Realizing Video Analytic Service in the Fog-Based Infrastructure-Less Environments

Deep learning has unleashed the great potential in many fields and now is the most significant facilitator for video analytics owing to its capability to providing more intelligent services in a complex scenario. Meanwhile, the emergence of fog computing has brought unprecedented opportunities to provision intelligence services in infrastructure-less environments like remote national parks and rural farms. However, most of the deep learning algorithms are computationally intensive and impossible to be executed in such environments due to the needed supports from the cloud. In this paper, we develop a video analytic framework, which is tailored particularly for the fog devices to realize video analytic service in a rapid manner. Also, the convolution neural networks are used as the core processing unit in the framework to facilitate the image analysing process

Parton Distributions from Boosted Fields in the Coulomb Gauge

We propose a new method to calculate parton distribution functions (PDFs) from correlations of boosted quarks and gluons in the Coulomb gauge. Compared to the widely used quasi-PDFs defined from gauge-invariant Wilson-line operators, such correlations offer advantages including absence of linear power divergence, enhanced long-range precision, and accessibility to larger off-axis momenta. We verify the validity of this method at next-to-leading order in perturbation theory and use it to calculate the pion valence quark PDF on a lattice with spacing $a=0.06$ fm and valence pion mass $m_\pi=300$ MeV. Our result agrees with that from the gauge-invariant quasi-PDF at similar precision, achieved with only half the computational cost through a large off-axis momentum $|\vec{p}|\sim2.2$ GeV. This opens the door to a more efficient way to calculate parton physics on the lattice.Comment: 5 pages, 4 figures, and the appendi

A Socio-Technical Metaverse Development Framework in Higher Education

The concept of the metaverse has recently generated a great deal of attention in academia and industry, with an increasing number of educational institutions expressing interest in its implementation. However, existing studies on metaverse development in higher education are still in their early stages, leaving institutions with little guidance on how to develop and implement a metaverse. Employing socio-technical theory, we propose a comprehensive nine-stage metaverse development framework (MDF) that incorporates both social and technical aspects of a metaverse initiative, thus providing a holistic approach to metaverse development. Leveraging case studies of three large universities and blending them with MDF, our study provides evidence of the applicability of our MDF and offers a better contextual understanding of metaverse development in educational settings. This paper is useful for educational institutions that are developing or considering metaverse initiatives. It contributes to the emerging literature on metaverse development in higher education

Hybrid Variational Autoencoder for Time Series Forecasting

Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables. Recent studies show that VAE can flexibly learn the complex temporal dynamics of time series and achieve more promising forecasting results than deterministic models. However, a major limitation of existing works is that they fail to jointly learn the local patterns (e.g., seasonality and trend) and temporal dynamics of time series for forecasting. Accordingly, we propose a novel hybrid variational autoencoder (HyVAE) to integrate the learning of local patterns and temporal dynamics by variational inference for time series forecasting. Experimental results on four real-world datasets show that the proposed HyVAE achieves better forecasting results than various counterpart methods, as well as two HyVAE variants that only learn the local patterns or temporal dynamics of time series, respectively

Ceria–terbia solid solution nanobelts with high catalytic activities for CO oxidation

Ceria–terbia solid solution nanobelts were prepared by an electrochemical route and tested as catalysts of high activity for CO oxidation

Hybrid quantum device based on NV centers in diamond nanomechanical resonators plus superconducting waveguide cavities

We propose and analyze a hybrid device by integrating a microscale diamond beam with a single built-in nitrogen-vacancy (NV) center spin to a superconducting coplanar waveguide (CPW) cavity. We find that under an ac electric field the quantized motion of the diamond beam can strongly couple to the single cavity photons via dielectric interaction. Together with the strong spin-motion interaction via a large magnetic field gradient, it provides a hybrid quantum device where the dia- mond resonator can strongly couple both to the single microwave cavity photons and to the single NV center spin. This enables coherent information transfer and effective coupling between the NV spin and the CPW cavity via mechanically dark polaritons. This hybrid spin-electromechanical de- vice, with tunable couplings by external fields, offers a realistic platform for implementing quantum information with single NV spins, diamond mechanical resonators, and single microwave photons.Comment: Accepted by Phys. Rev. Applie

SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems

Federated learning (FL) utilizes edge computing devices to collaboratively train a shared model while each device can fully control its local data access. Generally, FL techniques focus on learning model on independent and identically distributed (iid) dataset and cannot achieve satisfiable performance on non-iid datasets (e.g. learning a multi-class classifier but each client only has a single class dataset). Some personalized approaches have been proposed to mitigate non-iid issues. However, such approaches cannot handle underlying data distribution shift, namely data distribution skew, which is quite common in real scenarios (e.g. recommendation systems learn user behaviors which change over time). In this work, we provide a solution to the challenge by leveraging smart-contract with federated learning to build optimized, personalized deep learning models. Specifically, our approach utilizes smart contract to reach consensus among distributed trainers on the optimal weights of personalized models. We conduct experiments across multiple models (CNN and MLP) and multiple datasets (MNIST and CIFAR-10). The experimental results demonstrate that our personalized learning models can achieve better accuracy and faster convergence compared to classic federated and personalized learning. Compared with the model given by baseline FedAvg algorithm, the average accuracy of our personalized learning models is improved by 2% to 20%, and the convergence rate is about 2$\times$ faster. Moreover, we also illustrate that our approach is secure against recent attack on distributed learning.Comment: 12 pages, 9 figure