arXiv:2602.18502v1 Announce Type: new Abstract: Although deep learning models in medical imaging often achieve excellent classification performance, they can rely on shortcut learning, exploiting spurious correlations or confounding factors that are not causally related to the target task. This poses risks in clinical settings, where models must generalize across institutions, populations, and acquisition conditions. Feature disentanglement is a promising approach to mitigate shortcut learning by separating task-relevant information from confounder-related features in latent representations. In this study, we […]

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arXiv:2603.24392v1 Announce Type: new Abstract: Privacy and algorithmic fairness have become two central issues in modern machine learning. Although each has separately emerged as a rapidly growing research area, their joint effect remains comparatively under-explored. In this paper, we systematically study the joint impact of differential privacy and fairness on classification in a federated setting, where data are distributed across multiple servers. Targeting demographic disparity constrained classification under federated differential privacy, we propose a two-step algorithm, namely FDP-Fair. […]

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arXiv:2602.07144v1 Announce Type: cross Abstract: Bayesian optimization (BO) is a popular technique for sample-efficient optimization of black-box functions. In many applications, the parameters being tuned come with a carefully engineered default configuration, and practitioners only want to deviate from this default when necessary. Standard BO, however, does not aim to minimize deviation from the default and, in practice, often pushes weakly relevant parameters to the boundary of the search space. This makes it difficult to distinguish between important […]

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For years, emerging projects were competing for distribution and attention with 2 types of brands: large mature businesses with unlimited marketing budgets (think, Binance, Coinbase, or Ethereum) and new projects with a growth-hacking mindset pulling guerrilla stunts (e.g., Grass, Cluely). But, in 2026, the most powerful distribution channel won’t belong to any of them – it will be owned by governments. Here’s why. The crypto world used to run on one simple emotional loop: greed and fear. Greed […]

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arXiv:2602.02590v1 Announce Type: new Abstract: Visual navigation is fundamental to autonomous systems, yet generating reliable trajectories in cluttered and uncertain environments remains a core challenge. Recent generative models promise end-to-end synthesis, but their reliance on unstructured noise priors often yields unsafe, inefficient, or unimodal plans that cannot meet real-time requirements. We propose StepNav, a novel framework that bridges this gap by introducing structured, multimodal trajectory priors derived from variational principles. StepNav first learns a geometry-aware success probability field […]

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arXiv:2601.16348v1 Announce Type: new Abstract: Art technological investigations of historical panel paintings rely on acquiring multi-modal image data, including visual light photography, infrared reflectography, ultraviolet fluorescence photography, x-radiography, and macro photography. For a comprehensive analysis, the multi-modal images require pixel-wise alignment, which is still often performed manually. Multi-modal image registration can reduce this laborious manual work, is substantially faster, and enables higher precision. Due to varying image resolutions, huge image sizes, non-rigid distortions, and modality-dependent image content, registration […]

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