March 2026

Synchronous federated learning scales poorly due to the straggler effect. Asynchronous algorithms increase the update throughput by processing updates upon arrival, but they introduce two fundamental challenges: gradient staleness, which degrades convergence, and bias toward faster clients under heterogeneous data distributions. Although algorithms such as AsyncSGD and Generalized AsyncSGD mitigate this bias via client-side task queues, most existing analyses neglect the underlying queueing dynamics and lack closed-form characterizations of the update throughput and gradient staleness. To close this […]

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Shugert is a Shopify Select Partner agency helping 600+ e-commerce brands grow through custom development, SEO, and AI-driven analytics. With 1,200+ completed projects, flexible pricing, and a focus on conversion optimization, Shugert makes enterprise-level Shopify expertise accessible to businesses of all sizes, driving measurable growth and better performance.

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Adgager’s GAG Token powers an AI-driven market research platform that rewards verified users for high-quality feedback. By combining fraud detection, real-time analytics, and Web3 incentives on BNB Smart Chain, it delivers faster, more reliable insights for brands. With 300+ clients and active exchange listings, it bridges traditional research with tokenized engagement.

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Continual learning seeks to maintain stable adaptation under non-stationary environments, yet this problem becomes particularly challenging in object detection, where most existing methods implicitly assume relatively balanced visual conditions. In extreme-sparsity regimes, such as those observed in space-based resident space object (RSO) detection scenarios, foreground signals are overwhelmingly dominated by background observations. Under such conditions, we analytically demonstrate that background-driven gradients destabilize the feature backbone during sequential domain shifts, causing progressive representation drift. This exposes a structural limitation […]

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We introduce the Geometric Evolution Graph Convolutional Network (GEGCN), a novel framework that enhances graph representation learning by modeling geometric evolution on graphs. Specifically, GEGCN employs a Long Short-Term Memory to model the structural sequence generated by discrete Ricci flow, and the learned dynamic representations are infused into a Graph Convolutional Network. Extensive experiments demonstrate that GEGCN achieves state-of-the-art performance on classification tasks across various benchmark datasets, with its performance being particularly outstanding on heterophilic graphs.

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Recent work has questioned whether large language models (LLMs) can perform genuine in-context learning (ICL) for scientific experimental design, with prior studies suggesting that LLM-based agents exhibit no sensitivity to experimental feedback. We shed new light on this question by carrying out 800 independently replicated experiments on iterative perturbation discovery in Cell Painting high-content screening. We compare an LLM agent that iteratively updates its hypotheses using experimental feedback to a zero-shot baseline that relies solely on pretraining knowledge […]

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How Small Can Image Classifiers Get? My Experiments with Ultra-Lightweight Models Most image classification work today is about pushing accuracy higher and higher, usually by throwing more parameters at the problem. This project goes in the opposite direction: how small can the model get while still being useful? This post covers two tasks from TinyVision (v3): a cat vs dog classifier built around a handcrafted feature pipeline, and a CIFAR-10 classifier that ditches the filter bank entirely and […]

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:::info Astounding Stories of Super-Science October 2022, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. THE MURDER OF ROGER ACKROYD – AN EVENING AT MAH JONG Astounding Stories of Super-Science October 2022: THE MURDER OF ROGER ACKROYD – AN EVENING AT MAH JONG By Agatha Christie ::: That night we had a little Mah Jong party. This kind of simple entertainment is very popular in King’s Abbot. […]

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Most analysts don’t spend their time analysing data. They spend it copying, cleaning, and rebuilding the same reports every week. Across roles in e-commerce at Amazon, financial services at American Express, and the non-profit sector, I kept seeing the same pattern: manual reporting quietly consuming time, introducing errors, and slowing down decisions. At some point, improving reports stopped making sense. The process itself needed to go. The Problem No One Talks About On the surface, everything looked fine: […]

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