Logarithmic Corrections to Schwarzschild and Other Non-extremal Black Hole Entropy in Different Dimensions

Euclidean gravity method has been successful in computing logarithmic corrections to extremal black hole entropy in terms of low energy data, and gives results in perfect agreement with the microscopic results in string theory. Motivated by this success we apply Euclidean gravity to compute logarithmic corrections to the entropy of various non-extremal black holes in different dimensions, taking special care of integration over the zero modes and keeping track of the ensemble in which the computation is done. These results provide strong constraint on any ultraviolet completion of the theory if the latter is able to give an independent computation of the entropy of non-extremal black holes from microscopic description. For Schwarzschild black holes in four space-time dimensions the macroscopic result seems to disagree with the existing result in loop quantum gravity.Comment: LaTeX, 40 pages; corrected small typos and added reference

Dimension and Dimensional Reduction in Quantum Gravity

A number of very different approaches to quantum gravity contain a common thread, a hint that spacetime at very short distances becomes effectively two dimensional. I review this evidence, starting with a discussion of the physical meaning of "dimension" and concluding with some speculative ideas of what dimensional reduction might mean for physics.Comment: 33 page draft of solicited review article -- comments and added references welcome; v2: many added references, minor clean-u

Exploiting supplier capabilities to maximise product design opportunities in the fuzzy front end activities

This paper explores the Fuzzy Front-End (FFE), i.e. the first phase of the Product Design and Development process where a company formulates a product concept to be developed and decides whether or not to invest resources in the further development of an idea. Our goal is to understand how companies leverage supply chain capabilities to improve product design opportunities in order to obtain optimized product concepts in the FFE. From the analysis of our pilot study, the results suggest that FFE is organized differently depending on design requirements and supply chain capabilities and that matching design requirements with supplier capabilities during the FFE improves performance. Therefore, the findings indicate that the proposed Conceptual Framework has the potential to be used by companies to design their FFE and to enhance the use of supply chain capabilities in their product design activities

Unpacking the black box of improvement

During the Salzburg Global Seminar Session 565-Better Health Care: How do we learn about improvement, participants discussed the need to unpack the black box of improvement. The black box refers to the fact that when quality improvement interventions are described or evaluated, there is a tendency to assume a simple, linear path between the intervention and the outcomes it yields. It is also assumed that it is enough to evaluate the results without understanding the process of by which the improvement took place. However, quality improvement interventions are complex, nonlinear and evolve in response to local settings. To accurately assess the effectiveness of quality improvement and disseminate the learning, there must be a greater understanding of the complexity of quality improvement work. To remain consistent with the language used in Salzburg, we refer to this as unpacking the black box of improvement. To illustrate the complexity of improvement, this article introduces four quality improvement case studies. In unpacking the black box, we present and demonstrate how Cynefin framework from complexity theory can be used to categorize and evaluate quality improvement interventions. Many quality improvement projects are implemented in complex contexts, necessitating an approach defined as probesense- respond. In this approach, teams experiment, learn and adapt their changes to their local setting. Quality improvement professionals intuitively use the probe-sense-respond approach in their work but document and evaluate their projects using language for simple or complicated' contexts, rather than the complex contexts in which they work. As a result, evaluations tend to ask 'How can we attribute outcomes to the intervention, rather than 'What were the adaptations that took place. By unpacking the black box of improvement, improvers can more accurately document and describe their interventions, allowing evaluators to ask the right questions and more adequately evaluate quality improvement interventions.Fil: Ramaswamy, Rohit. University of North Carolina; Estados UnidosFil: Reed, Julie. Nihr Clarch Northwest London; Estados UnidosFil: Livesley, Nigel. Institute for Healthcare Improvement; Estados UnidosFil: Boguslavsky, Victor. University Research Co; Estados UnidosFil: Garcia Elorrio, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Sax, Sylvia. University of Heidelberg; AlemaniaFil: Houleymata, Diarra. Applying Science to Strengthen and Improve Systems Project,; MalíFil: Kimble, Leighann. University Research Co; Estados UnidosFil: Parry, Gareth. Institute of Healthcare Improvement; Estados Unido

Explanation and trust: what to tell the user in security and AI?

There is a common problem in artificial intelligence (AI) and information security. In AI, an expert system needs to be able to justify and explain a decision to the user. In information security, experts need to be able to explain to the public why a system is secure. In both cases, the goal of explanation is to acquire or maintain the users' trust. In this paper, we investigate the relation between explanation and trust in the context of computing science. This analysis draws on literature study and concept analysis, using elements from system theory as well as actor-network theory. We apply the conceptual framework to both AI and information security, and show the benefit of the framework for both fields by means of examples. The main focus is on expert systems (AI) and electronic voting systems (security). Finally, we discuss consequences of our analysis for ethics in terms of (un)informed consent and dissent, and the associated division of responsibilities