Solar Physics with the Square Kilometre Array

The Square Kilometre Array (SKA) will be the largest radio telescope ever built, aiming to provide collecting area larger than 1 km$^2$. The SKA will have two independent instruments, SKA-LOW comprising of dipoles organized as aperture arrays in Australia and SKA-MID comprising of dishes in South Africa. Currently the phase-1 of SKA, referred to as SKA1, is in its late design stage and construction is expected to start in 2020. Both SKA1-LOW (frequency range of 50-350 MHz) and SKA1-MID Bands 1, 2, and 5 (frequency ranges of 350-1050, 950-1760, and 4600-15300 MHz, respectively) are important for solar observations. In this paper we present SKA's unique capabilities in terms of spatial, spectral, and temporal resolution, as well as sensitivity and show that they have the potential to provide major new insights in solar physics topics of capital importance including (i) the structure and evolution of the solar corona, (ii) coronal heating, (iii) solar flare dynamics including particle acceleration and transport, (iv) the dynamics and structure of coronal mass ejections, and (v) the solar aspects of space weather. Observations of the Sun jointly with the new generation of ground-based and space-borne instruments promise unprecedented discoveries.Comment: Accepted for publication in Advances in Space Researc

Business on Chain: A Comparative Case Study of Five Blockchain-Inspired Business Models

Blockchain technology, despite its origins as the underlying infrastructure for value transfer in the era of cryptocurrency, has been touted as the main disruptive force in modern businesses. Blockchain has the capacity to chronologically capture and store transactional data in a standardized and tamper-proof format that is transparent to all stakeholders involved in the transaction. This, in turn, has prompted companies to rethink preexisting business practices, thereby yielding a myriad of fascinating business models anchored in blockchain technology. In this study, we advance contemporary knowledge of business applications of blockchain by drawing on the theoretical lens of the digital business model and value configuration to decipher how pioneers in this space are leveraging blockchain to create and capture value. Through a comparative, multiple case study approach, we analyzed five companies in mainland China that have rolled out blockchain initiatives. From our case analyses, we derived a typology of five blockchain-inspired business models, each of which embodies a distinctive logic for market differentiation. For each business model, we offer insights into its value creation logic, its value capturing mechanism, and the challenges that could threaten its longer-term viability. Grounded in our findings, we discuss key implications for theory and practice

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Deep neural networks in the cloud: Review, applications, challenges and research directions

Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide range of important real-world applications. DNNs consist of a huge number of parameters that require millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A more effective method is to implement DNNs in a cloud computing system equipped with centralized servers and data storage sub-systems with high-speed and high-performance computing capabilities. This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. We also present an extensive overview of different cloud computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications already deployed in cloud computing systems are reviewed to demonstrate the advantages of using cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.The EGIA project (KK-2022/00119The Consolidated Research Group MATHMODE (IT1456-22

IT Leadership in Transition - The Impact of Digitalization on Finnish Organizations

Digitalization is transforming business models across industries. As information technology (IT) is becoming embedded in products and services, IT leadership has an increasingly dualistic role in supporting the organization and also serving its customers' changing needs. The ACIO research program studied how Finnish industry and public sector organizations utilize information technology in developing and managing critical business capabilities. The focus was on understanding and analyzing contemporary approaches to IT leadership. This research report summarizes some of the key research findings, providing scholars and practitioners with insights into and understanding of digitalization and changes in IT leadership in Finnish informationintensive organizations

Individual-based artificial ecosystems for design and optimization

Individual-based modeling has gained popularity over the last decade, mainly due to the paradigm\u27s proven ability to address a variety of problems seen in many disciplines, including modeling complex systems from bottom-up, providing relationship between component level and system level parameters, and discovering the emergence of system-level behaviors from simple component level interactions. Availability of computational power to run simulation models with thousands to millions of agents is another driving force in the widespread adoption of individual-based modeling. This thesis proposes an individual-based modeling approach for solving engineering design and optimization problems using artificial ecosystems --Abstract, page iii

Essays on the discursive formation of emerging organizational fields: The role of technology, institutional logics, and identity

The thesis aims at shedding light on the process of organizational field emergence as resulting from a collectively enacted discursive endeavor. The investigation hinges upon the perspective that the generation of institutional and organizational domains of activity is primarily driven by the encounter of multiple and potentially misaligned social constituencies who work out their mutual incongruences by engaging in communicative-dialectical activities. Overall, the thesis contributes to an ongoing conversation on meaning making as the fundamental process driving the formation of organizational fields. These processes are explored both theoretically (essay 1) and empirically in the context of the nascent civil drone field (essays 2 and 3). Essay 1 provides a framework of dialectical meaning-making in emerging technological fields. It aims to show how fields emerge when meaning making processes are activated for the definition of the new technological artifacts, the identities of the actors involved with it, and the institutional infrastructures that shall govern their interactions. Essay 2 explores the role of multiple institutional logics as driver of field emergence. The emerging civil drone field is permeated by discursive activity on a number of issues that are framed according to multiple institutional logics. By analyzing how actors debate these issues in field conferences, the paper tracks a number of discursive interaction patterns that are generative of new meanings by combining potentially conflicting logics. Essay 3 addresses the identity formation process in nascent fields populated by heterogeneous organizations. By performing content analysis of a sample of field’s organizational mission statements and of a publication dedicated to drones, the paper highlights that the emergence of a field-specific identity is driven by an isomorphic tendency to mimic professionalized vocabularies and is shaped by ideational field-level discourse. This ensures legitimacy to the emerging field, while creating a distinctive field identity forged by drone-specific issues

Automated Machine Learning for Multi-Label Classification

Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand. For supervised learning tasks, most notably binary and multinomial classification, aka single-label classification (SLC), such AutoML approaches have shown promising results. However, the task of multi-label classification (MLC), where data points are associated with a set of class labels instead of a single class label, has received much less attention so far. In the context of multi-label classification, the data-specific selection and configuration of multi-label classifiers are challenging even for experts in the field, as it is a high-dimensional optimization problem with multi-level hierarchical dependencies. While for SLC, the space of machine learning pipelines is already huge, the size of the MLC search space outnumbers the one of SLC by several orders. In the first part of this thesis, we devise a novel AutoML approach for single-label classification tasks optimizing pipelines of machine learning algorithms, consisting of two algorithms at most. This approach is then extended first to optimize pipelines of unlimited length and eventually configure the complex hierarchical structures of multi-label classification methods. Furthermore, we investigate how well AutoML approaches that form the state of the art for single-label classification tasks scale with the increased problem complexity of AutoML for multi-label classification. In the second part, we explore how methods for SLC and MLC could be configured more flexibly to achieve better generalization performance and how to increase the efficiency of execution-based AutoML systems

POSSIBLE AND PREFERABLE SCENARIOS OF A SUSTAINABLE FUTURE: TOWARDS 2030 AND BEYOND

Investigating the Future is an established practice for the academy and the world of crafts and industry. From the Chicago Columbian Exhibition of 1893 to the two Worlds Fairs of New York City (1939 and 1965) and so on, the future has been foreseen as filled with technology and amazing architecture but not every vision of the future has described promising scenarios. The four visions of the future proposed by Norman Henchey (1978) are conceptualized in classes – "possible" (any future), "plausible" (future that makes sense), "probable" (highly likely to happen), "preferable" (the best that could happen) – and have been brilliantly described in the ‘Futures Cone’ reinterpreted by Joseph Voros (2003). As we move away from the present, the ‘possible’ tends to ‘preferable’ due to the lack of elements and data on which to base the programming and the planning: in fact, the certainty on the type of technologies and production methods that will be available, on the social structure and user uses, and so on decreases. By 2030, the world will already be different: Thomas L. Friedman (2016) highlights that the three main forces of our Planet – Moore’s Law (technology), the Market (globalization) and Mother Nature (climate change and biodiversity loss) – are all pressing at the same time, with inevitable consequences for the territory, cities, architecture, products and services that will be designed, developed and used in the future. The 17 2030 Sustainable Development Goals presented by the United Nations provide an answer for this time horizon, tracing the path towards a model to achieve a better and more sustainable future for everyone. But will these Goals be able to accelerate sustainable innovation? Paraphrasing Luciano Floridi, philosopher of Information and Technology at the University of Oxford, we ask ourselves if ‘green’ (of natural and artificial environments) and "blue" (of science, technology and therefore the digital world) will succeed in guiding a vision of the future capable of replacing ‘things’ (objects) with "relationships", "individual planning" with "common planning", the "experience economy" (and not consumption) with a "policy of care and relationships" (and not production). A vision of a sustainable future of living, by looking at the two-time horizons of 2030 and 2050, will be played on an increasingly synergistic work aimed at providing answers to many questions. In this regard, the book ‘Possible and Preferable Scenarios of a Sustainable Future – Towards 2030 and Beyond’ collects essays and critical thoughts, research and experimentations on the subject providing some starting points for debate for the international scientific Community