Results 131 to 140 of about 5,853,511 (292)

Enhancing Synaptic Plasticity and Multistate Retention of Organic Neuromorphic Devices Using Anion‐Excessive Gel Electrolyte

Advanced Functional Materials, EarlyView.
Anion‐excessive gel‐based organic synaptic transistors (AEG‐OSTs) that can maintain electrical neutrality are developed to enhance synaptic plasticity and multistate retention. Key improvement is attributed to the maintenance of electrical neutrality in the electrolyte even after electrochemical doping, which reduces the Coulombic force acting on ...
Yousang Won, Jeongwoo Lee, Hyunwook Kim, Joon Hak Oh   +3 more
wiley   +1 more source

Augmentation invariant manifold learning

Journal of the Royal Statistical Society Series B: Statistical Methodology
Abstract Data augmentation is a widely used technique and an essential ingredient in the recent advance in self-supervised representation learning. By preserving the similarity between augmented data, the resulting data representation can improve various downstream analyses and achieve state-of-the-art performance in many applications ...
openaire   +2 more sources

Manifold learning for parameter reduction

Journal of Computational Physics, 2019
Large scale dynamical systems (e.g. many nonlinear coupled differential equations) can often be summarized in terms of only a few state variables (a few equations), a trait that reduces complexity and facilitates exploration of behavioral aspects of otherwise intractable models.
Holiday, Alexander, Kooshkbaghi, Mahdi, Bello-Rivas, Juan M., William Gear, C., Zagaris, Antonios, Kevrekidis, Ioannis G.   +5 more
openaire   +5 more sources

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

Deep Grassmannian multiview subspace clustering with contrastive learning

Electronic Research Archive
This paper investigated the problem of multiview subspace clustering, focusing on feature learning with submanifold structure and exploring the invariant representations of multiple views.
Rui Wang, Haiqiang Li, Chen Hu, Xiao-Jun Wu, Yingfang Bao   +4 more
doaj   +1 more source

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

Advanced Functional Materials, EarlyView.
Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley   +1 more source

Non-parametric manifold learning

Electronic Journal of Statistics
We introduce an estimator for distances in a compact Riemannian manifold based on graph Laplacian estimates of the Laplace-Beltrami operator. We upper bound the error in the estimate of manifold distances, or more precisely an estimate of a spectrally truncated variant of manifold distance of interest in non-commutative geometry (cf.
openaire   +2 more sources

Artificial Intelligence as the Next Visionary in Liquid Crystal Research

Advanced Functional Materials, EarlyView.
The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam, Ouassim L'Karkouri, Danqing Liu   +2 more
wiley   +1 more source

Unsupervised manifold embedding to encode molecular quantum information for supervised learning of chemical data

Communications Chemistry
Molecular representation is critical in chemical machine learning. It governs the complexity of model development and the fulfillment of training data to avoid either over- or under-fitting. As electronic structures and associated attributes are the root
Tonglei Li, Nicholas J. Huls, Shan Lu, Peng Hou   +3 more
doaj   +1 more source

When Locally Linear Embedding Hits Boundary

, 2019
Based on the Riemannian manifold model, we study the asymptotic behavior of a widely applied unsupervised learning algorithm, locally linear embedding (LLE), when the point cloud is sampled from a compact, smooth manifold with boundary.
Wu, Hau-tieng, Wu, Nan
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