Interior Estimates for the nn-dimensional Abreu's Equation

We study the Abreu's equation in n-dimensional polytopes and derive interior estimates of solutions under the assumption of the uniform K-stability.Comment: 14 pages. Comments are welcom

Zeno effect of the open quantum system in the presence of 1/f noise

We study the quantum Zeno effect (QZE) and quantum anti-Zeno effect (QAZE) in a two-level system(TLS) interacting with an environment owning 1/f noise. Using a numerically exact method based on the thermo field dynamics(TFD) theory and the matrix product states(MPS), we obtain exact evolutions of the TLS and bath(environment) under repetitive measurements at both zero and finite temperatures. At zero temperature, we observe a novel transition from a pure QZE in the short time scale to a QZE-QAZE crossover in the long time scale, by considering the measurement induced non-Markvoian effect. At finite temperature, we exploit that the thermal fluctuation suppresses the decay of the survival probability in the short time scale, whereas it enhances the decay in the long time scale.Comment: 9 pages, 6 figure

Known-class Aware Self-ensemble for Open Set Domain Adaptation

Existing domain adaptation methods generally assume different domains have the identical label space, which is quite restrict for real-world applications. In this paper, we focus on a more realistic and challenging case of open set domain adaptation. Particularly, in open set domain adaptation, we allow the classes from the source and target domains to be partially overlapped. In this case, the assumption of conventional distribution alignment does not hold anymore, due to the different label spaces in two domains. To tackle this challenge, we propose a new approach coined as Known-class Aware Self-Ensemble (KASE), which is built upon the recently developed self-ensemble model. In KASE, we first introduce a Known-class Aware Recognition (KAR) module to identify the known and unknown classes from the target domain, which is achieved by encouraging a low cross-entropy for known classes and a high entropy based on the source data from the unknown class. Then, we develop a Known-class Aware Adaptation (KAA) module to better adapt from the source domain to the target by reweighing the adaptation loss based on the likeliness to belong to known classes of unlabeled target samples as predicted by KAR. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our approach

Generative Poisoning Attack Method Against Neural Networks

Poisoning attack is identified as a severe security threat to machine learning algorithms. In many applications, for example, deep neural network (DNN) models collect public data as the inputs to perform re-training, where the input data can be poisoned. Although poisoning attack against support vector machines (SVM) has been extensively studied before, there is still very limited knowledge about how such attack can be implemented on neural networks (NN), especially DNNs. In this work, we first examine the possibility of applying traditional gradient-based method (named as the direct gradient method) to generate poisoned data against NNs by leveraging the gradient of the target model w.r.t. the normal data. We then propose a generative method to accelerate the generation rate of the poisoned data: an auto-encoder (generator) used to generate poisoned data is updated by a reward function of the loss, and the target NN model (discriminator) receives the poisoned data to calculate the loss w.r.t. the normal data. Our experiment results show that the generative method can speed up the poisoned data generation rate by up to 239.38x compared with the direct gradient method, with slightly lower model accuracy degradation. A countermeasure is also designed to detect such poisoning attack methods by checking the loss of the target model

Storage of polarization-encoded cluster states in an atomic system

We present a scheme for entanglement macroscopic atomic ensembles which are four spatially separate regions of an atomic cloud using cluster-correlated beams. We show that the cluster-type polarization-encoded entanglement could be mapped onto the long-lived collective ground state of the atomic ensembles, and the stored entanglement could be retrieved based on the technique of electromagnetically induced transparency. We also discuss the efficiency of, the lifetime of, and some quantitative restrictions to the proposed quantum memory.Comment: 7 pages, 4 figure

Prescribed Scaler Curvatures for Homogeneous Toric Bundles

In this paper, we study the generalized Abreu equation on a Delzant ploytope $\Delta \subset \mathbb{R}^2$ and prove the existence of the constant scalar metrics of homogeneous toric bundles under the assumption of an appropriate stability.Comment: 28 pages,1 figure. Any comments are welcom

Phase Structure and QNMs of A Charged AdS Dilaton Black Hole

We investigate the phase structure of a charged AdS dilaton black hole in the extended phase space which takes the cosmological constant, i.e. the AdS-$\Lambda$ parameter as pressures. Through both thermal ensemble and quasinormal mode analysis, we find that stable phase of the black hole with non-trivial dilaton profiles always exists for both large and small couplings when the AdS-$\Lambda$ is considered dynamical degrees of freedom. This forms a somewhat contrast with previous works which does not do so. Our results provide new examples for the parallelism or equivalences between thermal ensemble methods and dynamic perturbation analysis for black hole phase structures.Comment: version to appear in Phys. Rev.

On the quantification of the dissolved hydroxyl radicals in the plasma-liquid system using the molecular probe method

Hydroxyl (OH) radical is the most important reactive species produced by the plasma-liquid interactions, and the OH in the liquid phase (dissolved OH radical, OHdis) takes effect in many plasma-based applications due to its high reactivity. Therefore, the quantification of the OHdis in the plasma-liquid system is of great importance, and a molecular probe method usually used for the OHdis detection might be applied. Herein we investigate the validity of using the molecular probe method to estimate the [OHdis] in the plasma-liquid system. Dimethyl sulfoxide is used as the molecular probe to estimate the [OHdis] in an air plasma-liquid system, and the partial OHdis is related to the formed formaldehyde (HCHO) which is the OHdis-induced derivative. The analysis indicates that the true concentration of the OHdis should be estimated from the sum of three terms: the formed HCHO, the existing OH scavengers, and the OHdis generated H2O2. The results show that the measured [HCHO] needs to be corrected since the HCHO destruction is not negligible in the plasma-liquid system. We conclude from the results and the analysis that the molecular probe method generally underestimates the [OHdis] in the plasma-liquid system. If one wants to obtain the true concentration of the OHdis in the plasma-liquid system, one needs to know the destruction behavior of the OHdis-induced derivatives, the information of the OH scavengers (such as hydrated electron, atomic hydrogen besides the molecular probe), and also the knowledge of the OHdis generated H2O2.Comment: 17 pages, 4 figures,3 table

HRank: A Path based Ranking Framework in Heterogeneous Information Network

Recently, there is a surge of interests on heterogeneous information network analysis. As a newly emerging network model, heterogeneous information networks have many unique features (e.g., complex structure and rich semantics) and a number of interesting data mining tasks have been exploited in this kind of networks, such as similarity measure, clustering, and classification. Although evaluating the importance of objects has been well studied in homogeneous networks, it is not yet exploited in heterogeneous networks. In this paper, we study the ranking problem in heterogeneous networks and propose the HRank framework to evaluate the importance of multiple types of objects and meta paths. Since the importance of objects depends upon the meta paths in heterogeneous networks, HRank develops a path based random walk process. Moreover, a constrained meta path is proposed to subtly capture the rich semantics in heterogeneous networks. Furthermore, HRank can simultaneously determine the importance of objects and meta paths through applying the tensor analysis. Extensive experiments on three real datasets show that HRank can effectively evaluate the importance of objects and paths together. Moreover, the constrained meta path shows its potential on mining subtle semantics by obtaining more accurate ranking results.Comment: 12 pages, 11 figure

CSIFT Based Locality-constrained Linear Coding for Image Classification

In the past decade, SIFT descriptor has been witnessed as one of the most robust local invariant feature descriptors and widely used in various vision tasks. Most traditional image classification systems depend on the luminance-based SIFT descriptors, which only analyze the gray level variations of the images. Misclassification may happen since their color contents are ignored. In this article, we concentrate on improving the performance of existing image classification algorithms by adding color information. To achieve this purpose, different kinds of colored SIFT descriptors are introduced and implemented. Locality-constrained Linear Coding (LLC), a state-of-the-art sparse coding technology, is employed to construct the image classification system for the evaluation. The real experiments are carried out on several benchmarks. With the enhancements of color SIFT, the proposed image classification system obtains approximate 3% improvement of classification accuracy on the Caltech-101 dataset and approximate 4% improvement of classification accuracy on the Caltech-256 dataset.Comment: 9 pages, 5 figure