Tootfinder

@zachleat@zachleat.com
2025-10-18 03:40:09

no — *you* are working on the archives page on your blog on friday night — not me
https://www.zachleat.com/web/

@axbom@axbom.me
2025-10-18 05:47:50

@… @… I have this page for embedding YouTube in a privacy-enhanced way. Don't know if that works for you but just wanted to let you know.

I'm also encouraging people to use somethin…

@scott@carfree.city
2025-10-18 00:31:50

the last numbered page of the new Carton and Malm book is 629, but the last page excluding the notes, index and acknowledgements is 425. I can't imagine it's good for sales to make your book seem like 50% more of an unapproachable doorstop than it is. wonder why they don't put the notes online at that point

@NFL@darktundra.xyz
2025-10-17 20:44:08

Cincy hopes benching gets Taylor-Britt 'to respond' https://www.espn.com/nfl/story/_/id/46629262/bengals-hoping-cam-taylor-britt-turn-page-benching

Bengals hoping Cam Taylor-Britt can 'turn the page' after benching - ESPN
Bengals hoping Cam Taylor-Britt can 'turn the page' after benching

@bilbo_le_hobbit@mamot.fr
2025-10-18 13:13:30

J'ai trouvé #pedantix nº1254 en 22 coups !
🟩🟩🟩🟩🟩🟩🟩🟧🟧🟧🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥
https://pedantix.certitudes.org/

pédantix - Découvrez la page Wikipédia !
Découvrez la page Wikipédia en devinant les mots cachés.

@brian_gettler@mas.to
2025-10-17 15:51:18

New book on the history of McGill University - written and edited by historians, not the university's comms department. I wrote a chapter on how Indigenous dispossession and colonial politicians saved McGill when it was in danger of going broke. Others cover McGill's ties to chattel slavery, Japanese exclusion, research of various sorts, student lives, queer experiences, and more.
Check it out:

A cream and red book on a desk. It features two black-and-white photos of McGill in the nineteenth-century, one zoomed in and the other in a broad view.
Brian Lewis, Don Nerbas, and Melissa N. Shaw, McGill in History.

The first page of a chapter: Brian Gettler, "'To Relieve It from Present Embarrassments': Fiduciary Colonialism in Service to McGill College"

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:26:41

GenExam: A Multidisciplinary Text-to-Image Exam
Zhaokai Wang, Penghao Yin, Xiangyu Zhao, Changyao Tian, Yu Qiao, Wenhai Wang, Jifeng Dai, Gen Luo
https://arxiv.org/abs/2509.14232

GenExam: A Multidisciplinary Text-to-Image Exam
Exams are a fundamental test of expert-level intelligence and require integrated understanding, reasoning, and generation. Existing exam-style benchmarks mainly focus on understanding and reasoning tasks, and current generation benchmarks emphasize the illustration of world knowledge and visual concepts, neglecting the evaluation of rigorous drawing exams. We introduce GenExam, the first benchmark for multidisciplinary text-to-image exams, featuring 1,000 samples across 10 subjects with exam-st…

@@arXiv_physicsatomph_bot@mastoxiv.page@mastoxiv.page
2025-10-17 07:50:24

[2025-10-17 Fri (UTC), 1 new article found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:20:21

Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision
Dulhan Jayalath, Shashwat Goel, Thomas Foster, Parag Jain, Suchin Gururangan, Cheng Zhang, Anirudh Goyal, Alan Schelten
https://arxiv.org/abs/2509.14234

Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision
Where do learning signals come from when there is no ground truth in post-training? We propose turning exploration into supervision through Compute as Teacher (CaT), which converts the model's own exploration at inference-time into reference-free supervision by synthesizing a single reference from a group of parallel rollouts and then optimizing toward it. Concretely, the current policy produces a group of rollouts; a frozen anchor (the initial policy) reconciles omissions and contradictions to…

@marcel@waldvogel.family
2025-12-17 10:33:17

There should be two classes of war criminals in the future: Those named #Trump or friends of his, and those who can be prosecuted by the #ICC.

Screenshot from the page: "Exclusive: US threatens new ICC sanctions unless court pledges not to prosecute Trump"

@arXiv_csCL_bot@mastoxiv.page
2025-09-18 10:10:51

Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale
Hasan Abed Al Kader Hammoud, Mohammad Zbeeb, Bernard Ghanem
https://arxiv.org/abs/2509.14008

Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale
We present Hala, a family of Arabic-centric instruction and translation models built with our translate-and-tune pipeline. We first compress a strong AR$\leftrightarrow$EN teacher to FP8 (yielding $\sim$2$\times$ higher throughput with no quality loss) and use it to create high-fidelity bilingual supervision. A lightweight language model LFM2-1.2B is then fine-tuned on this data and used to translate high-quality English instruction sets into Arabic, producing a million-scale corpus tailored to…

@arXiv_quantph_bot@mastoxiv.page
2025-09-18 10:10:01

Quantum Utility in Simulating the Real-time Dynamics of the Fermi-Hubbard Model using Superconducting Quantum Computers
Talal Ahmed Chowdhury, Vladimir Korepin, Vincent R. Pascuzzi, Kwangmin Yu
https://arxiv.org/abs/2509.14196

Quantum Utility in Simulating the Real-time Dynamics of the Fermi-Hubbard Model using Superconducting Quantum Computers
The Fermi-Hubbard model is a fundamental model in condensed matter physics that describes strongly correlated electrons. On the other hand, quantum computers are emerging as powerful tools for exploring the complex dynamics of these quantum many-body systems. In this work, we demonstrate the quantum simulation of the one-dimensional Fermi-Hubbard model using IBM's superconducting quantum computers, employing over 100 qubits. We introduce a first-order Trotterization scheme and extend it to an o…

@arXiv_csRO_bot@mastoxiv.page
2025-09-18 10:10:41

SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning
Zewei Yang, Zengqi Peng, Jun Ma
https://arxiv.org/abs/2509.13956

SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning
Autonomous parking is a critical component for achieving safe and efficient urban autonomous driving. However, unstructured environments and dynamic interactions pose significant challenges to autonomous parking tasks. To address this problem, we propose SEG-Parking, a novel end-to-end offline reinforcement learning (RL) framework to achieve interaction-aware autonomous parking. Notably, a specialized parking dataset is constructed for parking scenarios, which include those without interference…

@nelson@tech.lgbt
2025-10-16 20:50:50

Cries for help from our dying local newspaper:
Due to external issues we have been unable to produce a 16 page newspaper and have had to make the tough decision to go down in our regular page count from two 8 page sections, to 12 pages yesterday, and 10 pages in today's edition.

@arXiv_csCG_bot@mastoxiv.page
2025-10-17 07:32:04

[2025-10-17 Fri (UTC), 1 new article found for cs.CG Computational Geometry]
toXiv_bot_toot

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:19:31

NIRVANA: Structured pruning reimagined for large language models compression
Mengting Ai, Tianxin Wei, Sirui Chen, Jingrui He
https://arxiv.org/abs/2509.14230 https://

NIRVANA: Structured pruning reimagined for large language models compression
Structured pruning of large language models (LLMs) offers substantial efficiency improvements by removing entire hidden units, yet current approaches often suffer from significant performance degradation, particularly in zero-shot settings, and necessitate costly recovery techniques such as supervised fine-tuning (SFT) or adapter insertion. To address these critical shortcomings, we introduce NIRVANA, a novel pruning method explicitly designed to balance immediate zero-shot accuracy preservatio…

@@arXiv_physicsatomph_bot@mastoxiv.page@mastoxiv.page
2025-10-17 13:20:22

Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Two-color magneto-optical trapping of ytterbium atoms
Xiao Li, Yufei Wang, Ligeng Yu, Bo Song

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:26:31

Cin\'{e}aste: A Fine-grained Contextual Movie Question Answering Benchmark
Nisarg A. Shah, Amir Ziai, Chaitanya Ekanadham, Vishal M. Patel
https://arxiv.org/abs/2509.14227 h…

Cinéaste: A Fine-grained Contextual Movie Question Answering Benchmark
While recent advancements in vision-language models have improved video understanding, diagnosing their capacity for deep, narrative comprehension remains a challenge. Existing benchmarks often test short-clip recognition or use template-based questions, leaving a critical gap in evaluating fine-grained reasoning over long-form narrative content. To address these gaps, we introduce $\mathsf{Cin\acute{e}aste}$, a comprehensive benchmark for long-form movie understanding. Our dataset comprises 3,…

@arXiv_csCL_bot@mastoxiv.page
2025-09-18 10:10:31

Early Stopping Chain-of-thoughts in Large Language Models
Minjia Mao, Bowen Yin, Yu Zhu, Xiao Fang
https://arxiv.org/abs/2509.14004 https://arxiv.org/pdf/2…

Early Stopping Chain-of-thoughts in Large Language Models
Reasoning large language models (LLMs) have demonstrated superior capacities in solving complicated problems by generating long chain-of-thoughts (CoT), but such a lengthy CoT incurs high inference costs. In this study, we introduce ES-CoT, an inference-time method that shortens CoT generation by detecting answer convergence and stopping early with minimal performance loss. At the end of each reasoning step, we prompt the LLM to output its current final answer, denoted as a step answer. We then…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:19:11

Defending Diffusion Models Against Membership Inference Attacks via Higher-Order Langevin Dynamics
Benjamin Sterling, Yousef El-Laham, M\'onica F. Bugallo
https://arxiv.org/abs/2509.14225

Defending Diffusion Models Against Membership Inference Attacks via Higher-Order Langevin Dynamics
Recent advances in generative artificial intelligence applications have raised new data security concerns. This paper focuses on defending diffusion models against membership inference attacks. This type of attack occurs when the attacker can determine if a certain data point was used to train the model. Although diffusion models are intrinsically more resistant to membership inference attacks than other generative models, they are still susceptible. The defense proposed here utilizes criticall…

@@arXiv_physicsatomph_bot@mastoxiv.page@mastoxiv.page
2025-10-17 11:39:50

Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Nucleon Electric Dipole Moments in Paramagnetic Molecules through Effective Field Theory
Wouter Dekens, Jordy de Vries, Lemonia Gialidi, Javier Men\'endez, Heleen Muld…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:26:21

Dense Video Understanding with Gated Residual Tokenization
Haichao Zhang, Wenhao Chai, Shwai He, Ang Li, Yun Fu
https://arxiv.org/abs/2509.14199 https://ar…

Dense Video Understanding with Gated Residual Tokenization
High temporal resolution is essential for capturing fine-grained details in video understanding. However, current video large language models (VLLMs) and benchmarks mostly rely on low-frame-rate sampling, such as uniform sampling or keyframe selection, discarding dense temporal information. This compromise avoids the high cost of tokenizing every frame, which otherwise leads to redundant computation and linear token growth as video length increases. While this trade-off works for slowly changin…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:18:21

Language models' activations linearly encode training-order recency
Dmitrii Krasheninnikov, Richard E. Turner, David Krueger
https://arxiv.org/abs/2509.14223 https://…

Language models' activations linearly encode training-order recency
We show that language models' activations linearly encode when information was learned during training. Our setup involves creating a model with a known training order by sequentially fine-tuning Llama-3.2-1B on six disjoint but otherwise similar datasets about named entities. We find that the average activations of test samples for the six training datasets encode the training order: when projected into a 2D subspace, these centroids are arranged exactly in the order of training and lie on a s…

@@arXiv_physicsatomph_bot@mastoxiv.page@mastoxiv.page
2025-10-17 08:23:34

Impact of rotation on a cold atom interferometer and compensation strategy
No\'emie Marquet, Yannick Bidel, Malo Cadoret, Alexis Bonnin, Sylvain Schwartz, Phuong-Anh Huynh, Alexandre Bresson, Antoine Godard, Franck Pereira Dos Santos, Olivier Carraz, Nassim Zahzam
https://arxiv.org/abs/2510.14755…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:26:11

Where Do Tokens Go? Understanding Pruning Behaviors in STEP at High Resolutions
Michal Szczepanski, Martyna Poreba, Karim Haroun
https://arxiv.org/abs/2509.14165 https://…

Where Do Tokens Go? Understanding Pruning Behaviors in STEP at High Resolutions
Vision Transformers (ViTs) achieve state-of-the-art performance in semantic segmentation but are hindered by high computational and memory costs. To address this, we propose STEP (SuperToken and Early-Pruning), a hybrid token-reduction framework that combines dynamic patch merging and token pruning to enhance efficiency without significantly compromising accuracy. At the core of STEP is dCTS, a lightweight CNN-based policy network that enables flexible merging into superpatches. Encoder blocks …

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:16:01

Data Denoising and Derivative Estimation for Data-Driven Modeling of Nonlinear Dynamical Systems
Jiaqi Yao, Lewis Mitchell, John Maclean, Hemanth Saratchandran
https://arxiv.org/abs/2509.14219

Data Denoising and Derivative Estimation for Data-Driven Modeling of Nonlinear Dynamical Systems
Data-driven modeling of nonlinear dynamical systems is often hampered by measurement noise. We propose a denoising framework, called Runge-Kutta and Total Variation Based Implicit Neural Representation (RKTV-INR), that represents the state trajectory with an implicit neural representation (INR) fitted directly to noisy observations. Runge-Kutta integration and total variation are imposed as constraints to ensure that the reconstructed state is a trajectory of a dynamical system that remains clo…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:26:01

BEVUDA : Geometric-aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection
Rongyu Zhang, Jiaming Liu, Xiaoqi Li, Xiaowei Chi, Dan Wang, Li Du, Yuan Du, Shanghang Zhang
https://arxiv.org/abs/2509.14151

BEVUDA++: Geometric-aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection
Vision-centric Bird's Eye View (BEV) perception holds considerable promise for autonomous driving. Recent studies have prioritized efficiency or accuracy enhancements, yet the issue of domain shift has been overlooked, leading to substantial performance degradation upon transfer. We identify major domain gaps in real-world cross-domain scenarios and initiate the first effort to address the Domain Adaptation (DA) challenge in multi-view 3D object detection for BEV perception. Given the complexit…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:15:41

A Universal Banach--Bregman Framework for Stochastic Iterations: Unifying Stochastic Mirror Descent, Learning and LLM Training
Johnny R. Zhang (Independent Researcher), Xiaomei Mi (University of Manchester), Gaoyuan Du (Amazon), Qianyi Sun (Microsoft), Shiqi Wang (Meta), Jiaxuan Li (Amazon), Wenhua Zhou (Independent Researcher)
https://arx…

A Universal Banach--Bregman Framework for Stochastic Iterations: Unifying Stochastic Mirror Descent, Learning and LLM Training
Stochastic optimization powers the scalability of modern artificial intelligence, spanning machine learning, deep learning, reinforcement learning, and large language model training. Yet, existing theory remains largely confined to Hilbert spaces, relying on inner-product frameworks and orthogonality. This paradigm fails to capture non-Euclidean settings, such as mirror descent on simplices, Bregman proximal methods for sparse learning, natural gradient descent in information geometry, or Kullb…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:25:51

An Exploratory Study on Abstract Images and Visual Representations Learned from Them
Haotian Li, Jianbo Jiao
https://arxiv.org/abs/2509.14149 https://arxiv…

An Exploratory Study on Abstract Images and Visual Representations Learned from Them
Imagine living in a world composed solely of primitive shapes, could you still recognise familiar objects? Recent studies have shown that abstract images-constructed by primitive shapes-can indeed convey visual semantic information to deep learning models. However, representations obtained from such images often fall short compared to those derived from traditional raster images. In this paper, we study the reasons behind this performance gap and investigate how much high-level semantic content…

@@arXiv_physicsatomph_bot@mastoxiv.page@mastoxiv.page
2025-11-18 17:31:26

Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Temporal dynamics in the Bragg reflection of light by cold atoms: flash effect and superradiant d...
S. Asselie, J. -M. Nazon, R. Caldani, C. Roux-Spitz, W. Guerin

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:15:11

A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
Juan Diego Toscano, Daniel T. Chen, Vivek Oommen, George Em Karniadakis
https://arxiv.org/abs/2509.14198

A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
Residual-based adaptive strategies are widely used in scientific machine learning but remain largely heuristic. We introduce a unifying variational framework that formalizes these methods by integrating convex transformations of the residual. Different transformations correspond to distinct objective functionals: exponential weights target the minimization of uniform error, while linear weights recover the minimization of quadratic error. Within this perspective, adaptive weighting is equivalen…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:25:41

MARS2 2025 Challenge on Multimodal Reasoning: Datasets, Methods, Results, Discussion, and Outlook
Peng Xu, Shengwu Xiong, Jiajun Zhang, Yaxiong Chen, Bowen Zhou, Chen Change Loy, David A. Clifton, Kyoung Mu Lee, Luc Van Gool, Ruiming He, Ruilin Yao, Xinwei Long, Jirui Huang, Kai Tian, Sa Yang, Yihua Shao, Jin Feng, Yue Zhong, Jiakai Zhou, Cheng Tang, Tianyu Zou, Yifang Zhang, Junming Liang, Guoyou Li, Zhaoxiang Wang, Qiang Zhou, Yichen Zhao, Shili Xiong, Hyeongjin Nam, Jaerin Lee, Jaey…

MARS2 2025 Challenge on Multimodal Reasoning: Datasets, Methods, Results, Discussion, and Outlook
This paper reviews the MARS2 2025 Challenge on Multimodal Reasoning. We aim to bring together different approaches in multimodal machine learning and LLMs via a large benchmark. We hope it better allows researchers to follow the state-of-the-art in this very dynamic area. Meanwhile, a growing number of testbeds have boosted the evolution of general-purpose large language models. Thus, this year's MARS2 focuses on real-world and specialized scenarios to broaden the multimodal reasoning applicati…

@@arXiv_physicsatomph_bot@mastoxiv.page@mastoxiv.page
2025-11-18 14:48:24

Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Lellouch-L\"uscher relation for ultracold few-atom systems under confinement
Jing-Lun Li, Paul S. Julienne, Johannes Hecker Denschlag, Jos\'e P. D'Incao
…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:15:01

Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Yifan Hu, Jie Yang, Tian Zhou, Peiyuan Liu, Yujin Tang, Rong Jin, Liang Sun
https://arxiv.org/abs/2509.14181

Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Representation learning techniques like contrastive learning have long been explored in time series forecasting, mirroring their success in computer vision and natural language processing. Yet recent state-of-the-art (SOTA) forecasters seldom adopt these representation approaches because they have shown little performance advantage. We challenge this view and demonstrate that explicit representation alignment can supply critical information that bridges the distributional gap between input hist…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:25:31

Deceptive Beauty: Evaluating the Impact of Beauty Filters on Deepfake and Morphing Attack Detection
Sara Concas, Simone Maurizio La Cava, Andrea Panzino, Ester Masala, Giulia Orr\`u, Gian Luca Marcialis
https://arxiv.org/abs/2509.14120

Deceptive Beauty: Evaluating the Impact of Beauty Filters on Deepfake and Morphing Attack Detection
Digital beautification through social media filters has become increasingly popular, raising concerns about the reliability of facial images and videos and the effectiveness of automated face analysis. This issue is particularly critical for digital manipulation detectors, systems aiming at distinguishing between genuine and manipulated data, especially in cases involving deepfakes and morphing attacks designed to deceive humans and automated facial recognition. This study examines whether beau…

@@arXiv_physicsatomph_bot@mastoxiv.page@mastoxiv.page
2025-11-18 08:58:34

Optical investigation of ultra-slow spin relaxation in $^{171}$Yb$^{3 }$:Y$_2$SiO$_5$ single crystals
Federico Chiossi, Alexey Tiranov, Luois Nicolas, Diana Serrano, Felix Montjovet-Basset, Elo\"ise Lafitte-Houssat, Alban Ferrier, Sacha Welinski, Lo\"ic Morvan, Perrine Berger, Mikael Afzelius, Philippe Goldner
https://arxiv.org/a…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:14:11

TGPO: Tree-Guided Preference Optimization for Robust Web Agent Reinforcement Learning
Ziyuan Chen, Zhenghui Zhao, Zhangye Han, Miancan Liu, Xianhang Ye, Yiqing Li, Hongbo Min, Jinkui Ren, Xiantao Zhang, Guitao Cao
https://arxiv.org/abs/2509.14172

TGPO: Tree-Guided Preference Optimization for Robust Web Agent Reinforcement Learning
With the rapid advancement of large language models and vision-language models, employing large models as Web Agents has become essential for automated web interaction. However, training Web Agents with reinforcement learning faces critical challenges including credit assignment misallocation, prohibitively high annotation costs, and reward sparsity. To address these issues, we propose Tree-Guided Preference Optimization (TGPO), an offline reinforcement learning framework that proposes a tree-s…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:25:21

Generative AI for Misalignment-Resistant Virtual Staining to Accelerate Histopathology Workflows
Jiabo MA, Wenqiang Li, Jinbang Li, Ziyi Liu, Linshan Wu, Fengtao Zhou, Li Liang, Ronald Cheong Kin Chan, Terence T. W. Wong, Hao Chen
https://arxiv.org/abs/2509.14119

Generative AI for Misalignment-Resistant Virtual Staining to Accelerate Histopathology Workflows
Accurate histopathological diagnosis often requires multiple differently stained tissue sections, a process that is time-consuming, labor-intensive, and environmentally taxing due to the use of multiple chemical stains. Recently, virtual staining has emerged as a promising alternative that is faster, tissue-conserving, and environmentally friendly. However, existing virtual staining methods face significant challenges in clinical applications, primarily due to their reliance on well-aligned pai…

@@arXiv_physicsatomph_bot@mastoxiv.page@mastoxiv.page
2025-11-18 08:24:45

K-shell ionization and characteristic x-ray radiation by high-energy electrons and positrons in oriented silicon crystals
S. V. Trofymenko, I. V. Kyryllin
https://arxiv.org/abs/2511.13407

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:14:01

TopoSizing: An LLM-aided Framework of Topology-based Understanding and Sizing for AMS Circuits
Ziming Wei, Zichen Kong, Yuan Wang, David Z. Pan, Xiyuan Tang
https://arxiv.org/abs/2509.14169

TopoSizing: An LLM-aided Framework of Topology-based Understanding and Sizing for AMS Circuits
Analog and mixed-signal circuit design remains challenging due to the shortage of high-quality data and the difficulty of embedding domain knowledge into automated flows. Traditional black-box optimization achieves sampling efficiency but lacks circuit understanding, which often causes evaluations to be wasted in low-value regions of the design space. In contrast, learning-based methods embed structural knowledge but are case-specific and costly to retrain. Recent attempts with large language m…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:25:11

CSMoE: An Efficient Remote Sensing Foundation Model with Soft Mixture-of-Experts
Leonard Hackel, Tom Burgert, Beg\"um Demir
https://arxiv.org/abs/2509.14104 https://…

CSMoE: An Efficient Remote Sensing Foundation Model with Soft Mixture-of-Experts
Self-supervised learning through masked autoencoders has attracted great attention for remote sensing (RS) foundation model (FM) development, enabling improved representation learning across diverse sensors and downstream tasks. However, existing RS FMs often either suffer from substantial computational complexity during both training and inference or exhibit limited representational capacity. These issues restrict their practical applicability in RS. To address this limitation, we propose an a…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:13:51

Deconstructing Intraocular Pressure: A Non-invasive Multi-Stage Probabilistic Inverse Framework
Md Rezwan Jaher, Abul Mukid Mohammad Mukaddes, A. B. M. Abdul Malek
https://arxiv.org/abs/2509.14167

Deconstructing Intraocular Pressure: A Non-invasive Multi-Stage Probabilistic Inverse Framework
Many critical healthcare decisions are challenged by the inability to measure key underlying parameters. Glaucoma, a leading cause of irreversible blindness driven by elevated intraocular pressure (IOP), provides a stark example. The primary determinant of IOP, a tissue property called trabecular meshwork permeability, cannot be measured in vivo, forcing clinicians to depend on indirect surrogates. This clinical challenge is compounded by a broader computational one: developing predictive model…

@@arXiv_physicsatomph_bot@mastoxiv.page@mastoxiv.page
2025-11-18 08:02:53

Prospects for a Solid-State Nuclear Clock
Steven M. Girvin (Yale), Leo Radzihovsky (CU Boulder)
https://arxiv.org/abs/2511.13017 https://arxiv.org/pdf/2511…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:25:01

Teacher-Guided Pseudo Supervision and Cross-Modal Alignment for Audio-Visual Video Parsing
Yaru Chen, Ruohao Guo, Liting Gao, Yang Xiang, Qingyu Luo, Zhenbo Li, Wenwu Wang
https://arxiv.org/abs/2509.14097

Teacher-Guided Pseudo Supervision and Cross-Modal Alignment for Audio-Visual Video Parsing
Weakly-supervised audio-visual video parsing (AVVP) seeks to detect audible, visible, and audio-visual events without temporal annotations. Previous work has emphasized refining global predictions through contrastive or collaborative learning, but neglected stable segment-level supervision and class-aware cross-modal alignment. To address this, we propose two strategies: (1) an exponential moving average (EMA)-guided pseudo supervision framework that generates reliable segment-level masks via a…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:13:41

A Compositional Kernel Model for Feature Learning
Feng Ruan, Keli Liu, Michael Jordan
https://arxiv.org/abs/2509.14158 https://arxiv.org/pdf/2509.14158

A Compositional Kernel Model for Feature Learning
We study a compositional variant of kernel ridge regression in which the predictor is applied to a coordinate-wise reweighting of the inputs. Formulated as a variational problem, this model provides a simple testbed for feature learning in compositional architectures. From the perspective of variable selection, we show how relevant variables are recovered while noise variables are eliminated. We establish guarantees showing that both global minimizers and stationary points discard noise coordin…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:24:51

AD-DINOv3: Enhancing DINOv3 for Zero-Shot Anomaly Detection with Anomaly-Aware Calibration
Jingyi Yuan, Jianxiong Ye, Wenkang Chen, Chenqiang Gao
https://arxiv.org/abs/2509.14084

AD-DINOv3: Enhancing DINOv3 for Zero-Shot Anomaly Detection with Anomaly-Aware Calibration
Zero-Shot Anomaly Detection (ZSAD) seeks to identify anomalies from arbitrary novel categories, offering a scalable and annotation-efficient solution. Traditionally, most ZSAD works have been based on the CLIP model, which performs anomaly detection by calculating the similarity between visual and text embeddings. Recently, vision foundation models such as DINOv3 have demonstrated strong transferable representation capabilities. In this work, we are the first to adapt DINOv3 for ZSAD. However, …

@@arXiv_physicsatomph_bot@mastoxiv.page@mastoxiv.page
2025-11-18 07:56:43

Second-order correlations in directed emissions in sodium atoms
Ara Tonoyan, Sushree Subhadarshinee Sahoo, Anahit Gogyan, Oleg Tretiak, Razmik Aramyan, Alexander Akulshin, Dmitry Budker
https://arxiv.org/abs/2511.12228

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:13:11

Breaking the Cycle of Incarceration With Targeted Mental Health Outreach: A Case Study in Machine Learning for Public Policy
Kit T. Rodolfa, Erika Salomon, Jin Yao, Steve Yoder, Robert Sullivan, Kevin McGuire, Allie Dickinson, Rob MacDougall, Brian Seidler, Christina Sung, Claire Herdeman, Rayid Ghani
https://arxiv.org/abs/2509.14129

Breaking the Cycle of Incarceration With Targeted Mental Health Outreach: A Case Study in Machine Learning for Public Policy
Many incarcerated individuals face significant and complex challenges, including mental illness, substance dependence, and homelessness, yet jails and prisons are often poorly equipped to address these needs. With little support from the existing criminal justice system, these needs can remain untreated and worsen, often leading to further offenses and a cycle of incarceration with adverse outcomes both for the individual and for public safety, with particularly large impacts on communities of …

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:24:41

VSE-MOT: Multi-Object Tracking in Low-Quality Video Scenes Guided by Visual Semantic Enhancement
Jun Du, Weiwei Xing, Ming Li, Fei Richard Yu
https://arxiv.org/abs/2509.14060 ht…

VSE-MOT: Multi-Object Tracking in Low-Quality Video Scenes Guided by Visual Semantic Enhancement
Current multi-object tracking (MOT) algorithms typically overlook issues inherent in low-quality videos, leading to significant degradation in tracking performance when confronted with real-world image deterioration. Therefore, advancing the application of MOT algorithms in real-world low-quality video scenarios represents a critical and meaningful endeavor. To address the challenges posed by low-quality scenarios, inspired by vision-language models, this paper proposes a Visual Semantic Enhanc…

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2025-11-18 07:51:42

Response time of electrons to light in strong-field ionization of polar molecules
Jiayin Che, Sheng Ye, Shiqi Shen, Weiyan Li, Yanjun Chen
https://arxiv.org/abs/2511.12087 https…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:13:01

From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecasting
Alessandro Brusaferri, Danial Ramin, Andrea Ballarino
https://arxiv.org/abs/2509.14113

From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecasting
While neural networks are achieving high predictive accuracy in multi-horizon probabilistic forecasting, understanding the underlying mechanisms that lead to feature-conditioned outputs remains a significant challenge for forecasters. In this work, we take a further step toward addressing this critical issue by introducing the Quantile Neural Basis Model, which incorporates the interpretability principles of Quantile Generalized Additive Models into an end-to-end neural network training framewo…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:24:31

Wan-Animate: Unified Character Animation and Replacement with Holistic Replication
Gang Cheng, Xin Gao, Li Hu, Siqi Hu, Mingyang Huang, Chaonan Ji, Ju Li, Dechao Meng, Jinwei Qi, Penchong Qiao, Zhen Shen, Yafei Song, Ke Sun, Linrui Tian, Feng Wang, Guangyuan Wang, Qi Wang, Zhongjian Wang, Jiayu Xiao, Sheng Xu, Bang Zhang, Peng Zhang, Xindi Zhang, Zhe Zhang, Jingren Zhou, Lian Zhuo

Wan-Animate: Unified Character Animation and Replacement with Holistic Replication
We introduce Wan-Animate, a unified framework for character animation and replacement. Given a character image and a reference video, Wan-Animate can animate the character by precisely replicating the expressions and movements of the character in the video to generate high-fidelity character videos. Alternatively, it can integrate the animated character into the reference video to replace the original character, replicating the scene's lighting and color tone to achieve seamless environmental i…

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2025-11-18 07:51:32

Hyperfine-Resolved Spectroscopy of Dysprosium Monoxide (DyO) for Precision Measurements of the Nuclear Schiff Moment
Zack D. Lasner, Aidan T. Ohl, Nicole M. Albright, Kendall L. Rice, Charlene Peng, Lan Cheng, John M. Doyle, Benjamin L. Augenbraun
https://arxiv.org/abs/2511.11975

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:12:01

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
Robiul Islam, Dmitry I. Ignatov, Karl Kaberg, Roman Nabatchikov
https://arxiv.org/abs/2509.14078

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
This study investigates classifier performance across EEG frequency bands using various optimizers and evaluates efficient class prediction for the left and right hemispheres. Three neural network architectures - a deep dense network, a shallow three-layer network, and a convolutional neural network (CNN) - are implemented and compared using the TensorFlow and PyTorch frameworks. Results indicate that the Adagrad and RMSprop optimizers consistently perform well across different frequency bands,…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:24:21

PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings
Suhang You, Carla Pitarch-Abaigar, Sanket Kachole, Sumedh Sonawane, Juhyung Ha, Anish Sudarshan Gada, David Crandall, Rakesh Shiradkar, Spyridon Bakas
https://arxiv.org/abs/2509.14051

PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings
Almost 30% of prostate cancer (PCa) patients undergoing radical prostatectomy (RP) experience biochemical recurrence (BCR), characterized by increased prostate specific antigen (PSA) and associated with increased mortality. Accurate early prediction of BCR, at the time of RP, would contribute to prompt adaptive clinical decision-making and improved patient outcomes. In this work, we propose prostate cancer BCR prediction via fused multi-modal embeddings (PROFUSEme), which learns cross-modal int…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:11:21

Online Bayesian Risk-Averse Reinforcement Learning
Yuhao Wang, Enlu Zhou
https://arxiv.org/abs/2509.14077 https://arxiv.org/pdf/2509.14077

Online Bayesian Risk-Averse Reinforcement Learning
In this paper, we study the Bayesian risk-averse formulation in reinforcement learning (RL). To address the epistemic uncertainty due to a lack of data, we adopt the Bayesian Risk Markov Decision Process (BRMDP) to account for the parameter uncertainty of the unknown underlying model. We derive the asymptotic normality that characterizes the difference between the Bayesian risk value function and the original value function under the true unknown distribution. The results indicate that the Baye…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:24:11

SAIL-VL2 Technical Report
Weijie Yin, Yongjie Ye, Fangxun Shu, Yue Liao, Zijian Kang, Hongyuan Dong, Haiyang Yu, Dingkang Yang, Jiacong Wang, Han Wang, Wenzhuo Liu, Xiao Liang, Shuicheng Yan, Chao Feng
https://arxiv.org/abs/2509.14033

SAIL-VL2 Technical Report
We introduce SAIL-VL2, an open-suite vision-language foundation model (LVM) for comprehensive multimodal understanding and reasoning. As the successor to SAIL-VL, SAIL-VL2 achieves state-of-the-art performance at the 2B and 8B parameter scales across diverse image and video benchmarks, demonstrating strong capabilities from fine-grained perception to complex reasoning. Three core innovations drive its effectiveness. First, a large-scale data curation pipeline with scoring and filtering strategi…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:08:51

Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing
Chiara De Luca, Elisa Donati
https://arxiv.org/abs/2509.14061 https://

Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing
Queen bee presence is essential for the health and stability of honeybee colonies, yet current monitoring methods rely on manual inspections that are labor-intensive, disruptive, and impractical for large-scale beekeeping. While recent audio-based approaches have shown promise, they often require high power consumption, complex preprocessing, and are susceptible to ambient noise. To overcome these limitations, we propose a lightweight, multimodal system for queen detection based on environmenta…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:24:01

Performance Optimization of YOLO-FEDER FusionNet for Robust Drone Detection in Visually Complex Environments
Tamara R. Lenhard, Andreas Weinmann, Tobias Koch
https://arxiv.org/abs/2509.14012

Performance Optimization of YOLO-FEDER FusionNet for Robust Drone Detection in Visually Complex Environments
Drone detection in visually complex environments remains challenging due to background clutter, small object scale, and camouflage effects. While generic object detectors like YOLO exhibit strong performance in low-texture scenes, their effectiveness degrades in cluttered environments with low object-background separability. To address these limitations, this work presents an enhanced iteration of YOLO-FEDER FusionNet -- a detection framework that integrates generic object detection with camouf…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:07:31

Deep Learning-Driven Peptide Classification in Biological Nanopores
Samuel Tovey, Julian Ho{\ss}bach, Sandro Kuppel, Tobias Ensslen, Jan C. Behrends, Christian Holm
https://arxiv.org/abs/2509.14029

Deep Learning-Driven Peptide Classification in Biological Nanopores
A device capable of performing real time classification of proteins in a clinical setting would allow for inexpensive and rapid disease diagnosis. One such candidate for this technology are nanopore devices. These devices work by measuring a current signal that arises when a protein or peptide enters a nanometer-length-scale pore. Should this current be uniquely related to the structure of the peptide and its interactions with the pore, the signals can be used to perform identification. While s…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:23:51

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment
Elena Camuffo, Francesco Barbato, Mete Ozay, Simone Milani, Umberto Michieli
https://arxiv.org/abs/2509.14001 https…

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment
We introduce MOCHA (Multi-modal Objects-aware Cross-arcHitecture Alignment), a knowledge distillation approach that transfers region-level multimodal semantics from a large vision-language teacher (e.g., LLaVa) into a lightweight vision-only object detector student (e.g., YOLO). A translation module maps student features into a joint space, where the training of the student and translator is guided by a dual-objective loss that enforces both local alignment and global relational consistency. Un…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:07:21

Differentially private federated learning for localized control of infectious disease dynamics
Raouf Kerkouche, Henrik Zunker, Mario Fritz, Martin J. K\"uhn
https://arxiv.org/abs/2509.14024

Differentially private federated learning for localized control of infectious disease dynamics
In times of epidemics, swift reaction is necessary to mitigate epidemic spreading. For this reaction, localized approaches have several advantages, limiting necessary resources and reducing the impact of interventions on a larger scale. However, training a separate machine learning (ML) model on a local scale is often not feasible due to limited available data. Centralizing the data is also challenging because of its high sensitivity and privacy constraints. In this study, we consider a localiz…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:23:41

Can Current AI Models Count What We Mean, Not What They See? A Benchmark and Systematic Evaluation
Gia Khanh Nguyen, Yifeng Huang, Minh Hoai
https://arxiv.org/abs/2509.13939 htt…

Can Current AI Models Count What We Mean, Not What They See? A Benchmark and Systematic Evaluation
Visual counting is a fundamental yet challenging task, especially when users need to count objects of a specific type in complex scenes. While recent models, including class-agnostic counting models and large vision-language models (VLMs), show promise in counting tasks, their ability to perform fine-grained, intent-driven counting remains unclear. In this paper, we introduce PairTally, a benchmark dataset specifically designed to evaluate fine-grained visual counting. Each of the 681 high-reso…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:06:01

Deep Temporal Graph Networks for Real-Time Correction of GNSS Jamming-Induced Deviations
Ivana Kesi\'c, Alja\v{z} Blatnik, Carolina Fortuna, Bla\v{z} Bertalani\v{c}
https://arxiv.org/abs/2509.14000

Deep Temporal Graph Networks for Real-Time Correction of GNSS Jamming-Induced Deviations
Global Navigation Satellite Systems (GNSS) are increasingly disrupted by intentional jamming, degrading availability precisely when positioning and timing must remain operational. We address this by reframing jamming mitigation as dynamic graph regression and introducing a receiver-centric deep temporal graph network that predicts, and thus corrects, the receivers horizontal deviation in real time. At each 1 Hz epoch, the satellite receiver environment is represented as a heterogeneous star gra…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:23:31

Noise-Level Diffusion Guidance: Well Begun is Half Done
Harvey Mannering, Zhiwu Huang, Adam Prugel-Bennett
https://arxiv.org/abs/2509.13936 https://arxiv.o…

Noise-Level Diffusion Guidance: Well Begun is Half Done
Diffusion models have achieved state-of-the-art image generation. However, the random Gaussian noise used to start the diffusion process influences the final output, causing variations in image quality and prompt adherence. Existing noise-level optimization approaches generally rely on extra dataset construction, additional networks, or backpropagation-based optimization, limiting their practicality. In this paper, we propose Noise Level Guidance (NLG), a simple, efficient, and general noise-le…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:05:51

Personalization on a Budget: Minimally-Labeled Continual Learning for Resource-Efficient Seizure Detection
Amirhossein Shahbazinia, Jonathan Dan, Jose A. Miranda, Giovanni Ansaloni, David Atienza
https://arxiv.org/abs/2509.13974

Personalization on a Budget: Minimally-Labeled Continual Learning for Resource-Efficient Seizure Detection
Objective: Epilepsy, a prevalent neurological disease, demands careful diagnosis and continuous care. Seizure detection remains challenging, as current clinical practice relies on expert analysis of electroencephalography, which is a time-consuming process and requires specialized knowledge. Addressing this challenge, this paper explores automated epileptic seizure detection using deep learning, focusing on personalized continual learning models that adapt to each patient's unique electroenceph…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:23:21

Towards Robust Defense against Customization via Protective Perturbation Resistant to Diffusion-based Purification
Wenkui Yang, Jie Cao, Junxian Duan, Ran He
https://arxiv.org/abs/2509.13922

Towards Robust Defense against Customization via Protective Perturbation Resistant to Diffusion-based Purification
Diffusion models like Stable Diffusion have become prominent in visual synthesis tasks due to their powerful customization capabilities, which also introduce significant security risks, including deepfakes and copyright infringement. In response, a class of methods known as protective perturbation emerged, which mitigates image misuse by injecting imperceptible adversarial noise. However, purification can remove protective perturbations, thereby exposing images again to the risk of malicious fo…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:05:41

eXtended Physics Informed Neural Network Method for Fracture Mechanics Problems
Amin Lotfalian, Mohammad Reza Banan, Pooyan Broumand
https://arxiv.org/abs/2509.13952 https://

eXtended Physics Informed Neural Network Method for Fracture Mechanics Problems
This paper presents eXtended Physics-Informed Neural Network (X-PINN), a novel and robust framework for addressing fracture mechanics problems involving multiple cracks in fractured media. To address this, an energy-based loss function, customized integration schemes, and domain decomposition procedures are proposed. Inspired by the Extended Finite Element Method (XFEM), the neural network solution space is enriched with specialized functions that allow crack body discontinuities and singularit…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:23:11

Towards Rationale-Answer Alignment of LVLMs via Self-Rationale Calibration
Yuanchen Wu, Ke Yan, Shouhong Ding, Ziyin Zhou, Xiaoqiang Li
https://arxiv.org/abs/2509.13919 https://…

Towards Rationale-Answer Alignment of LVLMs via Self-Rationale Calibration
Large Vision-Language Models (LVLMs) have manifested strong visual question answering capability. However, they still struggle with aligning the rationale and the generated answer, leading to inconsistent reasoning and incorrect responses. To this end, this paper introduces the Self-Rationale Calibration (SRC) framework to iteratively calibrate the alignment between rationales and answers. SRC begins by employing a lightweight "rationale fine-tuning" approach, which modifies the model's respons…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:05:31

Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
Qiyue Li, Yingxin Liu, Hang Qi, Jieping Luo, Zhizhang Liu, Jingjin Wu
https://arxiv.org/abs/2509.13933

Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
We consider the client selection problem in wireless Federated Learning (FL), with the objective of reducing the total required time to achieve a certain level of learning accuracy. Since the server cannot observe the clients' dynamic states that can change their computation and communication efficiency, we formulate client selection as a restless multi-armed bandit problem. We propose a scalable and efficient approach called the Whittle Index Learning in Federated Q-learning (WILF-Q), which us…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:23:01

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation
Jiyun Im, SuBeen Lee, Miso Lee, Jae-Pil Heo
https://arxiv.org/abs/2509.13907 https://

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation
Few-Shot 3D Point Cloud Segmentation (FS-PCS) aims to predict per-point labels for an unlabeled point cloud, given only a few labeled examples. To extract discriminative representations from the limited support set, existing methods have constructed prototypes using conventional algorithms such as farthest point sampling. However, we point out that its initial randomness significantly affects FS-PCS performance and that the prototype generation process remains underexplored despite its prevalen…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:04:01

Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
Divya Thuremella, Yi Yang, Simon Wanna, Lars Kunze, Daniele De Martini
https://arxiv.org/abs/2509.13914 http…

Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
This work explores the application of ensemble modeling to the multidimensional regression problem of trajectory prediction for vehicles in urban environments. As newer and bigger state-of-the-art prediction models for autonomous driving continue to emerge, an important open challenge is the problem of how to combine the strengths of these big models without the need for costly re-training. We show how, perhaps surprisingly, combining state-of-the-art deep learning models out-of-the-box (withou…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:22:51

EvHand-FPV: Efficient Event-Based 3D Hand Tracking from First-Person View
Zhen Xu, Guorui Lu, Chang Gao, Qinyu Chen
https://arxiv.org/abs/2509.13883 https://

EvHand-FPV: Efficient Event-Based 3D Hand Tracking from First-Person View
Hand tracking holds great promise for intuitive interaction paradigms, but frame-based methods often struggle to meet the requirements of accuracy, low latency, and energy efficiency, especially in resource-constrained settings such as Extended Reality (XR) devices. Event cameras provide $μ$s-level temporal resolution at mW-level power by asynchronously sensing brightness changes. In this work, we present EvHand-FPV, a lightweight framework for egocentric First-Person-View 3D hand tracking fro…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:03:21

APFEx: Adaptive Pareto Front Explorer for Intersectional Fairness
Priyobrata Mondal, Faizanuddin Ansari, Swagatam Das
https://arxiv.org/abs/2509.13908 https://

APFEx: Adaptive Pareto Front Explorer for Intersectional Fairness
Ensuring fairness in machine learning models is critical, especially when biases compound across intersecting protected attributes like race, gender, and age. While existing methods address fairness for single attributes, they fail to capture the nuanced, multiplicative biases faced by intersectional subgroups. We introduce Adaptive Pareto Front Explorer (APFEx), the first framework to explicitly model intersectional fairness as a joint optimization problem over the Cartesian product of sensiti…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:22:41

Invisible Yet Detected: PelFANet with Attention-Guided Anatomical Fusion for Pelvic Fracture Diagnosis
Siam Tahsin Bhuiyan, Rashedur Rahman, Sefatul Wasi, Naomi Yagi, Syoji Kobashi, Ashraful Islam, Saadia Binte Alam
https://arxiv.org/abs/2509.13873

Invisible Yet Detected: PelFANet with Attention-Guided Anatomical Fusion for Pelvic Fracture Diagnosis
Pelvic fractures pose significant diagnostic challenges, particularly in cases where fracture signs are subtle or invisible on standard radiographs. To address this, we introduce PelFANet, a dual-stream attention network that fuses raw pelvic X-rays with segmented bone images to improve fracture classification. The network em-ploys Fused Attention Blocks (FABlocks) to iteratively exchange and refine fea-tures from both inputs, capturing global context and localized anatomical detail. Trained in…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:03:11

TFMAdapter: Lightweight Instance-Level Adaptation of Foundation Models for Forecasting with Covariates
Afrin Dange, Sunita Sarawagi
https://arxiv.org/abs/2509.13906 https://

TFMAdapter: Lightweight Instance-Level Adaptation of Foundation Models for Forecasting with Covariates
Time Series Foundation Models (TSFMs) have recently achieved state-of-the-art performance in univariate forecasting on new time series simply by conditioned on a brief history of past values. Their success demonstrates that large-scale pretraining across diverse domains can acquire the inductive bias to generalize from temporal patterns in a brief history. However, most TSFMs are unable to leverage covariates -- future-available exogenous variables critical for accurate forecasting in many appl…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:22:31

Distractor-Aware Memory-Based Visual Object Tracking
Jovana Videnovic, Matej Kristan, Alan Lukezic
https://arxiv.org/abs/2509.13864 https://arxiv.org/pdf/2…

Distractor-Aware Memory-Based Visual Object Tracking
Recent emergence of memory-based video segmentation methods such as SAM2 has led to models with excellent performance in segmentation tasks, achieving leading results on numerous benchmarks. However, these modes are not fully adjusted for visual object tracking, where distractors (i.e., objects visually similar to the target) pose a key challenge. In this paper we propose a distractor-aware drop-in memory module and introspection-based management method for SAM2, leading to DAM4SAM. Our design …

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:02:51

FedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated Learning
Zhanting Zhou, Jinshan Lai, Fengchun Zhang, Zeqin Wu, Fengli Zhang
https://arxiv.org/abs/2509.13895

FedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated Learning
Non-IID data and partial participation induce client drift and inconsistent local optima in federated learning, causing unstable convergence and accuracy loss. We present FedSSG, a stochastic sampling-guided, history-aware drift alignment method. FedSSG maintains a per-client drift memory that accumulates local model differences as a lightweight sketch of historical gradients; crucially, it gates both the memory update and the local alignment term by a smooth function of the observed/expected p…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:22:21

LamiGauss: Pitching Radiative Gaussian for Sparse-View X-ray Laminography Reconstruction
Chu Chen, Ander Biguri, Jean-Michel Morel, Raymond H. Chan, Carola-Bibiane Sch\"onlieb, Jizhou Li
https://arxiv.org/abs/2509.13863

LamiGauss: Pitching Radiative Gaussian for Sparse-View X-ray Laminography Reconstruction
X-ray Computed Laminography (CL) is essential for non-destructive inspection of plate-like structures in applications such as microchips and composite battery materials, where traditional computed tomography (CT) struggles due to geometric constraints. However, reconstructing high-quality volumes from laminographic projections remains challenging, particularly under highly sparse-view acquisition conditions. In this paper, we propose a reconstruction algorithm, namely LamiGauss, that combines G…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:01:11

Masked Diffusion Models as Energy Minimization
Sitong Chen, Shen Nie, Jiacheng Sun, Zijin Feng, Zhenguo Li, Ji-Rong Wen, Chongxuan Li
https://arxiv.org/abs/2509.13866 https://…

Masked Diffusion Models as Energy Minimization
We present a systematic theoretical framework that interprets masked diffusion models (MDMs) as solutions to energy minimization problems in discrete optimal transport. Specifically, we prove that three distinct energy formulations--kinetic, conditional kinetic, and geodesic energy--are mathematically equivalent under the structure of MDMs, and that MDMs minimize all three when the mask schedule satisfies a closed-form optimality condition. This unification not only clarifies the theoretical fo…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:22:11

EDITS: Enhancing Dataset Distillation with Implicit Textual Semantics
Qianxin Xia, Jiawei Du, Guoming Lu, Zhiyong Shu, Jielei Wang
https://arxiv.org/abs/2509.13858 https://

EDITS: Enhancing Dataset Distillation with Implicit Textual Semantics
Dataset distillation aims to synthesize a compact dataset from the original large-scale one, enabling highly efficient learning while preserving competitive model performance. However, traditional techniques primarily capture low-level visual features, neglecting the high-level semantic and structural information inherent in images. In this paper, we propose EDITS, a novel framework that exploits the implicit textual semantics within the image data to achieve enhanced distillation. First, exter…

@arXiv_csLG_bot@mastoxiv.page
2025-09-18 10:00:31

Graph-Regularized Learning of Gaussian Mixture Models
Shamsiiat Abdurakhmanova, Alex Jung
https://arxiv.org/abs/2509.13855 https://arxiv.org/pdf/2509.13855…

Graph-Regularized Learning of Gaussian Mixture Models
We present a graph-regularized learning of Gaussian Mixture Models (GMMs) in distributed settings with heterogeneous and limited local data. The method exploits a provided similarity graph to guide parameter sharing among nodes, avoiding the transfer of raw data. The resulting model allows for flexible aggregation of neighbors' parameters and outperforms both centralized and locally trained GMMs in heterogeneous, low-sample regimes.

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:22:01

SpecDiff: Accelerating Diffusion Model Inference with Self-Speculation
Jiayi Pan, Jiaming Xu, Yongkang Zhou, Guohao Dai
https://arxiv.org/abs/2509.13848 https://

SpecDiff: Accelerating Diffusion Model Inference with Self-Speculation
Feature caching has recently emerged as a promising method for diffusion model acceleration. It effectively alleviates the inefficiency problem caused by high computational requirements by caching similar features in the inference process of the diffusion model. In this paper, we analyze existing feature caching methods from the perspective of information utilization, and point out that relying solely on historical information will lead to constrained accuracy and speed performance. And we prop…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:21:51

Consistent View Alignment Improves Foundation Models for 3D Medical Image Segmentation
Puru Vaish, Felix Meister, Tobias Heimann, Christoph Brune, Jelmer M. Wolterink
https://arxiv.org/abs/2509.13846

Consistent View Alignment Improves Foundation Models for 3D Medical Image Segmentation
Many recent approaches in representation learning implicitly assume that uncorrelated views of a data point are sufficient to learn meaningful representations for various downstream tasks. In this work, we challenge this assumption and demonstrate that meaningful structure in the latent space does not emerge naturally. Instead, it must be explicitly induced. We propose a method that aligns representations from different views of the data to align complementary information without inducing false…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:21:31

Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models
Weihang Wang, Xinhao Li, Ziyue Wang, Yan Pang, Jielei Zhang, Peiyi Li, Qiang Zhang, Longwen Gao
https://arxiv.org/abs/2509.13836

Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models
Object hallucination in Large Vision-Language Models (LVLMs) significantly impedes their real-world applicability. As the primary component for accurately interpreting visual information, the choice of visual encoder is pivotal. We hypothesize that the diverse training paradigms employed by different visual encoders instill them with distinct inductive biases, which leads to their diverse hallucination performances. Existing benchmarks typically focus on coarse-grained hallucination detection a…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:21:21

Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation
Nguyen Lan Vi Vu, Thanh-Huy Nguyen, Thien Nguyen, Daisuke Kihara, Tianyang Wang, Xingjian Li, Min Xu
https://arxiv.org/abs/2509.13834

Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation
Semi-supervised learning has been employed to alleviate the need for extensive labeled data for histopathology image segmentation, but existing methods struggle with noisy pseudo-labels due to ambiguous gland boundaries and morphological misclassification. This paper introduces Semi-MOE, to the best of our knowledge, the first multi-task Mixture-of-Experts framework for semi-supervised histopathology image segmentation. Our approach leverages three specialized expert networks: A main segmentati…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:21:01

Data-Efficient Spectral Classification of Hyperspectral Data Using MiniROCKET and HDC-MiniROCKET
Nick Theisen, Kenny Schlegel, Dietrich Paulus, Peer Neubert
https://arxiv.org/abs/2509.13809

Data-Efficient Spectral Classification of Hyperspectral Data Using MiniROCKET and HDC-MiniROCKET
The classification of pixel spectra of hyperspectral images, i.e. spectral classification, is used in many fields ranging from agricultural, over medical to remote sensing applications and is currently also expanding to areas such as autonomous driving. Even though for full hyperspectral images the best-performing methods exploit spatial-spectral information, performing classification solely on spectral information has its own advantages, e.g. smaller model size and thus less data required for …

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2025-09-18 10:20:41

Masked Feature Modeling Enhances Adaptive Segmentation
Wenlve Zhou, Zhiheng Zhou, Tiantao Xian, Yikui Zhai, Weibin Wu, Biyun Ma
https://arxiv.org/abs/2509.13801 https://

Masked Feature Modeling Enhances Adaptive Segmentation
Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer models from a labeled source domain to an unlabeled target domain. While auxiliary self-supervised tasks-particularly contrastive learning-have improved feature discriminability, masked modeling approaches remain underexplored in this setting, largely due to architectural incompatibility and misaligned optimization objectives. We propose Masked Feature Modeling (MFM), a novel auxiliary task that performs feature mas…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:20:31

SWA-PF: Semantic-Weighted Adaptive Particle Filter for Memory-Efficient 4-DoF UAV Localization in GNSS-Denied Environments
Jiayu Yuan, Ming Dai, Enhui Zheng, Chao Su, Nanxing Chen, Qiming Hu, Shibo Zhu, Yibin Cao
https://arxiv.org/abs/2509.13795

SWA-PF: Semantic-Weighted Adaptive Particle Filter for Memory-Efficient 4-DoF UAV Localization in GNSS-Denied Environments
Vision-based Unmanned Aerial Vehicle (UAV) localization systems have been extensively investigated for Global Navigation Satellite System (GNSS)-denied environments. However, existing retrieval-based approaches face limitations in dataset availability and persistent challenges including suboptimal real-time performance, environmental sensitivity, and limited generalization capability, particularly in dynamic or temporally varying environments. To overcome these limitations, we present a large-s…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:20:11

Bridging the Synthetic-Real Gap: Supervised Domain Adaptation for Robust Spacecraft 6-DoF Pose Estimation
Inder Pal Singh, Nidhal Eddine Chenni, Abd El Rahman Shabayek, Arunkumar Rathinam, Djamila Aouada
https://arxiv.org/abs/2509.13792

Bridging the Synthetic-Real Gap: Supervised Domain Adaptation for Robust Spacecraft 6-DoF Pose Estimation
Spacecraft Pose Estimation (SPE) is a fundamental capability for autonomous space operations such as rendezvous, docking, and in-orbit servicing. Hybrid pipelines that combine object detection, keypoint regression, and Perspective-n-Point (PnP) solvers have recently achieved strong results on synthetic datasets, yet their performance deteriorates sharply on real or lab-generated imagery due to the persistent synthetic-to-real domain gap. Existing unsupervised domain adaptation approaches aim to…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:19:41

BWCache: Accelerating Video Diffusion Transformers through Block-Wise Caching
Hanshuai Cui, Zhiqing Tang, Zhifei Xu, Zhi Yao, Wenyi Zeng, Weijia Jia
https://arxiv.org/abs/2509.13789

BWCache: Accelerating Video Diffusion Transformers through Block-Wise Caching
Recent advancements in Diffusion Transformers (DiTs) have established them as the state-of-the-art method for video generation. However, their inherently sequential denoising process results in inevitable latency, limiting real-world applicability. Existing acceleration methods either compromise visual quality due to architectural modifications or fail to reuse intermediate features at proper granularity. Our analysis reveals that DiT blocks are the primary contributors to inference latency. Ac…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:19:21

CETUS: Causal Event-Driven Temporal Modeling With Unified Variable-Rate Scheduling
Hanfang Liang, Bing Wang, Shizhen Zhang, Wen Jiang, Yizhuo Yang, Weixiang Guo, Shenghai Yuan
https://arxiv.org/abs/2509.13784

CETUS: Causal Event-Driven Temporal Modeling With Unified Variable-Rate Scheduling
Event cameras capture asynchronous pixel-level brightness changes with microsecond temporal resolution, offering unique advantages for high-speed vision tasks. Existing methods often convert event streams into intermediate representations such as frames, voxel grids, or point clouds, which inevitably require predefined time windows and thus introduce window latency. Meanwhile, pointwise detection methods face computational challenges that prevent real-time efficiency due to their high computati…

@arXiv_csCV_bot@mastoxiv.page
2025-09-18 10:19:01

Morphology-optimized Multi-Scale Fusion: Combining Local Artifacts and Mesoscopic Semantics for Deepfake Detection and Localization
Chao Shuai, Gaojian Wang, Kun Pan, Tong Wu, Fanli Jin, Haohan Tan, Mengxiang Li, Zhenguang Liu, Feng Lin, Kui Ren
https://arxiv.org/abs/2509.13776

Morphology-optimized Multi-Scale Fusion: Combining Local Artifacts and Mesoscopic Semantics for Deepfake Detection and Localization
While the pursuit of higher accuracy in deepfake detection remains a central goal, there is an increasing demand for precise localization of manipulated regions. Despite the remarkable progress made in classification-based detection, accurately localizing forged areas remains a significant challenge. A common strategy is to incorporate forged region annotations during model training alongside manipulated images. However, such approaches often neglect the complementary nature of local detail and…

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