22,645 research outputs found
Over-the-Air Computation via Intelligent Reflecting Surfaces
Over-the-air computation (AirComp) becomes a promising approach for fast
wireless data aggregation via exploiting the superposition property in a
multiple access channel. To further overcome the unfavorable signal propagation
conditions for AirComp, in this paper, we propose an intelligent reflecting
surface (IRS) aided AirComp system to build controllable wireless environments,
thereby boosting the received signal power significantly. This is achieved by
smartly tuning the phase shifts for the incoming electromagnetic waves at IRS,
resulting in reconfigurable signal propagations. Unfortunately, it turns out
that the joint design problem for AirComp transceivers and IRS phase shifts
becomes a highly intractable nonconvex bi-quadratic programming problem, for
which a novel alternating difference-of-convex (DC) programming algorithm is
developed. This is achieved by providing a novel DC function representation for
the rank-one constraint in the low-rank matrix optimization problem via matrix
lifting. Simulation results demonstrate the algorithmic advantages and
admirable performance of the proposed approaches compared with the state-of-art
solutions
Scalable Spectral Algorithms for Community Detection in Directed Networks
Community detection has been one of the central problems in network studies
and directed network is particularly challenging due to asymmetry among its
links. In this paper, we found that incorporating the direction of links
reveals new perspectives on communities regarding to two different roles,
source and terminal, that a node plays in each community. Intriguingly, such
communities appear to be connected with unique spectral property of the graph
Laplacian of the adjacency matrix and we exploit this connection by using
regularized SVD methods. We propose harvesting algorithms, coupled with
regularized SVDs, that are linearly scalable for efficient identification of
communities in huge directed networks. The proposed algorithm shows great
performance and scalability on benchmark networks in simulations and
successfully recovers communities in real network applications.Comment: Single column, 40 pages, 6 figures and 7 table
CNNs based Viewpoint Estimation for Volume Visualization
Viewpoint estimation from 2D rendered images is helpful in understanding how
users select viewpoints for volume visualization and guiding users to select
better viewpoints based on previous visualizations. In this paper, we propose a
viewpoint estimation method based on Convolutional Neural Networks (CNNs) for
volume visualization. We first design an overfit-resistant image rendering
pipeline to generate the training images with accurate viewpoint annotations,
and then train a category-specific viewpoint classification network to estimate
the viewpoint for the given rendered image. Our method can achieve good
performance on images rendered with different transfer functions and rendering
parameters in several categories. We apply our model to recover the viewpoints
of the rendered images in publications, and show how experts look at volumes.
We also introduce a CNN feature-based image similarity measure for similarity
voting based viewpoint selection, which can suggest semantically meaningful
optimal viewpoints for different volumes and transfer functions
A coupled discrete unified gas-kinetic scheme for Boussinesq flows
Recently, the discrete unified gas-kinetic scheme (DUGKS) [Z. L. Guo \emph{et
al}., Phys. Rev. E , 033305 (2013)] based on the Boltzmann equation
is developed as a new multiscale kinetic method for isothermal flows. In this
paper, a thermal and coupled discrete unified gas-kinetic scheme is derived for
the Boussinesq flows, where the velocity and temperature fields are described
independently. Kinetic boundary conditions for both velocity and temperature
fields are also proposed. The proposed model is validated by simulating several
canonical test cases, including the porous plate problem, the
Rayleigh-b\'{e}nard convection, and the natural convection with Rayleigh number
up to in a square cavity. The results show that the coupled DUGKS is
of second order accuracy in space and can well describe the convection
phenomena from laminar to turbulent flows. Particularly, it is found that this
new scheme has better numerical stability in simulating high Rayleigh number
flows compared with the previous kinetic models
Transfer Learning by Ranking for Weakly Supervised Object Annotation
Most existing approaches to training object detectors rely on fully
supervised learning, which requires the tedious manual annotation of object
location in a training set. Recently there has been an increasing interest in
developing weakly supervised approach to detector training where the object
location is not manually annotated but automatically determined based on binary
(weak) labels indicating if a training image contains the object. This is a
challenging problem because each image can contain many candidate object
locations which partially overlaps the object of interest. Existing approaches
focus on how to best utilise the binary labels for object location annotation.
In this paper we propose to solve this problem from a very different
perspective by casting it as a transfer learning problem. Specifically, we
formulate a novel transfer learning based on learning to rank, which
effectively transfers a model for automatic annotation of object location from
an auxiliary dataset to a target dataset with completely unrelated object
categories. We show that our approach outperforms existing state-of-the-art
weakly supervised approach to annotating objects in the challenging VOC
dataset.Comment: BMVC 201
A Maximum-Principle-Satisfying High-order Finite Volume Compact WENO Scheme for Scalar Conservation Laws
In this paper, a maximum-principle-satisfying finite volume compact scheme is
proposed for solving scalar hyperbolic conservation laws. The scheme combines
WENO schemes (Weighted Essentially Non-Oscillatory) with a class of compact
schemes under a finite volume framework, in which the nonlinear WENO weights
are coupled with lower order compact stencils. The maximum-principle-satisfying
polynomial rescaling limiter in [Zhang and Shu, JCP, 2010] is adopted to
construct the present schemes at each stage of an explicit Runge-Kutta method,
without destroying high order accuracy and conservativity. Numerical examples
for one and two dimensional problems including incompressible flows are
presented to assess the good performance, maximum principle preserving,
essentially non-oscillatory and highly accurate resolution of the proposed
method
A Positivity-preserving High Order Finite Volume Compact-WENO Scheme for Compressible Euler Equations
In this paper, a positivity-preserving fifth-order finite volume compact-WENO
scheme is proposed for solving compressible Euler equations. As we know
conservative compact finite volume schemes have high resolution properties
while WENO (Weighted Essentially Non-Oscillatory) schemes are essentially
non-oscillatory near flow discontinuities. We extend the main idea of WENO
schemes to some classical compact finite volume schemes [32], where lower order
compact stencils are combined with WENO nonlinear weights to get a higher order
finite volume compact-WENO scheme. The newly developed positivity-preserving
limiter [46,44] is used to preserve positive density and internal energy for
compressible Euler equations of fluid dynamics. The HLLC (Harten, Lax, and van
Leer with Contact) approximate Riemann solver [39,2] is used to get the
numerical flux at the cell interfaces. Numerical tests are presented to
demonstrate the high-order accuracy, positivity-preserving, high-resolution and
robustness of the proposed scheme
Charmonium dissociation in collision with phi meson in hadronic matter
The phi-charmonium dissociation reactions in hadronic matter are studied.
Unpolarised cross sections for 12 reactions are calculated in the Born
approximation, in the quark-interchange mechanism and with a
temperature-dependent quark potential. The potential leads to remarkable
temperature dependence of the cross sections. With the cross sections and the
phi distribution function we calculate the dissociation rates of the charmonia
in the interactions with the phi meson in hadronic matter. The dependence of
the rates on temperature and charmonium momentum is meaningful to the influence
of phi mesons on charmonium suppression.Comment: 21 pages, 12 figure
Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation
We address the problem of localisation of objects as bounding boxes in images
with weak labels. This weakly supervised object localisation problem has been
tackled in the past using discriminative models where each object class is
localised independently from other classes. We propose a novel framework based
on Bayesian joint topic modelling. Our framework has three distinctive
advantages over previous works: (1) All object classes and image backgrounds
are modelled jointly together in a single generative model so that "explaining
away" inference can resolve ambiguity and lead to better learning and
localisation. (2) The Bayesian formulation of the model enables easy
integration of prior knowledge about object appearance to compensate for
limited supervision. (3) Our model can be learned with a mixture of weakly
labelled and unlabelled data, allowing the large volume of unlabelled images on
the Internet to be exploited for learning. Extensive experiments on the
challenging VOC dataset demonstrate that our approach outperforms the
state-of-the-art competitors.Comment: iccv 201
Variational Study of Fermionic and Bosonic Systems with Non-Gaussian States: Theory and Applications
We present a new variational method for investigating the ground state and
out of equilibrium dynamics of quantum many-body bosonic and fermionic systems.
Our approach is based on constructing variational wavefunctions which extend
Gaussian states by including generalized canonical transformations between the
fields. The key advantage of such states compared to simple Gaussian states is
presence of non-factorizable correlations and the possibility of describing
states with strong entanglement between particles. In contrast to the commonly
used canonical transformations, such as the polaron or Lang-Firsov
transformations, we allow parameters of the transformations to be time
dependent, which extends their regions of applicability. We derive equations of
motion for the parameters characterizing the states both in real and imaginary
time using the differential structure of the variational manifold. The ground
state can be found by following the imaginary time evolution until it converges
to a steady state. Collective excitations in the system can be obtained by
linearizing the real-time equations of motion in the vicinity of the imaginary
time steady-state solution. Our formalism allows us not only to determine the
energy spectrum of quasiparticles and their lifetime, but to obtain the
complete spectral functions and to explore far out of equilibrium dynamics such
as coherent evolution following a quantum quench. We illustrate and benchmark
this framework with several examples: a single polaron in the Holstein and
Su-Schrieer-Heeger models, non-equilibrium dynamics in the spin-boson and Kondo
models, the superconducting to charge density wave phase transitions in the
Holstein model.Comment: 45 pages, 14 figure
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