Results 71 to 80 of about 235,048 (260)
How Neuromorphic Microstructures Control In Vitro Early‐Stage Neuronal Outgrowth
Advanced Science, EarlyView.Biomimetic approach to neuronal interaction with neuromorphic microstructures. ABSTRACT Neuromorphic biomaterials represent a novel class of materials designed to replicate the architecture and functionality of neuronal structures, offering new opportunities in tissue engineering and bioelectronics.
Claudia Latte Bovio +5 more
wiley +1 more source
Advanced Science, EarlyView.A time‐multiplexed nanobody‐functionalized organic electrochemical transistor enables rapid and sensitive detection of three respiratory viral proteins from saliva. Sequentially addressed multi‐gate electrodes on a shared channel achieve around 1 fm detection within 15 min without sample preprocessing. Clinical validation demonstrates high specificity,
Tianrui Chang +13 more
wiley +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 +1 more
core
, 2023 Auxetics are a rare class of materials that exhibit a negative Poisson\u27s ratio. The existence of these auxetic materials is rare but has a large number of applications in designing exotic materials.
Raheel, Hammad, Sownyak , Mondal
core +1 more source
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 +13 more
wiley +1 more source
Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces
, 2012 Riechmann H, Finke A. Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces. In: ESANN 2012 proceedings. i6doc.com; 2012: 121-126.Non-stationarity is inherent in EEG data.
Riechmann, Hannes +1 more
core
Negative sampling in semi-supervised learning
CoRR, 2019 We introduce Negative Sampling in Semi-Supervised Learning (NS3L), a simple, fast, easy to tune algorithm for semi-supervised learning (SSL). NS3L is motivated by the success of negative sampling/contrastive estimation. We demonstrate that adding the NS3L loss to state-of-the-art SSL algorithms, such as the Virtual Adversarial Training (VAT ...
John Chen 0002 +2 more
openaire +3 more sources
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 +5 more
wiley +1 more source
Convex Multiview Semi-Supervised Classification
, 2017 In many practical applications, there are a great number of unlabeled samples available, while labeling them is a costly and tedious process. Therefore, how to utilize unlabeled samples to assist digging out potential information about the problem is ...
Feiping Nie +5 more
core +1 more source
Semi-Supervised Learning with Ladder Networks
CoRR, 2015 We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training.
Antti Rasmus +4 more
openaire +3 more sources