A Hybrid Quantum Encoding Algorithm of Vector Quantization for Image Compression

Many classical encoding algorithms of Vector Quantization (VQ) of image compression that can obtain global optimal solution have computational complexity O(N). A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45sqrt(N) times approximately. In this paper, a hybrid quantum VQ encoding algorithm between classical method and quantum algorithm is presented. The number of its operations is less than sqrt(N) for most images, and it is more efficient than the pure quantum algorithm. Key Words: Vector Quantization, Grover's Algorithm, Image Compression, Quantum AlgorithmComment: Modify on June 21. 10pages, 3 figure

CoO2-Layer-Thickness Dependence of Magnetic Properties and Possible Two Different Superconducting States in NaxCoO2.yH2O

In order to understand the experimentally proposed phase diagrams of NaxCoO2.yH2O, we theoretically study the CoO2-layer-thickness dependence of magnetic and superconducting (SC) properties by analyzing a multiorbital Hubbard model using the random phase approximation. When the Co valence (s) is +3.4, we show that the magnetic fluctuation exhibits strong layer-thickness dependence where it is enhanced at finite (zero) momentum in the thicker (thinner) layer system. A magnetic order phase appears sandwiched by two SC phases, consistent with the experiments. These two SC phases have different pairing states where one is the singlet extended s-wave state and the other is the triplet p-wave state. On the other hand, only a triplet p-wave SC phase with dome-shaped behavior of Tc is predicted when s=+3.5, which is also consistent with the experiments. Controversial experimental results on the magnetic properties are also discussed.Comment: 5 pages, 4 figures. Submitted to Journal of the Physical Society of Japa

Coexistence of Superconductivity and Antiferromagnetism in Multilayered High-TcT_c Superconductor HgBa2_2Ca4_4Cu5_5Oy_y: A Cu-NMR Study

We report a coexistence of superconductivity and antiferromagnetism in five-layered compound HgBa$_2$Ca$_4$Cu$_5$O$_y$ (Hg-1245) with $T_c=108$ K, which is composed of two types of CuO$_2$ planes in a unit cell; three inner planes (IP's) and two outer planes (OP's). The Cu-NMR study has revealed that the optimallydoped OP undergoes a superconducting (SC) transition at $T_c=108$ K, whereas the three underdoped IP's do an antiferromagnetic (AF) transition below $T_N\sim$ 60 K with the Cu moments of $\sim (0.3-0.4)\mu_B$. Thus bulk superconductivity with a high value of $T_c=108$ K and a static AF ordering at $T_N=60$ K are realized in the alternating AF and SC layers. The AF-spin polarization at the IP is found to induce the Cu moments of $\sim0.02\mu_B$ at the SC OP, which is the AF proximity effect into the SC OP.Comment: 6 pages, 8 figure

Mass independence and asymmetry of the reaction: Multi-fragmentation as an example

We present our recent results on the fragmentation by varying the mass asymmetry of the reaction between 0.2 and 0.7 at an incident energy of 250 MeV/nucleon. For the present study, the total mass of the system is kept constant (ATOT = 152) and mass asymmetry of the reaction is defined by the asymmetry parameter (? = | (AT - AP)/(AT + AP) |). The measured distributions are shown as a function of the total charge of all projectile fragments, Zbound. We see an interesting outcome for rise and fall in the production of intermediate mass fragments (IMFs) for large asymmetric colliding nuclei. This trend, however, is completely missing for large asymmetric nuclei. Therefore, experiments are needed to verify this prediction

Assessing mental health for China's police: psychometric features of the Self-Rating Depression Scale and Symptom Checklist 90-Revised

Police mental health is important because police officers usually encounter stressors that cause high levels of stress. In order to better understand mental health for Chinese police, the Zung Self-Rating Depression Scale (SDS) and Symptom Checklist 90-Revised (SCL-90-R) are commonly used in mainland China. Unfortunately, both the SDS and SCL-90-R lack detailed information on their psychometric properties. More specifically, factor structures of the SDS and SCL-90-R have yet to be confirmed among the police population in mainland China. Therefore, the present study compared several factor structures of the SDS and SCL-90-R proposed by prior research and to determine an appropriate structure for the police population. Utilizing cluster sampling, 1151 traffic police officers (1047 males; mean age = 36.6 years [SD = 6.10]) from 49 traffic police units in Jiangxi Province (China) participated in this study. Confirmatory factor analysis (CFA) with Akaike information criterion (AIC) was used to decide the best fit structure. In the SDS, the three-factor model (first posited by Kitamura et al.) had the smallest AIC and outperformed other models. In the SCL-90-R, the eight-factor model had the smallest AIC and outperformed the one-factor and nine-factor models. CFA fit indices also showed that both the three-factor model in the SDS and the eight-factor model in the SCL-90-R had satisfactory fit. The present study's results support the use of both SDS and SCL-90-R for police officers in mainland China

Fast Meta Learning for Adaptive Beamforming

This paper studies the deep learning based adaptive downlink beamforming solution for the signal-to-interference-plus-noise ratio balancing problem. Adaptive beamforming is an important approach to enhance the performance in dynamic wireless environments in which testing channels have different distributions from training channels. We propose an adaptive method to achieve fast adaptation of beamforming based on the principle of meta learning. Specifically, our method first learns an embedding model by training a deep neural network as a transferable feature extractor. In the adaptation stage, it fits a support vector regression model using the extracted features and testing data of the new environment. Simulation results demonstrate that compared to the state of the art meta learning method, our proposed algorithm reduces the complexities in both training and adaptation processes by more than an order of magnitude, while achieving better adaptation performance

Occupation probability of harmonic-oscillator quanta for microscopic cluster-model wave functions

We present a new and simple method of calculating the occupation probability of the number of total harmonic-oscillator quanta for a microscopic cluster-model wave function. Examples of applications are given to the recent calculations including $\alpha+n+n$-model for $^6$He, $\alpha+t+n+n$-model for $^9$Li, and $\alpha+\alpha+n$-model for $^9$Be as well as the classical calculations of $\alpha+p+n$-model for $^6$Li and $\alpha+\alpha+\alpha$-model for $^{12}$C. The analysis is found to be useful for quantifying the amount of excitations across the major shell as well as the degree of clustering. The origin of the antistretching effect is discussed.Comment: 9 page

Familial Clustering For Weakly-labeled Android Malware Using Hybrid Representation Learning

IEEE Labeling malware or malware clustering is important for identifying new security threats, triaging and building reference datasets. The state-of-the-art Android malware clustering approaches rely heavily on the raw labels from commercial AntiVirus (AV) vendors, which causes misclustering for a substantial number of weakly-labeled malware due to the inconsistent, incomplete and overly generic labels reported by these closed-source AV engines, whose capabilities vary greatly and whose internal mechanisms are opaque (i.e., intermediate detection results are unavailable for clustering). The raw labels are thus often used as the only important source of information for clustering. To address the limitations of the existing approaches, this paper presents ANDRE, a new ANDroid Hybrid REpresentation Learning approach to clustering weakly-labeled Android malware by preserving heterogeneous information from multiple sources (including the results of static code analysis, the metainformation of an app, and the raw-labels of the AV vendors) to jointly learn a hybrid representation for accurate clustering. The learned representation is then fed into our outlieraware clustering to partition the weakly-labeled malware into known and unknown families. The malware whose malicious behaviours are close to those of the existing families on the network, are further classified using a three-layer Deep Neural Network (DNN). The unknown malware are clustered using a standard density-based clustering algorithm. We have evaluated our approach using 5,416 ground-truth malware from Drebin and 9,000 malware from VIRUSSHARE (uploaded between Mar. 2017 and Feb. 2018), consisting of 3324 weakly-labeled malware. The evaluation shows that ANDRE effectively clusters weaklylabeled malware which cannot be clustered by the state-of-theart approaches, while achieving comparable accuracy with those approaches for clustering ground-truth samples

Na content dependence of superconductivity and the spin correlations in Na_{x}CoO_{2}\cdot 1.3H_{2}O

We report systematic measurements using the ^{59}Co nuclear quadrupole resonance(NQR) technique on the cobalt oxide superconductors Na_{x}CoO_{2}\cdot 1.3H_{2}O over a wide Na content range x=0.25\sim 0.34. We find that T_c increases with decreasing x but reaches to a plateau for x \leq0.28. In the sample with x \sim 0.26, the spin-lattice relaxation rate 1/T_1 shows a T^3 variation below T_c and down to T\sim T_c/6, which unambiguously indicates the presence of line nodes in the superconducting (SC) gap function. However, for larger or smaller x, 1/T_1 deviates from the T^3 variation below T\sim 2 K even though the T_c (\sim 4.7 K) is similar, which suggests an unusual evolution of the SC state. In the normal state, the spin correlations at a finite wave vector become stronger upon decreasing x, and the density of states at the Fermi level increases with decreasing x, which can be understood in terms of a single-orbital picture suggested on the basis of LDA calculation.Comment: version published in J. Phys. Condens. Matter (references updated and more added