Results 101 to 110 of about 391,322 (253)

Target detection technology of coal mine wheeled robot combining improved CNN and self attention mechanism

open access: yesMeikuang Anquan
In complex coal mine environment and poor lighting conditions, the existing target detection technology is difficult to meet the needs of intelligent inspection.
Junfei TANG   +5 more
doaj   +1 more source

MOGAD Is the Most Common Cause of Isolated Optic Neuritis in Children

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objectives The study aimed to characterize the clinical features, etiologies, and outcomes of isolated, first‐time pediatric ON in the post‐MOG‐IgG era. Methods This was a single‐center retrospective cohort study at Texas Children's Hospital of patients diagnosed with first‐time ON between 2018–2024, with follow‐up data collected through 2025.
Chaitanya Aduru   +13 more
wiley   +1 more source

Human Activity Recognition Based on Self-Attention Mechanism in WiFi Environment

open access: yesIEEE Access
In recent years, the use of WiFi Channel State Information (CSI) for Human Activity Recognition (HAR) has attracted widespread attention, thanks to its low cost and non-intrusive advantages.
Fei Ge   +6 more
doaj   +1 more source

Unraveling 4‐Phenylbutyrate's Therapeutic Role in SLC6A1 Disorders: Pharmacochaperoning Over HDAC Inhibition

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Variants in SLC6A1, encoding the GABA transporter 1 (GAT‐1), cause epilepsy, autism spectrum disorder, and developmental delay via loss of GABA uptake, impaired trafficking, and ER retention. We previously found that 4‐Phenylbutyrate (PBA), an FDA‐approved drug, restores GABA uptake and reduces seizures in SLC6A1‐related disorders ...
Melissa B. DeLeeuw   +5 more
wiley   +1 more source

Spatiotemporal Sequence Prediction Based on Spatiotemporal Self-Attention Mechanism

open access: yesINTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
This paper introduces the GCN-Transformer model, an innovative approach that combines Graph Convolutional Networks (GCNs) and Transformer architectures to enhance spatiotemporal sequence prediction. Targeted at applications requiring precise analysis of complex spatial and temporal data, the model was tested on two distinct datasets: PeMSD8 for traffic
Yuan Zhao, Junlin Lu
openaire   +1 more source

Sex‐Stratified Association of Regional Dopamine Transporter Binding With Disease Progression in Amyotrophic Lateral Sclerosis

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To clarify the clinical relevance of dopamine transporter single‐photon emission computed tomography (DAT‐SPECT) abnormalities in amyotrophic lateral sclerosis (ALS), with a prespecified focus on sex‐stratified associations with disease progression and short‐term prognosis.
Tomoya Kawazoe   +7 more
wiley   +1 more source

Image compressed sensing reconstruction network based on self-attention mechanism

open access: yesJournal of Measurement Science and Instrumentation
For image compression sensing reconstruction, most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers, which usually have defects of obvious block effects, high computational complexity, and long ...
LIU Yuhong, LIU Xiaoyan, CHEN Manyin
doaj  

Digital Cognitive Testing in Mitochondrial Disease: Validity and Challenges for Clinical Trial Use

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Primary mitochondrial disease is a group of genetic disorders caused by pathogenic variants in nuclear or mitochondrial DNA, often resulting in progressive neurodegeneration and cognitive decline. Current management is primarily supportive, though recent research offers hope for disease‐modifying treatments in the future.
Oksana Pogoryelova   +9 more
wiley   +1 more source

Factors Associated With the Rising Trend in Self‐Reported Cognitive Disability Among U.S. Adults Aged 18–39 From 2013–2024

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Building on our prior Behavioral Risk Factor Surveillance System analysis identifying adults aged 18–39 as the primary driver of the national increase in self‐reported cognitive disability, we examined factors associated with this rise using 2013–2024 U.S. BRFSS data. Methods We analyzed U.S.
Adam de Havenon   +9 more
wiley   +1 more source

DistrAttention: An Efficient and Flexible Self-Attention Mechanism on Modern GPUs

open access: yesCoRR
The Transformer architecture has revolutionized deep learning, delivering the state-of-the-art performance in areas such as natural language processing, computer vision, and time series prediction. However, its core component, self-attention, has the quadratic time complexity relative to input sequence length, which hinders the scalability of ...
Haolin Jin   +6 more
openaire   +2 more sources

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