Agglomeration Externalities and Technical Efficiency in Pig Production

The objective of the paper is to assess the effects of agglomeration on technical efficiency of French pig farms. We use a two-stage method to evaluate the effects of agglomeration on technical efficiency. The first stage consists in calculating pig activity’s efficiency scores with the non-parametric method Data Envelopment Analysis (DEA). The second stage is a truncated regression of these scores on agglomeration variables. Data are for 899 French pig producers in 2004. Results suggest that these farms were as much affected by positive agglomeration externalities (that are knowledge spillovers due to farms’ density, and also arise from farms’ closeness to downstream market) as any other businesses. There was however no evidence of negative externalities in the form of constraints in farmers’ land demand due to legal disposition relating to manure spreading.Agglomeration, Externalities, Data Envelopment Analysis, Industrial Organization, Livestock Production/Industries,

Knowledge-based machine vision systems for space station automation

Computer vision techniques which have the potential for use on the space station and related applications are assessed. A knowledge-based vision system (expert vision system) and the development of a demonstration system for it are described. This system implements some of the capabilities that would be necessary in a machine vision system for the robot arm of the laboratory module in the space station. A Perceptics 9200e image processor, on a host VAXstation, was used to develop the demonstration system. In order to use realistic test images, photographs of actual space shuttle simulator panels were used. The system's capabilities of scene identification and scene matching are discussed

A knowledge-based machine vision system for space station automation

A simple knowledge-based approach to the recognition of objects in man-made scenes is being developed. Specifically, the system under development is a proposed enhancement to a robot arm for use in the space station laboratory module. The system will take a request from a user to find a specific object, and locate that object by using its camera input and information from a knowledge base describing the scene layout and attributes of the object types included in the scene. In order to use realistic test images in developing the system, researchers are using photographs of actual NASA simulator panels, which provide similar types of scenes to those expected in the space station environment. Figure 1 shows one of these photographs. In traditional approaches to image analysis, the image is transformed step by step into a symbolic representation of the scene. Often the first steps of the transformation are done without any reference to knowledge of the scene or objects. Segmentation of an image into regions generally produces a counterintuitive result in which regions do not correspond to objects in the image. After segmentation, a merging procedure attempts to group regions into meaningful units that will more nearly correspond to objects. Here, researchers avoid segmenting the image as a whole, and instead use a knowledge-directed approach to locate objects in the scene. The knowledge-based approach to scene analysis is described and the categories of knowledge used in the system are discussed

A graph theoretic approach to scene matching

The ability to match two scenes is a fundamental requirement in a variety of computer vision tasks. A graph theoretic approach to inexact scene matching is presented which is useful in dealing with problems due to imperfect image segmentation. A scene is described by a set of graphs, with nodes representing objects and arcs representing relationships between objects. Each node has a set of values representing the relations between pairs of objects, such as angle, adjacency, or distance. With this method of scene representation, the task in scene matching is to match two sets of graphs. Because of segmentation errors, variations in camera angle, illumination, and other conditions, an exact match between the sets of observed and stored graphs is usually not possible. In the developed approach, the problem is represented as an association graph, in which each node represents a possible mapping of an observed region to a stored object, and each arc represents the compatibility of two mappings. Nodes and arcs have weights indicating the merit or a region-object mapping and the degree of compatibility between two mappings. A match between the two graphs corresponds to a clique, or fully connected subgraph, in the association graph. The task is to find the clique that represents the best match. Fuzzy relaxation is used to update the node weights using the contextual information contained in the arcs and neighboring nodes. This simplifies the evaluation of cliques. A method of handling oversegmentation and undersegmentation problems is also presented. The approach is tested with a set of realistic images which exhibit many types of sementation errors

Multiple testing for SNP-SNP interactions

Most genetic diseases are complex, i.e. associated to combinations of SNPs rather than individual SNPs. In the last few years, this topic has often been addressed in terms of SNP-SNP interaction patterns given as expressions linked by logical operators. Methods for multiple testing in high-dimensional settings can be applied when many SNPs are considered simultaneously. However, another less well-known multiple testing problem arises within a fixed subset of SNPs when the logic expression is chosen optimally. In this article, we propose a general asymptotic approach for deriving the distribution of the maximally selected chi-square statistic in various situations. We show how this result can be used for testing logic expressions - in particular SNP-SNP interaction patterns - while controlling for multiple comparisons. Simulations show that our method provides multiple testing adjustment when the logic expression is chosen such as to maximize the statistic. Its benefit is demonstrated through an application to a real dataset from a large population-based study considering allergy and asthma in KORA. An implementation of our method is available from the Comprehensive R Archive Network (CRAN) as R package 'SNPmaxsel'

Conformational control of anticancer activity: the application of arene-linked dinuclear ruthenium(II) organometallics

Dinuclear metal complexes have emerged as a promising class of biologically active compounds which possess unique anticancer activity. Here, we describe a novel series of arene-linked dinuclear organometallic Ru(II) complexes, where the relative conformation of the ruthenium centres is controlled by the stereochemical configuration of 1,2-diphenylethylenediamine linker moieties, as confirmed by X-ray crystallography. The reactivity and cytotoxicity of these compounds is compared to flexible dinuclear and mononuclear analogues, demonstrating in all cases the complexes can undergo aquation, coordinate to typical biological donor ligands and importantly, in the case of dinuclear analogues, crosslink oligonucleotide and peptide sequences. Differences in the conformation of the isomeric dinuclear compounds lead to significantly different levels of cytotoxicity against A2780, A2780cisR and HEK-293 cell lines; isomers with a closed conformation are significantly more cytotoxic than isomers with a more open conformation and they are also significantly less susceptible to acquired resistance mechanisms operating in the A2780cisR cell line. These rigid dinuclear compounds possess markedly increased cytotoxicity relative to the non-cytotoxic mononuclear analogues that does not appear to be related to differences in complex lipophilicity or cellular uptake, which, in general, remain similar in magnitude across the series. Thus, the molecular conformation of such dinuclear species may be crucial in determining the nature of the adducts formed on coordination to biological targets in a cellular environment, and opens up a novel route toward the development of more active metal-based anticancer agents

NVIDIA Tensor Core Programmability, Performance & Precision

The NVIDIA Volta GPU microarchitecture introduces a specialized unit, called "Tensor Core" that performs one matrix-multiply-and-accumulate on 4x4 matrices per clock cycle. The NVIDIA Tesla V100 accelerator, featuring the Volta microarchitecture, provides 640 Tensor Cores with a theoretical peak performance of 125 Tflops/s in mixed precision. In this paper, we investigate current approaches to program NVIDIA Tensor Cores, their performances and the precision loss due to computation in mixed precision. Currently, NVIDIA provides three different ways of programming matrix-multiply-and-accumulate on Tensor Cores: the CUDA Warp Matrix Multiply Accumulate (WMMA) API, CUTLASS, a templated library based on WMMA, and cuBLAS GEMM. After experimenting with different approaches, we found that NVIDIA Tensor Cores can deliver up to 83 Tflops/s in mixed precision on a Tesla V100 GPU, seven and three times the performance in single and half precision respectively. A WMMA implementation of batched GEMM reaches a performance of 4 Tflops/s. While precision loss due to matrix multiplication with half precision input might be critical in many HPC applications, it can be considerably reduced at the cost of increased computation. Our results indicate that HPC applications using matrix multiplications can strongly benefit from using of NVIDIA Tensor Cores.Comment: This paper has been accepted by the Eighth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES) 201

TensorFlow Doing HPC

TensorFlow is a popular emerging open-source programming framework supporting the execution of distributed applications on heterogeneous hardware. While TensorFlow has been initially designed for developing Machine Learning (ML) applications, in fact TensorFlow aims at supporting the development of a much broader range of application kinds that are outside the ML domain and can possibly include HPC applications. However, very few experiments have been conducted to evaluate TensorFlow performance when running HPC workloads on supercomputers. This work addresses this lack by designing four traditional HPC benchmark applications: STREAM, matrix-matrix multiply, Conjugate Gradient (CG) solver and Fast Fourier Transform (FFT). We analyze their performance on two supercomputers with accelerators and evaluate the potential of TensorFlow for developing HPC applications. Our tests show that TensorFlow can fully take advantage of high performance networks and accelerators on supercomputers. Running our TensorFlow STREAM benchmark, we obtain over 50% of theoretical communication bandwidth on our testing platform. We find an approximately 2x, 1.7x and 1.8x performance improvement when increasing the number of GPUs from two to four in the matrix-matrix multiply, CG and FFT applications respectively. All our performance results demonstrate that TensorFlow has high potential of emerging also as HPC programming framework for heterogeneous supercomputers.Comment: Accepted for publication at The Ninth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES'19

Signatures of Secondary Collisionless Magnetic Reconnection Driven by Kink Instability of a Flux Rope

The kinetic features of secondary magnetic reconnection in a single flux rope undergoing internal kink instability are studied by means of three-dimensional Particle-in-Cell simulations. Several signatures of secondary magnetic reconnection are identified in the plane perpendicular to the flux rope: a quadrupolar electron and ion density structure and a bipolar Hall magnetic field develop in proximity of the reconnection region. The most intense electric fields form perpendicularly to the local magnetic field, and a reconnection electric field is identified in the plane perpendicular to the flux rope. An electron current develops along the reconnection line in the opposite direction of the electron current supporting the flux rope magnetic field structure. Along the reconnection line, several bipolar structures of the electric field parallel to the magnetic field occur making the magnetic reconnection region turbulent. The reported signatures of secondary magnetic reconnection can help to localize magnetic reconnection events in space, astrophysical and fusion plasmas

Leser-Trélat Sign without Internal Malignancy

Leser-Trélat sign is characterized by the abrupt appearance of multiple seborrheic keratoses in association with underlying malignant disease. A case of Leser-Trélat sign in a 66-year-old healthy woman is presented. Evaluation and follow-up for the development of malignancy over a 2-year period failed to reveal any evidence of malignancy. To date, almost all cases of Leser-Trélat sign have been reported in association with an underlying malignancy. It is less known that Leser-Trélat sign can also occur in healthy individuals in the absence of internal malignancy