Results 41 to 50 of about 13,859 (190)

Fibroblast Transcriptomics in Molecular Diagnostics of a Comprehensive Dystonia Cohort

Annals of Neurology, EarlyView.
Objective Genomic sequencing leaves >50% of dystonia‐affected individuals without a diagnosis. Where DNA‐oriented approaches remain insufficient, integrating multiomics is essential to advance genome interpretation. Herein, we incorporated RNA sequencing (RNA‐seq) data from 167 patients with dystonia across a range of ages and presentations. Methods We
Alice Saparov, Ivana Dzinovic, Theresa Brunet, Vicente A. Yépez, Florian Hölzlwimmer, Elisabetta Indelicato, Birgit Assmann, Susann Badmann, Diana Ballhausen, Steffen Berweck, Felix Brechtmann, Melanie Brugger, Kevork Derderian, Felix Distelmaier, Philip Harrer, Denisa Harvanova, Petra Havrankova, Ann‐Kathrin Jaroszynski, Miriam Kolnikova, Robert Kopajtich, Anne Koy, Magdalena Krygier, Lukas Kunc, Katarina Kusikova, Oliver Maier, Maria Mazurkiewicz‐Bełdzińska, Christian Mertes, Ava Oberlack, Timo Roser, Alexandra Sitzberger, Ugo Sorrentino, Antonia M. Stehr, Katharina Vill, Matias Wagner, Holger Prokisch, Sylvia Boesch, Jan Necpal, Robert Jech, Juliane Winkelmann, Elisabeth Graf, Julien Gagneur, Matej Skorvanek, Michael Zech   +42 more
wiley   +1 more source

Hyperspectral anomaly detection via memory‐augmented autoencoders

CAAI Transactions on Intelligence Technology, 2023
Recently, the autoencoder (AE) based method plays a critical role in the hyperspectral anomaly detection domain. However, due to the strong generalised capacity of AE, the abnormal samples are usually reconstructed well along with the normal background ...
Zhe Zhao, Bangyong Sun
doaj   +1 more source

Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance

Naval Research Logistics (NRL), EarlyView.
ABSTRACT The rapid expansion of cross‐border e‐commerce (CBEC) has created significant opportunities for small‐ and medium‐sized sellers, yet financing remains a critical challenge due to their limited credit histories. Third‐party logistics (3PL)‐led supply chain finance (SCF) has emerged as a promising solution, leveraging in‐transit inventory as ...
Qingkai Zhang, L. Jeff Hong, Houmin Yan
wiley   +1 more source

Video Anomaly Detection Based on Convolutional Recurrent AutoEncoder

Sensors, 2022
As an essential task in computer vision, video anomaly detection technology is used in video surveillance, scene understanding, road traffic analysis and other fields.
Bokun Wang, Caiqian Yang
doaj   +1 more source

Reduced‐Order Modeling of Energetic Materials Using Physics‐Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)

Propellants, Explosives, Pyrotechnics, EarlyView.
Physics‐Aware Recurrent Convolutional Neural Networks (PARC) can reliably learn the thermomechanics of energetic materials as a function of morphology. This work introduces LatentPARC, which accelerates PARC by modeling the dynamics in a low‐dimensional latent space.
Zoë J. Gray, Joseph B. Choi, Youngsoo Choi, H. Keo Springer, H. S. Udaykumar, Stephen S. Baek   +5 more
wiley   +1 more source

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification

, 2020
Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic data. We introduce a novel GAN with Autoencoder (GAN-AE) architecture to generate synthetic samples for variable length, multi-feature ...
Ger, Stephanie, Klabjan, Diego
core  

DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection

IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley   +1 more source

Adversarial Example Detection and Restoration Defensive Framework for Signal Intelligent Recognition Networks

Applied Sciences, 2023
Deep learning-based automatic modulation recognition networks are susceptible to adversarial attacks, posing significant performance vulnerabilities. In response, we introduce a defense framework enriched by tailored autoencoder (AE) techniques.
Chao Han, Ruoxi Qin, Linyuan Wang, Weijia Cui, Dongyang Li, Bin Yan   +5 more
doaj   +1 more source

Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou, Juan Zou, Gan Ruan, Yuan Liu, Yizhang Xia   +4 more
wiley   +1 more source