arXiv:2601.08035v1 Announce Type: new Abstract: Information access systems such as search engines and generative AI are central to how people seek, evaluate, and interpret information. Yet most systems are designed to optimise retrieval rather than to help users develop better search strategies or critical awareness. This paper introduces a pedagogical perspective on information access, conceptualising search and conversational systems as instructive interfaces that can teach, guide, and scaffold users’ learning. We draw on seven didactic frameworks from education […]

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Hi everyone! I hope you’re having a great sunday. Me and my collegue decided this week to rewrite an api made with Laravel Lumen using ASP.NET Core. While doing so, I was wondering wheter some kind of tool existed to help generate the documentation of the API (since we all know writing a documentation is the least fun thing in programming) and I came across Swagger.

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Hi! I am trying to explore and learn some RL algorithms and implement them in Gym’s Car Racing environment ( https://gymnasium.farama.org/environments/box2d/car_racing/ ). Instead of using the image on the screen for my state, I measure the distance from the car to the edge of the track at 5 points (90º left, 45º left, forwards, 45º right, 90º right), along with the car’s current speed, and pass that as my state. I also give a fixed -1 reward if […]

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Federated learning (FL) and federated distillation (FD) are distributed learning paradigms that train UE models with enhanced privacy, each offering different trade-offs between noise robustness and learning speed. To mitigate their respective weaknesses, we propose a hybrid federated learning (HFL) framework in which each user equipment (UE) transmits either gradients or logits, and the base station (BS) selects the per-round weights of FL and FD updates. We derive convergence of HFL framework and introduce two methods to exploit […]

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arXiv:2601.00161v1 Announce Type: new Abstract: We present an NPT extension of Ewald summation with prolates (ESP), a spectrally accurate and scalable particle-mesh method for molecular dynamics simulations of periodic, charged systems. Building on the recently introduced ESP framework, this work focuses on rigorous and thermodynamically consistent pressure/stress evaluation in the isothermal–isobaric ensemble. ESP employs prolate spheroidal wave functions as both splitting and spreading kernels, reducing the Fourier grid size needed to reach a prescribed pressure accuracy compared with […]

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To address the practical challenges of diverse anomaly patterns, strongly coupled dependencies, and high labeling costs in large-scale complex infrastructures, this paper presents an unsupervised anomaly detection method that integrates graph neural networks with Transformer models. The approach learns normal system behavior and identifies deviations without relying on anomaly labels. Infrastructure components are abstracted as nodes in a dependency graph, where nodes are characterized by multiple source observability signals. A graph encoder aggregates neighborhood information to produce structure-enhanced […]

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arXiv:2405.19206v2 Announce Type: replace Abstract: Deep neural networks (DNNs) on Riemannian manifolds have garnered increasing interest in various applied areas. For instance, DNNs on spherical and hyperbolic manifolds have been designed to solve a wide range of computer vision and nature language processing tasks. One of the key factors that contribute to the success of these networks is that spherical and hyperbolic manifolds have the rich algebraic structures of gyrogroups and gyrovector spaces. This enables principled and effective […]

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