Results 121 to 130 of about 238,312 (311)

Nanoscale Spatial Organization of ARC High‐ and Low‐Order Assemblies at Excitatory Synapses

Advanced Science, EarlyView.
ARC (Activity‐Regulated Cytoskeleton‐Associated protein) mediates synaptic plasticity by forming nanoscale assemblies in neurons. Using super‐resolution microscopy and time‐resolved anisotropy with targeted tagging, the study reveals low‐order ARC assemblies at synapses colocalizing with AMPARs, semi‐circular structures at endocytic zones, and 60–80 nm
Martina Damenti, Giovanna Coceano, Mariline Mendes Silva, Jonatan Alvelid, Chiara Sgattoni, Andrea Volpato, Lea Rems, Luciano A. Masullo, Eduard M. Unterauer, Rafal Kowalewski, Lucie Delemotte, Erdinc Sezgin, Ralf Jungmann, Ilaria Testa   +13 more
wiley   +1 more source

Online learning over graphs

, 2005
We apply classic online learning techniques similar to the perceptron algorithm to the problem of learning a function defined on a graph. The benefit of our approach includes simple algorithms and performance guarantees that we naturally interpret in ...
Mark Herbster, Herbster, M., Lisa Wainer, Massimiliano Pontil, Pontil, M., Wainder, L.   +5 more
core   +1 more source

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

Advanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju, Adithya Umanath Rai, Jose Thomson, Marzie Ghorbani, Shujing Zhao, Nick Birbilis   +5 more
wiley   +1 more source

The Effectiveness of Semi-Supervised Learning Techniques in Identifying Calcifications in X-ray Mammography and the Impact of Different Classification Probabilities

Applied Sciences
Identifying calcifications in mammograms is crucial for early breast cancer detection, and semi-supervised learning, which utilizes a small dataset for supervised learning combined with deep learning, is anticipated to be an effective approach for ...
Miu Sakaida, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki Sugimori, Kenji Hirata, Kohsuke Kudo   +6 more
doaj   +1 more source

Stress History Establishes a Transient Tolerant State That Shapes Antibiotic Survival Upon Resuscitation

Advanced Science, EarlyView.
High‐throughput single‐cell analysis of resuscitating bacteria reveals a starvation‐history‐dependent transiently tolerant subpopulation that survives β$\beta$‐lactam exposure by temporarily reducing growth. Distinct from classical persisters, these actively growing yet dynamically modulated cells dominate survival across clinically relevant antibiotic
Kieran Abbott, Georgeos Hardo, Ruizhe Li, Jack Bradley, Ashraf Zarkan, Somenath Bakshi   +5 more
wiley   +1 more source

Learning from Partial Labels with Minimum Entropy [PDF]

This paper introduces the minimum entropy regularizer for learning from partial labels. This learning problem encompasses the semi-supervised setting, where a decision rule is to be learned from labeled and unlabeled examples.
Yoshua Bengio, Yves Grandvalet
core  

A Computational Strategy for Identifying Self‐Assembling Food‐Derived Molecules for Antiparasitic Nanotherapy

Advanced Science, EarlyView.
An integrated computational screening strategy identified ursolic acid (UA) and 18β‐glycyrrhetinic acid (18βGA) as a self‐assembling food‐derived molecular pair. The resulting carrier‐free nanoparticles (UA‐18βGA) showed synergistic antiparasitic activity, reduced combined toxicity, and host‐protective anti‐inflammatory effects in zebrafish and murine ...
Shenye Qu, Ting Wang, Jietao Liu, Jiacheng Qin, Bin Yang, Yihang Liu, Pengfei Li, Gaoxue Wang, Fei Ling   +8 more
wiley   +1 more source

Semi-supervised learning : from Gaussian fields to Gaussian processes

, 2018
: "We show that the Gaussian random fields and harmonic energy minimizing function framework for semi-supervised learning can be viewed in terms of Gaussian processes, with covariance matrices derived from the graph Laplacian.
Zoubin Ghahramani (5363936), John Lafferty (5399966), Xiaojin Zhu (5364317)   +2 more
core   +2 more sources

Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization

International Journal of Distributed Sensor Networks, 2017
The research of localization technology based on received signal strength and machine learning has recently attracted a lot of attentions, since with the help of enough labeled training data this technology is able to achieve high positioning accuracy ...
Fagui Liu, Hengrui Qin, Xin Yang, Yi Yu
doaj   +1 more source

Minimally-Supervised Morphological Segmentation using Adaptor Grammars

, 2013
This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model ...
Sirts, Kairit, Goldwater, Sharon; id_orcid   +1 more
core