Purification of Mixed State with Closed Timelike Curve is not Possible

In ordinary quantum theory any mixed state can be purified in an enlarged Hilbert space by bringing an ancillary system. The purified state does not depend on the state of any extraneous system with which the mixed state is going to interact and on the physical interaction. Here, we prove that it is not possible to purify a mixed state that traverses a closed time like curve (CTC) and allowed to interact in a consistent way with a causality-respecting (CR) quantum system in the same manner. Thus, in general for arbitrary interactions between CR and CTC systems there is no universal 'Church of the larger Hilbert space' for mixed states with CTC. This shows that in quantum theory with CTCs there can exist 'proper' and 'improper' mixtures.Comment: Latex2e, No Figs, 4 + pages, An error corrected, Results unchange

Quantum Frequency Translation of Single-Photon States in Photonic Crystal Fiber

We experimentally demonstrate frequency translation of a nonclassical optical field via the Bragg scattering four-wave mixing process in a photonic crystal fiber (PCF). The high nonlinearity and the ability to control dispersion in PCF enable efficient translation between photon channels within the visible to-near-infrared spectral range, useful in quantum networks. Heralded single photons at 683 nm were translated to 659 nm with an efficiency of $28.6 \pm 2.2$ percent. Second-order correlation measurements on the 683-nm and 659-nm fields yielded $g^{(2)}_{683}(0) = 0.21 \pm 0.02$ and $g^{(2)}_{659}(0) = 0.19 \pm 0.05$ respectively, showing the nonclassical nature of both fields.Comment: 5 pages, 3 figure

Darboux transformation for two component derivative nonlinear Schr\"odinger equation

In this paper, we consider the two component derivative nonlinear Schr\"{o}dinger equation and present a simple Darboux transformation for it. By iterating this Darboux transformation, we construct a compact representation for the $N-$soliton solutions.Comment: 12 pages, 2 figure

Observations of near-bottom flow in a wave-dominated nearshore environment

To provide observational data for analysis of near-bottom, wave-induced flows, a downward-looking laser Doppler velocimeter (LDV) was deployed to profile the near-bed velocity structure of a six meter water column at a site just outside the surfzone off the coast of North Carolina. 90 second "snap-shots" of the velocity at six elevations below 20 cm above bottom were measured at 25 Hz, while pressure was concurrently measured at 126 cm above bottom. The near-bottom data were supplemented with a benthic acoustic stress sensor (BASS) at approximately 20 cm above bottom which concurrently measured velocity components at 10 Hz. The purposes of this report are to document the collection, processing and archival of these data and to present the profiles for evaluation.Funding was provided by the Coastal Sciences Program of the Office of Naval Research under Grant N00014-92-J-12300

Peristaltic Transport of a Physiological Fluid in an Asymmetric Porous Channel in the Presence of an External Magnetic Field

The paper deals with a theoretical investigation of the peristaltic transport of a physiological fluid in a porous asymmetric channel under the action of a magnetic field. The stream function, pressure gradient and axial velocity are studied by using appropriate analytical and numerical techniques. Effects of different physical parameters such as permeability, phase difference, wave amplitude and magnetic parameter on the velocity, pumping characteristics, streamline pattern and trapping are investigated with particular emphasis. The computational results are presented in graphical form. The results are found to be in perfect agreement with those of a previous study carried out for a non-porous channel in the absence of a magnetic field

Using patterns position distribution for software failure detection

Pattern-based software failure detection is an important topic of research in recent years. In this method, a set of patterns from program execution traces are extracted, and represented as features, while their occurrence frequencies are treated as the corresponding feature values. But this conventional method has its limitation due to ignore the pattern’s position information, which is important for the classification of program traces. Patterns occurs in the different positions of the trace are likely to represent different meanings. In this paper, we present a novel approach for using pattern’s position distribution as features to detect software failure. The comparative experiments in both artificial and real datasets show the effectiveness of this method