Real-world scientific applications frequently encounter incomplete observational data due to sensor limitations, geographic constraints, or measurement costs. Although neural operators significantly advanced PDE solving in terms of computational efficiency and accuracy, their underlying assumption of fully-observed spatial inputs severely restricts applicability in real-world applications. We introduce the first systematic framework for learning neural operators from partial observation. We identify and formalize two fundamental obstacles: (i) the supervision gap in unobserved regions that prevents effective learning of physical correlations, […]

Ver mais

arXiv:2602.16568v1 Announce Type: cross Abstract: Sparse recovery is among the most well-studied problems in learning theory and high-dimensional statistics. In this work, we investigate the statistical and computational landscapes of sparse recovery with $ell_infty$ error guarantees. This variant of the problem is motivated by emph{variable selection} tasks, where the goal is to estimate the support of a $k$-sparse signal in $mathbb{R}^d$. Our main contribution is a provable separation between the emph{oblivious} (“for each”) and emph{adaptive} (“for all”) models […]

Ver mais

arXiv:2601.07640v1 Announce Type: new Abstract: This paper develops an approach for multi-step forecasting of dynamical systems by integrating probabilistic input forecasting with physics-informed output prediction. Accurate multi-step forecasting of time series systems is important for the automatic control and optimization of physical processes, enabling more precise decision-making. While mechanistic-based and data-driven machine learning (ML) approaches have been employed for time series forecasting, they face significant limitations. Incomplete knowledge of process mathematical models limits mechanistic-based direct employment, while purely […]

Ver mais

arXiv:2602.13311v1 Announce Type: new Abstract: In industrial millimeter-wave (mmWave) multi-hop Integrated Access and Backhaul (IAB) networks, dynamic blockages caused by moving obstacles pose a severe threat to robust and continuous networks. While Packet Duplication (PD) enhances reliability by path diversity, it inevitably doubles the traffic load, leading to severe congestion and degraded Age of Information (AoI). To navigate this reliability-congestion trade-off, we formulated an optimization problem in a multi-hop IAB scenario that minimizes the average AOI while satisfying […]

Ver mais

arXiv:2602.11760v1 Announce Type: new Abstract: Feature-importance methods show promise in transforming machine learning models from predictive engines into tools for scientific discovery. However, due to data sampling and algorithmic stochasticity, expressive models can be unstable, leading to inaccurate variable importance estimates and undermining their utility in critical biomedical applications. Although ensembling offers a solution, deciding whether to explain a single ensemble model or aggregate individual model explanations is difficult due to the nonlinearity of importance measures and remains […]

Ver mais

The Day Your LLM Stops Talking and Starts Doing There’s a moment in every LLM project where you realize the “chat” part is the easy bit. The hard part is everything that happens between the user request and the final output: gathering missing facts, choosing which tools to call (and in what order), handling failures, remembering prior decisions, and not spiraling into confident nonsense when the world refuses to match the model’s assumptions. That’s the moment you’re no […]

Ver mais

In this quiz, you’ll review hash functions, collision resolution strategies, hash function distribution, the avalanche effect, and key principles of Test-Driven Development. For more practice and context, explore Build a Hash Table in Python With TDD. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Ver mais

arXiv:2602.13292v1 Announce Type: new Abstract: Ethics review is a foundational mechanism of modern research governance, yet contemporary systems face increasing strain as ethical risks arise as structural consequences of large-scale, interdisciplinary scientific practice. The demand for consistent and defensible decisions under heterogeneous risk profiles exposes limitations in institutional review capacity rather than in the legitimacy of ethics oversight. Recent advances in large language models (LLMs) offer new opportunities to support ethics review, but their direct application remains limited […]

Ver mais

Evidence-based clinical guidelines are essential for high-quality care yet translating them into personalized clinical decision support remains resource-intensive and time-consuming. Large language models (LLMs) show promise for supporting clinical decision-making, but their limited access to patient-specific data and explicit guideline sources constrains trustworthiness, personalization, and clinical applicability. Retriev-al-augmented generation (RAG) addresses part of this challenge by grounding model outputs in curated evidence sources; however, true personalization requires structured access to electronic health record data. This study presents FHIR-RAG-MEDS, […]

Ver mais