Results 111 to 120 of about 18,691 (261)

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models

Angewandte Chemie, EarlyView.
AI‐empowered catalysis research via integrated database platform, universal machine learning interatomic potentials (MLIPs), and large language models (LLMs). ABSTRACT The integration of artificial intelligence (AI) into catalysis is fundamentally reshaping the research paradigm of catalyst discovery.
Di Zhang, Yuanzheng Chen, Chuanyu Liu, Yan Liu, Hongliang Xin, Jiayu Peng, Pengfei Ou, Hao Li   +7 more
wiley   +2 more sources

VAE+DDPG: An Attention‐Enhanced Variational Autoencoder for Deep Reinforcement Learning‐Based Autonomous Navigation in Low‐Light Environments

Advanced Intelligent Systems, EarlyView.
Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee, Chanhong Hwang, Hyunseok Kang, Yechan Park, Hangwoo Jin, Tuan Anh Nguyen, Eunmi Choi, Dugki Min   +7 more
wiley   +1 more source

Staged Diversity‐Constrained Machine Learning for High‐Dimensional Reaction Condition Optimization

Angewandte Chemie, EarlyView.
Staged diversity‐constrained modeling enables efficient navigation of high‐dimensional reaction spaces, validated on cross‐coupling HTE data and applied to ruthenium‐catalyzed meta‐C─H functionalization. ABSTRACT Optimizing reaction conditions in high‐dimensional chemical spaces remains a central challenge in modern synthesis.
Shu‐Wen Li, Shan Chen, João C. A. Oliveira, Shuo‐Qing Zhang, Lutz Ackermann, Xin Hong   +5 more
wiley   +2 more sources

gnSPADE: Incorporating Gene Network Structures Enhances Reference‐Free Deconvolution in Spatial Transcriptomics

Advanced Intelligent Systems, EarlyView.
gnSPADE integrates gene‐network structures into a probabilistic topic modeling framework to achieve reference‐free cell‐type deconvolution in spatial transcriptomics. By embedding gene connectivity within the generative process, gnSPADE enhances biological interpretability and accuracy across simulated and real datasets, revealing spatial organization ...
Aoqi Xie, Yuehua Cui
wiley   +1 more source

GraphNeuralCloth: A Graph‐Neural‐Network‐Based Framework for Non‐Skinning Cloth Simulation

Advanced Intelligent Systems, EarlyView.
This study presents a cloth motion capture system and a point‐cloud‐to‐mesh processing method to support the prediction of real‐world fabric deformation. GraphNeuralCloth, a graph neural‐network (GNN)‐based framework is also proposed to estimate the cloth morphology change in real time.
Yingqi Li, Xiaomei Wang, Xiaoqi Song, Chenchen Fan, Bohao Zhu, Jianan Peng, Qixin Liang, James Lam, Kam‐Yim Sze, Ka‐Wai Kwok   +9 more
wiley   +1 more source

Enabling Stochastic Dynamic Games for Robotic Swarms

Advanced Intelligent Systems, EarlyView.
This paper scales stochastic dynamic games to large swarms of robots through selective agent modeling and variable partial belief space planning. We formulate these games using a belief space variant of iterative Linear Quadratic Gaussian (iLQG). We scale to teams of 50 agents through selective modeling based on the estimated influence of agents ...
Kamran Vakil, Alyssa Pierson
wiley   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Maneuverable Multilegged Locomotion through Anisotropically Arranged Soft Backbones in a Single‐Actuator Modular Miniature Robot

Advanced Intelligent Systems, EarlyView.
A modular eight‐legged robot exploits anisotropically oriented soft I‐beam backbones to transmit vibration from a single unbalanced‐mass actuator, producing frequency‐dependent multimodal gaits. A pseudo‐rigid‐body model enables high‐fidelity MuJoCo simulation, while Bayesian parameter identification and reinforcement learning yield robust control ...
Yiğit Yaman, Burak Arslan, Ömer Çağrı Ergin, Daniel M. Aukes, Onur Özcan   +4 more
wiley   +1 more source

Parameterized codes over some embedded sets and their applications to complete graphs

Mathematical Communications, 2013
Let $K$ be a finite field, let $X \subset \mathbb{P}^{m-1}$ and $X' \subset \mathbb{P}^{r-1}$, with ...
González Sarabia, Manuel, Rentería Márquez, Carlos, Sarmiento Rosales, Eliseo   +2 more
openaire   +2 more sources