Large scale estimation of distribution algorithms for continuous optimisation

Modern real world optimisation problems are increasingly becoming large scale. However, searching in high dimensional search spaces is notoriously difficult. Many methods break down as dimensionality increases and Estimation of Distribution Algorithm (EDA) is especially prone to the curse of dimensionality. In this thesis, we device new EDA variants that are capable of searching in large dimensional continuous domains. We in particular (i) investigated heavy tails search distributions, (ii) we clarify a controversy in the literature about the capabilities of Gaussian versus Cauchy search distributions, (iii) we constructed a new way of projecting a large dimensional search space to low dimensional subspaces in a way that gives us control of the size of covariance of the search distribution and we develop adaptation techniques to exploit this and (iv) we proposed a random embedding technique in EDA that takes advantage of low intrinsic dimensional structure of problems. All these developments avail us with new techniques to tackle high dimensional optimization problems

A review of population-based metaheuristics for large-scale black-box global optimization: Part A

Scalability of optimization algorithms is a major challenge in coping with the ever growing size of optimization problems in a wide range of application areas from high-dimensional machine learning to complex large-scale engineering problems. The field of large-scale global optimization is concerned with improving the scalability of global optimization algorithms, particularly population-based metaheuristics. Such metaheuristics have been successfully applied to continuous, discrete, or combinatorial problems ranging from several thousand dimensions to billions of decision variables. In this two-part survey, we review recent studies in the field of large-scale black-box global optimization to help researchers and practitioners gain a bird’s-eye view of the field, learn about its major trends, and the state-of-the-art algorithms. Part of the series covers two major algorithmic approaches to large-scale global optimization: problem decomposition and memetic algorithms. Part of the series covers a range of other algorithmic approaches to large-scale global optimization, describes a wide range of problem areas, and finally touches upon the pitfalls and challenges of current research and identifies several potential areas for future research

p-Kernel Stein Variational Gradient Descent for Data Assimilation and History Matching

A Bayesian method of inference known as “Stein variational gradient descent” was recently implemented for data assimilation problems, under the heading of “mapping particle filter”. In this manuscript, the algorithm is applied to another type of geoscientific inversion problems, namely history matching of petroleum reservoirs. In order to combat the curse of dimensionality, the commonly used Gaussian kernel, which defines the solution space, is replaced by a p-kernel. In addition, the ensemble gradient approximation used in the mapping particle filter is rectified, and the data assimilation experiments are re-run with more relevant settings and comparisons. Our experimental results in data assimilation are rather disappointing. However, the results from the subsurface inverse problem show more promise, especially as regards the use of p-kernels.publishedVersio

Characterizing cortical myosin mini-filament regulation, length and its macroscopic implications in cytokinetic dynamics

Au cours de la cytokinèse, le génome dédoublé est compartimentalisé en deux cellules filles. L’anneau contractile, une structure dynamique, est constitué d’actine, myosine (NMY-II) et d’autres protéines accessoires. NMY-2 est le seul moteur protéique impliqué dans la contraction de l’anneau durant la cytokinèse. Depuis longtemps, il a été considéré que celle-ci glissait le long des filaments d’actine grâce à sa capacité de traction. Récemment, plusieurs études ont découvert que son activité réticulante joue un rôle en cytokinèse et il est connu que la NMY-2 peut s’assembler en filaments bipolaires à partir de dimères. Ainsi, nous postulons que leur dimension (nombre de moteurs ATPasiques) pourrait dicter leur contribution en activité motrice et réticulante. Afin de déterminer la composition des filaments corticaux de NMY-2, nous avons utilisé une technique d'imagerie de molécules individuelles à l’aide de la microscopie TIRF. J’ai trouvé à travers l’analyse statistique de la distribution des NMY-2 mesurés que les filaments sont assemblés à deux dimensions constantes: Des filaments composés de 20 dimères et 30 dimères. La kinase Rho est une activatrice de NMY-2 nécessaire pour les niveaux physiologiques de NMY-2 sur l’anneau contractile, pour des cinétiques et fermeture concentrique de l’anneau. La déplétion de RhoK augmente l’abondance relative des filaments de 20 dimères. Ainsi, RhoK pourrait réguler le recrutement de la NMY et aussi l’assemblage des filaments corticaux de NMY-2. De plus, à l’aide de la microscopie confocale à temps réel, j’ai trouvé que lors de la déplétion de RhoK, il se produit une réduction du recrutement et du délai d’initiation du sillon, une fermeture lente et une augmentation significative de la concentricité de l’anneau. De plus, j’ai mesuré des défauts dans l’organisation corticale de l’anneau contractile en patch. La déplétion de MRCK-1 n’affecte pas l’initiation du sillon, les cinétiques de fermeture, ou la fermeture concentrique de l’anneau. Paradoxalement, la déplétion de MRCK-1 augmente le recrutement cortical de NMY-2, mais quand depleté simultanément avec Rho-K il diminue NMY-2 à l’équateur comparé à la déplétion seule de Rho-K. De plus, la double déplétion, conduit à un phénotype de concentricité de l’anneau, suivie d’un recentrage.Non-muscle myosin II (myosin) is important for many cellular processes, including cytokinesis. Myosin is a major component of the contractile ring, which constricts to close the connection between the two daughter cells. It was long accepted that actomyosin contractile filament sliding closes the cytokinetic ring. However, several recent papers conclude that myosin’s actin crosslinking activity is more important than its motor activity. These two functions likely relate to the number of actin-binding heads per bipolar myosin mini-filament. I then measured the size of cortical bipolar myosin mini-filaments and tested how mini-filament size and abundance influences cytokinesis. To measure the composition of individual myosin-containing cortical features, I developed a TIRF microscopy-based assay to calculate the number of NMY-2 dimers per feature from a ratio of endogenous/functional NMY-2-GFP. Interestingly control cells possess 2 populations: mini-filaments with an average of 20 dimers and with 30 dimers that are built to consistent specifications. Depletion of the NMY-2 activator Rho-Kinase or Anillin, a contractile ring scaffold protein, significantly alters the relative abundance of small and larger NMY-2 filament populations. I then tested the macroscopic implications of perturbations that alter cortical NMY-2 assembly. I measured NMY-2 regulators depleted cells and measured NMY-2 cortical recruitment, organization, and the kinetics (speed and concentricity) of cytokinesis. Rho-K depletion decreases NMY-II cortical recruitment and organization, slows ring closure and makes it more concentric. Depletion of MRCK-1, a less well-understood conserved myosin kinase, increased myosin cortical recruitment but had little effect on furrowing kinetics. Following simultaneous depletion of MRCK and RhoK, cortical myosin organization and recruitment were drastically reduced and, as expected for a much weaker cortex, a unique concentric phenotype emerged. Thus, while Rho-kinase is the more important kinase for myosin activation, MRCK-1 contributes to myosin organization and contractile ring dynamics. We conclude that myosin is recruited to the cortex as multi-headed mini-filaments whose assembly is tightly regulated and which impacts several aspects of contractile ring function

Smart Technologies for Precision Assembly

This open access book constitutes the refereed post-conference proceedings of the 9th IFIP WG 5.5 International Precision Assembly Seminar, IPAS 2020, held virtually in December 2020. The 16 revised full papers and 10 revised short papers presented together with 1 keynote paper were carefully reviewed and selected from numerous submissions. The papers address topics such as assembly design and planning; assembly operations; assembly cells and systems; human centred assembly; and assistance methods in assembly

Single atom imaging with time-resolved electron microscopy

Developments in scanning transmission electron microscopy (STEM) have opened up new possibilities for time-resolved imaging at the atomic scale. However, rapid imaging of single atom dynamics brings with it a new set of challenges, particularly regarding noise and the interaction between the electron beam and the specimen. This thesis develops a set of analytical tools for capturing atomic motion and analyzing the dynamic behaviour of materials at the atomic scale. Machine learning is increasingly playing an important role in the analysis of electron microscopy data. In this light, new unsupervised learning tools are developed here for noise removal under low-dose imaging conditions and for identifying the motion of surface atoms. The scope for real-time processing and analysis is also explored, which is of rising importance as electron microscopy datasets grow in size and complexity. These advances in image processing and analysis are combined with computational modelling to uncover new chemical and physical insights into the motion of atoms adsorbed onto surfaces. Of particular interest are systems for heterogeneous catalysis, where the catalytic activity can depend intimately on the atomic environment. The study of Cu atoms on a graphene oxide support reveals that the atoms undergo anomalous diffusion as a result of spatial and energetic disorder present in the substrate. The investigation is extended to examine the structure and stability of small Cu clusters on graphene oxide, with atomistic modelling used to understand the significant role played by the substrate. Finally, the analytical methods are used to study the surface reconstruction of silicon alongside the electron beam-induced motion of adatoms on the surface. Taken together, these studies demonstrate the materials insights that can be obtained with time-resolved STEM imaging, and highlight the importance of combining state-ofthe- art imaging with computational analysis and atomistic modelling to quantitatively characterize the behaviour of materials with atomic resolution.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement 291522–3DIMAGE, as well as from the European Union Seventh Framework Programme under Grant Agreement 312483-ESTEEM2 (Integrated Infrastructure Initiative -I3)

Methods for enhanced learning using wearable technologies. A study of the maritime sector

Maritime safety is a critical concern due to the potential for serious consequences or accidents for the crew, passengers, environment, and assets resulting from navigation errors or unsafe acts. Traditional training methods face challenges in the rapidly evolving maritime industry, and innovative training methods are being explored. This study explores the use of wearable sensors with biosignal data collection to improve training performance in the maritime sector. Three experiments were conducted progressively to investigate the relationship between navigators' experience levels and biosignal data results, the effects of different training methods on cognitive workload, trainees' stress levels, and their decision-making skills, and the classification of scenario complexity and the biosignal data obtained by the trainees. questionnaire data on stress levels, workload, and user satisfaction of auxiliary training equipment; performance evaluation data on navigational abilities, decision-making skills, and ship-handling abilities; and biosignal data, including electrodermal activity (EDA), body temperature, blood volume pulse (BVP), inter-beat interval (IBI), and heart rate (HR). Several statistical methods and machine-learning algorithms were used in the data analysis. The present dissertation contributes to the advancement of the field of maritime education and training by exploring methods for enhancing learning in complex situations. The use of biosignal data provides insights into the interplay between stress levels and training outcomes in the maritime industry. The proposed conceptual training model underscores the relationship between trainees' stress and safety factors and offers a framework for the development and evaluation of advanced biosignal data-based training systems