Build a solar flare detection system on SageMaker AI LSTM networks and ESA STIX data

Build a solar flare detection system on SageMaker AI LSTM networks and ESA STIX data

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The effective monitoring and characterization of solar flares demands sophisticated analysis of X-ray emissions across multiple energy spectrums. Machine learning-based anomaly detection serves as a powerful tool for identifying significant patterns that could indicate notable solar activity. Through the identification of distinct radiation signatures, key solar event characteristics can be detected, analyzed, and comprehensively understood. These detected patterns are essential for various applications, including space weather forecasting, solar physics investigations, and satellite operation planning. In recent years, solar […]

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Associative Memory System via Threshold Linear Networks

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arXiv:2603.28873v1 Announce Type: new Abstract: Humans learn and form memories in stochastic environments. Auto-associative memory systems model these processes by storing patterns and later recovering them from corrupted versions. Here, memories are learned by associating each pattern with an attractor in a latent space. After learning, when (possibly corrupted) patterns are presented to the system, latent dynamics facilitate retrieval of the appropriate uncorrupted pattern. In this work, we propose a novel online auto-associative memory system. In contrast to […]

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Conjecture About the Composition of Prime Numbers

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What are the numbers made of? More precisely, what are prime numbers made of? I posed this question to myself on the evening of August 19, 2025, which prompted prolonged introspection and profound contemplation. Then, I began constructing a numerical pyramid with prime numbers. The number one took the place of the central axis. Therefore, it is possible that large prime numbers could be surrounded by prime numbers on either side of one. However, this property extends to […]

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Nestology: A New Framework for the Hierarchical Structure and Operational Logic of the Underlying Architecture of Artificial General Intelligence

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This paper proposes Nestology, a formal analytical framework for the underlying architecture of Artificial General Intelligence (AGI). It defines core concepts (system, parent/subsystem, rules/strategies), deduces five axioms, and derives nine theorems. The core insight is that AGI architectures exhibit a finitely nested structure of “logical non-containment—rule containment,” and subsystems possess dual attributes: absolute rule dependence and relative independence of four elements. Nestology uniquely addresses four core AGI problems: explaining black-box opacity via a quantitative model of cognitive distortion; […]

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The brothers meet Yoshi in Super Mario Galaxy Movie trailer

The brothers meet Yoshi in Super Mario Galaxy Movie trailer

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Universal Pictures and Illumination dropped a new trailer for the upcoming Super Mario Galaxy Movie, and gaming fans will no doubt be delighted at the news that Yoshi, the little green dinosaur, features prominently, along with plenty of other Easter eggs for sharp-eyed fans. As previously reported, the first attempt at a Super Mario movie adaptation in 1993 was notoriously a dismal failure, although it still has its ’90s-nostalgic fans. But 2023’s Super Mario Bros. Movie won over […]

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Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

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arXiv:2603.07169v1 Announce Type: cross Abstract: Optimizing GPU kernels manually is a challenging and time-consuming task. With the rapid development of LLMs, automated GPU kernel optimization is gradually becoming a tangible reality. However, current LLM-driven automated optimization methods narrowly focus on machine learning applications, such as PyTorch operator optimization, while overlooking broader domains like sparse matrix operations in scientific computing. Extending to these broader applications brings new challenges for the benchmark and algorithm. Therefore, developing a general-purpose automated kernel […]

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LLM University, Generative AI, AI Product Moats

LLM University, Generative AI, AI Product Moats

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Hi! As we collectively wrap our heads around the new age of AI and how it changes our world, I’d love to share with you: Thanks for reading Language Models and Machine Learning! Subscribe for free to receive new posts and support my work. LLM University Eight key observations from what I’m learning about the space. LLM University I’m lucky to get to team up with some of my favorite ML educators to bring you Cohere’s LLM University […]

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Casewise and Cellwise Robust Multilinear Principal Component Analysis

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arXiv:2503.07327v2 Announce Type: replace-cross Abstract: Multilinear Principal Component Analysis (MPCA) is an important tool for analyzing tensor data. It performs dimension reduction similar to PCA for multivariate data. However, standard MPCA is sensitive to outliers. It is highly influenced by observations deviating from the bulk of the data, called casewise outliers, as well as by individual outlying cells in the tensors, so-called cellwise outliers. This latter type of outlier is highly likely to occur in tensor data, as […]

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Teaching LLMs to Ask: Self-Querying Category-Theoretic Planning for Under-Specified Reasoning

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arXiv:2601.20014v1 Announce Type: new Abstract: Inference-time planning with large language models frequently breaks under partial observability: when task-critical preconditions are not specified at query time, models tend to hallucinate missing facts or produce plans that violate hard constraints. We introduce textbf{Self-Querying Bidirectional Categorical Planning (SQ-BCP)}, which explicitly represents precondition status (texttt{Sat}/texttt{Viol}/texttt{Unk}) and resolves unknowns via (i) targeted self-queries to an oracle/user or (ii) emph{bridging} hypotheses that establish the missing condition through an additional action. SQ-BCP performs bidirectional search […]

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Mitra: Mixed synthetic priors for enhancing tabular foundation models

Mitra: Mixed synthetic priors for enhancing tabular foundation models

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Mitra: Mixed synthetic priors for enhancing tabular foundation models Generating diverse synthetic prior distributions leads to a tabular foundation model that outperforms task-specific baselines. Machine learning Xiyuan Zhang Danielle Maddix Robinson July 22, 01:40 PM February 19, 12:55 PM Tabular data powers critical decisions across domains such as healthcare, finance, e-commerce, and the sciences. The machine learning methods traditionally used for tabular data, however such as random forests and XGBoost typically result in models tailored to individual datasets, […]

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