Cooperation of Spaln and Prrn5 for Construction of Gene-Structure-Aware Multiple Sequence Alignment.
Sign Up to like & getrecommendations! Published in 2021 at "Methods in molecular biology"
DOI: 10.1007/978-1-0716-1036-7_5
Abstract: Gene-structure-aware multiple sequence alignment (GSA-MSA) is conventionally used as a tool for analyzing evolutionary changes in gene structure, i.e., gain and loss of introns during the course of evolution of homologous eukaryotic genes. Recently, however,… read more here.
Keywords: gene structure; structure aware; gene; aware multiple ... See more keywords
A structure-aware axis-aligned grid deformation approach for robust image retargeting
Sign Up to like & getrecommendations! Published in 2017 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-017-4674-1
Abstract: Content-based image retargeting is a technique that resizes an input image to a given target resolution while minimizing distortions of important objects caused by aspect ratio variations. Conventional approaches have shared a similar methodology which… read more here.
Keywords: structure aware; image; aligned grid; axis aligned ... See more keywords
Structure-aware fusion network for 3D scene understanding
Sign Up to like & getrecommendations! Published in 2021 at "Chinese Journal of Aeronautics"
DOI: 10.1016/j.cja.2021.07.012
Abstract: Abstract In this paper, we propose a Structure-Aware Fusion Network (SAFNet) for 3D scene understanding. As 2D images present more detailed information while 3D point clouds convey more geometric information, fusing the two complementary data… read more here.
Keywords: structure aware; scene understanding; point clouds; fusion network ... See more keywords
Structure-Aware Multimodal Deep Learning for Drug-Protein Interaction Prediction
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c00060
Abstract: Identifying drug-protein interactions (DPIs) is crucial in drug discovery, and a number of machine learning methods have been developed to predict DPIs. Existing methods usually use unrealistic data sets with hidden bias, which will limit… read more here.
Keywords: structure aware; prediction; protein; drug ... See more keywords
Structure-Aware Multikernel Learning for Hyperspectral Image Classification
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2021.3111740
Abstract: Recently, the inclusion of spatial information has drawn increasing attention in hyperspectral image (HSI) applications due to its effectiveness in terms of improving classification accuracy. However, most of the techniques that include such spatial knowledge… read more here.
Keywords: hyperspectral image; multikernel learning; structure aware; aware multikernel ... See more keywords
Structure-Aware Reinforcement Learning for Node-Overload Protection in Mobile Edge Computing
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Cognitive Communications and Networking"
DOI: 10.1109/tccn.2022.3195503
Abstract: Mobile Edge Computing (MEC) involves placing computational capability and applications at the edge of the network, providing benefits such as reduced latency, reduced network congestion, and improved performance of applications. The performance and reliability of… read more here.
Keywords: mobile edge; reinforcement learning; structure; structure aware ... See more keywords
Edge Repartitioning via Structure-Aware Group Migration
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Computational Social Systems"
DOI: 10.1109/tcss.2021.3090373
Abstract: Graph partitioning is a mandatory step in distributed graph computing systems. Some existing systems use edge partitioning methods to partition static graphs. However, the structure of the real-world graphs changes dynamically, which leads to unnecessary… read more here.
Keywords: vertex replicas; structure; structure aware; group ... See more keywords
Structure-Aware Collaborative Representation for Hyperspectral Image Classification
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2019.2912507
Abstract: Recently, collaborative representation (CR) has drawn increasing attention in hyperspectral image classification due to its simplicity and effectiveness. However, existing representation-based classifiers do not explicitly utilize class label information of training samples in estimating representation… read more here.
Keywords: representation; hyperspectral image; collaborative representation; image classification ... See more keywords
Structure-Aware Weakly Supervised Network for Building Extraction From Remote Sensing Images
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3217830
Abstract: The use of fully supervised deep learning methods to extract buildings from remote sensing images (RSIs) has shown excellent performance, which requires large amounts of training data with laborious per-pixel labeling. Compared with pixelated intensive… read more here.
Keywords: remote sensing; structure; weakly supervised; structure aware ... See more keywords
Global Context Assisted Structure-Aware Vehicle Retrieval
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2020.3009000
Abstract: In vehicle retrieval, the vehicle patch should first be localized to remove the irrelevant background information. Moreover, the negative samples are much more prevalent than the positive samples, and the information from the negative samples… read more here.
Keywords: vehicle; structure aware; global context; vehicle retrieval ... See more keywords
Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on medical imaging"
DOI: 10.1109/tmi.2022.3149281
Abstract: Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis. However, the current methods are time-consuming and suffer from large biases in landmark localization,… read more here.
Keywords: aware long; long short; short term; structure ... See more keywords