<p>Hundreds of different factors adorn the eukaryotic genome, binding to it in large number. These DNA binding factors (DBFs) include nucleosomes, transcription factors (TFs), and other proteins and protein complexes, such as the origin recognition complex (ORC). DBFs compete with one another for binding along the genome, yet many current models of genome binding do not consider different types of DBFs together simultaneously. Additionally, binding is a stochastic process that results in a continuum of binding probabilities at any position along the genome, but many current models tend to consider positions as being either binding sites or not.</p><p>Here, we present a model that allows a multitude of DBFs, each at different concentrations, to compete with one another for binding sites along the genome. The result is an 'occupancy profile', a probabilistic description of the DNA occupancy of each factor at each position. We implement our model efficiently as the software package COMPETE. We demonstrate genome-wide and at specific loci how modeling nucleosome binding alters TF binding, and vice versa, and illustrate how factor concentration influences binding occupancy. Binding cooperativity between nearby TFs arises implicitly via mutual competition with nucleosomes. Our method applies not only to TFs, but also recapitulates known occupancy profiles of a well-studied replication origin with and without ORC binding.</p><p>We then develop a statistical framework for tuning our model concentrations to further improve its predictions. Importantly, this tuning optimizes with respect to actual biological data. We take steps to ensure that our tuned parameters are biologically plausible.</p><p>Finally, we discuss novel extensions and applications of our model, suggesting next steps in its development and deployment.</p> / Dissertation

This thesis investigates the recognition rate improvement strategies for a Japanese speech recognition system. Both training data development and consonant correction scheme are studied. For training data development, a database of 995 two-syllable Japanese words is established by phonetic balanced sieving. Furthermore, feature models for the 188 common Japanese mono-syllables are derived through mixed position training scheme to increase recognition rate. For consonant correction, a sub-syllable model is developed to enhance the consonant recognition accuracy, and hence further improve the overall correct rate for the whole Japanese phrases. Experimental results indicate that the average correct rate for Japanese phrase recognition system with 34 thousand phrases can be improved from 86.91% to 92.38%.

Stock market plays an important role in the modern capital market. As a result, the prediction of financial assets attracts people in different areas. Moreover, it is commonly accepted that stock price movement generally follows a major trend. As a result, forecasting the market trend becomes an important mission for a prediction method. Accordingly, we will predict the long term trend rather than the movement of near future or change in a trading day as the target of our predicting approach. Although there are various kinds of analyses for trend prediction, most of them use clear cuts or certain thresholds to classify the trends. Users (or investors) are not informed with the degrees of confidence associated with the recommendation or the trading signal. Therefore, in this research, we would like to study an approach that could offer the confidence of the trend analysis by providing the probabilities of each possible state given its historical data through Dynamic Bayesian Network. We will incorporate the well-known principles of Dow¡¦s Theory to better model the trend of stock movements. Through the results of our experiment, we may say that the financial performance of the proposed model is able to defeat the buy and hold trading strategy when the time scope covers the entire cycle of a trend. It also means that for the long term investors, our approach has high potential to win the excess return. At the same time, the trading frequency and correspondently trading costs can be reduced significantly.

A Mandarin phrase recognition system based on MFCC, LPC scaled excitation, vowel model, hidden Markov model (HMM) and Viterbi algorithm is proposed in this thesis. HMM, which is broadly used in speech recognition at present, is adopted in the main structure of recognition. In order to speed up the recognition time, we take advantage of stability of vowels in Mandarin and incorporate with vowel class recognition in our system. For the speaker-dependent case, a single Mandarin phrase recognition can be accomplished within 1 seconds on average in the laboratory environment.

Advanced process control solutions are oftentimes inadequate in their handling of uncertainty and disturbances. The main contribution of this work is to address this issue by providing solutions of immediate relevance to process control practitioners. To meet increasing performance demands, this work considers a Hidden Markov Model-based framework for describing non-stationary disturbance signals of practical interest such as intermittent drifts and abrupt jumps. The result is a more sophisticated model used by the state estimator for jump systems. At the expense of slightly higher computational costs (due to the state estimator), the proposed HMM disturbance model provides better tracking compared to a state estimator based on the commonly employed (in process control) integrated white noise disturbance model. Better tracking performance translates to superior closed loop performance without any redesign of the controller, through the typical assumption of separation and certainty equivalence. As a result, this provides a tool that can be readily adopted by process control practitioners. In line with this, the second aim is to develop approximate dynamic programming techniques for the rigorous control of nonlinear stochastic jump systems. The contribution is the creation of a framework that treats uncertainty in a systematic manner whilst leveraging existing off-the-shelf optimization solvers commonly employed by control practitioners.

<p>Avsikten med denna studie var att undersöka vilka spår av teknikundervisning somfanns i grundskolans år ett till tre. Våra upplevelser var att lokala arbetsplaner ochmedvetet arbete inom teknikämnet saknades i år ett till tre, vilket vi ansåg försvåraelevernas chanser att uppnå målen för teknikämnet i slutet av år fem. Trots upplevelsenav att teknikämnet försummas, har vi ändå sett att lärare undervisar i teknik i år ett tilltre, dock omedvetet. Syftet blev därmed att studera teknikämnets förekomst och karaktärmer systematiskt och genom observation och analys kritiskt granska hur teknikämnetkom till uttryck i den vanliga undervisningen. Detta analyserades utifrån en kvalitativmetod med deltagande observationer och berättande observationsanteckningar sominsamlingsdata. Datainsamlingen genomfördes på två olika skolor och baseras påtjugoen lektionsobservationer.</p><p>Resultatet visar att lärare i år ett till tre skapar möjligheter för eleverna att tillägna sigtekniska kunskaper och färdigheter, dock omedvetet, vilket vi benämner som doldteknikundervisning. Lärares omedvetna teknikundervisning resulterar i att elever i år etttill tre, genom handling eller resonemang, kan knyta an till kursplanemålen förteknikämnet i år fem. Det framkommer dock att lärarna inte alltid tar tillvara påelevernas initiativ, vilket genererar i att tillfällen att lyfta tekniken går förlorade.</p><p>Vår uppfattning är att lärare är omedvetna om att delar i deras vanliga undervisningtillhör teknikämnet. Vi har sett dold teknikundervisning och tror att den förkommer istor utsträckning, därmed finns det dold måluppfyllelse som inte heller blir synlig förlärarna vid bedömning av kursplanemålen.</p> / <p>The purpose of this study, through our own experience of the course outline andconsciously work on the subject of technology, is that it is inadequate through years oneto three. This, we felt prevent students' chances of achieving the objectives in thesubject technology at the end of year five. Despite our experience of the subjecttechnology’s negligence, we have still seen that teachers teach technology in year one tothree, but unconsciously. With this background, we became curious about the traces oftechnology education that do exist in primary school’s years one to three. The aim wasto study technology’s prevalence and nature of a more systematic and thorough analysisto critically examine how the subject of technology was reflected in mainstreameducation. The method of study is qualitative in nature, involving observations andnarrative field notes as data collection. The data collection was carried out at twodifferent schools and is based on twenty-one lesson observations.</p><p>The results show that teachers' unconsciously technology teachings actually succeed inthe students in year one to three, by act or reasoning may relate to curriculum objectivesfor the subject in year five. The results found that teachers in years one to three createopportunities for students to acquire technical knowledge and skills, however,unconsciously, which we refer to as hidden technology education. We have also seenthat the teachers do not always take advantage of their student’s initiative, which resultsin lost opportunities to further develop the subject.</p><p>Our view remains that teachers are unaware that part of their regular teachings actuallybelongs to the technology topic. We have seen hidden technology teaching and believethat it is lost to a large extent; therefore, there is hidden effectiveness that does notbecome visible to the teachers.</p>

<p>In this master's thesis, hidden Markov models (HMM) are evaluated as a tool for forecasting movements in a currency cross. With an ever increasing electronic market, making way for more automated trading, or so called algorithmic trading, there is constantly a need for new trading strategies trying to find alpha, the excess return, in the market.</p><p>HMMs are based on the well-known theories of Markov chains, but where the states are assumed hidden, governing some observable output. HMMs have mainly been used for speech recognition and communication systems, but have lately also been utilized on financial time series with encouraging results. Both discrete and continuous versions of the model will be tested, as well as single- and multivariate input data.</p><p>In addition to the basic framework, two extensions are implemented in the belief that they will further improve the prediction capabilities of the HMM. The first is a Gaussian mixture model (GMM), where one for each state assign a set of single Gaussians that are weighted together to replicate the density function of the stochastic process. This opens up for modeling non-normal distributions, which is often assumed for foreign exchange data. The second is an exponentially weighted expectation maximization (EWEM) algorithm, which takes time attenuation in consideration when re-estimating the parameters of the model. This allows for keeping old trends in mind while more recent patterns at the same time are given more attention.</p><p>Empirical results shows that the HMM using continuous emission probabilities can, for some model settings, generate acceptable returns with Sharpe ratios well over one, whilst the discrete in general performs poorly. The GMM therefore seems to be an highly needed complement to the HMM for functionality. The EWEM however does not improve results as one might have expected. Our general impression is that the predictor using HMMs that we have developed and tested is too unstable to be taken in as a trading tool on foreign exchange data, with too many factors influencing the results. More research and development is called for.</p>

In this dissertation work, a methodology is proposed to enable a robot to identify an object to be grasped and its intended grasp configuration while a human is teleoperating a robot towards the desired object. Based on the detected object and grasp configuration, the human is assisted in the teleoperation task. The environment is unstructured and consists of a number of objects, each with various possible grasp configurations. The identification of the object and the grasp configuration is carried out in real time, by recognizing the intention of the human motion. Simultaneously, the human user is assisted to preshape over the desired grasp configuration. This is done by scaling the components of the remote arm end-effector motion that lead to the desired grasp configuration and simultaneously attenuating the components that are in perpendicular directions. The complete process occurs while manipulating the master device and without having to interact with another interface. Intention recognition from motion is carried out by using Hidden Markov Model (HMM) theory. First, the objects are classified based on their shapes. Then, the grasp configurations are preselected for each object class. The selection of grasp configurations is based on the human knowledge of robust grasps for the various shapes. Next, an HMM for each object class is trained by having a skilled teleoperator perform repeated preshape trials over each grasp configuration of the object class in consideration. The grasp configurations are modeled as the states of each HMM whereas the projections of translation and orientation vectors, over each reference vector, are modeled as observations. The reference vectors are the ideal translation and rotation trajectories that lead the remote arm end-effector towards a grasp configuration. During an actual grasping task performed by a novice or a skilled user, the trained model is used to detect their intention. The output probability of the HMM associated with each object in the environment is computed as the user is teleoperating towards the desired object. The object that is associated with the HMM which has the highest output probability, is taken as the desired object. The most likely Viterbi state sequence of the selected HMM gives the desired grasp configuration. Since an HMM is associated with every object, objects can be shuffled around, added or removed from the environment without the need to retrain the models. In other words, the HMM for each object class needs to be trained only once by a skilled teleoperator. The intention recognition algorithm was validated by having novice users, as well as the skilled teleoperator, grasp objects with different grasp configurations from a dishwasher rack. Each object had various possible grasp configurations. The proposed algorithm was able to successfully detect the operator's intention and identify the object and the grasp configuration of interest. This methodology of grasping was also compared with unassisted mode and maximum-projection mode. In the unassisted mode, the operator teleoperated the arm without any assistance or intention recognition. In the maximum-projection mode, the maximum projection of the motion vectors was used to determine the intended object and the grasp configuration of interest. Six healthy and one wheelchair-bound individuals, each executed twelve pick-and-place trials in intention-based assisted mode and unassisted mode. In these trials, they picked up utensils from the dishwasher and laid them on a table located next to it. The relative positions and orientations of the utensils were changed at the end of every third trial. It was observed that the subjects were able to pick-and-place the objects 51% faster and with less number of movements, using the proposed method compared to the unassisted method. They found it much easier to execute the task using the proposed method and experienced less mental and overall workloads. Two able-bodied subjects also executed three preshape trials over three objects in intention-based assisted and maximum projection mode. For one of the subjects, the objects were shuffled at the end of the six trials and she was asked to carry out three more preshape trials in the two modes. This time, however, the subject was made to change their intention when she was about to preshape to the grasp configurations. It was observed that intention recognition was consistently accurate through the trajectory in the intention-based assisted method except at a few points. However, in the maximum-projection method the intention recognition was consistently inaccurate and fluctuated. This often caused to subject to be assisted in the wring directions and led to extreme frustration. The intention-based assisted method was faster and had less hand movements. The accuracy of the intention based method did not change when the objects were shuffled. It was also shown that the model for intention recognition can be trained by a skilled teleoperator and be used by a novice user to efficiently execute a grasping task in teleoperation.

This thesis looks at the introduction of educational action research as part of the national education reforms in Lao PDR. National policies on education emphasise concepts such as ‘education for all’ and ‘student-centred education’ taken from the globalised education reform agenda. Action research became a tool to implement the new pedagogy of student-centred education that was labelled ‘the five-pointed star’. The thesis contributes to the field of global policy studies. It combines global and contextual aspects in order to analyse how action research travelled from policy to practice. This process was part of a Lao national education reform that developed after the introduction of the new economic mechanism, when the previous socialist planned-economy system was replaced by a globalised market-oriented system. Data were collected from national policy documents, international donor documents, instructional material, and interviews with Lao educators involved with action research in different ways. Furthermore, we carried out action research as part of our own teaching duties in Lao PDR, which were subsequently documented and analysed. In this study of educational reform in Lao PDR we have found that an educational approach like action research that is introduced as part of a taken-for-granted global agenda of change, is reduced to a technical rationality and practices that resemble previous experiences. Our findings are explained from the theoretical perspectives of hidden policy ensembles and policy backlashes. Hidden policy ensembles reduce action research to a technical rationality due to their alien cultural and social connections that are not brought into the open at the reform arena. Policy backlashes become a way for practitioners to create meaning based on previous contextual practices, conceptions, and discourses as a consequence of the technical rationality created by the hidden policy ensembles and the use of the cascade model. The thesis concludes with an outline of a possible future educational development in the form of a critical and educative action research network in Lao PDR that is inspired by cross-cultural dialogue, a critical pedagogy of place, and our own action research experiences.

In service-oriented environments, services are put together in the form of a workflow with the aim of distributed problem solving. Capturing the execution details of the services' transformations is a significant advantage of using workflows. These execution details, referred to as provenance information, are usually traced automatically and stored in provenance stores. Provenance data contains the data recorded by a workflow engine during a workflow execution. It identifies what data is passed between services, which services are involved, and how results are eventually generated for particular sets of input values. Provenance information is of great importance and has found its way through areas in computer science such as: Bioinformatics, database, social, sensor networks, etc. Current exploitation and application of provenance data is very limited as provenance systems started being developed for specific applications. Thus, applying learning and knowledge discovery methods to provenance data can provide rich and useful information on workflows and services. Therefore, in this work, the challenges with workflows and services are studied to discover the possibilities and benefits of providing solutions by using provenance data. A multifunctional architecture is presented which addresses the workflow and service issues by exploiting provenance data. These challenges include workflow composition, abstract workflow selection, refinement, evaluation, and graph model extraction. The specific contribution of the proposed architecture is its novelty in providing a basis for taking advantage of the previous execution details of services and workflows along with artificial intelligence and knowledge management techniques to resolve the major challenges regarding workflows. The presented architecture is application-independent and could be deployed in any area. The requirements for such an architecture along with its building components are discussed. Furthermore, the responsibility of the components, related works and the implementation details of the architecture along with each component are presented.