Digital Switching in the Quantum Domain

In this paper, we present an architecture and implementation algorithm such that digital data can be switched in the quantum domain. First we define the connection digraph which can be used to describe the behavior of a switch at a given time, then we show how a connection digraph can be implemented using elementary quantum gates. The proposed mechanism supports unicasting as well as multicasting, and is strict-sense non-blocking. It can be applied to perform either circuit switching or packet switching. Compared with a traditional space or time domain switch, the proposed switching mechanism is more scalable. Assuming an n-by-n quantum switch, the space consumption grows linearly, i.e. O(n), while the time complexity is O(1) for unicasting, and O(log n) for multicasting. Based on these advantages, a high throughput switching device can be built simply by increasing the number of I/O ports.Comment: 24 pages, 16 figures, LaTe

Modeling Shallow Over-Saturated Mixtures on Arbitrary Rigid Topography

In this paper a system of depth-integrated equations for over-saturated debris flows on three-dimensional topography is derived. The lower layer is a saturated mixture of density preserving solid and fluid constituents, where the pore fluid is in excess, so that an upper fluid layer develops above the mixture layer. At the layer interface fluid mass exchange may exist and for this a parameterization is needed. The emphasis is on the description of the influence on the flow by the curvature of the basal surface, and not on proposing rheological models of the avalanching mass. To this end, a coordinate system fitted to the topography has been used to properly account for the geometry of the basal surface. Thus, the modeling equations have been written in terms of these coordinates, and then simplified by using (1) the depth-averaging technique and (2) ordering approximations in terms of an aspect ratio ϵ which accounts for the scale of the flowing mass. The ensuing equations have been complemented by closure relations, but any other such relations can be postulated. For a shallow two-layer debris with clean water in the upper layer, flowing on a slightly curved surface, the equilibrium free surface is shown to be horizonta

A hierarchy of avalanche models on arbitrary topography

We use the non-Cartesian, topography-based equations of mass and momentum balance for gravity driven frictional flows of Luca etal. (Math. Mod. Meth. Appl. Sci. 19, 127-171 (2009)) to motivate a study on various approximations of avalanche models for single-phase granular materials. By introducing scaling approximations we develop a hierarchy of model equations which differ by degrees in shallowness, basal curvature, peculiarity of constitutive formulation (non-Newtonian viscous fluids, Savage-Hutter model) and velocity profile parametrization. An interesting result is that differences due to the constitutive behaviour are largely eliminated by scaling approximations. Emphasis is on avalanche flows; however, most equations presented here can be used in the dynamics of other thin films on arbitrary surface

CORRECTION TO "QUASI-MONTE CARLO METHODS FOR HIGH-DIMENSIONAL INTEGRATION: THE STANDARD (WEIGHTED HILBERT SPACE) SETTING AND BEYOND”

ISSN:1446-1811ISSN:1446-873

Quasi-Monte Carlo methods for high-dimensional integration: the standard (weighted Hilbert space) setting and beyond

This paper is a contemporary review of quasi-Monte Carlo (QMC) methods, that is, equal-weight rules for the approximate evaluation of high-dimensional integrals over the unit cube $[0,1]^s$. It first introduces the by-now standard setting of weighted Hilbert spaces of functions with square-integrable mixed first derivatives, and then indicates alternative settings, such as non-Hilbert spaces, that can sometimes be more suitable. Original contributions include the extension of the fast component-by-component (CBC) construction of lattice rules that achieve the optimal convergence order (a rate of almost $1/N$, where $N$ is the number of points, independently of dimension) to so-called “product and order dependent†(POD) weights, as seen in some recent applications. Although the paper has a strong focus on lattice rules, the function space settings are applicable to all QMC methods. Furthermore, the error analysis and construction of lattice rules can be adapted to polynomial lattice rules from the family of digital nets. doi:10.1017/S144618111200007

The first operation and results of the Chung-Li VHF radar

The Chung-Li Very High Frequency (VHF) radar is used in the dual-mode operations, applying Doppler beam-swinging as well as the spaced-antenna-drift method. The design of the VHF radar is examined. Results of performance tests are discussed

Suppression of core polarization in halo nuclei

We present a microscopic study of halo nuclei, starting from the Paris and Bonn potentials and employing a two-frequency shell model approach. It is found that the core-polarization effect is dramatically suppressed in such nuclei. Consequently the effective interaction for halo nucleons is almost entirely given by the bare G-matrix alone, which presently can be evaluated with a high degree of accuracy. The experimental pairing energies between the two halo neutrons in $^6$He and $^{11}$Li nuclei are satisfactorily reproduced by our calculation. It is suggested that the fundamental nucleon-nucleon interaction can be probed in a clearer and more direct way in halo nuclei than in ordinary nuclei.Comment: 11 pages, RevTex, 2 postscript figures; major revisions, matches version to appear in Phys. Rev. Letter

Attentive Neural Architecture Incorporating Song Features For Music Recommendation

Recommender Systems are an integral part of music sharing platforms. Often the aim of these systems is to increase the time, the user spends on the platform and hence having a high commercial value. The systems which aim at increasing the average time a user spends on the platform often need to recommend songs which the user might want to listen to next at each point in time. This is different from recommendation systems which try to predict the item which might be of interest to the user at some point in the user lifetime but not necessarily in the very near future. Prediction of the next song the user might like requires some kind of modeling of the user interests at the given point of time. Attentive neural networks have been exploiting the sequence in which the items were selected by the user to model the implicit short-term interests of the user for the task of next item prediction, however we feel that the features of the songs occurring in the sequence could also convey some important information about the short-term user interest which only the items cannot. In this direction, we propose a novel attentive neural architecture which in addition to the sequence of items selected by the user, uses the features of these items to better learn the user short-term preferences and recommend the next song to the user.Comment: Accepted as a paper at the 12th ACM Conference on Recommender Systems (RecSys 18