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Single‐Cell Dissection of Microglial Dysfunction in Diabetes‐Associated Neurodegenerative Vulnerability

Advanced Science, EarlyView.
Using single‐cell transcriptomic and epigenomic profiling, researchers mapped microglial dysfunction across the lifespan of db/db mice. The authors uncovered a nonlinear microglial remodeling featuring early inflammatory priming, a mid‐stage transcriptional inflection, and late dysfunction.
Jizhou Zhang +6 more
wiley +1 more source

Efficient Unsupervised Deep Band Selection for Hyperspectral Imagery: A Comparative Study With Mamba-Based Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Band selection is a critical step in processing hyperspectral imagery (HSI); reducing input dimensionality allows models to mitigate redundancy, enhance computational efficiency, and improve learning accuracy.
Jacqueline Liu, Kara Combs, Jayson Boubin +2 more
doaj +1 more source

Photonic‐Enabled Energy‐Efficient Transparent Neuromorphic Computing Devices: A Review

Advanced Science, EarlyView.
Transparent photonic neuromorphic computing devices merge optics and brain‐inspired computing to overcome von Neumann bottlenecks with ultrafast, low‐energy processing. By exploiting transparent oxides, 2D materials, phase‐change materials, and hybrid heterostructures, these platforms enable photonic synapses, memory, and logic for see‐through edge ...
Shuvaraj Ghosh +8 more
wiley +1 more source

AUTOREGRESSIVE MODEL BASED ON BAYESIAN APPROACH FOR TEXTURE REPRESENTATION [PDF]

ICTACT Journal on Image and Video Processing, 2012
In this study autoregressive model based on Bayesian approach is proposed for texture classification. Based on auto correlation coefficients, micro textures are identified and represented locally and then globally.
T. Karthikeyan, R. Krishnamoorthy
doaj

Self-organizing maps on "what-where" codes towards fully unsupervised classification. [PDF]

Biol Cybern, 2023
Sa-Couto L, Wichert A.
europepmc +1 more source

unsupervised classification

Citation: 'unsupervised classification' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.10146 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms.
openaire +1 more source

Automating Chemical Reasoning in High‐Throughput Phase Identification With a Probabilistic, LLM‐Guided Framework

Advanced Science, EarlyView.
Autonomous laboratories can now synthesize materials faster than experts can interpret the resulting diffraction data. A probabilistic framework combines refinement‐fit metrics with large language model‐derived chemical reasoning to rank competing phase interpretations and flag those unsuitable for autonomous use.
Olympia Dartsi +7 more
wiley +1 more source

Unsupervised classification of plethysmography signals with advanced visual representations. [PDF]

Front Physiol, 2023
Germain T, Truong C, Oudre L, Krejci E.
europepmc +1 more source

Mixtures of Unsupervised Lexicon Classification

CoRR
A draft on lexicon classification unsupervised learning. It shows that aggregating lexicon scores is equivalent to a finite mixture of multinomial Naive Bayes models.
openaire +2 more sources

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

Advanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi +7 more
wiley +1 more source

classification
machine learning
unsupervised learning

deep learning