13699">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Curvature-enhanced Graph Convolutional Network for Biomolecular Interaction Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shen%2C+C">Cong Shen</a>, <a href="/search/cs?searchtype=author&query=Ding%2C+P">Pingjian Ding</a>, <a href="/search/cs?searchtype=author&query=Wee%2C+J">Junjie Wee</a>, <a href="/search/cs?searchtype=author&query=Bi%2C+J">Jialin Bi</a>, <a href="/search/cs?searchtype=author&query=Luo%2C+J">Jiawei Luo</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+K">Kelin 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="2306.13699v1-abstract-short" style="display: inline;"> Geometric deep learning has demonstrated a great potential in non-Euclidean data analysis. The incorporation of geometric insights into learning architecture is vital to its success. Here we propose a curvature-enhanced graph convolutional network (CGCN) for biomolecular interaction prediction, for the first time. Our CGCN employs Ollivier-Ricci curvature (ORC) to characterize network local struct… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.13699v1-abstract-full').style.display = 'inline'; document.getElementById('2306.13699v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.13699v1-abstract-full" style="display: none;"> Geometric deep learning has demonstrated a great potential in non-Euclidean data analysis. The incorporation of geometric insights into learning architecture is vital to its success. Here we propose a curvature-enhanced graph convolutional network (CGCN) for biomolecular interaction prediction, for the first time. Our CGCN employs Ollivier-Ricci curvature (ORC) to characterize network local structures and to enhance the learning capability of GCNs. More specifically, ORCs are evaluated based on the local topology from node neighborhoods, and further used as weights for the feature aggregation in message-passing procedure. Our CGCN model is extensively validated on fourteen real-world bimolecular interaction networks and a series of simulated data. It has been found that our CGCN can achieve the state-of-the-art results. It outperforms all existing models, as far as we know, in thirteen out of the fourteen real-world datasets and ranks as the second in the rest one. The results from the simulated data show that our CGCN model is superior to the traditional GCN models regardless of the positive-to-negativecurvature ratios, network densities, and network sizes (when larger than 500). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.13699v1-abstract-full').style.display = 'none'; document.getElementById('2306.13699v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> 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