7,647 research outputs found

    Optimizing Abstract Abstract Machines

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    The technique of abstracting abstract machines (AAM) provides a systematic approach for deriving computable approximations of evaluators that are easily proved sound. This article contributes a complementary step-by-step process for subsequently going from a naive analyzer derived under the AAM approach, to an efficient and correct implementation. The end result of the process is a two to three order-of-magnitude improvement over the systematically derived analyzer, making it competitive with hand-optimized implementations that compute fundamentally less precise results.Comment: Proceedings of the International Conference on Functional Programming 2013 (ICFP 2013). Boston, Massachusetts. September, 201

    DNA fragmentation assessment by flow cytometry and Sperm–Bos–Halomax (bright‐field microscopy and fluorescence microscopy) in bull sperm

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    P. 88-98The aim of this study was to find the relationship between fertility (as 90‐day non‐return rates) and DNA fragmentation assessed by two techniques [sperm chromatin structure assay (SCSA) and Sperm–Bos–Halomax (SBH)]. Furthermore, other quality parameters were achieved (motility, morphological abnormalities, cytoplasmic droplets, viability, capacitation and acrosomal and mitochondrial status) and their correlations with fertility were analysed. Bulls were divided into three fertility groups: high [non‐return rate (NRR) ≄ 80], medium (80  40). The results of this study indicate that there is a good correlation between fertility and different parameters of sperm quality (SBH and SCSA parameters, % of spermatozoa with head, neck and total abnormalities, and % of spermatozoa with proximal cytoplasmic droplets) and differences between fertility groups were observed in some of them (SBH and SCSA parameters and % of spermatozoa with head, neck and total abnormalities). In this sense, SBH parameters rendered good correlations with fertility (r = −0.42 using bright light microscope and r = −0.47 with fluorescence). Also, standard deviation of DNA fragmentation index (SD‐DFI) and DFIh (cells with High DNA fragmentation index) showed good correlations with fertility (r = −0.41 and r = −0.29). No correlations were observed between SCSA and SBH parameters. A multiple regression shows that four parameters (% of proximal cytoplasmic droplets, % of intact acrosomes in total population, SD‐DFI and percentage of fragmented DNA detected by bright light microscope) present a good predictive value of the fertility of sperm samples (r2 = 0.34, p < 0.001

    Aerospace Medicine and Biology. A continuing bibliography with indexes

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    This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included

    Adaptive Feedback Cancellation With Band-Limited LPC Vocoder in Digital Hearing Aids

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    Aerospace medicine and biology: A continuing bibliography with indexes, supplement 183

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    This bibliography lists 273 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1978

    SIMULATIONS-GUIDED DESIGN OF PROCESS ANALYTICAL SENSOR USING MOLECULAR FACTOR COMPUTING

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    Many areas of science now generate huge volumes of data that present visualization, modeling, and interpretation challenges. Methods for effectively representing the original data in a reduced coordinate space are therefore receiving much attention. The purpose of this research is to test the hypothesis that molecular computing of vectors for transformation matrices enables spectra to be represented in any arbitrary coordinate system. New coordinate systems are selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a spectrometer. A novel integrated sensing and processing system, termed Molecular Factor Computing (MFC) based near infrared (NIR) spectrometer, is proposed in this dissertation. In an MFC -based NIR spectrometer, spectral features are encoded by the transmission spectrum of MFC filters which effectively compute the calibration function or the discriminant functions by weighing the signals received from a broad wavelength band. Compared with the conventional spectrometers, the novel NIR analyzer proposed in this work is orders of magnitude faster and more rugged than traditional spectroscopy instruments without sacrificing the accuracy that makes it an ideal analytical tool for process analysis. Two different MFC filter-generating algorithms are developed and tested for searching a near-infrared spectral library to select molecular filters for MFC-based spectroscopy. One using genetic algorithms coupled with predictive modeling methods to select MFC filters from a spectral library for quantitative prediction is firstly described. The second filter-generating algorithm designed to select MFC filters for qualitative classification purpose is then presented. The concept of molecular factor computing (MFC)-based predictive spectroscopy is demonstrated with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument

    Analytic Performance Modeling and Analysis of Detailed Neuron Simulations

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    Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel computer performance has been supporting these developments, and at the same time maintainers of neuroscientific simulation code have strived to optimally and efficiently exploit new hardware features. Current state of the art software for the simulation of biological networks has so far been developed using performance engineering practices, but a thorough analysis and modeling of the computational and performance characteristics, especially in the case of morphologically detailed neuron simulations, is lacking. Other computational sciences have successfully used analytic performance engineering and modeling methods to gain insight on the computational properties of simulation kernels, aid developers in performance optimizations and eventually drive co-design efforts, but to our knowledge a model-based performance analysis of neuron simulations has not yet been conducted. We present a detailed study of the shared-memory performance of morphologically detailed neuron simulations based on the Execution-Cache-Memory (ECM) performance model. We demonstrate that this model can deliver accurate predictions of the runtime of almost all the kernels that constitute the neuron models under investigation. The gained insight is used to identify the main governing mechanisms underlying performance bottlenecks in the simulation. The implications of this analysis on the optimization of neural simulation software and eventually co-design of future hardware architectures are discussed. In this sense, our work represents a valuable conceptual and quantitative contribution to understanding the performance properties of biological networks simulations.Comment: 18 pages, 6 figures, 15 table

    Predicting spatial spread of rabies in skunk populations using surveillance data reported by the public

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    Background: Prevention and control of wildlife disease invasions relies on the ability to predict spatio-temporal dynamics and understand the role of factors driving spread rates, such as seasonality and transmission distance. Passive disease surveillance (i.e., case reports by public) is a common method of monitoring emergence of wildlife diseases, but can be challenging to interpret due to spatial biases and limitations in data quantity and quality. Methodology/Principal findings: We obtained passive rabies surveillance data from dead striped skunks (Mephitis mephitis) in an epizootic in northern Colorado, USA. We developed a dynamic patch-occupancy model which predicts spatio-temporal spreading while accounting for heterogeneous sampling. We estimated the distance travelled per transmission event, direction of invasion, rate of spatial spread, and effects of infection density and season. We also estimated mean transmission distance and rates of spatial spread using a phylogeographic approach on a subsample of viral sequences from the same epizootic. Both the occupancy and phylogeographic approaches predicted similar rates of spatio-temporal spread. Estimated mean transmission distances were 2.3 km (95% Highest Posterior Density (HPD95): 0.02, 11.9; phylogeographic) and 3.9 km (95% credible intervals (CI95): 1.4, 11.3; occupancy). Estimated rates of spatial spread in km/year were: 29.8 (HPD95: 20.8, 39.8; phylogeographic, branch velocity, homogenous model), 22.6 (HPD95: 15.3, 29.7; phylogeographic, diffusion rate, homogenous model) and 21.1 (CI95: 16.7, 25.5; occupancy). Initial colonization probability was twice as high in spring relative to fall. Conclusions/Significance: Skunk-to-skunk transmission was primarily local (&lt; 4 km) suggesting that if interventions were needed, they could be applied at the wave front. Slower viral invasions of skunk rabies in western USA compared to a similar epizootic in raccoons in the eastern USA implies host species or landscape factors underlie the dynamics of rabies invasions. Our framework provides a straightforward method for estimating rates of spatial spread of wildlife diseases
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