477 research outputs found

    Inference in particle tracking experiments by passing messages between images

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    Methods to extract information from the tracking of mobile objects/particles have broad interest in biological and physical sciences. Techniques based on simple criteria of proximity in time-consecutive snapshots are useful to identify the trajectories of the particles. However, they become problematic as the motility and/or the density of the particles increases due to uncertainties on the trajectories that particles followed during the images' acquisition time. Here, we report an efficient method for learning parameters of the dynamics of the particles from their positions in time-consecutive images. Our algorithm belongs to the class of message-passing algorithms, known in computer science, information theory and statistical physics as Belief Propagation (BP). The algorithm is distributed, thus allowing parallel implementation suitable for computations on multiple machines without significant inter-machine overhead. We test our method on the model example of particle tracking in turbulent flows, which is particularly challenging due to the strong transport that those flows produce. Our numerical experiments show that the BP algorithm compares in quality with exact Markov Chain Monte-Carlo algorithms, yet BP is far superior in speed. We also suggest and analyze a random-distance model that provides theoretical justification for BP accuracy. Methods developed here systematically formulate the problem of particle tracking and provide fast and reliable tools for its extensive range of applications.Comment: 18 pages, 9 figure

    Simulation of colloidal suspension systems

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    The research work is focused on the development of a simulation platform for colloidal suspension. Based on discrete element method (DEM), the model developed takes into account the crucial interactions, i.e. the electrostatic repulsion, van der Waals attraction, Brownian force, hydration effects and hydrodynamic force. The mechanism of colloid particle diffusion in confined space and the combined influences of fluid flow field, geometrical confinement, and the interparticle interactions on the self-assembly process are investigated

    Air-Fluidized Grains as a Model System: Self-Propelling and Jamming

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    This thesis examines two concepts -- self-propelling and jamming -- that have been employed to unify disparate non-equilibrium systems, in the context of a monolayer of grains fluidized by a temporally and spatially homogeneous upflow of air. The first experiment examines the single particle dynamics of air-fluidized rods. For Brownian rods, equipartition of energy holds and rotational motion sets a timescale after which directional memory is lost. Air-fluidized rods no longer obey equipartion; they self-propel, moving preferentially along their long axis. We show that self-propelling can be treated phenomenologically as an enhanced memory effect causing directional memory to persist for times longer than expected for thermal systems. The second experiment studies dense collections of self-propelling air-fluidized rods. We observe collective propagating modes that give rise to anomalously large fluctuations in the local number density. We quantify these compression waves by calculating the dynamic structure factor and show that the wavespeed is weakly linear with increasing density. It has been suggested that the observed behavior might be explained using the framework put forth by Baskaran et al. The third experiment seeks to determine whether a force analogous to the critical Casimir force in fluids exists for a large sphere fluidized in the presence of a background of smaller spheres. The behavior of such a large sphere is fully characterized showing that, rather than behaving like a sphere driven by turbulence, the large ball self-propels. We also show that the background is responsible for the purely attractive, intermediate-ranged interaction force between two simultaneously-fluidized large balls. The final experiment seeks to determine what parameters control the diverging relaxation timescale associated with the jamming transition. By tilting our apparatus, we quantify pressure, packing fraction, and temperature simultaneously with dynamics as we approach jamming. We obtain an equation of state that agrees well with simulation and free volume theory. We collapse the relaxation time by defining a time- and energy-scale using pressure, consistent with recent simulation. These experiments are further confirmation of the universality of the concepts of self-propelling and jamming

    Dynamical models and machine learning for supervised segmentation

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    This thesis is concerned with the problem of how to outline regions of interest in medical images, when the boundaries are weak or ambiguous and the region shapes are irregular. The focus on machine learning and interactivity leads to a common theme of the need to balance conflicting requirements. First, any machine learning method must strike a balance between how much it can learn and how well it generalises. Second, interactive methods must balance minimal user demand with maximal user control. To address the problem of weak boundaries,methods of supervised texture classification are investigated that do not use explicit texture features. These methods enable prior knowledge about the image to benefit any segmentation framework. A chosen dynamic contour model, based on probabilistic boundary tracking, combines these image priors with efficient modes of interaction. We show the benefits of the texture classifiers over intensity and gradient-based image models, in both classification and boundary extraction. To address the problem of irregular region shape, we devise a new type of statistical shape model (SSM) that does not use explicit boundary features or assume high-level similarity between region shapes. First, the models are used for shape discrimination, to constrain any segmentation framework by way of regularisation. Second, the SSMs are used for shape generation, allowing probabilistic segmentation frameworks to draw shapes from a prior distribution. The generative models also include novel methods to constrain shape generation according to information from both the image and user interactions. The shape models are first evaluated in terms of discrimination capability, and shown to out-perform other shape descriptors. Experiments also show that the shape models can benefit a standard type of segmentation algorithm by providing shape regularisers. We finally show how to exploit the shape models in supervised segmentation frameworks, and evaluate their benefits in user trials

    Structure and dynamics of the E. coli chemotaxis core signaling complex by cryo-electron tomography and molecular simulations

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    To enable the processing of chemical gradients, chemotactic bacteria possess large arrays of transmembrane chemoreceptors, the histidine kinase CheA, and the adaptor protein CheW, organized as coupled core-signaling units (CSU). Despite decades of study, important questions surrounding the molecular mechanisms of sensory signal transduction remain unresolved, owing especially to the lack of a high-resolution CSU structure. Here, we use cryo-electron tomography and sub-tomogram averaging to determine a structure of the Escherichia coli CSU at sub-nanometer resolution. Based on our experimental data, we use molecular simulations to construct an atomistic model of the CSU, enabling a detailed characterization of CheA conformational dynamics in its native structural context. We identify multiple, distinct conformations of the critical P4 domain as well as asymmetries in the localization of the P3 bundle, offering several novel insights into the CheA signaling mechanism

    Pattern-theoretic foundations of automatic target recognition in clutter

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    Issued as final reportAir Force Office of Scientific Research (U.S.
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