A system for computational analysis and reconstruction of 3D comminuted bone fractures

High energy impacts at joint locations often generate highly fragmented, or comminuted bone fractures. A leading current approach for treatment requires physicians qualitatively to classify the fracture to one of four possible fracture severity cases. Each case then has a sequence of best-practices for obtaining the best possible prognosis for the patient. It has been observed that qualitative evaluation of fracture severity by physicians can vary significantly which can lead to potential mis-classification and mis-treatment of these fracture cases. Major indicators of fracture severity are (i) fracture surface area, i.e., how much surface area was generated when the bone broke apart and (ii) dispersion, i.e., how far the fragments have rotated and translated from their original anatomic positions. Work in this dissertation develops computational tools that solve the bone puzzle-solving problem automatically or semi-automatically and extract previously unavailable quantitative information for these indicators from each bone fragment that are intended to assist physicians in making a more accurate and reliable fracture severity classification. The system applies novel three-dimensional (3D) puzzle-solving algorithms to identify the fracture fragments in the CT image data and piece them back together in a virtual environment. Doing so provides quantitative values for both fracture surface area and dispersion that reduce variability in fracture severity classifications and prevent mis-diagnosis for fracture cases that may be difficult to qualitatively classify using traditional approaches. This dissertation describes the system, the underlying algorithms and demonstrates the virtual reconstruction results and quantitative analysis of comminuted bone fractures from six clinical cases

Reinforcement Learning Applied to Trading Systems: A Survey

Financial domain tasks, such as trading in market exchanges, are challenging and have long attracted researchers. The recent achievements and the consequent notoriety of Reinforcement Learning (RL) have also increased its adoption in trading tasks. RL uses a framework with well-established formal concepts, which raises its attractiveness in learning profitable trading strategies. However, RL use without due attention in the financial area can prevent new researchers from following standards or failing to adopt relevant conceptual guidelines. In this work, we embrace the seminal RL technical fundamentals, concepts, and recommendations to perform a unified, theoretically-grounded examination and comparison of previous research that could serve as a structuring guide for the field of study. A selection of twenty-nine articles was reviewed under our classification that considers RL's most common formulations and design patterns from a large volume of available studies. This classification allowed for precise inspection of the most relevant aspects regarding data input, preprocessing, state and action composition, adopted RL techniques, evaluation setups, and overall results. Our analysis approach organized around fundamental RL concepts allowed for a clear identification of current system design best practices, gaps that require further investigation, and promising research opportunities. Finally, this review attempts to promote the development of this field of study by facilitating researchers' commitment to standards adherence and helping them to avoid straying away from the RL constructs' firm ground.Comment: 38 page

A System of Indicators for the Complex Evaluation of the Tax System

The main problem examined in the current article is the evaluation of tax system. It is suggested that tax systems should be evaluated through the hierarchical evaluation system consisting of primary, partially-integrated and complex-integrated indicators. Primary non-recurrent indicators are classified into three groups and aimed at evaluating a tax system through the prism of a certain aspect. The system is built taking into consideration the requirements of consistency, comparability and simplicity, and aimed at objective and accurate evaluation of tax systems. The synthesis of partially integrated indicators creates preconditions for complex evaluation of tax systems enabling identification of the quality of tax systems and comparison of tax systems of various countries. Complex tax system evaluation provides with a possibility to conduct a systematic analysis and to generate a quantitative estimate. The recommended complex tax system evaluation creates preconditions to analyse tax systems as a uniform totality, to identify their standing in various aspects correlated in quantitative terms, and to carry out dynamic and comparative analyses. Interstate comparative analysis of tax systems creates preconditions to reveal relevant quality of tax systems in various countries, to crystallise out the best practices that could be used for improvement of the quality of the tax system in the country at issue. Also, the evaluation system could be adapted to various needs of the evaluator: classification of indicators into groups enables elimination of some indicators or introduction of new ones without destroying the established system of significance of the indicators, simply by adjusting significance levels within the group

A systematic empirical comparison of different approaches for normalizing citation impact indicators

We address the question how citation-based bibliometric indicators can best be normalized to ensure fair comparisons between publications from different scientific fields and different years. In a systematic large-scale empirical analysis, we compare a traditional normalization approach based on a field classification system with three source normalization approaches. We pay special attention to the selection of the publications included in the analysis. Publications in national scientific journals, popular scientific magazines, and trade magazines are not included. Unlike earlier studies, we use algorithmically constructed classification systems to evaluate the different normalization approaches. Our analysis shows that a source normalization approach based on the recently introduced idea of fractional citation counting does not perform well. Two other source normalization approaches generally outperform the classification-system-based normalization approach that we study. Our analysis therefore offers considerable support for the use of source-normalized bibliometric indicators

Strategic market position of the European Crime Prevention Network

The activities and tasks of the European Crime Prevention Network (EUCPN), established in 2001, have significantly expanded over the past two decades. In view of the further implementation of its multiannual strategy, the EUCPN has commissioned a study into its current and future strategic market position, conducted with the financial support of the EU’s Internal Security Fund – Police. This book reflects the results. Whilst the EUCPN proves a well-equipped, versatile and multipurpose network in the EU crime prevention area, consolidation and further boosting are due. Key suggestions are to enhance outputs and visibility, to intensify existing partnerships, to broaden target and beneficiary audiences, including at local levels, to implement practice-oriented, multi-language and multimedia approaches, and to focus on the implementation, monitoring, coordination and evaluation of crime prevention policies or strategies, including through cooperation with academia