February 2026

Lossless compression is essential for efficient data storage and transmission. Although learning-based lossless compressors achieve strong results, most of them are designed for a single modality, leading to redundant compressor deployments in multi-modal settings. Designing a unified multi-modal compressor is critical yet challenging, as different data types vary largely in format, dimension, and statistics. Multi-modal large language models offer a promising resolution but remain too complex for practical use. Thus, we propose textbf{OmniZip}, textbf{a unified and lightweight lossless […]

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Objective: The objective of this study is to develop a machine learning (ML)-based framework for early risk stratification of clinical trials (CTs) according to their likelihood of exhibiting a high rate of dosing errors, using information available prior to trial initiation. Materials and Methods: We constructed a dataset from ClinicalTrials.gov comprising 42,112 CTs. Structured, semi-structured trial data, and unstructured protocol-related free-text data were extracted. CTs were assigned binary labels indicating elevated dosing error rate, derived from adverse event […]

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Federated learning (FL) facilitates the secure utilization of decentralized images, advancing applications in medical image recognition and autonomous driving. However, conventional FL faces two critical challenges in real-world deployment: ineffective knowledge fusion caused by model updates biased toward majority-class features, and prohibitive communication overhead due to frequent transmissions of high-dimensional model parameters. Inspired by the human brain’s efficiency in knowledge integration, we propose a novel Generative Federated Prototype Learning (GFPL) framework to address these issues. Within this framework, […]

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Differentially private federated learning faces a fundamental tension: privacy protection mechanisms that safeguard client data simultaneously create quantifiable privacy costs that discourage participation, undermining the collaborative training process. Existing incentive mechanisms rely on unbiased client selection, forcing servers to compensate even the most privacy-sensitive clients ("privacy stragglers"), leading to systemic inefficiency and suboptimal resource allocation. We introduce JSAM (Joint client Selection and privacy compensAtion Mechanism), a Bayesian-optimal framework that simultaneously optimizes client selection probabilities and privacy compensation to […]

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Fast matrix multiplication can be described as searching for low-rank decompositions of the matrix–multiplication tensor. We design a neural architecture, textsc{StrassenNet}, which reproduces the Strassen algorithm for $2times 2$ multiplication. Across many independent runs the network always converges to a rank-$7$ tensor, thus numerically recovering Strassen’s optimal algorithm. We then train the same architecture on $3times 3$ multiplication with rank $rin{19,dots,23}$. Our experiments reveal a clear numerical threshold: models with $r=23$ attain significantly lower validation error than those […]

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By Doug Needham, DataOps.live Technical debt is a challenge for any digital team. Suggestions to avoid it get overridden or ignored, and it’s a rare treat to get to fix the things we know need fixing. If only our PMs and data owners understood what we know—how terrifying technical debt really is—maybe they’d push back less when we fight to build things the right way.  I think I’ve found the way to tell them. Fairy tales, parables, and […]

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Hi everyone, We are currently expanding our team of experts to help train and evaluate the next generation of Large Language Models (LLMs). We are looking for high-quality contributors across various specialized domains and languages. Available Roles Include: Multimodal Eval & SFT/CoT: Creating high-quality reasoning and demonstration data. Expert RLHF: Ranking and grading model outputs based on preference. Red Teaming: Testing models for safety, bias, and edge cases. Specialized Annotation: Domain-specific labeling. We are seeking expertise in: STEM: […]

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