Results 211 to 220 of about 1,456,699 (307)

Upsampling DINOv2 Features for Unsupervised Vision Tasks and Weakly Supervised Materials Segmentation

Advanced Intelligent Systems, EarlyView.
Feature from recent image foundation models (DINOv2) are useful for vision tasks (segmentation, object localization) with little or no human input. Once upsampled, they can be used for weakly supervised micrograph segmentation, achieving strong results when compared to classical features (blurs, edge detection) across a range of material systems.
Ronan Docherty, Antonis Vamvakeros, Samuel J. Cooper   +2 more
wiley   +1 more source

ControlIt: A Universal Framework for Translational, Adaptive, and Online Brain–Computer Interfaces

Advanced Intelligent Systems, EarlyView.
Brain–computer interfaces (BCIs) lack a unified platform that works across signals and algorithms. ControlIt, an open‐source, modular ROS2‐based BCI framework supporting electroencephalography (EEG), electrocorticography (ECoG), and spike‐based decoding across both classification and regression tasks is presented.
Wanlin Yang, Chenyang Li, Xinxiu Xu, Xindong Wang, Yongcheng Guo, Xiaoke Chai, Tianqin Cao, Jiuxiang Song, Nan Wang, Xueming Zhang, Yi Yang, Cong Zheng, He Cui   +12 more
wiley   +1 more source

Listeners' baseline autonomic states associated with distinct music-physiology response patterns. [PDF]

Eur Heart J Imaging Methods Pract
Soliński M, Pope V, Lambiase P, Chew E.
europepmc   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
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

Multi-phase dataset for bulk Ti and the Ti-6Al-4V alloy. [PDF]

Sci Data
Allen CS, Bartók AP.
europepmc   +1 more source

Deformable Attention Multiscale Feature Fusion Network‐Dehaze: An Adaptive Image Dehazing Method Based on Deformable Attention Multiscale Fusion Network

Advanced Intelligent Systems, EarlyView.
This paper presents the deformable attention multiscale feature fusion network‐dehaze adaptive image dehazing network, which integrates three core modules (revised residual shrinkage unit, multiscale attention, cross‐scale feature fusion). It incorporates deformable convolution and multiscale attention mechanisms to address the detail loss issue of ...
Ruipeng Wang, Yajun Chen, Hao Liu, Mingrui Li, Zhiqi Li   +4 more
wiley   +1 more source

Leveraging historical trials to predict Fusarium head blight resistance in spring wheat breeding programs. [PDF]

Plant Genome
Brault C, Conley EJ, Green AJ, Glover KD, Cook JP, Gill HS, Read AC, Fiedler JD, Anderson JA.   +8 more
europepmc   +1 more source

Economy of Touch : Task‐Driven Information Selection in Electrical Impedance Tomography‐based Tactile Robotic Sensing

Advanced Intelligent Systems, EarlyView.
Electrical impedance tomography (EIT) tactile skins enable multiplexed measurements that trade sensing speed against information richness. This work introduces an economy‐of‐touch framework that treats tactile sensing as an information‐budgeting problem.
Xiaoxian Xu, David Hardman, Fumiya Iida
wiley   +1 more source

CEMUSA: a graph-based integrative metric for evaluating clusters in spatial transcriptomics. [PDF]

Bioinformatics
Hu J, Du Y, Hou S, Ding Y, Li J, Wu H, Sun X.   +6 more
europepmc   +1 more source