32,446 research outputs found
Simulating dynamical quantum Hall effect with superconducting qubits
We propose an experimental scheme to simulate the dynamical quantum Hall
effect and the related interaction-induced topological transition with a
superconducting-qubit array. We show that a one-dimensional Heisenberg model
with tunable parameters can be realized in an array of superconducting qubits.
The quantized plateaus, which is a feature of the dynamical quantum Hall
effect, will emerge in the Berry curvature of the superconducting qubits as a
function of the coupling strength between nearest neighbor qubits. We
numerically calculate the Berry curvatures of two-, four- and six-qubit arrays,
and find that the interaction-induced topological transition can be easily
observed with the simplest two-qubit array. Furthermore, we analyze some
practical conditions in typical experiments for observing such dynamical
quantum Hall effect.Comment: 9 pages, 6 figures, version accepted by PR
Occlusion Aware Unsupervised Learning of Optical Flow
It has been recently shown that a convolutional neural network can learn
optical flow estimation with unsupervised learning. However, the performance of
the unsupervised methods still has a relatively large gap compared to its
supervised counterpart. Occlusion and large motion are some of the major
factors that limit the current unsupervised learning of optical flow methods.
In this work we introduce a new method which models occlusion explicitly and a
new warping way that facilitates the learning of large motion. Our method shows
promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets.
Especially on KITTI dataset where abundant unlabeled samples exist, our
unsupervised method outperforms its counterpart trained with supervised
learning.Comment: CVPR 2018 Camera-read
Newton-MR: Inexact Newton Method With Minimum Residual Sub-problem Solver
We consider a variant of inexact Newton Method, called Newton-MR, in which
the least-squares sub-problems are solved approximately using Minimum Residual
method. By construction, Newton-MR can be readily applied for unconstrained
optimization of a class of non-convex problems known as invex, which subsumes
convexity as a sub-class. For invex optimization, instead of the classical
Lipschitz continuity assumptions on gradient and Hessian, Newton-MR's global
convergence can be guaranteed under a weaker notion of joint regularity of
Hessian and gradient. We also obtain Newton-MR's problem-independent local
convergence to the set of minima. We show that fast local/global convergence
can be guaranteed under a novel inexactness condition, which, to our knowledge,
is much weaker than the prior related works. Numerical results demonstrate the
performance of Newton-MR as compared with several other Newton-type
alternatives on a few machine learning problems.Comment: 35 page
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving
Autonomous driving has harsh requirements of small model size and energy
efficiency, in order to enable the embedded system to achieve real-time
on-board object detection. Recent deep convolutional neural network based
object detectors have achieved state-of-the-art accuracy. However, such models
are trained with numerous parameters and their high computational costs and
large storage prohibit the deployment to memory and computation resource
limited systems. Low-precision neural networks are popular techniques for
reducing the computation requirements and memory footprint. Among them, binary
weight neural network (BWN) is the extreme case which quantizes the float-point
into just bit. BWNs are difficult to train and suffer from accuracy
deprecation due to the extreme low-bit representation. To address this problem,
we propose a knowledge transfer (KT) method to aid the training of BWN using a
full-precision teacher network. We built DarkNet- and MobileNet-based binary
weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car,
pedestrian and cyclist detection. The experimental results show that the
proposed method maintains high detection accuracy while reducing the model size
of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB.Comment: Accepted by ICRA 201
Helicity hardens the gas
A screw generally works better than a nail, or a complicated rope knot better
than a simple one, in fastening solid matter, but a gas is more tameless.
However, a flow itself has a physical quantity, helicity, measuring the
screwing strength of the velocity field and the degree of the knottedness of
the vorticity ropes. It is shown that helicity favors the partition of energy
to the vortical modes, compared to others such as the dilatation and pressure
modes of turbulence; that is, helicity stiffens the flow, with nontrivial
implications for aerodynamics, such as aeroacoustics, and conducting fluids,
among others
Non-integrable stable approximation by Stein's method
We develop Stein's method for -stable approximation with
, continuing the recent line of research by Xu \cite{lihu} and
Chen, Nourdin and Xu \cite{C-N-X} in the case The main
results include an intrinsic upper bound for the error of the approximation in
a variant of Wasserstein distance that involves the characterizing differential
operators for stable distributions, and an application to the generalized
central limit theorem. Due to the lack of first moment for the approximating
sequence in the latter result, we appeal to an additional truncation procedure
and investigate fine regularity properties of the solution to Stein's equation
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