41 research outputs found

    Learning-based methods for planning and control of humanoid robots

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    Nowadays, humans and robots are more and more likely to coexist as time goes by. The anthropomorphic nature of humanoid robots facilitates physical human-robot interaction, and makes social human-robot interaction more natural. Moreover, it makes humanoids ideal candidates for many applications related to tasks and environments designed for humans. No matter the application, an ubiquitous requirement for the humanoid is to possess proper locomotion skills. Despite long-lasting research, humanoid locomotion is still far from being a trivial task. A common approach to address humanoid locomotion consists in decomposing its complexity by means of a model-based hierarchical control architecture. To cope with computational constraints, simplified models for the humanoid are employed in some of the architectural layers. At the same time, the redundancy of the humanoid with respect to the locomotion task as well as the closeness of such a task to human locomotion suggest a data-driven approach to learn it directly from experience. This thesis investigates the application of learning-based techniques to planning and control of humanoid locomotion. In particular, both deep reinforcement learning and deep supervised learning are considered to address humanoid locomotion tasks in a crescendo of complexity. First, we employ deep reinforcement learning to study the spontaneous emergence of balancing and push recovery strategies for the humanoid, which represent essential prerequisites for more complex locomotion tasks. Then, by making use of motion capture data collected from human subjects, we employ deep supervised learning to shape the robot walking trajectories towards an improved human-likeness. The proposed approaches are validated on real and simulated humanoid robots. Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3

    Visual and Camera Sensors

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    This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors

    Exploiting Novel Deep Learning Architecture in Character Animation Pipelines

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    This doctoral dissertation aims to show a body of work proposed for improving different blocks in the character animation pipelines resulting in less manual work and more realistic character animation. To that purpose, we describe a variety of cutting-edge deep learning approaches that have been applied to the field of human motion modelling and character animation. The recent advances in motion capture systems and processing hardware have shifted from physics-based approaches to data-driven approaches that are heavily used in the current game production frameworks. However, despite these significant successes, there are still shortcomings to address. For example, the existing production pipelines contain processing steps such as marker labelling in the motion capture pipeline or annotating motion primitives, which should be done manually. In addition, most of the current approaches for character animation used in game production are limited by the amount of stored animation data resulting in many duplicates and repeated patterns. We present our work in four main chapters. We first present a large dataset of human motion called MoVi. Secondly, we show how machine learning approaches can be used to automate proprocessing data blocks of optical motion capture pipelines. Thirdly, we show how generative models can be used to generate batches of synthetic motion sequences given only weak control signals. Finally, we show how novel generative models can be applied to real-time character control in the game production

    Exploiting Novel Deep Learning Architecture in Character Animation Pipelines

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    This doctoral dissertation aims to show a body of work proposed for improving different blocks in the character animation pipelines resulting in less manual work and more realistic character animation. To that purpose, we describe a variety of cutting-edge deep learning approaches that have been applied to the field of human motion modelling and character animation. The recent advances in motion capture systems and processing hardware have shifted from physics-based approaches to data-driven approaches that are heavily used in the current game production frameworks. However, despite these significant successes, there are still shortcomings to address. For example, the existing production pipelines contain processing steps such as marker labelling in the motion capture pipeline or annotating motion primitives, which should be done manually. In addition, most of the current approaches for character animation used in game production are limited by the amount of stored animation data resulting in many duplicates and repeated patterns. We present our work in four main chapters. We first present a large dataset of human motion called MoVi. Secondly, we show how machine learning approaches can be used to automate proprocessing data blocks of optical motion capture pipelines. Thirdly, we show how generative models can be used to generate batches of synthetic motion sequences given only weak control signals. Finally, we show how novel generative models can be applied to real-time character control in the game production

    The Combined Treatment Efficacy of Anti-CD123 CAR T cells with Azacitidine for the Treatment of Acute Myeloid Leukaemia

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    “Joint Degree Program between Adelaide Medical School, The University of Adelaide and Medizinischen Universitätsklinik, der Albert-Ludwigs-Universität Freiburg Im Breisgau Freiburg Im Breisgau, Deutschland”Chimeric antigen receptor (CAR) T cells have yielded impressive remission rates in treatment-refractory B cell malignancies (B-ALL and B-lymphomas) by targeting CD19, resulting in CAR T cell therapies entering into clinical practice. However, the utility of CAR T cells for acute myeloid leukaemia (AML) remains a challenge. CAR T cells against AML-associated antigens are typically hampered by cytotoxic effects against normal haematopoietic progenitor cells and by CAR T cell exhaustion. Current clinical trials using CAR T cells that target various antigens in AML have resulted in either transient leukaemia cell clearance, or complete clearance of leukaemia at the expense of severe on-target off-tumour toxicities. In this thesis, third-generation anti-CD123 CAR T cells were developed with a humanised binding moiety for CD123 incorporating two intracellular signalling domains. The CAR developed in this project demonstrated strong anti- AML activity without elimination of the healthy haematopoietic system or epithelial tissue damage in mouse xenograft models. However, a sustained and long-term tumour eradication was not observed in the mice. In the clinical setting, this would mean that patients have suboptimal responses to the CAR T cells and may relapse. The AML microenvironment is immunosuppressive by employing a variety of mechanisms to escape the host immune surveillance, which may hamper the efficacy of CAR T cell therapy. To improve the long-term efficacy of the CAR T cells, combination therapy with DNA methyltransferase inhibitors, such as azacitidine (AZA), was explored. AZA has previously been shown to upregulate the expression of leukaemia-associated antigens on AML thereby inducing more effective T cell responses. AZA was therefore combined with CD123 CAR T cells and evaluated in AML xenograft models. Priming of AML cells with AZA increased the expression of the target antigen, CD123, on the cell surface. CD123 CAR T cells were more effective at eliminating AML cells in vivo and induced long term eradication. Interestingly, the combined treatment strategy induced a CTLA-4negative CD123 CAR T cell population. Functionally, these CTLA-4negative CD123 CAR T cells exhibited superior cytotoxicity against AML cells with sustained tumour necrosis factor (TNF) production and higher proliferative capacity compared to CTLA-4positive CD123 CAR T cells. Furthermore, AML xenograft mice treated with CTLA-4negative CD123 CAR T cells survived longer than CTLA-4positive CD123 CAR T cell treated mice, and demonstrated recall immunity in secondary AML xenograft recipients. Mechanistically, when AML cells were primed with AZA, the CAR T cells demonstrated increased intracellular retention of CTLA-4 and reduced extracellular expression upon exposure to the AML cells. The decreased expression of extracellular CTLA-4 was associated with decreased numbers of regulatory CAR T cells. Normally, high extracellular CTLA-4 expression prevents the phosphorylation of Lck and Zap70; intracellular molecules required for effective T cell induction and function. In this case, a higher phosphorylation level of these molecules was observed in the CAR T cells exposed to AML cells previously primed with AZA compared to without priming. The findings in this thesis project indicate that AZA increases the target antigen, CD123, on AML cells, allowing enhanced recognition and elimination by cytotoxic CTLA-4negative CD123 CAR T cells. These novel findings pave the way for a clinical trial combining AZA and CD123 CAR T cells for AML treatment.Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 202

    Can Upward Brand Extensions be an Opportunity for Marketing Managers During the Covid-19 Pandemic and Beyond?

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    Early COVID-19 research has guided current managerial practice by introducing more products across different product categories as consumers tried to avoid perceived health risks from food shortages, i.e. horizontal brand extensions. For example, Leon, a fast-food restaurant in the UK, introduced a new range of ready meal products. However, when the food supply stabilised, availability may no longer be a concern for consumers. Instead, job losses could be a driver of higher perceived financial risks. Meanwhile, it remains unknown whether the perceived health or financial risks play a more significant role on consumers’ consumptions. Our preliminary survey shows perceived health risks outperform perceived financial risks to positively influence purchase intention during COVID-19. We suggest such a result indicates an opportunity for marketers to consider introducing premium priced products, i.e. upward brand extensions. The risk-as�feelings and signalling theories were used to explain consumer choice under risk may adopt affective heuristic processing, using minimal cognitive efforts to evaluate products. Based on this, consumers are likely to be affected by the salient high-quality and reliable product cue of upward extension signalled by its premium price level, which may attract consumers to purchase when they have high perceived health risks associated with COVID-19. Addressing this, a series of experimental studies confirm that upward brand extensions (versus normal new product introductions) can positively moderate the positive effect between perceived health risks associated with COVID-19 and purchase intention. Such an effect can be mediated by affective heuristic information processing. The results contribute to emergent COVID-19 literature and managerial practice during the pandemic but could also inform post-pandemic thinking around vertical brand extensions

    Radioisotopes

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    The book Radioisotopes - Applications in Physical Sciences is divided into three sections namely: Radioisotopes and Some Physical Aspects, Radioisotopes in Environment and Radioisotopes in Power System Space Applications. Section I contains nine chapters on radioisotopes and production and their various applications in some physical and chemical processes. In Section II, ten chapters on the applications of radioisotopes in environment have been added. The interesting articles related to soil, water, environmental dosimetry/tracer and composition analyzer etc. are worth reading. Section III has three chapters on the use of radioisotopes in power systems which generate electrical power by converting heat released from the nuclear decay of radioactive isotopes. The system has to be flown in space for space exploration and radioisotopes can be a good alternative for heat-to-electrical energy conversion. The reader will very much benefit from the chapters presented in this section

    Contribution to supervised representation learning: algorithms and applications.

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    278 p.In this thesis, we focus on supervised learning methods for pattern categorization. In this context, itremains a major challenge to establish efficient relationships between the discriminant properties of theextracted features and the inter-class sparsity structure.Our first attempt to address this problem was to develop a method called "Robust Discriminant Analysiswith Feature Selection and Inter-class Sparsity" (RDA_FSIS). This method performs feature selectionand extraction simultaneously. The targeted projection transformation focuses on the most discriminativeoriginal features while guaranteeing that the extracted (or transformed) features belonging to the sameclass share a common sparse structure, which contributes to small intra-class distances.In a further study on this approach, some improvements have been introduced in terms of theoptimization criterion and the applied optimization process. In fact, we proposed an improved version ofthe original RDA_FSIS called "Enhanced Discriminant Analysis with Class Sparsity using GradientMethod" (EDA_CS). The basic improvement is twofold: on the first hand, in the alternatingoptimization, we update the linear transformation and tune it with the gradient descent method, resultingin a more efficient and less complex solution than the closed form adopted in RDA_FSIS.On the other hand, the method could be used as a fine-tuning technique for many feature extractionmethods. The main feature of this approach lies in the fact that it is a gradient descent based refinementapplied to a closed form solution. This makes it suitable for combining several extraction methods andcan thus improve the performance of the classification process.In accordance with the above methods, we proposed a hybrid linear feature extraction scheme called"feature extraction using gradient descent with hybrid initialization" (FE_GD_HI). This method, basedon a unified criterion, was able to take advantage of several powerful linear discriminant methods. Thelinear transformation is computed using a descent gradient method. The strength of this approach is thatit is generic in the sense that it allows fine tuning of the hybrid solution provided by different methods.Finally, we proposed a new efficient ensemble learning approach that aims to estimate an improved datarepresentation. The proposed method is called "ICS Based Ensemble Learning for Image Classification"(EM_ICS). Instead of using multiple classifiers on the transformed features, we aim to estimate multipleextracted feature subsets. These were obtained by multiple learned linear embeddings. Multiple featuresubsets were used to estimate the transformations, which were ranked using multiple feature selectiontechniques. The derived extracted feature subsets were concatenated into a single data representationvector with strong discriminative properties.Experiments conducted on various benchmark datasets ranging from face images, handwritten digitimages, object images to text datasets showed promising results that outperformed the existing state-ofthe-art and competing methods

    Machine learning applications in operations management and digital marketing

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    In this dissertation, I study how machine learning can be used to solve prominent problems in operations management and digital marketing. The primary motivation is to show that the application of machine learning can solve problems in ways that existing approaches cannot. In its entirety, this dissertation is a study of four problems—two in operations management and two in digital marketing—and develops solutions to these problems via data-driven approaches by leveraging machine learning. These four problems are distinct, and are presented in the form of individual self-containing essays. Each essay is the result of collaborations with industry partners and is of academic and practical importance. In some cases, the solutions presented in this dissertation outperform existing state-of-the-art methods, and in other cases, it presents a solution when no reasonable alternatives are available. The problems are: consumer debt collection (Chapter 3), contact center staffing and scheduling (Chapter 4), digital marketing attribution (Chapter 5), and probabilistic device matching (Chapters 6 and 7). An introduction of the thesis is presented in Chapter 1 and some basic machine learning concepts are described in Chapter 2
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