arXiv:2601.03410v1 Announce Type: new Abstract: Molecular subtyping of PDAC into basal-like and classical has established prognostic and predictive value. However, its use in clinical practice is limited by cost, turnaround time, and tissue requirements, thereby restricting its application in the management of PDAC. We introduce PanSubNet, an interpretable deep learning framework that predicts therapy-relevant molecular subtypes directly from standard H&E-stained WSIs. PanSubNet was developed using data from 1,055 patients across two multi-institutional cohorts (PANCAN, n=846; TCGA, n=209) with […]

Ver mais

We establish the estimation of solutions of two classes of weakly singular Wendroff-type integral inequalities of multiple variables with multiple nonlinear terms, apply them to fractional partial differential equations, and give the proof procedures for uniqueness, boundedness and continuous dependence.

Ver mais

arXiv:2601.04204v1 Announce Type: new Abstract: The scalability of high-quality online education is hindered by the high costs and slow cycles of labor-intensive manual content creation. Despite advancements in video generation, current approaches often fail to ensure pedagogical structure and precise control due to their pixel-level, black-box nature. In this paper, we propose Generative Teaching, a novel paradigm that transitions educators from manual creators to high-level directors, allowing them to focus on pedagogical intent while autonomous agents handle the […]

Ver mais

How zero-cost, authoritative-sounding text is breaking institutional accountability On January 16, the chief constable of West Midlands Police in the United Kingdom stepped down after admitting that an AI tool had been used to help draft an official safety assessment that cited a football match that never took place. The report was used to justify banning supporters of Maccabi Tel Aviv from attending a Europa League match against Aston Villa in November 2025. Embedded in the assessment was […]

Ver mais

arXiv:2601.00254v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) presents new opportunities for automated software vulnerability detection, a crucial task in securing modern codebases. This paper presents a comparative study on the effectiveness of LLM-based techniques for detecting software vulnerabilities. The study evaluates three approaches, Retrieval-Augmented Generation (RAG), Supervised Fine-Tuning (SFT), and a Dual-Agent LLM framework, against a baseline LLM model. A curated dataset was compiled from Big-Vul and real-world code repositories from GitHub, […]

Ver mais

Every time you fire off a quick prompt or query to your AI assistant, be it ChatGPT, Gemini, or Claude, there’s a lot more going on behind the scenes than we realize. All those responses are powered by massive data centers that reportedly gulp millions of gallons of water to keep servers cool. Modern AI infrastructure is so thirsty that a single 100-megawatt data center can use up to 2 million liters of water per day for cooling […]

Ver mais

arXiv:2306.01468v3 Announce Type: replace-cross Abstract: Measurement error occurs when a covariate influencing a response variable is corrupted by noise. This can lead to misleading inference outcomes, particularly in problems where accurately estimating the relationship between covariates and response variables is crucial, such as causal effect estimation. Existing methods for dealing with measurement error often rely on strong assumptions such as knowledge of the error distribution or its variance and availability of replicated measurements of the covariates. We propose […]

Ver mais

arXiv:2601.10765v1 Announce Type: new Abstract: Neural networks are commonly trained in highly overparameterized regimes, yet empirical evidence consistently shows that many parameters become redundant during learning. Most existing pruning approaches impose sparsity through explicit intervention, such as importance-based thresholding or regularization penalties, implicitly treating pruning as a centralized decision applied to a trained model. This assumption is misaligned with the decentralized, stochastic, and path-dependent character of gradient-based training. We propose an evolutionary perspective on pruning: parameter groups (neurons, […]

Ver mais