arXiv:2602.09174v1 Announce Type: new Abstract: Processing large-scale graph datasets is computationally intensive and time-consuming. Processor-centric CPU and GPU architectures, commonly used for graph applications, often face bottlenecks caused by extensive data movement between the processor and memory units due to low data reuse. As a result, these applications are often memory-bound, limiting both performance and energy efficiency due to excessive data transfers. Processing-In-Memory (PIM) offers a promising approach to mitigate data movement bottlenecks by integrating computation directly within […]

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In the Symfony ecosystem, Doctrine is the de facto standard for database interaction. However, developers often hit a crossroads: should I use Doctrine’s object-oriented Query Language (DQL) or drop down to raw, Native SQL This article explores both approaches using Symfony 7.4 and PHP 8.4+. We will build real-world examples to demonstrate performance, maintainability and developer experience. Prerequisites & Environment To follow this guide, you need a standard Symfony 7.4 environment. We will use the official ORM pack. Installation: […]

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Hi everyone, Ross here—I’m an Investigative Data Journalist at The Markup. We publish a lot of words telling you what AI can (and often shouldn’t) do, but how can public policy keep AI in check? Governments are ramping up their role in keeping AI algorithms accountable and limiting their harms. We covered President Joe Biden’s AI order back in October; last month, a new executive order required all federal agencies to designate a Chief AI Officer, and by […]

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Grokking, the abrupt transition from memorization to generalisation after extended training, suggests the presence of competing solution basins with distinct statistical properties. We study this phenomenon through the lens of Singular Learning Theory (SLT), a Bayesian framework that characterizes the geometry of the loss landscape via the local learning coefficient (LLC), a measure of the local degeneracy of the loss surface. SLT links lower-LLC basins to higher posterior mass concentration and lower expected generalisation error. Leveraging this theory, […]

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arXiv:2601.18845v1 Announce Type: new Abstract: Deep learning has revolutionized numerous tasks within the computer vision field, including image classification, image segmentation, and object detection. However, the increasing deployment of deep learning models has exposed them to various adversarial attacks, including backdoor attacks. This paper presents a novel dynamic mask-based backdoor attack method, specifically designed for object detection models. We exploit a dataset poisoning technique to embed a malicious trigger, rendering any models trained on this compromised dataset vulnerable […]

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Anthropic’s AI chatbot Claude experienced a widespread outage on Monday, March 2, 2026. Over on Downdetector, users reported a spike in error rates yesterday morning. Many users complained about seeing HTTP 500 and 529 errors, login loops, slow responses, and complete denial of service when trying to interact with Claude’s web and mobile app. However, Anthropic restore the issue later yesterday. Claude AI faces outage two days in a row However, it seems the issue related to Claude […]

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arXiv:2602.22350v1 Announce Type: new Abstract: We introduce the concept of engineered simultaneity: a system design that (1) requires comparing events at spacelike-separated locations, (2) implements this comparison via an implicit simultaneity convention, and (3) represents the result as objective rather than conventional. The United States National Best Bid and Offer (NBBO), mandated by SEC Regulation NMS Rule 611, is shown to be an instance of engineered simultaneity. We prove that the NBBO is frame-dependent: its value depends on […]

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arXiv:2603.13674v1 Announce Type: cross Abstract: Most machine learning methods assume fixed probability distributions, limiting their applicability in nonstationary real-world scenarios. While continual learning methods address this issue, current approaches often rely on black-box models or require extensive user intervention for interpretability. We propose SyMPLER (Systems Modeling through Piecewise Linear Evolving Regression), an explainable model for time series forecasting in nonstationary environments based on dynamic piecewise-linear approximations. Unlike other locally linear models, SyMPLER uses generalization bounds from Statistical Learning […]

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Artificial intelligence (AI) is moving increasingly beyond prediction to support decisions in complex, uncertain, and dynamic environments. This shift creates a natural intersection with operations research and management sciences (OR/MS), which have long offered conceptual and methodological foundations for sequential decision-making under uncertainty. At the same time, recent advances in deep learning, including feedforward neural networks, LSTMs, transformers, and deep reinforcement learning, have expanded the scope of data-driven modeling and opened new possibilities for large-scale decision systems. This […]

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