Results 111 to 120 of about 5,414 (291)

Neural Fields for Highly Accelerated 2D Cine Phase Contrast MRI

Advanced Science, EarlyView.
ABSTRACT 2D cine phase contrast (CPC) MRI provides quantitative information on blood velocity and flow within the human vasculature. However, data acquisition is time‐consuming, motivating the reconstruction of the velocity field from undersampled measurements to reduce scan times. In this work, neural fields are proposed as a continuous spatiotemporal
Pablo Arratia, Martin J. Graves, Mary McLean, Carolin Pirkl, Carola‐Bibiane Schönlieb, Timo Schirmer, Florian Wiesinger, Matthias J. Ehrhardt   +7 more
wiley   +1 more source

Reproducing kernel Hilbert spaces

, 2021
U ovom radu upoznajemo se s teorijom Hilbertovih prostora reproducirajućih jezgri te objašnjavamo matematičku pozadinu primjene jezgrenih funkcija u umjetnoj inteligenciji. Istražujemo vezu između pozitivno semidefinitnih funkcija i jezgrenih funkcija te
Buljan, Antonio
core  

A Bioinspired Three‐Dimensional High‐Curvature Nano‐Interface Integrated Microfluidic Chip for Small Extracellular Vesicles Enrichment and Machine Learning‐Assisted Prostate Cancer Precision Diagnosis

Advanced Science, EarlyView.
A biotin‐modified artificial insertion peptide functionalized three‐dimensional high‐curvature‐TiO2 nano‐interface was engineered in a microfluidic chip to improve the isolation efficiency of small extracellular vesicles (sEVs). This chip balanced affinity, releasability, and extendibility, enabling high‐throughput recovery of sEVs for downstream ...
Le Wang, Yizhong Liang, Manan Sulaiman, Jiaqi Du, Ming Jiang, Zhihua Wang, Xu Yu, Li Xu   +7 more
wiley   +1 more source

Ridge Regression Learning Algorithm in Dual Variables

, 1998
In this paper we study a dual version of the Ridge Regression procedure. It allows us to perform non-linear regression by constructing a linear regression function in a high dimensional feature space.
C. Saunders, V. Vovk, Gammerman, A., Vovk, V., A. Gammerman, Saunders, C.   +5 more
core  

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

Advanced Science, EarlyView.
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, Adithya Umanath Rai, Jose Thomson, Marzie Ghorbani, Shujing Zhao, Nick Birbilis   +5 more
wiley   +1 more source

Reproducing kernel Hilbert space method for solutions of a coefficient inverse problem for the kinetic equation

, 2018
On the basis of a reproducing kernel Hilbert space, reproducing kernel functions for solving the coefficient inverse problem for the kinetic equation are given in this paper.
Esra Karatas Akgül
core   +1 more source

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
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, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

A Study of the Generalized Gabor Transform with Applications to Reproducing Kernel Theory

Mathematics
The aim of this paper is to establish an inversion and Calderón formulas for the generalized Gabor transform associated with a class of Sturm–Liouville operators.
Saifallah Ghobber, Hatem Mejjaoli
doaj   +1 more source

The Zero-Removing Property and Lagrange-Type Interpolation Series

, 2011
The classical Kramer sampling theorem, which provides a method for obtaining orthogonal sampling formulas, can be formulated in a more general nonorthogonal setting.
M. A. Hernández-Medina, García, A.G., P. E. Fernàndez-Moncada, A. G. García, Fernàndez-Moncada, P.E., Hernández Medina, Miguel Ángel   +5 more
core   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
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, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source