The MIRELA framework: modeling and analyzing mixed reality applications using timed automata

Mixed Reality (MR) aims to link virtual entities with the real world and has many applications such as military and medical domains [JBL+00, NFB07]. In many MR systems and more precisely in augmented scenes, one needs the application to render the virtual part accurately at the right time. To achieve this, such systems acquire data related to the real world from a set of sensors before rendering virtual entities. A suitable system architecture should minimize the delays to keep the overall system delay (also called end-to-end latency) within the requirements for real-time performance. In this context, we propose a compositional modeling framework for MR software architectures in order to specify, simulate and validate formally the time constraints of such systems. Our approach is first based on a functional decomposition of such systems into generic components. The obtained elements as well as their typical interactions give rise to generic representations in terms of timed automata. A whole system is then obtained as a composition of such defined components. To write specifications, a textual language named MIRELA (MIxed REality LAnguage) is proposed along with the corresponding compilation tools. The generated output contains timed automata in UPPAAL format for simulation and verification of time constraints. These automata may also be used to generate source code skeletons for an implementation on a MR platform. The approach is illustrated first on a small example. A realistic case study is also developed. It is modeled by several timed automata synchronizing through channels and including a large number of time constraints. Both systems have been simulated in UPPAAL and checked against the required behavioral properties.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Cryptographic properties of Boolean functions defining elementary cellular automata

In this work, the algebraic properties of the local transition functions of elementary cellular automata (ECA) were analysed. Specifically, a classification of such cellular automata was done according to their algebraic degree, the balancedness, the resiliency, nonlinearity, the propagation criterion and the existence of non-zero linear structures. It is shown that there is not any ECA satisfying all properties at the same time.

Fighting tax evasion: a cellular automata approach

Estudio de la dinámica de una población donde los individuos son contribuyentes (pagadores de impuestos) o no mediante un autómata celular 2D

Uniform Distributed Pushdown Automata Systems.

We consider here uniform distributed pushdown automata systems (UDPAS), namely distributed pushdown automata systems having all components identical pushdown automata. We consider here just a single protocol for activating/deactivating components, namely a component stays active as long as it can perform moves, as well as two ways of accepting the input word: by empty stacks (all components have empty stacks) or by final states (all components are in final states), when the input word is completely read. We mainly investigate the computational power of UDPAS accepting by empty stacks and a few decidability and closure properties of the families of languages they define. Some directions for further work and open problems are also discussed.

Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-election of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested on decentralized solution where the robots themselves autonomously and in an individual manner, are responsible of selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-tasks distribution problem and we propose a solution using two different approaches by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithm, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-selection of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested in a decentralized solution where the robots themselves autonomously and in an individual manner, are responsible for selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-task distribution problem and we propose a solution using two different approaches by applying Response Threshold Models as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Fusion of probabilistic knowledge-based classification rules and learning automata for automatic recognition of digital images

In this paper, the fusion of probabilistic knowledge-based classification rules and learning automata theory is proposed and as a result we present a set of probabilistic classification rules with self-learning capability. The probabilities of the classification rules change dynamically guided by a supervised reinforcement process aimed at obtaining an optimum classification accuracy. This novel classifier is applied to the automatic recognition of digital images corresponding to visual landmarks for the autonomous navigation of an unmanned aerial vehicle (UAV) developed by the authors. The classification accuracy of the proposed classifier and its comparison with well-established pattern recognition methods is finally reported.

Onconeural antigens and the paraneoplastic neurologic disorders: at the intersection of cancer, immunity, and the brain.

Paraneoplastic neurologic disorders (PNDs) are believed to be autoimmune neuronal degenerations that develop in some patients with systemic cancer. A series of genes encoding previously undiscovered neuronal proteins have been cloned using antiserum from PND patients. Identification of these onconeural antigens suggests a reclassification of the disorders into four groups: those in which neuromuscular junction proteins, nerve terminal/vesicle-associated proteins, neuronal RNA binding proteins, or neuronal signal-transduction proteins serve as target antigens. This review considers insights into basic neurobiology, tumor immunology, and autoimmune neuronal degeneration offered by the characterization of the onconeural antigens.

Postmortem/postpartum : the intersection between fertility and Mochican ancestral power

With no written record, the religious beliefs of the Pre-Columbian Mochica civilization are much of a mystery. This paper attempts to decipher the position of the deceased Mochicans, also known as ancestors, within the society as a whole. It discusses the ways in which we can use multiple sources of information, archaeological, iconographic, ethnohistoric and ethnographic to learn about the various aspects of Mochican culture. Specifically I will use these methods for collecting data to examine at how the Mochica viewed their deceased and to argue that part of the Mochica religious system granted their dead a supernatural ability to control human and agricultural fertility. This power would give Mochican ancestors a significant place within the society.

Seeding a Profession: The Intersection of the State, International Interests, and the Early Development of Brazilian Nursing

State and international entities can have profound effects on the development of a country’s nursing profession. Through a global health governance lens, this paper explores the development of nursing in Brazil during the early twentieth century, and its intersections with national and international interests. Accordingly, we will show how state policies established an environment that fostered the institutionalization of nursing as a profession in Brazil and supported it as a means to increase the presence of females in nation building processes. The State focused on recruiting elite women for nursing, in part due to the Rockefeller Foundation’s involvement in the country. Nurses who worked for Rockefeller came from well-educated classes within US society with specific ideas about who should be a nurse and the roles of nurses in a healthcare system. These women served as the primary vehicles for interacting with Brazilian health authorities responsible for health system development. Their early efforts did not, however, ensure a system capable of producing nursing human resources at a rate that, in present day Brazil, could meet the health needs of the country. Findings from this paper offer new avenues for historians to explore the early roots of professional nursing through a global health governance lens, improve the understanding of the intersection between international politics and professionalization, and highlight how these factors may impact nursing human resources production in the long term.

Zalpha-domains: At the intersection between RNA editing and innate immunity

The involvement of A to I RNA editing in antiviral responses was first indicated by the observation of genomic hyper-mutation for several RNA viruses in the course of persistent infections. However, in only a few cases an antiviral role was ever demonstrated and surprisingly, it turns out that ADARs - the RNA editing enzymes - may have a prominent pro-viral role through the modulation/down-regulation of the interferon response. A key role in this regulatory function of RNA editing is played by ADAR1, an interferon inducible RNA editing enzyme. A distinguishing feature of ADAR1, when compared with other ADARs, is the presence of a Z-DNA binding domain, Zalpha. Since the initial discovery of the specific and high affinity binding of Zalpha to CpG repeats in a left-handed helical conformation, other proteins, all related to the interferon response pathway, were shown to have similar domains throughout the vertebrate lineage. What is the biological function of this domain family remains unclear but a significant body of work provides pieces of a puzzle that points to an important role of Zalpha domains in the recognition of foreign nucleic acids in the cytoplasm by the innate immune system. Here we will provide an overview of our knowledge on ADAR1 function in interferon response with emphasis on Zalpha domains.