Tootfinder

Metrics vs Surveys: Can Quantitative Measures Replace Human Surveys in Social Robot Navigation? A Correlation Analysis
Stefano Trepella, Mauro Martini, No\'e P\'erez-Higueras, Andrea Ostuni, Fernando Caballero, Luis Merino, Marcello Chiaberge
https://arxiv.org/abs/2510.02941

Metrics vs Surveys: Can Quantitative Measures Replace Human Surveys in Social Robot Navigation? A Correlation Analysis
Social, also called human-aware, navigation is a key challenge for the integration of mobile robots into human environments. The evaluation of such systems is complex, as factors such as comfort, safety, and legibility must be considered. Human-centered assessments, typically conducted through surveys, provide reliable insights but are costly, resource-intensive, and difficult to reproduce or compare across systems. Alternatively, numerical social navigation metrics are easy to compute and faci…

Selective disruption of reach-related saccade timing following a middle-cerebral artery stroke
Mahya Beheshti, Todd E Hudson, Rajvardhan Gadde, Glenn Alvarez Arias, Karis Huh, Robert L Sainburg, JR Rizzo
https://arxiv.org/abs/2510.03076

Selective disruption of reach-related saccade timing following a middle-cerebral artery stroke
Background: Coordinated control of eye and hand movements is critical for nearly all goal-directed actions, underpinning tasks ranging from simple object manipulation to complex tool use. This coordination relies on temporal coupling between reach and saccade. Stroke disrupts this process at several levels. Methods: We conducted a comparative eye-tracking study on patients with history of stroke vs. control performing various tasks. We used the Kinereach motion-tracking system with integrated e…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Sample, Don't Search: Rethinking Test-Time Alignment for Language Models
Gon\c{c}alo Faria, Noah A. Smith
https://arxiv.org/abs/2504.03790 https://mastoxiv.page/@arXiv_csCL_bot/114301112970577326
- A Survey on Archetypal Analysis
Aleix Alcacer, Irene Epifanio, Sebastian Mair, Morten M{\o}rup
https://arxiv.org/abs/2504.12392 https://mastoxiv.page/@arXiv_statME_bot/114357826909813483
- The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations
Michael L. Wells, Kamel Lahouel, Bruno Jedynak
https://arxiv.org/abs/2505.11622 https://mastoxiv.page/@arXiv_statML_bot/114539065460187982
- BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions
Kingsley Yeon, Promit Ghosal, Mihai Anitescu
https://arxiv.org/abs/2505.12289 https://mastoxiv.page/@arXiv_mathNA_bot/114539035462135281
- Clustering and Pruning in Causal Data Fusion
Otto Tabell, Santtu Tikka, Juha Karvanen
https://arxiv.org/abs/2505.15215 https://mastoxiv.page/@arXiv_statML_bot/114550346291754635
- On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of ph...
Chloe H. Choi, Andrea Zanoni, Daniele E. Schiavazzi, Alison L. Marsden
https://arxiv.org/abs/2506.11683 https://mastoxiv.page/@arXiv_statML_bot/114692410563481289
- Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Maheshwari, Tang, Ock, Kolluru, Farimani, Kitchin
https://arxiv.org/abs/2506.14850 https://mastoxiv.page/@arXiv_physicschemph_bot/114709402590755731
- Quantifying Uncertainty in the Presence of Distribution Shifts
Yuli Slavutsky, David M. Blei
https://arxiv.org/abs/2506.18283 https://mastoxiv.page/@arXiv_statML_bot/114738165218533987
- ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models
Mina Namazi, Alexander Nemecek, Erman Ayday
https://arxiv.org/abs/2506.20915 https://mastoxiv.page/@arXiv_csCR_bot/114754394485208892
- SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars
Zhao, Huang, Xue, Kong, Liu, Tang, Beers, Ting, Luo
https://arxiv.org/abs/2507.01939 https://mastoxiv.page/@arXiv_astrophIM_bot/114788369702591337
- Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based I...
Ko Watanabe, Stanislav Frolov, Aya Hassan, David Dembinsky, Adriano Lucieri, Andreas Dengel
https://arxiv.org/abs/2507.17860 https://mastoxiv.page/@arXiv_csCV_bot/114912976717523345
- PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning
Yushi Feng, Junye Du, Yingying Hong, Qifan Wang, Lequan Yu
https://arxiv.org/abs/2508.10501 https://mastoxiv.page/@arXiv_csAI_bot/115032101532614110
- Unified Acoustic Representations for Screening Neurological and Respiratory Pathologies from Voice
Ran Piao, Yuan Lu, Hareld Kemps, Tong Xia, Aaqib Saeed
https://arxiv.org/abs/2508.20717 https://mastoxiv.page/@arXiv_csSD_bot/115111255835875066
- Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
Tasnuva Chowdhury, et al.
https://arxiv.org/abs/2509.11512 https://mastoxiv.page/@arXiv_csDC_bot/115213444524490263
- MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair
Ali Reza Ibrahimzada, Brandon Paulsen, Reyhaneh Jabbarvand, Joey Dodds, Daniel Kroening
https://arxiv.org/abs/2509.16187 https://mastoxiv.page/@arXiv_csSE_bot/115247172280557686
- Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation ...
Adam Lahouari, Jutta Rogal, Mark E. Tuckerman
https://arxiv.org/abs/2509.21647 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/115286737423175311
- Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference
Han Yuan, Yue Zhao, Li Zhang, Wuqiong Luo, Zheng Ma
https://arxiv.org/abs/2509.21791 https://mastoxiv.page/@arXiv_csCL_bot/115287166674809413
- The Generation Phases of Flow Matching: a Denoising Perspective
Anne Gagneux, S\'egol\`ene Martin, R\'emi Gribonval, Mathurin Massias
https://arxiv.org/abs/2510.24830 https://mastoxiv.page/@arXiv_csCV_bot/115462527449411627
- Data-driven uncertainty-aware seakeeping prediction of the Delft 372 catamaran using ensemble Han...
Giorgio Palma, Andrea Serani, Matteo Diez
https://arxiv.org/abs/2511.04461 https://mastoxiv.page/@arXiv_eessSY_bot/115507785247809767
- Generalized infinite dimensional Alpha-Procrustes based geometries
Salvish Goomanee, Andi Han, Pratik Jawanpuria, Bamdev Mishra
https://arxiv.org/abs/2511.09801 https://mastoxiv.page/@arXiv_statML_bot/115547135711272091
toXiv_bot_toot

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- CLAReSNet: When Convolution Meets Latent Attention for Hyperspectral Image Classification
Asmit Bandyopadhyay, Anindita Das Bhattacharjee, Rakesh Das
https://arxiv.org/abs/2511.12346 https://mastoxiv.page/@arXiv_csCV_bot/115570753208147835
- Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without ...
Dimitris Oikonomou, Nicolas Loizou
https://arxiv.org/abs/2512.02342 https://mastoxiv.page/@arXiv_mathOC_bot/115654870924418771
- Predictive Modeling of I/O Performance for Machine Learning Training Pipelines: A Data-Driven App...
Karthik Prabhakar, Durgamadhab Mishra
https://arxiv.org/abs/2512.06699 https://mastoxiv.page/@arXiv_csPF_bot/115688618582182232
- Minimum Bayes Risk Decoding for Error Span Detection in Reference-Free Automatic Machine Translat...
Lyu, Song, Kamigaito, Ding, Tanaka, Utiyama, Funakoshi, Okumura
https://arxiv.org/abs/2512.07540 https://mastoxiv.page/@arXiv_csCL_bot/115689532163491162
- In-Context Learning for Seismic Data Processing
Fabian Fuchs, Mario Ruben Fernandez, Norman Ettrich, Janis Keuper
https://arxiv.org/abs/2512.11575 https://mastoxiv.page/@arXiv_csCV_bot/115723040285820239
- Journey Before Destination: On the importance of Visual Faithfulness in Slow Thinking
Rheeya Uppaal, Phu Mon Htut, Min Bai, Nikolaos Pappas, Zheng Qi, Sandesh Swamy
https://arxiv.org/abs/2512.12218 https://mastoxiv.page/@arXiv_csCV_bot/115729165330908574
- Non-Resolution Reasoning (NRR): A Computational Framework for Contextual Identity and Ambiguity P...
Kei Saito
https://arxiv.org/abs/2512.13478 https://mastoxiv.page/@arXiv_csCL_bot/115729234145554554
- Stylized Synthetic Augmentation further improves Corruption Robustness
Georg Siedel, Rojan Regmi, Abhirami Anand, Weijia Shao, Silvia Vock, Andrey Morozov
https://arxiv.org/abs/2512.15675 https://mastoxiv.page/@arXiv_csCV_bot/115740141862163631
- mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs
Jonas Pai, Liam Achenbach, Victoriano Montesinos, Benedek Forrai, Oier Mees, Elvis Nava
https://arxiv.org/abs/2512.15692 https://mastoxiv.page/@arXiv_csRO_bot/115739947869830764
toXiv_bot_toot

On the quadratic complexity of subsets of $\mathbb{F}_p^n$ of bounded $\mathrm{VC_{2}}$-dimension
C. Terry, J. Wolf
https://arxiv.org/abs/2510.12767 https://

On the quadratic complexity of subsets of $\mathbb{F}_p^n$ of bounded $\mathrm{VC_{2}}$-dimension
In prior work, we showed that subsets of $\mathbb{F}_{p}^{n}$ of $\mathrm{VC_{2}}$-dimension at most $k$ are well approximated by a union of atoms of a quadratic factor of complexity $(\ell,q)$, where the complexity $\ell$ of the linear part and the complexity $q$ of the quadratic part are both bounded in terms of $k$, $p$, and the desired level of approximation $μ$. A key tool in the proof of this result was an arithmetic regularity lemma for the Gowers $U^3$-norm by Green and Tao, which resu…

Benchmarking Fake Voice Detection in the Fake Voice Generation Arms Race
Xutao Mao, Ke Li, Cameron Baird, Ezra Xuanru Tao, Dan Lin
https://arxiv.org/abs/2510.06544 https://

Benchmarking Fake Voice Detection in the Fake Voice Generation Arms Race
As advances in synthetic voice generation accelerate, an increasing variety of fake voice generators have emerged, producing audio that is often indistinguishable from real human speech. This evolution poses new and serious threats across sectors where audio recordings serve as critical evidence. Although fake voice detectors are also advancing, the arms race between fake voice generation and detection has become more intense and complex. In this work, we present the first large-scale, cross-do…

What Makes a Visualization Complex?
Mengdi Chu, Zefeng Qiu, Meng Ling, Shuning Jiang, Robert S. Laramee, Michael Sedlmair, Jian Chen
https://arxiv.org/abs/2510.08332 https://

What Makes a Visualization Complex?
We investigate the perceived visual complexity (VC) in data visualizations using objective image-based metrics. We collected VC scores through a large-scale crowdsourcing experiment involving 349 participants and 1,800 visualization images. We then examined how these scores align with 12 image-based metrics spanning information-theoretic, clutter, color, and our two object-based metrics. Our results show that both low-level image properties and the high-level elements affect perceived VC in vis…

ViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution
Long Cui, Weiyun Wang, Jie Shao, Zichen Wen, Gen Luo, Linfeng Zhang, Yanting Zhang, Yu Qiao, Wenhai Wang
https://arxiv.org/abs/2510.12793

ViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution
Existing Multimodal Large Language Models (MLLMs) suffer from increased inference costs due to the additional vision tokens introduced by image inputs. In this work, we propose Visual Consistency Learning (ViCO), a novel training algorithm that enables the model to represent images of varying semantic complexities using different numbers of vision tokens. The key idea behind our method is to employ multiple MLP connectors, each with a different image compression ratio, to downsample the vision …

VQArt-Bench: A semantically rich VQA Benchmark for Art and Cultural Heritage
A. Alfarano (University of Zurich, Max Planck Society), L. Venturoli (University of Zurich, Max Planck Society), D. Negueruela del Castillo (University of Zurich, Max Planck Society)
https://arxiv.org/abs/2510.12750

VQArt-Bench: A semantically rich VQA Benchmark for Art and Cultural Heritage
Multimodal Large Language Models (MLLMs) have demonstrated significant capabilities in joint visual and linguistic tasks. However, existing Visual Question Answering (VQA) benchmarks often fail to evaluate deep semantic understanding, particularly in complex domains like visual art analysis. Confined to simple syntactic structures and surface-level attributes, these questions fail to capture the diversity and depth of human visual inquiry. This limitation incentivizes models to exploit statisti…

SynthVC: Leveraging Synthetic Data for End-to-End Low Latency Streaming Voice Conversion
Zhao Guo, Ziqian Ning, Guobin Ma, Lei Xie
https://arxiv.org/abs/2510.09245 https://

SynthVC: Leveraging Synthetic Data for End-to-End Low Latency Streaming Voice Conversion
Voice Conversion (VC) aims to modify a speaker's timbre while preserving linguistic content. While recent VC models achieve strong performance, most struggle in real-time streaming scenarios due to high latency, dependence on ASR modules, or complex speaker disentanglement, which often results in timbre leakage or degraded naturalness. We present SynthVC, a streaming end-to-end VC framework that directly learns speaker timbre transformation from synthetic parallel data generated by a pre-traine…