Results 211 to 220 of about 57,147 (259)
Advanced Science, EarlyView.TSTScope is an interpretable AI framework that integrates single‐cell transcriptomes with TCR information through curated gene‐program constraints. By linking receptor context to functional T cell states, it reveals response‐associated tumor‐specific T cell programs in lung cancer immunotherapy cohorts and defines an MPR score associated with ...
Shiwei Cao +8 more
wiley +1 more source
Advanced Electronic Materials, EarlyView.A diagnostic method for reconfigurable intelligent surfaces (RIS) based on non‐uniform space‐time‐coding modulation is presented. Fault localization is achieved via amplitude‐only spectral measurements, eliminating the need for complex signal processing. A one‐to‐one mapping between harmonic components and RIS elements enables accurate detection.
Xiao Qing Chen +8 more
wiley +1 more source
Conformal Reconfigurable Intelligent Surfaces: A Cylindrical Geometry Perspective
Advanced Electronic Materials, EarlyView.Cylindrical reconfigurable intelligent surfaces are explored for low‐complexity beam steering using one‐bit meta‐atoms. A multi‐level modeling approach, including optimization‐based synthesis, demonstrates that even minimal hardware can support directive scattering.
Filippo Pepe +4 more
wiley +1 more source
Terahertz Channel Modeling, Estimation and Localization in RIS‐Assisted Systems
Advanced Electronic Materials, EarlyView.Reconfigurable intelligent surfaces have become a recent intensive research focus. Based on practical applications, channel strategies for RIS‐assisted terahertz wireless communication systems are categorized into three different types: channel modeling, channel estimation, and channel localization.
Hongjing Wang +9 more
wiley +1 more source
On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification
Advanced Electronic Materials, EarlyView.ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels +4 more
wiley +1 more source
Applied Economic Perspectives and Policy, EarlyView.ABSTRACT In recent decades, agriculture has become increasingly concentrated through horizontal mergers and acquisitions via corporate entities, and policy makers are concerned this will be exacerbated by the aging population of farm operators. To reduce market concentration in agriculture, many states have enacted policies to entice new prospective ...
Justin M. Ross +2 more
wiley +1 more source
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Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Sparse coding exhibits good performance in many computer vision applications. However, due to the overcomplete codebook and the independent coding process, the locality and the similarity among the instances to be encoded are lost. To preserve such locality and similarity information, we propose a Laplacian sparse coding (LSc) framework.
Ivor Tsang +2 more
exaly +3 more sources
Neurocomputing, 2014
Sparse coding has received an increasing amount of interest in recent years. It finds a basis set that captures high-level semantics in the data and learns sparse coordinates in terms of the basis set. However, most of the existing approaches fail to consider the geometrical structure of the data space.
Jiajun Bu, Miao Zheng
exaly +2 more sources
Sparse coding has received an increasing amount of interest in recent years. It finds a basis set that captures high-level semantics in the data and learns sparse coordinates in terms of the basis set. However, most of the existing approaches fail to consider the geometrical structure of the data space.
Jiajun Bu, Miao Zheng
exaly +2 more sources
Neural Computation, 2017
Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions.
Georgios Exarchakis, Jörg Lücke
openaire +3 more sources
Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions.
Georgios Exarchakis, Jörg Lücke
openaire +3 more sources
SC2Net: Sparse LSTMs for Sparse Coding
Proceedings of the AAAI Conference on Artificial Intelligence, 2018The iterative hard-thresholding algorithm (ISTA) is one of the most popular optimization solvers to achieve sparse codes. However, ISTA suffers from following problems: 1) ISTA employs non-adaptive updating strategy to learn the parameters on each dimension with a fixed learning rate. Such a strategy may lead to inferior performance due
Joey Tianyi Zhou +9 more
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