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Crosslisted article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/2]:
- Data-driven Head Motion Generation through Natural Gaze-Head Coordination
Xiaohan Liu, Yilin Wen, Yusuke Sugano
https://arxiv.org/abs/2605.25810
- TRIBE: Predicting Team Performance via Communication Behavior Ensembles
Ali Jalal-Kamali, Nikolos Gurney, David V. Pynadath, Fred Morstatter
https://arxiv.org/abs/2608.06926 https://mastoxiv.page/@arXiv_csAI_bot/117070249873784432
- Comprendia: AI-Augmented Code Comprehension
Costain Nachuma, Minhaz F. Zibran
https://arxiv.org/abs/2608.10290 https://mastoxiv.page/@arXiv_csSE_bot/117081500356613109
- Neural implants and human safety: single-fault detection for DC-coupled recording front ends
Dimitris Antoniadis, Timothy Constandinou
https://arxiv.org/abs/2608.10361 https://mastoxiv.page/@arXiv_eessSP_bot/117081505896378998
- A Neural Network Based Teleoperation for Remote Controlled Vehicles
Ning Ding, Azim Eskandarian
https://arxiv.org/abs/2608.10367 https://mastoxiv.page/@arXiv_csRO_bot/117081440193257425
- Leveraging Human Reading Behavior for Keyphrase Extraction: A Webcam-based Eye-tracking Corpus
Chengzhi Zhang, Xinyi Yan, Wenqi Yu
https://arxiv.org/abs/2608.10688 https://mastoxiv.page/@arXiv_csCL_bot/117081592176417112
- Your LLM, Your Style: Behavioral Mode Axes for LLM Behavioral Control
Haoze Liu, Run Liu, Haiying Xu, Jiahui Han, Siyuan Fang, Siyu Yan, Huiqi Deng, Guanchu Wang, Na Zou
https://arxiv.org/abs/2608.10703 https://mastoxiv.page/@arXiv_csLG_bot/117081615179660277
- The GENEA Challenge 2026: A Large-Scale Disentangled Evaluation of Speech-Driven Gesture Generati...
Nagy, Garc\'ia, Voss, Tsakov, Kucherenko, Yoon, Henter
https://arxiv.org/abs/2608.10839 https://mastoxiv.page/@arXiv_csCV_bot/117081628353061346
- Auditable AI-Assisted Research Writing: An Engineering Discipline with Pre-Registered Process Obs...
Yang Zhou, Chengqun Yu
https://arxiv.org/abs/2608.10858
- R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video
Ke Ma, Yamin Mao, Weiming Li, Shuai Tan, Yijie Zhong, Hao Chen, Haofen Wang, Meng Wang
https://arxiv.org/abs/2608.11017 https://mastoxiv.page/@arXiv_csCV_bot/117081647623530374
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ScoreField: Neural Inverse Scattering with Score-Based Generative Priors
Wenhan Guo, Yuan Gao, Yu Sun
https://arxiv.org/abs/2608.02937 https://arxiv.org/pdf/2608.02937 https://arxiv.org/html/2608.02937
arXiv:2608.02937v1 Announce Type: new
Abstract: Designing an effective electromagnetic inverse-scattering solver requires faithful enforcement of nonlinear full-wave physics together with an expressive prior on the unknown permittivity contrast. We propose ScoreField, a neural inverse scattering framework that integrates coupled implicit neural representations (INRs) with a pretrained score-based generative prior. ScoreField employs two INRs to parameterize the permittivity contrast and the induced current fields, and jointly optimize them under the Lippmann-Schwinger equations. In addition to the implicit regularization by the INR architecture, the score model provides a learned prior gradient on the contrast, which is propagated to the contrast INR through the chain rule. This formulation enables ScoreField to effectively handle strong multiple scattering, where nonlinear wave interactions require accurate modeling of the coupled full-wave physics. We evaluate ScoreField on simulated weak- and strong-scattering benchmarks, the canonical Austria phantom, and experimental Fresnel measurements. We note that ScoreField significantly improves reconstruction fidelity and suppresses artifacts relative to classical full-wave methods and deep learning baselines, achieving an average PSNR improvement of $1.8 \, \mathrm{dB}$ over the best competing method on real Fresnel data.
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