Across engineering and scientific domains, traditional deep learning (TDL) models perform well when training and test data share the same distribution. However, the dynamic nature of real-world data, broadly termed textit{data shift}, renders TDL models prone to rapid performance degradation, requiring costly relabeling and inefficient retraining. Meta-learning, which enables models to adapt quickly to new data with few examples, offers a promising alternative for mitigating these challenges. Here, we systematically compare TDL with fine-tuning and optimization-based meta-learning algorithms […]

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arXiv:2602.12389v1 Announce Type: new Abstract: Temporal knowledge graph (TKG) forecasting requires predicting future facts by jointly modeling structural dependencies within each snapshot and temporal evolution across snapshots. However, most existing methods are stateless: they recompute entity representations at each timestamp from a limited query window, leading to episodic amnesia and rapid decay of long-term dependencies. To address this limitation, we propose Entity State Tuning (EST), an encoder-agnostic framework that endows TKG forecasters with persistent and continuously evolving entity […]

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In an age of constant connectivity, millennials are quietly redefining what intimacy looks like. Traditional relationship models are shifting as digital communication, social platforms, and evolving cultural norms reshape how people form emotional bonds. Dating is no longer confined to physical proximity or conventional roles. Instead, many millennials are building more fluid networks of connection—blending friendships, romantic relationships, and digital companionship into new forms of emotional support. A new survey from Dating.com, one of Social Discovery Group`s products and a […]

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arXiv:2601.09856v1 Announce Type: new Abstract: Motivated by the vision of integrating mobile robots closer to humans in warehouses, hospitals, manufacturing plants, and the home, we focus on robot navigation in dynamic and spatially constrained environments. Ensuring human safety, comfort, and efficiency in such settings requires that robots are endowed with a model of how humans move around them. Human motion prediction around robots is especially challenging due to the stochasticity of human behavior, differences in user preferences, and […]

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Go developers regularly deal with warnings from the built-in static analyzer. But what if you need more features or want to find something specific in your project? Go provides powerful tools for parsing and analyzing code. In this article, we’ll talk about them and even create our first diagnostic rule. Intro We’ve got experience with Go tools for parsing code when we’ve started developing our own static analyzer in the Go language. This article will be useful for […]

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arXiv:2603.05646v1 Announce Type: new Abstract: Large-Language Models (LLMs) are changing the way learners acquire knowledge outside the classroom setting. Previous studies have shown that LLMs seem effective in generating to short and simple questions in introductory CS courses using high-resource programming languages such as Java or Python. In this paper, we evaluate the effectiveness of LLMs in the context of a low-resource programming language — OCaml, in an educational setting. In particular, we built three benchmarks to comprehensively […]

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If a number has a finite but nonzero fractional part, so do all its powers. I recently ran across a proof in [1] that is shorter than I expected. Theorem: Suppose r is a real number that is not an integer, and the decimal part of r terminates. Then rk is not an integer for any positive integer k. Proof: The number r can be written as a reduced fraction a / 10m for some positive m. If s = rk were an […]

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arXiv:2602.22251v1 Announce Type: new Abstract: General-purpose 3D chemical modeling encompasses molecules and materials, requiring both generative and predictive capabilities. However, most existing AI approaches are optimized for a single domain (molecules or materials) and a single task (generation or prediction), which limits representation sharing and transfer. We introduce Zatom-1, the first foundation model that unifies generative and predictive learning of 3D molecules and materials. Zatom-1 is a Transformer trained with a multimodal flow matching objective that jointly models […]

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arXiv:2602.18569v1 Announce Type: new Abstract: Many people could benefit from exoskeleton assistance during gait, for either medical or nonmedical purposes. But exoskeletons bring added mass and structure, which in turn require compensating for. In this work, we present a lightweight, low-complexity, soft bilateral ankle exoskeleton for plantarflexion assistance, with a shoe attachment design that can be mounted on top of any pair of shoes. Experimental tests show no significant difference in lower limb kinematics and kinetics when wearing […]

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arXiv:2603.05614v1 Announce Type: new Abstract: Real-time AI services increasingly operate across the device-edge-cloud continuum, where autonomous AI agents generate latency-sensitive workloads, orchestrate multi-stage processing pipelines, and compete for shared resources under policy and governance constraints. This article shows that the structure of service-dependency graphs, modelled as DAGs whose nodes represent compute stages and whose edges encode execution ordering, is a primary determinant of whether decentralised, price-based resource allocation can work reliably at scale. When dependency graphs are hierarchical […]

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