A Modified Optical Potential Approach to Low-energy Electron-helium Scattering

Optical potential approach to low energy electron- helium scatterin

The effect of shear and bulk viscosities on elliptic flow

In this work, we examine the effect of shear and bulk viscosities on elliptic flow by taking a realistic parameterization of the shear and bulk viscous coefficients, $\eta$ and $\zeta$, and their respective relaxation times, $\tau_{\pi}$ and $\tau_{\Pi}$. We argue that the behaviors close to ideal fluid observed at RHIC energies may be related to non-trivial temperature dependence of these transport coefficients.Comment: 6 pages, 4 figures, to appear in the proceedings of Strange Quark Matter 2009 (SQM09

Dynamical properties of dipolar Fermi gases

We investigate dynamical properties of a one-component Fermi gas with dipole-dipole interaction between particles. Using a variational function based on the Thomas-Fermi density distribution in phase space representation, the total energy is described by a function of deformation parameters in both real and momentum space. Various thermodynamic quantities of a uniform dipolar Fermi gas are derived, and then instability of this system is discussed. For a trapped dipolar Fermi gas, the collective oscillation frequencies are derived with the energy-weighted sum rule method. The frequencies for the monopole and quadrupole modes are calculated, and softening against collapse is shown as the dipolar strength approaches the critical value. Finally, we investigate the effects of the dipolar interaction on the expansion dynamics of the Fermi gas and show how the dipolar effects manifest in an expanded cloud.Comment: 14 pages, 8 figures, submitted to New J. Phy

The Influence of Dependence Structure And Relational Value on The Adoption Internet-enabled Supply Chain Management Systems

Based on resource dependency theory, this study investigates how the two dimensions of dependence – dependence asymmetry and mutual dependence – affect the adoption of internet-enabled supply chain management systems (eSCM). Drawing from the relational view of the firm, we argue that there are two types of relational value that can be provided by eSCM: relationship extendedness and relational depth. Dependence structure will influence firms’ incentive to obtain relationship extendedness and relational depth, which will in turn affect eSCM adoption. We collected data from mainland China using an online questionnaire and 212 valid samples were received. The emergent results show positive influence of dependence structure on relationship extendedness and relational depth. Positive effects of dependence structure and relationship relational depth on eSCM adoption are also found. However, the finding suggests a significant negative effect of relationship extendedness on eSCM, which is contradictory to the hypothesis. Future research is needed to interpret the counterintuitive finding

On the single mode approximation in spinor-1 atomic condensate

We investigate the validity conditions of the single mode approximation (SMA) in spinor-1 atomic condensate when effects due to residual magnetic fields are negligible. For atomic interactions of the ferromagnetic type, the SMA is shown to be exact, with a mode function different from what is commonly used. However, the quantitative deviation is small under current experimental conditions (for $^{87}$Rb atoms). For anti-ferromagnetic interactions, we find that the SMA becomes invalid in general. The differences among the mean field mode functions for the three spin components are shown to depend strongly on the system magnetization. Our results can be important for studies of beyond mean field quantum correlations, such as fragmentation, spin squeezing, and multi-partite entanglement.Comment: Revised, newly found analytic proof adde

Signatures of Strong Correlations in One-Dimensional Ultra-Cold Atomic Fermi Gases

Recent success in manipulating ultra-cold atomic systems allows to probe different strongly correlated regimes in one-dimension. Regimes such as the (spin-coherent) Luttinger liquid and the spin-incoherent Luttinger liquid can be realized by tuning the inter-atomic interaction strength and trap parameters. We identify the noise correlations of density fluctuations as a robust observable (uniquely suitable in the context of trapped atomic gases) to discriminate between these two regimes. Finally, we address the prospects to realize and probe these phenomena experimentally using optical lattices.Comment: 4 pages, 2 figure

Dipolar Relaxation in an ultra-cold Gas of magnetically trapped chromium atoms

We have investigated both theoretically and experimentally dipolar relaxation in a gas of magnetically trapped chromium atoms. We have found that the large magnetic moment of 6 $\mu_B$ results in an event rate coefficient for dipolar relaxation processes of up to $3.2\cdot10^{-11}$ cm$^{3}$s$^{-1}$ at a magnetic field of 44 G. We present a theoretical model based on pure dipolar coupling, which predicts dipolar relaxation rates in agreement with our experimental observations. This very general approach can be applied to a large variety of dipolar gases.Comment: 9 pages, 9 figure

Learning to learn graph topologies

Learning a graph topology to reveal the underlying relationship between data entities plays an important role in various machine learning and data analysis tasks. Under the assumption that structured data vary smoothly over a graph, the problem can be formulated as a regularised convex optimisation over a positive semidefinite cone and solved by iterative algorithms. Classic methods require an explicit convex function to reflect generic topological priors, e.g. the ℓ1 penalty for enforcing sparsity, which limits the flexibility and expressiveness in learning rich topological structures. We propose to learn a mapping from node data to the graph structure based on the idea of learning to optimise (L2O). Specifically, our model first unrolls an iterative primal-dual splitting algorithm into a neural network. The key structural proximal projection is replaced with a variational autoencoder that refines the estimated graph with enhanced topological properties. The model is trained in an end-to-end fashion with pairs of node data and graph samples. Experiments on both synthetic and real-world data demonstrate that our model is more efficient than classic iterative algorithms in learning a graph with specific topological properties

DEVELOPMENT OF A UNIFIED OPEN E-LOGISTICS STANDARDS DIFFUSION MODEL FOR MANUFACTURING SUPPLY CHAIN INTEGRATIONS

Open E-Logistics Standards (OELS) is important to facilitate the integration of the supply chain. In OELS, the transmission and the manipulation of data are governed by open data and process standards that define their format, structure, and the semantics of data flow between trading partners. Despite the significant investments made by governments and leading firms, there remain concerns about OELS’ slow development progress and low adoption rates. The potential failure of OELS represents a significant stumbling block for governments and supply chain practitioners who have envisioned a globalized supply chain network electronically enabled by OELS. Researchers are also concerned about the inadequate models that are used to explain and understand the adoption of OELS. Although analysing adopter configurations in what is known as configuration analysis has been examined in disciplines related to science and economics, its application in the study of OELS remains sparse. This research aims to integrate multiple theoretical views, and apply configuration analysis with an improved methodological approach in order to examine OELS diffusion decisions and processes. The project will result in a new algorithm integrating structural equation modelling and neural network, and a decision support system which helps firms improve their OELS adoption decision