Design / My work explores methods of creating illusions that make the imaginary and the real worlds appear to co-exist. More specifically, my animations look at ways of connecting the real and fantastical by using “low tech” materials. This report discusses existing work that combines animation with video-installation, live-performance, and advertisements; analyzes my research trajectory, explains my methodology for producing new hybrid work in animation; and then describes my projects. Each project is derived from a matrix I developed that forces integrations between two sets of criteria: (1) physical world action, objects and space, and (2) computer-generated images, representational images in an imaginary state and objects in physical space in an imaginary state. / text

ISO 26262 is an international standard about functional safety published on 2011, aiming to avoid failure in safety related electrical and electronic systems in passenger cars. A corresponding standard for heavy vehicles is expected to be published in a few years’ time. In order to be well-prepared, the heavy vehicle manufacturer Scania decides to start investigating what impact ISO26262 can bring. At Scania, modularization is one of the most important features of the product, which means several modules can be combined together into a vehicle in a variety of ways, so that highly configurable products can be provided for the customer. Huge number of unique module combinations bring big challenges to systems integration testing department REST/I in Scania because of limited time and resource availability for testing. Nowadays, people in REST/I deal with the variant mainly based on human experience, which is quite difficult to obtain the exact complete variant information and concrete testing coverage. In order to fulfill the requirement related with variant handling in ISO26262, better variant handling methods are proposed in this thesis, which can mainly be divided into two parts: method for variant generating and method for configuration selecting. To simplify the implementation work of this thesis, only the ECU components are focused on (other components such as sensors are ignored), and the risk-based feature is not added to the configuration selecting. Variant generating is to generate variant information from Allocation Element Diagram in Sesamm database systematically. According to the generated variant information, the configuration can be selected automatically by using Greedy best-first-search algorithm based on the proposed testing coverage metrics. Since all these work can be done automatically by computer, REST/I not only can work more efficiently by saving a lot of labor resource, but also can avoid mistakes caused by anthropogenic factors. However, not all the data needed for the automation are existed today, so the suggestions for consummation of the data to be ready for implementing the proposed methods are also mentioned in this thesis.

Combining Outputs from Machine Translation Systems By Fahim A. Salim Supervised by: Ing. Zdenek Zabokrtsky, Ph.D Institute of Formal and Applied Linguistics, Charles University in Prague 2010. Abstract: Due to the massive ongoing research there are many paradigms of Machine Translation systems with diverse characteristics. Even systems designed on the same paradigm might perform differently in different scenarios depending upon their training data used and other design decisions made. All Machine Translation Systems have their strengths and weaknesses and often weakness of one MT system is the strength of the other. No single approach or system seems to always perform best, therefore combining different approaches or systems i.e. creating systems of Hybrid nature, to capitalize on their strengths and minimizing their weaknesses in an ongoing trend in Machine Translation research. But even Systems of Hybrid nature has limitations and they also tend to perform differently in different scenarios. Thanks to the World Wide Web and open source, nowadays one can have access to many different and diverse Machine Translation systems therefore it is practical to have techniques which could combine the translation of different MT systems and produce a translation which is better than any of the individual systems....

Robôs móveis dependem de dados provenientes de sensores para ter uma representação do seu ambiente. Porém, os sensores geralmente fornecem informações incompletas, inconsistentes ou imprecisas. Técnicas de fusão de sensores têm sido empregadas com sucesso para aumentar a precisão de medidas obtidas com sensores. Este trabalho propõe e investiga o uso de técnicas de inteligência artificial para fusão de sensores com o objetivo de melhorar a precisão e acurácia de medidas de distância entre um robô e um objeto no seu ambiente de trabalho, obtidas com diferentes sensores. Vários algoritmos de aprendizado de máquina são investigados para fundir os dados dos sensores. O melhor modelo gerado com cada algoritmo é chamado de estimador. Neste trabalho, é mostrado que a utilização de estimadores pode melhorar significativamente a performance alcançada por cada sensor isoladamente. Mas os vários algoritmos de aprendizado de máquina empregados têm diferentes características, fazendo com que os estimadores tenham diferentes comportamentos em diferentes situações. Objetivando atingir um comportamento mais preciso e confiável, os estimadores são combinados em comitês. Os resultados obtidos sugerem que essa combinação pode melhorar a confiança e precisão das medidas de distâncias dos sensores individuais e estimadores usados para fusão de sensores. / Mobile robots rely on sensor data to have a representation of their environment. However, the sensors usually provide incomplete, inconsistent or inaccurate information. Sensor fusion has been successfully employed to enhance the accuracy of sensor measures. This work proposes and investigates the use of artificial intelligence techniques for sensor fusion. Its main goal is to improve the accuracy and reliability of a distance between a robot and an object in its work environment using measures obtained from different sensors. Several machine learning algorithms are investigated to fuse the sensors data. The best model generated with each algorithm are called estimator. It is shown that the employment of the estimators based on artificial intelligence can improve significantly the performance achieved by each sensor alone. The machine learning algorithms employed have different characteristics, causing the estimators to have different behaviour in different situations. Aiming to achieve more accurate and reliable behavior, the estimators are combined in committees. The results obtained suggest that this combination can improve the reliability and accuracy of the distance measures by the individual sensors and estimators used for sensor fusion.

Combination of p-values from multiple independent tests has been widely studied since 1930's. To find the optimal combination methods, various combiners such as Fisher's method, inverse normal transformation, maximal p-value, minimal p-value, etc. have been compared by different criteria. In this work, we focus on the criterion of Bahadur efficiency, and compare various methods under the TFisher. As a recently developed general family of combiners, TFisher cover Fisher's method, the rank truncated product method (RTP), the truncation product method (TPM, or the hard-thresholding method), soft-thresholding method, minimal p-value method, etc. Through the Bahadur asymptotics, we better understand the relative performance of these methods. In particular, through calculating the Bahadur exact slopes for the problem of detecting sparse signals, we reveal the relative advantages of truncation versus non-truncation, hard-thresholding versus soft-thresholding. As a result, the soft thresholding method is shown superior when signal strength is relatively weak and the ratio between the sample size of each p-value and the number of combining p-values is small.

The rise of antimicrobial resistance (AMR) has culminated in the most pressing problem in modern medicine. The situation is most acute with regards to the management of multi- drug resistant Gram-negative infections (MDRGNB) with common infections increasingly untreatable due to rapidly dwindling therapeutic options. A solution to the problem of AMR is unlikely to be easily found, but revisiting and re-purposing existing antimicrobials is a viable approach in the medium term. This study investigated the use of unorthodox antimicrobial combination therapies for the treatment of MDRGNB, with particular focus on agents of last resort. A systematic review of clinical studies highlighted the potential for polymyxin (colistin) combination therapies (e.g. colistin-rifampicin, colistin-carbapenems), although this could not be supported in a formal meta-analysis. A systematic approach for screening MDRAB for susceptibility to novel colistin combinations using multiple methods was employed and uncovered a number that were more potent than those previously identfied. The most potent combination that was consistently identified was colistin when combined with fusidic acid, despite this drug having no useful activity against MDRGNB on its own. The combination was further evaluated in static time-kill assays against a range of Gram-negative pathogens with defined resistance mechanisms, including to polymyxins and using invertebrate (Galleria mellonella) and murine models of MDRGNB infection. Colistin and fusidic acid combination therapy was subsequently used to successfully treat a case of ventilator-associated pneumonia due to MDR A. baumannii. This work highlights how older drugs can be re-purposed to tackle the problem of AMR using a precision medicine approach. Further studies to elucidate the mechanism of action of the colistin- fusidic acid combination and a formal clinical trial are warranted to investigate the potential utility of this combination in the treatment of MDRGNB with the expressed goal of bridging the current antimicrobial development gap.

A qualidade das hipóteses induzidas pelos atuais sistemas de Aprendizado de Máquina depende principalmente da quantidade e da qualidade dos atributos e exemplos utilizados no treinamento. Freqüentemente, resultados experimentais obtidos sobre grandes bases de dados, que possuem muitos atributos irrelevantes, resultam em hipóteses de baixa precisão. Por outro lado, muitos dos sistemas de aprendizado de máquina conhecidos não estão preparados para trabalhar com uma quantidade muito grande de exemplos. Assim, uma das áreas de pesquisa mais ativas em aprendizado de máquina tem girado em torno de técnicas que sejam capazes de ampliar a capacidade dos algoritmos de aprendizado para processar muitos exemplos de treinamento, atributos e classes. Para que conceitos sejam aprendidos a partir de grandes bases de dados utilizando Aprendizado de Máquina, pode-se utilizar duas abordagens. A primeira realiza uma seleção de exemplos e atributos mais relevantes, e a segunda ´e a abordagem de ensembles. Um ensemble ´e um conjunto de classificadores cujas decisões individuais são combinadas de alguma forma para classificar um novo caso. Ainda que ensembles classifiquem novos exemplos melhor que cada classificador individual, eles se comportam como caixas pretas, no sentido de n˜ao oferecer ao usuário alguma explicação relacionada à classificação por eles fornecida. O objetivo deste trabalho é propor uma forma de combinação de classificadores simbólicos, ou seja, classificadores induzidos por algoritmos de AM simbólicos, nos quais o conhecimento é descrito na forma de regras if-then ou equivalentes, para se trabalhar com grandes bases de dados. A nossa proposta é a seguinte: dada uma grande base de dados, divide-se esta base aleatoriamente em pequenas bases de tal forma que é viável fornecer essas bases de tamanho menor a um ou vários algoritmos de AM simbólicos. Logo após, as regras que constituem os classificadores induzidos por esses algoritmos são combinadas em um único classificador. Para analisar a viabilidade do objetivo proposto, foi implementado um sistema na linguagem de programação lógica Prolog, com a finalidade de (a) avaliar regras de conhecimento induzidas por algoritmos de Aprendizado de Máquina simbólico e (b) avaliar diversas formas de combinar classificadores simbólicos bem como explicar a classificação de novos exemplos realizada por um ensemble de classificares simbólicos. A finalidade (a) é implementada pelo Módulo de Análise de Regras e a finalidade (b) pelo Módulo de Combinação e Explicação. Esses módulos constituem os módulos principais do RuleSystem. Neste trabalho, são descritos os métodos de construção de ensembles e de combinação de classificadores encontrados na literatura, o projeto e a documentação do RuleSystem, a metodologia desenvolvida para documentar o sistema RuleSystem, a implementação do Módulo de Combinação e Explicação, objeto de estudo deste trabalho, e duas aplicações do Módulo de Combinação e Explicação. A primeira aplicação utilizou uma base de dados artificiais, a qual nos permitiu observar necessidades de modificações no Módulo de Combinação e Explicação. A segunda aplicação utilizou uma base de dados reais. / The hypothesis quality induced by current machine learning algorithms depends mainly on the quantity and quality of features and examples used in the training phase. Frequently, hypothesis with low precision are obtained in experiments using large databases with a large number of irrelevant features. Thus, one active research area in machine learning is to investigate techniques able to extend the capacity of machine learning algorithms to process a large number of examples, features and classes. To learn concepts from large databases using machine learning algorithms, two approaches can be used. The first approach is based on a selection of relevant features and examples, and the second one is the ensemble approach. An ensemble is a set of classifiers whose individual decisions are combined in some way to classify a new case. Although ensembles classify new examples better than each individual classifier, they behave like black-boxes, since they do not offer any explanation to the user about their classification. The purpose of this work is to consider a form of symbolic classifiers combination to work with large databases. Given a large database, it is equally divided randomly in small databases. These small databases are supplied to one or more symbolic machine learning algorithms. After that, the rules from the resulting classifiers are combined into one classifier. To analise the viability of this proposal, was implemented a system in logic programming language Prolog, called RuleSystem. This system has two purposes; the first one, implemented by the Rule Analises Module, is to evaluate rules induced by symbolic machine learning algorithms; the second one, implemented by the Combination and Explanation Module, is to evaluate several forms of combining symbolic classifiers as well as to explain ensembled classification of new examples. Both principal modules constitute the Rule System. This work describes ensemble construction methods and combination of classifiers methods found in the literature; the project and documentation of RuleSystem; the methodology developed to document the RuleSystem; and the implementation of the Combination and Explanation Module. Two different case studies using the Combination and Explanation Module are described. The first case study uses an artificial database. Through the use of this artificial database, it was possible to improve several of the heuristics used by the the Combination and Explanation Module. A real database was used in the second case study.

Platinum-based chemotherapies are effective agents in the treatment of a wide variety of human cancers. However, patients with recurrent disease can become resistant to platinum-based chemotherapy, leading to low overall survival rates. Activating transcription factor 3 (ATF3) is a stress-inducible gene that is a regulator of cisplatin-induced cytotoxicity. ATF3 protein expression was upregulated after cytotoxic doses of cisplatin treatment in a panel of cell lines. A chromatin immunoprecipitation assay showed that upon treatment with cisplatin, ATF3 directly bound to the CHOP gene promoter and this correlated with an increase in CHOP protein expression. In a 1200 compound library screen performed on cancer cell lines, disulfiram, a dithiocarbamate drug, was identified as an enhancer of the cytotoxic effects of cisplatin. This increased cytotoxic action was likely due to disulfiram and cisplatin’s ability to induce ATF3 independently through two separate mechanisms, namely the MAPK and integrated stress pathways. Furthermore, ATF3 protein and mRNA levels were variable amongst human ovarian and lung cancer tissues, suggesting the potential for basal expression of ATF3 to be predictive of cisplatin treatment response. Thus, understanding ATF3’s role in cisplatin-induced cytotoxicity will lead to novel therapeutic approaches that could improve this drug’s efficacy.

Our study investigated the potential for a combination of diabetes and nicotine treatment to affect blood pressure in the rat. We used streptozotocin injection and oral nicotine feeding as models of type-1 diabetes and smoking respectively. Blood pressure was assessed using the indirect tail-cuff technique. In an attempt to further characterize our experimental model, we also observed body weight, plasma glucose and the contractility of aortic segments in various treatment groups. Our data was expressed as mean ± SEM, and significance was regarded as P < 0.05. We found that a combination of streptozotocin and nicotine treatment resulted in a significant elevation of systolic blood pressure compared with either treatment alone, or control. Furthermore, assessment of aortic contractility showed alteration of reactivity to both phenylephrine and sodium nitroprusside as a result of the combination treatment. We also observed a trend for our combination treatment to exacerbate the elevation of plasma glucose level seen in streptozotocin induced diabetic rat models. This study serves as an experimental basis to underline the importance of cessation of tobacco use for individuals with diabetes mellitus.

Platinum-based chemotherapies are effective agents in the treatment of a wide variety of human cancers. However, patients with recurrent disease can become resistant to platinum-based chemotherapy, leading to low overall survival rates. Activating transcription factor 3 (ATF3) is a stress-inducible gene that is a regulator of cisplatin-induced cytotoxicity. ATF3 protein expression was upregulated after cytotoxic doses of cisplatin treatment in a panel of cell lines. A chromatin immunoprecipitation assay showed that upon treatment with cisplatin, ATF3 directly bound to the CHOP gene promoter and this correlated with an increase in CHOP protein expression. In a 1200 compound library screen performed on cancer cell lines, disulfiram, a dithiocarbamate drug, was identified as an enhancer of the cytotoxic effects of cisplatin. This increased cytotoxic action was likely due to disulfiram and cisplatin’s ability to induce ATF3 independently through two separate mechanisms, namely the MAPK and integrated stress pathways. Furthermore, ATF3 protein and mRNA levels were variable amongst human ovarian and lung cancer tissues, suggesting the potential for basal expression of ATF3 to be predictive of cisplatin treatment response. Thus, understanding ATF3’s role in cisplatin-induced cytotoxicity will lead to novel therapeutic approaches that could improve this drug’s efficacy.