Development Trends and Economic Assessment of the Integration Processes on the Metals Market

In the present paper, reasons for the increased interest in industrial policy in both developed and developing countries are explained. The systematisation of the results of the development of Russian industry from 1989 to 2014 showed a lack of systematic selection of its priorities, preventing the formation of a strategic vector of industrial policy. The target diversity of the industrial policy is established at the different economic development stages of the country. In the context of economic sanctions against Russia, it is shown that the emergence of a new industrial policy vector is connected to the need for import substitution and concomitant changes in the development model of the domestic economy. The dynamics and characteristics of the industrial development area are shown by the example of a highly developed region like the Central Urals. The total level of organisational innovation activity continues to be low and composes only 12%, although in the manufacturing sector this index is higher than the regional economy index by four absolute percentage points. The industrial policy of the Central Urals is analysed and innovation drivers of the industrial sector of the regional economy are established. The possibilities of the defence, civil engineering, mining, chemical/pharmaceutical and forestry complexes of the Sverdlovsk Region to implement its import substitution policy are explained. The most significant investment projects that will reduce the import dependence of the regional economy are presented. The possibilities of the research sector and created innovation infrastructure of the region in solving this problem are shown. It is necessary to develop the regional laws on the elaboration of industrial policy according to the basic regulations of the Federal Law “On Industrial Policy in the Russian Federation.”This article was prepared with support of the Grant of the Russian Foundation for Humanities No. 14-32-01030

JIDT: An information-theoretic toolkit for studying the dynamics of complex systems

Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave, Python and other environments. We present the principles behind the code design, and provide several examples to guide users.Comment: 37 pages, 4 figure

Information dynamics: patterns of expectation and surprise in the perception of music

This is a postprint of an article submitted for consideration in Connection Science © 2009 [copyright Taylor & Francis]; Connection Science is available online at:http://www.tandfonline.com/openurl?genre=article&issn=0954-0091&volume=21&issue=2-3&spage=8

A note on maximum likelihood estimation of a Pareto mixture

In this paper we study Maximum Likelihood Estimation of the parameters of a Pareto mixture. Application of standard techniques to a mixture of Pareto is problematic. For this reason we develop two alternative algorithms. The first one is the Simulated Annealing and the second one is based on Cross-Entropy minimization. The Pareto distribution is a commonly used model for heavy-tailed data. It is a two-parameter distribution whose shape parameter determines the degree of heaviness of the tail, so that it can be adapted to data with different features. This work is motivated by an application in the operational risk measurement field: we fit a Pareto mixture to operational losses recorded by a bank in two different business lines. Losses below an unknown threshold are discarded, so that the observed data are truncated. The thresholds used in the two business lines are unknown. Thus, under the assumption that each population follows a Pareto distribution, the appropriate model is a mixture of Pareto where all the parameters have to be estimated.

시스템 아키텍쳐 강건성 및 현업 엔지니어 일관성 평가를 위한 엔트로피 기반 측정법: 공식화 및 응용

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2019. 2. 서은석.본 연구에서는 시스템 아키텍쳐 설계 단계에서 다양한 이해관계자 관점에 강건한 아키텍쳐를 식별하는 정량적 메트릭을 연구하는 것을 목표로 한다. 복잡도가 높은 시스템 아키텍쳐의 개발 과정에서는 고객 뿐만 아니라 전체 수명주기의 이해관계자의 요구 사항을 감안하여 설계해야 한다. 기술의 발전과 소비자의 요구 사항에 따라서 개발되는 시스템의 복잡도와 수명주기가 지속적으로 증가함에 따라서 시스템의 전반적인 개발, 생산, 운용/유지보수 등 전 단계의 관점을 시스템 기본 설계 단계에서 미리 반영 및 수용을 하는 필요성이 대두되고 있다. 이와 같은 조직적, 기술적인 문제를 해소하는 관점이 어떻게 차이가 나는지 정량적인 평가의 중요성이 부각된다. 통계역학의 엔트로피 기반 메트릭을 개발하여 시스템 분해 관점에 대한 비교를 정량화한다. 개발한 메트릭으로 두 가지 사례연구를 통해서 다양한 관점에 강건한 시스템 아키텍쳐를 평가, 및 전문가의 시스템 분해의 일관성을 평가하는 과정을 거쳐서 제시된 메트릭의 실용성을 분석하였다. 이를 통해서 관점의 차이가 작게 나는 아키텍쳐의 개발에 기여를 하는 데에 목적을 둔다.This thesis proposes an entropy-based metric which quantifies complex system architecture robustness to different decomposition perspectives for resolving varying stakeholders architectural preferences during the critical stages of the system architecting process. The newly developed metric aims to identify architectures that are robust to different decomposition perspectives by quantifying pairwise comparisons between two different architectural decompositions that may arise from the system architecting process. While system architects typically rely on decomposing a system into its constituent functions and subfunctions, the architecture of a complex system may be interpreted differently by various stakeholders throughout the value chain, which can result in several different system decomposition perspectives, including, but not limited to, assembly or maintenance-based decomposition preferences. As such, the various modular configurations should be quantitatively assessed for the development of an architecture that is robust for different perspectives. The newly proposed module diffusion index adapts entropy, a statistical mechanics concept, to quantify the level of re-arrangement that is required for a modules components to be reassigned to another decomposition perspective as a means of assessing an architectures robustness to different stakeholder requirements. Two feasibility studies were conducted to observe how the newly proposed metric evaluates decomposition perspectives of three different architectures to find a perspective-robust architecture, and to assess the consistency at which industry professionals decompose a given architecture to different perspectives. The proposed metric aims to assist system architects as a quantitative evaluation criterion for analyzing different system architecture concepts during the early engineering phases of complex system design.Abstract ii Contents iv List of Tables vi List of Figures vii Chapter 1 Introduction 1 Chapter 2 Literature Review 5 2.1 System Architecture Development and Selection 5 2.2 Decomposition Perspectives of System Architecture 7 2.3 Quantitative System Architecture Assessment 9 2.4 Research Gap Analysis 10 Chapter 3 Entropy-based Metric Development 12 3.1 Metric Development Overview and Background 12 3.2 Module Diffusion Index Formulation 14 Chapter 4 Case Study: System Architecture Robustness Assessment for Different Stakeholder Perspectives 19 4.1 Introduction 19 4.2 Clock Architecture Overview 21 4.3 Stakeholders Decomposition Perspectives 23 4.4 Case Study Results 27 4.5 Case Study Discussion and Summary 32 Chapter 5 Case Study: Expert Evaluation for Decomposition Consistency 35 5.1 Introduction 35 5.2 Case Study Results 38 5.3 Case Study Discussion and Summary 40 Chapter 6 Conclusions and Directions for Future Work 43 6.1 Conclusions 43 6.2 Directions for Future Work 44 Bibliography 47 Appendix A: Bill of Materials for VFEC Architecture 53 Appendix B: Bill of Materials for FPC Architecture 58 Appendix C: Bill of Materials for CSC Architecture 62 Appendix D: DSM for for CSC Architecture 67 Appendix E: DSM for CSC Architecture 68 Appendix F: DSM for CSC Architecture 69 국문초록 70Maste