Results 181 to 190 of about 28,763 (261)
A Rapid Segmentation Method Based on Few-Shot Learning: A Case Study on Roadways. [PDF]
Cai H, Chen J, Yin Y, Yu J, Dong Z.
europepmc +1 more source
Upon mitotic entry, RTN4 relocalizes to the pericentrosomal region, forming a more tubular ER network around centrosomes. CDK1‐mediated phosphorylation of RTN4 increases its interaction with Rab11 GTPase, facilitating dynein‐dependent transport of RTN4 to the pericentrosomal region.
Xiangyu Xu +9 more
wiley +1 more source
SSNFNet: An Enhanced Few-Shot Learning Model for Efficient Poultry Farming Detection. [PDF]
Wang B, Liu D, Wu J.
europepmc +1 more source
Correcting the apparent priming effect resolves systematic biases in Asian rice fertilizer nitrogen accounting. Net soil retention drops below 7%, while 48% of fertilizer escapes, inflicting US$98.53 billion in annual reactive‐nitrogen damages. High‐resolution mapping uncovers N‐risk archetypes across 42% of the rice area, delivering a spatially ...
Xiuyun Liu +5 more
wiley +1 more source
Few-Shot Learning for Prostate Cancer Detection on MRI: Comparative Analysis with Radiologists' Performance. [PDF]
Yamagishi Y +5 more
europepmc +1 more source
Employing a digital single‐molecule activity tracker (dSMAT), this research demonstrates that high‐photon‐flux irradiation drives progressive oxidative scarring in polymerases. Unlike simple thermal denaturation, real‐time kinetic tracking dynamically visualizes enzymes degrading into multiple impaired subpopulations.
Anran Zheng +11 more
wiley +1 more source
Discovery of antimicrobial peptides targeting Acinetobacter baumannii via a pre-trained and fine-tuned few-shot learning-based pipeline. [PDF]
Huang J +9 more
europepmc +1 more source
Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho +6 more
wiley +1 more source
Multi-modal few-shot learning for anthesis prediction of individual wheat plants. [PDF]
Xie Y, Roy SJ, Schilling RK, Liu H.
europepmc +1 more source
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez +10 more
wiley +1 more source

