Large Language Models (LLMs) often exhibit inconsistent behavior when answering paraphrased questions, suggesting a reliance on surface-level patterns rather th...
Illumination inconsistency is a fundamental challenge in multi-view 3D reconstruction. Variations in sunlight direction, cloud cover, and shadows break the cons...
This study proposes a risk prediction method based on a Multi-Scale Temporal Alignment Network (MSTAN) to address the challenges of temporal irregularity, sampl...
Vision Language Action models have significantly advanced general purpose robotic manipulation by harnessing large scale pretrained vision and language represen...
Blockchain security is threatened by selfish mining, where a miner (operator) deviates from the protocol to increase their revenue. Selfish mining is exacerbate...
Human activity recognition (HAR) from inertial sensors is essential for ubiquitous computing, mobile health, and ambient intelligence. Conventional deep models ...
Reward feedback learning (ReFL) has proven effective for aligning image generation with human preferences. However, its extension to video generation faces sign...
The near-field (P2P) operator in the Multilevel Fast Multipole Algorithm (MLFMA) is a performance bottleneck on GPUs due to poor memory locality. This work intr...
Obtaining safety guarantees for reinforcement learning is a major challenge to achieve applicability for real-world tasks. Safety shields extend standard reinfo...
Alzheimer's disease is a debilitating disorder marked by a decline in cognitive function. Timely identification of the disease is essential for the development ...
A fundamental theoretical question in network analysis is to determine under which conditions community recovery is possible in polynomial time in the Stochasti...
The key limitation of the verification performance lies in the ability of error detection. With this intuition we designed several variants of pessimistic verif...
