Results 91 to 100 of about 98,849 (283)

Tuned Contrastive Learning

2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
In recent times, contrastive learning based loss functions have become increasingly popular for visual self-supervised representation learning owing to their state-of-the-art (SOTA) performance. Most of the modern contrastive learning methods generalize only to one positive and multiple negatives per anchor.
Animesh, Chaitanya, Chandraker, Manmohan
openaire   +2 more sources

Large‐scale bidirectional arrayed genetic screens identify OXR1 and EMC4 as modifiers of αSynuclein aggregation

FEBS Open Bio, EarlyView.
Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane, Lea Nikolić, Lorenzo Maraio, Thomas Goiran, Nathan Karpilovsky, Stefano Sellitto, Vangelis Bouris, Jiang‐An Yin, Ronald Melki, Edward A Fon, Adriano Aguzzi, Elena De Cecco   +11 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

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

Long‐Term Follow‐Up of Chemotherapy‐Associated Biological Aging in Women With Early Breast Cancer

Aging and Cancer, EarlyView.
Women threated with adjuvant chemotherapy for early breast cancer have sustained long‐term increase in p16INK4a,, a robust marker of cell senescence, suggesting a chemotherapy‐associated age acceleration. p16INK4a as well as other biomarkers may identify patients at greatest risk for senescence‐related diseases of aging.
Hyman B. Muss, Alexis C. Wardell, Allison M. Deal, Kirsten A. Nyrop, Allison Ross, Yara G. Abdou, Jeffrey D. Aldrich, Lisa A. Cary, E. Claire Dees, Katherine E. Reeder‐Hayes, Emily M. Ray, Natalia Mitin, Denis Tsygankov   +12 more
wiley   +1 more source

Contrast-reversal abolishes perceptual learning

Journal of Vision, 2009
We tested the effects of contrast reversal on perceptual learning in a 10AFC texture identification task. Four groups of subjects performed the task on two consecutive days. One group saw the same textures on both days, whereas three other groups saw novel, rotated (180 deg), or contrast-reversed textures on the second day.
Zahra, Hussain, Allison B, Sekuler, Patrick J, Bennett   +2 more
openaire   +2 more sources

Prediction of Myasthenia Gravis Worsening: A Machine Learning Algorithm Using Wearables and Patient‐Reported Measures

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Myasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life‐threatening crises. Timely interventions may be delayed by limited access to care and fragmented documentation. Our objective was to develop predictive algorithms for MG deterioration using multimodal telemedicine data ...
Maike Stein, Haoqi Sun, Sophie Lehnerer, Lea Gerischer, Maximilian Mönch, Christian Meisel, Andreas Meisel, Pushpa Narayanaswami   +7 more
wiley   +1 more source

Contrasting the landscape of contrastive and non-contrastive learning

, 2022
A lot of recent advances in unsupervised feature learning are based on designing features which are invariant under semantic data augmentations. A common way to do this is contrastive learning, which uses positive and negative samples. Some recent works however have shown promising results for non-contrastive learning, which does not require negative ...
Pokle, Ashwini, Tian, Jinjin, Li, Yuchen, Risteski, Andrej   +3 more
openaire   +1 more source

Hospital Readmission After Traumatic Brain Injury Hospitalization in Community‐Dwelling Older Adults

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To examine the risk of hospital readmission after an index hospitalization for TBI in older adults. Methods Using data from the Atherosclerosis Risk in Communities (ARIC) study, we used propensity score matching of individuals with an index TBI‐related hospitalization to individuals with (1) non‐TBI hospitalizations (primary analysis)
Rachel Thomas, Connor A. Law, Joan A. Casey, Thomas Mosley, Rebecca F. Gottesman, Ramon Diaz‐Arrastia, Holly Elser, Andrea L. C. Schneider   +7 more
wiley   +1 more source

Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu, Qi Wang, Suhui Kuang, Dezhi Cao, Ping Ding, Shaohui Zhang, Haihua Wei, Zhirong Wei, Jinshan Xu, Xinyu Huang, Bing Liu, Shuli Liang   +11 more
wiley   +1 more source