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Automatic Design of Artificial Neural Networks for Gamma-Ray Detection

The goal of this work is to investigate the possibility of improving current gamma/hadron discrimination based on their shower patterns recorded on the ground. To this end we propose the use of Convolutional Neural Networks (CNNs) for their ability to distinguish patterns based on automatically designed features. In order to promote the creation of CNNs that properly uncover the hidden patterns in the data, and at same time avoid the burden of hand-crafting the topology and learning hyper-parameters we resort to NeuroEvolution; in particular we use Fast-DENSER++, a variant of Deep Evolutionary Network Structured Representation. The results show that the best CNN generated by Fast-DENSER++ improves by a factor of 2 when compared with the results reported by classic statistical approaches. Additionally, we experiment ensembling the 10 best generated CNNs, one from each of the evolutionary runs; the ensemble leads to an improvement by a factor of 2.3. These results show that it is possible to improve the gamma/hadron discrimination based on CNNs that are automatically generated and are trained with instances of the ground impact patterns.info:eu-repo/semantics/publishedVersio

The promised territories: the production of branded housing projects in contemporary Turkey

Cities in Turkey, following the neoliberal restructuring of the country, have undergone a process of transformation in the last decade at a greater pace than experienced in previous periods. Through these processes, while new territories have been constructed, previous formations have been dismantled. While some of these constructed territories are abstract (e.g. Nomenclature of Units for Territorial Statistics [NUTS] regions), some are tangible and physically defined such as branded housing enclaves. Branded housing projects produce territories in the form of housing enclaves, which provide key services and facilities within their confines exclusively for project residents. By 2013, the number of branded housing projects located in Istanbul alone numbered 852 with the number of units provided by these projects amounting to 7.7% of the total housing stock the city (Sarıçayır 01/21/2014). This paper argues that these territories are co-produced by political society and civil society (in Gramscian terms): while political society regulates and directly contributes to the production of these territories through public actors involved in the branded housing projects, civil society contributes through the production of social consent for such developments. The article discusses the role of political society and civil society in the production of branded housing projects by focusing on the case of Emlak Konut GYO (Real Estate Partnership) projects developed in Istanbul between 2003 and 2014. Firstly, the role of political society is discussed through the roles of TOKI (Housing Development Administration of Turkey) and Emlak Konut GYO as major public actors in the development of these territories; and secondly, the role of civil society is discussed through excavating the traces of production of social consent for branded housing projects in news articles published on Emlak Konut GYO projects between 2003 and 2014. The paper concludes that branded housing projects are emerging as spatial territories in contemporary Turkey as a result of hegemonic struggle through political society and civil society

Learning Behavior Trees with Genetic Programming in Unpredictable Environments

Modern industrial applications require robots to be able to operate in unpredictable environments, and programs to be created with a minimal effort, as there may be frequent changes to the task. In this paper, we show that genetic programming can be effectively used to learn the structure of a behavior tree (BT) to solve a robotic task in an unpredictable environment. Moreover, we propose to use a simple simulator for the learning and demonstrate that the learned BTs can solve the same task in a realistic simulator, reaching convergence without the need for task specific heuristics. The learned solution is tolerant to faults, making our method appealing for real robotic applications

Synthesis of formation control for an aquatic swarm robotics system

Formations are the spatial organization of objects or entities according to some predefined pattern. They can be found in nature, in social animals such as fish schools, and insect colonies, where the spontaneous organization into emergent structures takes place. Formations have a multitude of applications such as in military and law enforcement scenarios, where they are used to increase operational performance. The concept is even present in collective sports modalities such as football, which use formations as a strategy to increase teams efficiency. Swarm robotics is an approach for the study of multi-robot systems composed of a large number of simple units, inspired in self-organization in animal societies. These have the potential to conduct tasks too demanding for a single robot operating alone. When applied to the coordination of such type of systems, formations allow for a coordinated motion and enable SRS to increase their sensing efficiency as a whole. In this dissertation, we present a virtual structure formation control synthesis for a multi-robot system. Control is synthesized through the use of evolutionary robotics, from where the desired collective behavior emerges, while displaying key-features such as fault tolerance and robustness. Initial experiments on formation control synthesis were conducted in simulation environment. We later developed an inexpensive aquatic robotic platform in order to conduct experiments in real world conditions. Our results demonstrated that it is possible to synthesize formation control for a multi-robot system making use of evolutionary robotics. The developed robotic platform was used in several scientific studies.As formações consistem na organização de objetos ou entidades de acordo com um padrão pré-definido. Elas podem ser encontradas na natureza, em animais sociais tais como peixes ou colónias de insetos, onde a organização espontânea em estruturas se verifica. As formações aplicam-se em diversos contextos, tais como cenários militares ou de aplicação da lei, onde são utilizadas para aumentar a performance operacional. O conceito está também presente em desportos coletivos tais como o futebol, onde as formações são utilizadas como estratégia para aumentar a eficiência das equipas. Os enxames de robots são uma abordagem para o estudo de sistemas multi-robô compostos de um grande número de unidades simples, inspirado na organização de sociedades animais. Estes têm um elevado potencial na resolução de tarefas demasiado complexas para um único robot. Quando aplicadas na coordenação deste tipo de sistemas, as formações permitem o movimento coordenado e o aumento da sensibilidade do enxame como um todo. Nesta dissertação apresentamos a síntese de controlo de formação para um sistema multi-robô. O controlo é sintetizado através do uso de robótica evolucionária, de onde o comportamento coletivo emerge, demonstrando ainda funcionalidadeschave tais como tolerância a falhas e robustez. As experiências iniciais na síntese de controlo foram realizadas em simulação. Mais tarde foi desenvolvida uma plataforma robótica para a condução de experiências no mundo real. Os nossos resultados demonstram que é possível sintetizar controlo de formação para um sistema multi-robô, utilizando técnicas de robótica evolucionária. A plataforma desenvolvida foi ainda utilizada em diversos estudos científicos

A Collaborative, Interactive and Context-Aware Drawing Agent for Co-Creative Design

Recent advances in text-conditioned generative models have provided us with neural networks capable of creating images of astonishing quality, be they realistic, abstract, or even creative. These models have in common that (more or less explicitly) they all aim to produce a high-quality one-off output given certain conditions, and in that they are not well suited for a creative collaboration framework. Drawing on theories from cognitive science that model how professional designers and artists think, we argue how this setting differs from the former and introduce CICADA: a Collaborative, Interactive Context-Aware Drawing Agent. CICADA uses a vector-based synthesis-by-optimisation method to take a partial sketch (such as might be provided by a user) and develop it towards a goal by adding and/or sensibly modifying traces. Given that this topic has been scarcely explored, we also introduce a way to evaluate desired characteristics of a model in this context by means of proposing a diversity measure. CICADA is shown to produce sketches of quality comparable to a human user's, enhanced diversity and most importantly to be able to cope with change by continuing the sketch minding the user's contributions in a flexible manner