Josephson dynamics of a spin-orbit coupled Bose-Einstein condensate in a double well potential

We investigate the quantum dynamics of an experimentally realized spin-orbit coupled Bose-Einstein condensate in a double well potential. The spin-orbit coupling can significantly enhance the atomic inter-well tunneling. We find the coexistence of internal and external Josephson effects in the system, which are moreover inherently coupled in a complicated form even in the absence of interatomic interactions. Moreover, we show that the spin-dependent tunneling between two wells can induce a net atomic spin current referred as spin Josephson effects. Such novel spin Josephson effects can be observable for realistically experimental conditions.Comment: 8 page

Hemodynamic evaluation using four-dimensional flow magnetic resonance imaging for a patient with multichanneled aortic dissection

The hemodynamic function of multichanneled aortic dissection (MCAD) requires close monitoring and effective management to avoid potentially catastrophic sequelae. This report describes a 47-year-old man who underwent endovascular repair based on findings from four-dimensional (4D) flow magnetic resonance imaging of an MCAD. The acquired 4D flow data revealed complex, bidirectional flow patterns in the false lumens and accelerated blood flow in the compressed true lumen. The collapsed abdominal true lumen expanded unsatisfactorily after primary tear repair, which required further remodeling with bare stents. This case study demonstrates that hemodynamic analysis using 4D flow magnetic resonance imaging can help understand the complex pathologic changes of MCAD

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

Momentum Distribution of Near-Zero-Energy Photoelectrons in the Strong-Field Tunneling Ionization in the Long Wavelength Limit

We investigate the ionization dynamics of Argon atoms irradiated by an ultrashort intense laser of a wavelength up to 3100 nm, addressing the momentum distribution of the photoelectrons with near-zero-energy. We find a surprising accumulation in the momentum distribution corresponding to meV energy and a \textquotedblleft V"-like structure at the slightly larger transverse momenta. Semiclassical simulations indicate the crucial role of the Coulomb attraction between the escaping electron and the remaining ion at extremely large distance. Tracing back classical trajectories, we find the tunneling electrons born in a certain window of the field phase and transverse velocity are responsible for the striking accumulation. Our theoretical results are consistent with recent meV-resolved high-precision measurements.Comment: 5 pages, 4 figure

Nonperturbative signatures in pair production for general elliptic polarization fields

The momentum signatures in nonperturbative multiphoton pair production for general elliptic polarization electric fields are investigated by employing the real-time Dirac-Heisenberg-Wigner formalism. For a linearly polarized electric field we find that the positions of the nodes in momenta spectra of created pairs depend only on the electric field frequency. The polarization of external fields could not only change the node structures or even make the nodes disappear but also change the thresholds of pair production. The momentum signatures associated to the node positions in which the even-number-photon pair creation process is forbid could be used to distinguish the orbital angular momentum of created pairs on the momenta spectra. These distinguishable momentum signatures could be relevant for providing the output information of created particles and also the input information of ultrashort laser pulses.Comment: 8 pages, 4 figures, submitted to Europhysics Letter

Stopping power of high-density alpha-particle clusters in warm dense deuterium-tritium fuels

The state of burning plasma had been achieved in inertial confinement fusion (ICF), which was regarded as a great milestone for high-gain laser fusion energy. In the burning plasma, alpha particles incident on the cryogenic (warm dense) fuels cannot be simply regarded as single particles, and the new physics brought about by the density effects of alpha particles should be considered. In this work, the collective interaction between them has been considered, namely the effect of the superposition of wake waves. The stopping power of alpha-particle clusters, i.e. the rate of energy loss per unit distance traveled has been calculated using both analytical and simulation approaches. In theory, we obtain the stopping power of alpha clusters in cryogenic (warm dense) fuel by the dielectric function method, which illustrates the importance of the effective interaction between particles. Simulation results using the LAPINS code show that the collective stopping power of the alpha cluster is indeed increased via coherent superposition of excitation fields (the excitation of high-amplitude wake waves). However, the comparison between simulation and theoretical results also illustrates a coherent-decoherent transition of the stopping power of the cluster. The initial conditions with various sizes and densities of the alpha clusters have been considered to verify the condition of decoherence transition. Our work provides a theoretical description of the transport properties of high-density alpha particles in warm dense cryogenic fuel and might give some theoretical guidance for the design of actual fusion processes.Comment: Junior undergraduate students Z. P. Fu, Z. W. Zhang and K. Lin contributed equally to this wor

The androgen receptor and signal-transduction pathways in hormone-refractory prostate cancer. Part 2: androgen-receptor cofactors and bypass pathways

Prostate cancer is the second leading cause of cancer related deaths in men from the western world. Treatment of prostate cancer has relied on androgen deprivation therapy for the past 50 years. Response rates are initially high (70-80%), however almost all patients develop androgen escape and subsequently die within 1-2 years. Unlike breast cancer, alternative approaches (chemotherapy and radiotherapy) do not increase survival time. The high rate of prostate cancer mortality is therefore strongly linked to both development of androgen escape and the lack of alternate therapies. AR mutations and amplifications can not explain all cases of androgen escape and post-translational modification of the AR has become an alternative theory. However recently it has been suggested that AR co-activators e.g. SRC-1 or pathways the bypass the AR (Ras/MAP kinase or PI3K/Akt) may stimulated prostate cancer progression independent of the AR. This review will focus on how AR coactivators may act to increase AR transactivation during sub-optimal DHT concentrations and also how signal transduction pathways may promote androgen escape via activation of transcription factors, e.g. AP-1, c-Myc and Myb, that induce cell proliferation or inhibit apoptosis