arXiv:2603.26841v1 Announce Type: new Abstract: We present FatigueFormer, a semi-end-to-end framework that deliberately combines saliency-guided feature separation with deep temporal modeling to learn interpretable and generalizable muscle fatigue dynamics from surface electromyography (sEMG). Unlike prior approaches that struggle to maintain…
arXiv:2603.26858v1 Announce Type: new Abstract: Single-cell RNA-seq data analysis typically requires representations that capture heterogeneous local structure across multiple scales while remaining stable and interpretable. In this work, we propose a hierarchical sheaf spectral embedding (HSSE) framework that constructs informative…
arXiv:2502.00472v3 Announce Type: replace Abstract: Forecasting multiscale chaotic dynamical systems, such as turbulent flows, with deep learning remains a formidable challenge due to the spectral bias of neural networks, which hinders the accurate representation of fine-scale structures in long-term predictions.…
arXiv:2601.02856v3 Announce Type: replace Abstract: Precise day-ahead forecasts for electricity prices are crucial to ensure efficient portfolio management, support strategic decision-making for power plant operations, enable efficient battery storage optimization, and facilitate demand response planning. However, developing an accurate prediction…
arXiv:2603.26889v1 Announce Type: new Abstract: Molecular discovery is increasingly framed as an inverse design problem: identifying molecular structures that satisfy desired property profiles under feasibility constraints. While recent generative models provide continuous latent representations of chemical space, targeted optimization within…
arXiv:2603.04427v4 Announce Type: replace Abstract: Standard Transformer attention uses identical dimensionality for queries, keys, and values, yet these components serve different roles: queries and keys produce scalar attention weights (selection), while values carry rich representations (value transfer). We show that…
arXiv:2603.26891v1 Announce Type: new Abstract: AI arenas, which rank generative models from pairwise preferences of users, are a popular method for measuring the relative performance of models in the course of their organic use. Because rankings are computed from noisy…
By quickly generating aesthetically accurate previews of fabricated objects, the VisiPrint system could make prototyping faster and less wasteful.
arXiv:2510.19372v2 Announce Type: replace-cross Abstract: We study reinforcement learning (RL) with transition look-ahead, where the agent may observe which states would be visited upon playing any sequence of $ell$ actions before deciding its course of action. While such predictive information…
By quickly generating aesthetically accurate previews of fabricated objects, the VisiPrint system could make prototyping faster and less wasteful.
