Results 111 to 120 of about 26,557 (311)

Relaxed Contrastive Learning for Federated Learning

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
We propose a novel contrastive learning framework to effectively address the challenges of data heterogeneity in federated learning. We first analyze the inconsistency of gradient updates across clients during local training and establish its dependence on the distribution of feature representations, leading to the derivation of the supervised ...
Seonguk Seo, Jinkyu Kim 0005, Geeho Kim, Bohyung Han   +3 more
openaire   +2 more sources

Super‐Refractory Status Epilepticus (SRSE) in a Patient With Compound Heterozygous OPA1 Variants: Case Report and Literature Review

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Super‐Refractory Status Epilepticus (SRSE) is a rare, life‐threatening neurological emergency with unclear etiology in many cases. Mitochondrial dysfunction, often due to disease‐causing genetic variants, is increasingly recognized as a cause, with each gene producing distinct pathophysiological mechanisms.
Pouria Mohammadi, Lara Basovic, Christopher Michael McGraw   +2 more
wiley   +1 more source

Partial contrastive point cloud self-supervised representation learning

Scientific Reports
Annotating 3D point cloud data is labor-intensive. Self-supervised representation learning can reduce the intense demand of manual annotation. However, the sparsity of point cloud, while containing rich geometric structural information, makes the self ...
Zijun Cheng, Yiguo Wang
doaj   +1 more source

Remote Assessment of Ataxia Severity in SCA3 Across Multiple Centers and Time Points

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Spinocerebellar ataxia type 3 (SCA3) is a genetically defined ataxia. The Scale for Assessment and Rating of Ataxia (SARA) is a clinician‐reported outcome that measures ataxia severity at a single time point. In its standard application, SARA fails to capture short‐term fluctuations, limiting its sensitivity in trials.
Marcus Grobe‐Einsler, Vivian Maas, Arian Taheri Amin, Jennifer Faber, Tamara Schaprian, Katharina Hill, Matthis Synofzik, Lisa H. Graf, Heike Jacobi, Manuela Lima, Ana F. Ferreira, Bart P. van de Warrenburg, Ilse H. J. Willemse, Dagmar Timmann, Andreas Thieme, Paola Giunti, Hector Garcia‐Moreno, Magda M. Santana, Jeannette Hübener‐Schmid, Elin H. Davies, Thomas Klockgether   +20 more
wiley   +1 more source

Line graph contrastive learning for node classification

Journal of King Saud University: Computer and Information Sciences
Existing graph contrastive learning methods often rely on differences in node features within subgraphs, lacking effective capture of the global structural information of the graph.
Mingyuan Li, Lei Meng, Zhonglin Ye, Yuzhi Xiao, Shujuan Cao, Haixing Zhao   +5 more
doaj   +1 more source

Clustering Algorithm Reveals Dopamine‐Motor Mismatch in Cognitively Preserved Parkinson's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. Methods n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort.
Rachele Malito, Chiara Meneghini, Alice Galli, Luca Gallo, Pierfrancesco Mitrotti, Paola Dimartino, Marianna Inglese, Cinzia Zatti, Alessandro Lupini, Andrea Pilotto, Alessandro Padovani, Micol Avenali, Enza Maria Valente, Cristina Tassorelli, Silvia P. Caminiti   +14 more
wiley   +1 more source

Evaluating Prefix-Driven Contrastive Learning for Semantic Search in High-Impact Societal Domains [PDF]

openSemantic search is a fundamental component of information retrieval, particularly in high- impact domains where extracting precise and contextually relevant information is crucial.
YILMAZ, CEREN
core  

Knowledge-aware contrastive heterogeneous molecular graph learning. [PDF]

Molecular representation learning is pivotal in predicting molecular properties and advancing drug design. Traditional methodologies, which predominantly rely on homogeneous graph encoding, are limited by their inability to integrate external knowledge ...
Jia Wu, Chen, Mukun, Hu, Wenbin, Wu, Jia, Bo Du, Shirui Pan, Fu Lin, Pan, Shirui, Gong, Xiuwen, Xiuwen Gong, Wenbin Hu, Du, Bo, Lin, Fu, Mukun Chen   +13 more
core   +2 more sources

Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning Model

Proceedings of the AAAI Conference on Artificial Intelligence
Graph contrastive learning (GCL) aims to learn representations from unlabeled graph data in a self-supervised manner and has developed rapidly in recent years. However, edge-level contrasts are not well explored by most existing GCL methods. Most studies in GCL only regard edges as auxiliary information while updating node features.
Yujun Li, Hongyuan Zhang, Yuan Yuan
openaire   +2 more sources

CX3CL1 in Early Detection of Alzheimer's Disease: Plasma Dynamics Across Age and Disease Stages

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Backgrounds Alzheimer's disease (AD) is characterized by amyloid‐beta plaques, tau tangles, and neuroinflammation. C‐X3‐C motif chemokine ligand 1 (CX3CL1, also known as fractalkine), a neuroimmune chemokine implicated in AD pathogenesis, shows inconsistent alterations in plasma/serum across studies.
Ling Wang, Yujie Liu, Fei Li, Xuelin Li, Lanlan Li, Jie Zhang, Yali Xu   +6 more
wiley   +1 more source