I first noticed the failure mode on a long chain when Scene 12 looked plausible—but it no longer felt like it belonged to Scene 1. Nothing was “broken” in any single step. The drift was death-by-a-thousand-cuts: each transition was slightly off, and those slight offs multiplied. That’s the moment I stopped thinking about consistency as a global memory problem. In Scenematic, I treat consistency like signal propagation in a circuit. You don’t carry the whole circuit state inside […]

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arXiv:2510.21935v2 Announce Type: replace-cross Abstract: Novelty detection in large scientific datasets faces two key challenges: the noisy and high-dimensional nature of experimental data, and the necessity of making statistically robust statements about any observed outliers. While there is a wealth of literature on anomaly detection via dimensionality reduction, most methods do not produce outputs compatible with quantifiable claims of scientific discovery. In this work we directly address these challenges, presenting the first step towards a unified pipeline for […]

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Signing PDFs has become an important task for businesses and individuals alike. Whether you’re handling contracts, legal agreements, or forms, the ability to quickly and securely sign PDFs online is essential. Fortunately, with the rise of online PDF signers, signing PDFs has never been easier. Common challenges in signing PDFs Signing PDFs might seem straightforward, but several challenges often arise during the process. Some of the most common issues include: File compatibility: Not all PDF editors or viewers […]

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Accurate crowd simulation is crucial for public safety management, emergency evacuation planning, and intelligent transportation systems. However, existing methods, which typically model crowds as a collection of independent individual trajectories, are limited in their ability to capture macroscopic physical laws. This microscopic approach often leads to error accumulation and compromises simulation stability. Furthermore, deep learning-driven methods tend to suffer from low inference efficiency and high computational overhead, making them impractical for large-scale, efficient simulations. To address these challenges, […]

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How are you, hacker? 🪐 What’s happening in tech today, January 2, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, The Palmer Raids began in the US in 1920, NASA launched its farthest exploration in 2019, The Federal Rules of Evidence were enacted in the US in 1975, and we present you with these top quality stories. From The Brain at the Edge of Chaos. When Predictive Coding Fails and Randomness […]

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:::info Authors: Alhussein Fawzi Matej Balog Aja Huang Thomas Hubert Bernardino Romera-Paredes Mohammadamin Barekatain Alexander Novikov Francisco J. R. Ruiz Julian Schrittwieser Grzegorz Swirszcz David Silver Demis Hassabis Pushmeet Kohli ::: Abstract Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primitive task, occurring in many systems—from neural networks to scientific computing routines. The automatic discovery […]

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In this review I summarize how machine learning can be used in lattice gauge theory simulations and what ap-proaches are currently available to improve the sampling of gauge field configurations, with a focus on applications in four-dimensional SU(3) gauge theories. These include approaches based on generative machine-learning models such as (stochastic) normalizing flows and diffusion processes, and an approach based on renormalization group (RG) transformations, more specifically the machine learning of RG-improved gauge actions using gauge-equivariant convolutional neural […]

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results D. Additional Results The hyperbolic partitioning trees of Cora is visualized in […]

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arXiv:2604.02397v1 Announce Type: new Abstract: Group Emotion Recognition (GER) aims to infer collective affect in social environments such as classrooms, crowds, and public events. Many existing approaches rely on explicit individual-level processing, including cropped faces, person tracking, or per-person feature extraction, which makes the analysis pipeline person-centric and raises privacy concerns in deployment scenarios where only group-level understanding is needed. This research proposes VE-MD, a Variational Encoder-Multi-Decoder framework for group emotion recognition under a privacy-aware functional design. Rather […]

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