Ideas for a regulatory definition of FinTech

Purpose: The aim of the paper is to develop the approach to a legal definition of FinTech. Design/Methodology/Approach: In this paper we evolve possible approaches of FinTech legal definition, investigate existing approaches at the international level and examine the policies applied at the national levels. Document analysis, as a form of qualitative research, was used in this study. Findings: We found that in most countries the legislation does not specifically address fintech companies, and the legal framework equally regulates the activities of traditional service providers and fintech operators. In our opinion, no specific legislation for FinTech companies needed, each type of activity provided by a financial or technology company is subject to a specific legislation/regulation with primary focus on services and products provided as payments, insurance, investments etc. Practical Implications: The term FinTech is freely used by policy makers, regulators, companies, researchers, academics and the public, both nationally and internationally. According to international organizations such as the IMF, the World Bank and the OECD, FinTech offers the opportunity to accelerate economic growth and expand financial affordability/inclusion in all countries. Some countries are increasingly striving to become global or international regional hubs for FinTech and are working hard to develop interagency government strategies with a supportive legal environment. Originality/Value: There is still confusion about the nature and dynamics of FinTech among politicians, scientists and practitioners, as well as about the legal framework of this area. The value of this article is to clarify and propose an apprach to definition of FinTech by combining different approaches in a very original and innovative way.peer-reviewe

Enhancing Energy Production with Exascale HPC Methods

High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the Intel Corporation, which enabled us to obtain the presented experimental results in uncertainty quantification in seismic imagingPostprint (author's final draft

Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services

One of the most widely-implemented service standards provided by the Open Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS). WMS is widely employed globally, but there is limited knowledge of the global distribution, adoption status or the service quality of these online WMS resources. To fill this void, we investigated global WMSs resources and performed distributed performance monitoring of these services. This paper explicates a distributed monitoring framework that was used to monitor 46,296 WMSs continuously for over one year and a crawling method to discover these WMSs. We analyzed server locations, provider types, themes, the spatiotemporal coverage of map layers and the service versions for 41,703 valid WMSs. Furthermore, we appraised the stability and performance of basic operations for 1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major reasons for request errors and performance issues, as well as the relationship between service response times and the spatiotemporal distribution of client monitoring sites. This paper will help service providers, end users and developers of standards to grasp the status of global WMS resources, as well as to understand the adoption status of OGC standards. The conclusions drawn in this paper can benefit geospatial resource discovery, service performance evaluation and guide service performance improvements.Comment: 24 pages; 15 figure

Complex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates

This study is a first, exploratory attempt to use quantitative semantics techniques and topological analysis to analyze systemic patterns arising in a complex political system. In particular, we use a rich data set covering all speeches and debates in the UK House of Commons between 1975 and 2014. By the use of dynamic topic modeling (DTM) and topological data analysis (TDA) we show that both members and parties feature specific roles within the system, consistent over time, and extract global patterns indicating levels of political cohesion. Our results provide a wide array of novel hypotheses about the complex dynamics of political systems, with valuable policy applications

Development and deployment of a microfluidic platform for water quality monitoring

There is an increasing demand for autonomous sensor devices which can provide reliable data on key water quality parameters at a higher temporal and geographical resolution than is achievable using current approaches to sampling and monitoring. Microfluidic technology, in combination with rapid and on-going developments in the area of wireless communications, has significant potential to address this demand due to a number of advantageous features which allow the development of compact, low-cost and low-powered analytical devices. Here we report on the development of a microfluidic platform for water quality monitoring. This system has been successfully applied to in-situ monitoring of phosphate in environmental and wastewater monitoring applications. We describe a number of the technical and practical issues encountered and addressed during these deployments and summarise the current status of the technology

Dried blood spots in toxicology : from the cradle to the grave?

About a century after its first described application by Ivar Bang, the potential of sampling via dried blood spots (DBS) as an alternative for classical venous blood sampling is increasingly recognized. Perhaps best known is the use of DBS in newborn screening programs, ignited by the hallmark paper by Guthrie and Susi half a century ago. However, it is only recently that both academia and industry have recognized the many advantages that DBS sampling may offer for bioanalytical purposes, as reflected by the strong increase in published reports during the last few years. Currently, major DBS applications include newborn screening for metabolic disorders, epidemiological surveys (e. g. HIV monitoring), therapeutic drug monitoring (TDM), as well as toxicology. In this review, we provide a comprehensive overview of the distinct subdisciplines of toxicology for which DBS sampling has been applied. DBS sampling for toxicological evaluation has been performed from birth until autopsy, aiming at the assessment of therapeutic drugs, drugs of abuse, environmental contaminants, toxins, as well as (trace) elements, with applications situated in fields as toxicokinetics, epidemiology and environmental and forensic toxicology. We discuss the strengths and limitations of DBS in the different subdisciplines and provide future prospects for the use of this promising sampling technique in toxicology

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2