Tootfinder

Language models align with brain regions that represent concepts across modalities
Maria Ryskina, Greta Tuckute, Alexander Fung, Ashley Malkin, Evelina Fedorenko
https://arxiv.org/abs/2508.11536

Language models align with brain regions that represent concepts across modalities
Cognitive science and neuroscience have long faced the challenge of disentangling representations of language from representations of conceptual meaning. As the same problem arises in today's language models (LMs), we investigate the relationship between LM--brain alignment and two neural metrics: (1) the level of brain activation during processing of sentences, targeting linguistic processing, and (2) a novel measure of meaning consistency across input modalities, which quantifies how consiste…

3D Reconstruction of Coronary Vessel Trees from Biplanar X-Ray Images Using a Geometric Approach
Ethan Koland, Lin Xi, Nadeev Wijesuriya, YingLiang Ma
https://arxiv.org/abs/2509.13358

3D Reconstruction of Coronary Vessel Trees from Biplanar X-Ray Images Using a Geometric Approach
X-ray angiography is widely used in cardiac interventions to visualize coronary vessels, assess integrity, detect stenoses and guide treatment. We propose a framework for reconstructing 3D vessel trees from biplanar X-ray images which are extracted from two X-ray videos captured at different C-arm angles. The proposed framework consists of three main components: image segmentation, motion phase matching, and 3D reconstruction. An automatic video segmentation method for X-ray angiography to enab…

Maybe it wasn’t a waste of time after all.
““We were able to win the following concession: That the United States could offer Article 5-like protection, which is one of the real reasons why Ukraine wants to be in NATO,” Witkoff told CNN’s “State of the Union.”
But the question is, what about the territory?
#RussiaUkraineWar
https://apnews.com/article/trump-witkoff-ukraine-russia-putin-war-048aa829a69b4020ca368577bfe18aee

Generalizable Holographic Reconstruction via Amplitude-Only Diffusion Priors
Jeongsol Kim, Chanseok Lee, Jong Chul Ye, Mooseok Jang
https://arxiv.org/abs/2509.12728 https://

Generalizable Holographic Reconstruction via Amplitude-Only Diffusion Priors
Phase retrieval in inline holography is a fundamental yet ill-posed inverse problem due to the nonlinear coupling between amplitude and phase in coherent imaging. We present a novel off-the-shelf solution that leverages a diffusion model trained solely on object amplitude to recover both amplitude and phase from diffraction intensities. Using a predictor-corrector sampling framework with separate likelihood gradients for amplitude and phase, our method enables complex field reconstruction witho…

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…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[6/8]:
- Occlusion-Aware Temporally Consistent Amodal Completion for 3D Human-Object Interaction Reconstru...
Hyungjun Doh, Dong In Lee, Seunggeun Chi, Pin-Hao Huang, Kwonjoon Lee, Sangpil Kim, Karthik Ramani

NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models
Xiaohan Bi, Binhang Qi, Hailong Sun, Xiang Gao, Yue Yu, Xiaojun Liang
https://arxiv.org/abs/2508.11348

NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models
With the growing incorporation of deep neural network (DNN) models into modern software systems, the prohibitive construction costs have become a significant challenge. Model reuse has been widely applied to reduce training costs, but indiscriminately reusing entire models may incur significant inference overhead. Consequently, DNN modularization has gained attention, enabling module reuse by decomposing DNN models. The emerging modularizing-while-training (MwT) paradigm, which incorporates mod…

Magnetic reconnection sustains the mass budget of the solar wind
Yajie Chen, Hardi Peter, Damien Przybylski, Haruhisa Iijima, Lakshmi Pradeep Chitta
https://arxiv.org/abs/2509.11692

Magnetic reconnection sustains the mass budget of the solar wind
The solar wind originates from regions of open magnetic fields on the Sun, but the relevant processes remain unsolved. We present a self-consistent numerical model of the source region of the wind, in which jets similar to those observed on the Sun naturally emerge due to magnetic reconnection between closed and open magnetic fields. In this process material is transferred from closed to open field lines and fed into the solar wind.We quantify the mass flux through the magnetic field connected …

Consistent Modeling of Non-equilibrium Dust Sublimation and the Interactions with Dust Evolution in the Inner Regions of Protoplanetary Disks
Sheng Xu, Lile Wang, Luis C. Ho, Renyue Cen, Shenzhen Xu
https://arxiv.org/abs/2509.11036

Consistent Modeling of Non-equilibrium Dust Sublimation and the Interactions with Dust Evolution in the Inner Regions of Protoplanetary Disks
The inner regions of protoplanetary disks are host to the sublimation of dust grains, a process traditionally modeled using equilibrium thermodynamics. We demonstrate through ab-initio density functional theory (DFT) and kinetic Monte Carlo (KMC) simulations that silicate dust sublimation is inherently a non-equilibrium kinetic process. The binding energies and vibrational frequencies governing desorption, calculated for MgSiO3 and other compositions, reveal that sublimation timescales far exce…

MSDNet: Efficient 4D Radar Super-Resolution via Multi-Stage Distillation
Minqing Huang, Shouyi Lu, Boyuan Zheng, Ziyao Li, Xiao Tang, Guirong Zhuo
https://arxiv.org/abs/2509.13149

MSDNet: Efficient 4D Radar Super-Resolution via Multi-Stage Distillation
4D radar super-resolution, which aims to reconstruct sparse and noisy point clouds into dense and geometrically consistent representations, is a foundational problem in autonomous perception. However, existing methods often suffer from high training cost or rely on complex diffusion-based sampling, resulting in high inference latency and poor generalization, making it difficult to balance accuracy and efficiency. To address these limitations, we propose MSDNet, a multi-stage distillation framew…