Nonequilibrium Phase Transitions of Vortex Matter in Three-Dimensional Layered Superconductors

Large-scale simulations on three-dimensional (3D) frustrated anisotropic XY model have been performed to study the nonequilibrium phase transitions of vortex matter in weak random pinning potential in layered superconductors. The first-order phase transition from the moving Bragg glass to the moving smectic is clarified, based on thermodynamic quantities. A washboard noise is observed in the moving Bragg glass in 3D simulations for the first time. It is found that the activation of the vortex loops play the dominant role in the dynamical melting at high drive.Comment: 3 pages,5 figure

Spin Qubits in Multi-Electron Quantum Dots

We study the effect of mesoscopic fluctuations on the magnitude of errors that can occur in exchange operations on quantum dot spin-qubits. Mid-size double quantum dots, with an odd number of electrons in the range of a few tens in each dot, are investigated through the constant interaction model using realistic parameters. It is found that the constraint of having short pulses and small errors implies keeping accurate control, at the few percent level, of several electrode voltages. In practice, the number of independent parameters per dot that one should tune depends on the configuration and ranges from one to four.Comment: RevTex, 6 pages, 5 figures. v3: two figures added, more details provided. Accepted for publication in PR

Investigations of the g factors and local structure for orthorhombic Cu^{2+}(1) site in fresh PrBa_{2}Cu_{3}O_{6+x} powders

The electron paramagnetic resonance (EPR) g factors g_x, g_y and g_z of the orthorhombic Cu^{2+}(1) site in fresh PrBa_{2}Cu_{3}O_{6+x} powders are theoretically investigated using the perturbation formulas of the g factors for a 3d^9 ion under orthorhombically elongated octahedra. The local orthorhombic distortion around the Cu^{2+}(1) site due to the Jahn-Teller effect is described by the orthorhombic field parameters from the superposition model. The [CuO6]^{10-} complex is found to experience an axial elongation of about 0.04 {\AA} along c axis and the relative bond length variation of about 0.09 {\AA} along a and b axes of the Jahn-Teller nature. The theoretical results of the g factors based on the above local structure are in reasonable agreement with the experimental data.Comment: 6 pages, 1 figur

Spin swap gate in the presence of qubit inhomogeneity in a double quantum dot

We study theoretically the effects of qubit inhomogeneity on the quantum logic gate of qubit swap, which is an integral part of the operations of a quantum computer. Our focus here is to construct a robust pulse sequence for swap operation in the simultaneous presence of Zeeman inhomogeneity for quantum dot trapped electron spins and the finite-time ramp-up of exchange coupling in a double dot. We first present a geometric explanation of spin swap operation, mapping the two-qubit operation onto a single-qubit rotation. We then show that in this geometric picture a square-pulse-sequence can be easily designed to perform swap in the presence of Zeeman inhomogeneity. Finally, we investigate how finite ramp-up times for the exchange coupling $J$ negatively affect the performance of the swap gate sequence, and show how to correct the problems numerically.Comment: published versio

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

Technology Road Mapping for Innovation Pathways of Fibrates: A Cross-Database Patent Review

Purpose: To examine international technology development of fibrates based on a cross-database quantitative patent review and to describe the evolution pathway for fibrates by means of a technology roadmap.Methods: The patent data were collected in March 2013 from United States Patent and Trademark Office (USPTO), European Patent Office (EPO) and China Intellectual Property Right Net (CNIPR) to broadly represent global patent activities.Results: This study selected and examined 84 patents from USPTO, 41 patents from EPO and 39 patents from CNIPR. It showed that most of the fibrate patents were fenofibrate patents (41.67 % at USPTO, 46.34 % at EPO and 33.33 % at CNIPR). The number of preparation patents (44 at USPTO, 24 at EPO and 17 at CNIPR) and combination patents (23 at USPTO, 11 at EPO and 15 at CNIPR) was obviously larger than other types of fibrate patents. The technology roadmap shows that new monomersor derivatives of fibrates can drive fibrate evolution into a new cycle of application-synthesiscombination- preparation.Conclusion: This study provides a comprehensive picture of fibrate development. It will aid researchers, entrepreneurs, investors and policymakers to identify foci for fibrate research and ensure better  decision-making