The thesis argues for the inclusion of the study of religion within the public school curriculum. It argues that the whole division between “religious” and “secular” spaces and institutions is itself rooted in a specific religious tradition. Using the theories of Jacques Derrida, I argue that, unless the present process of globalization is tempered with alternative models of organizing that don’t include this secular/sacred division, the very process of Western globalization acts as a moral religion. Derrida calls this process “globalatinization,” the imposition of Western defined institutions upon other cultures. The process creates a type of religious violence through act of imposing notions of “secular/public” and “sacred/private.” Drawing from Mark Juergensmeyer’s theory of religious violence, and Derrida’s and Foucault’s understanding of discursive formations, I argue that religious studies should enter this “secular/public” space in the form of educating about the world’s religions. Such education would go a long way in preventing the demonization of the “other” through promoting empathy, understanding, and respect for “other” traditions. Finally, education would provide a needed self-critique of the dividing of “secular/sacred” in contemporary Western life.

More than a decade after becoming a topic of intense research there is no transactional memory hardware nor any examples of software transactional memory use outside the research community. Using software transactional memory in large pieces of software needs copious source code annotations and often means that standard compilers and debuggers can no longer be used. At the same time, overheads associated with software transactional memory fail to motivate programmers to expend the needed effort to use software transactional memory. The only way around the overheads in the case of general unmanaged code is the anticipated availability of hardware support. On the other hand, architects are unwilling to devote power and area budgets in mainstream microprocessors to hardware transactional memory, pointing to transactional memory being a 'niche' programming construct. A deadlock has thus ensued that is blocking transactional memory use and experimentation in the mainstream. This dissertation covers the design and construction of a software transactional memory runtime system called SLE_x86 that can potentially break this deadlock by decoupling transactional memory from programs using it. Unlike most other STM designs, the core design principle is transparency rather than performance. SLE_x86 operates at the level of x86 machine code, thereby becoming immediately applicable to binaries for the popular x86 architecture. The only requirement is that the binary synchronise using known locking constructs or calls such as those in Pthreads or OpenMPlibraries. SLE_x86 provides speculative lock elision (SLE) entirely in software, executing critical sections in the binary using transactional memory. Optionally, the critical sections can also be executed without using transactions by acquiring the protecting lock. The dissertation makes a careful analysis of the impact on performance due to the demands of the x86 memory consistency model and the need to transparently instrument x86 machine code. It shows that both of these problems can be overcome to reach a reasonable level of performance, where transparent software transactional memory can perform better than a lock. SLE_x86 can ensure that programs are ready for transactional memory in any form, without being explicitly written for it.

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Understanding the fundamental mechanisms of solidification and the relative significance of different parameters governing these mechanisms is of vital importance for controlling the evolution of microstructure during solidification, and consequently, for improving the efficacy of a casting process. Towards achieving this goal, the present work attempts to study the effect of convection and shrinkage on solidification and microstructure formation primarily through the development of computational models which are complemented with experimental investigations and analytical solutions. Convection strongly influences the solutal and thermal distribution adjacent to the solidification interface and affects the growth rate and morphology of dendrites. To investigate this, a numerical model based on the enthalpy method is developed for binary alloy dendrite growth in presence of convection. The model results are validated with corresponding predictions using level-set method and micro-solvability theory. Subsequently, the model is applied for studying the effect of convection on the growth morphology of single dendrites. Results show that the presence of flow significantly affects the thermo-solutal distribution and consequently the growth rate and morphology of dendrites. Parametric studies performed using the model predict that thermal and solutal Peclet number and melt undercooling strongly influence the tip velocity of dendrites. Additionally, an analytical model is developed to quantify the effect of convection on dendrite tip velocity through the definition of an equivalent undercooling. An expression for this equivalent undercooling is derived in terms of the flow Nusselt and Sherwood numbers and the analytical equivalent undercooling values are compared with corresponding predictions obtained using the numerical model. Subsequently, the interaction of multiple dendrites growing in close proximity is studied. It is observed that the presence of neighbouring dendrites strongly influences the thermo-solutal distribution in the domain leading to significant changes in growth pattern. The effect of seed density on the growth morphology is investigated and it is observed that a higher initial seeding density leads to more spherical dendritic structure. Comparison with results from chilled casting of Al-6.5% Cu alloy with and without grain refiners show qualitative similarity in both the cases. The next part of the thesis presents a eutectic solidification model developed using the general enthalpy-based framework for dendritic solidification. New parameters and rules are defined and suitable modifications are made to incorporate the physics of eutectic solidification and account for the additional complexities arising due to the presence of multiple solid phases. The model simulates the presence of buoyancy driven convection and its interaction with the solidification process. i The model predictions are found to be in good agreement with the Jackson-Hunt theory. At first, the model is applied to simulate regular eutectic growth in a purely diffusive environment and it is observed that the model predicts the variation in interface profile with change in lamella width similar to those observed in experimental studies on eutectic solidification. Subsequently, a few case studies are performed to demonstrate the ability of the model in handling complex scenarios of eutectic growth such as width selection, lamella division and presence of solutal buoyancy. It is observed that solutal buoyancy gives rise to flow cells ahead of the eutectic interface facilitating the transfer of solute between the two phases. Apart from forced and natural convection, another important factor affecting solidification is the presence of shrinkage. Currently, solidification shrinkage is mostly modelled using empirical relations and criteria functions. In the present work, a phenomenological model for shrinkage driven convection is developed by incorporating the mechanism of solidification shrinkage in an existing framework of enthalpy based macro-scale solidification model. The effect of shrinkage flow on the free surface deformation is accounted for by using the volume-of-fluid method. The results predicted by the model are found to be in excellent agreement with analytical solutions for one-dimensional solidification with unequal phase densities. A set of controlled experiments are designed and executed for validating the numerical model. The experiments involve in-situ X-ray imaging of casting of pure aluminium in a rectangular cavity. The numerical predictions for solidification rate, free surface movement and temperature profiles are compared with corresponding experimental results obtained from the in-situ X-ray images and thermocouple data. Subsequent case studies, performed using the model, show significant influence of applied heat flux and mould geometry on the formation of shrinkage cavities. The shrinkage flow model provides the foundation for development of a generalized model to accurately predict the formation and morphology of internal porosity. The validated macro-scale shrinkage model is extended to the microscopic scale to study the influence of shrinkage flow on the growth rate of dendrites. Results demonstrate that shrinkage driven convection towards the dendrite strongly influences the solutal and thermal distribution adjacent to the solidification interface and consequently decreases the growth rate of the dendrite. Additionally, an analytical model is developed to quantify the effect of shrinkage driven convection through the definition of an equivalent undercooling for shrinkage flow. The present models provide significant physical insight into various mechanisms governing the process of solidification. Moreover, due to their similar framework, the individual models have the potential to be an effective foundation for the development of a generalized multi-scale solidification model incorporating the presence of important phenomena such as shrinkage and convection.

In the industry, a lot of companies are facing the explosion of big data. With this much information stored, companies want to make sense of the data and use it to help them for better decision making, especially for future prediction. A lot of money can be saved and huge revenue can be generated with the power of big data. When building statistical learning models for prediction, companies in the industry are aiming to build models with efficiency and high accuracy. After the learning models have been developed for production, new data will be generated. With the updated data, the models have to be updated as well. Due to this nature, the model performs best today doesn’t mean it will necessarily perform the same tomorrow. Thus, it is very hard to decide which algorithm should be used to build the learning model. This paper introduces a new method that ensembles the information generated by two different classification statistical learning algorithms together as inputs for another learning model to increase the final prediction power. The dataset used in this paper is NASA’s Turbofan Engine Degradation data. There are 49 numeric features (X) and the response Y is binary with 0 indicating the engine is working properly and 1 indicating engine failure. The model’s purpose is to predict whether the engine is going to pass or fail. The dataset is divided in training set and testing set. First, training set is used twice to build support vector machine (SVM) and neural network models. Second, it used the trained SVM and neural network model taking X of the training set as input to predict Y1 and Y2. Then, it takes Y1 and Y2 as inputs to build the Penalized Logistic Regression model, which is the ensemble model here. Finally, use the testing set follow the same steps to get the final prediction result. The model accuracy is calculated using overall classification accuracy. The result shows that the ensemble model has 92% accuracy. The prediction accuracies of SVM, neural network and ensemble models are compared to prove that the ensemble model successfully captured the power of the two individual learning model.

Wireless Sensor Networks (WSNs) have enticed a strong attention in the research community due to the broad range of applications and services they support. WSNs are composed of intelligent sensor nodes that have the capabilities to monitor different types of environmental phenomena or critical activities. Sensor nodes operate under stringent requirements of scarce power resources, limited storage capacities, limited processing capabilities, and hostile environmental surroundings. However, conserving sensor nodes’ power resources is the top priority requirement in the design of a WSN as it has a direct impact on its lifetime. The IEEE 802.15.4 standard defines a set of specifications for both the PHY layer and the MAC sub-layer that abide by the distinguished requirements of WSNs. The standard’s MAC protocol employs an intelligent backoff algorithm, called the Binary Exponent Backoff (BEB), that minimizes the drainage of power in these networks. In this thesis we present an in-depth study of the IEEE 802.15.4 MAC protocol to highlight both its strong and weak aspects. We show that we have enticing opportunities to improve the performance of this protocol in the context of WSNs. We propose three new backoff algorithms, namely, the Standby-BEB (SB-BEB), the Adaptive Backoff Algorithm (ABA), and the Priority-Based BEB (PB-BEB), to replace the standard BEB. The main contribution of the thesis is that it develops a new design concept that drives the design of efficient backoff algorithms for the IEEE 802.15.4-based WSNs. The concept dictates that controlling the algorithms parameters probabilistically has a direct impact on enhancing the backoff algorithm’s performance. We provide detailed discrete-time Markov-based models (for AB-BEB and ABA) and extensive simulation studies (for the three algorithms) to prove the superiority of our new algorithms over the standard BEB.

In this thesis, contributions are presented in the areas of shape parsing for view-based object recognition and spatio-temporal feature description for action recognition. A probabilistic model for parsing shapes into several distinguishable parts for accurate shape recognition is presented. This approach is based on robust geometric features that permit high recognition accuracy. As the second contribution in this thesis, a binary spatio-temporal feature descriptor is presented. Recent work shows that binary spatial feature descriptors are effective for increasing the efficiency of object recognition, while retaining comparable performance to state of the art descriptors. An extension of these approaches to action recognition is presented, facilitating huge gains in efficiency due to the computational advantage of computing a bag-of-words representation with the Hamming distance. A scene's motion and appearance is encoded with a short binary string. Exploiting the binary makeup of this descriptor greatly increases the efficiency while retaining competitive recognition performance.

Recently, automatic demographic classification has found its way into embedded applications such as targeted advertising in mobile devices, and in-car warning systems for elderly drivers. In this thesis, we present a complete framework for video-based gender classification and age estimation which can perform accurately on embedded systems in real-time and under unconstrained conditions. We propose a segmental dimensionality reduction technique utilizing Enhanced Discriminant Analysis (EDA) to minimize the memory and computational requirements, and enable the implementation of these classifiers for resource-limited embedded systems which otherwise is not achievable using existing resource-intensive approaches. On a multi-resolution feature vector we have achieved up to 99.5% compression ratio for training data storage, and a maximum performance of 20 frames per second on an embedded Android platform. Also, we introduce several novel improvements such as face alignment using the nose, and an illumination normalization method for unconstrained environments using bilateral filtering. These improvements could help to suppress the textural noise, normalize the skin color, and rectify the face localization errors. A non-linear Support Vector Machine (SVM) classifier along with a discriminative demography-based classification strategy is exploited to improve both accuracy and performance of classification. We have performed several cross-database evaluations on different controlled and uncontrolled databases to assess the generalization capability of the classifiers. Our experiments demonstrated competitive accuracies compared to the resource-demanding state-of-the-art approaches.

Unlike traditional stock exchanges, where bonds, shares and financial derivatives are traded, on the betting exchanges there are traded probabilistic estimates of the results of sporting or social events. The market price of bets, namely the market implied probability is influenced by estimate of the outcome. The specificity of betting exchanges is also a short period to maturity of contracts, and the possibility to trade with the estimated result of one real world event in several sub-markets simultaneously. In theoretical analysis, we have defined the bet, the underlying asset, and the binary betting contract, which is traded on betting exchanges. We have described some practical aspects of trading. Properties of the probabilistic contracts are demonstrated on several examples. Finally, we constructed the mathematical model of a tennis match, which is based on a binomial valuation model. This allows us to compare the market price of a contract with the price recommended by the model.