Modelling estimation and analysis of dynamic processes from image sequences using temporal random closed sets and point processes with application to the cell exocytosis and endocytosis

In this thesis, new models and methodologies are introduced for the analysis of dynamic processes characterized by image sequences with spatial temporal overlapping. The spatial temporal overlapping exists in many natural phenomena and should be addressed properly in several Science disciplines such as Microscopy, Material Sciences, Biology, Geostatistics or Communication Networks. This work is related to the Point Process and Random Closed Set theories, within Stochastic Geometry. The proposed models are an extension of Boolean Models in R2 by adding a temporal dimension. The study has been motivated for its application in a multidisciplinary project that combined Statistics, Computer Sciences, Biology and Microscopy, with the aim of analysing the cell exocytosis and endocytosis. Exocytosis is the process by which cells secrete vesicles outside the plasma membrane and endocytosis is the opposite mechanism. Our data were image sequences obtained by Electron Microscopy and Total Internal Reflection Fluorescence Microscopy. Fluorescent tagged-proteins are observed as overlapped clusters with random shape, area and duration. They can be modelled as realizations of a stationary and isotropic stochastic process. The methodology herein proposed could be used to analyze similar phenomena in other Fields of Science. First, the temporal Boolean model is introduced and some estimation methods for the parameters of the model are presented. Second, we proposed a method for the estimation of the event duration distribution function of a univariate temporal Boolean model based on spatial temporal covariance. A simulation study is performed with several duration probability density functions, and an application to the cell endocytosis is realized. Third, we introduce the bivariate temporal Boolean model to study interactions between two overlapped spatial temporal processes and to quantify their overlapping and dependencies. We propose a non-parametric approach based on a generalization of the Ripley K-function, the spatial-temporal covariance and the pair correlation functions for a bivariate temporal random closed set. A Monte Carlo test was performed to test the independence hypothesis. This methodology is not only a test procedure but also allows us to quantify the degree and spatial temporal interval of the interaction. No parametric assumption is needed. A simulation study has been conducted and an application to the study of proteins that mediate in cell endocytosis has been performed. Fourth, from high spatial resolution EM images, we model the distribution of exocytic vesicles (granules) within the cell cytoplasm as a realization of a finite point process (a point pattern), and the point patterns of several cell groups are considered replicates of different point processes. Our aim was to study differences between two treatment groups in terms of granule location. We characterize the spatial distribution of granules with respect to the plasma membrane by means of several functional descriptors, that allowed us to detect significant differences between the two cell groups that would not be observed by a classical approach. To perform image segmentation, we developed an automatic granule detection tool with similar performance to that of the manual one-by-one marking. Finally, we have implemented a software toolbox for the simulation and analysis of temporal Boolean models (available at http : ==www:uv:es=tracs=), so scientists and technicians of any discipline can apply the proposed methods. In summary, the spatial temporal stochastic models proposed allow modelling of dynamic processes from image sequences where several forms of random shape, size and duration overlap. It is the first time these tools are applied to the study of cell exo and endocytosis, and they would contribute to improve their understanding. Our methodologies will help future research in Cell Biology, e.g. in the study of diseases related to secretion dysfunctions, such as diabetes.En esta tesis presentamos nuevos modelos y metodolog as para el an alisis de pro- cesos din amicos a partir de secuencias de im agenes, con solapamiento espacial y tem- poral de los objetos de an alisis, un fen omeno habitual en la naturaleza. El trabajo realizado se enmarca en la teor a de Procesos Puntuales y Conjuntos Aleatorios Ce- rrados (RACS), dentro de la Geometr a Estoc astica. Los modelos propuestos son una extensi on de la teor a de modelos booleanos en R2 incorporando una componente temporal. La motivaci on del trabajo fue su aplicaci on a un proyecto multidisciplinar donde analizamos la exocitosis y la endocitosis celular, procesos en que la c elula segrega o absorbe sustancias a trav es de la membrana citoplasm atica, respectivamente. El es- tudio se realiz o utilizando secuencias de im agenes obtenidas con microscop a TIRFM, donde se observan las prote nas como agrupaciones uorescentes superpuestas. Mo- delizamos las im agenes como realizaciones de un proceso estoc astico estacionario e isotr opico. Esta metodolog a permite analizar fen omenos reales en otros campos de la Ciencia con superposici on espacio-temporal de objetos con formas y duraciones aleatorias, como Geolog a, Qu mica, Comunicaciones, etc. Primero, introducimos el modelo booleano temporal. Presentamos un m etodo de estimaci on de la funci on de distribuci on de la duraci on basado en la covarianza espacio-temporal, y el estudio de simulaci on realizado. Segundo, estudiamos la in- terrelaci on entre dos procesos espacio-temporales mediante la K-funci on de Ripley, la covarianza espacio-temporal y la funci on de correlaci on para conjuntos aleatorios bivariados. Realizamos un estudio de simulaci on y una aplicaci on a la endocitosis celular. Tercero, modelizamos la distribuci on de ves culas exoc ticas (gr anulos) en el cito- plasma celular como un proceso puntual nito. Caracterizamos su distribuci on espa- cial respecto a la membrana mediante varios descriptores funcionales. Para segmentar las im agenes, desarrollamos una herramienta autom atica de detecci on de gr anulos. Hemos desarrollado una herramienta de software completa para la simulaci on y es- timaci on de modelos booleanos temporales (disponible en http : ==www:uv:es=tracs=)

Data-Driven Representation Learning in Multimodal Feature Fusion

abstract: Modern machine learning systems leverage data and features from multiple modalities to gain more predictive power. In most scenarios, the modalities are vastly different and the acquired data are heterogeneous in nature. Consequently, building highly effective fusion algorithms is at the core to achieve improved model robustness and inferencing performance. This dissertation focuses on the representation learning approaches as the fusion strategy. Specifically, the objective is to learn the shared latent representation which jointly exploit the structural information encoded in all modalities, such that a straightforward learning model can be adopted to obtain the prediction. We first consider sensor fusion, a typical multimodal fusion problem critical to building a pervasive computing platform. A systematic fusion technique is described to support both multiple sensors and descriptors for activity recognition. Targeted to learn the optimal combination of kernels, Multiple Kernel Learning (MKL) algorithms have been successfully applied to numerous fusion problems in computer vision etc. Utilizing the MKL formulation, next we describe an auto-context algorithm for learning image context via the fusion with low-level descriptors. Furthermore, a principled fusion algorithm using deep learning to optimize kernel machines is developed. By bridging deep architectures with kernel optimization, this approach leverages the benefits of both paradigms and is applied to a wide variety of fusion problems. In many real-world applications, the modalities exhibit highly specific data structures, such as time sequences and graphs, and consequently, special design of the learning architecture is needed. In order to improve the temporal modeling for multivariate sequences, we developed two architectures centered around attention models. A novel clinical time series analysis model is proposed for several critical problems in healthcare. Another model coupled with triplet ranking loss as metric learning framework is described to better solve speaker diarization. Compared to state-of-the-art recurrent networks, these attention-based multivariate analysis tools achieve improved performance while having a lower computational complexity. Finally, in order to perform community detection on multilayer graphs, a fusion algorithm is described to derive node embedding from word embedding techniques and also exploit the complementary relational information contained in each layer of the graph.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Processing hidden Markov models using recurrent neural networks for biological applications

Philosophiae Doctor - PhDIn this thesis, we present a novel hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov Models (HMMs). Though sequence recognition problems could be potentially modelled through well trained HMMs, they could not provide a reasonable solution to the complicated recognition problems. In contrast, the ability of RNNs to recognize the complex sequence recognition problems is known to be exceptionally good. It should be noted that in the past, methods for applying HMMs into RNNs have been developed by other researchers. However, to the best of our knowledge, no algorithm for processing HMMs through learning has been given. Taking advantage of the structural similarities of the architectural dynamics of the RNNs and HMMs, in this work we analyze the combination of these two systems into the hybrid architecture. To this end, the main objective of this study is to improve the sequence recognition/classi_cation performance by applying a hybrid neural/symbolic approach. In particular, trained HMMs are used as the initial symbolic domain theory and directly encoded into appropriate RNN architecture, meaning that the prior knowledge is processed through the training of RNNs. Proposed algorithm is then implemented on sample test beds and other real time biological applications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Anomaly detection in unknown environments using wireless sensor networks

This dissertation addresses the problem of distributed anomaly detection in Wireless Sensor Networks (WSN). A challenge of designing such systems is that the sensor nodes are battery powered, often have different capabilities and generally operate in dynamic environments. Programming such sensor nodes at a large scale can be a tedious job if the system is not carefully designed. Data modeling in distributed systems is important for determining the normal operation mode of the system. Being able to model the expected sensor signatures for typical operations greatly simplifies the human designer’s job by enabling the system to autonomously characterize the expected sensor data streams. This, in turn, allows the system to perform autonomous anomaly detection to recognize when unexpected sensor signals are detected. This type of distributed sensor modeling can be used in a wide variety of sensor networks, such as detecting the presence of intruders, detecting sensor failures, and so forth. The advantage of this approach is that the human designer does not have to characterize the anomalous signatures in advance. The contributions of this approach include: (1) providing a way for a WSN to autonomously model sensor data with no prior knowledge of the environment; (2) enabling a distributed system to detect anomalies in both sensor signals and temporal events online; (3) providing a way to automatically extract semantic labels from temporal sequences; (4) providing a way for WSNs to save communication power by transmitting compressed temporal sequences; (5) enabling the system to detect time-related anomalies without prior knowledge of abnormal events; and, (6) providing a novel missing data estimation method that utilizes temporal and spatial information to replace missing values. The algorithms have been designed, developed, evaluated, and validated experimentally in synthesized data, and in real-world sensor network applications

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic