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Learning-To-Measure: In-context Active Feature Acquisition
Yuta Kobayashi, Zilin Jing, Jiayu Yao, Hongseok Namkoong, Shalmali Joshi
https://arxiv.org/abs/2510.12624 https://

Learning-To-Measure: In-context Active Feature Acquisition
Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to acquire. In practice, AFA methods often learn from retrospective data with systematic missingness in the features and limited task-specific labels. Most prior work addresses acquisition for a single predetermined task, limiting scalability. To address this limitation, we formalize the meta-AFA problem, where the goal…

Hyundai is making a major bet on EV batteries. The automaker is investing $900M to build a massive battery R&D campus in Korea by 2026.
The goal? Develop breakthrough battery tech in-house, optimize EV performance, and stay ahead in the global race to electrification.

Hyundai Motor Group Drives Next-Generation Battery Innovation with Future Mobility Battery Campus - CleanTechnica
Support CleanTechnica's work through a Substack subscription or on Stripe. KRW 1.2 trillion investment to establish Hyundai Motor Group’s first comprehensive battery research and development hub in Anseong, Korea, by the end of 2026 To internalize core battery technologies — cell design, process engineering and integrated control systems — to ... [continued]

Novel point cloud registration approach for noninvasive patient specific estimation of leaflet strain from 3D images of heart valves
Wensi Wu, Matthew Daemer, Jeffrey A. Weiss, Alison M. Pouch, Matthew A. Jolley
https://arxiv.org/abs/2510.06578

Novel point cloud registration approach for noninvasive patient specific estimation of leaflet strain from 3D images of heart valves
Valvular heart disease is prevalent and a major contributor to heart failure. Valve leaflet strain is a promising metric for evaluating the mechanics underlying the initiation and progression of valvular pathology. However, robust and generalizable methods for noninvasively quantifying valvular strain from clinically acquired patient images remain limited. In this work, we present a novel feature-tracking framework for quantifying leaflet strain in atrioventricular valves using 3D echocardiogra…