Structural Analysis of Deterministic Mass Fractals Using Small- Angle Scattering and Lacunarity Techniques

The structural characterization of deterministic mass fractals at nano- and microscales is presented in this chapter using two complementary techniques in both reciprocal and real spaces. In the former case, fractal and geometrical features are obtained from the small-angle scattering (SAS) (neutrons, X-rays, light) spectrum in the reciprocal space. The lacunarity technique is considered to extract structural properties and differentiate textures of fractals in real space. We present and discuss various types of mass fractals, such as thin and fat fractals, as well as fractals generated with the Chaos game representation (CGR). We show how the main structural properties of the fractals, such as the fractal dimension, the iteration number, the scaling factor, the overall size of the fractal, and the size of the basic units of the fractal, can be extracted by using SAS and lacunarity techniques

Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets

The goal of this dissertation is to develop unsupervised algorithms for discovering previously unknown subspace trends in massive multivariate biomedical data sets without the benefit of prior information. A subspace trend is a sustained pattern of gradual/progressive changes within an unknown subset of feature dimensions. A fundamental challenge to subspace trend discovery is the presence of irrelevant data dimensions, noise, outliers, and confusion from multiple subspace trends driven by independent factors that are mixed in with each other. These factors can obscure the trends in traditional dimension reduction and projection based data visualizations. To overcome these limitations, we propose a novel graph-theoretic neighborhood similarity measure for sensing concordant progressive changes across data dimensions. Using this measure, we present an unsupervised algorithm for trend-relevant feature selection and visualization. Additionally, we propose to use an efficient online density-based representation to make the algorithm scalable for massive datasets. The representation not only assists in trend discovery, but also in cluster detection including rare populations. Our method has been successfully applied to diverse synthetic and real-world biomedical datasets, such as gene expression microarray and arbor morphology of neurons and microglia in brain tissue. Derived representations revealed biologically meaningful hidden subspace trend(s) that were obscured by irrelevant features and noise. Although our applications are mostly from the biomedical domain, the proposed algorithm is broadly applicable to exploratory analysis of high-dimensional data including visualization, hypothesis generation, knowledge discovery, and prediction in diverse other applications.Electrical and Computer Engineering, Department o

Statistical and Fractal Analysis of Particle Data from Two-Dimensional Video Disdrometer

13301甲第4234号博士（工学）金沢大学博士論文本文Full 以下に掲載：Advances in Remote Sensing 4(1) pp.1-14 2015. Scientific Research. 共著者：Sergey Gavrilov, Mamoru Kubo, Vu Anh Tran, Duc Luu Ngo, Ngoc Giang Nguyen, Lan Ahn T. Nguyen, Favorisen Rosyking Lumbanraja, Dau Phan, Kenji Sato

네트워크 프랙탈 성질에 기반한 금융시장 예측 및 거래전략

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2020. 8. 장우진.Extensive academic research was performed for the financial market as it is closely connected to practical economy. Research in traditional financial economics resulted in economic indicators and the econometrics was instrumental for quantitative research in financial market. However, it proved to be difficult to predict the market behavior as it is a result of complex interaction among many agents with their own agenda. An effective tool to predict a change in market would be beneficial for policy makers and market participants to assist them with rational and consistent decision making. On the other hand, inconsistent prediction would lead to a suboptimal and inconsistent market activity which sometimes result in sudden collapse in the market as it did in 2008 Financial crisis and 1997 Asian financial crisis. The purpose of this dissertation is to develop approach based on econophysics and machine learning to systematically analyze the financial market. The main focus of this dissertation involves the network structure of stock market. To predict the change in market behavior, it is critical to understand the relationship or correlation among the market participants beforehand, and complex network analysis is one of the most prominent methods for such study. The fractal theory was employed as the primary approach to analyze the network structure of financial market. The empirical study shows that the network of financial market exhibits fractal properties. Also, analysis of fractal dimension and network topology led to two key discoveries. First, the fractal dimension and the Strong effective repulsion between distinct network nodes known as the hub are closely related. Second, the fractal dimension reveals the shortcut of network structure. Through further analysis, these two properties were proved to be useful for risk management in financial market. Three fractal measures were proposed to specify network structure for ease of implementation in future studies. In the second step, the fractal measures were implemented in a financial market to assess its ability to predict the market movement. Recently, studies were conducted to determine if a new measure or index improves the prediction accuracy for financial time series. These studies are advantageous for future studies as it proposes new indices for other implementation and further analysis rather than studying the precision of their own method. In this paper, machine learning algorithms were employed to assess the predictive properties of fractal measures. Empirical experiments were performed to predict direction of market movement, which is effectively a classification task, and prediction for returns, a regression task. The studies concludes that the fractal measure proposed were effective in prediction for long-term stock returns of more than three months period. Finally, a model to improve trading strategy based on learning-to-rank algorithm and the fractal measures was introduced. Previous studies are often based on the modern portfolio theory(MPT), but it is insufficient for real-world application as it doesnt provide any implication for rebalancing period of portfolio. The optimal rebalancing model proposed in this study allows its application with traditional portfolio methods. The experiments were carried out in two steps. The model learns to predict the better time period to perform rebalancing between two time periods in the future, followed by the empirical simulation to apply the model in real world trading scenario. Two traditional portfolio methods, equal weighted and maximized Sharpe ratio, were taken for experiment. The result affirms that the optimal rebalancing model was able to capture the better time period of rebalancing portfolio. In addition, the model outperformed a simple rebalancing method of fixed time period. When the fractal measures were employed as an input variable, the model performance was further improved. The primary contribution achieved through this model is that it allows application and expansion into all traditional portfolio models. Also, the fractal measures observed in the network structure grants insight regarding the market behavior and empirically proved that the measure provides benefit in prediction for the real-world stock market.금융시장에 대한 연구는 전반적인 경제 활동과 밀접한 연관성이 있기 때문에, 다양한 학계의 지식들과 연계되어 광범위하게 연구되고 있다. 전통적인 경제학 이론을 바탕으로 여러가지 경제 지표들이 개발되었고, 계량경제학의 발전으로 이를 정량적으로 분석하는 연구가 진행되었다. 하지만, 서로 다른 특징을 갖는 시장참여자들의 행위로 이루어진 금융시장의 복잡한 특성 때문에, 기존의 경제학 기반의 방법론들만으로 금융시장의 변화를 정밀하게 예측하기에는 한계가 있었다. 만약 금융시장의 변화를 효율적으로 예측 할 수 있다면, 국가 정책이나 기업들 및 시장 참여자들은 합리적인 의사결정을 통해서 건전한 금융 활동을 할 수 있을 것이다. 반면에 이러한 금융시장의 변화를 효율적으로 예측하지 못해 비이상적인 금융 활동이 지속된다면, 최악의 경우에는 글로벌 금융 위기와 같은 대규모 시장 붕괴 현상이 발생할 수 있을 것이아. 따라서 본 학위논문에서는 경제물리학과 머신러닝을 융합하여 체계적으로 금융시장 분석을 진행하고자 한다. 본 학위논문에서는 금융시장의 다양한 섹터 중에서 주식시장 네트워크 구조를 분석하는데 초점을 맞춘다. 미래의 주식시장의 변화를 올바르게 예측하기 위해서는 주식시장 구성원들간의 관계 파악이 선행되어야 하는데, 이에 대표적인 분석 방법이 복잡계 네트워크 분석(Complex network analysis)이기 때문이다. 본 연구에서는 주식시장 네트워크 구조를 분석하는 여러 방법론들 중 프랙탈 이론(Fractal theory)의 도입을 제안한다. 실험 결과 주식시장 네트워크의 구조는 프랙탈 특성을 가짐을 밝혀냈다. 또한, 측정된 프랙탈 차원(Fractal dimension)과 네트워크의 토폴로지(Topology)와의 관계를 살펴본 결과 두 가지 주요한 주식시장 네트워크의 구조적인 특징을 발견할 수 있었다. 첫번째는, 프랙탈 차원과 소위 허브(Hub)라고 불리우는 네트워크 상에서 연결이 많이된 노드들간의 강한 반발(Strong effective repulsion) 현상과 연관성이 있다는 점이다. 두번째는, 프랙탈 차원으로 네트워크의 지름길(Shortcut) 구조를 관찰할 수 있었다. 또한 이 두 가지 네트워크의 구조적인 특성은 주식시장의 위험 관리(Risk management) 관점에서 유용하게 쓰일 수 있음을 분석했다. 그리고, 위 특성들을 다른 연구들에 쉽게 적용 가능하도록 네트워크 구조를 표현하는 3가지 프랙탈 지표(Fractal measures)들을 제안한다. 다음 단계로 주식시장에서 측정한 프랙탈 지표가 미래에 주가 지수의 예측력 향상에 도움이 되는지를 검증했다. 최근 다양한 분야에서 새롭게 발견한 지표들이 금융 시계열 데이터에 대하여 예측력 향상에 도움이 되는지를 검증하는 연구들이 진행되고있다. 이러한 연구들은 발견한 지표들 만을 사용하여 정밀한 예측을 하는 목적이 아닌, 발견한 지표들이 예측력 향상에 도움이 된다는 점을 밝혀내는데 주 목적이 있다. 이렇게 예측력 향상이 있는것이 밝혀진 지표들은 다른 연구나 산업에 쉽게 적용할 수 있는 장점이 있다. 본 학위논문에서는 몇 가지 머신러닝 알고리즘을 활용하여 측정한 프랙탈 지표가 미래의 주가 지수의 예측력 향상에 도움이 되는지를 검증했다. 검증 실험은 가장 단순한 미래 주가 지수의 방향 분류(Classification) 부터, 주가 지수 수익률의 예측(Prediction) 까지 이루어진다. 그 결과 제안한 프랙탈 지표들은 약 3개월 이후의 장기 미래의 주가 지수에 대해 일관성있는 예측력 향상 효과가 있음을 밝혀냈다. 마지막으로 제안한 프랙탈 지표들과 Learning-to-rank 알고리즘을 활용하여 기존의 주식시장에 연구되었던 거래 전략(Trading strategy)의 성능을 개선할 수 있는 모델을 제안한다. 기존의 주식시장에서 연구된 거래 전략들 중 큰 비율을 차지하는 연구들은 현대 포트폴리오 이론(Modern portfolio theory)에 기반한 포트폴리오 구성 방법에 대한 연구들이다. 하지만, 실제 투자에 적용하기 위해서는 포트폴리오를 구성하는 방법론 뿐 만 아니라, 언제 포트폴리오를 재구성해야 하는지를 판단하는것 또한 중요한 의사결정 요소이다. 본 학위논문에서 제안하는 모델은 기존에 연구된 방법론들에 유연하게 접목하여 활용할 수 있는 최적 리밸런싱 시점 판단 모델(Optimal rebalancing model)이다. 실험은 두단계로 진행된다. 먼저, 제안한 모델로 학습 데이터 내의 서로 다른 두 시점 중 미래의 더 나은 성능을 보이는 리밸런싱 지점을 예측할수 있는지를 학습한다. 그후에, 학습된 모델들 중 좋은 성능을 갖는 파라미터를 선택하고, 시뮬레이션 분석을 통해서 실제 거래전략에 적용 가능성을 평가한다. 실험에서 사용된 기존의 포트폴리오 구성 방법론은, 관련 연구들에서 가장 대표적인 벤치마크로 활용되는 자산 균등 분배 포트폴리오 방식과 샤프 비율(Sharpe ratio) 최대화 포트폴리오 방식이다. 실험결과 두 방식 모두에서 본 연구에서 제안한 최적 리밸런싱 시점 판단 모델이 더 나은 포트폴리오 구성 시점을 구해냈다. 또한 시뮬레이션 결과 일정주기로 리밸런싱하는 동일한 포트폴리오 구성방식보다 더 나은 성능을 보였다. 특히 입력 변수로 프랙탈 지표들을 추가했을 때 가장 좋은 성능을 보임을 관찰했다. 본 모델은 연구된 기존의 모든 포트폴리오 구성 방법론들에 적용할 수 있는 확장성의 관점에서 중요한 기여가 있다. 그리고 프랙탈 지표를 통해서 관찰되는 네트워크의 구조적 특징들이 미래 시장을 판단하는데 도움이 됨을 보임으로써, 제안한 프랙탈 지표들이 실제 주식시장에 적용 가능한 실용적인 특성을 나타냄도 검증했다.Chapter 1 Introduction 1 1.1 Research Motivation and Purpose 1 1.2 Organization of the Research 5 Chapter 2 Literature Review 7 2.1 Complex Network 7 2.2 Market Prediction with Machine Learning 8 2.3 Trading Strategies 11 Chapter 3 Fractal Structure in Stock Market 13 3.1 Network Fractality 13 3.1.1 Threshold Network 13 3.1.2 Fractal Dimension 15 3.1.3 Fractal Measures 17 3.2 Fractal Analysis on Stock Market 21 3.2.1 Data Description 21 3.2.2 Fractality of S&P500 Network 25 3.2.3 Network Topology and Fractal Measures 27 3.3 Summary and Discussion 43 Chapter 4 Stock Market Prediction with Fractality 45 4.1 Classification of Stock Market 45 4.1.1 Classification Model 45 4.1.2 Classification Results 50 4.2 Fractal Measures and Predictive Power 55 4.2.1 Prediction of Stock Market Return 55 4.2.2 Parameter Analysis 59 4.2.3 Predictive Power Results 60 4.3 Summary and Discussion 66 Chapter 5 Trading Strategy with Optimal Rebalancing Model 69 5.1 Optimal Rebalancing Model 69 5.1.1 Portfolio Selection Method 69 5.1.2 Learning-to-rank algorithm 71 5.1.3 Proposed Modeling Method 73 5.1.4 Model Results 77 5.2 Simulation Analysis 84 5.2.1 Simulation Structure 84 5.2.2 Simulation Results 88 5.3 Summary and Discussion 101 Chapter 6 Conclusion 103 6.1 Conclusions 103 6.2 Future Works 106 Bibliography 107 국문초록 117Docto

Shannon Entropy in a European Seabass (Dicentrarchus labrax) System during the Initial Recovery Period after a Short-Term Exposure to Methylmercury

Methylmercury (MeHg) is an environmental contaminant of increasing relevance as a seafood safety hazard that affects the health and welfare of fish. Non-invasive, on-line methodologies to monitor and evaluate the behavior of a fish system in aquaculture may make the identification of altered systems feasible-for example, due to the presence of agents that compromise their welfare and wholesomeness-and find a place in the implementation of Hazard Analysis and Critical Control Points and Fish Welfare Assurance Systems. The Shannon entropy (SE) of a European seabass (Dicentrarchus labrax) system has been shown to differentiate MeHg-treated from non-treated fish, the former displaying a lower SE value than the latter. However, little is known about the initial evolution of the system after removal of the toxicant. To help to cover this gap, the present work aims at providing information about the evolution of the SE of a European seabass system during a recuperation period of 11 days following a two-week treatment with 4 mu g center dot MeHg/L. The results indicate that the SE of the system did not show a recovery trend during the examined period, displaying erratic responses with daily fluctuations and lacking a tendency to reach the initial SE values.We wish to thank Grupo Tinamenor (Cantabria, Spain) for providing the European sea bass, Urtzi Izagirre for his contribution to the design of the experimental treatments, and Xabier Lekube and Gregor Bwye for technical assistance. The work received financial support from the Spanish Ministry of Economy and Competitiveness Project number: CTM2012-40203-C02-01, Towards science-based standard biomarker methods, suitable to diagnose and monitor pollution biological effects in the Bay of Biscay for the purpose of implementing the European Marine Strategy Framework Directive-BMW and Project number: RTC-2014-2837-2, Minimizacion de la problematica del mercurio del atun y valorizacion del atun como alimento saludable-SELATUN

Microbiological modulation of suspended particulate matter dynamics: A study of biological flocculation in nutrient-enriched waters

The study of suspended particulate matter (SPM) dynamics has conventionally focused on physical and hydrodynamical interactions, with little attention paid on exploring the role of SPM as a micro-ecosystem that sustains a wide diversity of microbial colonies. This thesis puts forth a new paradigm of SPM dynamics that integrates mineral, chemical, and biological components into one framework to emphasize the role of microorganisms in altering the chemistry and structure of SPM, which further affect its transport and deposition. Microbiological modulation of SPM dynamics was investigated in this thesis by coupling experiments with numerical models. Experimental results revealed that the size of biomass-affected SPM was approximately 60% larger and the capacity dimension was 2% lower as compared to biomass-free SPM. In contrast, the average settling velocity was observed to be nearly invariant for all SPM types. It was also found that the probability for SPM to aggregate was highly dependent on SPM shape and surface asperity, suggesting that microorganisms can alter SPM collision and aggregation kinematics through their role in modifying SPM structure and shape. Analyses coupling experimental results and a biogeochemical model further reveal the feedback interactions between minerals, chemicals, and microorganisms. It shows how changes in sediment and water qualities can have impacts on microorganisms that in turn modify SPM characteristics and result in further alteration of sediment and water qualities. This thesis provides an insight into the role played by microorganisms in engineering the architecture and altering the chemistry of SPM, with experimental evidence and simulation results put forth to emphasize that the contributions of nutrients and microorganisms cannot be neglected in modelling and predicting SPM dynamics

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin