Bayesian plug & play methods for inverse problems in imaging.

Thèse de Doctorat de Mathématiques Appliquées (Université de Paris)Tesis de Doctorado en Ingeniería Eléctrica (Universidad de la República)This thesis deals with Bayesian methods for solving ill-posed inverse problems in imaging with learnt image priors. The first part of this thesis (Chapter 3) concentrates on two particular problems, namely joint denoising and decompression and multi-image super-resolution. After an extensive study of the noise statistics for these problem in the transformed (wavelet or Fourier) domain, we derive two novel algorithms to solve this particular inverse problem. One of them is based on a multi-scale self-similarity prior and can be seen as a transform-domain generalization of the celebrated non-local bayes algorithm to the case of non-Gaussian noise. The second one uses a neural-network denoiser to implicitly encode the image prior, and a splitting scheme to incorporate this prior into an optimization algorithm to find a MAP-like estimator. The second part of this thesis concentrates on the Variational AutoEncoder (VAE) model and some of its variants that show its capabilities to explicitly capture the probability distribution of high-dimensional datasets such as images. Based on these VAE models, we propose two ways to incorporate them as priors for general inverse problems in imaging : • The first one (Chapter 4) computes a joint (space-latent) MAP estimator named Joint Posterior Maximization using an Autoencoding Prior (JPMAP). We show theoretical and experimental evidence that the proposed objective function satisfies a weak bi-convexity property which is sufficient to guarantee that our optimization scheme converges to a stationary point. Experimental results also show the higher quality of the solutions obtained by our JPMAP approach with respect to other non-convex MAP approaches which more often get stuck in spurious local optima. • The second one (Chapter 5) develops a Gibbs-like posterior sampling algorithm for the exploration of posterior distributions of inverse problems using multiple chains and a VAE as image prior. We showhowto use those samples to obtain MMSE estimates and their corresponding uncertainty.Cette thèse traite des méthodes bayésiennes pour résoudre des problèmes inverses mal posés en imagerie avec des distributions a priori d’images apprises. La première partie de cette thèse (Chapitre 3) se concentre sur deux problèmes partic-uliers, à savoir le débruitage et la décompression conjoints et la super-résolutionmulti-images. Après une étude approfondie des statistiques de bruit pour ces problèmes dans le domaine transformé (ondelettes ou Fourier), nous dérivons deuxnouveaux algorithmes pour résoudre ce problème inverse particulie. L’un d’euxest basé sur une distributions a priori d’auto-similarité multi-échelle et peut êtrevu comme une généralisation du célèbre algorithme de Non-Local Bayes au cas dubruit non gaussien. Le second utilise un débruiteur de réseau de neurones pourcoder implicitement la distribution a priori, et un schéma de division pour incor-porer cet distribution dans un algorithme d’optimisation pour trouver un estima-teur de type MAP. La deuxième partie de cette thèse se concentre sur le modèle Variational Auto Encoder (VAE) et certaines de ses variantes qui montrent ses capacités à capturer explicitement la distribution de probabilité d’ensembles de données de grande dimension tels que les images. Sur la base de ces modèles VAE, nous proposons deuxmanières de les incorporer comme distribution a priori pour les problèmes inverses généraux en imagerie: •Le premier (Chapitre 4) calcule un estimateur MAP conjoint (espace-latent) nommé Joint Posterior Maximization using an Autoencoding Prior (JPMAP). Nous montrons des preuves théoriques et expérimentales que la fonction objectif proposée satisfait une propriété de bi-convexité faible qui est suffisante pour garantir que notre schéma d’optimisation converge vers un pointstationnaire. Les résultats expérimentaux montrent également la meilleurequalité des solutions obtenues par notre approche JPMAP par rapport à d’autresapproches MAP non convexes qui restent le plus souvent bloquées dans desminima locaux. •Le second (Chapitre 5) développe un algorithme d’échantillonnage a poste-riori de type Gibbs pour l’exploration des distributions a posteriori de problèmes inverses utilisant des chaînes multiples et un VAE comme distribution a priori. Nous montrons comment utiliser ces échantillons pour obtenir desestimations MMSE et leur incertitude correspondante.En esta tesis se estudian métodos bayesianos para resolver problemas inversos mal condicionados en imágenes usando distribuciones a priori entrenadas. La primera parte de esta tesis (Capítulo 3) se concentra en dos problemas particulares, a saber, el de eliminación de ruido y descompresión conjuntos, y el de superresolución a partir de múltiples imágenes. Después de un extenso estudio de las estadísticas del ruido para estos problemas en el dominio transformado (wavelet o Fourier),derivamos dos algoritmos nuevos para resolver este problema inverso en particular. Uno de ellos se basa en una distribución a priori de autosimilitud multiescala y puede verse como una generalización al dominio wavelet del célebre algoritmo Non-Local Bayes para el caso de ruido no Gaussiano. El segundo utiliza un algoritmo de eliminación de ruido basado en una red neuronal para codificar implícitamente la distribución a priori de las imágenes y un esquema de relajación para incorporar esta distribución en un algoritmo de optimización y así encontrar un estimador similar al MAP. La segunda parte de esta tesis se concentra en el modelo Variational AutoEncoder (VAE) y algunas de sus variantes que han mostrado capacidad para capturar explícitamente la distribución de probabilidad de conjuntos de datos en alta dimensión como las imágenes. Basándonos en estos modelos VAE, proponemos dos formas de incorporarlos como distribución a priori para problemas inversos genéricos en imágenes : •El primero (Capítulo 4) calcula un estimador MAP conjunto (espacio imagen y latente) llamado Joint Posterior Maximization using an Autoencoding Prior (JPMAP). Mostramos evidencia teórica y experimental de que la función objetivo propuesta satisface una propiedad de biconvexidad débil que es suficiente para garantizar que nuestro esquema de optimización converge a un punto estacionario. Los resultados experimentales también muestran la mayor calidad de las soluciones obtenidas por nuestro enfoque JPMAP con respecto a otros enfoques MAP no convexos que a menudo se atascan en mínimos locales espurios. •El segundo (Capítulo 5) desarrolla un algoritmo de muestreo tipo Gibbs parala exploración de la distribución a posteriori de problemas inversos utilizando múltiples cadenas y un VAE como distribución a priori. Mostramos cómo usar esas muestras para obtener estimaciones de MMSE y su correspondiente incertidumbr

Data collection system: Earth Resources Technology Satellite-1

Subjects covered at the meeting concerned results on the overall data collection system including sensors, interface hardware, power supplies, environmental enclosures, data transmission, processing and distribution, maintenance and integration in resources management systems

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity

Adding Context to Automated Text Input Error Analysis with Reference to Understanding How Children Make Typing Errors

Despite the enormous body of literature studying the typing errors of adults, children's typing errors remain an understudied area. It is well known in the field of Child-Computer Interaction that children are not 'little adults'. This means findings regarding how adults make typing mistakes cannot simply be transferred into how children make typing errors, without first understanding the differences. To understand how children differ from adults in the way they make typing mistakes, typing data were gathered from both children and adults. It was important that the data collected from the contrasting participant groups were comparable. Various methods of collecting typing data from adults were reviewed for suitability with children. Several issues were identified that could create a bias towards the adults. To resolve these issues, new tools and methods were designed, such as a new phrase set, a new data collector and new computer experience questionnaires. Additionally, there was a lack of an analysis method of typing data suitable for use with both children and adults. A new categorisation method was defined based on typing errors made by both children and adults. This categorisation method was then adapted into a Java program, which dramatically reduced the time required to carry out typing categorisation. Finally, in a large study, typing data collected from 231 primary school children, aged between 7 and 10 years, and 229 undergraduate computing students were analysed. Grouping the typing errors according to the context in which they occurred allowed for a much more detailed analysis than was possible with error rates. The analysis showed children have a set of errors they made frequently that adults rarely made. These errors that are specific to children suggest that differences exist between the ways the two groups make typing errors. This finding means that children's typing errors should be studied in their own right

Ecological modernisation theory and Bangladesh: Lessons from the environmental compliance upgrading experiences of Bangladeshi garments firms.

In this era of international supply chains where Least Developed Countries (LDCs) are exporting to Developed Countries (DCs), concerns about economic growth that is environmentally benign has meant that LDC factories are taking environmental upgrading measures to meet standards set by DC customers. This thesis looks at the applicability of ecological modernisation theory (EMT) to this situation by examining the Bangladeshi readymade garments (RMG) sector that is part of the global apparel value chain. EMT suggests that economic growth can continue while providing environmental protection in the long run due to proactive environmental actions by the market actors, civil society and the nation state. This thesis tests the tenets of EMT by looking at the apparel value chain in three parts (management networks within firms, economic networks of the supply chain, and policy networks) and then as a whole (EM network). Evidence from Bangladeshi garment factories (corporate culture, organisational change and environmental learning) suggests significant problems: factories are compliant with buyer codes only on paper and not in reality. Firms have a mixture of proactive and reactive greening measures and enjoy only an indirect competitive advantage from greening. The absence of "win win" gains can be pinned to buyer behaviour along the chains, coupled with their reluctance for closer collaboration and weak green customer pressures for clothing sourced in Bangladesh. Policymaking by the state has also been problematic: issue cognition and conflict, closed hierarchical networks, mistrust, political bargaining and prioritising national economic interests hampered the EM vision of the modern nation state. Overall, this thesis questions the adequacy of EMT for investing international supply chains. EMT needs to reconceptualise itself with hierarchical relationship realities, LDC cultural contexts, LDC growth trajectories, actor heterogeneity, "no win" situations, and the suitability of EM tools

The effectiveness of commitment devices: field experiments on health behaviour change

Behavioural public policy, as popularised by the “nudge” agenda, aims to help people make better choices in the face of their inherent biases (Thaler and Sunstein, 2008), including over diet and weight management (Liu et al, 2014). Present bias can lead to time inconsistency: individuals identify an optimal course of action but when the moment comes to take that action they delay or quit, prioritizing present gains at the expense of longer term benefits (O’ Donoghue and Rabin, 1999). Time inconsistency is explained in Thaler and Shefrin’s dual-self model (1981) as the result of an internal tussle between a myopic ‘doer’ and a far-sighted ‘planner’. Commitment devices – voluntary strategies to change future behaviours – can help people stay on track with their goals. Emerging empirical evidence from psychology, medicine, and behavioural economics bears out this prediction for health behaviours (Prestwich et al, 2012; Volpp et al, 2008; Giné et al, 2010), but commitment devices remain relatively under-researched (Perry et al, 2015). The dissertation sets out a fresh analytical framework applying, for the first time, planner-doer theory to health behaviours for weight loss. It also explores how commitment devices might work differently across sub-groups. The empirical strategy, combining quantitative and qualitative methods, centres on two field experiments testing for average and heterogeneous treatment effects of commitment devices on self-monitoring behaviour, participation in a weight loss programme, and weight loss outcomes. Results indicate commitment devices improve health behaviours, but have mixed effects on weight loss: highlighting the potential for commitment overload, and the importance of choosing the right dose of commitment. Qualitative evidence provides fresh insights for planner-doer theory. Differential impacts on sub-groups imply a need for careful targeting and design of commitment devices. The dissertation concludes there is scope for commitment devices to play an effective role in behaviour change programmes

Third International Symposium on Space Mission Operations and Ground Data Systems, part 2

Under the theme of 'Opportunities in Ground Data Systems for High Efficiency Operations of Space Missions,' the SpaceOps '94 symposium included presentations of more than 150 technical papers spanning five topic areas: Mission Management, Operations, Data Management, System Development, and Systems Engineering. The symposium papers focus on improvements in the efficiency, effectiveness, and quality of data acquisition, ground systems, and mission operations. New technology, methods, and human systems are discussed. Accomplishments are also reported in the application of information systems to improve data retrieval, reporting, and archiving; the management of human factors; the use of telescience and teleoperations; and the design and implementation of logistics support for mission operations. This volume covers expert systems, systems development tools and approaches, and systems engineering issues