162 research outputs found
Automatic Dataset Augmentation
Large scale image dataset and deep convolutional neural network (DCNN) are
two primary driving forces for the rapid progress made in generic object
recognition tasks in recent years. While lots of network architectures have
been continuously designed to pursue lower error rates, few efforts are devoted
to enlarge existing datasets due to high labeling cost and unfair comparison
issues. In this paper, we aim to achieve lower error rate by augmenting
existing datasets in an automatic manner. Our method leverages both Web and
DCNN, where Web provides massive images with rich contextual information, and
DCNN replaces human to automatically label images under guidance of Web
contextual information. Experiments show our method can automatically scale up
existing datasets significantly from billions web pages with high accuracy, and
significantly improve the performance on object recognition tasks by using the
automatically augmented datasets, which demonstrates that more supervisory
information has been automatically gathered from the Web. Both the dataset and
models trained on the dataset are made publicly available
Creation of Ghost Illusions Using Metamaterials in Wave Dynamics
The creation of wave-dynamic illusion functionality is of great interests to
various scientific communities, which can potentially transform an actual
perception into the pre-controlled perception, thus empowering unprecedented
applications in the advanced-material science, camouflage, cloaking, optical
and/or microwave cognition, and defense security, etc. By using the space
transformation theory and engineering capability of metamaterials, we propose
and realize a functional ghost illusion device, which is capable of creating
wave-dynamic virtual ghost images off the original object's position under the
illumination of electromagnetic waves. The scattering signature of the object
is thus ghosted and perceived as multiple ghost targets with different
geometries and compositions. The ghost-illusion material, being inhomogeneous
and anisotropic, was realized by thousands of varying unit cells working at
non-resonance. The experimental demonstration of the ghost illusion validates
our theory of scattering metamorphosis and opens a novel avenue to the
wave-dynamic illusion, cognitive deception, manipulate strange light or matter
behaviors, and design novel optical and microwave devices.Comment: 19 pages, 6 figure
Estimating the reciprocal of a binomial proportion
As a classic parameter from the binomial distribution, the binomial
proportion has been well studied in the literature owing to its wide range of
applications. In contrast, the reciprocal of the binomial proportion, also
known as the inverse proportion, is often overlooked, even though it also plays
an important role in various fields including clinical studies and random
sampling. The maximum likelihood estimator of the inverse proportion suffers
from the zero-event problem, and to overcome it, alternative methods have been
developed in the literature. Nevertheless, there is little work addressing the
optimality of the existing estimators, as well as their practical performance
comparison. Inspired by this, we propose to further advance the literature by
developing an optimal estimator for the inverse proportion in a family of
shrinkage estimators. We further derive the explicit and approximate formulas
for the optimal shrinkage parameter under different settings. Simulation
studies show that the performance of our new estimator performs better than, or
as well as, the existing competitors in most practical settings. Finally, to
illustrate the usefulness of our new method, we also revisit a recent
meta-analysis on COVID-19 data for assessing the relative risks of physical
distancing on the infection of coronavirus, in which six out of seven studies
encounter the zero-event problem
Solving eigenvalue PDEs of metastable diffusion processes using artificial neural networks
In this paper, we consider the eigenvalue PDE problem of the infinitesimal
generators of metastable diffusion processes. We propose a numerical algorithm
based on training artificial neural networks for solving the leading
eigenvalues and eigenfunctions of such high-dimensional eigenvalue problem. The
algorithm is able to find multiple leading eigenpairs by solving a single
training task. It is useful in understanding the dynamical behaviors of
metastable processes on large timescales. We demonstrate the capability of our
algorithm on a high-dimensional model problem, and on the simple molecular
system alanine dipeptide.Comment: revision with minor change
Placement and Resource Allocation of Wireless-Powered Multiantenna UAV for Energy-Efficient Multiuser NOMA
This paper investigates a new downlink nonorthogonal multiple access (NOMA)
system, where a multiantenna unmanned aerial vehicle (UAV) is powered by
wireless power transfer (WPT) and serves as the base station for multiple pairs
of ground users (GUs) running NOMA in each pair. An energy efficiency (EE)
maximization problem is formulated to jointly optimize the WPT time and the
placement for the UAV, and the allocation of the UAV's transmit power between
different NOMA user pairs and within each pair. To efficiently solve this
nonconvex problem, we decompose the problem into three subproblems using block
coordinate descent. For the subproblem of intra-pair power allocation within
each NOMA user pair, we construct a supermodular game with confirmed
convergence to a Nash equilibrium. Given the intra-pair power allocation,
successive convex approximation is applied to convexify and solve the
subproblem of WPT time allocation and inter-pair power allocation between the
user pairs. Finally, we solve the subproblem of UAV placement by using the
Lagrange multiplier method. Simulations show that our approach can
substantially outperform its alternatives that do not use NOMA and WPT
techniques or that do not optimize the UAV location.Comment: 15 pages, 11 figures, Accepted by IEEE Transactions on Wireless
Communication
Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation
Wireless federated learning (WFL) undergoes a communication bottleneck in
uplink, limiting the number of users that can upload their local models in each
global aggregation round. This paper presents a new multi-carrier
non-orthogonal multiple-access (MC-NOMA)-empowered WFL system under an adaptive
learning setting of Flexible Aggregation. Since a WFL round accommodates both
local model training and uploading for each user, the use of Flexible
Aggregation allows the users to train different numbers of iterations per
round, adapting to their channel conditions and computing resources. The key
idea is to use MC-NOMA to concurrently upload the local models of the users,
thereby extending the local model training times of the users and increasing
participating users. A new metric, namely, Weighted Global Proportion of
Trained Mini-batches (WGPTM), is analytically established to measure the
convergence of the new system. Another important aspect is that we maximize the
WGPTM to harness the convergence of the new system by jointly optimizing the
transmit powers and subchannel bandwidths. This nonconvex problem is converted
equivalently to a tractable convex problem and solved efficiently using
variable substitution and Cauchy's inequality. As corroborated experimentally
using a convolutional neural network and an 18-layer residential network, the
proposed MC-NOMA WFL can efficiently reduce communication delay, increase local
model training times, and accelerate the convergence by over 40%, compared to
its existing alternative.Comment: 33 pages, 16 figure
A Brief Introduction on Water Conservation and Drought Resistance Technique on Desert Grasslandslope
Experimental simulation of the Parity-Time-symmetric dynamics using photonics qubits
The concept of parity-time (PT) symmetry originates from the framework of
quantum mechanics, where if the Hamiltonian operator satisfies the commutation
relation with the parity and time operators, it shows all real eigen-energy
spectrum. Recently, PT symmetry was introduced into optics, electronic
circuits, acoustics, and so many other classical fields to further study the
dynamics of the Hamiltonian and the energy of the system. Focusing on the
dynamical evolution of the quantum state under the action of PT symmetric
Hamiltonian, here we experimentally demonstrated the general dynamical
evolution of a two-level system under the PT symmetric Hamiltonian using
single-photon system. By enlarging the system using ancillary qubits and
encoding the subsystem under the non-Hermitian Hamiltonian with post-selection,
the evolution of the state can be observed with a high fidelity when the
successfully parity-time symmetrically evolved subspace is solely considered.
Owing to the effectively operation of the dilation method, our work provides a
route for further exploiting the exotic properties of PT symmetric Hamiltonian
for quantum simulation and quantum information processing
Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing
The compact descriptors for visual search (CDVS) standard from ISO/IEC moving
pictures experts group (MPEG) has succeeded in enabling the interoperability
for efficient and effective image retrieval by standardizing the bitstream
syntax of compact feature descriptors. However, the intensive computation of
CDVS encoder unfortunately hinders its widely deployment in industry for
large-scale visual search. In this paper, we revisit the merits of low
complexity design of CDVS core techniques and present a very fast CDVS encoder
by leveraging the massive parallel execution resources of GPU. We elegantly
shift the computation-intensive and parallel-friendly modules to the
state-of-the-arts GPU platforms, in which the thread block allocation and the
memory access are jointly optimized to eliminate performance loss. In addition,
those operations with heavy data dependence are allocated to CPU to resolve the
extra but non-necessary computation burden for GPU. Furthermore, we have
demonstrated the proposed fast CDVS encoder can work well with those
convolution neural network approaches which has harmoniously leveraged the
advantages of GPU platforms, and yielded significant performance improvements.
Comprehensive experimental results over benchmarks are evaluated, which has
shown that the fast CDVS encoder using GPU-CPU hybrid computing is promising
for scalable visual search
Analysis and Optimization of Service Delay for Multi-quality Videos in Multi-tier Heterogeneous Network with Random Caching
Aiming to minimize service delay, we propose a new random caching scheme in
device-to-device (D2D)-assisted heterogeneous network. To support diversified
viewing qualities of multimedia video services, each video file is encoded into
a base layer (BL) and multiple enhancement layers (ELs) by scalable video
coding (SVC). A super layer, including the BL and several ELs, is transmitted
to every user. We define and quantify the service delay of multi-quality videos
by deriving successful transmission probabilities when a user is served by a
D2D helper, a small-cell base station (SBS) and a macro-cell base station
(MBS). We formulate a delay minimization problem subject to the limited cache
sizes of D2D helpers and SBSs. The structure of the optimal solutions to the
problem is revealed, and then an improved standard gradient projection method
is designed to effectively obtain the solutions. Both theoretical analysis and
Monte-Carlo simulations validate the successful transmission probabilities.
Compared with three benchmark caching policies, the proposed SVC-based random
caching scheme is superior in terms of reducing the service delay.Comment: 13 pages, 8 figures, IEEE Systems Journal, Accepte
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