Results 71 to 80 of about 525,499 (288)

Branching quantum convolutional neural networks

Physical Review Research, 2022
Neural-network-based algorithms have garnered considerable attention for their ability to learn complex patterns from very-high-dimensional data sets towards classifying complex long-range patterns of entanglement and correlations in many-body quantum ...
Ian MacCormack, Conor Delaney, Alexey Galda, Nidhi Aggarwal, Prineha Narang   +4 more
doaj   +1 more source

Recurrent Models of Visual Attention [PDF]

, 2014
Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels.
Alex Graves, Google Deepmind, Koray Kavukcuoglu, Nicolas Heess, Volodymyr Mnih   +4 more
core  

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

Deep Convolutional Neural Networks as Generic Feature Extractors [PDF]

, 2017
Recognizing objects in natural images is an intricate problem involving multiple conflicting objectives. Deep convolutional neural networks, trained on large datasets, achieve convincing results and are currently the state-of-the-art approach for this ...
Barth, Erhardt, Hertel, Lars, Käster, Thomas, Martinetz, Thomas   +3 more
core  

Electron–Matter Interactions During Electron Beam Nanopatterning

Advanced Functional Materials, EarlyView.
This article reviews the electron–matter interactions important to nanopatterning with electron beam lithography (EBL). Electron–matter interactions, including secondary electron generation routes, polymer radiolysis, and electron beam induced charging, are discussed.
Camila Faccini de Lima, Prathiba Thamizhavel, Taylor J. Woehl   +2 more
wiley   +1 more source

Toward Audio Beehive Monitoring: Deep Learning vs. Standard Machine Learning in Classifying Beehive Audio Samples

Applied Sciences, 2018
Electronic beehive monitoring extracts critical information on colony behavior and phenology without invasive beehive inspections and transportation costs.
Vladimir Kulyukin, Sarbajit Mukherjee, Prakhar Amlathe   +2 more
doaj   +1 more source

Spectrally Tuned Floating‐Gate Synapse Based on Blue‐ and Red‐Absorbing Organic Molecules for Wavelength‐Selective Neural Networks and Fashion Image Classifications

Advanced Functional Materials, EarlyView.
The characteristics of a vertical floating gate heterostructure transistor device that exhibits neuromorphic potentiation under visible light illumination are investigated. Due to spectrally‐tuned absorbance properties of each thin film layer and introduction of tunneling dielectric, the device enables wavelength‐selective tuning of synaptic plasticity
Seungme Kang, Jisoo Park, Jinwoong Hong, Jinmin Park, Jeongbo Lee, Hyeonjung Kim, Wonjun Shin, Eva Bestelink, Radu A. Sporea, Seyong Oh, Chung Whan Lee, Yun‐Hi Kim, Hocheon Yoo   +12 more
wiley   +1 more source

Intrinsic Nanopore‐Assisted SnP2S6 Memristors With Ti Ion Dynamics for Compact Logic‐In‐Memory Hardware

Advanced Functional Materials, EarlyView.
Tin hexathiophosphate memristors leverage intrinsic nanopores together with a guided filament formation strategy to regulate titanium ion motion and switching behavior. The devices support reliable nonvolatile memory and reconfigurable logic‐in‐memory, demonstrating 14 Boolean logic functions in a single cell.
Thaw Tint Te Tun, Sanchali Mitra, Hongxi Su, Jiali Huo, Haofei Zheng, Michel Bosman, Yee Sin Ang, Kah‐Wee Ang   +7 more
wiley   +1 more source

AAGCN: a graph convolutional neural network with adaptive feature and topology learning

Scientific Reports
In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power.
Bin Wang, Bodong Cai, Jinfang Sheng, Wenzhe Jiao   +3 more
doaj   +1 more source

From Bug to Feature: Harnessing Cross‐Sensitivity for Multiparametric Luminescence Sensing

Advanced Functional Materials, EarlyView.
Cross‐sensitivity in luminescence sensing is reframed from a limitation into a resource for multiparametric detection. Using ruby microspheres as a model system, cross‐sensitivity is quantitatively assessed and exploited through linear discriminant analysis, enabling simultaneous, correction‐free pressure and temperature sensing with a single ...
Nikita Panov, Liyan Ming, Stanislav Sinogeikin, Erving Ximendes, Daniel Jaque, Riccardo Marin   +5 more
wiley   +1 more source