1,345 research outputs found
Joint Resource Allocation and Cache Placement for Location-Aware Multi-User Mobile Edge Computing
With the growing demand for latency-critical and computation-intensive
Internet of Things (IoT) services, mobile edge computing (MEC) has emerged as a
promising technique to reinforce the computation capability of the
resource-constrained mobile devices. To exploit the cloud-like functions at the
network edge, service caching has been implemented to (partially) reuse the
computation tasks, thus effectively reducing the delay incurred by data
retransmissions and/or the computation burden due to repeated execution of the
same task. In a multiuser cache-assisted MEC system, designs for service
caching depend on users' preference for different types of services, which is
at times highly correlated to the locations where the requests are made. In
this paper, we exploit users' location-dependent service preference profiles to
formulate a cache placement optimization problem in a multiuser MEC system.
Specifically, we consider multiple representative locations, where users at the
same location share the same preference profile for a given set of services. In
a frequency-division multiple access (FDMA) setup, we jointly optimize the
binary cache placement, edge computation resources and bandwidth allocation to
minimize the expected weighted-sum energy of the edge server and the users with
respect to the users' preference profile, subject to the bandwidth and the
computation limitations, and the latency constraints. To effectively solve the
mixed-integer non-convex problem, we propose a deep learning based offline
cache placement scheme using a novel stochastic quantization based
discrete-action generation method. In special cases, we also attain suboptimal
caching decisions with low complexity leveraging the structure of the optimal
solution. The simulations verify the performance of the proposed scheme and the
effectiveness of service caching in general.Comment: 32 pages, 9 figures, submitted for possible journal publicatio
Invertible Mosaic Image Hiding Network for Very Large Capacity Image Steganography
The existing image steganography methods either sequentially conceal secret
images or conceal a concatenation of multiple images. In such ways, the
interference of information among multiple images will become increasingly
severe when the number of secret images becomes larger, thus restrict the
development of very large capacity image steganography. In this paper, we
propose an Invertible Mosaic Image Hiding Network (InvMIHNet) which realizes
very large capacity image steganography with high quality by concealing a
single mosaic secret image. InvMIHNet consists of an Invertible Image Rescaling
(IIR) module and an Invertible Image Hiding (IIH) module. The IIR module works
for downscaling the single mosaic secret image form by spatially splicing the
multiple secret images, and the IIH module then conceal this mosaic image under
the cover image. The proposed InvMIHNet successfully conceal and reveal up to
16 secret images with a small number of parameters and memory consumption.
Extensive experiments on ImageNet-1K, COCO and DIV2K show InvMIHNet outperforms
state-of-the-art methods in terms of both the imperceptibility of stego image
and recover accuracy of secret image
Spectrum Comparative Study of Commutation Failure and Short-Circuit Fault in UHVDC Transmission System
When commutation failure occurs in UHVDC transmission system, the transient process of DC voltage and current are similar to grounding short-circuit fault. In order to differentiate them effectively, the paper introduces mathematical morphology methods to analysis the spectrum of transient current. Base on Yunnan-Guangzhou kV UHVDC transmission system, the paper simulates the commutation failure and DC line short-circuit fault under different fault conditions in PSCAD/EMTDC. By modified morphology filter, the transient signal of DC () is decomposed into six scales, and morphological characteristics of aerial mode component of is analyzed under different scales. The simulation results show that when DC line short-circuit faults occurs, wherever in the rectifier side, in the DC transmission line midpoint or in the inverter side, the aerial mode component of have more high frequency weight in ~ and decays gradually; When commutation failures, which are caused by the inverter side AC system single-phase grounding fault, phase to phase fault, three phase grounding fault or the inverter side transformer ratio increased, the aerial mode component of have less frequency weight in
Mitochondrial oxidative stress promotes atrial fibrillation
Oxidative stress has been suggested to play a role in the pathogenesis of atrial fibrillation (AF). Indeed, the prevalence of AF increases with age as does oxidative stress. However, the mechanisms linking redox state to AF are not well understood. In this study we identify a link between oxidative stress and aberrant intracellular Ca(2+) release via the type 2 ryanodine receptor (RyR2) that promotes AF. We show that RyR2 are oxidized in the atria of patients with chronic AF compared with individuals in sinus rhythm. To dissect the molecular mechanism linking RyR2 oxidation to AF we used two murine models harboring RyR2 mutations that cause intracellular Ca(2+) leak. Mice with intracellular Ca(2+) leak exhibited increased atrial RyR2 oxidation, mitochondrial dysfunction, reactive oxygen species (ROS) production and AF susceptibility. Both genetic inhibition of mitochondrial ROS production and pharmacological treatment of RyR2 leakage prevented AF. Collectively, our results indicate that alterations of RyR2 and mitochondrial ROS generation form a vicious cycle in the development of AF. Targeting this previously unrecognized mechanism could be useful in developing effective interventions to prevent and treat AF
Quantum Software Analytics: Opportunities and Challenges
Quantum computing systems depend on the principles of quantum mechanics to
perform multiple challenging tasks more efficiently than their classical
counterparts. In classical software engineering, the software life cycle is
used to document and structure the processes of design, implementation, and
maintenance of software applications. It helps stakeholders understand how to
build an application. In this paper, we summarize a set of software analytics
topics and techniques in the development life cycle that can be leveraged and
integrated into quantum software application development. The results of this
work can assist researchers and practitioners in better understanding the
quantum-specific emerging development activities, challenges, and opportunities
in the next generation of quantum software
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Promp
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