Results 141 to 150 of about 26,557 (311)

Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation [PDF]

, 2023
Spatial-temporal graph learning has emerged as a promising solution for modeling structured spatial-temporal data and learning region representations for various urban sensing tasks such as crime forecasting and traffic flow prediction.
Yiu, Siuming, Xia, Lianghao, Wang, Zheng, Huang, Chao, Zhang, Qianru, Han, Ruihua   +5 more
core  

Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani, Emmanuel Barbeau, Béatrice Lemesle, Emilie Milongo‐Rigal, Sophie Fernandez, Sandrine Charpentier, Jean‐François Albucher, Nicolas Raposo, Fabrice Bonneville, Patrice Péran, Jérémie Pariente   +10 more
wiley   +1 more source

Unsupervised Bilingual POS Tagging with Markov Random Fields [PDF]

, 2011
In this paper, we give a treatment to the problem of bilingual part-of-speech induction with parallel data. We demonstrate that naive optimizationof log-likelihood with joint MRFs suffers from a severe problem of local maxima,and suggest an alternative –
Chen, Desai, Cohen, Shay, Smith, Noah A., Dyer, Chris   +3 more
core  

scSCC: A swapped contrastive learning‐based clustering method for single‐cell gene expression data

Quantitative Biology
Cell clustering plays a pivotal role in deciphering the intricacies of cell types, facilitating subsequent cell annotation endeavors within scRNA‐seq data analysis.
Xiang Wang, Sansheng Yang, Hongwei Li
doaj   +1 more source

A Good View for Graph Contrastive Learning

Entropy
Due to the success observed in deep neural networks with contrastive learning, there has been a notable surge in research interest in graph contrastive learning, primarily attributed to its superior performance in graphs with limited labeled data. Within
Xueyuan Chen, Shangzhe Li
doaj   +1 more source

Spatial and Volumetric Characteristics of Glioblastoma: Associations With Clinical Presentation and Survival

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective We aim to comprehensively analyze how regional tumor and edema characteristics are associated with clinical presentations and survival outcomes in a large cohort of glioblastoma patients. Methods Patients with IDH‐wildtype glioblastoma who received brain MRI from 2010 to 2023 were included.
Daniel J. Zhou, Marc Jaskir, Julian Gal, Yombe Fonkeu, Christos Davatzikos, MacLean P. Nasrallah, Jose Garcia, Rubo Xing, Stephen J. Bagley, Arati Desai, Richard E. Phillips, Steven Tobochnik, Colin A. Ellis, James J. Gugger, Nishant Sinha, Joel M. Stein, Kathryn A. Davis   +16 more
wiley   +1 more source

Statistical Contrastive Learning for Spatio-Temporal Anomaly Detection

Data Science in Science
Anomaly detection is an interdisciplinary research area which attracts substantial attention both in statistics and in machine learning due to its critical role in a wide range of diverse applications, from cybersecurity to health monitoring.
Zhiwei Zhen, Yuzhou Chen, Yulia R. Gel
doaj   +1 more source

Distortion-Disentangled Contrastive Learning

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Oral in ...
Jinfeng Wang 0008, Sifan Song, Jionglong Su, S. Kevin Zhou   +3 more
openaire   +2 more sources

Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Epilepsy surgery is severely underutilized despite proven efficacy, with substantial under‐referral of eligible patients in routine clinical practice. This study evaluated the potential role of large language models (LLMs) as decision‐support tools for screening unstructured clinical notes to identify epilepsy surgery candidates and ...
Uriel Fennig, Nadav Amir, Maya Schiller, Roni Loebenstein, Johnatan Nissan, Marina Boxer, Shany Guly Gofrit, Gal Goshen, Sándor Beniczky, Nicola Maggio   +9 more
wiley   +1 more source

Rethinking spatial-temporal contrastive learning for Urban traffic flow forecasting: multi-level augmentation framework

Complex & Intelligent Systems
Graph neural networks integrating contrastive learning have attracted growing attention in urban traffic flow forecasting. However, most existing graph contrastive learning methods do not perform well in capturing local–global spatial dependencies or ...
Lin Pan, Qianqian Ren, Zilong Li, Xingfeng Lv   +3 more
doaj   +1 more source