Contractual Testing

Variants of must testing approach have been successfully applied in Service Oriented Computing for capturing compliance between (contracts exposed by) a client and a service and for characterising safe replacement, namely the fact that compliance is preserved when a service exposing a ’smaller’ contract is replaced by another one with a ’larger’ contract. Nevertheless, in multi-party interactions, partners often lack full coordination capabilities. Such a scenario calls for less discriminating notions of testing in which observers are, e.g., the description of uncoordinated multiparty contexts or contexts that are unable to observe the complete behaviour of the process under test. In this paper we propose an extended notion of must preorder, called contractual preorder, according to which contracts are compared according to their ability to pass only the tests belonging to a given set. We show the generality of our framework by proving that preorders induced by existing notions of compliance in a distributed setting are instances of the contractual preorder when restricting to suitable sets of observers

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Advanced spectroscopic techniques for the analysis of illicit drugs and explosives

University of Technology Sydney. Faculty of Science.Law enforcement agencies are on a path to intelligence-led-policing, with an aim of gaining as much information as possible and interpreting that into actionable intelligence as quickly as possible. The focus on this information is not necessarily how accurate it is, but in what intelligence it can provide. In the illicit drugs environment, information about mixtures and purity could take months to obtain under current procedures. In the field of explosives analysis there is always a need for field- deployable methodologies that do not require the acquisition of expensive equipment. Currently available spectroscopic techniques used for the preliminary identification of illicit drugs are limited to “single point” spectroscopic methods. Samples that can prove particularly problematic for these methods include drug mixtures, especially those of low purity (e.g. tablets or powders with a range of diluents, adulterants and cutting agents) or new psychoactive substances (NPS) that have not previously been encountered. Furthermore, the information that these methods provide offers little value in the realm of intelligence to policing organisations. In a move to intelligence-led-policing and the desire for more data, ATR-FTIR hyperspectral imaging and Raman mapping are two techniques that have the potential to rapidly provide law enforcement with actionable intelligence on potential illicit drug samples. Both of these methods have been shown to have superior information content in comparison to their single point equivalents. This research compares the performance of the unsupervised chemometric techniques multivariate curve resolution (MCR) and simple-to-use interactive self-modelling mixture analysis (SIMPLISMA) in identifying components of mixtures (from hyperspectral image data) and estimating their purity, without the need for calibration. While all of the hyperspectral methods provided more information than current techniques, Raman mapping coupled with analysis by MCR was found to provide the most precise and accurate results. A feasibility study on the analysis of nitroaromatic explosives via fluorescence landscapes and PARAFAC was conducted. Although the initial aim of the project was to determine a field-deployable, ‘one-size-fits-all’ approach to nitro-containing explosives detection via reduction to amines, the reduction method was only found to be suitable for nitroaromatic explosives. Following the reduction to amines, derivatisation with -phthalaldehyde (OPA) was performed to form fluorescent isoindoles. This two-step derivatisation process was demonstrated to take less than 60 minutes and was assessed to be field-deployable. However, fluorescence landscapes of the derivatised amines were found to be too similar for PARAFAC to separate and quantify

Kino europejskie i idea Europy 1925–1995

Publikacja została sfinansowana ze środków Narodowego Programu Rozwoju Humanistyki w ramach projektu nr 12H 11 0004 8

Dealiasing techniques for high-order spectral element methods on regular and irregular grids

High-order methods are becoming increasingly attractive in both academia and industry, especially in the context of computational fluid dynamics. However, before they can be more widely adopted, issues such as lack of robustness in terms of numerical stability need to be addressed, particularly when treating industrial-type problems where challenging geometries and a wide range of physical scales, typically due to high Reynolds numbers, need to be taken into account. One source of instability is aliasing effects which arise from the nonlinearity of the underlying problem. In this work we detail two dealiasing strategies based on the concept of consistent integration. The first uses a localised approach, which is useful when the nonlinearities only arise in parts of the problem. The second is based on the more traditional approach of using a higher quadrature. The main goal of both dealiasing techniques is to improve the robustness of high order spectral element methods, thereby reducing aliasing-driven instabilities. We demonstrate how these two strategies can be effectively applied to both continuous and discontinuous discretisations, where, in the latter, both volumetric and interface approximations must be considered. We show the key features of each dealiasing technique applied to the scalar conservation law with numerical examples and we highlight the main differences in terms of implementation between continuous and discontinuous spatial discretisations

Propranolol as first-line treatment for life-threatening diffuse infantile hepatic hemangioma: a case report

[No abstract available

Utilization of big data to improve management of the emergency departments. Results of a systematic review

Background. The emphasis on using big data is growing exponentially in several sectors including biomedicine, life sciences and scientific research, mainly due to advances in information technologies and data analysis techniques. Actually, medical sciences can rely on a large amount of biomedical information and Big Data can aggregate information around multiple scales, from the DNA to the ecosystems. Given these premises, we wondered if big data could be useful to analyze complex systems such as the Emergency Departments (EDs) to improve their management and eventually patient outcomes. Methods. We performed a systematic review of the literature to identify the studies that implemented the application of big data in EDs and to describe what have already been done and what are the expectations, issues and challenges in this field. Results. Globally, eight studies met our inclusion criteria concerning three main activities: the management of ED visits, the ED process and activities and, finally, the prediction of the outcome of ED patients. Although the results of the studies show good perspectives regarding the use of big data in the management of emergency departments, there are still some issues that make their use still difficult. Most of the predictive models and algorithms have been applied only in retrospective studies, not considering the challenge and the costs of a real-time use of big data. Only few studies highlight the possible usefulness of the large volume of clinical data stored into electronic health records to generate evidence in real time. Conclusion. The proper use of big data in this field still requires a better management information flow to allow real-time application

Reflecting Together on Race, Privilege, and Teaching: Why Bank Street Needs Stronger Commitment to Teacher Education in Social Justice

This project explores the need for high quality teacher training in social justice education and the current program in early childhood education at Bank Street College