Data Driven Discovery in Astrophysics

We review some aspects of the current state of data-intensive astronomy, its methods, and some outstanding data analysis challenges. Astronomy is at the forefront of "big data" science, with exponentially growing data volumes and data rates, and an ever-increasing complexity, now entering the Petascale regime. Telescopes and observatories from both ground and space, covering a full range of wavelengths, feed the data via processing pipelines into dedicated archives, where they can be accessed for scientific analysis. Most of the large archives are connected through the Virtual Observatory framework, that provides interoperability standards and services, and effectively constitutes a global data grid of astronomy. Making discoveries in this overabundance of data requires applications of novel, machine learning tools. We describe some of the recent examples of such applications.Comment: Keynote talk in the proceedings of ESA-ESRIN Conference: Big Data from Space 2014, Frascati, Italy, November 12-14, 2014, 8 pages, 2 figure

Which conceptual foundations for environmental policies? An institutional and evolutionary framework of economic change

This paper draws on institutional and evolutionary economics and contributes to an approach to environmental policy which diverges from mainstream prescriptions. The 'socio-technical system' is the core concept: this is a complex made of co-evolving institutions, technologies, markets and actors that fulfils an overall societal need (such as housing, production, mobility, etc.). A systemic and dynamic analysis of those structural changes which are needed to create more sustainable socio-technical systems is provided; actors – and their ability to influence politics and policy – are explicitly taken into consideration. Unsustainable socio-technical systems feature a relevant resistance to change, because they are embedded in the very structure of our society and because of the conservative action of dominant stakeholders; this is why no environmental policy will be effective unless it aims at 'unlocking' our societies from their dominance. But also a constructive side of environmental policy is needed in order to establish new and more sustainable socio-technical systems; consistently, environmental policy is viewed as a combination of actions that can trigger, make viable and align those institutional, technological and economic changes which are needed to reach sustainability. Again, actors (for change) are at the heart of this vision of environmental policy: as subject, because the creation of new and sustainable socio-technical systems is made possible by (coalitions of) actors for change; as object, because environmental policy – to be effective – must actively support the empowerment, legitimation and social networking of such coalitions. A ‘chicken and egg’ problem remains: who comes first? Actors for change advocating policies for sustainability or policies for sustainability supporting actors for change?Environmental policy; Economic dynamics; Institutional economics; Evolutionary economics; Socio-technical systems

WHICH CONCEPTUAL FOUNDATIONS FOR ENVIRONMENTAL POLICIES? AN INSTITUTIONAL AND EVOLUTIONARY FRAMEWORK OF ECONOMIC CHANGE

This paper draws on institutional and evolutionary economics and contributes to an approach to environmental policy which diverges from mainstream prescriptions. The 'socio-technical system' is the core concept: this is a complex made of co-evolving institutions, technologies, markets and actors that fulfils an overall societal need (such as housing, production, mobility, etc.). A systemic and dynamic analysis of those structural changes which are needed to create more sustainable socio-technical systems is provided; actors – and their ability to influence politics and policy – are explicitly taken into consideration. Unsustainable socio-technical systems feature a relevant resistance to change, because they are embedded in the very structure of our society and because of the conservative action of dominant stakeholders; this is why no environmental policy will be effective unless it aims at 'unlocking' our societies from their dominance. But also a constructive side of environmental policy is needed in order to establish new and more sustainable socio-technical systems; consistently, environmental policy is viewed as a combination of actions that can trigger, make viable and align those institutional, technological and economic changes which are needed to reach sustainability. Again, actors (for change) are at the heart of this vision of environmental policy: as subject, because the creation of new and sustainable socio-technical systems is made possible by (coalitions of) actors for change; as object, because environmental policy – to be effective – must actively support the empowerment, legitimation and social networking of such coalitions. A 'chicken and egg' problem remains: who comes first? Actors for change advocating policies for sustainability or policies for sustainability supporting actors for change?

EGFC: Evolving Gaussian Fuzzy Classifier from Never-Ending Semi-Supervised Data Streams -- With Application to Power Quality Disturbance Detection and Classification

Power-quality disturbances lead to several drawbacks such as limitation of the production capacity, increased line and equipment currents, and consequent ohmic losses; higher operating temperatures, premature faults, reduction of life expectancy of machines, malfunction of equipment, and unplanned outages. Real-time detection and classification of disturbances are deemed essential to industry standards. We propose an Evolving Gaussian Fuzzy Classification (EGFC) framework for semi-supervised disturbance detection and classification combined with a hybrid Hodrick-Prescott and Discrete-Fourier-Transform attribute-extraction method applied over a landmark window of voltage waveforms. Disturbances such as spikes, notching, harmonics, and oscillatory transient are considered. Different from other monitoring systems, which require offline training of models based on a limited amount of data and occurrences, the proposed online data-stream-based EGFC method is able to learn disturbance patterns autonomously from never-ending data streams by adapting the parameters and structure of a fuzzy rule base on the fly. Moreover, the fuzzy model obtained is linguistically interpretable, which improves model acceptability. We show encouraging classification results.Comment: 10 pages, 6 figures, 1 table, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020

ERTS imagery as data source for updating aeronautical charts

There are no author-identified significant results in this report