Results 121 to 130 of about 63,273 (275)
High-Order Sequential Simulation via Statistical Learning in Reproducing Kernel Hilbert Space. [PDF]
Yao L, Dimitrakopoulos R, Gamache M.
europepmc +1 more source
Learnable Diffusion Framework for Mouse V1 Neural Decoding
We introduce Sensorium‐Viz, a diffusion‐based framework for reconstructing high‐fidelity visual stimuli from mouse primary visual cortex activity. By integrating a novel spatial embedding module with a Diffusion Transformer (DiT) and a synthetic‐response augmentation strategy, our model outperforms state‐of‐the‐art fMRI‐based baselines, enabling robust
Kaiwen Deng +2 more
wiley +1 more source
An entity‐centric foundation model, GloPath, is introduced for comprehensive glomerular lesion assessment from routine renal biopsy images. Trained on over one million glomeruli, the framework enables robust lesion recognition, grading, and cross modality diag nosis, while uncovering large‐scale clinicopathological associations.
Qiming He +28 more
wiley +1 more source
SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics
ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao +11 more
wiley +1 more source
Plant Genetic Engineering: Technological Pathways, Application Scenarios, and Future Directions
This review maps the fast‐evolving landscape of plant genetic engineering, linking enabling platforms with trait‐focused applications in architecture optimization, stress resilience, yield improvement, and quality enhancement. It highlights how genome editing, transgenic strategies, and emerging multi‐gene approaches reshape breeding pipelines, while ...
Peilin Wang +4 more
wiley +1 more source
An Integrated NLP‐ML Framework for Property Prediction and Design of Steels
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju +5 more
wiley +1 more source
Kernel VICReg for Self-Supervised Learning in Reproducing Kernel Hilbert Space
Self-supervised learning (SSL) has emerged as a powerful paradigm for representation learning by optimizing geometric objectives, such as invariance to augmentations, variance preservation, and feature decorrelation, without requiring labels.
M. Hadi Sepanj +3 more
doaj +1 more source
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo +6 more
wiley +1 more source
Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source
Mapping the “Supply–Demand–Flow” of Ecosystem Services for Ecosystem Management in China
This study develops a “supply–demand–flow” framework clarifies how ecosystem services move between regions by distinguishing potential and actual supply and demand. Using integrated biophysical–socioeconomic modeling, nine services in China were mapped.
Yikun Zhang +3 more
wiley +1 more source

