![Model rescaling, evaluated using the Brier Skill Score (BSS) as a function of a multiplicative scaling factor and the mean number of earthquakes [latex]\overline{n\_{\mathrm{eq}}}[/latex], demonstrates that empirical BSS-based rescaling-obtained by fitting along the ridge of positive skill-and logit-based prior correction consistently outperform uncorrected methods, with the latter further refined by an offset calibrated to optimize BSS, as evidenced by comparative performance across both training and independent test epochs and substantiated by the distribution of [latex]\overline{n\_{\mathrm{eq}}}[/latex] for all space-time samples and those culminating in earthquakes with [latex]M\_{W} \geq 5[/latex].](https://arxiv.org/html/2603.03079v1/2603.03079v1/x1.png)
A new study explores whether deep learning, combined with analysis of the Gutenberg-Richter b-value, can offer a marginal improvement in predicting earthquake occurrences.
![The additive model estimates entry-wise functions - termed “Partial Effect” and represented as [latex]\hat{f}_{j}(x)[/latex] - alongside corresponding “Predictive Value” expressed as [latex]\sigma\{\hat{h}_{j}(\mathbf{X})\}[/latex], where [latex]\hat{h}_{j}(\mathbf{X})[/latex] defines the function’s contribution, to discern patterns within datasets labeled with conditions such as inferior myocardial infarction (IMI), non-diagnostic T abnormalities (NDT), atrial fibrillation (AFIB), and left ventricular hypertrophy (LVH).](https://arxiv.org/html/2603.02616v1/2603.02616v1/x5.png)
A new approach leverages the power of artificial intelligence to identify indicators of structural heart disease directly from routine electrocardiograms.
Researchers have created a large-scale dataset, FinTexTS, designed to improve the link between financial news and stock price predictions.
As AI agents become increasingly reliant on external skills, ensuring the integrity of those skills is paramount, and this research introduces a novel framework to address this growing security challenge.
Researchers have developed an AI-powered multi-agent system that learns to proactively defend financial institutions against evolving cyber threats.
Information and communication technologies are becoming essential tools for building more effective, transparent, and sustainable carbon removal projects.
![A survey of thirty-one jurisdictions reveals a systemic lack of preparedness, as no region surpasses a ‘Partially Prepared’ status-a finding underscored by an overall mean score of [latex]33.0[/latex] and clearly demarcated tiers at [latex]20[/latex], [latex]40[/latex], and [latex]60[/latex].](https://arxiv.org/html/2603.01508v1/2603.01508v1/Fig1.png)
A new index reveals a critical lack of preparedness among assessed nations for the potential emergence of artificial sentience, exposing a gap in current AI governance frameworks.
![Learned econometric structures, visualized through a [latex]ggplot2[/latex] implementation, demonstrate discernible differences in their structural homology as determined by SHD comparison.](https://arxiv.org/html/2603.00041v1/2603.00041v1/Pairwise_SHD_Score_Comparison_Among_Learnt_Structures.png)
A new study challenges traditional econometric approaches by exploring how causal machine learning can provide more robust insights for policy decisions based on complex time-series data.
New research explores how machine learning can accurately assess the impact of software bugs, helping developers focus on the most critical issues.
New research reveals the complexities of categorizing AI systems under Europe’s landmark regulations and proposes solutions for consistent compliance.