Entanglement generation in spatially separated systems using quantum walk

We present a novel scheme to generate entanglement between two spatially separated systems. The scheme makes use of spatial entanglement generated by a single-particle quantum walk which is used to entangle two spatially separated, not necessarily correlated, systems. This scheme can be used to entangle any two systems which can interact with the spatial modes entangled during the quantum walk evolution. A notable feature is that we can control the quantum walk dynamics and its ability to localize leads to a substantial control and improvement in the entanglement output.Comment: 9 pages, 5 figure

Estimation from quantized Gaussian measurements: when and how to use dither

Subtractive dither is a powerful method for removing the signal dependence of quantization noise for coarsely quantized signals. However, estimation from dithered measurements often naively applies the sample mean or midrange, even when the total noise is not well described with a Gaussian or uniform distribution. We show that the generalized Gaussian distribution approximately describes subtractively dithered, quantized samples of a Gaussian signal. Furthermore, a generalized Gaussian fit leads to simple estimators based on order statistics that match the performance of more complicated maximum likelihood estimators requiring iterative solvers. The order statistics-based estimators outperform both the sample mean and midrange for nontrivial sums of Gaussian and uniform noise. Additional analysis of the generalized Gaussian approximation yields rules of thumb for determining when and how to apply dither to quantized measurements. Specifically, we find subtractive dither to be beneficial when the ratio between the Gaussian standard deviation and quantization interval length is roughly less than one-third. When that ratio is also greater than 0.822/K^0.930 for the number of measurements K > 20, estimators we present are more efficient than the midrange.https://arxiv.org/abs/1811.06856Accepted manuscrip

A novel strategy for multiagent coalitions in a dynamic hostile world

One of the main underpinning of the multi-agent systems community is how and why autonomous agents should cooperate with one another. Several formal and computational models of cooperative work or coalition are currently developed and used within multi-agent systems research. The coalition facilitates the achievement of cooperation among different agents. In this paper, a mental construct called attitude is proposed and its significance in coalition formation in a dynamic fire world is discussed. It shows that coalitions explore the attitudes and behaviours that help agents to achieve goals that cannot be achieved alone or to maximize net group utility. © 2007 Springer-Verlag Berlin Heidelberg

An attitude based modeling of agents in coalition

One of the main underpinning of the multi-agent systems community is how and why autonomous agents should cooperate with one another. Several formal and computational models of cooperative work or coalition are currently developed and used within multi-agent systems research. The coalition facilitates the achievement of cooperation among different agents. In this paper, a mental construct called attitude is proposed and its significance in coalition formation in a dynamic fire world is discussed. This paper presents ABCAS (Attitude Based Coalition Agent System) that shows coalitions in multi-agent systems are an effective way of dealing with the complexity of fire world. It shows that coalitions explore the attitudes and behaviors that help agents to achieve goals that cannot be achieved alone or to maximize net group utility

An attitude based cooperative negotiation model in a hostile multi-agent worldZ

In multi-agent setting agent teams often encounter conflicts in agents’ plans and actions. This paper presents a cooperative negotiation model (ABCON) that allows agents in a team to appropriately negotiate various options in a hostile and dynamic fire world. It shows that negotiations explore the attitudes and behaviors that help agents to manage conflict constructively. It says that cooperative negotiation is guided by the agents’ dynamic assessment of alternative actions given the different scenario conditions. The application and implementation of this model to a virtual fire-fighting domain has revealed a promising prospect in negotiating conflicts and solving them. © 2005 - IOS Press and the authors

Sports Retailinf in India: Opportunities, Constraints and Way Forward

Sports retail is a small but fast growing segment of modern retail in India. Recently, thecountry has been hosting many international sports and this has given a boost to thissector. Many foreign and domestic corporate retailers have entered sports retail. Sportsgoods manufacturing is a focus area in the Foreign Trade Policy (2009-2014) and thegovernment is taking a fresh look at the current foreign direct investment policy inretail.In the above context, this paper provides an overview of the sports retail sector in India.Specifically, it presents the different retail formats, consumer profile, retailers’ supplychain and sourcing. It also examines the retail and sports policies and their implicationsfor this segment of retail, analyses the barriers faced by this sector and suggests policyreforms.The study found that the policy of allowing 51 per cent FDI in the single-brand formathas not benefited this sector. The FDI ban on multi-brand retail is not an entry barriersince foreign retailers can establish their presence in India through other routes. Thestudy found that since this is a niche segment of retail, FDI would not have an adverseimpact on traditional retailers. It concludes that government should allow 51 per centFDI in multi-brand sports retail. This will increase sourcing from India, lead todiffusion of technology, proliferation of brands, investment in sports and sportspromotion, among others.Retail, Sports, Government Policy, Trade, Consumer Survey

Dead Time Compensation for High-Flux Ranging

Dead time effects have been considered a major limitation for fast data acquisition in various time-correlated single photon counting applications, since a commonly adopted approach for dead time mitigation is to operate in the low-flux regime where dead time effects can be ignored. Through the application of lidar ranging, this work explores the empirical distribution of detection times in the presence of dead time and demonstrates that an accurate statistical model can result in reduced ranging error with shorter data acquisition time when operating in the high-flux regime. Specifically, we show that the empirical distribution of detection times converges to the stationary distribution of a Markov chain. Depth estimation can then be performed by passing the empirical distribution through a filter matched to the stationary distribution. Moreover, based on the Markov chain model, we formulate the recovery of arrival distribution from detection distribution as a nonlinear inverse problem and solve it via provably convergent mathematical optimization. By comparing per-detection Fisher information for depth estimation from high- and low-flux detection time distributions, we provide an analytical basis for possible improvement of ranging performance resulting from the presence of dead time. Finally, we demonstrate the effectiveness of our formulation and algorithm via simulations of lidar ranging.Comment: Revision with added estimation results, references, and figures, and modified appendice

The University of Glasgow at ImageClefPhoto 2009

In this paper we describe the approaches adopted to generate the five runs submitted to ImageClefPhoto 2009 by the University of Glasgow. The aim of our methods is to exploit document diversity in the rankings. All our runs used text statistics extracted from the captions associated to each image in the collection, except one run which combines the textual statistics with visual features extracted from the provided images. The results suggest that our methods based on text captions significantly improve the performance of the respective baselines, while the approach that combines visual features with text statistics shows lower levels of improvements

Zonal trend-agrometeorological models for wheat yield estimation in Haryana

An attempt has been made to assess the impact of weather variables for district-level wheat yield estimation in Haryana. Fortnightly weather data and trend based yield were used for developing the zonal trendagrometeorological (agromet) models within the framework of multiple linear regression and discriminant function analyses. The district level wheat yield forecasts, percent deviations from the real time wheat yield (s) and root mean square error(s) at zonal level show a preference of using discriminant/weather scores as regressors in almost all the considered districts of the state. Zonal trend-agromet models provided considerable improvement in district-level wheat yield prediction moreover the yield estimates may be obtained 4-5 weeks in advance of the harvest time. The estimated yield(s) from the selected zonal models showed good agreement with State Department of Agriculture (DOA) wheat yields by showing less than 5 percent deviations in 9 districts and 6-11 percent deviations in the remaining 9 districts under consideration