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:2603.26842v1 Announce Type: new Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems. Existing methods require training one specific model for each dataset, which exhibits limited generalization capability across different target…
arXiv:2508.02343v2 Announce Type: replace Abstract: Quantization significantly accelerates inference in large language models (LLMs) by replacing original high-precision matrices with low-precision counterparts. Recent advances in weight-activation quantization have primarily focused on mapping both weights and activations to the INT4 format.…
arXiv:2603.26846v1 Announce Type: new Abstract: As Large Language Models (LLMs) expand in capability and application scope, their trustworthiness becomes critical. A vital risk is intrinsic deception, wherein models strategically mislead users to achieve their own objectives. Existing alignment approaches based…
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…
By quickly generating aesthetically accurate previews of fabricated objects, the VisiPrint system could make prototyping faster and less wasteful.
arXiv:2603.27743v1 Announce Type: cross Abstract: Empirical likelihood is an attractive inferential framework that respects natural parameter boundaries, but existing approaches typically require smoothness of the functional and miscalibrate substantially when these assumptions are violated. For the optimal-value functional central to…
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…
arXiv:2603.09645v2 Announce Type: replace-cross Abstract: Photonic Quantum Machine Learning (PQML) is an emerging method to implement scalable, energy-efficient quantum information processing by combining photonic quantum computing technologies with machine learning techniques. The features of photonic technologies offer several benefits: room-temperature…
arXiv:2602.03912v2 Announce Type: replace Abstract: This paper investigates the forecasting performance of Echo State Networks (ESNs) for univariate time series forecasting using a subset of the M4 Forecasting Competition dataset. Focusing on monthly and quarterly time series with at most…
