Results 71 to 80 of about 788,437 (264)
IEEE AccessImage restoration under adverse weather conditions refers to the process of removing degradation caused by weather particles while improving visual quality.
Zihan Shen, Yu Xuan, Qingyu Yang
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Rock CT Image Super-Resolution Using Residual Dual-Channel Attention Generative Adversarial Network
Energies, 2022 Because of its benefits in terms of high speed, non-destructiveness, and three-dimensionality, as well as ease of integration with computer simulation, computed tomography (CT) technology is widely applied in reservoir geology research.
Liqun Shan +4 more
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Is the Bellman residual a bad proxy? [PDF]
, 2017 This paper aims at theoretically and empirically comparing two standard optimization criteria for Reinforcement Learning: i) maximization of the mean value and ii) minimization of the Bellman residual.
Geist, Matthieu +2 more
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Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution
, 2016 In this work, we consider the image super-resolution (SR) problem. The main challenge of image SR is to recover high-frequency details of a low-resolution (LR) image that are important for human perception.
Feng, Jiashi +6 more
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Disordered but rhythmic—the role of intrinsic protein disorder in eukaryotic circadian timing
FEBS Letters, EarlyView.Unstructured domains known as intrinsically disordered regions (IDRs) are present in nearly every part of the eukaryotic core circadian oscillator. IDRs enable many diverse inter‐ and intramolecular interactions that support clock function. IDR conformations are highly tunable by post‐translational modifications and environmental conditions, which ...
Emery T. Usher, Jacqueline F. Pelham
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Remote Sensing, 2023 Landslide susceptibility evaluation can accurately predict the spatial distribution of potential landslides, which offers great usefulness for disaster prevention, disaster reduction, and land resource management.
Zhuolu Wang +7 more
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Deep Limits of Residual Neural Networks
, 2019 Neural networks have been very successful in many applications; we often, however, lack a theoretical understanding of what the neural networks are actually learning. This problem emerges when trying to generalise to new data sets.
Thorpe, Matthew, van Gennip, Yves
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Liquid biopsy epigenetics: establishing a molecular profile based on cell‐free DNA
Molecular Oncology, EarlyView.Cell‐free DNA (cfDNA) fragments in plasma from cancer patients carry epigenetic signatures reflecting their cells of origin. These epigenetic features include DNA methylation, nucleosome modifications, and variations in fragmentation. This review describes the biological properties of each feature and explores optimal strategies for harnessing cfDNA ...
Christoffer Trier Maansson +2 more
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A multi-crop disease identification approach based on residual attention learning
Journal of Intelligent Systems, 2023 In this work, a technique is proposed to identify the diseases that occur in plants. The system is based on a combination of residual network and attention learning. The work focuses on disease identification from the images of four different plant types
Kirti, Rajpal Navin
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Sensors, 2021 Convolutional neural networks (CNNs) are a state-of-the-art technique for speech emotion recognition. However, CNNs have mostly been applied to noise-free emotional speech data, and limited evidence is available for their applicability in emotional ...
Youngja Nam, Chankyu Lee
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