Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification

Multiple kernel learning (MKL) method is generally believed to perform better than single kernel method. However, some empirical studies show that this is not always true: the combination of multiple kernels may even yield an even worse performance than using a single kernel. There are two possible reasons for the failure: (i) most existing MKL methods assume that the optimal kernel is a linear combination of base kernels, which may not hold true; and (ii) some kernel weights are inappropriately assigned due to noises and carelessly designed algorithms. In this paper, we propose a novel MKL framework by following two intuitive assumptions: (i) each kernel is a perturbation of the consensus kernel; and (ii) the kernel that is close to the consensus kernel should be assigned a large weight. Impressively, the proposed method can automatically assign an appropriate weight to each kernel without introducing additional parameters, as existing methods do. The proposed framework is integrated into a unified framework for graph-based clustering and semi-supervised classification. We have conducted experiments on multiple benchmark datasets and our empirical results verify the superiority of the proposed framework.Comment: Accepted by IJCAI 2018, Code is availabl

Optimal measurements to access classical correlations of two-qubit states

We analyze the optimal measurements accessing classical correlations in arbitrary two-qubit states. Two-qubit states can be transformed into the canonical forms via local unitary operations. For the canonical forms, we investigate the probability distribution of the optimal measurements. The probability distribution of the optimal measurement is found to be centralized in the vicinity of a specific von Neumann measurement, which we call the maximal-correlation-direction measurement (MCDM). We prove that for the states with zero-discord and maximally mixed marginals, the MCDM is the very optimal measurement. Furthermore, we give an upper bound of quantum discord based on the MCDM, and investigate its performance for approximating the quantum discord.Comment: 8 pages, 3 figures, version accepted by Phys. Rev.

The role of inter-well tunneling strength on coherence dynamics of two-species Bose-Einstein condensates

Coherence dynamics of two-species Bose-Einstein condensates in double wells is investigated in mean field approximation. We show that the system can exhibit decoherence phenomena even without the condensate-environment coupling and the variation tendency of the degree of coherence depends on not only the parameters of the system but also the initial states. We also investigate the time evolution of the degree of coherence for a Rosen-Zener form of tunneling strength, and propose a method to get a condensate system with certain degree of coherence through a time-dependent tunneling strength

The Influence of Augmented Reality Technology on the Learning Interest, Achievement of Learning Goals and Cognitive Load of Middle School Students

In order to test the influence of augmented reality technology on the learning interest achievement of learning goals and cognitive load the instruction of topographic map was taken as an example for experimental study This paper selected 427 students from 8 classes of Grade one who all come from Zhantan Middle School in Xindu District of Chengdu as experimental samples set two classes with similar level in learning as one group the one is the experimental class and the other is the control class and formed 4 groups in total The experimental classes adopted AR three-dimensional videos as teaching aids to give new lessons and the control classes adopted traditional two- dimensional videos and then the students learning interest level achievement of learning goals and cognitive load were measured The results showed that the application of AR technology in teaching could improve students learning interest and achievement of learning goals but had no effect on reducing cognitive loa

Submodular Load Clustering with Robust Principal Component Analysis

Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behaviors at different areas, highly aggregated load shapes still share similar characteristics. Load clustering is to discover such intrinsic patterns and provide useful information to other load applications, such as load forecasting and load modeling. This paper proposes an efficient submodular load clustering method for transmission-level load areas. Robust principal component analysis (R-PCA) firstly decomposes the annual load profiles into low-rank components and sparse components to extract key features. A novel submodular cluster center selection technique is then applied to determine the optimal cluster centers through constructed similarity graph. Following the selection results, load areas are efficiently assigned to different clusters for further load analysis and applications. Numerical results obtained from PJM load demonstrate the effectiveness of the proposed approach.Comment: Accepted by 2019 IEEE PES General Meeting, Atlanta, G