arXiv:2601.12156v1 Announce Type: new Abstract: The deployment of Artificial Intelligence on edge devices (TinyML) is often constrained by the high power consumption and latency associated with traditional Artificial Neural Networks (ANNs) and their reliance on intensive Matrix-Multiply (MAC) operations. Neuromorphic computing offers a compelling alternative by mimicking biological efficiency through event-driven processing. This paper presents the design and implementation of a cycle-accurate, hardware-oriented Spiking Neural Network (SNN) core implemented in SystemVerilog. Unlike conventional accelerators, this design utilizes a […]

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arXiv:2604.08602v1 Announce Type: new Abstract: Background: Server-based screening tools impose subscription costs, while open-source alternatives require coding skills. Objectives: We developed a browser extension that provides no-code, serverless artificial intelligence (AI)-assisted title and abstract screening and examined its functionality. Methods: TiAb Review Plugin is an open-source Chrome browser extension (available at https://chromewebstore.google.com/detail/tiab-review-plugin/alejlnlfflogpnabpbplmnojgoeeabij). It uses Google Sheets as a shared database, requiring no dedicated server and enabling multi-reviewer collaboration. Users supply their own Gemini API key, stored locally and […]

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arXiv:2602.00977v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in domains where errors carry high social, scientific, or safety costs. Yet standard confidence estimators, such as token likelihood, semantic similarity and multi-sample consistency, remain brittle under distribution shift, domain-specialised text, and compute limits. In this work, we present Structural Confidence, a single-pass, model-agnostic framework that enhances output correctness prediction based on multi-scale structural signals derived from a model’s final-layer hidden-state trajectory. By combining spectral, local-variation, […]

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Radial basis function in machine learning serves as a powerful tool for handling non-linear data patterns through neural networks that use distance-based activations. This approach helps models approximate complex functions and make accurate predictions in tasks like classification and regression. This blog explains what radial basis function means, its formula, network structure, training steps, a solved example, and applications of radial basis function network. You will gain a clear understanding of how these networks work, their strengths, and […]

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In-context learning (ICL) performance depends critically on which demonstrations are placed in the prompt, yet most existing selectors prioritize heuristic notions of relevance or diversity and provide limited insight into the coverage of a demonstration set. We propose Unseen Coverage Selection (UKS), a training-free, subset-level coverage prior motivated by the principle that a good demonstration set should expose the model to latent cluster unrevealed by the currently selected subset. UCS operationalizes this idea by (1) inducing discrete latent […]

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Meta’s Scam Ads Are Finally Being Challenged — And It’s Long Overdue After years of warnings from consumer advocates, regulators and defrauded users, Meta Platforms is finally being dragged into court over what critics say has been an open-secret business model: knowingly allowing scam advertisements to run across Facebook and Instagram in the name of profit. The U.S. Virgin Islands has filed a lawsuit against the social media giant, alleging Meta deliberately profited from scam advertising while publicly […]

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arXiv:2603.20273v1 Announce Type: new Abstract: Prostate cancer is one of the most frequently diagnosed malignancies in men worldwide. However, precise prediction of biochemical recurrence (BCR) after radical prostatectomy remains challenging due to the multifocality of tumors distributed throughout the prostate gland. In this paper, we propose a novel AI framework that simultaneously processes a series of multi-section pathology slides to capture the comprehensive tumor landscape across the entire prostate gland. To develop this predictive AI model, we curated […]

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The ask changes you more than the answer does. Vanessa Bohns, a social psychologist at Cornell, has spent fifteen years asking a simple question: when you ask someone for something, how often do they say yes? She’s tested this across more than 14,000 interactions. Strangers asking strangers for favors, for help, for things that feel like impositions. Before each request, she has the person predict how many people will say yes. Then she sends them out to ask. […]

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