Maximum-likelihood coherent-state quantum process tomography

Coherent-state quantum process tomography (csQPT) is a method of completely characterizing a quantum-optical "black box" by probing it with coherent states and performing homodyne measurements on the output [M. Lobino et al, Science 322, 563 (2008)]. We present a technique for csQPT that is fully based on statistical inference, specifically, quantum expectation-maximization. The method relies on the Jamiolkowski isomorphism and iteratively reconstructs the process tensor in the Fock basis directly from the experimental data. This approach permits incorporation of a priori constraints into the reconstruction procedure, thereby guaranteeing that the resulting process tensor is physically consistent. Furthermore, our method is easier to implement and requires a narrower range of coherent states than its predecessors. We test its feasibility using simulations on several experimentally relevant processes.Comment: 17 pages, 4 figure

Hidden Subsidies

Many governments use price subsidisation (total costs less total revenues from user charges) to meet social protection objectives in lieu of, or in addition to, direct income transfers. Such subsidies may be perceived as influencing behaviour to further other socially desirable policies. For example, the price response induced by lowering the price of schooling will both lower the cost of living for the beneficiaries and also increase the investment in education more than a similar income transfer would achieve. The incidences of benefits from a general price subsidy are proportional to purchases and can be deduced from the pattern of expenditures. Some goods are inappropriate vehicles for redistribution since subsidies on them will not only accrue mainly to the rich they will actually increase inequality in welfare. It is therefore important to ensure that commodities chosen for price subsidisation are largely consumed by the lower income groups. Also, detailed data on such commodities should be made public to make the extent of subsidy easily tractable. In the case of Pakistan, the problem of lack of transparency of federal and provincial budgets is vividly demonstrated by the inability of such budgets to readily highlight the subsidy on the various economic and social services, which are essentially in the nature of ‘private’ goods, provided by such governments. This is not only a reflection of the problem of the nature of budgeting practices whereby, first, revenues and expenditures on different heads are shown separately and no account is made either of depreciation of assets or the costs of capital used to finance the acquisition of assets which yield a stream of services. Second, to the extent that the subsidies largely benefit the upper income groups, political compulsions dictate that such subsidies largely remain hidden.

Transport Through an Electrostatically Defined Quantum Dot Lattice in a Two-Dimensional Electron Gas

Quantum dot lattices (QDLs) have the potential to allow for the tailoring of optical, magnetic and electronic properties of a user-defined artificial solid. We use a dual gated device structure to controllably tune the potential landscape in a GaAs/AlGaAs two-dimensional electron gas, thereby enabling the formation of a periodic QDL. The current-voltage characteristics, I(V), follow a power law, as expected for a QDL. In addition, a systematic study of the scaling behavior of I(V) allows us to probe the effects of background disorder on transport through the QDL. Our results are particularly important for semiconductor-based QDL architectures which aim to probe collective phenomena.Comment: 6 pages, 4 figure

Generalized anxiety disorder: can we rest now?

Generalized anxiety disorder is a complex psychiatric syndrome. Current understanding on the epidemiological risk factors, genetic vulnerability and neurobiology of the GAD is beginning to unfold the complexities behind this disorder. This narrative review has attempted to put together the recent advances in the area of GAD research with intent to identify the gaps requiring further research

Hidden Subsidies

Many governments use price subsidisation (total costs less total revenues from user charges) to meet social protection objectives in lieu of, or in addition to, direct income transfers. Such subsidies may be perceived as influencing behaviour to further other socially desirable policies. For example, the price response induced by lowering the price of schooling will both lower the cost of living for the beneficiaries and also increase the investment in education more than a similar income transfer would achieve. The incidences of benefits from a general price subsidy are proportional to purchases and can be deduced from the pattern of expenditures. Some goods are inappropriate vehicles for redistribution since subsidies on them will not only accrue mainly to the rich they will actually increase inequality in welfare. It is therefore important to ensure that commodities chosen for price subsidisation are largely consumed by the lower income groups. Also, detailed data on such commodities should be made public to make the extent of subsidy easily tractable

A pricing optimization modelling for assisted decision making in telecommunication product-service bundling

Product service bundle (PSB) is a marketing strategy that offers attractive product-service packages with competitive pricing to ensure sustained profitability. However, designing suitable pricing for PSB is a non-trivial task that involves complex decision-making. This paper explores the significance of pricing optimization in the telecommunication industry, focusing on product-service bundling (PSB). It delves into the challenges associated with pricing PSB and highlights the transformative impact of big data analytics on decision-making for PSB strategies. The study presents a data-driven pricing optimization model tailored for designing appropriate pricing structures for product-service bundles within the telecommunication services domain. This model integrates customer preference knowledge and involves intricate decision-making processes. To demonstrate the feasibility of the proposed approach, the paper conducts a case study encompassing two design scenarios, wherein the results reveal that the model offers competitive pricing compared to existing telecommunication service providers, facilitating PSB design and decision-making. The findings from the case study indicate that the data-driven pricing optimization model can significantly aid PSB design and decision-making, leading to competitive pricing strategies that open avenues for new market exploration and ensure business sustainability. By considering both product and service features concurrently, the proposed model provides a pricing reference for optimal decision-making. The case study validates the feasibility and effectiveness of the approach within the telecommunication industry and highlights its potential for broader applications. The model's capability to generate competitive pricing strategies offers opportunities for new market exploration, ensuring business growth and adaptability

The Effect of Service Quality on Purchase Intention with Mediating Role of Store Image

This study emphasizes to measure the effect of service quality on purchase intention with mediating role of store image. The study majorly focused on paint brands available at stores of Manama, Bahrain. Reliability of data, descriptive analysis and Pearson correlation matrix was calculated by using SPSS. The relationship among study variables is tested trough structural equation modeling (SEM) by using AMOS 24. Results highlighted that there is significant and positive relation between service quality and purchase intention of private label paint brands. Moreover, it was found that store image mediates the relationship between service quality and purchase intention of private label paint brands

A Multi-Factorial Risk Prioritization Framework for Food-Borne Pathogens

To lower the incidence of human food-borne disease, experts and stakeholders have urged the development of a science- and risk-based management system in which food-borne hazards are analyzed and prioritized. A literature review shows that most approaches to risk prioritization developed to date are based on measures of health outcomes and do not systematically account for other factors that may be important to decision making. The Multi-Factorial Risk Prioritization Framework developed here considers four factors that may be important to risk managers: public health, consumer risk perceptions and acceptance, market-level impacts, and social sensitivity. The framework is based on the systematic organization and analysis of data on these multiple factors. The basic building block of the information structure is a three-dimensional cube based on pathogen-food-factor relationships. Each cell of the cube has an information card associated with it and data from the cube can be aggregated along different dimensions. The framework is operationalized in three stages, with each stage adding another dimension to decision-making capacity. The first stage is the information cards themselves that provide systematic information that is not pre-processed or aggregated across factors. The second stage maps the information on the various information cards into cobweb diagrams that create a graphical profile of, for example, a food-pathogen combination with respect to each of the four risk prioritization factors. The third stage is formal multi-criteria decision analysis in which decision makers place explicit values on different criteria in order to develop risk priorities. The process outlined above produces a ‘List A’ of priority food-pathogen combinations according to some aggregate of the four risk prioritization factors. This list is further vetted to produce ‘List B’, which brings in feasibility analysis by ranking those combinations where practical actions that have a significant impact are feasible. Food-pathogen combinations where not enough is known to identify any or few feasible interventions are included in ‘List C’. ‘List C’ highlights areas with significant uncertainty where further research may be needed to enhance the precision of the risk prioritization process. The separation of feasibility and uncertainty issues through the use of ‘Lists A, B, and C’ allows risk managers to focus separately on distinct dimensions of the overall prioritization. The Multi-Factorial Risk Prioritization Framework provides a flexible instrument that compares and contrasts risks along four dimensions. Use of the framework is an iterative process. It can be used to establish priorities across pathogens for a particular food, across foods for a particular pathogen and/or across specific food-pathogen combinations. This report provides a comprehensive conceptual paper that forms the basis for a wider process of consultation and for case studies applying the framework.risk analysis, risk prioritization, food-borne pathogens, benefits and costs