arXiv:2602.08980v1 Announce Type: cross Abstract: Neural ordinary differential equations (neural ODEs) can effectively learn dynamical systems from time series data, but their behavior on graph-structured data remains poorly understood, especially when applied to graphs with different size or structure than encountered during training. We study neural ODEs ($mathtt{nODE}$s) with vector fields following the Barab’asi-Barzel form, trained on synthetic data from five common dynamical systems on graphs. Using the $mathbb{S}^1$-model to generate graphs with realistic and tunable structure, we […]

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Braking system, the key module to ensure the safety and steer-ability of current vehicles, relies on extensive manual calibration during production. Reducing labor and time consumption while maintaining the Vehicle Braking Controller (VBC) performance greatly benefits the vehicle industry. Model-based methods in offline reinforcement learning, which facilitate policy exploration within a data-driven dynamics model, offer a promising solution for addressing real-world control tasks. This work proposes ReinVBC, which applies an offline model-based reinforcement learning approach to deal with […]

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Modern seismic and volcanic monitoring is increasingly shaped by continuous, multi-sensor observations and by the need to extract actionable information from nonstationary, noisy wavefields. In this context, machine learning has moved from a research curiosity to a practical ingredient of processing chains for detection, phase picking, classification, denoising, and anomaly tracking. However, improved accuracy on a fixed dataset is not sufficient for operational use. Models must remain reliable under domain shift (new stations, changing noise, evolving volcanic activity), […]

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arXiv:2601.03327v1 Announce Type: new Abstract: Wildfires are highly imbalanced natural hazards in both space and severity, making the prediction of extreme events particularly challenging. In this work, we introduce the first ordinal classification framework for forecasting wildfire severity levels directly aligned with operational decision-making in France. Our study investigates the influence of loss-function design on the ability of neural models to predict rare yet critical high-severity fire occurrences. We compare standard cross-entropy with several ordinal-aware objectives, including the […]

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Individual phishing URLs are often short-lived, but underlying infrastructure such as domains, IP addresses, and certificates exhibits recurring patterns. We propose a graph-based detection framework that models a heterogeneous network comprising domains, IP addresses, TLS certificates, and registrars. Node embeddings are learned using a relational graph convolutional network (R-GCN) trained on 3.1 million domains, of which 210,000 are labeled as phishing-related. Structural features such as shared-IP communities, certificate reuse, and registrar clusters are incorporated into the model. The […]

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Around 1637, Pierre de Fermat famously wrote in the margin of a book that he had a proof for the equation $a^n + b^n = c^n$ having no positive integer solutions for exponents $n$ greater than 2. This statement, now known as Fermat’s Last Theorem, remained unproven for centuries, despite the efforts of countless mathematicians. Andrew Wiles’ work in 1994 finally provided a rigorous proof of Fermat’s Last Theorem. However, Wiles’ proof relied on advanced mathematical techniques that […]

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This work addresses the fundamental problem of unbounded metric movement costs in bandit online convex optimization, by considering high-dimensional dynamic quadratic hitting costs and $ell_2$-norm switching costs in a noisy bandit feedback model. For a general class of stochastic environments, we provide the first algorithm SCaLE that provably achieves a distribution-agnostic sub-linear dynamic regret, without the knowledge of hitting cost structure. En-route, we present a novel spectral regret analysis that separately quantifies eigenvalue-error driven regret and eigenbasis-perturbation driven […]

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