惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

H
Help Net Security
T
ThreatConnect
SecWiki News
SecWiki News
F
Future of Privacy Forum
AWS News Blog
AWS News Blog
C
Cisco Blogs
A
Arctic Wolf
Vercel News
Vercel News
The GitHub Blog
The GitHub Blog
Scott Helme
Scott Helme
V
V2EX
博客园 - 叶小钗
阮一峰的网络日志
阮一峰的网络日志
K
Kaspersky official blog
G
Google Developers Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
P
Privacy International News Feed
C
Cyber Attacks, Cyber Crime and Cyber Security
N
News | PayPal Newsroom
Schneier on Security
Schneier on Security
NISL@THU
NISL@THU
Microsoft Azure Blog
Microsoft Azure Blog
量子位
The Hacker News
The Hacker News
Stack Overflow Blog
Stack Overflow Blog
Security Latest
Security Latest
M
Microsoft Research Blog - Microsoft Research
Google Online Security Blog
Google Online Security Blog
博客园_首页
C
CXSECURITY Database RSS Feed - CXSecurity.com
I
InfoQ
Google DeepMind News
Google DeepMind News
Y
Y Combinator Blog
The Cloudflare Blog
Microsoft Security Blog
Microsoft Security Blog
Martin Fowler
Martin Fowler
Cisco Talos Blog
Cisco Talos Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
T
Troy Hunt's Blog
F
Fox-IT International blog
S
Security @ Cisco Blogs
博客园 - 司徒正美
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
C
Comments on: Blog
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
L
LINUX DO - 最新话题
GbyAI
GbyAI
Project Zero
Project Zero
腾讯CDC
T
Tailwind CSS Blog

cs.LG updates on arXiv.org

Momentum Streams for Optimizer-Inspired Transformers Cross-Domain Energy-Guided Diffusion Generation for Off-Dynamics Reinforcement Learning RL with Learnable Textual Feedback: A Bilevel Approach Feature Learning in Wide Neural Networks under $μ$P: Identifiability and Sparse-Dictionary Decomposition of the Mean-Field Limit Zeroth-Order Nonconvex Nonsmooth Optimization with Heavy-Tailed Noise Refined Analysis of Entropy-Regularized Actor-Critic Batch Normalization Amplifies Memorization and Privacy Risks Learning Laplacian Eigenspace with Mass-Aware Neural Operators on Point Clouds Not All Transitions Matter: Evidence from PPO PILOT: Policy-Informed Learned Optimization for Adaptive Deep Network Training From One-Pass SGD to Data Reuse: Mini-Batch Scaling Laws in Sketched Linear Regression LLMs Show No Signs Of Individuated Metacognition A Large-Scale Dataset and Benchmark: Do Protein-Ligand Models Learn Binding Sites or Just Binding Likelihood? Rethinking Federated Unlearning via the Lens of Memorization Mixture of Complementary Agents for Robust LLM Ensemble Reinforcement Learning for Reachability: Guaranteeing Asymptotic Optimality Representation-Guided Discrete Molecular Graph Retrosynthesis Verified SHAP: Provable Bounds for Exact Shapley Values of Neural Networks A Contractive Feedback Semantics for Reinforcement Learning CAFD: Concept-Aware DNN Fault Detection using VLMs GEESE: Genotype-aware End-to-End Spatio-temporal Embedding for Behavioral Phenotyping Iterative Refinement Neural Operators are Learned Fixed-Point Solvers: A Principled Approach to Spectral Bias Mitigation CAffNet: Hard Constraint-Affine Neural Networks The Normalized Maximum Likelihood for Regular Non-Smooth Models: Measure-Theoretic Foundations and Geometric Sampling Fourier Feature Pyramids for Physics-Informed Neural Networks Extracting Training Data from Diffusion Language Models via Infilling PrivFusion: A Privacy-preserving Multi-Agent Framework for Harmonizing Distributed Datasets Spectral Probe-Circuits: A Three-Step Recipe for Identifying Attention-Head Circuits in Pretrained Transformers TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models Complement Submodular Information Measures for Balanced and Robust Data Selection DriftingMol: Decoder-Coupled Drift for One-Pass Property-Conditional Molecular Generation Disentangled Double Machine Learning for Accurate Causal Effect Estimation Cascade-KDE: Robust Time-Series Restoration under Out-of-Distribution Impulse Corruptions On the Stability and Realizability of Recurrent Polynomial Surrogate Ternary Logic Gate Networks Towards Verifiable Transformers: Solver-Checkable Circuit Explanations An Effective-Rank Audit of Alignment-Induced Activation Shifts: Confound Control, Constructive Calibration, and Limits Discovering Lexical Gaps Using Embeddings from Multilingual LLMs IterInject: Indirect Prompt Injection Against LLM Agents via Feedback-Guided Iterative Optimization Beyond Generative Priors: Minority Sampling with JEPA-Guided Diffusion Signs Beat Floats: Low-Rank Double-Binary Adaptation for On-Device Fine-Tuning Hardware-Aware Federated Learning for Speech Emotion Recognition Synheart Capacity: A Theory-Driven Physiological Representation of Cognitive Capacity Dynamics from Wearable Signals A lift for input-convex neural network training PromptAudit: Auditing Prompt Sensitivity in LLM-Based Vulnerability Detection Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning The Perception-Physics Paradox: Probing Scientific Alignment with TC-Bench Feature Lottery? A Bifurcation Theory of Concept Emergence ChaosBench-Logic v2: Evaluating LLM Logical Reasoning over Dynamical Systems at Scale CONF-KV: Confidence-Aware KV Cache Eviction with Mixed-Precision Storage for Long-Horizon LLM Multicalibration Boosting: Theory, Convergence, and Transferability Optimizing Digital Therapeutic Interventions: Online Learning under Endogenous Adherence LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots A computational phase transition for learning-to-sample from Ising models Interdomain Attention: Beyond Token-Level Key-Value Memory Faithfulness as Information Flow: Evaluating and Training Faithful Chain-of-Thought Reasoning Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection Evolving Robustness--Exploration Trade-off in Online Reinforcement Learning via Quantile Bayesian Risk MDPs AvAtar: Learning to Align via Active Optimal Transport Omissive Bias in Religious Representation: Benchmarking LLM Answers to Everyday Ethical Decision-making Riemannian Archetypal Analysis: Interpretable non-linear data analysis on deformed star distributions Treatment Effect Estimation with Differentiated Networked Effect on Graph Data ChainLearn: A Blockchain-Based Capacity-Aware Framework for Federated Ensemble Learning Lake Detection and Water Quality Estimation in Sentinel-2 Data Polymorphism Is Rotation: Operational Mechanistic Interpretability from a Two-Layer Transformer to Pythia-70m Beyond Fixed Points: Superpolynomial Capacity of Asymmetric Hopfield Networks Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs Deep ZakaiJ: Structured Filtering for Jump-Diffusion Time Series Forecasting MindAlign: Bridging EEG, Vision, and Language for Zero-Shot Visual Decoding Algometrics: Forecasting Under Algorithmic Feedback WLNO: Wavelet-Laplace Neural Operator for Solving Partial Differential Equations What Are We Actually Decoding? Source Attribution for Non-Invasive Brain-to-Language Retrieval High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications Knowledge Graph Modulated Deep Learning for Limited-Sample Clinical Data Analysis Bilevel Optimization of Synthetic Trajectories for Multi-Turn LLM Fine-Tuning Overcoming "Physics Shock" in Earth Observation A Heteroscedastic Uncertainty Framework for PINN-based Flood Inference Trajectory-Based Difficulty Scoring for Reliable Learning on Tabular Data Hermite-NGP: Gradient-Augmented Hash Encoding for Learning PDEs Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing ECHO: Terminal Agents Learn World Models for Free Filtered Posterior Mean Collections: A Unified Framework for Analytical Models of Diffusion Generalization Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion Assessing the Operational Viability of Foundation Models for Time Series Forecasting Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions A Unified Python Framework for Direct PPO-based Control of AHUs with Economizer Logic and CO2-Constrained Ventilation CurveRL: Principled Distribution-Aware Context Reweighting for LLM Reasoning Agent-ToM: Learning to Monitor Autonomous LLM Agents via Theory-of-Mind Reasoning Private Adaptive Covariance Estimation via Gaussian Graphical Models Hidden-State Privacy Has an Empty Middle Active Learning for Stochastic Contextual Linear Bandits Federated Learning over Human-Body Communication for On-Body Edge Intelligence: A Survey, Taxonomy, and BODYFED-HBC Scheduling Vignette Muon in Vision Transformers: Optimizer-Recipe Interactions and Gradient Spectra Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents LLM-AutoSciLab: Closed-Loop Scientific Discovery via Active Experimentation with LLMs Balancing Fairness, Privacy, and Accuracy: A Multitask Adversarial Framework for Centralized Data-Driven Systems Aligning Molecular Graph Explanations with Chemical Identity via InChIfied Invariants Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Components LAPLEX: The FFT of Learnable Laplace Kernels SemanticZip: A Pilot Framework for Lossy Text Compression with LLMs as Semantic Decompressors ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks Characterizing the Representational Capacity of Neural Processes
Seeing Inside the Storm: Improving Nowcasting by Integrating Meteorological Drivers
Minghui Qiu, · 2026-05-26 · via cs.LG updates on arXiv.org

View PDF HTML (experimental)

Abstract:Most nowcasting systems, built on radar reflectivity, focus on current precipitation, ignoring the atmospheric precursors -- such as low-level convergence, turbulent eddies, and latent heating -- that offer a fleeting window to foresee storm birth. We introduce MeteoLogist, a physics-inspired radar intelligence framework that models the full life cycle of convection -- from its precursors to organized storm evolution. However, exploiting these precursors is non-trivial: they originate from multiple meteorological drivers -- thermodynamic, kinematic, and microphysical -- that evolve asynchronously (C1) and remain spatially fragmented (C2). To this end, MeteoLogist designs three tightly integrated components. The Physics-Tailored Encoders process radar echoes according to their intrinsic physical scales and semantics, forming thermodynamic, kinematic, and microphysical streams that capture distinct dynamical regimes. The Temporal-Phase Aligner addresses C1 by leveraging causal temporal attention to capture when and how different drivers interact and activate. The Cross-Field Spatial Aggregator addresses C2 through cross-regional fusion, aligning weak and scattered precursors across neighboring cells to expose upstream triggers and enforce spatial coherence. Evaluated on 3D-NEXRAD (2020--2022, US-wide), MeteoLogist boosts high-impact detection (CSI40) by +9.7% over strong baselines, and achieves a remarkable 37.67% gain during the storm-developing stage -- demonstrating true foresight in sensing storms before they appear. The code can be found in the supplementary material.
Subjects: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
Cite as: arXiv:2605.24067 [physics.ao-ph]
  (or arXiv:2605.24067v1 [physics.ao-ph] for this version)
  https://doi.org/10.48550/arXiv.2605.24067

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Minghui Qiu [view email]
[v1] Fri, 22 May 2026 08:15:35 UTC (11,631 KB)