27,862 research outputs found
Interplay of interaction and disorder in the steady state of an open quantum system
Many types of dissipative processes can be found in nature or be engineered,
and their interplay with a system can give rise to interesting phases of
matter. Here we study the interplay among interaction, tunneling, and disorder
in the steady state of a spin chain coupled to a tailored bath. We consider a
dissipation which, in contrast to disorder, tends to generate a homogeneously
polarized steady state. We find that the steady state can be highly sensitive
even to weak disorder. We also establish that, in the presence of such
dissipation, even in the absence of interaction, a finite amount of disorder is
needed for localization. Last, we show that for strong disorder the system
reveals signatures of localization both in the weakly and strongly interacting
regimes.Comment: 5 pages, 5 figure
Performance Evaluation of Deep Learning Tools in Docker Containers
With the success of deep learning techniques in a broad range of application
domains, many deep learning software frameworks have been developed and are
being updated frequently to adapt to new hardware features and software
libraries, which bring a big challenge for end users and system administrators.
To address this problem, container techniques are widely used to simplify the
deployment and management of deep learning software. However, it remains
unknown whether container techniques bring any performance penalty to deep
learning applications. The purpose of this work is to systematically evaluate
the impact of docker container on the performance of deep learning
applications. We first benchmark the performance of system components (IO, CPU
and GPU) in a docker container and the host system and compare the results to
see if there's any difference. According to our results, we find that
computational intensive jobs, either running on CPU or GPU, have small overhead
indicating docker containers can be applied to deep learning programs. Then we
evaluate the performance of some popular deep learning tools deployed in a
docker container and the host system. It turns out that the docker container
will not cause noticeable drawbacks while running those deep learning tools. So
encapsulating deep learning tool in a container is a feasible solution.Comment: Conference: BIgCom2017, 9 page
The Minimal and Maximal Sensitivity of the Simplified Weighted Sum Function
Sensitivity is an important complexity measure of Boolean functions. In this
paper we present properties of the minimal and maximal sensitivity of the
simplified weighted sum function. A simple close formula of the minimal
sensitivity of the simplified weighted sum function is obtained. A phenomenon
is exhibited that the minimal sensitivity of the weighted sum function is
indeed an indicator of large primes, that is, for large prime number p, the
minimal sensitivity of the weighted sum function is always equal to one.Comment: 6 page
An improved immersed finte element particle-in-cell method for plasma simulation
The particle-in-cell (PIC) method has been widely used for plasma simulation,
because of its noise-reduction capability and moderate computational cost. The
immersed finite element (IFE) method is efficient for solving interface
problems on Cartesian meshes, which is desirable for PIC method. The
combination of these two methods provides an effective tool for plasma
simulation with complex interface/boundary. This paper introduces an improved
IFE-PIC method that enhances the performance in both IFE and PIC aspects. For
the electric field solver, we adopt the newly developed partially penalized IFE
method with enhanced accuracy. For PIC implementation, we introduce a new
interpolation technique to ensure the conservation of the charge. Numerical
examples are provided to demonstrate the features of the improved IFE-PIC
method
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
Fabricating neural models for a wide range of mobile devices demands for a
specific design of networks due to highly constrained resources. Both evolution
algorithms (EA) and reinforced learning methods (RL) have been dedicated to
solve neural architecture search problems. However, these combinations usually
concentrate on a single objective such as the error rate of image
classification. They also fail to harness the very benefits from both sides. In
this paper, we present a new multi-objective oriented algorithm called MoreMNAS
(Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search) by
leveraging good virtues from both EA and RL. In particular, we incorporate a
variant of multi-objective genetic algorithm NSGA-II, in which the search space
is composed of various cells so that crossovers and mutations can be performed
at the cell level. Moreover, reinforced control is mixed with a natural
mutating process to regulate arbitrary mutation, maintaining a delicate balance
between exploration and exploitation. Therefore, not only does our method
prevent the searched models from degrading during the evolution process, but it
also makes better use of learned knowledge. Our experiments conducted in
Super-resolution domain (SR) deliver rivalling models compared to some
state-of-the-art methods with fewer FLOPS.Comment: Deep Learning, Neural Architecture Search, Multi-objective,
Reinforcement Learnin
Thermalization with detailed-balanced two-site Lindblad dissipators
The use of two-site Lindblad dissipator to generate thermal states and study
heat transport raised to prominence since [J. Stat. Mech. (2009) P02035] by
Prosen and \v{Z}nidari\v{c}. Here we propose a variant of this method based on
detailed balance of internal levels of the two site Hamiltonian and
characterize its performance. We study the thermalization profile in the chain,
the effective temperatures achieved by different single and two-site
observables, and we also investigate the decay of two-time correlations. We
find that at a large enough temperature the steady state approaches closely a
thermal state, with a relative error below 1% for the inverse temperature
estimated from different observables.Comment: 9 pages, 7 figure
Variational Semi-supervised Aspect-term Sentiment Analysis via Transformer
Aspect-term sentiment analysis (ATSA) is a longstanding challenge in natural
language understanding. It requires fine-grained semantical reasoning about a
target entity appeared in the text. As manual annotation over the aspects is
laborious and time-consuming, the amount of labeled data is limited for
supervised learning. This paper proposes a semi-supervised method for the ATSA
problem by using the Variational Autoencoder based on Transformer (VAET), which
models the latent distribution via variational inference. By disentangling the
latent representation into the aspect-specific sentiment and the lexical
context, our method induces the underlying sentiment prediction for the
unlabeled data, which then benefits the ATSA classifier. Our method is
classifier agnostic, i.e., the classifier is an independent module and various
advanced supervised models can be integrated. Experimental results are obtained
on the SemEval 2014 task 4 and show that our method is effective with four
classical classifiers. The proposed method outperforms two general
semisupervised methods and achieves state-of-the-art performance.Comment: Accepted by CoNLL 201
Computing optimal interfacial structure of ordered phases
We propose a general framework of computing interfacial structure. If an
ordered phase is involved, the interfacial structure can be obtained by simply
minimizing the free energy with compatible boundary conditions. The framework
is applied to Landau- Brazovskii model and works efficiently
Computing optimal interfacial structure of modulated phases
We propose a general framework of computing interfacial structures between
two modulated phases. Specifically we propose to use a computational box
consisting of two half spaces, each occupied by a modulated phase with given
position and orientation. The boundary conditions and basis functions are
chosen to be commensurate with the bulk structures. It is observed that the
ordered nature of modulated structures stabilizes the interface, which enables
us to obtain optimal interfacial structures by searching local minima of the
free energy landscape. The framework is applied to the Landau-Brazovskii model
to investigate interfaces between modulated phases with different relative
positions and orientations. Several types of novel complex interfacial
structures are obtained from the calculations.Comment: 15 pages, 7 figures. arXiv admin note: substantial text overlap with
arXiv:1511.0362
Perceiving Motion Cues Inspired by Microsoft Kinect Sensor on Game Experiencing
This paper proposed a novel method to replace the traditional mouse
controller by using Microsoft Kinect Sensor to realize the functional
implementation on human-machine interaction. With human hand gestures and
movements, Kinect Sensor could accurately recognize the participants intention
and transmit our order to desktop or laptop. In addition, the trend in current
HCI market is giving the customer more freedom and experiencing feeling by
involving human cognitive factors more deeply. Kinect sensor receives the
motion cues continuously from the humans intention and feedback the reaction
during the experiments. The comparison accuracy between the hand movement and
mouse cursor demonstrates the efficiency for the proposed method. In addition,
the experimental results on hit rate in the game of Fruit Ninja and Shape
Touching proves the real-time ability of the proposed framework. The
performance evaluation built up a promise foundation for the further
applications in the field of human-machine interaction. The contribution of
this work is the expansion on hand gesture perception and early formulation on
Mac iPad.Comment: 4 pages,4 figures Confernece: 1st International Workshop on
Bio-neuromorphic Systems and Human-Robot Interactio
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