arXiv:2511.10878v1 Announce Type: new Abstract: Time-efficient estimation of muscle activations and forces across multi-joint systems is critical for clinical assessment and assistive device control. However, conventional approaches are computationally expensive and lack a high-quality labeled dataset for multi-joint applications. To…
arXiv:2508.01727v2 Announce Type: replace Abstract: Time series forecasting is fundamental to diverse applications, with recent approaches leverage large vision models (LVMs) to capture temporal patterns through visual representations. We reveal that while vision models enhance forecasting performance, 99% of their…
arXiv:2511.10893v1 Announce Type: new Abstract: We address polymer property prediction with a multi-view design that exploits complementary representations. Our system integrates four families: (i) tabular RDKit/Morgan descriptors, (ii) graph neural networks, (iii) 3D-informed representations, and (iv) pretrained SMILES language models,…
arXiv:2509.01348v2 Announce Type: replace Abstract: Accurate precipitation forecasting is becoming increasingly important in the context of climate change. In response, machine learning-based approaches have recently gained attention as an emerging alternative to traditional methods such as numerical weather prediction and…
arXiv:2511.10898v1 Announce Type: new Abstract: Natural disasters such as hurricanes, wildfires, and winter storms have induced large-scale power outages in the U.S., resulting in tremendous economic and societal impacts. Accurately predicting power outage recovery and impact is key to resilience…
arXiv:2511.00108v2 Announce Type: replace Abstract: This report presents Pelican-VL 1.0, a new family of open-source embodied brain models with parameter scales ranging from 7 billion to 72 billion. Our explicit mission is clearly stated as: To embed powerful intelligence into…
arXiv:2511.10915v1 Announce Type: new Abstract: Federated clustering addresses the critical challenge of extracting patterns from decentralized, unlabeled data. However, it is hampered by the flaw that current approaches are forced to accept a compromise between performance and privacy: textit{transmitting embedding…
arXiv:2511.08544v3 Announce Type: replace Abstract: Learning manipulable representations of the world and its dynamics is central to AI. Joint-Embedding Predictive Architectures (JEPAs) offer a promising blueprint, but lack of practical guidance and theory has led to ad-hoc R&D. We present…
arXiv:2511.10936v1 Announce Type: new Abstract: Graph unlearning has emerged as a promising solution for complying with “the right to be forgotten” regulations by enabling the removal of sensitive information upon request. However, this solution is not foolproof. The involvement of…
arXiv:2511.10572v2 Announce Type: replace-cross Abstract: Equitably allocating limited resources in high-stakes domains-such as education, employment, and healthcare-requires balancing short-term utility with long-term impact, while accounting for delayed outcomes, hidden heterogeneity, and ethical constraints. However, most learning-based allocation frameworks either assume…
