1,682 research outputs found
Edge-as-a-Service: Towards Distributed Cloud Architectures
We present an Edge-as-a-Service (EaaS) platform for realising distributed
cloud architectures and integrating the edge of the network in the computing
ecosystem. The EaaS platform is underpinned by (i) a lightweight discovery
protocol that identifies edge nodes and make them publicly accessible in a
computing environment, and (ii) a scalable resource provisioning mechanism for
offloading workloads from the cloud on to the edge for servicing multiple user
requests. We validate the feasibility of EaaS on an online game use-case to
highlight the improvement in the QoS of the application hosted on our
cloud-edge platform. On this platform we demonstrate (i) low overheads of less
than 6%, (ii) reduced data traffic to the cloud by up to 95% and (iii)
minimised application latency between 40%-60%.Comment: 10 pages; presented at the EdgeComp Symposium 2017; will appear in
Proceedings of the International Conference on Parallel Computing, 201
Video-based evidence analysis and extraction in digital forensic investigation
As a result of the popularity of smart mobile devices and the low cost of surveillance systems, visual data are increasingly being used in digital forensic investigation. Digital videos have been widely used as key evidence sources in evidence identification, analysis, presentation, and report. The main goal of this paper is to develop advanced forensic video analysis techniques to assist the forensic investigation. We first propose a forensic video analysis framework that employs an efficient video/image enhancing algorithm for the low quality of footage analysis. An adaptive video enhancement algorithm based on contrast limited adaptive histogram equalization (CLAHE) is introduced to improve the closed-circuit television (CCTV) footage quality for the use of digital forensic investigation. To assist the video-based forensic analysis, a deep-learning-based object detection and tracking algorithm are proposed that can detect and identify potential suspects and tools from footages
Universal Treatment of Reduction for One-Loop Integrals in Projective Space
Recently a nice work about the understanding of one-loop integrals has been
done in [1] using the tricks of the projective space language associated to
their Feynman parametrization. We find this language is also very suitable to
deal with the reduction problem of one-loop integrals with general tensor
structures as well as propagators with arbitrary higher powers. In this paper,
we show that how to combine Feynman parametrization and embedding formalism to
give a universal treatment of reductions for general one-loop integrals, even
including the degenerated cases, such as the vanishing Gram determinant.
Results from this method can be written in a compact and symmetric form.Comment: 32 pages, 1 figur
FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent
The theoretical landscape of federated learning (FL) undergoes rapid
evolution, but its practical application encounters a series of intricate
challenges, and hyperparameter optimization is one of these critical
challenges. Amongst the diverse adjustments in hyperparameters, the adaptation
of the learning rate emerges as a crucial component, holding the promise of
significantly enhancing the efficacy of FL systems. In response to this
critical need, this paper presents FedHyper, a novel hypergradient-based
learning rate adaptation algorithm specifically designed for FL. FedHyper
serves as a universal learning rate scheduler that can adapt both global and
local rates as the training progresses. In addition, FedHyper not only
showcases unparalleled robustness to a spectrum of initial learning rate
configurations but also significantly alleviates the necessity for laborious
empirical learning rate adjustments. We provide a comprehensive theoretical
analysis of FedHyper's convergence rate and conduct extensive experiments on
vision and language benchmark datasets. The results demonstrate that FEDHYPER
consistently converges 1.1-3x faster than FedAvg and the competing baselines
while achieving superior final accuracy. Moreover, FedHyper catalyzes a
remarkable surge in accuracy, augmenting it by up to 15% compared to FedAvg
under suboptimal initial learning rate settings
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Stiff, strong, and tough hydrogels with good chemical stability
Most hydrogels have poor mechanical properties, severely limiting their scope of applications. Here a hybrid hydrogel, consisting of hydrophilic and crystalline polymer networks, achieves an elastic modulus of 5 MPa, a strength of 2.5 MPa, and a fracture energy of 14 000 J m−2, while maintaining physical integrity in concentrated electrolyte solutions.Engineering and Applied Science
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A model of ideal elastomeric gels for polyelectrolyte gels
The concept of the ideal elastomeric gel is extended to polyelectrolyte gels and verified using a polyacrylamide-co-acrylic acid hydrogel as a model material system. A comparison between mixing and ion osmosis shows that the mixing osmosis is larger than the ion osmosis for small swelling ratios, while the ion osmosis dominates for large swelling ratios. We show further that the non-Gaussian chain effect becomes important in the elasticity of the polymer network at the very large swelling ratios that may occur under certain conditions of pH and salinity. We demonstrate that the Gent model captures the non-Gaussian chain effect well and that it provides a good description of the free energy associated with the stretching of the network. The model of ideal elastomeric gels fits the experimental data very well.Engineering and Applied Science
Generative Adversarial Networks Selection Approach for Extremely Imbalanced Fault Diagnosis of Reciprocating Machinery
At present, countless approaches to fault diagnosis in reciprocating machines have been proposed, all considering that the available machinery dataset is in equal proportions for all conditions. However, when the application is closer to reality, the problem of data imbalance is increasingly evident. In this paper, we propose a method for the creation of diagnoses that consider an extreme imbalance in the available data. Our approach first processes the vibration signals of the machine using a wavelet packet transform-based feature-extraction stage. Then, improved generative models are obtained with a dissimilarity-based model selection to artificially balance the dataset. Finally, a Random Forest classifier is created to address the diagnostic task. This methodology provides a considerable improvement with 99% of data imbalance over other approaches reported in the literature, showing performance similar to that obtained with a balanced set of data.National Natural Science Foundation of China, under Grant 51605406National Natural Science Foundation of China under Grant 7180104
Data-driven method to learn the most probable transition pathway and stochastic differential equations
Transition phenomena between metastable states play an important role in
complex systems due to noisy fluctuations. In this paper, the physics informed
neural networks (PINNs) are presented to compute the most probable transition
pathway. It is shown that the expected loss is bounded by the empirical loss.
And the convergence result for the empirical loss is obtained. Then, a sampling
method of rare events is presented to simulate the transition path by the
Markovian bridge process. And we investigate the inverse problem to extract the
stochastic differential equation from the most probable transition pathway data
and the Markovian bridge process data, respectively. Finally, several numerical
experiments are presented to verify the effectiveness of our methods
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