PPF - A Parallel Particle Filtering Library

We present the parallel particle filtering (PPF) software library, which enables hybrid shared-memory/distributed-memory parallelization of particle filtering (PF) algorithms combining the Message Passing Interface (MPI) with multithreading for multi-level parallelism. The library is implemented in Java and relies on OpenMPI's Java bindings for inter-process communication. It includes dynamic load balancing, multi-thread balancing, and several algorithmic improvements for PF, such as input-space domain decomposition. The PPF library hides the difficulties of efficient parallel programming of PF algorithms and provides application developers with the necessary tools for parallel implementation of PF methods. We demonstrate the capabilities of the PPF library using two distributed PF algorithms in two scenarios with different numbers of particles. The PPF library runs a 38 million particle problem, corresponding to more than 1.86 GB of particle data, on 192 cores with 67% parallel efficiency. To the best of our knowledge, the PPF library is the first open-source software that offers a parallel framework for PF applications.Comment: 8 pages, 8 figures; will appear in the proceedings of the IET Data Fusion & Target Tracking Conference 201

Enduring rural poverty:Stigma, class practices and social networks in a town in the Groninger Veenkoloniën

In the Groninger Veenkoloniën, a former peat region in the northeast of the Netherlands, persistent poverty is more prevalent compared to other rural regions in the country. Grounded in participant observations and supplemented by in-depth interviews capturing the social life history of 21 participants, this paper paints a detailed picture of the social networks and class practices of those experiencing persisting poverty in the examined town and surrounding region. In addition, we explore the relations between the rural context and lived experiences of class and poverty. Our findings highlight the complex experience as well as spatial embeddedness of persisting poverty. We find that, although the specific circumstances to which the participants are exposed vary greatly, the repercussions in terms of the characteristics of their social networks and practices are very similar. In general, the social networks of participants are fragmented and small, tightly knit, and characterized by clear power imbalances. The most formative experiences that result in the isolation of networks of poor are found to occur in the home and family situation during childhood years. We argue that poverty and the region's history are intricately interwoven resulting in a socio-spatial stigma which in turn contributes to the persistent and intergenerational character of poverty in the rural context of our study. Due to the long history of stigmatization, dismantling the socio-spatial stigma attached to the Groninger Veenkoloniën will presumably take multiple generations

The time and place of social mixing:Everyday rhythms of long-term residents and newcomers in a Dutch neighborhood

Despite research questioning the beneficial effects of social mixing interventions, urban governments continue to strive for a social mix. In this paper we examine the effects of social mixing through the concept of rhythm. We paint an ethnographic portrait of a disadvantaged area in the city of Groningen, The Netherlands, which was targeted by a social mixing intervention. We analyze everyday rhythms of newcomers and long-term low-income residents in order to shed light on the effects of the mixing intervention on perceptions of social division and disadvantage. By introducing ‘exemplary’ newcomers, the social mixing intervention improves the area in terms of e.g. livability scores and socio-economic indicators. However, looking through the lens of rhythm, we found how social divisions between advantaged and disadvantaged groups become exacerbated as a result of ‘arrhythmias’ occurring. We argue that institutional actors fail to align the social mixing intervention with long-term residents’ daily rhythms, which impinges upon that group’s right to the social production of their neighborhood. On a wider scale, we assert the social mixing intervention renders the problem of socio-economic disadvantage spatially insignificant under the guise of improved livability. Therefore, we implore future urban policy to explicitly imagine the ways in which socio-spatial interventions might affect daily rhythms of inequality within neighborhoods

‘Everywhere they are trying to hide poverty. I hate it!’:Spatial practices of the urban poor in Calgary, Canada

Globalizing cities such as Calgary, Canada's center of the oil and gas industry, are confronted with increasing socio-spatial inequalities and uneven development. The aim of this paper is to comprehend poverty in the disadvantaged area of Greater Forest Lawn (GFL) in Calgary through everyday spatial practices of the urban poor and to examine how these practices are affected by urban developments in the area. We provide an in-depth ethnographic account of everyday routines and social conventions of people experiencing poverty in GFL. Our findings reveal how spatial practices that enable poor residents in GFL to meet basic needs are precariously balanced with many intersecting social, spatial, economic, and political structures. They also portray how many residents feel new developments in the area attempt to hide the presence of poverty by oppressing the undesirable aspects associated with it. Consequently, GFL as a social space is increasingly torn between the spatial practices of those trying to cope with poverty and the urban development which imposes a spatial code of desirability and consumption. Consequently, we see urban development in the case of GFL as oppressive and recommend a shift from thinking about urban development in terms of desirability and profitability to becoming more aware of and involved in local practices. Overall, we argue that the right to the city includes the right to urban development in harmony with one's own everyday spatial practices

Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process

Multiple sparse representations classification

Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy.We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and sparsity level

Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation

Microscopic images of neuronal cells provide essential structural information about the key constituents of the brain and form the basis of many neuroscientific studies. Computational analyses of the morphological properties of the captured neurons require first converting the structural information into digital tree-like reconstructions. Many dedicated computational methods and corresponding software tools have been and are continuously being developed with the aim to automate this step while achieving human-comparable reconstruction accuracy. This pursuit is hampered by the immense diversity and intricacy of neuronal morphologies as well as the often low quality and ambiguity of the images. Here we present a novel method we developed in an effort to improve the robustness of digital reconstruction against these complicating factors. The method is based on probabilistic filtering by sequential Monte Carlo estimation and uses prediction and update models designed specifically for tracing neuronal branches in microscopic image stacks. Moreover, it uses multiple probabilistic traces to arrive at a more robust, ensemble reconstruction. The proposed method was evaluated on fluorescence microscopy image stacks of single neurons and dense neuronal networks with expert manual annotations serving as the gold standard, as well as on synthetic images with known ground truth. The results indicate that our method performs well under varying experimental conditions and compares favorably to state-of-the-art alternative methods

Toward a morphodynamic model of the cell: Signal processing for cell modeling

From a systems biology perspective, the cell is the principal element of information integration. Therefore, understanding the cell in its spatiotemporal context is the key to unraveling many of the still unknown mechanisms of life and disease. This article reviews image processing aspects relevant to the quantification of cell morphology and dynamics. We cover both acquisition (hardware) and analysis (software) related issues, in a multiscale fashion, from the detection of cellular components to the description of the entire cell in relation to its extracellular environment. We then describe ongoing efforts to integrate all this vast and diverse information along with data about the biomechanics of the cell to create a credible model of cell morphology and behavior.Carlos Ortiz-de-Solorzano and Arrate Muñoz-Barrutia were supported by the Spanish Ministry of Economy and Competitiveness grants with reference DPI2012-38090-C03-02 and TEC2013-48552-C02, respectively. Michal Kozubek was supported by the Czech Science Foundation (302/12/G157)