Intermediality in Theatre and Performance: Definitions, Perceptions and Medial Relationships

This article provides a brief overview of the discourse on the relationships between the arts and media over the twentieth century, with specific reference to the concepts of mediality: multi-, trans- and intermediality set in discourse of arts and media relationships. I discuss the concepts, together with the impact of the growth of media technological developments, on the perception of audiences to the works of Wagner, Kandinsky, Meyerhold, Balázs, Eisenstein, Brecht, and to contemporary theatre and performance-makers, before concluding with a short presentation of my own current thinking about the concept and purpose of intermediality.Este artículo presenta una panorámica general del discurso sobre las relaciones entre las artes y los medios de comunicación a lo largo del siglo XX, con una mención especial a los conceptos de medialidad, multimedia, transmedia e intermedialidad. Se aborda el impacto del desarrollo tecnológico de los medios en la percepción de la audiencia, centrándose en el trabajo de Wagner, Kandisky, Meyerhold, Balázs, Eisenstein, Brech y el teatro y la representación contemporáneos. Se concluye con una apreciación personal sobre el estado actual del concepto y propósito de la intermedialidad

An Object-Oriented Framework for Explicit-State Model Checking

This paper presents a conceptual architecture for an object-oriented framework to support the development of formal verification tools (i.e. model checkers). The objective of the architecture is to support the reuse of algorithms and to encourage a modular design of tools. The conceptual framework is accompanied by a C++ implementation which provides reusable algorithms for the simulation and verification of explicit-state models as well as a model representation for simple models based on guard-based process descriptions. The framework has been successfully used to develop a model checker for a subset of PROMELA

HBsAg-vectored DNA vaccines elicit concomitant protective responses to multiple CTL epitopes relevant in human disease.

Vaccines capable of controlling neoplastic and infectious diseases which depend on the cellular immune response for their resolution, have proven difficult to develop. We, and others, have previously demonstrated that the potent immunogenicity of hepatitis B surface antigen (HBsAg), the already- licensed human vaccine for hepatitis B infection, may be exploited to deliver foreign antigens for cytotoxic T-lymphocyte (CTL) induction. In this study we demonstrate that recombinant (r) HBsAg DNA delivering a CTL polyepitope appended at the C' terminus elicits concomitant responses to multiple epitopes restricted through a diversity of MHC class I haplotypes, which are relevant in a number of human diseases. We show that the rHBsAg DNA vaccine elicits concomitant protection against neoplastic and infectious disease. These studies vindicate the use of HBsAg as a powerful vector to deliver CTL responses to foreign antigens, and have implications for a multi-disease vaccine applicable to the HLA-polymorphic human population

Probabilistic Timed Automata with Clock-Dependent Probabilities

Probabilistic timed automata are classical timed automata extended with discrete probability distributions over edges. We introduce clock-dependent probabilistic timed automata, a variant of probabilistic timed automata in which transition probabilities can depend linearly on clock values. Clock-dependent probabilistic timed automata allow the modelling of a continuous relationship between time passage and the likelihood of system events. We show that the problem of deciding whether the maximum probability of reaching a certain location is above a threshold is undecidable for clock-dependent probabilistic timed automata. On the other hand, we show that the maximum and minimum probability of reaching a certain location in clock-dependent probabilistic timed automata can be approximated using a region-graph-based approach.Comment: Full version of a paper published at RP 201

Probabilistic Guarantees for Safe Deep Reinforcement Learning

Deep reinforcement learning has been successfully applied to many control tasks, but the application of such agents in safety-critical scenarios has been limited due to safety concerns. Rigorous testing of these controllers is challenging, particularly when they operate in probabilistic environments due to, for example, hardware faults or noisy sensors. We propose MOSAIC, an algorithm for measuring the safety of deep reinforcement learning agents in stochastic settings. Our approach is based on the iterative construction of a formal abstraction of a controller's execution in an environment, and leverages probabilistic model checking of Markov decision processes to produce probabilistic guarantees on safe behaviour over a finite time horizon. It produces bounds on the probability of safe operation of the controller for different initial configurations and identifies regions where correct behaviour can be guaranteed. We implement and evaluate our approach on agents trained for several benchmark control problems

Encephalitis caused by a Lyssavirus in fruit bats in Australia.

This report describes the first pathologic and immunohistochemical recognition in Australia of a rabies-like disease in a native mammal, a fruit bat, the black flying fox (Pteropus alecto). A virus with close serologic and genetic relationships to members of the Lyssavirus genus of the family Rhabdoviridae was isolated in mice from the tissue homogenates of a sick juvenile animal