Tootfinder

AFFORD2ACT: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation
Anukriti Singh, Kasra Torshizi, Khuzema Habib, Kelin Yu, Ruohan Gao, Pratap Tokekar
https://arxiv.org/abs/2510.01433

AFFORD2ACT: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation
Vision-based robot learning often relies on dense image or point-cloud inputs, which are computationally heavy and entangle irrelevant background features. Existing keypoint-based approaches can focus on manipulation-centric features and be lightweight, but either depend on manual heuristics or task-coupled selection, limiting scalability and semantic understanding. To address this, we propose AFFORD2ACT, an affordance-guided framework that distills a minimal set of semantic 2D keypoints from a…

A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs
Dong Yan, Nanyi Zhang, Junyi Guo
https://arxiv.org/abs/2509.02267

A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs
In this paper, we first conduct a study of the portfolio selection problem, incorporating both exogenous (proportional) and endogenous (resulting from liquidity risk, characterized by a stochastic process) transaction costs through the utility-based approach. We also consider the intrinsic relationship between these two types of costs. To address the associated nonlinear two-dimensional Hamilton-Jacobi-Bellman (HJB) equation, we propose an innovative deep learning-driven policy iteration scheme…

VLT/MUSE Detection of the AB Aurigae b Protoplanet with $H _{\rm \alpha}$ Spectroscopy
Thayne Currie, Jun Hashimoto, Yuhiko Aoyama, Ruobing Dong, Misato Fukagawa, Takayuki Muto, Erica Dykes, Mona El Morsy, Motohide Tamura
https://arxiv.org/abs/2508.18351

VLT/MUSE Detection of the AB Aurigae b Protoplanet with $H _{\rm α}$ Spectroscopy
We analyze high-contrast, medium-spectral-resolution $H_{\rm α}$ observations of the star AB Aurigae using the Very Large Telescope's Multi Unit Spectroscopic Explorer (MUSE). In multiple epochs, MUSE detects the AB Aur b protoplanet discovered from Subaru/SCExAO data in emission at wavelengths slightly blue-shifted from the $H_{\rm α}$ line center (i.e. at 6558.88--6560.13 Å; $\sim$ -100 km s$^{-1}$) and in absorption at redshifted wavelengths (6562.8--6565.1 Å; $\sim$ 75 km s$^{-1}$). AB …

😲 Antipsychotic-treated patients with schizophrenia see benefits from semaglutide, study claims
https://medicalxpress.com/news/2025-09-antipsychotic-patients-schizophrenia-benefits-semaglutide.html

Antipsychotic-treated patients with schizophrenia see benefits from semaglutide, study claims
A multicenter collaboration of Danish researchers reports that once-weekly semaglutide for 30 weeks lowered blood sugar levels and body weight and improved physical quality of life in antipsychotic-treated adults with schizophrenia and prediabetes.

Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
Niklas Bubeck, Suprosanna Shit, Chen Chen, Can Zhao, Pengfei Guo, Dong Yang, Georg Zitzlsberger, Daguang Xu, Bernhard Kainz, Daniel Rueckert, Jiazhen Pan
https://arxiv.org/abs/2508.13826

Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
Cardiac Magnetic Resonance (CMR) imaging is a critical tool for diagnosing and managing cardiovascular disease, yet its utility is often limited by the sparse acquisition of 2D short-axis slices, resulting in incomplete volumetric information. Accurate 3D reconstruction from these sparse slices is essential for comprehensive cardiac assessment, but existing methods face challenges, including reliance on predefined interpolation schemes (e.g., linear or spherical), computational inefficiency, an…

Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation
Chu Zhao, Eneng Yang, Yizhou Dang, Jianzhe Zhao, Guibing Guo, Xingwei Wang
https://arxiv.org/abs/2508.07243

Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation
Heuristic negative sampling enhances recommendation performance by selecting negative samples of varying hardness levels from predefined candidate pools to guide the model toward learning more accurate decision boundaries. However, our empirical and theoretical analyses reveal that unobserved environmental confounders (e.g., exposure or popularity biases) in candidate pools may cause heuristic sampling methods to introduce false hard negatives (FHNS). These misleading samples can encourage the …

An interferometric mid-infrared study of the eruptive star binary Z CMa with MATISSE/VLTI. I. Imaging the protoplanetary disks during the 2023 outburst
F. Lykou, J. Varga, F. Cruz-Sa\'enz de Miera, P. \'Abrah\'am, \'A. K\'osp\'al, B. Lopez, T. Henning, S. Wolf, G. Weigelt, F. Millour, M. Hogerheijde, L. Chen, T. Ratzka, W. Danchi, P. Boley, J. -C. Augereau, P. Priolet

An interferometric mid-infrared study of the eruptive star binary Z CMa with MATISSE/VLTI. I. Imaging the protoplanetary disks during the 2023 outburst
The mid-infrared (MIR) emitting regions of the individual protoplanetary disks in the binary system Z CMa are resolved by MATISSE/VLTI. The observations were obtained during a serendipitous large outburst of the HBe star that lasted more than 100 days, while the FUor companion is presumed to be in quiescence. The size of the MIR-emitting disk region of the more massive HBe star increases toward longer wavelengths from $<14$ mas at 3.5$μm$ to $\ll 50$ mas at 11.5$μm$ . The lack of substructure…

Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search
He Wang, Liang Zeng
https://arxiv.org/abs/2508.03661

Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search
Computational scientific discovery increasingly relies on algorithms to process complex data and identify meaningful patterns - yet faces persistent challenges in gravitational-wave signal identification. While existing algorithmic approaches like matched filtering (MF) and deep neural networks (DNNs) have achieved partial success, their limitations directly stem from fundamental limitations: MF's excessive computational demands arise from its reliance on predefined theoretical waveform templat…

MachineLearningLM: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML
Haoyu Dong, Pengkun Zhang, Mingzhe Lu, Yanzhen Shen, Guolin Ke
https://arxiv.org/abs/2509.06806

MachineLearningLM: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML
Large language models (LLMs) possess broad world knowledge and strong general-purpose reasoning ability, yet they struggle to learn from many in-context examples on standard machine learning (ML) tasks, that is, to leverage many-shot demonstrations purely via in-context learning (ICL) without gradient descent. We introduce MachineLearningLM, a portable continued-pretraining framework that equips a general-purpose LLM with robust in-context ML capability while preserving its general knowledge an…

Wearable Music2Emotion : Assessing Emotions Induced by AI-Generated Music through Portable EEG-fNIRS Fusion
Sha Zhao, Song Yi, Yangxuan Zhou, Jiadong Pan, Jiquan Wang, Jie Xia, Shijian Li, Shurong Dong, Gang Pan
https://arxiv.org/abs/2508.04723

Wearable Music2Emotion : Assessing Emotions Induced by AI-Generated Music through Portable EEG-fNIRS Fusion
Emotions critically influence mental health, driving interest in music-based affective computing via neurophysiological signals with Brain-computer Interface techniques. While prior studies leverage music's accessibility for emotion induction, three key limitations persist: \textbf{(1) Stimulus Constraints}: Music stimuli are confined to small corpora due to copyright and curation costs, with selection biases from heuristic emotion-music mappings that ignore individual affective profiles. \text…