Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection

Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics. An important computation for reconstruction is the detection of neuronal boundaries. Images acquired by serial section EM, a leading 3D EM technique, are highly anisotropic, with inferior quality along the third dimension. For such images, the 2D max-pooling convolutional network has set the standard for performance at boundary detection. Here we achieve a substantial gain in accuracy through three innovations. Following the trend towards deeper networks for object recognition, we use a much deeper network than previously employed for boundary detection. Second, we incorporate 3D as well as 2D filters, to enable computations that use 3D context. Finally, we adopt a recursively trained architecture in which a first network generates a preliminary boundary map that is provided as input along with the original image to a second network that generates a final boundary map. Backpropagation training is accelerated by ZNN, a new implementation of 3D convolutional networks that uses multicore CPU parallelism for speed. Our hybrid 2D-3D architecture could be more generally applicable to other types of anisotropic 3D images, including video, and our recursive framework for any image labeling problem

Environmental Issues to be Addressed in Indian Alumina Refineries and their Possible Solutions

India is endowed with a vast bauxite reserve totalling 3037 million tons,more than 87% of which is deemed fit for the production of metallurgical grade alumina by the Bayer 's process. In spite of having such a vast bauxite reserve, the country 's alumina production is very meagre and poised for augmentation in the near future. Also, the existing alumina plants except for Nalco, which has been set-up in the eighties , all the other existing plants are quite old and require modernisation and capacity expansion. With further additional capacities, may it be new green field plants or brown field expansions, there would be an increasing concern mainly for bauxite residue disposal, the working environment and nearby surroundings. The working environment in the alumina plant is critical due to the handling of corrosive chemicals, toxic fumes, air borne emissions, noise hazards and require necessary safety monitoring system. Based on plant operation experience and critical literature survey it is thought that the environmental measures possible under the Indian conditions would match even the best available in the alumina industry abroad, so that there will be a very little negative impact. This paper attempts to bring out the possible areas of concern with measures available and comparison of those with the best possible under the Indian conditions. The overall assessment of impact on the environment including the social , cultural and economic would be highly beneficial to the project planners, local community and the country in genera

Automated computation of arbor densities: a step toward identifying neuronal cell types

The shape and position of a neuron convey information regarding its molecular and functional identity. The identification of cell types from structure, a classic method, relies on the time-consuming step of arbor tracing. However, as genetic tools and imaging methods make data-driven approaches to neuronal circuit analysis feasible, the need for automated processing increases. Here, we first establish that mouse retinal ganglion cell types can be as precise about distributing their arbor volumes across the inner plexiform layer as they are about distributing the skeletons of the arbors. Then, we describe an automated approach to computing the spatial distribution of the dendritic arbors, or arbor density, with respect to a global depth coordinate based on this observation. Our method involves three-dimensional reconstruction of neuronal arbors by a supervised machine learning algorithm, post-processing of the enhanced stacks to remove somata and isolate the neuron of interest, and registration of neurons to each other using automatically detected arbors of the starburst amacrine interneurons as fiducial markers. In principle, this method could be generalizable to other structures of the CNS, provided that they allow sparse labeling of the cells and contain a reliable axis of spatial reference

Genetic structure and affinities among tribal populations of southern India: a study of 24 autosomal DNA markers

We describe the genetic structure and affinities of five Dravidian-speaking tribal populations inhabiting the Nilgiri hills of Tamil Nadu, in south India, using 24 autosomal DNA markers. Our goals were: (i) to examine what evolutionary forces have most significantly impacted south Indian tribal genetic variation, and (ii) to test whether the phenotypic similarities of some south Indian tribal groups to Africans represent a signature of close relationship to Africans or are due to convergence. All loci were polymorphic and average heterozygosities were substantial (range: 0.347-0.423). Genetic differentiation was high (Gst= 6.7%) and genetic distances were not significantly correlated with geographic distances. Genetic drift therefore probably played a significant role in shaping the patterns of genetic variation observed in southern Indian tribal populations. Otherwise, analyses of population relationships showed that Indian populations are closely related to one another, regardless of phenotypic characteristics, and do not show particular affinities to Africans. We conclude that the phenotypic similarities of some Indian groups to Africans do not reflect a close relationship between these groups, but are better explained by convergence

Toward the PSTN/Internet Inter-Networking--Pre-PINT Implementations

This document contains the information relevant to the development of the inter-networking interfaces underway in the Public Switched Telephone Network (PSTN)/Internet Inter-Networking (PINT) Working Group. It addresses technologies, architectures, and several (but by no means all) existing pre-PINT implementations of the arrangements through which Internet applications can request and enrich PSTN telecommunications services. The common denominator of the enriched services (a.k.a. PINT services) is that they combine the Internet and PSTN services in such a way that the Internet is used for non-voice interactions, while the voice (and fax) are carried entirely over the PSTN. One key observation is that the pre-PINT implementations, being developed independently, do not inter-operate. It is a task of the PINT Working Group to define the inter-networking interfaces that will support inter-operation of the future implementations of PINT services

Strategic CSR: A Concept Building Meta-Analysis

This study develops the concept of Strategic Corporate Social Responsibility (Strategic CSR) by meta-analyzing the available empirical evidence on the relationship between CSR and corporate financial performance (CFP). Using meta-analytic structural equation modeling on effect size data from 344 primary studies, our study documents four empirical mechanisms explaining how CSR positively affects CFP: by 1) enhancing firm reputation, 2) increasing stakeholder reciprocation, 3) mitigating firm risk, and 4) strengthening innovation capacity. We propose these four mechanisms to identify four causally relevant attributes that allow us to conceptually distinguish Strategic CSR from CSR more generally. Our findings indicate that the four mechanisms combined explain 20 per cent of the CSR-CFP relationship, suggesting that considerable room remains for future empirical research. The development of an empirically informed, causal conceptualization of Strategic CSR responds to a long-heard call for better-specified concepts in empirical CSR research

Visibility graphs of random scalar fields and spatial data

The family of visibility algorithms were recently introduced as mappings between time series and graphs. Here we extend this method to characterize spatially extended data structures by mapping scalar fields of arbitrary dimension into graphs. After introducing several possible extensions, we provide analytical results on some topological properties of these graphs associated to some types of real-valued matrices, which can be understood as the high and low disorder limits of real-valued scalar fields. In particular, we find a closed expression for the degree distribution of these graphs associated to uncorrelated random fields of generic dimension, extending a well known result in one-dimensional time series. As this result holds independently of the field's marginal distribution, we show that it directly yields a statistical randomness test, applicable in any dimension. We showcase its usefulness by discriminating spatial snapshots of two-dimensional white noise from snapshots of a two-dimensional lattice of diffusively coupled chaotic maps, a system that generates high dimensional spatio-temporal chaos. We finally discuss the range of potential applications of this combinatorial framework, which include image processing in engineering, the description of surface growth in material science, soft matter or medicine and the characterization of potential energy surfaces in chemistry, disordered systems and high energy physics. An illustration on the applicability of this method for the classification of the different stages involved in carcinogenesis is briefly discussed

Passenger transport decarbonization in emerging economies: policy lessons from modelling long-term deep decarbonization pathways

Reaching the goal of the Paris Agreement will not be possible without a deep decarbonization of the passenger transport sector. In emerging economies experiencing rapid economic growth and social transformations, and large-scale development of urban areas and associated infrastructure, opportunities and challenges exist when considering a broader set of mitigation options. In this paper, we apply the Deep Decarbonization Pathways (DDP) approach to develop and report scenarios on the passenger transport sector in Brazil, India, Indonesia, and South Africa. This approach supports an increase in the sectoral ambition of covering all drivers of change in transport mobility and facilitating collective comparison and policy discussions on the barriers and enablers of transitions. The scenario analysis illustrates that all four countries can achieve reductions in emissions per passenger kilometres of 59% and up to 92% by 2050 while meeting growing mobility needs. Lastly, the analysis identifies short-term policy needed to address barriers and promote enablers

Probabilistic Clustering of Time-Evolving Distance Data

We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows the number of objects and clusters to differ at every time point, and no identification on the identities of the objects is needed. Further, the model does not require the number of clusters being specified in advance -- they are instead determined automatically using a Dirichlet process prior. We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods. Finally, we use our dynamic clustering model to analyze and illustrate the evolution of brain cancer patients over time

Hierarchies and Ranks for Persistence Pairs

We develop a novel hierarchy for zero-dimensional persistence pairs, i.e., connected components, which is capable of capturing more fine-grained spatial relations between persistence pairs. Our work is motivated by a lack of spatial relationships between features in persistence diagrams, leading to a limited expressive power. We build upon a recently-introduced hierarchy of pairs in persistence diagrams that augments the pairing stored in persistence diagrams with information about which components merge. Our proposed hierarchy captures differences in branching structure. Moreover, we show how to use our hierarchy to measure the spatial stability of a pairing and we define a rank function for persistence pairs and demonstrate different applications.Comment: Topology-based Methods in Visualization 201