Dissertations / Theses on the topic 'Set valued theory'

The relationship between the convergence of a sequence of self mappings of a metric space and their fixed points, known as the stability (or continuity) of fixed points has been of continuing interest and widely studied in fixed point theory. In this thesis we study the stability of common fixed points in a Hausdorff uniform space whose uniformity is generated by a family of pseudometrics, by using some general notations of convergence. These results are then extended to 2-metric spaces due to S. Gähler. In addition, a well-known theorem of T. Suzuki that generalized the Banach Contraction Principle is also extended to 2-metric spaces and applied to obtain a coincidence theorem for a pair of mappings on an arbitrary set with values in a 2-metric space. Further, we prove the existence of coincidence and fixed points of Ćirić type weakly generalized contractions in metric spaces. Subsequently, the above result is utilized to discuss applications to the convergence of modified Mann and Ishikawa iterations in a convex metric space. Finally, we obtain coincidence, fixed and stationary point results for multi-valued and hybrid pairs of mappings on a metric space.

Orientador: Maria Sueli Marconi Roversi
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica
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Resumo: Estudamos um tipo especial de função denominada função ponto a conjunto, que associa a cada elemento de um espaço métrico um único subconjunto não vazio de outro espaço métrico. A noção de continuidade das funções usuais caracterizada por propriedades equivalentes, enun-ciadas em termos de vizinhanças ou em termos de seqüências, deram origem a versões corres-pondentes para as funções ponto a conjunto. As propriedades adaptadas, não mais equivalentes, são conhecidas como semicontinuidade superior e semicontinuidade inferior, respectivamente. Uma condição do tipo Lipschitz e um tipo de continuidade propriamente, obtido munindo-se o contradomínio da métrica de Hausdorff, foram relacionados à semicontinuidade. Algumas propriedades algébricas ou topológicas dos conjuntos imagem foram essenciais para os resulta-dos obtidos. Abordamos adaptações de alguns resultados clássicos da análise funcional como os teoremas da limitação uniforme, da aplicação aberta e do gráfico fechado para as funções ponto a conjunto caracterizadas como processos convexos, que são os análogos dos operadores lineares. Estabelecemos também uma versão do teorema de Schauder sobre pontos fixos para funções ponto a conjunto e também para as do tipo contração
Abstract: We study a mapping called a set-valued map which associates with each point of a metric space a non empty subset of another metric space. In the case of single-valued maps, contin-uous functions are characterized by two equivalent properties: one in terms of neighborhood and other in terms of sequences. These two properties can be adapted to the case of set-valued maps, are no longer equivalent and are called upper semi continuity and lower semi continuity, respectively. We adapt to the set-valued case the concept of Lipschitz applications and also a type of continuity when the range is enjoyed with the Hausdorff metric. We related them with the conditions of semi continuity. Some of the results depends on algebraic or topological prop-erties of the images. We adapt to closed convex process the principIe of uniform boundedness, the Banach open mapping and closed graph theorems. The closed convex processes are the set-valued analogues of continuous linear operators. We also establish two fixed point result for set-valued maps: the first generalizes the Schauder fixed point theorem and the second considers that of contraction type
Mestrado
Matematica
Mestre em Matemática

In traditional classification problems, all classes in the test set are assumed to also occur in the training set, also referred to as the closed-set assumption. However, in practice, new classes may occur in the test set, which reduces the performance of machine learning models trained under the closed-set assumption. Machine learning models should be able to accurately classify instances of classes known during training while concurrently recognizing instances of previously unseen classes (also called the open set assumption). This open set assumption is motivated by real world applications of classifiers wherein its improbable that sufficient data can be collected a priori on all possible classes to reliably train for them. For example, motivated by the DARPA WASH project at WPI, a disease classifier trained on data collected prior to the outbreak of COVID-19 might erroneously diagnose patients with the flu rather than the novel coronavirus. State-of-the-art open set methods based on the Extreme Value Theory (EVT) fail to adequately model class distributions with unequal variances. We propose the Variational Open-Set Recognition (VOSR) model that leverages all class-belongingness probabilities to reject unknown instances. To realize the VOSR model, we design a novel Multi-Modal Variational Autoencoder (MMVAE) that learns well-separated Gaussian Mixture distributions with equal variances in its latent representation. During training, VOSR maps instances of known classes to high-probability regions of class-specific components. By enforcing a large distance between these latent components during training, VOSR then assumes unknown data lies in the low-probability space between components and uses a multivariate form of Extreme Value Theory to reject unknown instances. Our VOSR framework outperforms state-of-the-art open set classification methods with a 15% F1 score increase on a variety of benchmark datasets.

Sidman and Tailby (1982) established procedures to analyze the nature of stimulus to stimulus relations established by conditional discriminations. Their research describes specific behavioral tests to determine the establishment of properties that define the relations of equivalence. An equivalence relation requires the demonstration of three conditional relations: reflexivity, symmetry, and transitivity. The equivalence stimulus paradigm provides a method to account for novel responding. The research suggests that equivalence relations provide a more efficient and effective approach to the assessment, analysis, and instruction of skills. The present research examined the effectiveness of the formation of an equivalence class in teaching young children coin value relations. The second aspect of the study was to determine if there was a relationship between equivalence class formation and generalization of the skills established to other settings. Five children, 4- and 5-years old, were selected to participate in the study based on their lack of skills in the area of coin values and purchasing an item with dimes or quarters equaling fifth cents. The experimental task was presented on a Macintosh computer with HyperCard programming. The experimental stimuli consisted of pictures of dimes, quarters, and Hershey candy bars presented in match-to-sample procedures. Two conditional discriminations were taught (if A then B and if B then C.). The formation of an equivalence class was evaluated by if C then A. Generalization across settings was tested after the formation of an equivalence class by having the children purchase a Hershey candy bar with dimes at a play store. A multiple baseline experimentaldesign was used to demonstrate a functional relationship between the formation of an equivalence class and generalization of skills across settings. The present research provides supportive evidence that coin value relations can be taught to young children using equivalence procedures. The study also demonstrated generalization of novel, untaught stimuli across settings, after the formation of an equivalence class. A posttest on generalization across settings was conducted 3 months after the study. Long-term stability of equivalence relations was demonstrated by three of the subjects.
Department of Special Education

Motivation and background The enormous amount of capabilities that every human learns throughout his life, is probably among the most remarkable and fascinating aspects of life. Learning has therefore drawn lots of interest from scientists working in very different fields like philosophy, biology, sociology, educational sciences, computer sciences and mathematics. This thesis focuses on the information theoretical and mathematical aspects of learning. We are interested in the learning process of an agent (which can be for example a human, an animal, a robot, an economical institution or a state) that interacts with its environment. Common models for this interaction are Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). Learning is then considered to be the maximization of the expectation of a predefined reward function. In order to formulate general principles (like a formal definition of curiosity-driven learning or avoidance of unpleasant situation) in a rigorous way, it might be desirable to have a theoretical framework for the optimization of more complex functionals of the underlying process law. This might include the entropy of certain sensor values or their mutual information. An optimization of the latter quantity (also known as predictive information) has been investigated intensively both theoretically and experimentally using computer simulations by N. Ay, R. Der, K Zahedi and G. Martius. In this thesis, we develop a mathematical theory for learning in the sensorimotor loop beyond expected reward maximization. Approaches and results This thesis covers four different topics related to the theory of learning in the sensorimotor loop. First of all, we need to specify the model of an agent interacting with the environment, either with learning or without learning. This interaction naturally results in complex causal dependencies. Since we are interested in asymptotic properties of learning algorithms, it is necessary to consider infinite time horizons. It turns out that the well-understood theory of causal networks known from the machine learning literature is not powerful enough for our purpose. Therefore we extend important theorems on causal networks to infinite graphs and general state spaces using analytical methods from measure theoretic probability theory and the theory of discrete time stochastic processes. Furthermore, we prove a generalization of the strong Markov property from Markov processes to infinite causal networks. Secondly, we develop a new idea for a projected stochastic constraint optimization algorithm. Generally a discrete gradient ascent algorithm can be used to generate an iterative sequence that converges to the stationary points of a given optimization problem. Whenever the optimization takes place over a compact subset of a vector space, it is possible that the iterative sequence leaves the constraint set. One possibility to cope with this problem is to project all points to the constraint set using Euclidean best-approximation. The latter is sometimes difficult to calculate. A concrete example is an optimization over the unit ball in a matrix space equipped with operator norm. Our idea consists of a back-projection using quasi-projectors different from the Euclidean best-approximation. In the matrix example, there is another canonical way to force the iterative sequence to stay in the constraint set: Whenever a point leaves the unit ball, it is divided by its norm. For a given target function, this procedure might introduce spurious stationary points on the boundary. We show that this problem can be circumvented by using a gradient that is tailored to the quasi-projector used for back-projection. We state a general technical compatibility condition between a quasi-projector and a metric used for gradient ascent, prove convergence of stochastic iterative sequences and provide an appropriate metric for the unit-ball example. Thirdly, a class of learning problems in the sensorimotor loop is defined and motivated. This class of problems is more general than the usual expected reward maximization and is illustrated by numerous examples (like expected reward maximization, maximization of the predictive information, maximization of the entropy and minimization of the variance of a given reward function). We also provide stationarity conditions together with appropriate gradient formulas. Last but not least, we prove convergence of a stochastic optimization algorithm (as considered in the second topic) applied to a general learning problem (as considered in the third topic). It is shown that the learning algorithm converges to the set of stationary points. Among others, the proof covers the convergence of an improved version of an algorithm for the maximization of the predictive information as proposed by N. Ay, R. Der and K. Zahedi. We also investigate an application to a linear Gaussian dynamic, where the policies are encoded by the unit-ball in a space of matrices equipped with operator norm.

Master of Science
Department of Industrial & Manufacturing Systems Engineering
David H. Ben-Arieh
Quest for continuous improvement among the organizations and issue of missing data for data analysis are never ending. This thesis brings these two topics under one roof, i.e., to evaluate the productivity of organizations with sparse data. This study focuses on Data Envelopment Analysis (DEA) to determine the efficiency of 41 member clinics of Kansas Association of Medically Underserved (KAMU) with missing data. The primary focus of this thesis is to develop new reliable methods to determine the missing values and to execute DEA. DEA is a linear programming methodology to evaluate relative technical efficiency of homogenous Decision Making Units, using multiple inputs and outputs. Effectiveness of DEA depends on the quality and quantity of data being used. DEA outcomes are susceptible to missing data, thus, creating a need to supplement sparse data in a reliable manner. Determining missing values more precisely improves the robustness of DEA methodology. Three methods to determine the missing values are proposed in this thesis based on three different platforms. First method named as Average Ratio Method (ARM) uses average value, of all the ratios between two variables. Second method is based on a modified Fuzzy C-Means Clustering algorithm, which can handle missing data. The issues associated with this clustering algorithm are resolved to improve its effectiveness. Third method is based on interval approach. Missing values are replaced by interval ranges estimated by experts. Crisp efficiency scores are identified in similar lines to how DEA determines efficiency scores using the best set of weights. There exists no unique way to evaluate the effectiveness of these methods. Effectiveness of these methods is tested by choosing a complete dataset and assuming varying levels of data as missing. Best set of recovered missing values, based on the above methods, serves as a source to execute DEA. Results show that the DEA efficiency scores generated with recovered values are close within close proximity to the actual efficiency scores that would be generated with the complete data. As a summary, this thesis provides an effective and practical approach for replacing missing values needed for DEA.

The design of hydropower projects requires a comprehensive planning process in order to achieve the objective to maximise exploitation of the existing hydropower potential as well as future revenues of the plant. For this purpose and to satisfy approval requirements for a complex hydropower development, it is imperative at planning stage, that the conceptual development contemplates a wide range of influencing design factors and ensures appropriate consideration of all related aspects. Since the majority of technical and economical parameters that are required for detailed and final design cannot be precisely determined at early planning stages, crucial design parameters such as design discharge and hydraulic head have to be examined through an extensive optimisation process. One disadvantage inherent to commonly used deterministic analysis is the lack of objectivity for the selection of input parameters. Moreover, it cannot be ensured that the entire existing parameter ranges and all possible parameter combinations are covered. Probabilistic methods utilise discrete probability distributions or parameter input ranges to cover the entire range of uncertainties resulting from an information deficit during the planning phase and integrate them into the optimisation by means of an alternative calculation method. The investigated method assists with the mathematical assessment and integration of uncertainties into the rational economic appraisal of complex infrastructure projects. The assessment includes an exemplary verification to what extent the Random Set Theory can be utilised for the determination of input parameters that are relevant for the optimisation of hydropower projects and evaluates possible improvements with respect to accuracy and suitability of the calculated results
Die Auslegung von Wasserkraftanlagen stellt einen komplexen Planungsablauf dar, mit dem Ziel das vorhandene Wasserkraftpotential möglichst vollständig zu nutzen und künftige, wirtschaftliche Erträge der Kraftanlage zu maximieren. Um dies zu erreichen und gleichzeitig die Genehmigungsfähigkeit eines komplexen Wasserkraftprojektes zu gewährleisten, besteht hierbei die zwingende Notwendigkeit eine Vielzahl für die Konzepterstellung relevanter Einflussfaktoren zu erfassen und in der Projektplanungsphase hinreichend zu berücksichtigen. In frühen Planungsstadien kann ein Großteil der für die Detailplanung entscheidenden, technischen und wirtschaftlichen Parameter meist nicht exakt bestimmt werden, wodurch maßgebende Designparameter der Wasserkraftanlage, wie Durchfluss und Fallhöhe, einen umfangreichen Optimierungsprozess durchlaufen müssen. Ein Nachteil gebräuchlicher, deterministischer Berechnungsansätze besteht in der zumeist unzureichenden Objektivität bei der Bestimmung der Eingangsparameter, sowie der Tatsache, dass die Erfassung der Parameter in ihrer gesamten Streubreite und sämtlichen, maßgeblichen Parameterkombinationen nicht sichergestellt werden kann. Probabilistische Verfahren verwenden Eingangsparameter in ihrer statistischen Verteilung bzw. in Form von Bandbreiten, mit dem Ziel, Unsicherheiten, die sich aus dem in der Planungsphase unausweichlichen Informationsdefizit ergeben, durch Anwendung einer alternativen Berechnungsmethode mathematisch zu erfassen und in die Berechnung einzubeziehen. Die untersuchte Vorgehensweise trägt dazu bei, aus einem Informationsdefizit resultierende Unschärfen bei der wirtschaftlichen Beurteilung komplexer Infrastrukturprojekte objektiv bzw. mathematisch zu erfassen und in den Planungsprozess einzubeziehen. Es erfolgt eine Beurteilung und beispielhafte Überprüfung, inwiefern die Random Set Methode bei Bestimmung der für den Optimierungsprozess von Wasserkraftanlagen relevanten Eingangsgrößen Anwendung finden kann und in wieweit sich hieraus Verbesserungen hinsichtlich Genauigkeit und Aussagekraft der Berechnungsergebnisse ergeben

The phenomenon of Live Video Shopping (LVS) has gained increased attention in recent years. Sinceapproximately 2017, the Chinese market has brought LVSto the attention of the public. In terms of overall market share, LVS was projected to account for roughly 20% of the overalle-commerce volume in Chinauntil 2021. Despite that, within Europe LVS has not seen such rapid growth and seems to be still a neglected domain,both in practice and in academia.Most academic research on LVS has originated in China and took place in an Asian setting. From a European standpoint, these findings can onlyserve as a starting point for further researchdue to cultural differences. Sincemost extant research on the topic of LVS adopted a quantitative research design, we could clearly identify a lack of qualitative studies in a European context. Therefore, our study aims to fill this research gap by investigating European consumersperceived value of LVS and their motivations to engage in it. A qualitative research design is particularly suitable for the exploratory nature of ourresearch. For the collection of data, we conducted three synchronous online focus groups through purposive sampling of European participants. In addition to that, and as a triangulation for our data sources, Pål Burman, the CEO of the LVS provider Zellma, was interviewed. With this we were able to includedifferent perspectives on the topic. It also helped us to increase thevalidityofthemanagerial implications.Usingthematic analysis, we could identify themes that theparticipants associated with the concept of LVS. The findings suggest that the perceived value of LVS for European consumers is not indisputable. Based on the theoretical concept of perceived value,LVS has to solve the trade-off between give and get components. This means that the committed time for attending an LVS stream has to be compensated by certainbenefits, such asdiscounts, enhanced product information, exclusivity of content,or inspiration. By using the theoretical framework of Uses and Gratifications Theory (UGT), we were able to divide the motivations of European consumers to engage in LVS in three types of gratifications: hedonic, utilitarian, and social. As opposed to prior Asian research on the topic, social gratifications only played a minor role forEuropean consumers. Reasons for an interaction between the viewer and the broadcaster were primarily product-related and utilitarian by nature. Engagement for the sake of social motives could not be confirmed. Utilitarian gratifications desired byEuropean consumers were mainly connected to theobtainment of enhanced production information, leading to more informed purchase decisions. Hedonic motivations were for exampleentertainmentand inspiration. When connecting UGT to the TechnologyAcceptance Model (TAM), we have also been able to identify consumers' approachesto LVS as a new technology.Crucial here weretheperceived ease of use and perceived usefulnessof LVS.It was shown that consumers require LVS to be as easy or easier than normal e-commerce shopping is already.LVS would further be perceived as useful if it could provide the viewers with something uniquethat old technologies are not able to provide.Asa new digital phenomenon, itis expected to serve as a complement to physical and online stores. In fact, LVS might not only serve as a platform of sales, but also tocreate long-lasting relationships between a brand and its consumers with consumer engagementas a main desired outcome.

The importance that governments put into open government data policies has increased over the last decade. However, a decreasing speed in this trend is potentially ongoing due to the objectives of these policies not being perceived as completed. Therefore, locating the impacts and measuring their relative value generation aids the understanding of how these objectives can succeed. This study examines the impacts of open government data in Sweden and their potential value generation, focusing on the financial ones. In this study, we developed a measurement model that comprehends six different impacts that generate a value. These impacts are innovation for established firms, innovative start-ups, innovation for public institutions, anti-corruption, and democracy/civil participation. The study has used 24 semi-structured interview findings to develop the model using the grounded theory method. The model was then subsequentially tested and validated by conducting a survey.  We used PLS-SEM as a method of analysis of the 69 responses on the survey from Swedish experts in the field. The results show a positive influence on the open government data financial value generation in the Swedish context, originating from data-driven innovation in established firms. Adding to this, positive impacts on the social value generated from open government data originate from innovative start-ups and product innovation in public institutions. The social value generated was also found to influence the financial value generation. Overall, the results also confirmed that the measurement model assessed is suited for evaluating the value generation of open government data. Thus, the study contributes to policies by visualizing the potential impacts and values that specific policy decisions may yield. Besides, the study contributes to theory thanks to developing a measurement model that could be applied to different contexts. Finally, a unique method that combines model development, context understanding, and model testing is used in the research. This method is considered a contribution due to its potential to be applied to future case study research.

[EN] After-sales services have become very important in the automobile industry. However, this area has not been sufficiently researched, particularly with regard to China, the most important car market globally. In this respect, German manufacturers play a leading role because they dominate the premium market segment. When it comes to services, the one thing that is especially important in China is culture. At the same time, this is exactly where a scientific gap exists because the cultural aspect in automotive services has been mostly neglected in the research literature. Thus, specific knowledge with regard to Chinese service demand behaviour is lacking, which could become a crucial issue because of the enormous differences between Chinese and Western cultures. This paper addresses this limitation by providing a guideline for how the entire process chain of after-sales services could be researched in China. In addition, it also introduces Schwartz's individual level value theory as a beneficial operationalisation approach to culture. Thereby, values are modelled as exogenous variables in order to show which ones are really causal. This significant advantage cannot be provided by national comparison studies, which are the ones that are most often conducted. A total of 301 Chinese workshop customers of Audi, BMW and Mercedes-Benz were surveyed in order to assess the critical success factors of after-sales services via partial least squares structural equation modelling.
[ES] Los servicios post venta en el sector del automóvil se han convertido en un elemento esencial en su mercadotecnia global. Sin embargo, no se han investigado suficientemente y, especialmente en países emergentes con mercados crecientes como China, el mercado más relevante a nivel mundial. Aquí, los fabricantes alemanes juegan un rol fundamental al dominar el segmento premium (o de cuasi lujo) del mercado. Cuando analizamos los servicios, un factor importante en China es la cultura. Sin embargo existe, en este campo un hueco en la investigación académica ya que en la literatura de investigación del sector automóvil, la cultura es un elemento poco analizado. Por ello, no se pueden aplicar conocimientos de mercadotecnia específicos en relación con el comportamiento de la demanda de servicios en China, en un tema esencial, como es la cultura China, muy diferente a la occidental. Esta tesis trata de enfocar las limitaciones mencionadas; en primer lugar, proporcionando una guía de cómo la cadena de proceso de servicios postventa puede ser investigada en países emergentes como China. Y en segundo lugar, porque se utiliza la teoría de cultura de Schwartz como un enfoque útil de instrumentación de los valores culturales. Así, estos se modelan como variables externas, para mostrar claramente cuáles son los valores realmente relevantes en su conjunto. Para ello se encuestaron a 301 clientes de talleres post venta chinos de las marcas Audi, BMW y Mercedes-Benz, con el fin de evaluar los factores críticos de éxito mediante modelos de ecuaciones estructurales de mínimos cuadrados parciales (PLS).
[CAT] Els serveis post venda en el sector de l'automòbil s'han convertit en un element essencial del màrqueting global. No obstant això, no s'han investigat prou i, especialment en països emergents amb mercats creixents com la Xina, el mercat més rellevant a nivell mundial. Aquí, els fabricants alemanys juguen un paper fonamental en dominar el segment premium (o de quasi luxe) del mercat. Quan analitzem els serveis, un factor important a la Xina és la cultura. No obstant això existeix, en aquest camp un buit en la investigació acadèmica ja que en la literatura de recerca del sector automòbil, la cultura és un element poc analitzat. Per això, no es poden aplicar coneixements de màrqueting específics en relació amb el comportament de la demanda de serveis a la Xina, en un tema essencial, com és la cultura Xina, molt diferent a l'occidental. Aquesta tesis tracta d'enfocar les limitacions esmentades; en primer lloc, proporcionant una guia de com la cadena de procés de serveis postvenda pot ser investigada en països emergents com la Xina. I en segon lloc, perquè s'utilitza la teoria de cultura de Schwartz com un enfocament útil d'instrumentació dels valors culturals. Així, aquests es modelen com a variables externes, per mostrar clarament quins són els valors realment rellevants en el seu conjunt. Per a això es van enquestar a 301 clients de tallers post venda xinesos de les marques Audi, BMW i Mercedes-Benz, per tal d'avaluar els factors crítics d'èxit mitjançant models d'equacions estructurals de mínims quadrats
Frass, A. (2015). Impact of After-Sales Performances of German Automobile Manufacturers in China in Service Satisfaction and Loyalty. With a Particular Focus on the Influences of Cultural Determinants [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59251
TESIS

L’adaptation côtière est un impératif pour faire face à l’élévation du niveau marin,conséquence directe du réchauffement climatique. Cependant, la mise en place d’actions et de stratégies est souvent entravée par la présence de diverses et importantes incertitudes lors de l’estimation des aléas et risques futurs. Ces incertitudes peuvent être dues à une connaissance limitée (de l’élévation du niveau marin futur par exemple) ou à la variabilité naturelle de certaines variables (les conditions de mer extrêmes). La prise en compte des incertitudes dans la chaîne d’évaluation des risques est essentielle pour une adaptation efficace.L’objectif de ce travail est de proposer une méthodologie pour la quantification des incertitudes basée sur les fonctions de croyance – un formalisme de l’incertain plus flexible que les probabilités. Les fonctions de croyance nous permettent de décrire plus fidèlement l’information incomplète fournie par des experts (quantiles,intervalles, etc.), et de combiner différentes sources d’information. L’information statistique peut quand à elle être décrite par de fonctions des croyance définies à partir de la fonction de vraisemblance. Pour la propagation d’incertitudes, nous exploitons l’équivalence mathématique entre fonctions de croyance et intervalles aléatoires, et procédons par échantillonnage Monte Carlo. La méthodologie est appliquée dans l’estimation des projections de la remontée du niveau marin global à la fin du siècle issues de la modélisation physique, d’élicitation d’avis d’experts, et de modèle semi-empirique. Ensuite, dans une étude de cas, nous évaluons l’impact du changement climatique sur les conditions de mers extrêmes et évaluons le renforcement nécessaire d’une structure afin de maintenir son niveau de performance fonctionnelle
Coastal adaptation is an imperative to deal with the elevation of the global sealevel caused by the ongoing global warming. However, when defining adaptationactions, coastal engineers encounter substantial uncertainties in the assessment of future hazards and risks. These uncertainties may stem from a limited knowledge (e.g., about the magnitude of the future sea-level rise) or from the natural variabilityof some quantities (e.g., extreme sea conditions). A proper consideration of these uncertainties is of principal concern for efficient design and adaptation.The objective of this work is to propose a methodology for uncertainty analysis based on the theory of belief functions – an uncertainty formalism that offers greater features to handle both aleatory and epistemic uncertainties than probabilities.In particular, it allows to represent more faithfully experts’ incomplete knowledge (quantiles, intervals, etc.) and to combine multi-sources evidence taking into account their dependences and reliabilities. Statistical evidence can be modeledby like lihood-based belief functions, which are simply the translation of some inference principles in evidential terms. By exploiting the mathematical equivalence between belief functions and random intervals, uncertainty can be propagated through models by Monte Carlo simulations. We use this method to quantify uncertainty in future projections of the elevation of the global sea level by 2100 and evaluate its impact on some coastal risk indicators used in coastal design. Sea-level rise projections are derived from physical modelling, expert elicitation, and historical sea-level measurements. Then, within a methodologically-oriented case study,we assess the impact of climate change on extreme sea conditions and evaluate there inforcement of a typical coastal defence asset so that its functional performance is maintained

The present paper extends previous research in terms of integrating social learning with morality theories, under the framework of moral educators’ and their conflicting moral influences. Specifically, this study aims to investigate the impact of witnessing moral educators breaking (their) moral teachings on individual’s morality and criminal behavior using a sample of two countries, Sweden and Greece, with similar population but entirely different cultural and social characteristics. We focus on three research questions regarding the correlations and (i) the explanatory influence of witnessing this conflict on moral emotions and values by gender and country, (ii) its impact on traditional crime by gender and country and (iii) the impact that witnessing the conflict and morality mutually have on traditional crime in the two countries. Our findings emerge in three key points. First, we found that witnessing moral educators influenced both moral emotions differentially in each country and gender, but only affected Swedish males’ moral values. Secondly, our results showed that witnessing moral educators can explain a moderate to small variance of traditional crime only for males in the two countries. Lastly, we found that witnessing moral educators together with morality can explain a moderate variance of traditional crime in the two countries, while gender is highly important for both countries. Findings are discussed in relation to theory and previous research. Future research is recommended in order to expand the understanding of the cultural and social learning processes that inhibit (im)moral contexts and subsequently affect morality and offending.

Stochastic approximation algorithms encompass a class of iterative schemes that converge to a sought value through a series of successive approximations. Such algorithms converge even when the observations are erroneous. Errors in observations may arise due to the stochastic nature of the problem at hand or due to extraneous noise. In other words, stochastic approximation algorithms are self-correcting schemes, in that the errors are wiped out in the limit and the algorithms still converge to the sought values. The rst stochastic approximation algorithm was developed by Robbins and Monro in 1951 to solve the root- nding problem. In 1977 Ljung showed that the asymptotic behavior of a stochastic approximation algorithm can be studied by associating a deterministic ODE, called the associated ODE, and studying it's asymptotic behavior instead. This is commonly referred to as the ODE method. In 1996 Bena•m and Bena•m and Hirsch [1] [2] used the dynamical systems approach in order to develop a framework to analyze generalized stochastic approximation algorithms, given by the following recursion: xn+1 = xn + a(n) [h(xn) + Mn+1] ; (1) where xn 2 Rd for all n; h : Rd ! Rd is Lipschitz continuous; fa(n)gn 0 is the given step-size sequence; fMn+1gn 0 is the Martingale difference noise. The assumptions of [1] later became the `standard assumptions for convergence'. One bottleneck in deploying this framework is the requirement on stability (almost sure boundedness) of the iterates. In 1999 Borkar and Meyn developed a unified set of assumptions that guaranteed both stability and convergence of stochastic approximations. However, the aforementioned frameworks did not account for scenarios with set-valued mean fields. In 2005 Bena•m, Hofbauer and Sorin [3] showed that the dynamical systems approach to stochastic approximations can be extended to scenarios with set-valued mean- fields. Again, stability of the fiterates was assumed. Note that stochastic approximation algorithms with set-valued mean- fields are also called stochastic recursive inclusions (SRIs). The Borkar-Meyn theorem for SRIs [10] As stated earlier, in many applications stability of the iterates is a hard assumption to verify. In Chapter 2 of the thesis, we present an extension of the original theorem of Borkar and Meyn to include SRIs. Specifically, we present two different (yet related) easily-verifiable sets of assumptions for both stability and convergence of SRIs. A SRI is given by the following recursion in Rd: xn+1 = xn + a(n) [yn + Mn+1] ; (2) where 8 n yn 2 H(xn) and H : Rd ! fsubsets of Rdg is a given Marchaud map. As a corollary to one of our main results, a natural generalization of the original Borkar and Meyn theorem is seen to follow. We also present two applications of our framework. First, we use our framework to provide a solution to the `approximate drift problem'. This problem can be stated as follows. When an experimenter runs a traditional stochastic approximation algorithm such as (1), the exact value of the drift h cannot be accurately calculated at every stage. In other words, the recursion run by the experimenter is given by (2), where yn is an approximation of h(xn) at stage n. A natural question arises: Do the errors due to approximations accumulate and wreak havoc with the long-term behavior (convergence) of the algorithm? Using our framework, we show the following: Suppose a stochastic approximation algorithm without errors can be guaranteed to be stable, then it's `approximate version' with errors is also stable, provided the errors are bounded at every stage. For the second application, we use our framework to relax the stability assumptions involved in the original Borkar-Meyn theorem, hence making the framework more applicable. It may be noted that the contents of Chapter 2 are based on [10]. Analysis of gradient descent methods with non-diminishing, bounded errors [9] Let us consider a continuously differentiable function f. Suppose we are interested in nding a minimizer of f, then a gradient descent (GD) scheme may be employed to nd a local minimum. Such a scheme is given by the following recursion in Rd: xn+1 = xn a(n)rf(xn): (3) GD is an important implementation tool for many machine learning algorithms, such as the backpropagation algorithm to train neural networks. For the sake of convenience, experimenters often employ gradient estimators such as Kiefer-Wolfowitz estimator, simultaneous perturbation stochastic approximation, etc. These estimators provide an estimate of the gradient rf(xn) at stage n. Since these estimators only provide an approximation of the true gradient, the experimenter is essentially running the recursion given by (2), where yn is a `gradient estimate' at stage n. Such gradient methods with errors have been previously studied by Bertsekas and Tsitsiklis [5]. However, the assumptions involved are rather restrictive and hard to verify. In particular, the gradient-errors are required to vanish asymptotically at a prescribed rate. This may not hold true in many scenarios. In Chapter 3 of the thesis, the results of [5] are extended to GD with bounded, non-diminishing errors, given by the following recursion in Rd: xn+1 = xn a(n) [rf(xn) + (n)] ; (4) where k (n)k for some fixed > 0. As stated earlier, previous literature required k (n)k ! 0, as n ! 1, at a `prescribed rate'. Sufficient conditions are presented for both stability and convergence of (4). In other words, the conditions presented in Chapter 3 ensure that the errors `do not accumulate' and wreak havoc with the stability or convergence of GD. Further, we show that (4) converges to a small neighborhood of the minimum set, which in turn depends on the error-bound . To the best of our knowledge this is the first time that GD with bounded non-diminishing errors has been analyzed. As an application, we use our framework to present a simplified implementation of simultaneous perturbation stochastic approximation (SPSA), a popular gradient descent method introduced by Spall [13]. Traditional convergence-analysis of SPSA involves assumptions that `couple' the `sensitivity parameters' of SPSA and the step-sizes. These assumptions restrict the choice of step-sizes available to the experimenter. In the context of machine learning, the learning rate may be adversely affected. We present an implementation of SPSA using `constant sensitivity parameters', thereby `decoupling' the step-sizes and sensitivity parameters. Further, we show that SPSA with constant sensitivity parameters can be analyzed using our framework. Finally, we present experimental results to support our theory. It may be noted that contents of Chapter 3 are based on [9]. b(n) a(n) Stochastic recursive inclusions with two timescales [12] There are many scenarios wherein the traditional single timescale framework cannot be used to analyze the algorithm at hand. Consider for example, the adaptive heuristic critic approach to reinforcement learning, which requires a stationary value iteration (for a fixed policy) to be executed between two policy iterations. To analyze such schemes Borkar [6] introduced the two timescale framework, along with a set of sufficient conditions which guarantee their convergence. Perkins and Leslie [8] extended the framework of Borkar to include set-valued mean- fields. However, the assumptions involved were still very restrictive and not easily verifiable. In Chapter 4 of the thesis, we present a generalization of the aforementioned frameworks. The framework presented is more general when compared to the frameworks of [6] and [8], and the assumptions involved are easily verifiable. A SRI with two timescales is given by the following coupled iteration: xn+1 = xn + a(n) un + Mn1+1 ; (5) yn+1 = yn + b(n) vn + Mn2+1 ; (6) where xn 2 R d and yn 2 R k for all n 0; un 2 h(xn; yn) and vn 2 g(xn; yn) for all n 0, where h : Rd Rk ! fsubsets of Rdg and g : Rd Rk ! fsubsets of Rkg are two given Marchaud maps; fa(n)gn 0 and fb(n)gn 0 are the step-size sequences satisfying ! 0 as n ! 1; fMn1+1gn 0 and fMn2+1 gn 0 constitute the Martingale noise terms. Our main contribution is in the weakening of the key assumption that `couples' the behavior of the x and y iterates. As an application of our framework we analyze the two timescale algorithm which solves the `constrained Lagrangian dual optimization problem'. The problem can be stated as thus: Given two functions f : Rd ! R and g : Rd ! Rk, we want to minimize f(x) subject to the condition that g(x) 0. This problem can be stated in the following primal form: inf sup f(x) + T g(x) : (7) 2R 2R0 x d k Under strong duality, solving the above equation is equivalent to solving it's dual: sup inf f(x) + T g(x) : (8) 2Rk x2Rd 0 The corresponding two timescale algorithm to solve the dual is given by: xn+1 = xn a(n) rx f(xn) + nT g(xn) + Mn2+1 ; (9) n+1 = n + b(n) f(xn) + nT g(xn) + Mn1+1 : r We use our framework to show that (9) converges to a solution of the dual given by (8). Further, as a consequence of our framework, the class of objective and constraint functions, for which (9) can be analyzed, is greatly enlarged. It may be noted that the contents of Chapter 4 are based on [12]. Stochastic approximation driven by `controlled Markov' process and temporal difference learning [11] In the field of reinforcement learning, one encounters stochastic approximation algorithms that are driven by Markov processes. The groundwork for analyzing the long-term behavior of such algorithms was laid by Benveniste et. al. [4]. Borkar [7] extended the results of [4] to include algorithms driven by `controlled Markov' processes i.e., algorithms where the `state process' was in turn driven by a time varying `control' process. Another important extension was that multiple stationary distributions were allowed, see [7] for details. The convergence analysis of [7] assumed that the iterates were stable. In reinforcement learning applications, stability is a hard assumption to verify. Hence, the stability assumption poses a bottleneck when deploying the aforementioned framework for the analysis of reinforcement algorithms. In Chapter 5 of the thesis we present sufficient conditions for both stability and convergence of stochastic approximations driven by `controlled Markov' processes. As an application of our framework, sufficient conditions for stability of temporal difference (T D) learning algorithm, an important policy-evaluation method, are presented that are compatible with existing conditions for convergence. The conditions are weakened two-fold in that (a) the Markov process is no longer required to evolve in a finite state space and (b) the state process is not required to be ergodic under a given stationary policy. It may be noted that the contents of Chapter 5 are based on [11].

Autonomous aerial transportation has multiple potential applications including emergency cases and rescue missions where ground intervention may be difficult. In this context, the following work will address the control of multi-agent Vertical Take-off and Landing aircraft (VTOL) transportation system. We develop a decentralized method. The advantage of such a solution is that it can provide better maneuverability and lifting capabilities compared to existing systems. First, we consider a cooperative group of VTOLs transporting one payload. The main idea is that each agent perceive the interaction with other agents as a disturbance while assuming a negotiated motion model and imposing certain magnitude bounds on each agent. The theoretical model will be then validated using a numerical simulation illustrating the interesting features of the presented control method. Results show that under specified disturbances, the algorithm is able to guarantee the tracking with a minimal error. We describe a toolbox that has been developed for this purpose. Then, a system of multiple VTOLs lifting payloads will be studied. The algorithm assures that the VTOLs are coordinated with minimal communication. Additionally, a novel gripper design for ferrous objects is presented that enables the transportation of ferrous objects without a cable. Finally, we discuss potential connections to human in the loop transportation systems.

碩士
國立台北師範學院
數學暨資訊教育學系(含數學教育碩士班)
93
We consider the bounded above and upper semicontinuous functions on complete metric spaces. We establish some results about set-valued mapping, and apply them to established some fixed points of set-valued mapping, which are generalizations of those in Caristi [2].

碩士
國立新竹教育大學
進修部數理教育碩士班(數學組)
92
In this paper, we shall prove a common fixed point theorem in probabilistic metric space of set-valued functions with　-contractive condition, which generalize some results of V.M. Sehgal & A.T. Bharucha-Reid [12].

碩士
國立彰化師範大學
數學研究所
92
In this paper, we give some Ekeland's variational principle for vectorial-valued map, and set-valued map. Some of them have no the condition of boundedness for map. The petal theorem of vectorial form is given in the last section of this paper.

碩士
義守大學
工業工程與管理學系碩士班
97
An enterprise has to put a lot of effort in customer relationship management in order to gain advantages and survive in the keen competition. Not only can an enterprise gains profit from original customer but an enterprise can also gains profit from new customer and increase the contribution of customer profit. This research will make use of RFM model and Rough Sets Theory in order to carry out the goal of customer relationship management. Because Rough Set Theory is a good tool to find the association among data, down the data size, category the data, further more, it does not need to claim the hypothesis previously. Therefore, this research apply Min-Min Rough of Rough Sets Theory to achieve customer segmentation and then the enterprise can figure out a better marketing strategy to strengthen customer relationship and create profit.

Motivation and background The enormous amount of capabilities that every human learns throughout his life, is probably among the most remarkable and fascinating aspects of life. Learning has therefore drawn lots of interest from scientists working in very different fields like philosophy, biology, sociology, educational sciences, computer sciences and mathematics. This thesis focuses on the information theoretical and mathematical aspects of learning. We are interested in the learning process of an agent (which can be for example a human, an animal, a robot, an economical institution or a state) that interacts with its environment. Common models for this interaction are Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). Learning is then considered to be the maximization of the expectation of a predefined reward function. In order to formulate general principles (like a formal definition of curiosity-driven learning or avoidance of unpleasant situation) in a rigorous way, it might be desirable to have a theoretical framework for the optimization of more complex functionals of the underlying process law. This might include the entropy of certain sensor values or their mutual information. An optimization of the latter quantity (also known as predictive information) has been investigated intensively both theoretically and experimentally using computer simulations by N. Ay, R. Der, K Zahedi and G. Martius. In this thesis, we develop a mathematical theory for learning in the sensorimotor loop beyond expected reward maximization. Approaches and results This thesis covers four different topics related to the theory of learning in the sensorimotor loop. First of all, we need to specify the model of an agent interacting with the environment, either with learning or without learning. This interaction naturally results in complex causal dependencies. Since we are interested in asymptotic properties of learning algorithms, it is necessary to consider infinite time horizons. It turns out that the well-understood theory of causal networks known from the machine learning literature is not powerful enough for our purpose. Therefore we extend important theorems on causal networks to infinite graphs and general state spaces using analytical methods from measure theoretic probability theory and the theory of discrete time stochastic processes. Furthermore, we prove a generalization of the strong Markov property from Markov processes to infinite causal networks. Secondly, we develop a new idea for a projected stochastic constraint optimization algorithm. Generally a discrete gradient ascent algorithm can be used to generate an iterative sequence that converges to the stationary points of a given optimization problem. Whenever the optimization takes place over a compact subset of a vector space, it is possible that the iterative sequence leaves the constraint set. One possibility to cope with this problem is to project all points to the constraint set using Euclidean best-approximation. The latter is sometimes difficult to calculate. A concrete example is an optimization over the unit ball in a matrix space equipped with operator norm. Our idea consists of a back-projection using quasi-projectors different from the Euclidean best-approximation. In the matrix example, there is another canonical way to force the iterative sequence to stay in the constraint set: Whenever a point leaves the unit ball, it is divided by its norm. For a given target function, this procedure might introduce spurious stationary points on the boundary. We show that this problem can be circumvented by using a gradient that is tailored to the quasi-projector used for back-projection. We state a general technical compatibility condition between a quasi-projector and a metric used for gradient ascent, prove convergence of stochastic iterative sequences and provide an appropriate metric for the unit-ball example. Thirdly, a class of learning problems in the sensorimotor loop is defined and motivated. This class of problems is more general than the usual expected reward maximization and is illustrated by numerous examples (like expected reward maximization, maximization of the predictive information, maximization of the entropy and minimization of the variance of a given reward function). We also provide stationarity conditions together with appropriate gradient formulas. Last but not least, we prove convergence of a stochastic optimization algorithm (as considered in the second topic) applied to a general learning problem (as considered in the third topic). It is shown that the learning algorithm converges to the set of stationary points. Among others, the proof covers the convergence of an improved version of an algorithm for the maximization of the predictive information as proposed by N. Ay, R. Der and K. Zahedi. We also investigate an application to a linear Gaussian dynamic, where the policies are encoded by the unit-ball in a space of matrices equipped with operator norm.

Global warming is affecting the Earth climate year by year, the biggest difference being observable in increasing temperatures in the World Ocean. Following the long- term global ocean warming trend, average sea surface temperatures across the global tropics and subtropics have increased by 0.4–1◦C in the last 40 years. These rates become even higher in semi-enclosed southern seas, such as the Red Sea, threaten- ing the survival of thermal-sensitive species. As average sea surface temperatures are projected to continue to rise, careful study of future developments of extreme temper- atures is paramount for the sustainability of marine ecosystem and biodiversity. In this thesis, we use Extreme-Value Theory to study sea surface temperature extremes from a gridded dataset comprising 16703 locations over the Red Sea. The data were provided by Operational SST and Sea Ice Analysis (OSTIA), a satellite-based data system designed for numerical weather prediction. After pre-processing the data to account for seasonality and global trends, we analyze the marginal distribution of ex- tremes, defined as observations exceeding a high spatially varying threshold, using the Generalized Pareto distribution. This model allows us to extrapolate beyond the ob- served data to compute the 100-year return levels over the entire Red Sea, confirming the increasing trend of extreme temperatures. To understand the dynamics govern- ing the dependence of extreme temperatures in the Red Sea, we propose a flexible local approach based on R-Pareto processes, which extend the univariate Generalized Pareto distribution to the spatial setting. Assuming that the sea surface temperature varies smoothly over space, we perform inference based on the gradient score method over small regional neighborhoods, in which the data are assumed to be stationary in space. This approach allows us to capture spatial non-stationarity, and to reduce the overall computational cost by taking advantage of distributed computing resources. Our results reveal an interesting extremal spatial dependence structure: in particular, from our estimated model, we conclude that significant extremal dependence prevails for distances up to about 2500 km, which roughly corresponds to the Red Sea length.

The design of hydropower projects requires a comprehensive planning process in order to achieve the objective to maximise exploitation of the existing hydropower potential as well as future revenues of the plant. For this purpose and to satisfy approval requirements for a complex hydropower development, it is imperative at planning stage, that the conceptual development contemplates a wide range of influencing design factors and ensures appropriate consideration of all related aspects. Since the majority of technical and economical parameters that are required for detailed and final design cannot be precisely determined at early planning stages, crucial design parameters such as design discharge and hydraulic head have to be examined through an extensive optimisation process. One disadvantage inherent to commonly used deterministic analysis is the lack of objectivity for the selection of input parameters. Moreover, it cannot be ensured that the entire existing parameter ranges and all possible parameter combinations are covered. Probabilistic methods utilise discrete probability distributions or parameter input ranges to cover the entire range of uncertainties resulting from an information deficit during the planning phase and integrate them into the optimisation by means of an alternative calculation method. The investigated method assists with the mathematical assessment and integration of uncertainties into the rational economic appraisal of complex infrastructure projects. The assessment includes an exemplary verification to what extent the Random Set Theory can be utilised for the determination of input parameters that are relevant for the optimisation of hydropower projects and evaluates possible improvements with respect to accuracy and suitability of the calculated results.
Die Auslegung von Wasserkraftanlagen stellt einen komplexen Planungsablauf dar, mit dem Ziel das vorhandene Wasserkraftpotential möglichst vollständig zu nutzen und künftige, wirtschaftliche Erträge der Kraftanlage zu maximieren. Um dies zu erreichen und gleichzeitig die Genehmigungsfähigkeit eines komplexen Wasserkraftprojektes zu gewährleisten, besteht hierbei die zwingende Notwendigkeit eine Vielzahl für die Konzepterstellung relevanter Einflussfaktoren zu erfassen und in der Projektplanungsphase hinreichend zu berücksichtigen. In frühen Planungsstadien kann ein Großteil der für die Detailplanung entscheidenden, technischen und wirtschaftlichen Parameter meist nicht exakt bestimmt werden, wodurch maßgebende Designparameter der Wasserkraftanlage, wie Durchfluss und Fallhöhe, einen umfangreichen Optimierungsprozess durchlaufen müssen. Ein Nachteil gebräuchlicher, deterministischer Berechnungsansätze besteht in der zumeist unzureichenden Objektivität bei der Bestimmung der Eingangsparameter, sowie der Tatsache, dass die Erfassung der Parameter in ihrer gesamten Streubreite und sämtlichen, maßgeblichen Parameterkombinationen nicht sichergestellt werden kann. Probabilistische Verfahren verwenden Eingangsparameter in ihrer statistischen Verteilung bzw. in Form von Bandbreiten, mit dem Ziel, Unsicherheiten, die sich aus dem in der Planungsphase unausweichlichen Informationsdefizit ergeben, durch Anwendung einer alternativen Berechnungsmethode mathematisch zu erfassen und in die Berechnung einzubeziehen. Die untersuchte Vorgehensweise trägt dazu bei, aus einem Informationsdefizit resultierende Unschärfen bei der wirtschaftlichen Beurteilung komplexer Infrastrukturprojekte objektiv bzw. mathematisch zu erfassen und in den Planungsprozess einzubeziehen. Es erfolgt eine Beurteilung und beispielhafte Überprüfung, inwiefern die Random Set Methode bei Bestimmung der für den Optimierungsprozess von Wasserkraftanlagen relevanten Eingangsgrößen Anwendung finden kann und in wieweit sich hieraus Verbesserungen hinsichtlich Genauigkeit und Aussagekraft der Berechnungsergebnisse ergeben.

碩士
南華大學
企業管理學系管理科學碩博士班
107
Due to Cambodian tends to start to learn on the internet, this research aims to study the interrelationship between each research construct by using technology innovation, Technology Adoption Model (TAM), Value-based Adoption Model (VAM) and Self-Determination Theory (SDT) to determine the behavioral intention of the user in e-learning with subjective norm and perceived risk as the moderation effect. This study is conducted by using the quantitative method of surveying the questionnaire through the social application and e-mail with a total of 369 respondents who get employed in Cambodia. The results found that there is a relationship between the three theories that mentioned which had moderated affect by the subjective norm and perceived risk. It suggests the users need to be aware of the essential of studying on e-learning to adapt with the modern era while the developer of website and application should consider about the reliable system, accurate knowledge, and convenience way to attract the more user to use theirs.