Anomalous Magnetic and Thermal Behavior in Some RMn2O5 Oxides

The RMn2O5 (R=Pr, Nd, Sm, and Eu) oxides showing magnetoelectric (ME) behavior have been prepared in polycrystalline form by a standard citrate route. The lattice parameters, obtained from the powder XRD analysis, follow the rare-earth contraction indicating the trivalent character of the R ions. Cusp-like anomalies in the magnetic susceptibility curve and sharp peaks in the specific heat were reported at the corresponding temperatures in RMn2O5 (R=Pr, Nd, Sm, and Eu) indicating the magnetic or electric ordering transitions.Comment: 2 pages, 1 table, 3 figures, will be published in the Proceedings of the 24th International Conference on Low Temperature Physic

Weak-Light, Zero to -\pi Lossless Kerr-Phase Gate in Quantum-well System via Tunneling Interference Effect

We examine a Kerr phase gate in a semiconductor quantum well structure based on the tunnelling interference effect. We show that there exist a specific signal field detuning, at which the absorption/amplification of the probe field will be eliminated with the increase of the tunnelling interference. Simultaneously, the probe field will acquire a -\pi phase shift at the exit of the medium. We demonstrate with numerical simulations that a complete 180^\circ phase rotation for the probe field at the exit of the medium is achieved, which may result in many applications in information science and telecommunication

Critical Fluctuation of Wind Reversals in Convective Turbulence

The irregular reversals of wind direction in convective turbulence are found to have fluctuating intervals that can be related to critical behavior. It is shown that the net magnetization of a 2D Ising lattice of finite size fluctuates in the same way. Detrended fluctuation analysis of the wind reversal time series results in a scaling behavior that agrees with that of the Ising problem. The properties found suggest that the wind reversal phenomenon exhibits signs of self-organized criticality.Comment: 4 RevTeX pages + 3 figures in ep

Bose-Einstein Condensation of Atoms in a Trap

We point out that the local density approximation (LDA) of Oliva is an adaptation of the Thomas-Fermi method, and is a good approximation when $\varepsilon = \hbar\omega/kT 0$, the LDA leads to a quantitative result (14') easily checked by experiments. Critical remarks are made about the physics of the many body problem in terms of the scattering length $a$.Comment: 9 pages, latex. one figure, available from author

The contribution of ultracompact dark matter minihalos to the isotropic radio background

The ultracompact minihalos could be formed during the earlier epoch of the universe. The dark matter annihilation within them is very strong due to the steep density profile, $\rho \sim r^{-2.25}$. The high energy electrons and positrons from the dark matter annihilation can inverse Compton scatter (ICS) with the background photons, such as CMB photons, to acquire higher energy. On the other hand, the synchrotron radiation can also be produced when they meet the magnetic field. In this paper, we study the signals from the UCMHs due to the dark matter annihilation for the radio, X-ray and $\gamma$-ray band. We found that for the radio emission the UCMHs can provide one kind of source for the radio excess observed by ARCADE 2. But the X-ray signals due to the ICS effect or the $\gamma$-ray signals mainly due to the prompt emission from dark matter would exceed the present observations, such as Fermi, COMPTEL and CHANDRA. We found that the strongest limits on the fraction of UCMHs come from the X-ray observations and the constraints from the radio data are the weakest.Comment: 6 pages, 8 figures, Comments Welcome! Some Refs. are added, some presentation have been corrected. The conclusions remain unchanged. One important reference has been corrected. Some presentations are changed and added according to the referee's comments. Accepted for publication in PR

Experimental evidence for a two-gap structure of superconducting NbSe_2: a specific heat study in external magnetic fields

To resolve the discrepancies of the superconducting order parameter in quasi-two-dimensional NbSe_2, comprehensive specific-heat measurements have been carried out. By analyzing both the zero-field and mixed-state data with magnetic fields perpendicular to and parallel to the c axis of the crystal and using the two-gap model, we conclude that (1) more than one energy scale of the order parameter is required for superconducting NbSe_2 due to the thermodynamic consistency; (2)delta_L=1.26 meV and delta_S=0.73 meV are obtained; (3) N_S(0)/N(0)=11%~20%; (4) The observation of the kink in gamma(H) curve suggests that the two-gap scenario is more favorable than the anisotropic s-wave model to describe the gap structure of NbSe_2; and (5)delta_S is more isotropic and has a three-dimensional-like feature and is located either on the Se or the bonding Nb Fermi sheets.Comment: 16 pages, 4 figure

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

As many-core accelerators keep integrating more processing units, it becomes increasingly more difficult for a parallel application to make effective use of all available resources. An effective way for improving hardware utilization is to exploit spatial and temporal sharing of the heterogeneous processing units by multiplexing computation and communication tasks - a strategy known as heterogeneous streaming. Achieving effective heterogeneous streaming requires carefully partitioning hardware among tasks, and matching the granularity of task parallelism to the resource partition. However, finding the right resource partitioning and task granularity is extremely challenging, because there is a large number of possible solutions and the optimal solution varies across programs and datasets. This article presents an automatic approach to quickly derive a good solution for hardware resource partition and task granularity for task-based parallel applications on heterogeneous many-core architectures. Our approach employs a performance model to estimate the resulting performance of the target application under a given resource partition and task granularity configuration. The model is used as a utility to quickly search for a good configuration at runtime. Instead of hand-crafting an analytical model that requires expert insights into low-level hardware details, we employ machine learning techniques to automatically learn it. We achieve this by first learning a predictive model offline using training programs. The learnt model can then be used to predict the performance of any unseen program at runtime. We apply our approach to 39 representative parallel applications and evaluate it on two representative heterogeneous many-core platforms: a CPU-XeonPhi platform and a CPU-GPU platform. Compared to the single-stream version, our approach achieves, on average, a 1.6x and 1.1x speedup on the XeonPhi and the GPU platform, respectively. These results translate to over 93% of the performance delivered by a theoretically perfect predictor