arXiv:2601.14872v2 Announce Type: replace-cross Abstract: We study a linear observation model with an unknown permutation called textit{permuted/shuffled linear regression}, where responses and covariates are mismatched and the permutation forms a discrete, factorial-size parameter. The permutation is a key component of the data-generating process, yet its statistical investigation remains challenging due to its discrete nature. We develop a general statistical inference framework on the permutation and regression coefficients. First, we introduce a localization step that reduces the permutation space […]

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arXiv:2601.05330v1 Announce Type: new Abstract: Predicting enzyme-substrate interactions has long been a fundamental problem in biochemistry and metabolic engineering. While existing methods could leverage databases of expert-curated enzyme-substrate pairs for models to learn from known pair interactions, the databases are often sparse, i.e., there are only limited and incomplete examples of such pairs, and also labor-intensive to maintain. This lack of sufficient training data significantly hinders the ability of traditional enzyme prediction models to generalize to unseen interactions. […]

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As has been said by many commentators, rare earths are not particularly rare.  Via source, here is an estimate of their abundance in the Earth’s surface: Note by the way the Y-axis is logarithmic so small changes in vertical position can mean a factor of 10 or more difference in concentration.  But the rare earths are not unreasonably far off fairly common industrial metals like lead, nickel, copper, and molybdenum and well more common than gold, silver, and […]

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arXiv:2410.19815v2 Announce Type: replace-cross Abstract: Deep learning is advancing EEG processing for automated epileptic seizure detection and onset zone localization, yet its performance relies heavily on high-quality annotated training data. However, scalp EEG is susceptible to high noise levels, which in turn leads to imprecise annotations of the seizure timing and characteristics. This “label noise” presents a significant challenge in model training and generalization. In this paper, we introduce Bayesian UncertaiNty-aware Deep Learning (BUNDL), a novel algorithm that […]

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Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an indispensable paradigm for enhancing reasoning in Large Language Models (LLMs). However, standard policy optimization methods, such as Group Relative Policy Optimization (GRPO), often converge to low-entropy policies, leading to severe mode collapse and limited output diversity. We analyze this issue from the perspective of sampling probability dynamics, identifying that the standard objective disproportionately reinforces the highest-likelihood paths, thereby suppressing valid alternative reasoning chains. To address this, we propose […]

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Vehicle-sharing platforms are constantly gaining ground in smart cities around the world, reducing the number of traditional fuel-based vehicles on the roads in busy areas and thus contributing to the development of a sustainable environment. On the other hand, the availability of a plethora of shared vehicles of different types across a city increases the need for their seamless combination, so that they are considered part of a unified transportation system within a smart city rather than independent […]

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In software engineering, code without tests rarely makes it to production. In data systems, the bar is often much lower: “as long as the table isn’t empty, it’s probably fine.” But this assumption is expensive. Data bugs rarely crash services. Instead, they quietly produce broken dashboards, misleading metrics, incorrect business decisions, and long debugging sessions after trust is already lost. The problem isn’t that teams don’t care about quality, it’s that data failures rarely look like failures. They look like slightly […]

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Instead of catching you off-guard with a jump scare this Halloween season, EFF is here to catch you up on the latest digital rights news with our EFFector newsletter! In this issue, we’re helping you take control of your online privacy with Opt Out October; explaining the UK’s attack on encryption and why it’s bad for all users; and covering shocking new details about an abortion surveillance case in Texas. Prefer to listen in? Check out our audio […]

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