Decoherence of Einstein-Podolsky-Rosen steering

We consider two systems A and B that share Einstein-Podolsky-Rosen (EPR) steering correlations and study how these correlations will decay, when each of the systems are independently coupled to a reservoir. EPR steering is a directional form of entanglement, and the measure of steering can change depending on whether the system A is steered by B, or vice versa. First, we examine the decay of the steering correlations of the two-mode squeezed state. We find that if the system B is coupled to a reservoir, then the decoherence of the steering of A by B is particularly marked, to the extent that there is a sudden death of steering after a finite time. We find a different directional effect, if the reservoirs are thermally excited. Second, we study the decoherence of the steering of a Schr\"odinger cat state, modeled as the entangled state of a spin and harmonic oscillator, when the macroscopic system (the cat) is coupled to a reservoir

Quantifying wind and pressure effects on trace gas fluxes across the soil–atmosphere interface

Acknowledgements. We would like to acknowledge the manufacturers of the inner toroid: Mark Bentley and Steve Howarth from the University of York, Dept. of Biology, mechanical and electronics workshops respectively. Furthermore, we would like to acknowledge the Forestry Commission for access and aid at Wheldrake Forest, Mike Bailey and Natural Resources Wales for access and assistance at Cors Fochno, and Norrie Russell and the Royal Society for the Protection of Birds for access and aid at Forsinard. We would also like to thank Graham Hambley, James Robinson, and Elizabeth Donkin for equipment preparation and sampling. Phil Ineson is thanked for the loan of essential equipment, site suggestions, and accessible power supply. Funding was provided by the University of York, Dept. of Biology, and by a grant to YAT by the UK Natural Environment Research Council (NE/H01182X/1).Peer reviewedPublisher PD

Real-time information processing of environmental sensor network data using Bayesian Gaussian processes

In this article, we consider the problem faced by a sensor network operator who must infer, in real time, the value of some environmental parameter that is being monitored at discrete points in space and time by a sensor network. We describe a powerful and generic approach built upon an efficient multi-output Gaussian process that facilitates this information acquisition and processing. Our algorithm allows effective inference even with minimal domain knowledge, and we further introduce a formulation of Bayesian Monte Carlo to permit the principled management of the hyperparameters introduced by our flexible models. We demonstrate how our methods can be applied in cases where the data is delayed, intermittently missing, censored, and/or correlated. We validate our approach using data collected from three networks of weather sensors and show that it yields better inference performance than both conventional independent Gaussian processes and the Kalman filter. Finally, we show that our formalism efficiently reuses previous computations by following an online update procedure as new data sequentially arrives, and that this results in a four-fold increase in computational speed in the largest cases considered

Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 201

Comparison between controlled and uncontrolled spray-DIC modeling for dehydration process

The work reported here focuses on the controllability expressions in the mathematical modeling of dehydration process of food concentrates in producing powder using spray-DIC (spray-Détente Instantaneé Controlee or spray-instant controlled pressure drop). This paper presents the second-order partial differential equations for mathematical modeling of moisture and heat transfer in spray-DIC process. This paper proposes the enhancement in the simple model of DIC technique with controllability expression to be used in the spray-DIC. The controllability expression in the drying process models gives better results when compared to the models without the controllability expression. The results were computed and shown by MATLAB 2013 with Windows 8 operating systems. The controllability expression in dehydration process model using the spray-DIC drier manage to succesfully control the dehydration process

The 2011 Retrovirology Prize winner Masao Matsuoka: forward looking and antisense

Masao Matsuoka wins the 2011 Retrovirology Prize

Parallel computing of numerical schemes and big data analytic for solving real life applications

This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance

Energy Efficient virtualization framework for 5G F-RAN

Fog radio access network (F-RAN) and virtualisation are promising technologies for 5G networks. In F-RAN, the fog and cloud computing are integrated where the conventional C-RAN functions are diverged to the edge devices of radio access networks. F-RAN is adopted to mitigate the burden of front-haul and improve the end to end (E2E) latency. On other hand, virtualization and network function virtualization (NFV) are IT techniques that aim to convert the functions from hardware to software based functions. Many merits could be brought by the employment of NFV in mobile networks including a high degree of reliability, flexibility and energy efficiency. In this paper, a virtualization framework is introduced for F-RAN to improve the energy efficiency in 5G networks. In this framework, a gigabit passive optical network (GPON) is leveraged as a backbone network for the proposed F-RAN architecture where it connects several evolved nodes B (eNodeBs) via fibre cables. The energy-efficiency of the proposed F-RAN architecture has been investigated and compared with the conventional C-RAN architecture in two different scenarios using mixed integer linear programming (MILP) models. The MILP results indicate that on average a 30% power saving can be achieved by the F-RAN architecture compared with the C-RAN architecture