mother / me is a visual exploration and analysis of the biological and constructed maternal realms of artist Gray Swartzel’s life. Orienting and navigating childhood influences, Swartzel explains his desire to use Craigslist to seek out surrogates, or mother figures. Interrogating his queer body within the psychological space between himself and his biological and surrogate mothers, he challenges and interrogates conceptions of the nuclear family, critiquing heteronormative assumptions of family. Swartzel tasks himself as an agent to inspect family as a social construct within a larger Lacanian orientation, while seeking out the objet petit a, or cause of desire in such relationships. He details the influences of early twentieth century glamour photography and maternal theory and outlines how they manifest in performances of the self. mother / me is an experiment to investigate the queer relationship between camp and the twenty-first century dandy through the collaboration of a mother and a child to construct visual images.

Modern nonlinear programming solvers can efficiently handle very large scale optimization problems when accurate derivative information is available. However, black box or derivative free modeling components are often unavoidable in practice when the modeled phenomena may cross length and time scales. This work is motivated by examples in chemical process optimization where most unit operations have well-known equation oriented representations, but some portion of the model (e.g. a complex reactor model) may only be available with an external function call. The concept of a surrogate model is frequently used to solve this type of problem. A surrogate model is an equation oriented approximation of the black box that allows traditional derivative based optimization to be applied directly. However, optimization tends to exploit approximation errors in the surrogate model leading to inaccurate solutions and repeated rebuilding of the surrogate model. Even if the surrogate model is perfectly accurate at the solution, this only guarantees that the original problem is feasible. Since optimality conditions require gradient information, a higher degree of accuracy is required. In this work, we consider the general problem of hybrid glass box/black box optimization, or gray box optimization, with focus on guaranteeing that a surrogate-based optimization strategy converges to optimal points of the original detailed model. We first propose an algorithm that combines ideas from SQP filter methods and derivative free trust region methods to solve this class of problems. The black box portion of the model is replaced by a sequence of surrogate models (i.e. surrogate models) in trust region subproblems. By carefully managing surrogate model construction, the algorithm is guaranteed to converge to true optimal solutions. Then, we discuss how this algorithm can be modified for effective application to practical problems. Performance is demonstrated on a test set of benchmarks as well as a set of case studies relating to chemical process optimization. In particular, application to the oxycombustion carbon capture power generation process leads to significant efficiency improvements. Finally, extensions of surrogate-based optimization to other contexts is explored through a case study with physical properties.

Surrogacy - abstract This master thesis deals with the phenomenon of surrogate motherhood (surrogacy) and analyses it from the point of view of Czech and British law, especially the Private law. Its main objective is to approximate to the answer to the question whether surrogacy should be entrenched in Czech law and, if so, what should the main features of such legal regulation be. In order to achieve this aim, it, firstly, focuses on the currently valid and effective Czech legislation. It is a well-known fact that, apart from the provision of § 804 of the Czech Civil Code, Czech law does not provide for surrogacy, at all. Surrogacy is, therefore, only partially regulated in some of its aspects, namely assisted reproduction, legal parenthood and adoption. Since the United Kingdom is often mentioned as one of the possible sources of inspiration for the future Czech legal regulation of surrogacy, British law concerning this phenomenon is considered in the following part of this thesis - especially in the light of the extensive case law. In particular, issues such as surrogacy arrangements, assisted reproduction, legal parenthood and transfer of legal parenthood (that is adoption and parental orders) are analysed. These are then subjected to critical assessment. In its last chapter, the thesis returns to the...

Surrogate motherhood is considered one of the most disputed and controversial method of assisted reproduction available to childless couples. This contemporary topic assists rising numbers of couples with reproductive disorders. Professional and nonprofessional discourse on the topic of surrogacy has grown in the Czech Republic since 2009. This discussion has contributed to increasing destigmatisation of the parties that make use of the option of surrogate motherhood. As a result, we have first- hand accounts on how the Czech legal system in its current form responded. This diploma thesis presents an overview of the methods of assisted reproduction and their history as well as the legal basis for assisted reproduction in international, European and national law. Furthermore, the thesis touches upon the topic of surrogate motherhood with a view to identifying the risks and dangers of surrogate motherhood both in general and with a focus on the specific legal issues in the Czech Republic. In its conclusion it examines the politico-legal developments regarding surrogate motherhood both on the level of the European Union as well as the Council of Europe. The diploma thesis aims to map the legislation of surrogate motherhood in the Czech legal system including the first contact of the parties, the validity and...

Detection canines represent the fastest and most versatile means of illicit material detection. This research endeavor in its most simplistic form is the improvement of detection canines through training, training aids, and calibration. This study focuses on developing a universal calibration compound for which all detection canines, regardless of detection substance, can be tested daily to ensure that they are working with acceptable parameters. Surrogate continuation aids (SCAs) were developed for peroxide based explosives along with the validation of the SCAs already developed within the International Forensic Research Institute (IFRI) prototype surrogate explosives kit. Storage parameters of the SCAs were evaluated to give recommendations to the detection canine community on the best possible training aid storage solution that minimizes the likelihood of contamination. Two commonly used and accepted detection canine imprinting methods were also evaluated for the speed in which the canine is trained and their reliability. As a result of the completion of this study, SCAs have been developed for explosive detection canine use covering: peroxide based explosives, TNT based explosives, nitroglycerin based explosives, tagged explosives, plasticized explosives, and smokeless powders. Through the use of these surrogate continuation aids a more uniform and reliable system of training can be implemented in the field than is currently used today. By examining the storage parameters of the SCAs, an ideal storage system has been developed using three levels of containment for the reduction of possible contamination. The developed calibration compound will ease the growing concerns over the legality and reliability of detection canine use by detailing the daily working parameters of the canine, allowing for Daubert rules of evidence admissibility to be applied. Through canine field testing, it has been shown that the IFRI SCAs outperform other commercially available training aids on the market. Additionally, of the imprinting methods tested, no difference was found in the speed in which the canines are trained or their reliability to detect illicit materials. Therefore, if the recommendations discovered in this study are followed, the detection canine community will greatly benefit through the use of scientifically validated training techniques and training aids.

Handheld devices are becoming increasingly common, and they have varied range of resources. Mobile Cloud Computing (MCC) allows resource constrained devices to offload computation and use storage capacities of more resourceful surrogate machines. This enables creation of new and interesting applications for all devices. We propose a scheme that constructs a high-performance de-centralized system by a group of volunteer mobile devices which come together to form a resourceful unit (cloudlet). The idea is to design a model to operate as a public-resource between mobile devices in close geographical proximity. This cloudlet can provide larger storage capability and can be used as a computational resource by other devices in the network. The system needs to watch the movement of the participating nodes and restructure the topology if some nodes that are providing support to the cloudlet fail or move out of the network. In this work, we discuss the need of the system, our goals and design issues in building a scalable and reconfigurable system. We achieve this by leveraging the concept of virtual dominating set to create an overlay in the broads of the network and distribute the responsibilities in hosting a cloudlet server. We propose an architecture for such a system and develop algorithms that are requited for its operation. We map the resources available in the network by first scoring each device individually, and then gathering these scores to determine suitable candidate cloudlet nodes. We have simulated cloudlet functionalities for several scenarios and show that our approach is viable alternative for many applications such as sharing GPS, crowd sourcing, natural language processing, etc.

Optimizing the loading process of a front loader vehicle is a challenging task. The design space is large and depends on the design of the vehicle, the strategy of the loading process, the nature of the material to load etcetera. Finding an optimal loading strategy, with respect to production and damage on equipment would greatly improve the production and environmental impacts in mining and construction. In this thesis, a method for exploring the design space of a loading strategy is presented. The loading strategy depends on four design variables that controls the shape of the trajectory relative to the shape of the pile. The responses investigated is the production, vehicle damage and work interruptions due to rock spill. Using multi-body dynamic simulations many different strategies can be tested with little cost. The result of these simulations are then used to build surrogate models of the original unknown function. The surrogate models are used to visualize and explore the design space and construct Pareto fronts for the competing responses. The surrogate models were able to predict the production function from the simulations well. The damage and rock spill surrogate models was moderately good in predicting the simulations but still good enough to explore how the design variables affect the response. The produced Pareto fronts makes it easy for the decision maker to compare sets of design variables and choose an optimal design for the loading strategy.

Surrogate-model-based optimization is widely used to solve black-box optimization problems if the evaluation of a target system is expensive. However, when the optimization budget is limited to a single or several evaluations, surrogate-model-based optimization may not perform well due to the lack of knowledge about the search space. In this case, transfer learning helps to get a good optimization result due to the usage of experience from the previous optimization runs. And if the budget is not strictly limited, transfer learning is capable of improving the final results of black-box optimization. The recent work in surrogate-model-based optimization showed that using multiple surrogates (i.e., applying multi-surrogate-model optimization) can be extremely efficient in complex search spaces. The main assumption of this thesis suggests that transfer learning can further improve the quality of multi-surrogate-model optimization. However, to the best of our knowledge, there exist no approaches to transfer learning in the multi-surrogate-model context yet. In this thesis, we propose an approach to transfer learning for multi-surrogate-model optimization. It encompasses an improved method of defining the expediency of knowledge transfer, adapted multi-surrogate-model recommendation, multi-task learning parameter tuning, and few-shot learning techniques. We evaluated the proposed approach with a set of algorithm selection and parameter setting problems, comprising mathematical functions optimization and the traveling salesman problem, as well as random forest hyperparameter tuning over OpenML datasets. The evaluation shows that the proposed approach helps to improve the quality delivered by multi-surrogate-model optimization and ensures getting good optimization results even under a strictly limited budget.:1 Introduction 1.1 Motivation 1.2 Research objective 1.3 Solution overview 1.4 Thesis structure 2 Background 2.1 Optimization problems 2.2 From single- to multi-surrogate-model optimization 2.2.1 Classical surrogate-model-based optimization 2.2.2 The purpose of multi-surrogate-model optimization 2.2.3 BRISE 2.5.0: Multi-surrogate-model-based software product line for parameter tuning 2.3 Transfer learning 2.3.1 Definition and purpose of transfer learning 2.4 Summary of the Background 3 Related work 3.1 Questions to transfer learning 3.2 When to transfer: Existing approaches to determining the expediency of knowledge transfer 3.2.1 Meta-features-based approaches 3.2.2 Surrogate-model-based similarity 3.2.3 Relative landmarks-based approaches 3.2.4 Sampling landmarks-based approaches 3.2.5 Similarity threshold problem 3.3 What to transfer: Existing approaches to knowledge transfer 3.3.1 Ensemble learning 3.3.2 Search space pruning 3.3.3 Multi-task learning 3.3.4 Surrogate model recommendation 3.3.5 Few-shot learning 3.3.6 Other approaches to transferring knowledge 3.4 How to transfer (discussion): Peculiarities and required design decisions for the TL implementation in multi-surrogate-model setup 3.4.1 Peculiarities of model recommendation in multi-surrogate-model setup 3.4.2 Required design decisions in multi-task learning 3.4.3 Few-shot learning problem 3.5 Summary of the related work analysis 4 Transfer learning for multi-surrogate-model optimization 4.1 Expediency of knowledge transfer 4.1.1 Experiments’ similarity definition as a variability point 4.1.2 Clustering to filter the most suitable experiments 4.2 Dynamic model recommendation in multi-surrogate-model setup 4.2.1 Variable recommendation granularity 4.2.2 Model recommendation by time and performance criteria 4.3 Multi-task learning 4.4 Implementation of the proposed concept 4.5 Conclusion of the proposed concept 5 Evaluation 5.1 Benchmark suite 5.1.1 APSP for the meta-heuristics 5.1.2 Hyperparameter optimization of the Random Forest algorithm 5.2 Environment setup 5.3 Evaluation plan 5.4 Baseline evaluation 5.5 Meta-tuning for a multi-task learning approach 5.5.1 Revealing the dependencies between the parameters of multi-task learning and its performance 5.5.2 Multi-task learning performance with the best found parameters 5.6 Expediency determination approach 5.6.1 Expediency determination as a variability point 5.6.2 Flexible number of the most similar experiments with the help of clustering 5.6.3 Influence of the number of initial samples on the quality of expediency determination 5.7 Multi-surrogate-model recommendation 5.8 Few-shot learning 5.8.1 Transfer of the built surrogate models’ combination 5.8.2 Transfer of the best configuration 5.8.3 Transfer from different experiment instances 5.9 Summary of the evaluation results 6 Conclusion and Future work

Indiana University-Purdue University Indianapolis (IUPUI) / Over the years, car driving experience has evolved drastically. Many new and useful technologies have emerged, which have enhanced safety and reliability measures. The Automotive world is now trying to build capabilities for driverless or vehicle assisted driving. Building capabilities for driverless cars practically means first developing training methods, then training the machine, evaluating the test results, and then based on testing results; develop a confidence interval for trusting the machine. One of the critical models is the model adopting the Road Departure Assisting Techniques (RDAT). These techniques are primarily the standards for alleviating the risk of roadside fatalities. The different models developed or proposed for RDAT falls under “The Road Departure Mitigation System” (RDMS). But, almost every RDMS to date has over-reliance on the presence and the quality of the lane markings. In the absence of lane markings or of proper lane markings, these RDMS are unreliable. Therefore, RDMS requires new references such as roadside objects and road edges for detecting road departures. This new system should propose and establish a standard for RDMS testing with roadside objects. As the foremost task, this new system requires the creation of a testing environment consisting of soft, robust, and reusable surrogates. Critically, these surrogates must have comparable sensors characteristics to those of real roadside objects from various commonly used object detection sensors on the vehicles such as camera, radar, and LIDAR. One of such everyday roadside objects is the curbs. For developing a surrogate for the curb, the first step is to recognize what the roadside objects should look like concerning different sensors, and the next step is to design and develop a surrogate curb that successfully follows the properties of the real roadside objects. This thesis first demonstrates and proposes the methods for extracting the color, Radar reflectivity, and the LiDAR reflectance properties of real roadside curbs. That is, the study deals with what all color combinations and patterns represent the US roadside curbs, what should be the range of Radar reflectivity values, and LiDAR reflectance bounds that a surrogate curb should satisfy. The later part of the thesis illustrates methods and steps on how to mimic the extracted properties, design a surrogate curb as per federal standards, and then develop a surrogate curb. Finally, the surrogate curbs were subjected to crash tests for testing their robustness.

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