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This thesis deals with optimization problems with main focus on logistic Vehicle Routing Problem (VRP). In the first part term optimization is established and most important optimization problems are presented. Next section deals with methods, which are capable of solving those problems. Furthermore it is explored how to apply those methods to specific VRP, along with presenting some enhancement of those algorithms. This thesis also introduces learning method capable of using knowledge of previous solutions. At the end of the paper, experiments are performed to tune the parameters of used algorithms and to discuss benefit of suggested improvements.

Dans cette thèse, nous considérons le problème de la classification supervisée sur un flux de données sujets à des changements de concepts. Afin de pouvoir apprendre dans cet environnement, nous pensons qu’un algorithme d’apprentissage doit combiner plusieurs caractéristiques. Il doit apprendre en ligne, ne pas faire d’hypothèses sur le concept ou sur la nature des changements de concepts et doit être autorisé à s’abstenir de prédire lorsque c’est nécessaire. Les algorithmes en ligne sont un choix évident pour traiter les flux de données. De par leur structure, ils sont capables de continuellement affiner le modèle appris à l’aide des dernières observations reçues. La structure instance based a des propriétés qui la rende particulièrement adaptée pour traiter le problème des flux de données sujet à des changements de concept. En effet, ces algorithmes font très peu d’hypothèses sur la nature du concept qu’ils essaient d’apprendre ce qui leur donne une flexibilité qui les rend capable d’apprendre un vaste éventail de concepts. Une autre force est que stocker certaines des observations passées dans la mémoire peux amener de précieuses meta-informations qui pourront être utilisées par la suite par l’algorithme. Enfin, nous mettons en valeur l’importance de permettre à un algorithme d’apprentissage de s’abstenir de prédire lorsque c’est nécessaire. En effet, les changements de concepts peuvent être la source de beaucoup d’incertitudes et, parfois, l’algorithme peux ne pas avoir suffisamment d’informations pour donner une prédiction fiable. / In this thesis, we investigate the problem of supervised classification on a data stream subject to concept drifts. In order to learn in this environment, we claim that a successful learning algorithm must combine several characteristics. It must be able to learn and adapt continuously, it shouldn’t make any assumption on the nature of the concept or the expected type of drifts and it should be allowed to abstain from prediction when necessary. On-line learning algorithms are the obvious choice to handle data streams. Indeed, their update mechanism allows them to continuously update their learned model by always making use of the latest data. The instance based (IB) structure also has some properties which make it extremely well suited to handle the issue of data streams with drifting concepts. Indeed, IB algorithms make very little assumptions about the nature of the concept they are trying to learn. This grants them a great flexibility which make them likely to be able to learn from a wide range of concepts. Another strength is that storing some of the past observations into memory can bring valuable meta-informations which can be used by an algorithm. Furthermore, the IB structure allows the adaptation process to rely on hard evidences of obsolescence and, by doing so, adaptation to concept changes can happen without the need to explicitly detect the drifts. Finally, in this thesis we stress the importance of allowing the learning algorithm to abstain from prediction in this framework. This is because the drifts can generate a lot of uncertainties and at times, an algorithm might lack the necessary information to accurately predict.

Strongly inspired by an understanding of mammalian cortical structure and function, the Hierarchical Temporal Memory Cortical Learning Algorithm (HTM CLA) is a promising new approach to problems of recognition and inference in space and time. Only a subset of the theoretical framework of this algorithm has been studied, but it is already clear that there is a need for more information about the performance of HTM CLA with real data and the associated computational costs. For the work presented here, a complete implementation of Numenta's current algorithm was done in C++. In validating the implementation, first and higher order sequence learning was briefly examined, as was algorithm behavior with noisy data doing simple pattern recognition. A pattern recognition task was created using sequences of handwritten digits and performance analysis of the sequential implementation was performed. The analysis indicates that the resulting rapid increase in computing load may impact algorithm scalability, which may, in turn, be an obstacle to widespread adoption of the algorithm. Two critical hotspots in the sequential code were identified and a parallelized version was developed using OpenMP multi-threading. Scalability analysis of the parallel implementation was performed on a state of the art multi-core computing platform. Modest speedup was readily achieved with straightforward parallelization. Parallelization on multi-core systems is an attractive choice for moderate sized applications, but significantly larger ones are likely to remain infeasible without more specialized hardware acceleration accompanied by optimizations to the algorithm.

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<p> This work focuses on solving the general optimal control problems with smart-learning-enabled and theory-supported optimal control (SET-OC) approaches. The proposed SET-OC includes two main directions. Firstly, according to the basic idea of the direct method, the smart-learning-enabled iterative optimization algorithm (SEIOA) is proposed for solving discrete optimal control problems. Via discretization and reformulation, the optimal control problem is converted into a general quadratically constrained quadratic programming (QCQP) problem. Then, the SEIOA is applied to solving QCQPs. To be specific, first, a structure-exploiting decomposition scheme is introduced to reduce the complexity of the original problem. Next, an iterative search, combined with an intersection-cutting plane, is developed to achieve global convergence. Furthermore, considering the implicit relationship between the algorithmic parameters and the convergence rate of the iterative search, deep learning is applied to design the algorithmic parameters from an appropriate amount of training data to improve convergence property. To demonstrate the effectiveness and improved computational performance of the proposed SEIOA, the developed algorithms have been implemented in extensive real-world application problems, including unmanned aerial vehicle path planning problems and general QCQP problems. According to the theoretical analysis of global convergence and the simulation results, the efficiency, robustness, and improved convergence rate of the optimization framework compared to the state-of-the-art optimization methods for solving general QCQP problems are analyzed and verified. Secondly, the onboard learning-based optimal control method (L-OCM) is proposed to solve the optimal control problems. Supported by the optimal control theory, the necessary conditions of optimality for optimal control of the optimal control problem can be derived, which leads to two two-point-boundary-value-problems (TPBVPs). Then, critical parameters are identified to approximate the complete solutions of the TPBVPs. To find the implicit relationship between the initial states and these critical parameters, deep neural networks are constructed to learn the values of these critical parameters in real-time with training data obtained from the offline solutions.  To demonstrate the effectiveness and improved computational performance of the proposed L-OCM approaches, the developed algorithms have been implemented in extensive real-world application problems, including two-dimensional human-Mars entry, powered-descent, landing guidance problems, and fuel-optimal powered descent guidance (PDG) problems. In addition, considering there is no thorough analysis of the properties of the optimal control profile for PDG when considering the state constraints, a rigid theoretical analysis of the fuel-optimal PDG problem with state constraints is further provided. According to the theoretical analysis and simulation results, the optimality, robustness, and real-time performance of the proposed L-OCM are analyzed and verified, which indicates the potential for onboard implementation. </p>

В диссертации были изучены особенности использования методов машинного обучения в сфере страхования. Рассмотрены возможности архитектурного подхода в разработке модели машинного обучения. Осуществлен анализ тенденций цифровой трансформации сферы страхования. Осуществлена оценка результативности использования машинного обучения в страховании. Построена полная модель архитектуры ПАО СК «Росгосстрах». Разработана аналитическая модель машинного обучения в сфере тарификации страховой компании. На основе процессного подхода детально рассмотрены все фазы проекта по внедрению модели машинного обучения в деятельность страховой компании. Разработана и реализована имитационная модель управления проектом разработки и внедрения модели машинного обучения в деятельность страховой компании на основе различных сценарием. / The dissertation studied the features of using machine learning methods in the field of insurance. The possibilities of the architectural approach in the development of a machine learning model are considered. The analysis of trends in the digital transformation of the insurance industry has been carried out. The effectiveness of the use of machine learning in insurance has been evaluated. A complete model of the architecture of PJSC IC Rosgosstrakh was built. An analytical model of machine learning in the field of tariffing of an insurance company has been developed. Based on the process approach, all phases of the project to introduce a machine learning model into the activities of an insurance company are considered in detail. A simulation model for project management for the development and implementation of a machine learning model in the activities of an insurance company has been developed and implemented based on various scenarios.

<p dir="ltr">In this work, first, unsupervised machine learning is proposed as a study for detecting and classifying jamming attacks targeting unmanned aerial vehicles (UAV) operating at a 2.4 GHz band. Three scenarios are developed with a dataset of samples extracted from meticulous experimental routines using various unsupervised learning algorithms, namely K-means, density-based spatial clustering of applications with noise (DBSCAN), agglomerative clustering (AGG) and Gaussian mixture model (GMM). These routines characterize attack scenarios entailing barrage (BA), single- tone (ST), successive-pulse (SP), and protocol-aware (PA) jamming in three different settings. In the first setting, all extracted features from the original dataset are used (i.e., nine in total). In the second setting, Spearman correlation is implemented to reduce the number of these features. In the third setting, principal component analysis (PCA) is utilized to reduce the dimensionality of the dataset to minimize complexity. The metrics used to compare the algorithms are homogeneity, completeness, v-measure, adjusted mutual information (AMI) and adjusted rank index (ARI). The optimum model scored 1.00, 0.949, 0.791, 0.722, and 0.791, respectively, allowing the detection and classification of these four jamming types with an acceptable degree of confidence.</p><p dir="ltr">Second, following a different study, supervised learning (i.e., random forest modeling) is developed to achieve a binary classification to ensure accurate clustering of samples into two distinct classes: clean and jamming. Following this supervised-based classification, two-class and three-class unsupervised learning is implemented considering three of the four jamming types: BA, ST, and SP. In this initial step, the four aforementioned algorithms are used. This newly developed study is intended to facilitate the visualization of the performance of each algorithm, for example, AGG performs a homogeneity of 1.0, a completeness of 0.950, a V-measure of 0.713, an ARI of 0.557 and an AMI of 0.713, and GMM generates 1, 0.771, 0.645, 0.536 and 0.644, respectively. Lastly, to improve the classification of this study, semi-supervised learning is adopted instead of unsupervised learning considering the same algorithms and dataset. In this case, GMM achieves results of 1, 0.688, 0.688, 0.786 and 0.688 whereas DBSCAN achieves 0, 0.036, 0.028, 0.018, 0.028 for homogeneity, completeness, V-measure, ARI and AMI respectively. Overall, this unsupervised learning is approached as a method for jamming classification, addressing the challenge of identifying newly introduced samples.</p>

Introdução: Ruminação é a perseveração mal-adaptativa de pensamentos auto-centrados. Evidências sinalizam que ela está associada com início e manutenção de episódios depressivos, e ocorre em múltiplos transtornos mentais. A ruminação está associada com marcadores de desenvolvimento psicopatológico, como volumetria cerebral, memória, genes do BDNF e serotonina. É necessário aprofundar o conhecimento da ruminação enquanto traço dimensional, e conhecer melhor sua associação com variáveis sóciodemográficas, biológicas e clínicas para entender quando passa a ser um sintoma. Entretanto, aferi-la é um desafio, considerando que só existem escalas psicométricas. A mais utilizada, Ruminative Response Scale (RRS), foi validada em amostras não-clínicas. Objetivos: Avaliar ruminação transdiagnosticamente e determinar a validade de constructo da RRS em amostra clínica, buscando determinar fatores sócio-demográficos, clínicos e neurobiológicos associados a maiores escores de ruminação. Métodos: Estudo transversal, amostra não-probabilística. Foram convidados a participar 944 pacientes em atendimento psiquiátrico ambulatorial no HCPA entre março/2015 e junho/2016, maiores de 18 anos, que soubessem ler e escrever, e portadores de transtornos bipolares, depressão, esquizofrenia, esquizoafetivo, ansiedade generalizada, pânico, fobia específica e obsessivocompulsivo. Foram excluídos 373 com doenças que alteram resposta inflamatória, dependência química, gravidez, lactação, doenças neurológicas, vasculares e degenerativas. Recusaram-se a participar 254. Foram incluídos 317 pacientes, e 200 completaram a coleta de dados, que foi realizada em 4 etapas: 1) perfil sócio-demográfico e escalas auto-aplicáveis: ruminação, preocupação e funcionalidade; 2) amostras de sangue e entrevista clínica para aplicação das escalas de sintomas: depressão, mania, ansiedade e gravidade; 3) confirmação diagnóstica; e 4) processamento, armazenamento e análises bioquímicas das amostras de sangue. No primeiro artigo, revisamos sistematicamente a literatura sobre ruminação nos transtornos bipolares. No segundo, determinamos as validades de construto e externa da RRS. No terceiro, usamos machine learning para encontrar padrões de ruminação e determinar quais variáveis associadas preveem ruminação. Resultados: Ruminação está presente em todas as fases do transtorno bipolar, e é um sintoma estável independente do estado de humor, apesar de ter relação estreia com ele. Verificou-se também que mulheres ruminam mais que homens. Os escores de ruminação foram menores nos portadores de esquizofrenia que nos com depressão maior, bipolaridade e ansiedade. RRS apresentou boa confiabilidade, com 2-fatores correlacionados, brooding e ponderação, que apresentaram similaridade nas correlações com medidas clínicas, confirmando a validade externa transdiagnóstica. Por fim, encontrou-se que as variáveis associadas aos pacientes que mais ruminam são preocupação, sintomas de ansiedade generalizada e depressão, gravidade, nível socioeconômico e diagnóstico atual de pânico, sinalizando que ruminação pode ser um marcador de maior sensibilidade à ansiedade. Discussão: Ruminação parece ser um sintoma transdiagnóstico marcador de sofrimento. Os resultados desta tese contribuem para ampliar a discussão sobre diagnóstico psiquiátrico, agregando evidências para aprimorar as definições de limites e sobreposições diagnósticas entre as doenças mentais em que a ruminação ocorre. Por fim, conhecer melhor os mecanismos bioquímicos e clínicos envolvidos na ruminação contribuem na compreensão sobre quando ela deixa de ser um traço normal e vira um sintoma que necessita de tratamento. / Introduction: Rumination has been described as maladaptive perseveration of self-centered thoughts. Evidence indicates that rumination is associated with onset and maintenance of depressive episodes, it’s present in several mental disorders. Rumination is associated with markers of development of psychopathology, such as cerebral volumetry, memory, BDNF and serotonin genes. Measuring rumination is a challenge, considering that are available only psychometric scales. The most used, the Ruminative Responses Scale (RRS), was validated on non-clinical samples. Objectives: To evaluate transdiagnostically the rumination and to determine construct validity of the RRS in outpatients, in order to determine which associated factors lead the patients to ruminate. Methods: Cross-sectional study, non-probabilistic sample. A total of 944 patients in psychiatric outpatient treatment at HCPA between March / 2015 and June / 2016, major than 18 years old, knowing read and write, presenting bipolar disorder, schizophrenia, schizoaffective disorder, generalized anxiety disorder, panic disorder, phobia specific and obsessive-compulsive disorder were invited to participate. We excluded 373 patients with diseases that alter inflammatory response, chemical dependence, pregnancy, lactation, neurological, vascular and degenerative diseases. Two hundred fifty four refused to participate, 317 were included, and 200 completed the data collection, which was performed in 4 stages: 1) socio-demographic profile and self-applicable scales: rumination, worry and functionality; 2) blood samples and clinical interview for the application of symptom scales: depression, mania, anxiety and severity; 3) diagnostic confirmation; and 4) processing, storage and biochemical analyzes of blood samples. In the first article, we systematically reviewed the literature on rumination in bipolar disorders. In the second, we evaluated construct and external validity of RRS. In the third, we used machine learning algorithms to find patterns of rumination and to determine which associated variables predict rumination. Results: Rumination is present in all phases of bipolar disorder, it is a stable symptom, independent of mood, despite it has close relationship with it. It has also been found that women ruminate more than men. Rumination scores were lower in patients with schizophrenia than in major depression, bipolarity and anxiety patients. RRS presented good reliability, with correlated 2-factors, brooding and pondering, which presented similar correlations with clinical measures, confirming the external transdiagnostic validity. Finally, it was found that the variables associated with the greater scores of rumination are worry, symptoms of generalized anxiety and depression, severity of symptoms, socioeconomic level and current diagnosis of panic, signaling that rumination may be a marker of greater sensitivity to anxiety. Discussion: Rumination seems to be a transdiagnostic symptom of suffering. The results of this thesis contribute to broadening the discussion about psychiatric diagnostic, adding evidence to improve the definitions of limits and diagnostic overlaps between mental illnesses in which rumination occurs. Finally, a better understanding of the biochemical and clinical mechanisms involved in rumination may contribute to understanding of when rumination ceases to be a normal trait and becomes a symptom that requires treatment.

大部的類神經網路均為解決特定問題而設計，並非真正去模擬人腦的功能 ，在本論文中介紹一個模擬人類學習方式的類神經網路，稱為推理類神經 網路（The Reasoning Neural Network），其主要兩個組成為強記（ cram -ming）及推理（reasoning）部份，透過彈性的組合這兩個部份可 使類神經網路具有類似人類的學習程序。在本論文中介紹其中一個學習程 序並用四個實驗來評估推理類神經網路的績效，從實結果得知，推理類神 經網路能以合理的隱藏節點數（hidden nodes）達到學習的目標，並建立 一個網路內部表示方式（internal representation），及具有好的推理 能力（g eneralization ability）。 / Most of artification Neural Networks are designed to resolve spe -cific problems, rather than to model the brain. The Reasoning N -eural Network (RNN) that imitates the way of human learning is presented here. Two key components of RNN are the cramming and t -he reasoning. These components coulds be arranged flexibly to a -chieve the human-like learning procedure. One edition of the RNN used in experiments is introduces, and four different proble -ms are used to evaluate the RNN's performance. From simulation results, the RNN accomplishes the goal of learning with a reason -able number of hidden nodes, and evolves a good internal repres -entation and a generalization ability.