Learning Bayesian networks from data: An information-theory based approach

This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.

Catalytic role of gold in gold-based catalysts: A density functional theory study on the CO oxidation on gold

Gold-based catalysts have been of intense interests in recent years, being regarded as a new generation of catalysts due to their unusually high catalytic performance. For example, CO oxidation on Au/TiO2 has been found to occur at a temperature as low as 200 K. Despite extensive studies in the field, the microscopic mechanism of CO oxidation on Au-based catalysts remains controversial. Aiming to provide insight into the catalytic roles of Au, we have performed extensive density functional theory calculations for the elementary steps in CO oxidation on Au surfaces. O atom adsorption, CO adsorption, O-2 dissociation, and CO oxidation on a series of Au surfaces, including flat surfaces, defects and small clusters, have been investigated in detail. Many transition states involved are located, and the lowest energy pathways are determined. We find the following: (i) the most stable site for O atom on Au is the bridge site of step edge, not a kink site; (ii) O-2 dissociation on Au (O-2-->20(ad)) is hindered by high barriers with the lowest barrier being 0.93 eV on a step edge; (iii) CO can react with atomic O with a substantially lower barrier, 0.25 eV, on Au steps where CO can adsorb; (iv) CO can react with molecular O-2 on Au steps with a low barrier of 0.46 eV, which features an unsymmetrical four-center intermediate state (O-O-CO); and (v) O-2 can adsorb on the interface of Au/TiO2 with a reasonable chemisorption energy. On the basis of our calculations, we suggest that (i) O-2 dissociation on Au surfaces including particles cannot occur at low temperatures; (ii) CO oxidation on Au/inactive-materials occurs on Au steps via a two-step mechanism: CO+O-2-->CO2+O, and CO+O-->CO2; and (iii) CO oxidation on Au/active-materials also follows the two-step mechanism with reactions occurring at the interface.

A constraints-based resource discovery model for multi-provider cloud environments

Increasingly infrastructure providers are supplying the cloud marketplace with storage and on-demand compute resources to host cloud applications. From an application user's point of view, it is desirable to identify the most appropriate set of available resources on which to execute an application. Resource choice can be complex and may involve comparing available hardware specifications, operating systems, value-added services, such as network configuration or data replication, and operating costs, such as hosting cost and data throughput. Providers' cost models often change and new commodity cost models, such as spot pricing, have been introduced to offer significant savings. In this paper, a software abstraction layer is used to discover infrastructure resources for a particular application, across multiple providers, by using a two-phase constraints-based approach. In the first phase, a set of possible infrastructure resources are identified for a given application. In the second phase, a heuristic is used to select the most appropriate resources from the initial set. For some applications a cost-based heuristic is most appropriate; for others a performance-based heuristic may be used. A financial services application and a high performance computing application are used to illustrate the execution of the proposed resource discovery mechanism. The experimental result shows the proposed model could dynamically select an appropriate set of resouces that match the application's requirements.

Random Regret Minimization: Exploration of a New Choice Model for Environmental and Resource Economics

This paper introduces the discrete choice model-paradigm of Random Regret Minimization (RRM) to the field of environmental and resource economics. The RRM-approach has been very recently developed in the context of travel demand modelling and presents a tractable, regret-based alternative to the dominant choice-modelling paradigm based on Random Utility Maximization-theory (RUM-theory). We highlight how RRM-based models provide closed form, logit-type formulations for choice probabilities that allow for capturing semi-compensatory behaviour and choice set-composition effects while being equally parsimonious as their utilitarian counterparts. Using data from a Stated Choice-experiment aimed at identifying valuations of characteristics of nature parks, we compare RRM-based models and RUM-based models in terms of parameter estimates, goodness of fit, elasticities and consequential policy implications.

Moving to evidence-based practice in long-term care:the role of a Best Practise Resource Centre in two long-term care settings.

In this article, the authors provide an overview on the development of a Long-Term Care Best Practise Resource Centre. The results of both a feasibility study and the outcomes of a 1-year demonstration project are presented. The demonstration project involved a hospital as the information service provider and two demonstration sites, a home care service agency and a nursing home that used the services of the Centre. The goals of the Centre were threefold: provide access to literature for staff in long-term care (LTC) settings; improve the information management skills of health care providers; and support research and the integration of best practices in LTC organizations. The results of the pilot study contributed to the development of a collaborative information access system for LTC clinicians and managers that provides timely, up-to-date information contributing to improving the quality of care for adults receiving LTC. Based on this demonstration project, strategies for successful innovation in LTC are identified.

Resource utilization and cost analyses of home-based palliative care service provision:The Niagara West End-of-Life Shared-Care Project

Background: Increasing emphasis is being placed on the economics of health care service delivery - including home-based palliative care. Aim: This paper analyzes resource utilization and costs of a shared-care demonstration project in rural Ontario (Canada) from the public health care system's perspective. Design: To provide enhanced end-of-life care, the shared-care approach ensured exchange of expertise and knowledge and coordination of services in line with the understood goals of care. Resource utilization and costs were tracked over the 15 month study period from January 2005 to March 2006. Results: Of the 95 study participants (average age 71 years), 83 had a cancer diagnosis (87%); the non-cancer diagnoses (12 patients, 13%) included mainly advanced heart diseases and COPD. Community Care Access Centre and Enhanced Palliative Care Team-based homemaking and specialized nursing services were the most frequented offerings, followed by equipment/transportation services and palliative care consults for pain and symptom management. Total costs for all patient-related services (in 2007 CAN) were 1,625,658.07 - or 17,112.19 per patient/117.95 per patient day. Conclusion: While higher than expenditures previously reported for a cancer-only population in an urban Ontario setting, the costs were still within the parameters of the US Medicare Hospice Benefits, on a par with the per diem funding assigned for long-term care homes and lower than both average alternate level of care and hospital costs within the Province of Ontario. The study results may assist service planners in the appropriate allocation of resources and service packaging to meet the complex needs of palliative care populations. © 2012 The Author(s).

Adaptive Measurement-Based Policy-Driven QoS Management with Fuzzy-Rule-based Resource Allocation

Fixed and wireless networks are increasingly converging towards common connectivity with IP-based core networks. Providing effective end-to-end resource and QoS management in such complex heterogeneous converged network scenarios requires unified, adaptive and scalable solutions to integrate and co-ordinate diverse QoS mechanisms of different access technologies with IP-based QoS. Policy-Based Network Management (PBNM) is one approach that could be employed to address this challenge. Hence, a policy-based framework for end-to-end QoS management in converged networks, CNQF (Converged Networks QoS Management Framework) has been proposed within our project. In this paper, the CNQF architecture, a Java implementation of its prototype and experimental validation of key elements are discussed. We then present a fuzzy-based CNQF resource management approach and study the performance of our implementation with real traffic flows on an experimental testbed. The results demonstrate the efficacy of our resource-adaptive approach for practical PBNM systems