Results 221 to 230 of about 36,522 (258)
Integrative Zoology, EarlyView.This study integrates genomics and landscape genetics to analyze African goat environmental adaptation. Analyzing 1591 samples, it finds population structure differentiates geographically into four groups, with gene flow between wild Yura goats and North Africans.
Weifeng Peng +19 more
wiley +1 more source
Developmental emergence of sparse and structured synaptic connectivity in the hippocampal CA3 memory circuit. [PDF]
Nat CommunVargas-Barroso V +4 more
europepmc +1 more source
Integrative Zoology, EarlyView.A high‐quality chromosome‐level reference genome was constructed for Hucho bleekeri. Population structure and environmental adaptation of Hucho species were revealed by whole‐genome resequencing. ABSTRACT Salmonidae represents an important family in the study of genome evolution following genome duplication.
Yeyu Chen +9 more
wiley +1 more source
Dynamic graph convolution with comprehensive pruning and GNN classification for precise lymph node metastasis detection. [PDF]
Sci RepH N C, N S, S AR, Singh C, Y R.
europepmc +1 more source
Interventional oncology in children: Where are we now?
Journal of Medical Imaging and Radiation Oncology, EarlyView.Abstract Paediatric Interventional Oncology (IO) lags behind adult IO due to a scarcity of specific outcome data. The suboptimal way to evolve this field is relying heavily on adult experiences. The distinct tumour types prevalent in children, such as extracranial germ cell tumours, sarcomas, and neuroblastoma, differ strongly from those found in ...
Premal Amrishkumar Patel +1 more
wiley +1 more source
Transcriptomic analysis reveals synaptic dysregulation and mitochondrial dysfunction in autism spectrum disorder. [PDF]
Brain Behav Immun HealthLiao X, Shao J, Chen Z.
europepmc +1 more source
Neuroimmune Clearance and EEG Biomarkers: A Unified Model of ASD and Dyslexia. [PDF]
Neural PlastEroğlu G.
europepmc +1 more source
Pruning-aware Sparse Regularization for Network Pruning
Machine Intelligence Research, 2023 Structural neural network pruning aims to remove the redundant channels in the deep convolutional neural networks (CNNs) by pruning the filters of less importance to the final output accuracy. To reduce the degradation of performance after pruning, many methods utilize the loss with sparse regularization to produce structured sparsity.
Xu Zhao, Nanfei Jiang
exaly +3 more sources
Filter Sketch for Network Pruning [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 We propose a novel network pruning approach by information preserving of pre-trained network weights (filters). Network pruning with the information preserving is formulated as a matrix sketch problem, which is efficiently solved by the off-the-shelf Frequent Direction method.
Rongrong Ji, Yonghong Tian, Qi Tian
exaly +4 more sources
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Pruning Weightless Neural Networks
ESANN 2022 proceedings, 2022Weightless neural networks (WNNs) are a type of machine learning model which perform prediction using lookup tables (LUTs) instead of arithmetic operations. Recent advancements in WNNs have reduced model sizes and improved accuracies, reducing the gap in accuracy with deep neural networks (DNNs).
Zachary Susskind +11 more
openaire +2 more sources