Tootfinder

TComQA: Extracting Temporal Commonsense from Text
Lekshmi R Nair, Arun Sankar, Koninika Pal
https://arxiv.org/abs/2508.15274 https://arxiv.org/pdf/2508.152…

TComQA: Extracting Temporal Commonsense from Text
Understanding events necessitates grasping their temporal context, which is often not explicitly stated in natural language. For example, it is not a trivial task for a machine to infer that a museum tour may last for a few hours, but can not take months. Recent studies indicate that even advanced large language models (LLMs) struggle in generating text that require reasoning with temporal commonsense due to its infrequent explicit mention in text. Therefore, automatically mining temporal commo…

Derek Stingley Jr. is comparable to Deion Sanders in one key area, Texans coach Demeco Ryans explains

https://www.cbssports.com/nfl/news/derek-s

Communications to Circulations: 3D Wind Field Retrieval and Real-Time Prediction Using 5G GNSS Signals and Deep Learning
Yuchen Ye, Hong Liang, Chaoxia Yuan, Mingyu Li, Aoqi Zhou, Chunqing Shang, Hua Cai, Peixi Liu, Kezuan Wang, Yifeng Zheng
https://arxiv.org/abs/2509.16068

Communications to Circulations: 3D Wind Field Retrieval and Real-Time Prediction Using 5G GNSS Signals and Deep Learning
Accurate atmospheric wind field information is crucial for various applications, including weather forecasting, aviation safety, and disaster risk reduction. However, obtaining high spatiotemporal resolution wind data remains challenging due to limitations in traditional in-situ observations and remote sensing techniques, as well as the computational expense and biases of numerical weather prediction (NWP) models. This paper introduces G-WindCast, a novel deep learning framework that leverages …

On Splitting Lightweight Semantic Image Segmentation for Wireless Communications
Ebrahim Abu-Helalah, Jordi Serra, Jordi Perez-Romero
https://arxiv.org/abs/2507.14199

On Splitting Lightweight Semantic Image Segmentation for Wireless Communications
Semantic communication represents a promising technique towards reducing communication costs, especially when dealing with image segmentation, but it still lacks a balance between computational efficiency and bandwidth requirements while maintaining high image segmentation accuracy, particularly in resource-limited environments and changing channel conditions. On the other hand, the more complex and larger semantic image segmentation models become, the more stressed the devices are when process…

Twisting Signals for Joint Radar-Communications: An OAM Vortex Beam Approach
Wanghan Lv, Kumar Vijay Mishra, Jinsong Hu
https://arxiv.org/abs/2509.15601 https://

Twisting Signals for Joint Radar-Communications: An OAM Vortex Beam Approach
Orbital angular momentum (OAM) technology has attracted much research interest in recent years because of its characteristic helical phase front twisting around the propagation axis and natural orthogonality among different OAM states to encode more degrees of freedom than classical planar beams. Leveraging upon these features, OAM technique has been applied to wireless communication systems to enhance spectral efficiency and radar systems to distinguish spatial targets without beam scanning. L…

Lorentz-Equivariance without Limitations
Luigi Favaro, Gerrit Gerhartz, Fred A. Hamprecht, Peter Lippmann, Sebastian Pitz, Tilman Plehn, Huilin Qu, Jonas Spinner
https://arxiv.org/abs/2508.14898

Lorentz-Equivariance without Limitations
Lorentz Local Canonicalization (LLoCa) ensures exact Lorentz-equivariance for arbitrary neural networks with minimal computational overhead. For the LHC, it equivariantly predicts local reference frames for each particle and propagates any-order tensorial information between them. We apply it to graph networks and transformers. We showcase its cutting-edge performance on amplitude regression, end-to-end event generation, and jet tagging. For jet tagging, we introduce a large top tagging dataset…

Developing Shared Vocabulary System For Collaborative Software Engineering
Carey Lai Zheng Hui, Johnson Britto Jessia Esther Leena, Kumuthini Subramanian, Zhao Chenyu, Shubham Rajeshkumar Jariwala
https://arxiv.org/abs/2507.14396

Developing Shared Vocabulary System For Collaborative Software Engineering
Effective communication is a critical factor in successful software engineering collaboration. However, communication gaps remain a persistent challenge, often leading to misunderstandings, inefficiencies, and defects. This research investigates the technical factors contributing to such misunderstandings and explores the measurable benefits of establishing shared vocabulary systems within software documentation and codebases. Using a Design Science Research (DSR) framework, the study was struc…

Chunk Based Speech Pre-training with High Resolution Finite Scalar Quantization
Yun Tang, Cindy Tseng
https://arxiv.org/abs/2509.15579 https://arxiv.org/pd…

Chunk Based Speech Pre-training with High Resolution Finite Scalar Quantization
Low latency speech human-machine communication is becoming increasingly necessary as speech technology advances quickly in the last decade. One of the primary factors behind the advancement of speech technology is self-supervised learning. Most self-supervised learning algorithms are designed with full utterance assumption and compromises have to made if partial utterances are presented, which are common in the streaming applications. In this work, we propose a chunk based self-supervised learn…

Personalized Federated Learning with Heat-Kernel Enhanced Tensorized Multi-View Clustering
Kristina P. Sinaga
https://arxiv.org/abs/2509.16101 https://arxi…

Personalized Federated Learning with Heat-Kernel Enhanced Tensorized Multi-View Clustering
We present a robust personalized federated learning framework that leverages heat-kernel enhanced tensorized multi-view fuzzy c-means clustering with advanced tensor decomposition techniques. Our approach integrates heat-kernel coefficients adapted from quantum field theory with Tucker decomposition and canonical polyadic decomposition (CANDECOMP/PARAFAC) to transform conventional distance metrics and efficiently represent high-dimensional multi-view structures. The framework employs matriculat…

Robust ISAC Transceiver Beamforming Design under Low-Resolution AD/DA Converters
Tiantian Xu, Zhenyao He, Jindan Xu, Wei Xu, Jianfeng Wang, Derrick Wing Kwan Ng
https://arxiv.org/abs/2507.15373

Robust ISAC Transceiver Beamforming Design under Low-Resolution AD/DA Converters
In this letter, we investigate the robust beamforming design for an integrated sensing and communication (ISAC) system featuring low-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs). Taking into account quantization noise, we aim at maximizing the radar signal-to-quantization-plus-noise ratio (SQNR) while guaranteeing the minimum required signal-to-quantization-plus-interference-plus-noise ratio (SQINR) for communication users. To address this nonconvex des…