arXiv:2603.19792v1 Announce Type: cross Abstract: Efficient and scalable non-parametric or semi-parametric regression analysis and density estimation are of crucial importance to the fields of statistics and machine learning. However, available methods are limited in their ability to handle large-scale data. We address this issue by developing a novel coreset construction for multivariate conditional transformation models (MCTMs) to enhance their scalability and training efficiency. To the best of our knowledge, these are the first coresets for semi-parametric distributional models. […]

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arXiv:2601.03265v1 Announce Type: new Abstract: This paper introduces Jailbreak-Zero, a novel red teaming methodology that shifts the paradigm of Large Language Model (LLM) safety evaluation from a constrained example-based approach to a more expansive and effective policy-based framework. By leveraging an attack LLM to generate a high volume of diverse adversarial prompts and then fine-tuning this attack model with a preference dataset, Jailbreak-Zero achieves Pareto optimality across the crucial objectives of policy coverage, attack strategy diversity, and prompt […]

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Introduction The hypermedia renaissance is in full swing. After years of SPA dominance, developers are rediscovering the power of server-driven architectures that leverage HTML as the engine of application state. The framework leading this charge, HTMX, focuses exclusively of hypermedia. An alternative, Lightview, doesn’t lock you into hypermedia-only patterns. Unlike HTMX’s focused hypermedia approach, Lightview is a multi-paradigm framework that supports and hypermedia patterns, functional programming with template tag functions like BauJS [https://github.com/grucloud/bau], and JSON representation (like JurisJS […]

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arXiv:2505.20754v2 Announce Type: replace Abstract: Approximation of a target probability distribution using a finite set of points is a problem of fundamental importance in numerical integration. Several authors have proposed to select points by minimising a maximum mean discrepancy (MMD), but the non-convexity of this objective typically precludes global minimisation. Instead, we consider the concept of emph{stationary points of the MMD} which, in contrast to points globally minimising the MMD, can be accurately computed. Our main contributions are […]

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Blockchain technology is no longer just the backbone of cryptocurrencies; it has become a powerful tool for real businesses. In a world still reeling from supply-chain disruptions and data breaches, blockchain promises to reinvent trust in multi-party systems. As Deloitte notes, “using blockchain can improve both supply chain transparency and traceability” while cutting costs. Kevin Werbach of Wharton puts it succinctly: “Blockchain is not the end of trust. Blockchain is a new structure of trust, a ‘new architecture […]

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During my childhood I was highly passionate about biology, and especially genetics. I liked to solve inheritance genetics problems and understand mechanisms of molecular biology. Later I started to learn programming and with time I became a Data Scientist, partially forgetting about my childhood passion. During my Master I re-found my passion in biology, understanding that I can combine my passion to biology and my experience in Data Science by pursuing the Bioinformatics field. The National Human Genome […]

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arXiv:2604.04969v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucinations in Multimodal Large Language Models (MLLMs), yet existing systems struggle with complex cross-modal reasoning. Flat vector retrieval often ignores structural dependencies, while current graph-based methods rely on costly “translation-to-text” pipelines that discard fine-grained visual information. To address these limitations, we propose textbf{MG$^2$-RAG}, a lightweight textbf{M}ulti-textbf{G}ranularity textbf{G}raph textbf{RAG} framework that jointly improves graph construction, modality fusion, and cross-modal retrieval. MG$^2$-RAG constructs a hierarchical multimodal knowledge graph by combining lightweight […]

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arXiv:2602.00909v1 Announce Type: new Abstract: Recent advances in deep learning have produced highly accurate but increasingly large and complex DNNs, making traditional fault-injection techniques impractical. Accurate fault analysis requires RTL-accurate hardware models. However, this significantly slows evaluation compared with software-only approaches, particularly when combined with expensive HDL instrumentation. In this work, we show that such high-overhead methods are unnecessary for systolic array (SA) architectures and propose ENFOR-SA, an end-to-end framework for DNN transient fault analysis on SAs. Our […]

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Neural operators have emerged as fast surrogate models for physics simulations, yet they remain acutely vulnerable to adversarial perturbations, a critical liability for safety-critical digital twin deployments. We present a synergistic defense that combines active learning-based data generation with an input denoising architecture. The active learning component adaptively probes model weaknesses using differential evolution attacks, then generates targeted training data at discovered vulnerability locations while an adaptive smooth-ratio safeguard preserves baseline accuracy. The input denoising component augments the […]

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