There has been a growing interest in stochastic modelling and learning with complex data, whose elements are structured and interdependent. One of the most successful methods to model data dependencies is graphical models, which is a combination of graph theory and probability theory. This thesis focuses on a special type of graphical models known as Conditional Random Fields (CRFs) (Lafferty et al., 2001), in which the output state spaces, when conditioned on some observational input data, are represented by undirected graphical models. The contributions of thesis involve both (a) broadening the current applicability of CRFs in the real world and (b) deepening the understanding of theoretical aspects of CRFs. On the application side, we empirically investigate the applications of CRFs in two real world settings. The first application is on a novel domain of Vietnamese accent restoration, in which we need to restore accents of an accent-less Vietnamese sentence. Experiments on half a million sentences of news articles show that the CRF-based approach is highly accurate. In the second application, we develop a new CRF-based movie recommendation system called Preference Network (PN). The PN jointly integrates various sources of domain knowledge into a large and densely connected Markov network. We obtained competitive results against well-established methods in the recommendation field. / On the theory side, the thesis addresses three important theoretical issues of CRFs: feature selection, parameter estimation and modelling recursive sequential data. These issues are all addressed under a general setting of partial supervision in that training labels are not fully available. For feature selection, we introduce a novel learning algorithm called AdaBoost.CRF that incrementally selects features out of a large feature pool as learning proceeds. AdaBoost.CRF is an extension of the standard boosting methodology to structured and partially observed data. We demonstrate that the AdaBoost.CRF is able to eliminate irrelevant features and as a result, returns a very compact feature set without significant loss of accuracy. Parameter estimation of CRFs is generally intractable in arbitrary network structures. This thesis contributes to this area by proposing a learning method called AdaBoost.MRF (which stands for AdaBoosted Markov Random Forests). As learning proceeds AdaBoost.MRF incrementally builds a tree ensemble (a forest) that cover the original network by selecting the best spanning tree at a time. As a result, we can approximately learn many rich classes of CRFs in linear time. The third theoretical work is on modelling recursive, sequential data in that each level of resolution is a Markov sequence, where each state in the sequence is also a Markov sequence at the finer grain. One of the key contributions of this thesis is Hierarchical Conditional Random Fields (HCRF), which is an extension to the currently popular sequential CRF and the recent semi-Markov CRF (Sarawagi and Cohen, 2004). Unlike previous CRF work, the HCRF does not assume any fixed graphical structures. / Rather, it treats structure as an uncertain aspect and it can estimate the structure automatically from the data. The HCRF is motivated by Hierarchical Hidden Markov Model (HHMM) (Fine et al., 1998). Importantly, the thesis shows that the HHMM is a special case of HCRF with slight modification, and the semi-Markov CRF is essentially a flat version of the HCRF. Central to our contribution in HCRF is a polynomial-time algorithm based on the Asymmetric Inside Outside (AIO) family developed in (Bui et al., 2004) for learning and inference. Another important contribution is to extend the AIO family to address learning with missing data and inference under partially observed labels. We also derive methods to deal with practical concerns associated with the AIO family, including numerical overflow and cubic-time complexity. Finally, we demonstrate good performance of HCRF against rivals on two applications: indoor video surveillance and noun-phrase chunking.

<p>Fast addition is essential in many DSP algorithms. Various structures have been introduced to speed up the time critical carry propagation. For high throughput applications, however, it may be necessary to introduce pipelining. In this report the power consumption of four different adder structures, with varying word length and different number of pipeline cuts, is compared. </p><p>Out of the four adder structures compared, the Kogge-Stone parallel prefix adder proves to be the best choice most of the time. The Brent-Kung parallel prefix adder is also a good choice, but the maximal throughput does not reach as high as the maximal throughput of the Kogge-Stone parallel prefix adder.</p>

Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2008.

In everyday conversation &quot;if&quot; is one of the most frequently used conjunctions. This dissertation investigates what meaning an everyday conditional transmits and what inferences it licenses. It is suggested that the nature of the relation between the two propositions in a conditional might play a major role for both questions. Thus, in the experiments reported here conditional statements that describe a causal relationship (e.g., &quot;If you touch that wire, you will receive an electric shock&quot;) were compared to arbitrary conditional statements in which there is no meaningful relation between the antecedent and the consequent proposition (e.g., &quot;If Napoleon is dead, then Bristol is in England&quot;).<br> Initially, central assumptions from several approaches to the meaning and the reasoning from causal conditionals will be integrated into a common model. In the model the availability of exceptional situations that have the power to generate exceptions to the rule described in the conditional (e.g., the electricity is turned off), reduces the subjective conditional probability of the consequent, given the antecedent (e.g., the probability of receiving an electric shock when touching the wire). This conditional probability determines people's degree of belief in the conditional, which in turn affects their willingness to accept valid inferences (e.g., &quot;Peter touches the wire, therefore he receives an electric shock&quot;) in a reasoning task. Additionally to this indirect pathway, the model contains a direct pathway: Cognitive availability of exceptional situations directly reduces the readiness to accept valid conclusions.<br> The first experimental series tested the integrated model for conditional statements embedded in pseudo-natural cover stories that either established a causal relation between the antecedent and the consequent event (causal conditionals) or did not connect the propositions in a meaningful way (arbitrary conditionals). The model was supported for the causal, but not for the arbitrary conditional statements. Furthermore, participants assigned lower degrees of belief to arbitrary than to causal conditionals. Is this effect due to the presence versus absence of a semantic link between antecedent and consequent in the conditionals?<br> This question was one of the starting points for the second experimental series. Here, the credibility of the conditionals was manipulated by adding explicit frequency information about possible combinations of presence or absence of antecedent and consequent events to the problems (i.e., frequencies of cases of 1. true antecedent with true consequent, 2. true antecedent with false consequent, 3. false antecedent with true consequent, 4. false antecedent with false consequent). This paradigm allows testing different approaches to the meaning of conditionals (Experiment 4) as well as theories of conditional reasoning against each other (Experiment 5).<br> The results of Experiment 4 supported mainly the conditional probability approach to the meaning of conditionals (Edgington, 1995) according to which the degree of belief a listener has in a conditional statement equals the conditional probability that the consequent is true given the antecedent (e.g., the probability of receiving an electric shock when touching the wire). Participants again assigned lower degrees of belief to the arbitrary than the causal conditionals, although the conditional probability of the consequent given the antecedent was held constant within every condition of explicit frequency information. This supports the hypothesis that the mere presence of a causal link enhances the believability of a conditional statement. In Experiment 5 participants solved conditional reasoning tasks from problems that contained explicit frequency information about possible relevant cases. The data favored the probabilistic approach to conditional reasoning advanced by Oaksford, Chater, and Larkin (2000).<br> The two experimental series reported in this dissertation provide strong support for recent probabilistic theories: for the conditional probability approach to the meaning of conditionals by Edgington (1995) and the probabilistic approach to conditional reasoning by Oaksford et al. (2000). In the domain of conditional reasoning, there was additionally support for the modified mental model approaches by Markovits and Barrouillet (2002) and Schroyens and Schaeken (2003). Probabilistic and mental model approaches could be reconciled within a dual-process-model as suggested by Verschueren, Schaeken, and d&#39;Ydewalle (2003). / Im Laufe eines Tages verwenden die meisten Menschen mehrfach Konditionalsätze: Das Wörtchen &quot;wenn&quot; gehört zu den häufigsten Konjunktionen in Alltag, Wissenschaft und Literatur. Die vorliegende Dissertation beschäftigt sich mit der Frage, welche Bedeutung ein Konditionalsatz im alltäglichen Sprachgebrauch übermittelt und welche Inferenzen er erlaubt. Es wird die Vermutung aufgestellt, dass dabei die Art der Relation zwischen den zwei Propositionen in einem Konditional eine bedeutsame Rolle spielen könnte. Daher werden in den Experimenten Konditionalsätze, die eine kausale Beziehung beschreiben (z.B. &quot;Wenn Du das Kabel berührst, bekommst Du einen elektrischen Schlag&quot;) mit arbiträren Konditionalen verglichen, in denen keinerlei sinnvolle Relation zwischen Antezedens und Konsequens besteht (z.B. &quot;Wenn Napoleon tot ist, liegt Bristol in England&quot;).<br> Als erstes werden zentrale Annahmen von mehreren Ansätzen zur Bedeutung und zum Schlußfolgern mit kausalen Konditionalsätzen in ein gemeinsames Modell integriert. In dem Modell reduziert die kognitive Verfügbarkeit von Situationen, die zu Ausnahmen von der Regel im Konditionalsatz führen können (z.B. der Strom ist abgestellt), die subjektive bedingte Wahrscheinlichkeit des Konsequens gegeben das Antezedens (z.B. die Wahrscheinlichkeit, einen Schlag zu bekommen gegeben, dass man das Kabel berührt). Diese subjektive bedingte Wahrscheinlichkeit bestimmt die Glaubwürdigkeit des Konditionals, die wiederum die Bereitwilligkeit beeinflußt, mit der gültige Schlußfolgerungen (z.B. &quot;Peter berührt das Kabel, daher erhält er einen elektrischen Schlag&quot;) in einer Schlußfolgerungsaufgabe akzeptiert werden. Zusätzlich zu dem gerade beschriebenen indirekten Pfad enthält das integrierte Modell zusätzlich einen direkten Pfad: Die kognitive Verfügbarkeit von Ausnahme-Situationen reduziert unmittelbar die Bereitschaft, gültige Schlüsse zu akzeptieren. Die erste Experimentalreihe testete das entwickelte integrierte Model für Konditionalsätze, die in pseudo-natürliche Kontextgeschichten eingebettet wurden. Die Kontextgeschichten stellten entweder eine kausale Relation zwischen Antezedens und Konsequens her (kausale Konditionale) oder stellten die Propositionen in keinerlei sinnvollen Zusammenhang (arbiträre Konditionale). Die empirischen Daten stützen das Modell für die kausalen, aber nicht für die arbiträren Konditionale. Außerdem schätzten die TeilnehmerInnen die arbiträren Konditionalsätzen als weniger glaubwürdig ein als die kausalen Konditionale. Es stellt sich die Frage, ob dieser Unterschied in der Glaubwürdigkeit auf die An- bzw. Abwesenheit einer bedeutungshaltigen Relation zwischen Antezedens und Konsequens im Konditional zurückgeführt werden kann.<br> Diese Frage war einer der Ausgangspunkte für die zweite Experimentalreihe. In dieser wurde die Glaubwürdigkeit der Konditionalsätze kontrolliert manipuliert, indem in den Kontextgeschichten explizite Häufigkeitsinformationen über die vier möglichen Kombinationen von An- bzw. Abwesenheit von Antezedens und Konsequens gegeben wurden (d.h. die Häufigkeit von Fällen mit 1. wahrem Antezedens und wahrem Konsequens, 2. wahrem Antezedens und falschem Konsequens, 3. falschem Antezedens und wahrem Konsequens, 4. falschem Antezedens und falschem Konsequens). Dieses Paradigma ermöglichte ferner, unterschiedliche Ansätze zur Bedeutung des Konditionals (Experiment 4) ebenso wie Theorien zum konditionalen Schlußfolgern gegeneinander zu testen (Experiment 5). Die Befunde aus Experiment 4 stützen im wesentlichen Edgingtons Ansatz zur Bedeutung von Konditionalen (Edgington, 1995). Nach diesem Ansatz entspricht die Glaubwürdigkeit, die ein Zuhörer einem Konditionalsatz einräumt, der bedingten Wahrscheinlichkeit, dass das Konsequens wahr ist gegeben das Antezedens (z.B. die Wahrscheinlichkeit, einen Schlag zu bekommen gegeben, dass man das Kabel berührt). Erneut schrieben die TeilnehmerInnen den arbiträren Konditionalsätzen geringere Glaubwürdigkeit zu als den kausalen Sätzen, obwohl diesmal innerhalb jeder Häufigkeitsbedingung die bedingte Wahrscheinlichkeit des Konsequens gegeben das Antezedens konstant gehalten wurde. Dieses Ergebnis stützt die Hypothese, dass die bloße Anwesenheit einer kausalen Beziehung die Glaubwürdigkeit eines Konditionalsatzes erhöht. In Experiment 5 lösten die TeilnehmerInnen konditionale Schlußfolgerungsaufgaben, dabei wurden die Kontextgeschichten um explizite Häufigkeitsangaben ergänzt. Die Ergebnisse favorisieren die probabilistische Theorie zum konditionalen Schließen, die von Oaksford, Chater, und Larkin (2000) vorgeschlagen wurde. Beide Experimentalreihen lieferten deutliche Evidenz für probabilistische Theorien: für Edgingtons Ansatz zur Bedeutung von Konditionalsätzen (Edgington, 1995) und für die probabilistische Theorie des konditionalen Schließens von Oaksford et al. (2000). Im Bereich des konditionalen Schließens stützen die Daten gleichzeitig die modifizierten mentalen Modell-Theorien von Markovits und Barrouillet (2002) und Schroyens und Schaeken (2003). Probabilistische und mentale Modell-Theorien könnten im Rahmen eines Dualen-Prozeß-Modells wie es von Verschueren, Schaeken und d&#39;Ydewalle (2003) vorgeschlagen wurde, miteinander versöhnt werden.

A key issue in neuroscience is to determine the connection between neuronal circuits and behaviour. In the adult brain, all neuronal circuits include a glutamatergic component. Three proteins designated Vesicular glutamate transporter 1-3 (VGLUT1-3) possess the capability of packaging glutamate into presynaptic vesicles for release of glutamate at the nerve terminal. The present study aimed at determining the role of VGLUT2 in neuronal circuits of higher brain function, emotion, and reward-pocessing. A conditional knockout (cKO) strategy was utilised, and three different mouse lines were produced to delete VGLUT2 in specific neuronal circuits in a temporally and spatially controlled manner. First, we produced a cKO mouse in which Vglut2 was deleted in specific subpopulations of the cortex, amygdala and hippocampus from preadolescence. This resulted in blunted aspects in cognitive, emotional and social behaviour in a schizophrenia-related phenotype. Furthermore, we showed a downstream effect of the targeted deletion on the dopaminergic system. In a subsequent analysis of the same cKO mice, we showed that female cKO mice were more affected their male counterparts, and we also found that female schizophrenia patients, but not male patients, had increased Vglut2 levels in the cortex.  Second, we produced and analysed cKO mice in which Vglut2 was deleted in the cortex, amygdala and hippocampus already from midgestation, and could show that this deletion affected emotional, but not cognitive, function. Third, we addressed the role of VGLUT2 in midbrain dopamine neurons by targeting Vglut2 specifically in these neurons. These cKO mice showed a blunted activational response to the psychostimulant amphetamine and increased operant self-administration of both sugar and cocaine reinforcers. Further, the cKO mice displayed strongly enhanced cocaine-seeking in response to cocaine-associated cues, a behaviour of relevance for addiction in humans. In summary, this thesis work has addressed the role of the presynaptic glutamatergic neuron in different neuronal circuits and shown that the temporal and spatial distribution of VGLUT2 is of great significance for normal brain function.

In the field of cognitive science, as well as the area of Artificial Intelligence (AI), the role of context has been investigated in many forms, and for many purposes. It is clear in both areas that consideration of contextual information is important. However, the significance of context has not been emphasized in the Bayesian networks literature. We suggest that consideration of context is necessary for acquiring knowledge about a situation and for refining current representational models that are potentially erroneous due to hidden independencies in the data.<p>In this thesis, we make several contributions towards the automation of contextual consideration by discovering useful contexts from probability distributions. We show how context-specific independencies in Bayesian networks and discovery algorithms, traditionally used for efficient probabilistic inference can contribute to the identification of contexts, and in turn can provide insight on otherwise puzzling situations. Also, consideration of context can help clarify otherwise counter intuitive puzzles, such as those that result in instances of Simpson's paradox. In the social sciences, the branch of attribution theory is context-sensitive. We suggest a method to distinguish between <i>dispositional causes</i> and <i>situational factors</i> by means of contextual models. Finally, we address the work of Cheng and Novick dealing with causal attribution by human adults. Their <i>probabilistic contrast model</i> makes use of contextual information, called focal sets, that must be determined by a human expert. We suggest a method for discovering complete <i>focal sets</i> from probabilistic distributions, without the human expert.

The main purpose of this thesis is to assess the potential of emerging stock markets and commodity markets in attracting the attention of international investors who utilize various portfolio diversification strategies to reduce the cumulative risk of their portfolio. A successful portfolio diversification strategy requires low correlation among financial markets. However, it is now well documented that the correlations among financial markets in developed countries are very high and hence the benefits of international portfolio diversification among these markets have been very limited. This fact suggests that investors should look for alternative markets whose correlations with developed markets are low (or even negative if possible) and which have high growth potentials. In this thesis, two emerging countries&#039 / stock markets and two commodity markets are considered as alternative markets. Among emerging countries, Turkey and China are chosen due to their promising growth performance since the mid-2000s. As commodity markets, agricultural commodity and precious metal markets are selected because of the outstanding performance of the former and the &quot / safe harbor&quot / property of the latter. The structures and properties of dependence between these markets and stock markets in developed countries are examined by modeling the conditional correlation in the dynamic conditional correlation framework. The results reveal that upward trend hypothesis is valid for almost all correlations among market pairs and market volatility plays significant role in time varying structures of correlations.

Rational expectations provide people or economic agents making future decision with available information and past experiences. The first approach to the idea of rational expectations was given approximately fifty years ago by John F. Muth. Many models in economics have been studied using the rational expectations idea. The most familiar one among them is the rational expectations version of the Cagans hyperination model where the expectation for tomorrow is formed using all the information available today. This model was reinterpreted by Thomas J. Sargent and Neil Wallace in 1973. After that time, many solution techniques were suggested to solve the Cagan type rational expectations (CTRE) model. Some economists such as Muth [13], Taylor [26] and Shiller [27] consider the solutions admitting an infinite moving-average representation. Blanchard and Kahn [28] find solutions by using a recursive procedure. A general characterization of the solution was obtained using the martingale approach by Broze, Gourieroux and Szafarz in [22], [23]. We choose to study martingale solution of CTRE model. This thesis is comprised of five chapters where the main aim is to study the CTRE model on isolated time scales. Most of the models studied in economics are continuous or discrete. Discrete models are more preferable by economists since they give more meaningful and accurate results. Discrete models only contain uniform time domains. Time scale calculus enables us to study on m-periodic time domains as well as non periodic time domains. In the first chapter, we give basics of time scales calculus and stochastic calculus. The second chapter is the brief introduction to rational expectations and the CTRE model. Moreover, many other solution techniques are examined in this chapter. After we introduce the necessary background, in the third chapter we construct the CTRE Model on isolated time scales. Then we give the general solution of this model in terms of martingales. We continue our work with defining the linear system and higher order CTRE on isolated time scales. We use Putzer Algorithm to solve the system of the CTRE Model. Then, we examine the existence and uniqueness of the solution of the CTRE model. In the fourth chapter, we apply our solution algorithm developed in the previous chapter to models in Finance and stochastic growth models in Economics.

Modern day cars are often equipped with a variety of sensors that collect information about the car and its surroundings. The stereo camera is an example of a sensor that in addition to regular images also provides distances to points in its environment. This information can, for example, be used for detecting approaching obstacles and warn the driver if a collision is imminent or even automatically brake the vehicle. Objects that constitute a potential danger are usually located on the road in front of the vehicle which makes the road surface a suitable reference level from which to measure the object's heights. This Master's thesis describes how an estimate of the road surface can be found to in order to make these height measurements. The thesis describes how the large amount of data generated by the stereo camera can be scaled down to a more effective representation in the form of an elevation map. The report discusses a method for relating data from different instances in time using information from the vehicle's motion sensors and shows how this method can be used for temporal filtering of the elevation map. For estimating the road surface two different methods are compared, one that uses a RANSAC-approach to iterate for a good surface model fit and one that uses conditional random fields for modeling the probability of different parts of the elevation map to be part of the road. A way to detect curb lines and how to use them to improve the road surface estimate is shown. Both methods for road classification show good results with a few differences that are discussed towards the end of the report. An example of how the road surface estimate can be used to detect obstacles is also included.

The impact of the introduction of derivatives on the underlying stock is a debatable topic among the researchers. The issue is quite controversial as contradictory results have been obtained by researchers in various stock markets. The purpose of this study is to examine the volatility and the liquidity effect on the underlying stock after the introduction of index options. We have investigated volatility and liquidity effect by collecting sample data from the stock markets of India, Korea, Taiwan, Hong Kong, Japan, Thailand, Malaysia and Singapore, only markets which are offering index options in Asia.   Applying the generalized autoregressive conditional heteroscedasticity (GARCH) model, we have examined the conditional volatility of intraday (high frequency) returns for each stock market, before and after the introduction of index options. We have also examined the liquidity effect through t-test and Wilcoxon Signed Rank Test. We used t-test to determine the mean differences between the trading volume of pre-index and post-index options periods.    By comparing the estimated parameters and the coefficient of conditional volatility in pre and post period of index options introductions, we have examined that the derivatives trading dramatically increases the persistence of the conditional volatility for all the selected stock markets. We also observed mixed evidence in context to liquidity effect. In the stock exchanges of Hong Kong, Japan, Korea, Taiwan and Thailand, we found that the respective markets become more liquid in the post index options periods in contrast to pre index options period. In these markets trading volume increased significantly after the introduction of index options.  On the other hand, India, Malaysia and Singapore stock markets show no liquidity effect in the post-index option period.   Finally, the empirical results of our study conclude that the introduction of index options on the selected Asian stock markets have increased in stock return volatility and liquidity on the underlying stocks.