381,128 research outputs found
RCSB PDB Mobile: iOS and Android mobile apps to provide data access and visualization to the RCSB Protein Data Bank.
SummaryThe Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) resource provides tools for query, analysis and visualization of the 3D structures in the PDB archive. As the mobile Web is starting to surpass desktop and laptop usage, scientists and educators are beginning to integrate mobile devices into their research and teaching. In response, we have developed the RCSB PDB Mobile app for the iOS and Android mobile platforms to enable fast and convenient access to RCSB PDB data and services. Using the app, users from the general public to expert researchers can quickly search and visualize biomolecules, and add personal annotations via the RCSB PDB's integrated MyPDB service.Availability and implementationRCSB PDB Mobile is freely available from the Apple App Store and Google Play (http://www.rcsb.org)
Mobile-Based Video Caching Architecture Based on Billboard Manager
Video streaming services are very popular today. Increasingly, users can now
access multimedia applications and video playback wirelessly on their mobile
devices. However, a significant challenge remains in ensuring smooth and
uninterrupted transmission of almost any size of video file over a 3G network,
and as quickly as possible in order to optimize bandwidth consumption. In this
paper, we propose to position our Billboard Manager to provide an optimal
transmission rate to enable smooth video playback to a mobile device user
connected to a 3G network. Our work focuses on serving user requests by mobile
operators from cached resource managed by Billboard Manager, and transmitting
the video files from this pool. The aim is to reduce the load placed on
bandwidth resources of a mobile operator by routing away as much user requests
away from the internet for having to search a video and, subsequently, if
located, have it transferred back to the user.Comment: 8 pages, 1 figure, GridCom-201
Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks
An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive h -mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks
Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach
Efficient identification of people and objects, segmentation of regions of
interest and extraction of relevant data in images, texts, audios and videos
are evolving considerably in these past years, which deep learning methods,
combined with recent improvements in computational resources, contributed
greatly for this achievement. Although its outstanding potential, development
of efficient architectures and modules requires expert knowledge and amount of
resource time available. In this paper, we propose an evolutionary-based neural
architecture search approach for efficient discovery of convolutional models in
a dynamic search space, within only 24 GPU hours. With its efficient search
environment and phenotype representation, Gene Expression Programming is
adapted for network's cell generation. Despite having limited GPU resource time
and broad search space, our proposal achieved similar state-of-the-art to
manually-designed convolutional networks and also NAS-generated ones, even
beating similar constrained evolutionary-based NAS works. The best cells in
different runs achieved stable results, with a mean error of 2.82% in CIFAR-10
dataset (which the best model achieved an error of 2.67%) and 18.83% for
CIFAR-100 (best model with 18.16%). For ImageNet in the mobile setting, our
best model achieved top-1 and top-5 errors of 29.51% and 10.37%, respectively.
Although evolutionary-based NAS works were reported to require a considerable
amount of GPU time for architecture search, our approach obtained promising
results in little time, encouraging further experiments in evolutionary-based
NAS, for search and network representation improvements.Comment: Accepted for presentation at the IEEE Congress on Evolutionary
Computation (IEEE CEC) 202
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