884 research outputs found
Condensation of Eigen Microstate in Statistical Ensemble and Phase Transition
In a statistical ensemble with microstates, we introduce an
correlation matrix with the correlations between microstates as its elements.
Using eigenvectors of the correlation matrix, we can define eigen microstates
of the ensemble. The normalized eigenvalue by represents the weight factor
in the ensemble of the corresponding eigen microstate. In the limit , weight factors go to zero in the ensemble without localization of
microstate. The finite limit of weight factor when indicates a
condensation of the corresponding eigen microstate. This indicates a phase
transition with new phase characterized by the condensed eigen microstate. We
propose a finite-size scaling relation of weight factors near critical point,
which can be used to identify the phase transition and its universality class
of general complex systems. The condensation of eigen microstate and the
finite-size scaling relation of weight factors have been confirmed by the Monte
Carlo data of one-dimensional and two-dimensional Ising models.Comment: 9 pages, 16 figures, accepted for publication in Sci. China-Phys.
Mech. Astro
Recommended from our members
Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects
A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm
Neural networks play an increasingly important role in the field of machine
learning and are included in many applications in society. Unfortunately,
neural networks suffer from adversarial samples generated to attack them.
However, most of the generation approaches either assume that the attacker has
full knowledge of the neural network model or are limited by the type of
attacked model. In this paper, we propose a new approach that generates a
black-box attack to neural networks based on the swarm evolutionary algorithm.
Benefiting from the improvements in the technology and theoretical
characteristics of evolutionary algorithms, our approach has the advantages of
effectiveness, black-box attack, generality, and randomness. Our experimental
results show that both the MNIST images and the CIFAR-10 images can be
perturbed to successful generate a black-box attack with 100\% probability on
average. In addition, the proposed attack, which is successful on distilled
neural networks with almost 100\% probability, is resistant to defensive
distillation. The experimental results also indicate that the robustness of the
artificial intelligence algorithm is related to the complexity of the model and
the data set. In addition, we find that the adversarial samples to some extent
reproduce the characteristics of the sample data learned by the neural network
model
Observation of vacancy-induced suppression of electronic cooling in defected graphene
Previous studies of electron-phonon interaction in impure graphene have found
that static disorder can give rise to an enhancement of electronic cooling. We
investigate the effect of dynamic disorder and observe over an order of
magnitude suppression of electronic cooling compared with clean graphene. The
effect is stronger in graphene with more vacancies, confirming its
vacancy-induced nature. The dependence of the coupling constant on the phonon
temperature implies its link to the dynamics of disorder. Our study highlights
the effect of disorder on electron-phonon interaction in graphene. In addition,
the suppression of electronic cooling holds great promise for improving the
performance of graphene-based bolometer and photo-detector devices.Comment: 13 pages, 4 figure
Weighted-Sampling Audio Adversarial Example Attack
Recent studies have highlighted audio adversarial examples as a ubiquitous
threat to state-of-the-art automatic speech recognition systems. Thorough
studies on how to effectively generate adversarial examples are essential to
prevent potential attacks. Despite many research on this, the efficiency and
the robustness of existing works are not yet satisfactory. In this paper, we
propose~\textit{weighted-sampling audio adversarial examples}, focusing on the
numbers and the weights of distortion to reinforce the attack. Further, we
apply a denoising method in the loss function to make the adversarial attack
more imperceptible. Experiments show that our method is the first in the field
to generate audio adversarial examples with low noise and high audio robustness
at the minute time-consuming level.Comment: https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuXL.9260.pd
Defining Urban Boundaries by Characteristic Scales
Defining an objective boundary for a city is a difficult problem, which
remains to be solved by an effective method. Recent years, new methods for
identifying urban boundary have been developed by means of spatial search
techniques (e.g. CCA). However, the new algorithms are involved with another
problem, that is, how to determine the characteristic radius of spatial search.
This paper proposes new approaches to looking for the most advisable spatial
searching radius for determining urban boundary. We found that the
relationships between the spatial searching radius and the corresponding number
of clusters take on an exponential function. In the exponential model, the
scale parameter just represents the characteristic length that we can use to
define the most objective urban boundary objectively. Two sets of China's
cities are employed to test this method, and the results lend support to the
judgment that the characteristic parameter can well serve for the spatial
searching radius. The research may be revealing for making urban spatial
analysis in methodology and implementing identification of urban boundaries in
practice.Comment: 26 pages, 5 figures, 7 table
The linear dependence problem for power linear maps
AbstractLet Bl, l=1,…,k, be m×nl complex matrices and let x[l]∈Cnl,l=1,…,k, be complex vector variables. We show that the components of the map H=(B1x[1])(d1)∘⋯∘(Bkx[k])(dk) are linearly dependent over C if and only if det(B1B1∗)(d1)∘⋯∘(BkBk∗)(dk)=0, where ∘ means the Hadamard product, X∗ and X(d) denote the conjugate transpose and the dth Hadamard power of a matrix X respectively. Connections are established between the Homogenous Dependence Problem (HDP(n,d)), which arises in the study of the Jacobian Conjecture, and the dependence problem for power linear maps (PLDP(n,d)). An algorithm is given to compute counterexamples to PLDP(n,d) from those to HDP(n,d), and counterexamples to PLDP(n,3) are obtained for all n⩾67
- …