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

A Novel Spatio-Temporal Data Storage and Index Method for ARM-Based Hadoop Server

This project is supported by Science and Technology Development Plan of Jilin Province (20140204010SF) and Chinese National Natural Science Foundation (61472159). WP is supported by the PECE bursary from The Scottish Informatics and Computer Science Alliance (SICSA).Postprin

Deep learning assisted jet tomography for the study of Mach cones in QGP

Mach cones are expected to form in the expanding quark-gluon plasma (QGP) when energetic quarks and gluons (called jets) traverse the hot medium at a velocity faster than the speed of sound in high-energy heavy-ion collisions. The shape of the Mach cone and the associated diffusion wake are sensitive to the initial jet production location and the jet propagation direction relative to the radial flow because of the distortion by the collective expansion of the QGP and large density gradient. The shape of jet-induced Mach cones and their distortions in heavy-ion collisions provide a unique and direct probe of the dynamical evolution and the equation of state of QGP. However, it is difficult to identify the Mach cone and the diffusion wake in current experimental measurements of final hadron distributions because they are averaged over all possible initial jet production locations and propagation directions. To overcome this difficulty, we develop a deep learning assisted jet tomography which uses the full information of the final hadrons from jets to localize the initial jet production positions. This method can help to constrain the initial regions of jet production in heavy-ion collisions and enable a differential study of Mach-cones with different jet path length and orientation relative to the radial flow of the QGP in heavy-ion collisions

Design and Development of the Reactive BGP peering in Software-Defined Routing Exchanges

The Software-Defined Networking (SDN) is considered to be an improved solution for applying flexible control and operation recently in the network. Its characteristics include centralized management, global view, as well as fast adjustment and adaptation. Many experimental and research networks have already migrated to the SDN-enabled architecture. As the global network continues to grow in a fast pace, how to use SDN to improve the networking fields becomes a popular topic in research. One of the interesting topics is to enable routing exchanges among the SDN-enabled network and production networks. However, considering that many production networks are still operated on legacy architecture, the enabled SDN routing functionalities have to support hybrid mode in operation. In this paper, we propose a routing exchange mechanism by enabling reactive BGP peering actions among the SDN and legacy network components. The results of experiments show that our SDN controller is able to mask as an Autonomous System (AS) to exchange routing information with other BGP routers