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Dolphins Announce Ex-Raiders Executive Will Serve as Interim GM https://heavy.com/sports/nfl/las-vegas-raiders/dolphins-interim-gm-champ-kelly/?adt_ei=[email]

You Only Train Once: Differentiable Subset Selection for Omics Data
Daphn\'e Chopard, Jorge da Silva Gon\c{c}alves, Irene Cannistraci, Thomas M. Sutter, Julia E. Vogt
https://arxiv.org/abs/2512.17678 https://arxiv.org/pdf/2512.17678 https://arxiv.org/html/2512.17678
arXiv:2512.17678v1 Announce Type: new
Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling. However, most existing feature selection approaches either operate as multi-stage pipelines or rely on post hoc feature attribution, making selection and prediction weakly coupled. In this work, we present YOTO (you only train once), an end-to-end framework that jointly identifies discrete gene subsets and performs prediction within a single differentiable architecture. In our model, the prediction task directly guides which genes are selected, while the learned subsets, in turn, shape the predictive representation. This closed feedback loop enables the model to iteratively refine both what it selects and how it predicts during training. Unlike existing approaches, YOTO enforces sparsity so that only the selected genes contribute to inference, eliminating the need to train additional downstream classifiers. Through a multi-task learning design, the model learns shared representations across related objectives, allowing partially labeled datasets to inform one another, and discovering gene subsets that generalize across tasks without additional training steps. We evaluate YOTO on two representative single-cell RNA-seq datasets, showing that it consistently outperforms state-of-the-art baselines. These results demonstrate that sparse, end-to-end, multi-task gene subset selection improves predictive performance and yields compact and meaningful gene subsets, advancing biomarker discovery and single-cell analysis.
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Mass-Centered GCM Framework in Perturbations of Kerr(-Newman)
Allen Juntao Fang, Elena Giorgi, Jingbo Wan
https://arxiv.org/abs/2510.10811 https://arxiv.or…

Mass-Centered GCM Framework in Perturbations of Kerr(-Newman)
The nonlinear stability problem for black hole solutions of the Einstein equations critically depends on choosing an appropriate geometric gauge. In the vacuum setting, the use of Generally Covariant Modulated (GCM) spheres and hypersurfaces has played a central role in the proof of stability for slowly rotating Kerr spacetime. In this work, we develop an alternative GCM framework, that we call mass-centered, designed to overcome the breakdown of the standard GCM construction in the charged cas…

RA-Gen: A Controllable Code Generation Framework Using ReAct for Multi-Agent Task Execution
Aofan Liu, Haoxuan Li, Bin Wang, Ao Yang, Hui Li
https://arxiv.org/abs/2510.08665 htt…

RA-Gen: A Controllable Code Generation Framework Using ReAct for Multi-Agent Task Execution
Code generation models based on large language models (LLMs) have gained wide adoption, but challenges remain in ensuring safety, accuracy, and controllability, especially for complex tasks. Existing methods often lack dynamic integration of external tools, transparent reasoning, and user control over safety. To address these issues, we propose a controllable code generation framework utilizing the ReAct paradigm for multi-agent task execution. This framework is a multi-agent system designed to…

A Neural Surrogate-Enhanced Multi-Method Framework for Robust Wing Design Optimization
Arash Fath Lipaei, AmirHossein Ghaemi, Melika Sabzikari
https://arxiv.org/abs/2510.08582 h…

A Neural Surrogate-Enhanced Multi-Method Framework for Robust Wing Design Optimization
This paper introduces a modular and scalable design optimization framework for the wing design process that enables faster early-phase design while ensuring aerodynamic stability. The pipeline starts with the generation of initial wing geometries and then proceeds to optimize the wing using several algorithms. Aerodynamic performance is assessed using a Vortex Lattice Method (VLM) applied to a carefully selected dataset of wing configurations. These results are employed to develop surrogate neu…

LM Fight Arena: Benchmarking Large Multimodal Models via Game Competition
Yushuo Zheng, Zicheng Zhang, Xiongkuo Min, Huiyu Duan, Guangtao Zhai
https://arxiv.org/abs/2510.08928 h…

LM Fight Arena: Benchmarking Large Multimodal Models via Game Competition
Existing benchmarks for large multimodal models (LMMs) often fail to capture their performance in real-time, adversarial environments. We introduce LM Fight Arena (Large Model Fight Arena), a novel framework that evaluates LMMs by pitting them against each other in the classic fighting game Mortal Kombat II, a task requiring rapid visual understanding and tactical, sequential decision-making. In a controlled tournament, we test six leading open- and closed-source models, where each agent operat…

Raiders Insider Reveals Major QB Decision Ahead of Eagles Game https://heavy.com/sports/nfl/las-vegas-raiders/kenny-pickett-geno-smith-decision-eagles/

A Non-Intrusive Framework for Deferred Integration of Cloud Patterns in Energy-Efficient Data-Sharing Pipelines
Sepideh Masoudi, Mark Edward Michael Daly, Jannis Kiesel, Stefan Tai
https://arxiv.org/abs/2510.12354

A Non-Intrusive Framework for Deferred Integration of Cloud Patterns in Energy-Efficient Data-Sharing Pipelines
As data mesh architectures gain traction in federated environments, organizations are increasingly building consumer-specific data-sharing pipelines using modular, cloud-native transformation services. Prior work has shown that structuring these pipelines with reusable transformation stages enhances both scalability and energy efficiency. However, integrating traditional cloud design patterns into such pipelines poses a challenge: predefining and embedding patterns can compromise modularity, re…

Channel-Aware Deep Learning for Superimposed Pilot Power Allocation and Receiver Design
Run Gu, Renjie Xie, Wei Xu, Zhaohui Yang, Kaibin Huang
https://arxiv.org/abs/2510.11294 h…

Channel-Aware Deep Learning for Superimposed Pilot Power Allocation and Receiver Design
Superimposed pilot (SIP) schemes face significant challenges in effectively superimposing and separating pilot and data signals, especially in multiuser mobility scenarios with rapidly varying channels. To address these challenges, we propose a novel channel-aware learning framework for SIP schemes, termed CaSIP, that jointly optimizes pilot-data power (PDP) allocation and a receiver network for pilot-data interference (PDI) elimination, by leveraging channel path gain information, a form of la…

Raiders’ Kenny Pickett Gets Blunt Words Amid Geno Smith Injury https://heavy.com/sports/nfl/las-vegas-raiders/kenny-pickett-geno-smith-injury/?adt_ei=[email]