2,067 research outputs found
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 fm and valence pion mass 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 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 faster. Moreover, we also illustrate that our approach is
secure against recent attack on distributed learning.Comment: 12 pages, 9 figure
- …