Estudos empíricos [1],[2] demonstraram que o trafego das redes Internet Protocol (IP) possui propriedades fractais tais como impulsividade, auto-similaridade e dependência de longa duração em diversas escalas de agregação temporal, na faixa de milissegundos a minutos. Essas características tem motivado o desenvolvimento de novos modelos fractais de teletráfego e de novos algoritmos de controle de trafego em redes convergentes. Este trabalho propõe um novo modelo de trafego no espaço de estados baseado numa aproximação finito-dimensional do processo AutoRegressive Fractionally Integrated Moving Average (ARFIMA). A modelagem por meio de processos auto-regressivos (AR) também é investigada. A analise estatística de series simuladas e de series reais de trafego mostra que a aplicação de modelos AR de ordem alta em esquemas de previsão de teletráfego é fortemente prejudicada pelo problema da identificação da ordem do modelo. Também demonstra-se que a modelagem da memória longa pode ser obtida as custas do posicionamento de um ou mais pólos nas proximidades do circulo de raio unitário. Portanto, a implementação do modelo AR ajustado pode ser instável devido a efeitos de quantização dos coeficientes do filtro digital. O modelo de memória longa proposto oferece as seguintes vantagens: a) possibilidade de implementação pratica, pois não requer memória infinita, b) modelagem (explícita) da região das baixas freqüências do espectro e c) viabilização da utilização do filtro de Kalman. O estudo de caso apresentado demonstra que é possível aplicar o modelo de memória longa proposto em trechos estacionários de sinais de teletráfego fractal. Os resultados obtidos mostram que a dinâmica do parâmetro de Hurst de sinais de teletráfego pode ser bastante lenta na pratica. Sendo assim, o novo modelo proposto é adequado para esquemas de previsão de trafego, tais como Controle de Admissão de Conexões (CAC) e alocação dinâmica de banda, dado que o parâmetro de Hurst pode ser estimado em tempo real por meio da aplicação da transformada wavelet discreta (Discrete Wavelet Transform (DWT)). / Empirical studies [1],[2] demonstrated that heterogeneous IP traffic has fractal properties such as impulsiveness, self-similarity, and long-range dependence over several time scales, from miliseconds to minutes. These features have motivated the development of new traffic models and traffic control algorithms. This work presents a new state-space model for teletraffic which is based on a finite-dimensional representation of the ARFIMA random process. The modeling via AutoRegressive (AR) processes is also investigated. The statistical analysis of simulated time series and real traffic traces show that the application of high-order AR models in schemes of teletraffic prediction can be highly impaired by the model identification problem. It is also demonstrated that the modeling of the long memory can be obtained at the cost of positioning one or more poles near the unit circle. Therefore, the implementation of the adjusted AR model can be unstable due to the quantization of the digital filter coefficients. The proposed long memory model has the following advantages: a) possibility of practical implementation, inasmuch it does not require infinite memory, b) explicit modeling of the low frequency region of the power spectrum, and c) forecasts can be performed via the Kalman predictor. The presented case study suggests one can apply the proposed model in periods where stationarity can be safely assumed. The results indicate that the dynamics of the Hurst parameter can be very slow in practice. Hence, the new proposed model is suitable for teletraffic prediction schemes, such as CAC and dynamic bandwidth allocation, given that the Hurst parameter can be estimated on-line via DWT.

To better understand why machine learning works, we cast learning problems as searches and characterize what makes searches successful. We prove that any search algorithm can only perform well on a narrow subset of problems, and show the effects of dependence on raising the probability of success for searches. We examine two popular ways of understanding what makes machine learning work, empirical risk minimization and compression, and show how they fit within our search frame-work. Leveraging the “dependence-first” view of learning, we apply this knowledge to areas of unsupervised time-series segmentation and automated hyperparameter optimization, developing new algorithms with strong empirical performance on real-world problem classes.

Systems and tools that finds similarities among essays and reports are widely used by todays universities and schools to detect plagiarism. Such tools are however insufficient when used for source code comparisons since they are fragile to the most simplest forms of diguises. Other methods that analyses intermediate forms such as token strings, syntax trees and graph representations have shown to be more effective than using simple textual matching methods. In this master thesis report we discuss how program dependence graphs, an abstract representation of a programs semantics, can be used to find similar procedures. We also present an implementation of a system that constructs approximated program dependence graphs from the abstract syntax tree representation of a program. Matching procedures are found by testing graph pairs for either sub-graph isomorphism or graph monomorphism depending on whether structured transfer of control has been used. Under a scenario based evaluation our system is compared to Moss, a popular plagiarism detection tool. The result shows that our system is more or least as effective than Moss in finding plagiarized procedured independently on the type of modifications used. / System och verktyg som hittar likheter mellan uppsatser och rapporter används i stor omfattning av dagens universitet och skolor för att hitta plagiat bland studenters inlämningar. Sådana verktyg är dock otillräckliga när de används för att jämföra programkod eftersom de är svaga mot de enklaste formerna av modifikationer. Andra metoder som analyserar mellanstegsformer såsom tokensträngar, syntaxträd och grafrepresentationer har visat sig vara mer effektiva än att använda sig av enkla textuella metoder. I denna examensuppsats diskuterar vi hur programberoendegrafer, en abstrakt representation av en programs semantik, kan användas för att hitta jämförelsevis liknande procedurer. Vi presenterar också ett system som konstruerar approximerade programberoendegrafer från det abstrakta syntaxträdet av ett program. Matchande procedurer hittas genom att testa grafpar för antingen sub-graf isomorfism eller monomorfism beroende på om strukturerad byte av kontrolflöde har använts. I en scenariobaserad utvärdering jämför vi vårt system mot Moss, ett populärt verktyg för att detektera plagiat. Resultaten visar att vårt system är lika eller mer effektivt som Moss att detektera plagierade procedurer oberoende av de typer av modifikationer som använts.

Prescription opioid (PO) abuse is a growing public health concern worldwide as evidenced by an increasing number of opioid-related hospital admissions with a striking lack of research examining the neural basis underlying cognitive symptomatology. Drugs of abuse, through their impact on the dopaminergic system, are thought to disrupt the cognitive network regulating impulse control and incentive salience through inhibition of goal-oriented behaviour and drug-induced attentional biases. The objective of the present study is to examine neurocognitive processes in PO abusers (vs. healthy controls) by relying on the enhanced temporal resolution (1ms) of event-related potentials (ERPs) to track information processing abnormalities associated with cognitive control. In a naturalistic clinical study, 16 patients actively using prescription opioids and 16 healthy controls (matched for age, gender, educational level and smoking status) were assessed using a Go/NoGo and cue reactivity paradigm. Analysis revealed no significant differences in N2 or P3 amplitude, measures of inhibitory control, between groups after successful NoGo trials and no significant differences in ERN or Pe amplitude, measures of error processing, between groups after unsuccessful NoGo trials. Cue reactivity analysis of attention-related ERP components in patients demonstrated significantly (p<0.005) smaller P2 amplitudes, indexing the commencement of attentional processing, for drug pictures compared to neutral and affective pictures. Furthermore, stimulus type did not significantly modulate LPP amplitudes, indexing sustained attention, in patients however arousal ratings for drug pictures were positively correlated with LPP amplitudes in patients. These ERP results of altered cognitive control and incentive salience suggest the neural mechanisms underlying these cognitions are affected by chronic opioid abuse. Investigating the cognitive abnormalities experienced by PO abusers is an important factor in understanding the neural correlates of substance abuse and in predicting successful outcomes to ensure the best chance at long-term recovery for addicted individuals.

Abstract The sample consisted of 419 twins, born in 1965–1973, from Northern Finland who had been followed at ten-year intervals, at 2–10 years, 12–20 years and at 22–30 years of age. Data on psychosomatic symptoms, parent-twin and inter-twin relationships were elicited, and twins completed the Children's Depression Inventory modified for age. Middle adolescence appeared to be the most difficult phase of puberty for twins as far as depressive and psychosomatic/somatic symptoms were concerned. When different twin pairs were evaluated separately, the males of opposite-sex twin pairs seemed to be in the most favourable position, particularly in late adolescence, as they reported least depressive symptoms. Depressive and psychosomatic symptoms were evaluated in relation to parental preference among young adult twins. Parental preference was evaluated in two directions: which one of the parents was reported to feel as being closer to the twin – experienced parental preference evaluated by the twin, and which one of the parents the twin felt closer to – twin's own preference. Those males who were equally close to both parents (experienced parental preference) had least total depressiveness, while females in intermediate situation had the highest self-confidence and least anhedonia and nervousness. According to twins' own preference, twins who felt equally close to both parents had least total depressiveness and anhedonia. The intermediate position seems to be the best alternative, as these twins had the least symptoms. Psychosomatic and depressive symptoms were evaluated in relation to co-twin dependence in young adult twins. MZ twins, especially MZ females, reported most often co-twin dependence at all ages. There were no significant differences in depressive symptoms between dependent and independent twins. Twin's subjective experience about co-twin dependence appeared to be important for the twin's mental well-being, as dependence-independence imbalance within twin pair was associated with elevated levels of depressive symptom reporting, especially in twins who perceived themselves as dependent and the co-twin as independent. Dominance-submissiveness between co-twins and its relationship to mental health was assessed in young adulthood. Dominance-submissiveness in the twin relationships was assessed separately in three domains of life: physical and psychological dominance-submissiveness and the role of a spokesperson. Submissiveness in the psychological domain seemed to be associated with increased depressiveness, nervous complaints and psychosomatic symptoms in males of male-female twin pairs. Among females of same-sex twin pairs, submissiveness in the psychological domain was most clearly associated with depressive symptoms. We conclude that being submissive, especially in the psychological domain, to a female twin partner seems to be stressful, whereas it is easier, especially for females, to be submissive to a male twin partner. This was in contrast to co-twin dependency, which was experienced positively when occurring towards a twin sister.

This master's thesis deals with the concept of the weaker party in private law, especially in

This dissertation deals with the rise of new industries through entrepreneurial activities. The aim is to investigate how differences in contexts might encourage or discourage entrepreneurial activities. This contextualization of entrepreneurship enhanced our understanding of when, how and why entrepreneurial activities happen. Entrepreneurship is recognized to be a spatially uneven process and, in addition to previous research that has examined the actions of individual entrepreneurs, we also need to understand the context in which entrepreneurship occurs. We have a good understanding of how structural conditions like industry structure, organization structure and agglomeration effects influence the context, but we know little about how the social dimension of the context is the transmitting medium between structural conditions for entrepreneurship and the decision to act upon identified entrepreneurial opportunities. Following this line of argument, this dissertation is built on the assumption that entrepreneurship is a social phenomenon which gives strong arguments for including local culture in entrepreneurship research. The temporal persistence and the pronounced differences of culture and structural conditions between places reflect path-dependent processes. I therefore use regional path dependence as an interpretative lens to study the contextualization of entrepreneurship in two Swedish cities. Although each context is unique, some generalizations can be drawn from the four individual papers in this dissertation. The first is that industrial legacy leads to the formation of a distinct local culture and that the persistency of this culture influences the subsequent entrepreneurial activities in new local industries. The second is that this persistency of culture suggests that entrepreneurs who are outsiders, geographically or socially, are the driving forces for the emergence of new local industries. Finally, new industry emergence is a result of a combination of exogenous forces and initial local conditions, but it is the entrepreneurial individuals who translate these forces and conditions into entrepreneurial activities.

What constitutes effective corporate governance? Which director characteristics render boards effective at positively influencing firm-level performance outcomes? This dissertation examines these questions by taking a multilevel, multidisciplinary approach to corporate governance. I explore the individual-, team-, and firm- level factors that enable directors to serve effectively as strategic resources during international expansion. I argue that directors’ international experience improves their ability to serve as effective strategic consultants and resource providers to firms during the complex internationalization process. However, unlike prior research, which tends to assume that directors with the potential to provide important resources uniformly do so, I acknowledge contextual factors (i.e. board cohesiveness, strategic relevance of directors’ experience) that affect their propensity to actually influence outcomes. I explore these issues in three essays: one review essay and two empirical essays. In the first empirical essay, I integrate resource dependence theory with insights from social-psychological research to explore the influence of board capital on firms’ cross-border M&A performance. Using a sample of cross-border M&As completed by S&P 500 firms from 2004-2009, I find evidence that directors’ depth of international experience is associated with superior pre-deal outcomes. This suggests that boards’ deep, market-specific knowledge is valuable during the target selection phase. I further find that directors’ breadth of international experience is associated with superior post-deal performance, suggesting that these directors’ global mindset helps firms in the post-M&A integration phase. I also find that these relationships are positively moderated by board cohesiveness, measured by boards’ internal social ties. In the second empirical essay, I explore the boundary conditions of international board capital by examining how the characteristics of firms’ internationalization strategy moderate the relationship between board capital and firm performance. Using a panel of 377 S&P 500 firms observed from 2004-2011, I find that boards’ depth of international experience and social capital are more important during early stages of internationalization, when firms tend to lack market knowledge and legitimacy in the host markets. On the other hand, I find that breadth of international experience has a stronger relationship with performance when firms’ have higher scope of internationalization, when information-processing demands are higher.

This study sought to examine power, resources and environmental negotiation through an examination of the operations and governance of two Community Sport Organizations (CSOs) located in a mid-sized city in the Midwest region of the United States. This was undertaken by answering three research questions: (1) How does power shape the allocation of resources within CSOs? (2) How do CSOs secure access to resources from their organizational environments? and (3) How do CSOs attempt to manipulate their organizational environment? The dissertation took a case study approach, combining documentary review with in-depth semi-structured interviews to develop a greater understanding of the CSOs under study and of the dynamics of power that animate the organizations’ activities, decisions, and outlook. By using two theories of power, Lukes’ Three Dimensional (3D) approach and Resource Dependence Theory (RDT), the dissertation examined both an institutional and episodic view of power, providing a richer view of power within the organizations under study. In RQ1, the study finds that CSOs are willing to allocate resources to the social construction of their sport; that they are sensitive to threshold effects in resource allocation, meaning they provide resources up to the point that a need is met, and not beyond; and that gender played a role in internal resource allocation. In RQ2, the results indicated that the CSOs under study were able to secure resources from their environments through not valuing their institutional existence, and through working with their multi-level governance structures. RQ3 finds that CSOs used anticipatory compliance with environmental actors and borrowing capacity of means to change their organizational environments. The overall conclusion of the study notes that low organizational capacity in CSOs has considerable benefits to go with the drawbacks previously noted in the CSO and not-for-profit literatures. The study outlines that CSOs are able to use their low capacity status to help ensure their organizational environment remains passive, allowing them to maintain a focus on their members and mission while securing sufficient resources to survive. Further discussion of volunteer leadership being a type of participation in sport and of the challenges of studying CSOs, as well as participant recruitment, are also included.

A set of observations from a random process which exhibit correlations that decay slower than an exponential rate is regarded as long-range dependent. This phenomenon has stimulated great interest in the scientific community as it appears in a wide range of areas of knowledge. For example, this property has been observed in data pertaining to electronics, econometrics, hydrology and biomedical signals.There exist several estimation methods for finding model parameters that help explain the set of observations exhibiting long-range dependence. Among these methods, maximum likelihood is attractive, given its desirable statistical properties such as asymptotic consistency and efficiency. However, its computational complexity makes the implementation of maximum likelihood prohibitive.This thesis presents a group of computationally efficient estimators based on the maximum likelihood framework. The thesis consists of two main parts. The first part is devoted to developing a computationally efficient alternative to the maximum likelihood estimate. This alternative is based on the circulant embedding concept and it is shown to maintain the desirable statistical properties of maximum likelihood.Interesting results are obtained by analysing the circulant embedding estimate. In particular, this thesis shows that the maximum likelihood based methods are ill-conditioned; the estimators' performance will deteriorate significantly when the set of observations is corrupted by errors. The second part of this thesis focuses on developing computationally efficient estimators with improved performance under the presence of errors in the observations.