March 2026

arXiv:2603.02411v1 Announce Type: new Abstract: Dataset Distillation (DD) compresses large datasets into compact synthetic ones that maintain training performance. However, current methods mainly target sample reduction, with limited consideration of data precision and its impact on efficiency. We propose Quantization-aware Dataset Distillation (QuADD), a unified framework that jointly optimizes dataset compactness and precision under fixed bit budgets. QuADD integrates a differentiable quantization module within the distillation loop, enabling end-to-end co-optimization of synthetic samples and quantization parameters. Guided by […]

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arXiv:2603.02406v1 Announce Type: new Abstract: Generative models have recently advanced $textit{de novo}$ protein design by learning the statistical regularities of natural structures. However, current approaches face three key limitations: (1) Existing methods cannot jointly learn protein geometry and design tasks, where pretraining can be a solution; (2) Current pretraining methods mostly rely on local, non-rigid atomic representations for property prediction downstream tasks, limiting global geometric understanding for protein generation tasks; and (3) Existing approaches have yet to effectively […]

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arXiv:2603.02405v1 Announce Type: new Abstract: We present a one-fits-all programmatic approach to reason about a plethora of objectives on probabilistic programs. The first ingredient is to add a reward-statement to the language. We then define a program transformation applying a monotone function to the cumulative reward of the program. The key idea is that this transformation uses incremental differences in the reward. This simple, elegant approach enables to express e.g., higher moments, threshold probabilities of rewards, the expected […]

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arXiv:2603.02400v1 Announce Type: new Abstract: In this paper, we investigate the impact of the broadcast effect arising in filterless optical networks on the computational complexity of the wavelength assignment problem. We model conflicts using an appropriate interference digraph, whose proper colourings correspond to feasible wavelength assignments. Minimizing the number of required wavelengths therefore amounts to determining the chromatic number of this interference digraph. Within this framework, we first present a polynomial-time 2-approximation algorithm for minimizing the number of […]

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arXiv:2603.02398v1 Announce Type: new Abstract: An open-source C++ framework for discovering fast matrix multiplication schemes using the flip graph approach is presented. The framework supports multiple coefficient rings — binary ($mathbb{Z}_2$), modular ternary ($mathbb{Z}_3$) and integer ternary ($mathbb{Z}_T = {-1,0,1}$) — and implements both fixed-dimension and meta-dimensional search operators. Using efficient bit-level encoding of coefficient vectors and OpenMP parallelism, the tools enable large-scale exploration on commodity hardware. The study covers 680 schemes ranging from $(2 times 2 times […]

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arXiv:2603.02396v1 Announce Type: new Abstract: Platelets expire within five days. Blood banks face uncertain daily demand and must balance ordering decisions between costly wastage from overstocking and life-threatening shortages from understocking. Reinforcement learning (RL) can learn effective ordering policies for this Markov decision process (MDP), but the resulting neural policies remain black boxes, hindering trust and adoption in safety-critical domains. We apply COOL-MC, a tool that combines RL with probabilistic model checking and explainable RL, to verify and […]

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arXiv:2603.02392v1 Announce Type: new Abstract: Smartphone manufacturers continue to release new models annually, yet the pace of meaningful innovation has slowed, with most changes limited to incremental updates in design, performance, or software. This study examines whether such updates deliver tangible user benefits, as perceived by expert reviewers. Using a grounded theory approach, guided by Rogers Diffusion of Innovation (DOI) framework, the research analyses reviewer discourse from 2021 2025 across three technology commentators. The analysis identifies three interrelated […]

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arXiv:2603.02390v1 Announce Type: new Abstract: Smart factories use advanced technologies to optimize production and increase efficiency. To this end, the recognition of worker activity allows for accurate quantification of performance metrics, improving efficiency holistically while contributing to worker safety. OpenMarcie is, to the best of our knowledge, the biggest multimodal dataset designed for human action monitoring in manufacturing environments. It includes data from wearables sensing modalities and cameras distributed in the surroundings. The dataset is structured around two […]

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arXiv:2603.02386v1 Announce Type: new Abstract: Earth observation machine learning pipelines differ fundamentally from standard computer vision workflows. Imagery is typically delivered as large, georeferenced scenes, labels may be raster masks or vector geometries in distinct coordinate reference systems, and both training and evaluation often require spatially aware sampling and splitting strategies. TorchGeo is a PyTorch-based domain library that provides datasets, samplers, transforms and pre-trained models with the goal of making it easy to use geospatial data in machine […]

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arXiv:2603.02379v1 Announce Type: new Abstract: We propose a decision-theoretic framework in which a robot strategically can shape inferred human’s prosocial state during repeated interactions. Modeling the human’s prosociality as a latent state that evolves over time, the robot learns to infer and influence this state through its own actions, including helping and signaling. We formalize this as a latent-state POMDP with limited observations and learn the transition and observation dynamics using expectation maximization. The resulting belief-based policy balances […]

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