Results 161 to 170 of about 61,624 (324)
Two new strategies that include the convergence of the one‐particle reduced density matrix (1‐RDM) were introduced in Variational Quantum Eigensolvers (VQE). Improved energy and, most importantly, density‐based properties (dipole moments, electron density) were obtained.
Amanda Marques de Lima +3 more
wiley +1 more source
Design and Applications of Multi‐Frequency Programmable Metamaterials for Adaptive Stealth
This article provides a comprehensive overview of metamaterials, including their fundamental principles, properties, synthesis techniques, and applications in stealth, as well as their challenges and future prospects. It covers topics that are more advanced than those typically discussed in existing review articles, while still being closely connected ...
Jonathan Tersur Orasugh +4 more
wiley +1 more source
Training a learning vector quantization network for biomedical classification
Christoforos Anagnostopoulos +5 more
openalex +1 more source
Spatiotemporal Reservoir Computing with a Reconfigurable Multifunctional Memristor Array
This study presents a hardware physical reservoir computing system using a tri‐modal memristive crossbar array. Stochastic masking, bistable nonlinear activation, and analog readout enable fully in‐memory spatiotemporal processing. Demonstrations on cellular automata, Lorenz prediction, ADHD EEG classification, and chaotic KS modeling highlight its ...
Sungho Kim +10 more
wiley +1 more source
Optimasi Identifikasi Adenokarsinoma Dari Citra X-Ray Dengan Metode Learning Vector Quantization [PDF]
Aziz Amrullah Septian +1 more
openalex +1 more source
Recent efforts of memristor array‐based hardware neuromorphic computing are discussed for efficient application of VMM on‐chip level in terms of circuit integration and actual application of AI algorithms. The parallel data processing principle of VMM operation is briefly reviewed, and hardware VMM is presented including convolutional transformation ...
Jingon Jang, Sang‐gyun Gi
wiley +1 more source
This study presents CPMP, a weakly supervised transformer model that predicts MammaPrint recurrence risk directly from routine histopathological images of early‐stage HR+/HER2− breast cancer patients. CPMP enables spatial heatmap visualization, analysis of cellular‐level interaction patterns, and an in‐depth characterization of morphological phenotypes
Chaoyang Yan +8 more
wiley +1 more source
Breast Tumor Diagnosis Based on Molecular Learning Vector Quantization Neural Networks. [PDF]
Huang C +6 more
europepmc +1 more source
Learning Vector Quantization And Neural Predictive Coding For Nonlinear Speech Feature Extraction
Mohamed Chétouani +2 more
openalex +1 more source

