![The FLNet model establishes a novel architecture for [latex]f(x) = w^T x[/latex], enabling efficient and scalable feature learning through a learned network of weights, <i>w</i>, and input features, <i>x</i>.](https://arxiv.org/html/2601.03884v1/x1.png)
A new deep learning pipeline leverages freely available satellite data to pinpoint flood damage at the individual farm level, offering a cost-effective solution for disaster response and agricultural monitoring.
Research reveals how data from our smartphones, combined with advanced artificial intelligence, can predict changes in mental health with increasing accuracy.
![The study demonstrates that compounding data and weight recursion-where each generation of synthetic text refines training from the previous generation’s weights-results in measurable drift, quantified as the change in [latex]\Delta U_{\mathrm{LLN,cov}}(\delta)[/latex] and [latex]\Delta G_{\mathrm{KF}}(\delta)[/latex], relative to a baseline checkpoint.](https://arxiv.org/html/2601.03385v1/x2.png)
New research offers a powerful framework for understanding and predicting when large language models begin to lose coherence during prolonged, synthetic data training.
New research reveals that financial networks exhibit surprisingly fragile stability, with even modest increases in interconnectedness potentially triggering cascading defaults.
New research models the dynamic interplay between artificial intelligence, physical capital, and the workforce in China, revealing both opportunities and risks for sustainable growth.
A new comparative analysis reveals fundamentally different approaches to AI governance across the globe, driven by divergent institutional priorities and understandings of ‘AI safety’.
Researchers have developed a method to pinpoint the key transmission channels of risk within complex financial systems by analyzing how shocks propagate through the network.
A new wave of AI is transforming wireless networks, leveraging powerful foundation models to predict behavior and optimize performance.
A new machine learning approach rapidly and accurately determines stellar properties by analyzing their natural vibrations, opening doors to more precise models of stellar evolution.
![The comparative analysis of anomaly detection methods-tested against the KDDCup99, IoTID20, and WUSTL-IIoT datasets-demonstrates that performance, as measured by the Area Under the Curve [latex] AUC [/latex], fluctuates with the volume of records processed, highlighting the critical need for drift adaptation in maintaining reliable anomaly identification across evolving data streams.](https://arxiv.org/html/2601.03085v1/Figures/WUSTL.png)
A new method leverages machine learning to proactively identify anomalies in real-time data streams from industrial IoT devices, minimizing downtime and maximizing efficiency.