699 research outputs found

    Digital support for alcohol moderation and smoking cessation in cancer survivors

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    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiïŹed Proportional ConïŹ‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiïŹers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well

    Management strategies and contributory factors for resistance exercise-induced muscle damage: an exploration of dietary protein, exercise load, and sex

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    The World Health Organisation recommends that resistance exercise be performed at least twice per week to benefit general health and wellbeing. However, resistance exercise is associated with acute muscle damage that potentially can dampen muscle adaptations promoted by chronic resistance training. The extent to which muscle is damaged by exercise is influenced by various factors, including age, training status, exercise type, and – notable to this thesis – sex. To this end, establishing sex-specific management strategies for exercise-induced muscle damage (EIMD) is important to optimise the benefits of exercise. Two EIMD management strategies were focussed on in this thesis: dietary protein supplementation and exercise load manipulation. It was identified in this thesis that research into the impact both of protein supplementation and exercise load on EIMD heavily underrepresent female populations (chapters 3 and 5), despite well-documented sex differences in EIMD responses. Therefore, future research priority should be placed on bridging the sex data gap by conducting high-quality studies centralising around female-focussed and sex-comparative methodological designs. Both peri-exercise protein supplementation and exercise load manipulation in favour of lighter loads were revealed to be effective management strategies for resistance EIMD in males through systematic and scoping review of the current literature (chapters 3 and 5, respectively). Due to a lack of data from females, it is only appropriate for these strategies to be recommended for males at present. To decipher whether protein supplementation and lower exercise loads are beneficial for managing EIMD in females, a randomised controlled trial (RCT) (chapter 4) and a protocol for an RCT (chapter 6) involving male and female participants are presented in this thesis. The incorporation of ecologically-valid resistance exercise in the RCT in chapter 4 highlighted that even mild muscle damage is attenuated in females, reflected in diminished increases in post-exercise creatine kinase concentration and muscle soreness compared with males; however, the reason for this difference requires further investigation. This study, while supporting sex differences, contrasted previous studies, as neither males nor females experienced an attenuation of EIMD during milk protein supplementation. This difference likely owed to the lower severity of muscle damage induced in the current study relative to previous studies, and accordingly, future research should seek to discover alternative management strategies for mild EIMD. A protocol for an RCT examining the impact of exercise load on EIMD in untrained males and females is described in Chapter 6 of this thesis and may be used as guidance for researchers developing similar, sex-comparative studies. It was hypothesised that females will experience attenuated muscle damage relative to males and low-load exercise will induce less muscle damage than high-load exercise in both sexes. A lack of methodological consistency among EIMD studies was a recurring finding throughout this thesis, which posed an issue when attempting to compare between-study outcomes and reach a consensus. Achieving greater uniformity in study designs by adopting comparable methods relating to EIMD markers and time-points of assessment would help improve understanding of the factors influencing the magnitude of EIMD and effective management strategies. While there are limitations with several EIMD markers – for example the variability of biomarkers and subjectivity of perceptual assessments – once the optimal markers are determined, these should be consistently used moving forward. Overall, this thesis has contributed to the current body of knowledge by demonstrating that milk protein ingestion is not an effective management strategy for muscle damage following ecologically-valid resistance exercise; therefore, alternative strategies to mitigate mild muscle damage should be investigated. Further, this work supported previous reports of sex differences in EIMD and indicated that the attenuation of EIMD in females relative to males was not attributed to sex differences in body composition; thus, the aetiology of such differences necessitates further exploration by means of high-quality sex comparative research. Finally, this thesis reached the consensus recommendation that lower exercise loads can be utilised to reduce muscle damage in males; nonetheless, supporting evidence for the application of this recommendation to females is required

    Diamagnetic Levitation of Bubbles and Droplets

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    This thesis describes the use of diamagnetic levitation to study fluids in a zero-gravity environment, particularly focusing on bubbles and droplets. We use a strong nonhomogeneous magnetic field (maximum field strength 18.5~T) generated by a superconducting solenoid magnet to repel/attract materials at a molecular level allowing for a net zero body force to be experienced by bubbles/droplets. A new technique that allows for the suspension of spherical gas bubbles in liquids at room temperature is presented. The development of this technique allowed for several novel experiments to be carried out. Firstly, we use this technique to observe the coalescence of multiple pairs of air bubbles in water, starting from hydrostatic equilibrium. The coalescence creates large axisymmetric perturbations to the surface of the bubble which leads to the ejection of satellite bubbles. For the first time, we experimentally observe the simultaneous ejection of two satellite bubbles from the coalescence of a pair of air bubbles. After satellite bubbles are ejected, the bubble formed from the coalescence of the parent bubbles undergoes large nonlinear axisymmetric surface oscillations. We analyse these surface oscillations for two cases: a symmetric case, where the initial parent bubbles have equal radii (within experimental error) and an asymmetric case where the ratio of the radii of the two parent bubbles is ∌1.5\sim1.5. We compare our results to the analytical model of Tsamopoulos and Brown and find that in the symmetric case, when only a single large amplitude surface mode is dominant, that experiment and simulation agree well with theory and the oscillation frequency of the dominant mode behaves as a function of the square of its amplitude. But, in the case several surface modes are oscillating with moderate or large amplitudes, agreement between the model of Tsamopoulos and Brown and what is observed in experiment and simulation is seen to be less accurate. Secondly, we use this technique to observe and manipulate bubble clusters. We show that if a small amount of surfactant is added to the liquid, that air bubbles levitating in the liquid may remain in contact with each other without coalescing for an indefinite period of time. This allows for the creation of clusters of multiple diamagnetically levitated spherical air bubbles. We present bubble clusters created from up to 21 bubbles and show how the arrangement of these clusters may be altered by simply altering the current in the superconducting solenoid. Future use cases are hypothesised for bubble clusters, such as the production of new acoustic metamaterials and a new technique for the study of the nonlinear interaction of bubbles in an oscillating acoustic field. The final section of this thesis describes a new experimental technique ‘Sonomaglev’. This new technique combines both acoustic and diamagnetic levitation, allowing for the manipulation of multiple levitated spherical water droplets, using a superconducting magnet fitted with low-power ultrasonic transducers. We show that multiple droplets, arranged horizontally along a line, can be stably levitated with this system, and demonstrate controlled contactless coalescence of two droplets. Numerical simulation of the magnetogravitational and acoustic potential reproduces the multiple stable equilibrium points observed in our experiments

    Teaching Unknown Objects by Leveraging Human Gaze and Augmented Reality in Human-Robot Interaction

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    Roboter finden aufgrund ihrer außergewöhnlichen Arbeitsleistung, PrĂ€zision, Effizienz und Skalierbarkeit immer mehr Verwendung in den verschiedensten Anwendungsbereichen. Diese Entwicklung wurde zusĂ€tzlich begĂŒnstigt durch Fortschritte in der KĂŒnstlichen Intelligenz (KI), insbesondere im Maschinellem Lernen (ML). Mit Hilfe moderner neuronaler Netze sind Roboter in der Lage, Objekte in ihrer Umgebung zu erkennen und mit ihnen zu interagieren. Ein erhebliches Manko besteht jedoch darin, dass das Training dieser Objekterkennungsmodelle, in aller Regel mit einer zugrundeliegenden AbhĂ€ngig von umfangreichen DatensĂ€tzen und der VerfĂŒgbarkeit großer Datenmengen einhergeht. Dies ist insbesondere dann problematisch, wenn der konkrete Einsatzort des Roboters und die Umgebung, einschließlich der darin befindlichen Objekte, nicht im Voraus bekannt sind. Die breite und stĂ€ndig wachsende Palette von Objekten macht es dabei praktisch unmöglich, das gesamte Spektrum an existierenden Objekten allein mit bereits zuvor erstellten DatensĂ€tzen vollstĂ€ndig abzudecken. Das Ziel dieser Dissertation war es, einem Roboter unbekannte Objekte mit Hilfe von Human-Robot Interaction (HRI) beizubringen, um ihn von seiner AbhĂ€ngigkeit von Daten sowie den EinschrĂ€nkungen durch vordefinierte Szenarien zu befreien. Die Synergie von Eye Tracking und Augmented Reality (AR) ermöglichte es dem als Lehrer fungierenden Menschen, mit dem Roboter zu kommunizieren und ihn mittels des menschlichen Blickes auf Objekte hinzuweisen. Dieser holistische Ansatz ermöglichte die Konzeption eines multimodalen HRI-Systems, durch das der Roboter Objekte identifizieren und dreidimensional segmentieren konnte, obwohl sie ihm zu diesem Zeitpunkt noch unbekannt waren, um sie anschließend aus unterschiedlichen Blickwinkeln eigenstĂ€ndig zu inspizieren. Anhand der Klasseninformationen, die ihm der Mensch mitteilte, war der Roboter daraufhin in der Lage, die entsprechenden Objekte zu erlernen und spĂ€ter wiederzuerkennen. Mit dem Wissen, das dem Roboter durch diesen auf HRI basierenden Lehrvorgang beigebracht worden war, war dessen FĂ€higkeit Objekte zu erkennen vergleichbar mit den FĂ€higkeiten modernster Objektdetektoren, die auf umfangreichen DatensĂ€tzen trainiert worden waren. Dabei war der Roboter jedoch nicht auf vordefinierte Klassen beschrĂ€nkt, was seine Vielseitigkeit und AnpassungsfĂ€higkeit unter Beweis stellte. Die im Rahmen dieser Dissertation durchgefĂŒhrte Forschung leistete bedeutende BeitrĂ€ge an der Schnittstelle von Machine Learning (ML), AR, Eye Tracking und Robotik. Diese Erkenntnisse tragen nicht nur zum besseren VerstĂ€ndnis der genannten Felder bei, sondern ebnen auch den Weg fĂŒr weitere interdisziplinĂ€re Forschung. Die in dieser Dissertation enthalten wissenschaftlichen Artikel wurden auf hochrangigen Konferenzen in den Bereichen Robotik, Eye Tracking und HRI veröffentlicht.Robots are becoming increasingly popular in a wide range of environments due to their exceptional work capacity, precision, efficiency, and scalability. This development has been further encouraged by advances in Artificial Intelligence (AI), particularly Machine Learning (ML). By employing sophisticated neural networks, robots are given the ability to detect and interact with objects in their vicinity. However, a significant drawback arises from the underlying dependency on extensive datasets and the availability of substantial amounts of training data for these object detection models. This issue becomes particularly problematic when the specific deployment location of the robot and the surroundings, including the objects within it, are not known in advance. The vast and ever-expanding array of objects makes it virtually impossible to comprehensively cover the entire spectrum of existing objects using preexisting datasets alone. The goal of this dissertation was to teach a robot unknown objects in the context of Human-Robot Interaction (HRI) in order to liberate it from its data dependency, unleashing it from predefined scenarios. In this context, the combination of eye tracking and Augmented Reality (AR) created a powerful synergy that empowered the human teacher to seamlessly communicate with the robot and effortlessly point out objects by means of human gaze. This holistic approach led to the development of a multimodal HRI system that enabled the robot to identify and visually segment the Objects of Interest (OOIs) in three-dimensional space, even though they were initially unknown to it, and then examine them autonomously from different angles. Through the class information provided by the human, the robot was able to learn the objects and redetect them at a later stage. Due to the knowledge gained from this HRI based teaching process, the robot’s object detection capabilities exhibited comparable performance to state-of-the-art object detectors trained on extensive datasets, without being restricted to predefined classes, showcasing its versatility and adaptability. The research conducted within the scope of this dissertation made significant contributions at the intersection of ML, AR, eye tracking, and robotics. These findings not only enhance the understanding of these fields, but also pave the way for further interdisciplinary research. The scientific articles included in this dissertation have been published at high-impact conferences in the fields of robotics, eye tracking, and HRI

    Science with the Einstein Telescope: a comparison of different designs

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    The Einstein Telescope (ET), the European project for a third-generation gravitational-wave detector, has a reference configuration based on a triangular shape consisting of three nested detectors with 10 km arms, where each detector has a ‘xylophone’ configuration made of an interferometer tuned toward high frequencies, and an interferometer tuned toward low frequencies and working at cryogenic temperature. Here, we examine the scientific perspectives under possible variations of this reference design. We perform a detailed evaluation of the science case for a single triangular geometry observatory, and we compare it with the results obtained for a network of two L-shaped detectors (either parallel or misaligned) located in Europe, considering different choices of arm-length for both the triangle and the 2L geometries. We also study how the science output changes in the absence of the low-frequency instrument, both for the triangle and the 2L configurations. We examine a broad class of simple ‘metrics’ that quantify the science output, related to compact binary coalescences, multi-messenger astronomy and stochastic backgrounds, and we then examine the impact of different detector designs on a more specific set of scientific objectives

    Digital support for alcohol moderation and smoking cessation in cancer survivors

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    Computer-assisted detection of lung cancer nudules in medical chest X-rays

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    Diagnostic medicine was revolutionized in 1895 with Rontgen's discovery of x-rays. X-ray photography has played a very prominent role in diagnostics of all kinds since then and continues to do so. It is true that more sophisticated and successful medical imaging systems are available. These include Magnetic Resonance Imaging (MRI), Computerized Tomography (CT) and Positron Emission Tomography (PET). However, the hardware instalment and operation costs of these systems remain considerably higher than x-ray systems. Conventional x-ray photography also has the advantage of producing an image in significantly less time than MRI, CT and PET. X-ray photography is still used extensively, especially in third world countries. The routine diagnostic tool for chest complaints is the x-ray. Lung cancer may be diagnosed by the identification of a lung cancer nodule in a chest x-ray. The cure of lung cancer depends upon detection and diagnosis at an early stage. Presently the five-year survival rate of lung cancer patients is approximately 10%. If lung cancer can be detected when the tumour is still small and localized, the five-year survival rate increases to about 40%. However, currently only 20% of lung cancer cases are diagnosed at this early stage. Giger et al wrote that "detection and diagnosis of cancerous lung nodules in chest radiographs are among the most important and difficult tasks performed by radiologists"

    Statistical learning of random probability measures

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    The study of random probability measures is a lively research topic that has attracted interest from different fields in recent years. In this thesis, we consider random probability measures in the context of Bayesian nonparametrics, where the law of a random probability measure is used as prior distribution, and in the context of distributional data analysis, where the goal is to perform inference given avsample from the law of a random probability measure. The contributions contained in this thesis can be subdivided according to three different topics: (i) the use of almost surely discrete repulsive random measures (i.e., whose support points are well separated) for Bayesian model-based clustering, (ii) the proposal of new laws for collections of random probability measures for Bayesian density estimation of partially exchangeable data subdivided into different groups, and (iii) the study of principal component analysis and regression models for probability distributions seen as elements of the 2-Wasserstein space. Specifically, for point (i) above we propose an efficient Markov chain Monte Carlo algorithm for posterior inference, which sidesteps the need of split-merge reversible jump moves typically associated with poor performance, we propose a model for clustering high-dimensional data by introducing a novel class of anisotropic determinantal point processes, and study the distributional properties of the repulsive measures, shedding light on important theoretical results which enable more principled prior elicitation and more efficient posterior simulation algorithms. For point (ii) above, we consider several models suitable for clustering homogeneous populations, inducing spatial dependence across groups of data, extracting the characteristic traits common to all the data-groups, and propose a novel vector autoregressive model to study of growth curves of Singaporean kids. Finally, for point (iii), we propose a novel class of projected statistical methods for distributional data analysis for measures on the real line and on the unit-circle

    Connectome-Constrained Artificial Neural Networks

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    In biological neural networks (BNNs), structure provides a set of guard rails by which function is constrained to solve tasks effectively, handle multiple stimuli simultaneously, adapt to noise and input variations, and preserve energy expenditure. Such features are desirable for artificial neural networks (ANNs), which are, unlike their organic counterparts, practically unbounded, and in many cases, initialized with random weights or arbitrary structural elements. In this dissertation, we consider an inductive base case for imposing BNN constraints onto ANNs. We select explicit connectome topologies from the fruit fly (one of the smallest BNNs) and impose these onto a multilayer perceptron (MLP) and a reservoir computer (RC), in order to craft “fruit fly neural networks” (FFNNs). We study the impact on performance, variance, and prediction dynamics from using FFNNs compared to non-FFNN models on odour classification, chaotic time-series prediction, and multifunctionality tasks. From a series of four experimental studies, we observe that the fly olfactory brain is aligned towards recalling and making predictions from chaotic input data, with a capacity for executing two mutually exclusive tasks from distinct initial conditions, and with low sensitivity to hyperparameter fluctuations that can lead to chaotic behaviour. We also observe that the clustering coefficient of the fly network, and its particular non-zero weight positions, are important for reducing model variance. These findings suggest that BNNs have distinct advantages over arbitrarily-weighted ANNs; notably, from their structure alone. More work with connectomes drawn across species will be useful in finding shared topological features which can further enhance ANNs, and Machine Learning overall
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