MRM-Lasso: A Sparse Multiview Feature Selection Method via Low-Rank Analysis

© 2015 IEEE. Learning about multiview data involves many applications, such as video understanding, image classification, and social media. However, when the data dimension increases dramatically, it is important but very challenging to remove redundant features in multiview feature selection. In this paper, we propose a novel feature selection algorithm, multiview rank minimization-based Lasso (MRM-Lasso), which jointly utilizes Lasso for sparse feature selection and rank minimization for learning relevant patterns across views. Instead of simply integrating multiple Lasso from view level, we focus on the performance of sample-level (sample significance) and introduce pattern-specific weights into MRM-Lasso. The weights are utilized to measure the contribution of each sample to the labels in the current view. In addition, the latent correlation across different views is successfully captured by learning a low-rank matrix consisting of pattern-specific weights. The alternating direction method of multipliers is applied to optimize the proposed MRM-Lasso. Experiments on four real-life data sets show that features selected by MRM-Lasso have better multiview classification performance than the baselines. Moreover, pattern-specific weights are demonstrated to be significant for learning about multiview data, compared with view-specific weights

Out of plane effect on the superconductivity of Sr2-xBaxCuO3+y with Tc up to 98K

A series of new Sr2-xBaxCuO3+y (0 x 0.6) superconductors were prepared using high-pressure and high-temperature synthesis. A Rietveld refinement based on powder x-ray diffraction confirms that the superconductors crystallize in the K2NiF4-type structure of a space group I4/mmm similar to that of La2CuO4 but with partially occupied apical oxygen sites. It is found that the superconducting transition temperature Tc of this Ba substituted Sr2CuO3+y superconductor with constant carrier doping level, i.e., constant d, is controlled not only by order/disorder of apical-O atoms but also by Ba content. Tcmax =98 K is achieved in the material with x=0.6 that reaches the record value of Tc among the single-layer copper oxide superconductors, and is higher than Tc=95K of Sr2CuO3+y with optimally ordered apical-O atoms. There is Sr-site disorder in Sr2-xBaxCuO3+y which might lead to a reduction of Tc. The result indicates that another effect surpasses the disorder effect that is related either to the increased in-plane Cu-O bond length or to elongated apical-O distance due to Ba substitution with larger cation size. The present experiment demonstrates that the optimization of local geometry out of the Cu-O plane can dramatically enhance Tc in the cuprate superconductors.Comment: 23 Pages, 1 Table, 5 Figure

The missing link between thermodynamics and structure in F_1-ATPase

F_1F_o-ATP synthase is the enzyme responsible for most of the ATP synthesis in living systems. The catalytic domain F_1 of the F_1F_o complex, F_1-ATPase, has the ability to hydrolyze ATP. A fundamental problem in the development of a detailed mechanism for this enzyme is that it has not been possible to determine experimentally the relation between the ligand binding affinities measured in solution and the different conformations of the catalytic β subunits (β_(TP), β_(DP), β_E) observed in the crystal structures of the mitochondrial enzyme, MF_1. Using free energy difference simulations for the hydrolysis reaction ATP+H_2O → ADP+P_i in the β_(TP) and β_(DP) sites and unisite hydrolysis data, we are able to identify β_(TP) as the “tight” (K_D = 10^(−12) M, MF_1) binding site for ATP and β_(DP) as the “loose” site. An energy decomposition analysis demonstrates how certain residues, some of which have been shown to be important in catalysis, modulate the free energy of the hydrolysis reaction in the β_(TP) and β_(DP) sites, even though their structures are very similar. Combined with the recently published simulations of the rotation cycle of F_1-ATPase, the present results make possible a consistent description of the binding change mechanism of F_1-ATPase at an atomic level of detail

A symplectic analytical singular element for steady-state thermal conduction with singularities in composite structures

In modern design of composite structures, multiple materials with different properties are bound together. Accurate prediction of the strength of the interface between different materials, especially with the existence of cracks under thermal loading, is demanded in engineering. To this end, detailed knowledge on the distribution of temperature and heat flux is required. This study conducts a systematical investigation on the cracks terminated at material interface under steady-state thermal conduction. A new symplectic analytical singular element is constructed for the numerical modeling. Combining the proposed element with conventional finite elements, the generalized flux intensity factors can be solved accurately

Generalized Limits for Parameter Sensitivity via Quantum Ziv-Zakai Bound

We study the generalized limit for parameter sensitivity in quantum estimation theory considering the effects of repeated and adaptive measurements. Based on the quantum Ziv-Zakai bound, we derive some lower bounds for parameter sensitivity when the Hamiltonian of system is unbounded and when the adaptive measurements are implemented on the system. We also prove that the parameter sensitivity is bounded by the limit of the minimum detectable parameter. In particular, we examine several known states in quantum phase estimation with non-interacting photons, and show that they can not perform better than Heisenberg limit in a much simpler way with our result.Comment: 8pages, 5 figure

Recommendation with multi-source heterogeneous information

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Network embedding has been recently used in social network recommendations by embedding low-dimensional representations of network items for recommendation. However, existing item recommendation models in social networks suffer from two limitations. First, these models partially use item information and mostly ignore important contextual information in social networks such as textual content and social tag information. Second, network embedding and item recommendations are learned in two independent steps without any interaction. To this end, we in this paper consider item recommendations based on heterogeneous information sources. Specifically, we combine item structure, textual content and tag information for recommendation. To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source Deep Network Embedding (CDNE for short) is proposed to learn different latent representations. Experimental results on two real-world data sets demonstrate that CDNE can use network representation learning to boost the recommendation performance

Reconstruction of lymphatic vessels in the mouse tail after cupping therapy

Background: The aim of the study was to investigate the regulatory mechanism of local lymphatic reconstruction after cupping therapy in a mouse model. Materials and methods: The lymphatic reconstruction process in the mouse tail after cupping therapy as well as the expression levels of the vascular endothelial identification molecule CD34, prospero homeobox protein 1 (PROX1), and lymphatic vessel endothelial hyaluronan receptor 1 (LYVE-1) were investigated for a duration of 4 days through immunohistochemistry experiments. Results: On day 1 after cupping therapy, the CD34+ and LYVE-1+ cell densities were significantly increased, and the formed CD34+LYVE-1+ tubular structure started to express PROX1. This was followed by a decrease in both the CD34+ and LYVE-1+ stem cell densities to basal levels on the second day after cupping therapy. Both the CD34+ and LYVE-1+ cell densities subsequently increased again on the third day after cupping therapy. The increase in the LYVE-1+ density was accompanied by tubular structure formation, which is characteristic of lymphangiogenesis. In addition, the colocalisation of CD34+ and LYVE-1+ cells by immunohistochemistry suggests that the CD34+ stem cells differentiated into new lymphatic endothelial cells. Conclusions: Our findings indicate that the mechanism underlying the therapeutic effect of cupping therapy involves upregulation of vascular and lymphatic endothelial markers (CD34+, LYVE-1+, and CD34+LYVE-1+) in local tissues, which in turn promotes local new lymphatic vessel formation through the expression of PROX1