3-Uniform states and orthogonal arrays

In a recent paper (Phys. Rev. A 90, 022316 (2014) ), Goyeneche et al. established a link between the combinatorial notion of orthogonal arrays and k-uniform states and present open issue. (B) Find for what N there are 3-uniform states of N-qubits. In this paper, we demonstrate the existence of 3-uniform states of N-qubits for N=11,..,15"

Searching for the Evidence of Dynamical Dark Energy

In the statistical framework of model-independent Gaussian processes (GP), we search for the evidence of dynamical dark energy (DDE) using the "Joint Light-curve Analysis" (JLA) Type Ia supernovae (SNe Ia) sample, the 30 latest cosmic chronometer data points (H(z)), Planck's shift parameter from cosmic microwave background (CMB) anisotropies, the 156 latest HII galaxy measurements and 79 calibrated gamma-ray bursts (GRBs). We find that the joint constraint from JLA + H(z) + CMB + HII + GRB supports the global measurement of $H_0$ by Planck collaboration very much in the low redshift range $z\in[0, 0.76]$ at the $2\sigma$ confidence level (C.L.), gives a cosmological constant crossing (quintom-like) equation of state (EoS) of DE at the $2\sigma$ C.L. and implies that the evolution of the late-time Universe may be actually dominated by the DDE.Comment: EPJC accepted. 7 figures, 10 page

Heavy Quark Energy Loss in Nuclear Medium

Multiple scattering, modified fragmentation functions and radiative energy loss of a heavy quark propagating in a nuclear medium are investigated in perturbative QCD. Because of the quark mass dependence of the gluon formation time, the medium size dependence of heavy quark energy loss is found to change from a linear to a quadratic form when the initial energy and momentum scale are increased relative to the quark mass. The radiative energy loss is also significantly suppressed relative to a light quark due to the suppression of collinear gluon emission by a heavy quark.Comment: 4 pages in Revtex, 3 figure

Cross-Domain Image Retrieval with Attention Modeling

With the proliferation of e-commerce websites and the ubiquitousness of smart phones, cross-domain image retrieval using images taken by smart phones as queries to search products on e-commerce websites is emerging as a popular application. One challenge of this task is to locate the attention of both the query and database images. In particular, database images, e.g. of fashion products, on e-commerce websites are typically displayed with other accessories, and the images taken by users contain noisy background and large variations in orientation and lighting. Consequently, their attention is difficult to locate. In this paper, we exploit the rich tag information available on the e-commerce websites to locate the attention of database images. For query images, we use each candidate image in the database as the context to locate the query attention. Novel deep convolutional neural network architectures, namely TagYNet and CtxYNet, are proposed to learn the attention weights and then extract effective representations of the images. Experimental results on public datasets confirm that our approaches have significant improvement over the existing methods in terms of the retrieval accuracy and efficiency.Comment: 8 pages with an extra reference pag