Beyond the Numbers: Predicting Stock Performance with News and AI
A new approach combines financial time series data with real-time news sentiment analysis to deliver more accurate and generalizable stock price forecasts.
Science
Seizure Control Through Network Harmony
![Optimal control, achieved through Mean Field Game (MFG) techniques, successfully guides the trajectories of key sub-nodes - as evidenced by the shift from uncontrolled red lines to controlled blue lines - and correspondingly reshapes their amplitude probability density functions, bringing them closer to those of a healthy baseline state, a process reflected in the convergence of training losses for both the φ and [latex]G_{\theta}[/latex] functions within the APAC-Net framework.](https://arxiv.org/html/2603.18035v1/Picture/MFG11.png)
Researchers are exploring a new approach to epilepsy treatment by leveraging computational models of whole-brain dynamics to optimize targeted neuromodulation.
Blind Spots in the Code: How AI Review Tools Can Miss Critical Flaws
New research reveals that AI-powered code review systems are vulnerable to confirmation bias, potentially overlooking security vulnerabilities.
Decoding Attacks with AI: A New Era for Security Analysis
Researchers are harnessing the power of artificial intelligence to automatically analyze security incidents, reconstruct attack timelines, and deliver clearer, more actionable intelligence.
Smart Queries: Balancing Power and Cost in Natural Language to SQL Conversion
A new system intelligently combines smaller, efficient AI models with larger language models to translate natural language questions into database queries.
Seeing What Harmful Content Detection Models *Really* See
New research delves into the inner workings of content moderation AI, revealing the complexities of explanation and the critical need for human oversight.
The Edge of Chaos: Neural Networks Find Stability in Heavy Tails
![The system’s stability, characterized by the curvature of the Lyapunov potential [latex]1-\mathcal{L}\_{\<i>}[/latex] against the stationary population rate [latex]m\_{\</i>}\[/latex], demonstrates a robust stable regime across a range of activities, though nearing criticality ([latex]m\_{\<i>}\to 0[/latex]) induces a diminished curvature, while an observed “divisive brake” - manifested as inverse scaling of closed-loop susceptibility [latex]d\sigma\_{\</i>}/dh[/latex] with activity - suggests automatic gain control, all supported by analysis using biphasic kernels [latex]G(t)=h\_{\tau\_{1}}(t)-bh\_{\tau\_{2}}(t)[/latex] where [latex]h\_{\tau}(t)=\tau^{-1}e^{-t/\tau}\Theta(t)[/latex].](https://arxiv.org/html/2603.18478v1/sand.png)
New research reveals that neural networks with specific connection patterns naturally self-regulate, offering a more robust and controllable alternative to traditional designs.
Decoding Earthquakes with AI: A New Approach to Moment Tensor Solutions
A novel simulation-based inference method enhances the accuracy and reliability of earthquake source analysis, even with incomplete knowledge of Earth’s internal structure.
Navigating the Swarm: Smarter Prediction for Dense Crowds
![The system addresses dense crowd management through dynamic clustering, initiating with a nested agglomerative approach and continuously evaluating cluster stability via Local Outlier Factor [latex]LOF[/latex] to identify and reassign outliers, while centroid trajectories are calculated based on membership deviation-a process that recursively refines clustering as unassigned members accumulate, ensuring adaptability in crowded environments.](https://arxiv.org/html/2603.18166v1/ICPR_2026_LaTeX_Templates/proposed_method.png)
A new approach to pedestrian trajectory prediction leverages dynamic clustering to improve accuracy and efficiency in crowded environments.
Robots Need to Know What They Don’t Know
New research tackles the challenge of reliable failure detection in robotic systems powered by vision and language, focusing on pinpointing uncertainty at critical moments.