arXiv:2607. 24168v1 Announce Type: new Abstract: Road crashes remain among the gravest threats to public safety, and preventing them is a defining task of transportation systems worldwide.
arXiv:2608.20980v1 Announce Type: new Abstract: Graph neural networks (GNNs) are routinely employed for short-range forecasting on multivariate time series with a spatial graph structure. Despite the...
arXiv:2607. 20046v1 Announce Type: cross Abstract: With the widespread deployment of deep neural networks (DNNs) in safety-critical domains, reducing the cost of model validation under limited testing budgets has become increasingly important.
arXiv:2609.37650v1 Announce Type: new Abstract: Counterfactual explanations of graph neural networks identify edge deletions that flip a prediction. On heterogeneous graphs, however, existing methods...
arXiv:2607. 14416v1 Announce Type: new Abstract: The interconnected nature of global financial systems makes them vulnerable to systemic risks, where the failure of a few institutions can trigger catastrophic cascading defaults.
arXiv:2503. 17386v2 Announce Type: replace-cross Abstract: Crashworthiness is a key performance measure in the design of safety-critical vehicle panel components such as B-pillars.
arXiv:2607. 07716v1 Announce Type: cross Abstract: Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy.
The paper investigates how graph neural networks can quickly estimate the loss of algebraic connectivity after multiple road-link disruptions, comparing learned corrections to first‑order Fiedler sensitivity with analytical baselines. Experiments on synthetic failures and real OpenStreetMap data across six countries show that residual GCN and GraphSAGE models reduce mean absolute error for spatial and targeted failures, while second‑order perturbation offers minimal improvement. The study also demonstrates that sparse scaling allows the approach to scale to 20,000 nodes and that the spectral residual acts as a domain‑sensitive inductive bias for connectivity screening.
The paper investigates how reusing observations for ranking forecasts can create artificial associations between forecast and outcome ranks. It develops theoretical conditions that preserve these associations and introduces unbiased kernel and U‑statistic estimators for interaction terms. Empirical results on a Beijing air‑quality archive show that interaction explains over 90% of shared forecast scores, and a matched null experiment demonstrates that distinct references dramatically reduce false rejections.
arXiv:2609. 19210v1 Announce Type: cross Abstract: Graph neural networks are widely used for transductive node classification, with accuracy typically measured on randomly drawn train/validation/test splits.
arXiv:2608. 14903v1 Announce Type: new Abstract: Quantitative forecasts of frontier artificial intelligence often connect dated targets to trends in benchmark scores, training compute, release time, or expert belief.
arXiv:2606. 12500v1 Announce Type: cross Abstract: Traffic microsimulation combined with surrogate safety measures has increasingly been used as a proactive alternative to historical crash data for predicting crash frequency for current or planned road infrastructure designs.