Vision-based object stabilisation is an exciting and challenging area of research, and is one that promises great technical advancements in the field of computer vision. As humans, we are capable of a tremendous array of skilful interactions, particularly when balancing unstable objects that have complex, non-linear dynamics. These complex dynamics impose a difficult control problem, since the object must be stabilised through collaboration between applied forces and vision-based feedback. To coordinate our actions and facilitate delivery of precise amounts of muscle torque, we primarily use our eyes to provide feedback in a closed-loop control scheme. This ability to control an inherently unstable object by vision-only feedback demonstrates an exceptionally high degree of voluntary motor skill. Despite the pervasiveness of vision-based stabilisation in humans and animals, relatively little is known about the neural strategies used to achieve this task. In the last few decades, with advancements in technology, we have tried to impart the skill of vision-based object stabilisation to machines, with varying degrees of success. Within the context of this research, we continue this pursuit by employing the classic Cart Inverted Pendulum; an inherently unstable, non-linear system to investigate dynamic object balancing by vision-only feedback. The Inverted Pendulum is considered to be one of the most fundamental benchmark systems in control theory; as a platform, it provides us with a strong, well established test bed for this research. We seek to discover what strategies are used to stabilise the Cart Inverted Pendulum, and to determine if these strategies can be deployed in Real-Time, using cost-effective solutions. The thesis confronts, and overcomes the problems imposed by low-bandwidth USB cameras; such as poor colour-balance, image noise and low frame rates etc., to successfully achieve vision-based stabilisation. The thesis presents a comprehensive vision-based control system that is capable of balancing an inverted pendulum with a resting oscillation of approximately ±1º. We employ a novel, segment-based location and tracking algorithm, which was found to have excellent noise immunity and enhanced robustness. We successfully demonstrate the resilience of the tracking and pose estimation algorithm against visual disturbances in Real-Time, and with minimal recovery delay. The algorithm was evaluated against peer reviewed research; in terms of processing time, amplitude of oscillation, measurement accuracy and resting oscillation. For each key performance indicator, our system was found to be superior in many cases to that found in the literature. The thesis also delivers a complete test software environment, where vision-based algorithms can be evaluated. This environment includes a flexible tracking model generator to allow customisation of visual markers used by the system. We conclude by successfully performing off-line optimization of our method by means of Artificial Neural Networks, to achieve a significant improvement in angle measurement accuracy. / Goodrich Engine Control Systems and Balfour Beatty Rail Technologies

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This study evaluated the ability of segment-based classification paired with non-parametric methods (CART and kNN) to classify a chronosequence of Landsat TM/ETM+ imagery spanning from 1992 to 2002 within the state of Rondônia, Brazil. Pixel-based classification was also implemented for comparison. Interannual multitemporal composites were used in each classification in an attempt to increase the separation of primary forest, cleared, and re-vegetated classes within a given year. The kNN and CART classification methods, with the integration of multitemporal data, performed equally well with overall accuracies ranging from 77% to 91%. Pixel-based CART classification, although not different in terms of mean or median overall accuracy, did have significantly lower variability than all other techniques (3.2% vs. an average of 13.2%), and thus provided more consistent results. Segmentation did not improve classification success over pixel-based methods and was therefore an unnecessary processing step with the used dataset. Through the appropriate band selection methods of the respective non-parametric classifiers, multitemporal bands were chosen in 38 of the 44 total classifications, strongly suggesting the utility of interannual multitemporal data for the separation of cleared, re-vegetated, and primary forest classes. The separation of the primary forest class from the cleared and re-vegetated classes was particularly successful and may be a possible result of the incorporation of multitemporal data. The land cover maps from this study allow for an accurate annualized analysis of land cover and can be coupled with household data to gain a better understanding of landscape change in the region. / Master of Science

No / The square array was initially tested in the UK during the 1960s by Anthony Clark. However, since the development of the twin probe system, the square array has been seldom used in the UK, although greater use has been reported elsewhere, especially in France (Panissod et al., 1998). In the last few years, re-investigation of the square array¿s potential in an archaeological context has reignited interest and led to the development of a hand-pulled cart system by Dr. Roger Walker (Geoscan Research). This cart system incorporates earth resistance and gradiometer instruments to allow simultaneous surveys with a lightweight device. The main objective of the project is to test the application of the MSP40 on a variety of sites and to encourage the use of appropriate geophysical survey equipment to minimize the impact and therefore protect even the most vulnerable of archaeological sites.

Lorsque la protéine GILZ (glucocorticoid-induced leucine zipper) est surexprimée dans les cellules dendritiques (DC), elles acquièrent un phénotype tolérogène vecteur d’une fonction immunorégulatrice, notamment par l’induction de lymphocytes T CD4+ régulateurs de type Tr1. Au niveau moléculaire, GILZ peut être induite dans les DC par des signaux extracellulaires anti-inflammatoires comme l’IL-10 et le TGF-b, mais aussi par un cocktail de cytokines pro-inflammatoires (PGE2, IL-6, IL-1b, TNF).L’expression de GILZ dans les DC peut être dérégulée dans des immunopathologies qui reposent sur un déséquilibre du ratio T CD4+ effecteurs / T CD4+ régulateurs comme dans les allergies médiées par les IgE, les cancers.Compte tenu du rôle central joué par les DC dans la mise en œuvre des réponses immunitaires et leur dérégulation dans les infections virales chroniques, nous avons exploré l’expression et la fonction de la protéine GILZ dans les DC dans le cas de l’infection par le VIH-1, chez des patients en phase chronique et sous trithérapies efficaces mais aussi chez des patients elite controllers qui contrôlent naturellement l’infection et préservent des DC immunogènes tout en contrôlant le niveau d’inflammation. Nos données apportent des connaissances nouvelles sur les mécanismes de modulation des DC dans l’infection chronique par le VIH-1, entre l’immunogénicité bénéfique des DC d’elite controllers qui expriment faiblement GILZ et la tolérance délétère des DC de patients sous trithérapies efficaces qui expriment fortement GILZ. Ces découvertes peuvent potentiellement trouver des applications en vaccination, les DC jouant également un rôle crucial dans des stratégies vaccinales. / When the GILZ (glucocorticoid-induced leucine zipper) protein is overexpressed in dendritic cells (DC), they acquire a tolerogenic phenotype that support an immunoregulatory function, notably inducing regulatory CD4+ T cells of a Tr1 type. At the molecular level, GILZ can be induced by anti-inflammatory extracellular signals like IL-10 and TGF-b, as well as by some pro-inflammatory signals (PGE2, IL-6, IL-1b, TNF).A deregulation of GILZ expression in DC has been reported in immune pathologies relying on a disequilibrium between effectors and regulatory CD4+ T cells like allergic diseases and cancers.Considering the central role played by DC in the implementation of immune responses and their deregulation in chronic viral infections, we explored GILZ expression and function in DC in the context of HIV-1 infection, in chronically HIV-1-infected patients under efficacious antiretroviral therapies and in elite controllers that naturally control the infection and preserve immunogenic DC while controlling the level of inflammation. Our data bring new knowledge on the mechanisms of modulation of DC in the chronic infection by HIV-1, between beneficial immunogenicity of DC from elite controllers that barely express GILZ and deleterious tolerance of DC from patients under efficacious antiretroviral therapies that strongly express GILZ. These discoveries may potentially found applications in vaccination, DC also playing a crucial role in vaccine strategies.

Dans le cadre de la sélection de modèle par contraste pénalisé, L. Birgé and P. Massart ont prouvé que le phénomène de pénalité minimale se produit pour la sélection libre parmi des variables gaussiennes indépendantes. Nous étendons certains de leurs résultats à la partition d'un signal gaussien lorsque la famille de partitions envisagées est suffisamment riche, notamment dans le cas des arbres de régression. Nous montrons que le même phénomène se produit dans le cadre de l'estimation de densité. La richesse de la famille de modèle s'apparente à une forme d'isotropie. De ce point de vue le phénomène de pénalité minimale est intrinsèque. Pour corroborer et illustrer ce point de vue, nous montrons que le même phénomène se produit pour une famille de modèles d'orientation aléatoire uniforme. / L. Birgé and P. Massart proved that the minimum penalty phenomenon occurs in Gaussian model selection when the model family arises from complete variable selection among independent variables. We extend some of their results to discrete Gaussian signal segmentation when the model family corresponds to a sufficiently rich family of partitions of the signal's support. This is the case of regression trees. We show that the same phenomenon occurs in the context of density estimation. The richness of the model family can be related to a certain form of isotropy. In this respect the minimum penalty phenomenon is intrinsic. To corroborate this point of view, we show that the minimum penalty phenomenon occurs when the models are chosen randomly under an isotropic law.

This thesis was conducted in cooperation with the Swedish bank SEB, who expressed interest in getting an increased understanding of how the marketing measure Customer Lifetime Value could be implemented and used in the retail banking industry. Accordingly, the purpose of this thesis was to provide insight into how Customer Lifetime Value could be modelled in an appropriate way in the retail banking industry and provide an increased understanding of necessary considerations for the modelling process. First, performance requirements for models of Customer Lifetime Value in the retail banking industry were identified through literary analysis and interviews with SEB. These requirements were then used to evaluate six general modelling approaches: RFM, Probability, Econometric, Persistance, Diffusion/Growth and Computer science. Based on the evaluation, the computer science approach and the econometric approach were identified as suitable for further investigation. This was achieved by implementing and analysing the performance of two models chosen as examples of respective approach. Specifically, a computer science model based on the \textit{random forest} algorithm and an econometric model based on \textit{Markov chains} were chosen. The results indicate that both approaches could be appropriate for the retail banking industry, but that an econometric approach could have the advantage of higher interpretability while a computer science approach can have the advantage of higher predictive accuracy.  In conclusion, the results indicate that the specific considerations and performance requirements for models of Customer Lifetime Value in the retail banking context should be based on a specific use case and area of business application. However, the discussions, considerations and examples of implementations provided in this thesis could serve as a foundation for future research and model development in this context. / Detta arbete genomfördes i samarbete med SEB, som uttryckt ett intresse för att öka sin kunskap kring hur marknadsföringsmåttet Customer Lifetime Value skulle kunna implementeras och användas i retail banking-branschen. Syftet med denna uppsats var följaktligen att ge en ökad förståelse för vad som är en lämplig modell av Customer Lifetime Value i branschen, samt ge en ökad förståelse för nödvändiga hänsynstaganden i modelleringsprocessen. Detta gjordes genom att först identifiera existerande modellkrav genom litteraturanalyys och intervjuer med SEB. Kraven användes sedan för att utvärdera sex generella modelltyper: RFM, Probability, Econometric, Persistance, Diffusion/Growth and Computer science. Baserat på utvärderingen identifierades Econometric och Computer science som lämpliga modelltyper för vidare undersökning, vilken gjordes genom att implementera en modell från respektive modelltyp. Specifikt valdes en Computer science-metod baserad på algoritmen random forest och en Econometric-metod baserad på Markovkedjor. Resultaten indikerade att båda modelltyper är lämpliga för implementering i retail banking-branschen, men att en Econometric-metod skulle kunna ha större tolkbarhet och att en Computer science-metod skulle kunna ha bättre precision. Sammanfattningsvis konstateras att hänsynstaganden och modellkrav på modeller av Customer Lifetime Value i retail banking-branschen bör utformas utifrån det specifika tilltänkta användningsområdet. De diskussioner, hänsynstaganden och implementationsexempel som presenteras i detta arbete kan dock fungera som grund för vidare forskning och modellutveckling i kontexten.

本論文是研究在台灣開放型的股票型共同基金，並且利用資料採掘的技術加以分析並分類所謂優異績效及劣質績效的共同基金。我們使用分類決策樹(Classification and regression trees, CART)的方法來進行共同基金績效的分析及預測。本篇論文，我們採用了13種重要的變數來建構樹並找出優質基金，此外更驗證CART對於我們進行台灣共同基金績效的分析是穩定且有效的。最後，我們利用cross-validation test進行兩個月的基金的選取及持有，並各透過一個月的持有來視其績效。我們特別發現利用此方法選取出來的基金，其平均績效將優於所有共同基金的績效，並且其中有一個月的平均報酬率高於僅投資於高科技股的共同基金平均報酬率。 / We study the performance of open-end mutual funds in Taiwan, and use the technology of data mining to classify the outperforming and underperforming mutual funds. Classification and regression tree (CART) is the method to evaluate and predict the performance of mutual funds. In this paper, we utilize thirteen crucial factors to build trees and pick mutual funds by its classification rules. Moreover, we will verify precision of each tree. We find that the CART is a good tool to evaluate the performance of mutual funds in Taiwan because of its stability in outperforming - underperforming spreads. Moreover, we use two kinds of learning sample to build two trees and pick mutual funds to compose of them into the fund of funds. The results are better than the total average returns monthly, and one of them is better than the mutual funds that its investing target is high-tech stocks.

Disertacijoje nagrinėjamos dvi iki šiol netyrinėtos problemos: 1. Lietuvių kalbos garsų trukmių prognozavimo modelių kūrimas Iki šiol visi darbai, kuriuose yra nagrinėjamos lietuvių kalbos garsų trukmės, yra atlikti kalbininkų, tačiau šie tyrimai yra daugiau aprašomosios statistikos pobūdžio ir apsiriboja pavienių požymių įtakos garso trukmei analize. Šiame darbe, mašininio mokymo algoritmo pagalba, požymių įtaka garsų trukmei yra išmokstama iš duomenų ir užrašoma sprendimo medžio pavidalu. 2. Nuo kalbos nepriklausomų garsų trukmių prognozavimo modelių kūrimo metodas, naudojant didelės apimties daugelio, kalbėtojų automatiškai, anotuotą garsyną. Dėl skirtingų kalbėtojų tarties specifikos ir dėl automatinio anotavimo netikslumų, kuriant garsų trukmės modelius visame pasaulyje yra apsiribojama vieno kalbėtojo ekspertų anotuotais nedidelės apimties garsynais. Darbe pasiūlyti skirtingų kalbėtojų tarties ypatybių normalizavimo ir garsyno duomenų triukšmo atmetimo algoritmai leidžia garsų trukmių modelių kūrimui naudoti didelės apimties, daugelio kalbėtojų automatiškai anotuotus garsynus. Darbo metu atliktas audicinis tyrimas, kurio pagalba parodoma, kad šnekos signalą sudarančių garsų trukmės turi įtakos klausytojų/respondentų suvokiamam šnekos signalo natūralumui; kontekstinės informacijos panaudojimas garsų trukmių prognozavimo uždavinio sprendime yra svarbus faktorius įtakojantis sintezuotos šnekos natūralumą; natūralaus šnekos signalo atžvilgiu, geriausiai vertinamas yra... [toliau žr. visą tekstą] / Two heretofore unanalyzed aspects are addressed in this dissertation: 1. Building a model capable of predicting phone duration of Lithuanian. All existing investigations of phone durations of Lithuanian were performed by linguists. Usually these investigations are the kind of exploratory statistics and are limited to a single factor, affecting phone duration, analysis. Phone duration dependencies on contextual factors were estimated and written in explicit form (decision tree) in this work by means of machine learning method. 2. Construction of language independent method for creating phone duration models using very large, multi-speaker, automatically annotated speech corpus. Most of the researchers worldwide use speech corpus that are: relatively small scale, single speaker, manually annotated or at least validated by experts. Usually the referred reasons are: using multi-speaker speech corpora is inappropriate because different speakers have different pronunciation manners and speak in different speech rate; automatically annotated corpuses lack accuracy. The created method for phone duration modeling enables the use of such corpus. The main components of the created method are: the reduction of noisy data in speech corpus; normalization of speaker specific phone durations by using phone type clustering. The performed listening tests of synthesized speech, showed that: the perceived naturalness is affected by the underlying phones durations; The use of contextual... [to full text]