Cultural transition in Southeastern Europe: the creative city - crossing visions and new realities in the region

Contents: Chapter I: Conceptual Frameworks of the Creative City Debate: Lidia Varbanova: Our Creative Cities Online; Žaklina Gligorijević: Forces and Trends Shaping the Contemporary City: The Creative Sector in Creative Cities; Milena Dragičević-Šešić: Culture as a Resource of City Development; Jaka Primora: Attitudes of Cultural Workers towards Creative Industries Development and the City in Southeastern Europe; Ivana Jašić: Cities on the Global Market: Territorial Marketing Planning Strategies. Chapter I: Case Studies from the Region: Maja Breznik The Role of Culture in the Strategies of "City Regeneration"; Krisztina Keresztély: Cultural Policies and Urban Rehabilitation in Budapest; Nada Švob-Đokić: Zagreb: Urban Cultural Identities and City Growth; Inga Tomić-Koludrović, Mirko Petrić: New Cultural Tourists in a Southeastern European City: The Case of Split; Ana Žuvela: Developing Cultural Strategy in the City of Dubrovnik; Fatjon Dragoshi: TI-RAMA: My Creative City - Case study: Tirana; Nevena Daković; Cityscape and Cinema; Snežana Krstanović: The Position of Cultural Resources in the Urban Regeneration Process - Case study: Pančevo; Dona Kolar-Panov, Violeta Simjanovska, Katerina Mojančevska: City Regeneration Policies and Practices - Case study: Skopje

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network\u27s prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine

Hypernetworks based Radio Spectrum Profiling in Cognitive Radio Networks

This paper presents a novel concept of active radio spectrum profiling f or Cognitive Radio (CR) networks using evolutionary hypernetworks. Spectrum profiling enables cognitive radio nodes to abstract and predict usable spectrum opportunities in pre-defined P rimary Users ( PU) c hannels. T he PU c hannels a re actively monitored through spectrum sensing and the resulting binary time series are used for channel abstraction and prediction. An overlay spectrum sharing approach is assumed in this paper and the evolutionary hypernetworks are used for the realization of the radio spectrum profiling concept. The abstracted information not only facilitates the optimization of channel selection and mobility, but also improves the quality of service for the secondary user applications. This paper presents the main concepts, their application to CR ad hoc networks, and an analysis of its impact on the CR network performance

Hypernetworks based Radio Spectrum Profiling in Cognitive Radio Networks

This paper presents a novel concept of active radio spectrum profiling f or Cognitive Radio (CR) networks using evolutionary hypernetworks. Spectrum profiling enables cognitive radio nodes to abstract and predict usable spectrum opportunities in pre-defined P rimary Users ( PU) c hannels. T he PU c hannels a re actively monitored through spectrum sensing and the resulting binary time series are used for channel abstraction and prediction. An overlay spectrum sharing approach is assumed in this paper and the evolutionary hypernetworks are used for the realization of the radio spectrum profiling concept. The abstracted information not only facilitates the optimization of channel selection and mobility, but also improves the quality of service for the secondary user applications. This paper presents the main concepts, their application to CR ad hoc networks, and an analysis of its impact on the CR network performance

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction