Optimized ensemble Monte Carlo simulations of dense Lennard-Jones fluids

We apply the recently developed adaptive ensemble optimization technique to simulate dense Lennard-Jones fluids and a particle-solvent model by broad-histogram Monte Carlo techniques. Equilibration of the simulated fluid is improved by sampling an optimized histogram in radial coordinates that shifts statistical weight towards the entropic barriers between the shells of the liquid. Interstitial states in the vicinity of these barriers are identified with unprecedented accuracy by sharp signatures in the quickly converging histogram and measurements of the local diffusivity. The radial distribution function and potential of mean force are calculated to high precision.Comment: 4.2 pages, 6 figure

Impurity Effects in Highly Frustrated Diamond Lattice Antiferromagnets

We consider the effects of local impurities in highly frustrated diamond lattice antiferromagnets, which exhibit large but non-extensive ground state degeneracies. Such models are appropriate to many A-site magnetic spinels. We argue very generally that sufficiently dilute impurities induce an ordered magnetic ground state, and provide a mechanism of degeneracy breaking. The states which are selected can be determined by a "swiss cheese model" analysis, which we demonstrate numerically for a particular impurity model in this case. Moreover, we present criteria for estimating the stability of the resulting ordered phase to a competing frozen (spin glass) one. The results may explain the contrasting finding of frozen and ordered ground states in CoAl2O4 and MnSc2S4, respectively.Comment: 13 pages, 7 figure

A Phase Coded Disk Approach To Thick Curvilinear Line Detection

This paper examines the well-known problem of line detection, but where the lines are wider than one pixel. The motivation behind the paper is the extraction of road information from high resolution photogrammetry and Light Detection and Ranging (LIDAR) data. Wide lines cause varying problems during detection. The Hough or Radon transform approaches do not find the road centrelines accurately; diagonals of the thick lines are found instead whilst other methods also tend to be error prone. Our approach convolves a raw, pixelated, binary road classification with a complex-valued disk. The technique provides three separate pieces of information about the road or thick line: the centreline, the direction and the width of the road at any point along the centreline. The road centreline can be detected from the position of the peak of the magnitude image resulting from the complex convolution. Road width can also be estimated from the magnitude peak whilst the direction of the road may be obtained from the phase image

Resilient Quantum Computation: Error Models and Thresholds

Recent research has demonstrated that quantum computers can solve certain types of problems substantially faster than the known classical algorithms. These problems include factoring integers and certain physics simulations. Practical quantum computation requires overcoming the problems of environmental noise and operational errors, problems which appear to be much more severe than in classical computation due to the inherent fragility of quantum superpositions involving many degrees of freedom. Here we show that arbitrarily accurate quantum computations are possible provided that the error per operation is below a threshold value. The result is obtained by combining quantum error-correction, fault tolerant state recovery, fault tolerant encoding of operations and concatenation. It holds under physically realistic assumptions on the errors.Comment: 19 pages in RevTex, many figures, the paper is also avalaible at http://qso.lanl.gov/qc

The quantum query complexity of the hidden subgroup problem is polynomial

We present a quantum algorithm which identifies with certainty a hidden subgroup of an arbitrary finite group G in only a polynomial (in log |G|) number of calls to the oracle. This is exponentially better than the best classical algorithm. However our quantum algorithm requires exponential time, as in the classical case. Our algorithm utilizes a new technique for constructing error-free algorithms for non-decision problems on quantum computers.Comment: To appear in Information Processing Letters (IPL

Variability in second language learning:the roles of individual differences, learning conditions, and linguistic complexity

Second language learning outcomes are highly variable, due to a variety of factors, including individual differences, exposure conditions, and linguistic complexity. However, exactly how these factors interact to influence language learning is unknown. This paper examines the relationship between these three variables in language learners. Native English speakers were exposed to an artificial language containing three sentence patterns of varying linguistic complexity. They were randomly assigned to two groups – incidental and instructed – designed to promote the acquisition of implicit and explicit knowledge, respectively. Learning was assessed with a grammaticality judgment task, while subjective measures of awareness were used to measure whether exposure had resulted in implicit or explicit knowledge. Participants also completed cognitive tests. Awareness measures demonstrated that learners in the incidental group relied more on implicit knowledge, whereas learners in the instructed group relied more on explicit knowledge. Overall, exposure condition was the most significant predictor of performance on the grammaticality judgment task, with learners in the instructed group outperforming those in the incidental group. Performance on a procedural learning task accounted for additional variance. When outcomes were analysed according to linguistic complexity, exposure condition was the most significant predictor for two syntactic patterns, but it was not a predictor for the most complex sentence group; instead, procedural learning ability was

Cohesion and Repulsion in Bayesian Distance Clustering

Clustering in high-dimensions poses many statistical challenges. While traditional distance-based clustering methods are computationally feasible, they lack probabilistic interpretation and rely on heuristics for estimation of the number of clusters. On the other hand, probabilistic model-based clustering techniques often fail to scale and devising algorithms that are able to effectively explore the posterior space is an open problem. Based on recent developments in Bayesian distance-based clustering, we propose a hybrid solution that entails defining a likelihood on pairwise distances between observations. The novelty of the approach consists in including both cohesion and repulsion terms in the likelihood, which allows for cluster identifiability. This implies that clusters are composed of objects which have small "dissimilarities" among themselves (cohesion) and similar dissimilarities to observations in other clusters (repulsion). We show how this modelling strategy has interesting connection with existing proposals in the literature as well as a decision-theoretic interpretation. The proposed method is computationally efficient and applicable to a wide variety of scenarios. We demonstrate the approach in a simulation study and an application in digital numismatics.Comment: 1 supplementary PDF attached. To view the supplementary PDF, please download the attachment under "Ancilliary Files

Selling digital services abroad: How do extrinsic attributes influence foreign consumers’ purchase intentions?

© 2017 The Authors This article investigates, through the country-of-origin effect and value-in-use lenses, how the implementation of digital services creates opportunities for cultural industries to expand internationally. We argue that intrinsic attributes of cultural content such as the capacity to entertain are difficult to parameterize because they are somewhat experiential and subjective. This means that extrinsic cues are essential to foreign consumers when making a decision to purchase digital services. We specifically evaluate the influence of Britishness, cultural distance, exoticness, brand image, and flag-brand congruence on the purchase intentions of consumers in foreign markets. This study employs a unique consumer dataset with information on the internationalization of British cultural digital services. The depth and breadth of the survey data collected through collaboration with a UK media industry partner with a globally recognised brand is significantly richer than data used in previous studies. In particular, the study exploits a survey with 5,200 usable data points from consumers residing in fourteen geographically dispersed countries. Findings support theoretical predictions that Britishness, cultural distance, exoticness, brand image and flag-brand congruence are positively linked to the purchasing decisions. Theoretical and managerial implications are discussed