A Cellular Potts Model of single cell migration in presence of durotaxis

Cell migration is a fundamental biological phenomenon during which cells sense their surroundings and respond to different types of signals. In presence of durotaxis, cells preferentially crawl from soft to stiff substrates by reorganizing their cytoskeleton from an isotropic to an anisotropic distribution of actin filaments. In the present paper, we propose a Cellular Potts Model to simulate single cell migration over flat substrates with variable stiffness. We have tested five configurations: (i) a substrate including a soft and a stiff region, (ii) a soft substrate including two parallel stiff stripes, (iii) a substrate made of successive stripes with increasing stiffness to create a gradient and (iv) a stiff substrate with four embedded soft squares. For each simulation, we have evaluated the morphology of the cell, the distance covered, the spreading area and the migration speed. We have then compared the numerical results to specific experimental observations showing a consistent agreement

Robust and Efficient Inference of Scene and Object Motion in Multi-Camera Systems

Multi-camera systems have the ability to overcome some of the fundamental limitations of single camera based systems. Having multiple view points of a scene goes a long way in limiting the influence of field of view, occlusion, blur and poor resolution of an individual camera. This dissertation addresses robust and efficient inference of object motion and scene in multi-camera and multi-sensor systems. The first part of the dissertation discusses the role of constraints introduced by projective imaging towards robust inference of multi-camera/sensor based object motion. We discuss the role of the homography and epipolar constraints for fusing object motion perceived by individual cameras. For planar scenes, the homography constraints provide a natural mechanism for data association. For scenes that are not planar, the epipolar constraint provides a weaker multi-view relationship. We use the epipolar constraint for tracking in multi-camera and multi-sensor networks. In particular, we show that the epipolar constraint reduces the dimensionality of the state space of the problem by introducing a ``shared'' state space for the joint tracking problem. This allows for robust tracking even when one of the sensors fail due to poor SNR or occlusion. The second part of the dissertation deals with challenges in the computational aspects of tracking algorithms that are common to such systems. Much of the inference in the multi-camera and multi-sensor networks deal with complex non-linear models corrupted with non-Gaussian noise. Particle filters provide approximate Bayesian inference in such settings. We analyze the computational drawbacks of traditional particle filtering algorithms, and present a method for implementing the particle filter using the Independent Metropolis Hastings sampler, that is highly amenable to pipelined implementations and parallelization. We analyze the implementations of the proposed algorithm, and in particular concentrate on implementations that have minimum processing times. The last part of the dissertation deals with the efficient sensing paradigm of compressing sensing (CS) applied to signals in imaging, such as natural images and reflectance fields. We propose a hybrid signal model on the assumption that most real-world signals exhibit subspace compressibility as well as sparse representations. We show that several real-world visual signals such as images, reflectance fields, videos etc., are better approximated by this hybrid of two models. We derive optimal hybrid linear projections of the signal and show that theoretical guarantees and algorithms designed for CS can be easily extended to hybrid subspace-compressive sensing. Such methods reduce the amount of information sensed by a camera, and help in reducing the so called data deluge problem in large multi-camera systems

Rebuild continuity: reshaping the publicness of waterfront through civic infrastructure

MBArch - Màster Universitari en Estudis Avançats en Arquitectura-Barcelona: The Contemporary ProjectEs pren com a cas destudi principal la comunitat de CaoYang, que és el primer poble de treballadors de Xangai, ia causa del valor històric que tenia, la comunitat de CaoYang es va conservar com un fòssil vivent amb lesperança de preservar la memòria del que una vegada va ser, però mantenir-la intacta no és el mateix que preservar l'autenticitat, i el sistema d'espai públic obsolet i la infraestructura residual de CaoYang afecten greument la vida dels residents que viuen ara i aquí. En tractar el problema del declivi públic a escala urbana de la comunitat de Caoyang, el concepte és inusual perquè no opera una escala urbana gran i abstracta, sinó una microintervenció a escala de l'arquitectura. És possible fer operacions quirúrgiques en aquestes estructures abandonades per transformar la infraestructura en espai públic, com una llavor que provoca una sèrie d'esdeveniments seqüencials? Les múltiples infraestructures obsoletes que queden al Bucle del Riu CaoYang es converteixen en el punt d'entrada d'aquesta tesi, que intenta transformar-les comunitàriament i integrar-les sistemàticament. Això no només crearà un parc lineal continu davant de l'aigua o un cinturó verd públic, sinó que funcionarà a escala urbana integrant els sistemes urbans i naturals. Prenent com a oportunitat la reutilització urbana flexible i adaptable, el desenvolupament urbà emprendrà un nou camí de participació ascendent i universal.Se toma como caso de estudio principal la comunidad de CaoYang, que es el primer pueblo de trabajadores de Shangai, y debido al valor histórico que tenía, la comunidad de CaoYang se conservó como un fósil viviente con la esperanza de preservar la memoria de lo que una vez fue, pero mantenerla intacta no es lo mismo que preservar la autenticidad, y el sistema de espacio público obsoleto y la infraestructura residual de CaoYang afectan gravemente a la vida de los residentes que viven aquí y ahora. Al tratar el problema del declive público a escala urbana de la comunidad de Caoyang, el concepto es inusual porque no opera a una escala urbana grande y abstracta, sino a una microintervención a escala de la arquitectura. ¿Es posible realizar operaciones quirúrgicas en estas estructuras abandonadas para transformar la infraestructura en espacio público, como una semilla que provoca una serie de acontecimientos secuenciales? Las múltiples infraestructuras obsoletas que quedan en el Bucle del Río CaoYang se convierten en el punto de entrada de esta tesis, que intenta transformarlas comunitariamente, así como integrarlas sistemáticamente. Esto no sólo creará un parque lineal continuo frente al agua o un cinturón verde público, sino que funcionará a escala urbana integrando los sistemas urbanos y naturales. Tomando como oportunidad la reutilización urbana flexible y adaptable, el desarrollo urbano emprenderá un nuevo camino de participación ascendente y universal.CaoYang Community is taken as the main case study, which is the first workers’ village in Shanghai, and because of the historical value it carried, the CaoYang community was preserved as a living fossil in the hope of preserving the memory of what once was, but keeping it intact is not the same as preserving the authenticity, and the obsolete public space system and residual infrastructure of CaoYang seriously affects the lives of the residents living here and now. Discussing the problem of the urban-scale public decline of the Caoyang community, the concept is unusual in that it operates not on a large, abstract urban scale, but on a micro intervention at the scale of architecture. Is it possible to perform surgical operations on these abandoned structures to transform infrastructure into public space, like a seed that causes a series of sequential events? The multiple obsolete infrastructures left on the CaoYang River Loop become the entry point for this thesis, which attempts to transform them communally as well as integrate them systematically. This will not only create a continuous linear waterfront park or public green belt, but will function at the urban scale by integrating urban and natural systems. Taking flexible and adaptable urban reuse as an opportunity, urban development will embark on a new road of bottom-up and universal participation

Signal separation of musical instruments: simulation-based methods for musical signal decomposition and transcription

This thesis presents techniques for the modelling of musical signals, with particular regard to monophonic and polyphonic pitch estimation. Musical signals are modelled as a set of notes, each comprising of a set of harmonically-related sinusoids. An hierarchical model is presented that is very general and applicable to any signal that can be decomposed as the sum of basis functions. Parameter estimation is posed within a Bayesian framework, allowing for the incorporation of prior information about model parameters. The resulting posterior distribution is of variable dimension and so reversible jump MCMC simulation techniques are employed for the parameter estimation task. The extension of the model to time-varying signals with high posterior correlations between model parameters is described. The parameters and hyperparameters of several frames of data are estimated jointly to achieve a more robust detection. A general model for the description of time-varying homogeneous and heterogeneous multiple component signals is developed, and then applied to the analysis of musical signals. The importance of high level musical and perceptual psychological knowledge in the formulation of the model is highlighted, and attention is drawn to the limitation of pure signal processing techniques for dealing with musical signals. Gestalt psychological grouping principles motivate the hierarchical signal model, and component identifiability is considered in terms of perceptual streaming where each component establishes its own context. A major emphasis of this thesis is the practical application of MCMC techniques, which are generally deemed to be too slow for many applications. Through the design of efficient transition kernels highly optimised for harmonic models, and by careful choice of assumptions and approximations, implementations approaching the order of realtime are viable.Engineering and Physical Sciences Research Counci

Quantifying Registration Uncertainty with Sparse Bayesian Modelling

International audienceWe investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to automate the tuning of registration hyperparameters, such as the trade-off between the data and regularization functionals. Sparsity-inducing priors have recently been used to render the parametrization itself adaptive and data-driven. The sparse prior on transformation parameters effectively favors the use of coarse basis functions to capture the global trends in the visible motion while finer, highly localized bases are introduced only in the presence of coherent image information and motion. In earlier work, approximate inference under the sparse Bayesian model was tackled in an efficient Variational Bayes (VB) framework. In this paper we are interested in the theoretical and empirical quality of uncertainty estimates derived under this approximate scheme vs. under the exact model. We implement an (asymptotically) exact inference scheme based on reversible jump Markov Chain Monte Carlo (MCMC) sampling to characterize the posterior distribution of the transformation and compare the predictions of the VB and MCMC based methods. The true posterior distribution under the sparse Bayesian model is found to be meaningful: orders of magnitude for the estimated uncertainty are quantitatively reasonable, the uncertainty is higher in textureless regions and lower in the direction of strong intensity gradients

The Average Star Formation Histories of Galaxies in Dark Matter Halos from z=0-8

We present a robust method to constrain average galaxy star formation rates, star formation histories, and the intracluster light as a function of halo mass. Our results are consistent with observed galaxy stellar mass functions, specific star formation rates, and cosmic star formation rates from z=0 to z=8. We consider the effects of a wide range of uncertainties on our results, including those affecting stellar masses, star formation rates, and the halo mass function at the heart of our analysis. As they are relevant to our method, we also present new calibrations of the dark matter halo mass function, halo mass accretion histories, and halo-subhalo merger rates out to z=8. We also provide new compilations of cosmic and specific star formation rates; more recent measurements are now consistent with the buildup of the cosmic stellar mass density at all redshifts. Implications of our work include: halos near 10^12 Msun are the most efficient at forming stars at all redshifts, the baryon conversion efficiency of massive halos drops markedly after z ~ 2.5 (consistent with theories of cold-mode accretion), the ICL for massive galaxies is expected to be significant out to at least z ~ 1-1.5, and dwarf galaxies at low redshifts have higher stellar mass to halo mass ratios than previous expectations and form later than in most theoretical models. Finally, we provide new fitting formulae for star formation histories that are more accurate than the standard declining tau model. Our approach places a wide variety of observations relating to the star formation history of galaxies into a self-consistent framework based on the modern understanding of structure formation in LCDM. Constraints on the stellar mass-halo mass relationship and star formation rates are available for download at http://www.peterbehroozi.com/data.html .Comment: Revised to match ApJ accepted version, with additional corrections to Figs. 18+19 (superseding published version

Quantitative application of 4D seismic data for updating thin-reservoir models

A range of methods which allow quantitative integration of 4D seismic and reservoir simulation are developed. These methods are designed to work with thin reservoirs, where the seismic response is normally treated in a map-based sense due to the limited vertical resolution of seismic. The first group of methods are fast-track procedures for prediction of future saturation fronts, and reservoir permeability estimation. The input to these methods is pressure and saturation maps which are intended to be derived from time-lapse seismic attributes. The procedures employ a streamline representation of the fluid flow, and finite difference discretisation of the flow equations. The underlying ideas are drawn from the literature and merged with some innovative new ideas, particularly for the implementation and use. However my conclusions on the applicability of the methods are different from their literature counterparts, and are more conservative. The fast-track procedures are advantageous in terms of speed compared to history matching techniques, but are lacking coupling between the quantities which describe the reservoir fluid flow: permeabilities, pressures, and saturations. For this reason, these methods are very sensitive to the input noise, and currently cannot be applied to the real dataset with a robust outcome. Seismic history matching is the second major method considered here for integrating 4D seismic data with the reservoir simulation model. Although more computationally demanding, history matching is capable of tolerating high levels of the input noise, and is more readily applicable to the real datasets. The proposed implementation for seismic modelling within the history matching loop is based on a linear regression between the time-lapse seismic attribute maps and the reservoir dynamic parameter maps, thus avoiding the petro-elastic and seismic trace modelling. The idea for such regression is developed from a pressure/saturation inversion approach found in the literature. Testing of the seismic history matching workflow with the associated uncertainty estimation is performed for a synthetic model. A reduction of the forecast uncertainties is observed after addition of the 4D seismic information to the history matching process. It is found that a proper formulation of the covariance matrices for the seismic errors is essential to obtain favourable forecasts which have small levels of bias. Finally, the procedure is applied to a North Sea field dataset where a marginal reduction in the prediction uncertainties is observed for the wells located close to the major seismic anomalies. Overall, it is demonstrated that the proposed seismic history matching technique is capable of integrating 4D seismic data with the simulation model and increasing confidence in the latter