arXiv:2603.25940v1 Announce Type: new Abstract: Effective models for slender structures derived from well-known plate (or shell) theories are justified within the limit of a small thickness, and may therefore prove limited for intermediate slenderness. On the other hand, direct 3D simulation of such structures is sub-optimal because it does not take advantage of the presence of small dimensions in some directions and is sometimes too costly and ill-conditioned. In this context, the Proper Generalized Decomposition (PGD) method, a […]

Ver mais

arXiv:2604.13050v1 Announce Type: new Abstract: Urban areas are intricate systems shaped by socioeconomic, environmental, and infrastructural factors, with land use patterns serving as aspects of urban morphology. This paper proposes a novel methodology leveraging frequent item set mining and unsupervised learning techniques to identify similar cities based on co-occurring land use patterns. The Copernicus program’s Urban Atlas data are used as source data. The methodology involves data preprocessing, pattern mining using the negFIN algorithm, postprocessing, and knowledge extraction […]

Ver mais

arXiv:2601.21696v1 Announce Type: cross Abstract: Advances in data collection are producing growing volumes of temporal count observations, making adapted modeling increasingly necessary. In this work, we introduce a generative framework for independent component analysis of temporal count data, combining regime-adaptive dynamics with Poisson log-normal emissions. The model identifies disentangled components with regime-dependent contributions, enabling representation learning and perturbations analysis. Notably, we establish the identifiability of the model, supporting principled interpretation. To learn the parameters, we propose an efficient […]

Ver mais

In a simple gridworld example, everything stays the same except the discount factor γ. Reward for boundary/forbidden: -1 Reward for target: +1 Only γ changes Case 1: γ = 0.9 The agent is long-term oriented. Future rewards are discounted slowly: γ⁵ ≈ 0.59 So even if the agent takes a -1 penalty now (entering a forbidden area), the future reward is still valuable enough to justify it. Result: The optimal policy is willing to take short-term losses to […]

Ver mais

arXiv:2604.11253v1 Announce Type: new Abstract: Feature importance methods using unrestricted permutations are flawed due to extrapolation errors; such errors appear in all non-trivial variable importance approaches. We propose three new approaches: conditional model reliance and Knockoffs with Gaussian transformation, and restricted ALE plot designs. Theoretical and numerical results show our strategies reduce/eliminate extrapolation.

Ver mais

Machine Learning often involves various imprecise labels, leading to diverse weakly supervised settings. While recent methods aim for universal handling, they usually suffer from complex manual pre-work, ignore the relationships between associated labels, or are unable to batch process due to computational design flaws, resulting in long running times. To address these limitations, we propose a novel general framework that efficiently infers latent true label distributions across various weak supervisions. Our key idea is to express the label […]

Ver mais

arXiv:2510.21934v3 Announce Type: replace-cross Abstract: Risk assessment tools in healthcare commonly employ point-based scoring systems that map patients to ordinal risk categories via thresholds. While electronic health record (EHR) data presents opportunities for data-driven optimization of these tools, two fundamental challenges impede standard supervised learning: (1) labels are often available only for extreme risk categories due to intervention-censored outcomes, and (2) misclassification cost is asymmetric and increases with ordinal distance. We propose a mixed-integer programming (MIP) framework that […]

Ver mais

arXiv:2601.02535v1 Announce Type: new Abstract: Selecting a single high-quality output from multiple stochastic generations remains a fundamental challenge for large language models (LLMs), particularly in open-ended tasks where no canonical answer exists. While Best-of-N and self-consistency methods show that aggregating multiple generations can improve performance, existing approaches typically rely on external evaluators, reward models, or exact string-match voting, limiting their applicability and efficiency. We propose Mode Extraction (ModeX), an evaluator-free Best-of-N selection framework that generalizes majority voting to […]

Ver mais

The crypto industry is now worth trillions, but Rome wasn’t built in a day. Over the years, many developers, enthusiasts, and users of all kinds and places have put their own blocks to raise this bright skyscraper. We’ve crossed some huge milestones, sometimes through careful planning, sometimes through happy accidents, and often through bold experiments. Of course, the first one is Bitcoin itself: the first-ever decentralized cryptocurrency, born on January 3, 2009. This modest snowball would create an […]

Ver mais

I have been doing this for years, and the hardest parts of the job were never about typing out code. I have always struggled most with understanding systems, debugging things that made no sense, designing architectures that wouldn’t collapse under heavy load, and making decisions that would save months of pain later. None of these problems can be solved LLMs. They can suggest code, help with boilerplate, sometimes can act as a sounding board. But they don’t understand […]

Ver mais