text:
},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/The Annals of Applied Statistics/Reich, Shaby - 2012 - A hierarchical max-stable spatial model for extreme precipitation.pdf:pdf},
	Issn = {1932-6157},
	Journal = {The Annals of Applied Statistics},
	Keywords = {Regional climate model,gaussian extreme value process,generalized extreme value distribution,positive stable distribution},
	Month = {dec},
	Number = {4},
	Pages = {1430--1451},
	Title = {{A hierarchical max-stable spatial model for extreme precipitation}},
	Volume = {6},
	Year = {2012}
}

@article{Reich2014,
	Abstract = {Heat waves take a major toll on human populations, with negative impacts on the economy, agriculture, and human health. As a result, there is great interest in studying the changes over time in the probability and magnitude of heat waves. In this paper we propose a hierarchical Bayesian model for serially-dependent extreme temperatures. We assume the marginal temperature distribution follows the generalized Pareto distribution (GPD) above a location-specific threshold, and capture dependence between subsequent days using a transformed max-stable process. Our model allows both the parameters in the marginal GPD and the temporal dependence function to change over time. This allows Bayesian inference on the change in likelihood of a heat wave. We apply this methodology to daily high temperatures in nine cities in the western US for 1979--2010. Our analysis reveals increases in the probability of a heat wave in several US cities. This article has supplementary material online.},
	Author = {Reich, Brian J. and Shaby, Benjamin A. and Cooley, Daniel},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Agricultural, Biological, and Environmental Statistics/Reich, Shaby, Cooley - 2014 - A Hierarchical Model for Serially-Dependent Extremes A Study of Heat Waves in the Western US.pdf:pdf},
	Issn = {10857117},
	Journal = {Journal of Agricultural, Biological, and Environmental Statistics},
	Keywords = {Bayesian modeling,Climate change,Generalized Pareto distribution,Markov model},
	Number = {1},
	Pages = {119--135},
	Title = {{A Hierarchical Model for Serially-Dependent Extremes: A Study of Heat Waves in the Western US}},
	Volume = {19},
	Year = {2014}
}

@article{Reich2013a,
	Abstract = {Tropospheric ozone is one of six criteria pollutants regulated by the US EPA, and has been linked to respiratory and cardiovascular endpoints and adverse effects on vegetation and ecosystems. Regional photochemical models have been developed to study the impacts of emission reductions on ozone levels. The standard approach is to run the deterministic model under new emission levels and attribute the change in ozone concentration to the emission control strategy. However, running the deterministic model requires substantial computing time, and this approach does not provide a measure of uncertainty for the change in ozone levels. Recently, a reduced form model (RFM) has been proposed to approximate the complex model as a simple function of a few relevant inputs. In this paper, we develop a new statistical approach to make full use of the RFM to study the effects of various control strategies on the probability and magnitude of extreme ozone events. We fuse the model output with monitoring data to calibrate the RFM by modeling the conditional distribution of monitoring data given the RFM using a combination of flexible semiparametric quantile regression for the center of the distribution where data are abundant and a parametric extreme value distribution for the tail where data are sparse. Selected parameters in the conditional distribution are allowed to vary by the RFM value and the spatial location. Also, due to the simplicity of the RFM, we are able to embed the RFM in our Bayesian hierarchical framework to obtain a full posterior for the model input parameters, and propagate this uncertainty to the estimation of the effects of the control strategies. We use the new framework to evaluate three potential control strategies, and find that reducing mobile-source emissions has a larger impact than reducing point-source emissions or a combination of several emission sources.},
	Author = {Reich, Brian and Cooley, Daniel and Foley, Kristen and Napelenok, Sergey and Shaby, Benjamin},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/The Annals of Applied Statistics/Reich et al. - 2013 - Extreme value analysis for evaluating ozone control strategies.pdf:pdf},
	Issn = {1932-6157},
	Journal = {The Annals of Applied Statistics},
	Keywords = {Bayesian hierarchical modeling,Generalized Pareto distribution,Spatial data analysis,Statistical downscaling},
	Month = {jun},
	Number = {2},
	Pages = {739--762},
	Title = {{Extreme value analysis for evaluating ozone control strategies}},
	Volume = {7},
	Year = {2013}
}

@article{Rootzen2006,
	Abstract = {Statistical inference for extremes has been a subject of intensive research over the past couple of decades. One approach is based on modelling exceedances of a random variable over a high threshold with the generalized Pareto (GP) distribution. This has proved to be an important way to apply extreme value theory in practice and is widely used. We introduce a multivariate analogue of the GP distribution and show that it is characterized by each of following two properties: first, exceedances asymptotically have a multivariate GP distribution if and only if maxima asymptotically are extreme value distributed; and second, the multivariate GP distribution is the only one which is preserved under change of exceedance levels. We also discuss a bivariate example and lower-dimensional marginal distributions.},
	Author = {Rootz{\'{e}}n, Holger and Tajvidi, Nader},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Bernoulli/Rootz{\'{e}}n, Tajvidi - 2006 - Multivariate generalized Pareto distributions.pdf:pdf},
	Issn = {1350-7265},
	Journal = {Bernoulli},
	Month = {oct},
	Number = {5},
	Pages = {917--930},
	Title = {{Multivariate generalized Pareto distributions}},
	Volume = {12},
	Year = {2006}
}

@article{Sahu2003,
	Author = {Sahu, Sujit K. and Dey, Dipak K. and Branco, M{\'{a}}rcia D.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Canadian Journal of Statistics/Sahu, Dey, Branco - 2003 - A new class of multivariate skew distributions with applications to bayesian regression models.pdf:pdf},
	Issn = {03195724},
	Journal = {Canadian Journal of Statistics},
	Keywords = {62105,62h12,andphrases,bayesian inference,elliptical distributions,gibbs sampler,heavy tailed error dis-,markov chain monte carlo,msc 2000,multivariate skewness,primary 62f15,secondary 62e15,tribution},
	Month = {jun},
	Number = {2},
	Pages = {129--150},
	Title = {{A new class of multivariate skew distributions with applications to bayesian regression models}},
	Volume = {31},
	Year = {2003}
}

@techreport{Samet2000,
	Author = {Samet, Jonathan M and Dominici, Francesca and Zeger, Scott L and Schwartz, Joel and Dockery, Douglas W},
	File = {:Users/samorris/Dropbox/Mendeley/Unknown/Samet et al. - 2000 - The National Morbidity, Mortalityand Air Pollution Study Part I Methods and Methodologic Issues.pdf:pdf},
	Number = {94},
	Title = {{The National Morbidity, Mortalityand Air Pollution Study Part I : Methods and Methodologic Issues}},
	Year = {2000}}

@phdthesis{Sang2008,
	Abstract = {Extreme value theory finds wide applications in areas such as environmental science, financial strategy of risk management and biomedical data processing. In this thesis, we present two spatial extreme value studies related to weather and climate events observed in space and in time, one of which motivates a novel methodology in constructing continuous spatial process for extreme values. Motivated by finding multiscale spatial dependence in extreme climate studies, we offer a new Bayesian analysis tool for learning about both large scale spatial dependence and microscale dependence. The last chapter presents a novel application of space-time models to synoptic climatology. The first investigation is a development of hierarchical modeling approach for explaining a collection of spatially-referenced time series of extreme values. We assume that the observations follow Generalized Extreme Value(GEV) distributions whose locations and scales are jointly spatially dependent where the dependence is captured using multivariate Markov random field models specified through coregionalization. There are various ways to provide appropriate specifications; we consider four choices. The models can be fitted using a Markov Chain Monte Carlo (MCMC) algorithm to enable inference for parameters and to provide spatio-temporal predictions. We fit the models to a set of gridded interpolated precipitation data collected over a 50 year period for the Cape Floristic Region in South Africa, summarizing results for what appears to be the best choice of model. In chapter 3, we extend the hierarchical modeling approach for explaining a collection of point-referenced time series of extreme values. Annual maxima are still assumed to follow GEV distributions, with parameters $\mu$, $\sigma$, and $\xi$ specified in the latent stage to reflect underlying spatio-temporal structure. Here, we relax the conditionally independence assumption previously imposed in the first stage hierarchical models for annual maxima. Instead, a continuous spatial process model is proposed to account for spatial dependence which is unexplained by the latent spatio-temporal specifications for the GEV parameters. In addition, we offer an approach to make spatial interpolation for extreme values based on this hierarchical models with smoothed residuals across space. A simulation study is illustrated to investigate the model fitting behavior. Motivated by the findings in extreme climate studies, which is, large scale spatial variations and small scale spatial variations coexist in some extreme climate phenomena, we present a Bayesian spatial modeling approach to make inference of both large scale and small scale spatial dependence in a general spatial setting. In particular, we focus on the investigation of microscale spatial dependence, which is defined as the dependence at distances smaller than the measurement scale of the spatial study. Since microscale spatial variation study often involves data observed at high resolution, we offer several potential solutions to tackle the computational difficulty of 'large n' problem. In the last chapter, the application we focus on is to synoptic climatology where the goal is to develop an array of atmospheric states to capture a collection of distinct circulations. In particular, Self Organizing Maps (SOMs) are one of the recently used techniques in the meteorology community with regard to developing synoptic weather states. Little discussion about this technique has been found in the statistics literature. We introduce the stochasticity in the form of a space-time process model aiming to illuminate and interpret its performance in the context of application to daily data collection. That is, the observed daily state vectors are viewed as a time series of multivariate process realizations which we try to understand under the dimension reduction achieved by the SOM procedure.},
	Author = {Sang, Huiyan},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Unknown/Sang - 2008 - Extreme value modeling for space-time data with meterological applications.pdf:pdf},
	Isbn = {9780549965022},
	School = {Duke University},
	Title = {{Extreme value modeling for space-time data with meterological applications}},
	Year = {2008}
}

@article{Sang2008a,
	Abstract = {We propose a hierarchical modeling approach for explaining a collection of spatially referenced time series of extreme values. We assume that the observations follow generalized extreme value (GEV) distributions whose locations and scales are jointly spatially dependent where the dependence is captured using multivariate Markov random field models specified through coregionalization. In addition, there is temporal dependence in the locations. There are various ways to provide appropriate specifications; we consider four choices. The models can be fitted using a Markov Chain Monte Carlo (MCMC) algorithm to enable inference for parameters and to provide spatio--temporal predictions. We fit the models to a set of gridded interpolated precipitation data collected over a 50-year period for the Cape Floristic Region in South Africa, summarizing results for what appears to be the best choice of model.},
	Author = {Sang, Huiyan and Gelfand, Alan E.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Environmental and Ecological Statistics/Sang, Gelfand - 2008 - Hierarchical modeling for extreme values observed over space and time.pdf:pdf},
	Issn = {1352-8505},
	Journal = {Environmental and Ecological Statistics},
	Keywords = {coregionalization,generalized extreme value distribution,markov,precipitation surfaces,random field,spatial random effects},
	Month = {jan},
	Number = {3},
	Pages = {407--426},
	Title = {{Hierarchical modeling for extreme values observed over space and time}},
	Volume = {16},
	Year = {2008}
}

@article{Sang2010,
	Author = {Sang, Huiyan and Gelfand, Alan E.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Agricultural, Biological, and Environmental Statistics/Sang, Gelfand - 2010 - Continuous Spatial Process Models for Spatial Extreme Values.pdf:pdf},
	Issn = {1085-7117},
	Journal = {Journal of Agricultural, Biological, and Environmental Statistics},
	Keywords = {copula gaussian process models,generalized extreme value distribution,maximum precipitation surfaces,spatial interpolation},
	Month = {jan},
	Number = {1},
	Pages = {49--65},
	Title = {{Continuous Spatial Process Models for Spatial Extreme Values}},
	Volume = {15},
	Year = {2010}
}

@article{Scarrott2012,
	Author = {Scarrott, Carl and MacDonald, A},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/REVSTAT--Statistical Journal/Scarrott, MacDonald - 2012 - A Review of Extreme Value Threshold Estimation and Uncertainty Quantification.pdf:pdf},
	Journal = {REVSTAT--Statistical Journal},
	Number = {1},
	Pages = {33--60},
	Title = {{A Review of Extreme Value Threshold Estimation and Uncertainty Quantification}},
	Volume = {10},
	Year = {2012}
}

@article{Schlather2003,
	Author = {Schlather, M. and Tawn, Jonathan A.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Biometrika/Schlather, Tawn - 2003 - A dependence measure for multivariate and spatial extreme values Properties and inference.pdf:pdf},
	Issn = {0006-3444},
	Journal = {Biometrika},
	Month = {mar},
	Number = {1},
	Pages = {139--156},
	Title = {{A dependence measure for multivariate and spatial extreme values: Properties and inference}},
	Volume = {90},
	Year = {2003}
}

@article{Schlather2002,
	Abstract = {Models for stationary max-stable random fields are revisited and illustrated by two-dimensional simulations. We introduce a new class of models, which are based on stationary Gaussian random fields, and whose realizations are not necessarily semi-continuous functions. The bivariate marginal distributions of these random fields can be calculated, and they form a new class of bivariate extreme value distributions.},
	Author = {Schlather, Martin},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Extremes/Schlather - 2002 - Models for Stationary Max-Stable Random Fields.pdf:pdf},
	Journal = {Extremes},
	Keywords = {Gaussian random field,bivariate extreme value distribution,dependence function,max-stable random field,rainfall modeling,simulation of max-stable processes},
	Number = {1},
	Pages = {33--44},
	Title = {{Models for Stationary Max-Stable Random Fields}},
	Volume = {5},
	Year = {2002}
}

@article{Schlather2002a,
	Abstract = {The extremal coefficients are the natural dependence measures for multivariate extreme value distributions. For an m-variate distribution 2m distinct extremal coefficients of different orders exist; they are closely linked and therefore a complete set of 2m coefficients cannot take any arbitrary values. We give a full characterization of all the sets of extremal coefficients. To this end, we introduce a simple class of extreme value distributions that allows for a 1-1 mapping to the complete sets of extremal coefficients. We construct bounds that higher order extremal coefficients need to satisfy to be consistent with lower order extremal coefficients. These bounds are useful as lower order extremal coefficients are the most easily inferred from data.},
	Author = {Schlather, Martin and Tawn, Jonathan a.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Unknown/Schlather, Tawn - 2002 - Inequalities for the extremal coefficients of multivariate extreme value distributions.pdf:pdf},
	Keywords = {QA Mathematics},
	Number = {1990},
	Pages = {87--102},
	Title = {{Inequalities for the extremal coefficients of multivariate extreme value distributions.}},
	Year = {2002}
}

@article{Schmidt2002,
	Author = {Schmidt, Rafael},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Mathematical Methods of Operations Research/Schmidt - 2002 - Tail dependence for elliptically contoured distributions.pdf:pdf},
	Issn = {1432-2994},
	Journal = {Mathematical Methods of Operations Research},
	Keywords = {elliptical distribution,regular variation,spherical distribution,tail dependence},
	Month = {may},
	Number = {2},
	Pages = {301--327},
	Title = {{Tail dependence for elliptically contoured distributions}},
	Volume = {55},
	Year = {2002}
}

@article{Segers2012,
	Author = {Segers, Johan},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Statistical Science/Segers - 2012 - Nonparametric Inference for Max-Stable Dependence.pdf:pdf},
	Issn = {0883-4237},
	Journal = {Statistical Science},
	Month = {may},
	Number = {2},
	Pages = {193--196},
	Title = {{Nonparametric Inference for Max-Stable Dependence}},
	Volume = {27},
	Year = {2012}
}

@article{Shaby2012,
	Author = {Shaby, Benjamin and Reich, Brian J.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Statistical Science/Shaby, Reich - 2012 - Discussion of ``Statistical Modeling of Spatial Extremes'' by A. C. Davison, S. A. Padoan and M. Ribatet.pdf:pdf},
	Issn = {0883-4237},
	Journal = {Statistical Science},
	Month = {may},
	Number = {2},
	Pages = {197--198},
	Title = {{Discussion of ``Statistical Modeling of Spatial Extremes'' by A. C. Davison, S. A. Padoan and M. Ribatet}},
	Volume = {27},
	Year = {2012}
}

@article{Silva2008,
	Abstract = {Copula functions and marginal distributions are combined to produce multivariate distributions. We show advantages of estimating all parameters of these models using the Bayesian approach, which can be done with standard Markov chain Monte Carlo algorithms. Deviance-based model selection criteria are also discussed when applied to copula models since they are invariant under monotone increasing transformations of the marginals. We focus on the deviance information criterion. The joint estimation takes into account all dependence structure of the parameters' posterior distributions in our chosen model selection criteria. Two Monte Carlo studies are conducted to show that model identification improves when the model parameters are jointly estimated. We study the Bayesian estimation of all unknown quantities at once considering bivariate copula functions and three known marginal distributions.},
	Author = {Silva, Ralph Dos Santos and Lopes, Hedibert Freitas},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Statistics and Computing/Silva, Lopes - 2008 - Copula, marginal distributions and model selection a Bayesian note.pdf:pdf},
	Issn = {0960-3174},
	Journal = {Statistics and Computing},
	Keywords = {Monte Carlo study,copula,deviance information criterion,marginal distribution,measure of dependence,skewness},
	Month = {mar},
	Number = {3},
	Pages = {313--320},
	Title = {{Copula, marginal distributions and model selection: a Bayesian note}},
	Volume = {18},
	Year = {2008}
}

@unpublished{Smith1990,
	Abstract = {Max-stable processes arise from an infinite-dimensional generalisation of extreme value theory. They form a natural class of processes when sample maxima are observed at each site of a spatial process, a problem of particular interest in connection with regional estimation methods in hydrology. A general representation of max-stable processes due to de Haan and Vatan is discussed, and examples are given to show how it may be used to generate explicit examples of max-stable process. As a side-product, it is possible to generate a number of known multivariate extreme value families in this way, and in one case this suggests an extension of the family. The main contribution of the paper, however, is to define two new max-stable stochastic processes, related to the multivariate normal and multivariate t distributions. Statistical estimation and model checking are discussed, and the concepts illustrated by being applied to rainfall data. Keywords.},
	Author = {Smith, Richard L.},
	File = {:Users/samorris/Dropbox/Mendeley/Unknown/Smith - 1990 - Max-stable processes and spatial extremes.pdf:pdf},
	Keywords = {Gaussian extreme value process,hydrological extremes,max-stable process,multivariate extreme value theory},
	Title = {{Max-stable processes and spatial extremes}},
	Year = {1990},
	Note = {Unpublished manuscript}
}

@article{Smith1997,
	Abstract = {In recent research on extreme value statistics, there has been an extensive development of threshold methods, first in the univariate case and subsequently in the multivariate case as well. In this paper, an alternative methodology for extreme values of univariate time series is developed, by assuming that the time series is Markovian and using bivariate extreme value theory to suggest appropriate models for the transition distributions. A new likelihood representation for threshold methods is presented which we apply to a Markovian time series. An important motivation for developing this kind of theory is the possibility of calculating probability distributions for functionals of extreme events. We address this issue by showing how a theory of compound Poisson limits for additive functionals can be combined with simulation to obtain numerical solutions for problems of practical interest. The methods are illustrated by application to temperature data.},
	Author = {Smith, Richard L. and Tawn, Jonathan A. and Coles, Stuart G.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Biometrika/Smith, Tawn, Coles - 1997 - Markov chain models for threshold exceedances.pdf:pdf},
	Issn = {0006-3444},
	Journal = {Biometrika},
	Keywords = {QA Mathematics},
	Pages = {249--268},
	Title = {{Markov chain models for threshold exceedances.}},
	Volume = {1},
	Year = {1997}
}

@article{Stephenson2003,
	Abstract = {Methods are given for simulating from symmetric and asymmetric versions of the multivariate logistic distribution, and from other multivariate extreme value distributions based on the well known logistic model. We consider two general approaches. The first approach uses transformations to derive random variables with a joint distribution function from which it is easy to simulate. The second approach derives from a specification of conditionally independent marginal components, conditioning on positive stable random variables. This specification extends to models of nested or hierarchical type and leads to an efficient way of incorporating marginal censoring. The algorithms presented in Sections 2 and 3 are available on request from the author. They are also included in the R (Ihaka and Gentleman, 1996) package evd (Stephenson, 2002), which is available from http://www.maths.lancs.ac.uk/{\~{}}stephena/.},
	Author = {Stephenson, Alec},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Extremes/Stephenson - 2003 - Simulating Multivariate Extreme Value Distributions of Logistic Type.pdf:pdf},
	Journal = {Extremes},
	Keywords = {multivariate extreme value distribution,positive stable distribution,simulation},
	Number = {1},
	Pages = {49--59},
	Title = {{Simulating Multivariate Extreme Value Distributions of Logistic Type}},
	Volume = {6},
	Year = {2003}
}

@article{Stephenson2009,
	Abstract = {Multivariate extreme events are typically modelled using multivariate extreme value distributions. Unfortunately, there exists no finite parametrization for the class of multivariate extreme value distributions. One common approach is to model extreme events using some flexible parametric subclass. This approach has been limited to only two or three dimensions, primarily because suitably flexible high-dimensional parametric models have prohibitively complex density functions. We present an approach that allows a number of popular flexible models to be used in arbitrarily high dimensions. The approach easily handles missing and censored data, and can be employed when modelling componentwise maxima and multivariate threshold exceedances. The approach is based on a representation using conditionally independent marginal components, conditioning on positive stable random variables. We use Bayesian inference, where the conditioning variables are treated as auxiliary variables within Markov chain Monte Carlo simulations. We demonstrate these methods with an application to sea-levels, using data collected at 10 sites on the east coast of England.},
	Author = {Stephenson, Alec G.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Australian {\&} New Zealand Journal of Statistics/Stephenson - 2009 - High-Dimensional Parametric Modelling of Multivariate Extreme Events.pdf:pdf},
	Issn = {13691473},
	Journal = {Australian {\&} New Zealand Journal of Statistics},
	Keywords = {markov chain monte carlo,maxima,multivariate extreme value distribution,positive stable distribution,sea-level},
	Month = {mar},
	Number = {1},
	Pages = {77--88},
	Title = {{High-dimensional parametric modelling of multivariate extreme events}},
	Volume = {51},
	Year = {2009}
}

@article{Stephenson2015,
	Author = {Stephenson, Alec G. and Shaby, Benjamin a. and Reich, Brian J. and Sullivan, Andrew L.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Applied Meteorology and Climatology/Stephenson et al. - 2015 - Estimating Spatially Varying Severity Thresholds of a Forest Fire Danger Rating System Using Max-Stable Extre.pdf:pdf},
	Issn = {1558-8424},
	Journal = {Journal of Applied Meteorology and Climatology},
	Number = {2},
	Pages = {395--407},
	Title = {{Estimating Spatially Varying Severity Thresholds of a Forest Fire Danger Rating System Using Max-Stable Extreme-Event Modeling}},
	Volume = {54},
	Year = {2015}
}

@article{Strokorb2014,
	Abstract = {We focus on two dependency quantities of a max-stable random field {\$}X{\$} on some space {\$}T{\$}: the extremal coefficient function {\$}\backslashtheta{\$} which we define on finite sets of {\$}T{\$} and the extremal correlation function {\$}\backslashchi(s,t)=\backslashlim{\_}{\{}x \backslashuparrow \backslashinfty{\}} \backslashPP(X{\_}s \backslashgeq x \backslashmid X{\_}t \backslashgeq x){\$}. We fully characterize extremal coefficient functions {\$}\backslashtheta{\$} by a property called complete alternation and construct a corresponding max-stable random field. Simple properties and consequences concerning the convex geometry of extremal coefficients are derived. We study how the continuity of {\$}X{\$}, {\$}\backslashtheta{\$} and {\$}\backslashchi{\$} are linked to each other, and we show that extremal correlation functions {\$}\backslashchi{\$} allow for convex combinations in general, and for products and pointwise limits if the resulting function is continuous. These are operations which are well-known for positive definite functions, but the latter are non-trivial for extremal correlation functions. Finally, we regard some additional implications, when the random field {\$}X{\$} on {\$}T=\backslashmathbb{\{}R{\}}{\^{}}d{\$} is stationary.},
	Author = {Strokorb, Kirstin and Schlather, Martin},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Bernoulli/Strokorb, Schlather - 2014 - An exeptional max-stable process fully parameterized by its extremal coefficients.pdf:pdf},
	Issn = {13507265},
	Journal = {Bernoulli},
	Keywords = {completely alternating,dependency set,extremal coefficient,max-,max-linear model,negative definite,semigroup,spectrally discrete,stable process,tawn-molchanov process},
	Number = {1},
	Pages = {276--302},
	Title = {{An exeptional max-stable process fully parameterized by its extremal coefficients}},
	Volume = {in press},
	Year = {2014},
	archivePrefix = {arXiv},
	eprint = {1205.1315}
}

@article{Sullivan2009,
	Abstract = {New technologies are rapidly changing the way we collect, archive, analyze, and share scientific data. For example, over the next several years it is estimated that more than one billion autonomous sensors will be deployed over large spatial and temporal scales, and will gather vast quantities of data. Networks of human observers play a major role in gathering scientific data, and whether in astronomy, meteorology, or observations of nature, they continue to contribute significantly. In this paper we present an innovative use of the Internet and information technologies that better enhances the opportunity for citizens to contribute their observations to science and the conservation of bird populations. eBird is building a web-enabled community of bird watchers who collect, manage, and store their observations in a globally accessible unified database. Through its development as a tool that addresses the needs of the birding community, eBird sustains and grows participation. Birders, scientists, and conservationists are using eBird data worldwide to better understand avian biological patterns and the environmental and anthropogenic factors that influence them. Developing and shaping this network over time, eBird has created a near real-time avian data resource producing millions of observations per year. ?? 2009 Elsevier Ltd. All rights reserved.},
	Author = {Sullivan, Brian L. and Wood, Christopher L. and Iliff, Marshall J. and Bonney, Rick E. and Fink, Daniel and Kelling, Steve},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Biological Conservation/Sullivan et al. - 2009 - eBird A citizen-based bird observation network in the biological sciences.pdf:pdf},
	Isbn = {0006-3207},
	Issn = {00063207},
	Journal = {Biological Conservation},
	Keywords = {Bird observations,Citizen-science,Observation network,Scale-dependent analysis,eBird},
	Number = {10},
	Pages = {2282--2292},
	Pmid = {1960744},
	Publisher = {Elsevier Ltd},
	Title = {{eBird: A citizen-based bird observation network in the biological sciences}},
	Volume = {142},
	Year = {2009}
}

@article{Szekely2013,
	Author = {Sz{\'{e}}kely, G{\'{a}}bor J. and Rizzo, Maria L.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Statistical Planning and Inference/Sz{\'{e}}kely, Rizzo - 2013 - Energy statistics A class of statistics based on distances.pdf:pdf},
	Issn = {03783758},
	Journal = {Journal of Statistical Planning and Inference},
	Keywords = {Distance correlation,Distance covariance,Energy distance,Goodness-of-fit,Multivariate independence},
	Month = {aug},
	Number = {8},
	Pages = {1249--1272},
	Publisher = {Elsevier},
	Title = {{Energy statistics: A class of statistics based on distances}},
	Volume = {143},
	Year = {2013}
}

@article{Szekely2005,
	Author = {Sz{\'{e}}kely, G{\'{a}}bor J. and Rizzo, Maria L.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Multivariate Analysis/Sz{\'{e}}kely, Rizzo - 2005 - A new test for multivariate normality.pdf:pdf},
	Issn = {0047259X},
	Journal = {Journal of Multivariate Analysis},
	Keywords = {bhep test,goodness-of-fit,henze,multivariate,multivariate kurtosis,projection pursuit,skewness,strictly negative definite,zirkler test},
	Month = {mar},
	Number = {1},
	Pages = {58--80},
	Title = {{A new test for multivariate normality}},
	Volume = {93},
	Year = {2005}
}

@phdthesis{Tajvidi1996,
	Author = {Tajvidi, Nader},
	File = {:Users/samorris/Dropbox/Mendeley/Unknown/Tajvidi - 1996 - Characterisation and Some Statistical Aspects of Univariate and Multivariate Generalised Pareto Distributions.pdf:pdf},
	Isbn = {91-7197-390-7},
	School = {G{\"{o}}teborg},
	Title = {{Characterisation and Some Statistical Aspects of Univariate and Multivariate Generalised Pareto Distributions}},
	Year = {1996}}

@article{Tawn1988,
	Author = {Tawn, JA},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Biometrika/Tawn - 1988 - Bivariate extreme value theory models and estimation.pdf:pdf},
	Issn = {0006-3444},
	Journal = {Biometrika},
	Number = {3},
	Pages = {397--415},
	Title = {{Bivariate extreme value theory: models and estimation}},
	Volume = {75},
	Year = {1988}
}

@article{Tawn1990,
	Abstract = {Multivariate extreme value distributions arise as the limiting joint distribution of normalized componentwise maxima/minima. No parametric family exists for the dependence between the margins. This paper extends to more than two variables the models and results for the bivariate case obtained by Tawn (1988). Two new families of physically motivated parametric models for the dependence structure are presented and are illustrated with an application to trivariate extreme sea level data.},
	Author = {Tawn, Jonathan A.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Biometrika/Tawn - 1990 - Modelling multivariate extreme value distributions(2).pdf:pdf},
	Issn = {0006-3444},
	Journal = {Biometrika},
	Keywords = {Extreme value theory,Generalized Pareto distribution,Multivariate exponential distribution,Nonregular estimation},
	Number = {2},
	Pages = {245--253},
	Title = {{Modelling multivariate extreme value distributions}},
	Volume = {77},
	Year = {1990}
}

@article{Thibaud2013,
	Abstract = {We propose an approach to spatial modeling of extreme rainfall, based on max-stable processes fitted using partial duration series and a censored threshold likelihood function. The resulting models are coherent with classical extreme-value theory and allow the consistent treatment of spatial dependence of rainfall using ideas related to those of classical geostatistics. We illustrate the ideas through data from the Val Ferret watershed in the Swiss Alps, based on daily cumulative rainfall totals recorded at 24 stations for four summers, augmented by a longer series from nearby. We compare the fits of different statistical models appropriate for spatial extremes, select that best fitting our data, and compare return level estimates for the total daily rainfall over the stations. The method can be used in other situations to produce simulations needed for hydrological models, and in particular, for the generation of spatially heterogeneous extreme rainfall fields over catchments.},
	Author = {Thibaud, E. and Mutzner, R. and Davison, A. C.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Water Resources Research/Thibaud, Mutzner, Davison - 2013 - Threshold modeling of extreme spatial rainfall.pdf:pdf},
	Isbn = {1944-7973},
	Issn = {00431397},
	Journal = {Water Resources Research},
	Keywords = {extremes,max-stable model,spatial rainfall},
	Number = {8},
	Pages = {4633--4644},
	Title = {{Threshold modeling of extreme spatial rainfall}},
	Volume = {49},
	Year = {2013}
}

@article{Thibaud2013a,
	Abstract = {Recent advances in extreme value theory have established [IMG]f1.gif" ALT="Formula" BORDER="0"{\textgreater}-Pareto processes as the natural limits for extreme events defined in terms of exceedances of a risk functional. In this paper we provide methods for the practical modelling of data based on a tractable yet flexible dependence model. We introduce the class of elliptical [IMG]f1.gif" ALT="Formula" BORDER="0"{\textgreater}-Pareto processes, which arise as the limits of threshold exceedances of certain elliptical processes characterized by a correlation function and a shape parameter. An efficient inference method based on maximizing a full likelihood with partial censoring is developed. Novel procedures for exact conditional and unconditional simulation are proposed. These ideas are illustrated using precipitation extremes in Switzerland.},
	Author = {Thibaud, Emeric and Opitz, Thomas},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Biometrika/Thibaud, Opitz - 2015 - Efficient inference and simulation for elliptical Pareto processes.pdf:pdf},
	Issn = {14643510},
	Journal = {Biometrika},
	Keywords = {Censored likelihood,Elliptical extremes,Extremal-t process,Pareto process,Simulation},
	Number = {4},
	Pages = {855--870},
	Title = {{Efficient inference and simulation for elliptical Pareto processes}},
	Volume = {102},
	Year = {2015},
	archivePrefix = {arXiv},
	eprint = {1401.0168v2}
}

@article{Varin2011,
	Abstract = {A survey of recent developments in the theory and application of composite likelihood is provided, building on the review paper of Varin (2008). A range of application areas, including geostatistics, spatial extremes, and space-time models, as well as clustered and longitudinal data and time series are considered. The important area of applications to statistical genetics is omitted, in light of Larribe and Fearnhead (2011). Emphasis is given to the development of the theory, and the current state of knowledge on efficiency and robustness of composite likelihood inference.},
	Author = {Varin, Cristiano and Reid, Nancy and Firth, David},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Statistica Sinica/Varin, Reid, Firth - 2011 - An overview of composite likelihood methods.pdf:pdf},
	Issn = {10170405},
	Journal = {Statistica Sinica},
	Keywords = {and phrases,copulas,generalized estimating equations,geostatistics,godambe information,hood,longitudinal data,multivariate binary data,pseudo-likeli-,quasi-likelihood,robustness,spatial extremes,time series},
	Pages = {5----42},
	Title = {{An overview of composite likelihood methods}},
	Volume = {21},
	Year = {2011}
}

@article{Wadsworth2014,
	Abstract = {Max-stable processes arise as the only possible nontrivial limits for maxima of affinely normalized identically distributed stochastic processes, and thus form an important class of models for the extreme values of spatial processes. Until recently, inference for max-stable processes has been restricted to the use of pairwise composite likelihoods, due to intractability of higher-dimensional distributions. In this work we consider random fields that are in the domain of attraction of a widely used class of max-stable processes, namely those constructed via manipulation of log-Gaussian random functions. For this class, we exploit limiting d-dimensional multivariate Poisson process intensities of the underlying process for inference on all d-vectors exceeding a high marginal threshold in at least one component, employing a censoring scheme to incorporate information below the marginal threshold. We also consider the d-dimensional distributions for the equivalent max-stable process, and perform full likelihood inference by exploiting the methods of Stephenson {\&} Tawn (2005), where information on the occurrence times of extreme events is shown to dramatically simplify the likelihood. The Stephenson-Tawn likelihood is in fact simply a special case of the censored Poisson process likelihood. We assess the improvements in inference from both methods over pairwise likelihood methodology by simulation.},
	Author = {Wadsworth, J. L. and Tawn, J. A.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Biometrika/Wadsworth, Tawn - 2014 - Efficient inference for spatial extreme value processes associated to log-Gaussian random functions.pdf:pdf},
	Issn = {0006-3444},
	Journal = {Biometrika},
	Number = {1},
	Pages = {1--15},
	Title = {{Efficient inference for spatial extreme value processes associated to log-Gaussian random functions}},
	Volume = {101},
	Year = {2014}
}

@article{Wadsworth2012,
	Abstract = {Current dependence models for spatial extremes are based upon max-stable processes. Within this class, there are few inferentially viable models available, and we propose one further model. More problematic are the restrictive assumptions that must be made when using max-stable processes to model dependence for spatial extremes: it must be assumed that the dependence structure of the observed extremes is compatible with a limiting model that holds for all events more extreme than those that have already occurred. This problem has long been acknowledged in the context of finite-dimensional multivariate extremes, in particular when data display dependence at observable levels, but are independent in the limit. We propose a flexible class of models that is suitable for such data in a spatial context. In addition, we consider the situation where the extremal dependence structure may vary with distance. We apply our models to spatially referenced significant wave height data from the North Sea, finding evidence that their extremal structure is not compatible with a limiting dependence model.},
	Author = {Wadsworth, Jennifer L. and Tawn, Jonathan A.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Biometrika/Wadsworth, Tawn - 2012 - Dependence modelling for spatial extremes.pdf:pdf},
	Issn = {00063444},
	Journal = {Biometrika},
	Keywords = {Asymptotic independence,Extremal dependence,Max-stable process,Significant wave height,Spatial extreme value theory},
	Number = {2},
	Pages = {253--272},
	Title = {{Dependence modelling for spatial extremes}},
	Volume = {99},
	Year = {2012}
}

@article{Wang2010,
	Abstract = {In the information system research, a question of particular interest is to interpret and to predict the probability of a firm to adopt a new technology such that market promotions are targeted to only those firms that were more likely to adopt the technology. Typically, there exists significant difference between the observed number of ``adopters'' and ``nonadopters,'' which is usually coded as binary response. A critical issue involved in modeling such binary response data is the appropriate choice of link functions in a regression model. In this paper we introduce a new flexible skewed link function for modeling binary response data based on the generalized extreme value (GEV) distribution. We show how the proposed GEV links provide more flexible and improved skewed link regression models than the existing skewed links, especially when dealing with imbalance between the observed number of 0's and 1's in a data. The flexibility of the proposed model is illustrated through simulated data sets and a billing data set of the electronic payments system adoption from a Fortune 100 company in 2005.},
	Author = {Wang, Xia and Dey, Dipak K.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/The Annals of Applied Statistics/Wang, Dey - 2010 - Generalized extreme value regression for binary response data An application to B2B electronic payments system adopti.pdf:pdf},
	Issn = {1932-6157},
	Journal = {The Annals of Applied Statistics},
	Keywords = {generalized extreme value distribution,latent variable,markov chain monte carlo,posterior distribution,skewness},
	Month = {dec},
	Number = {4},
	Pages = {2000--2023},
	Title = {{Generalized extreme value regression for binary response data: An application to B2B electronic payments system adoption}},
	Volume = {4},
	Year = {2010}
}

@article{Wiper2008,
	Author = {Wiper, M. P. and Gir{\'{o}}n, F. J. and Pewsey, Arthur},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Communications in Statistics - Theory and Methods/Wiper, Gir{\'{o}}n, Pewsey - 2008 - Objective Bayesian Inference for the Half-Normal and Half- t Distributions.pdf:pdf},
	Isbn = {0361092080210},
	Issn = {0361-0926},
	Journal = {Communications in Statistics - Theory and Methods},
	Keywords = {62f15,bias-correction,gaussian-modulated gamma distribution,gibbs,likelihood based inference,mathematics subject classification primary,model selection,normal-gamma distribution,right-truncated,sampling,secondary 62e15},
	Month = {sep},
	Number = {20},
	Pages = {3165--3185},
	Title = {{Objective Bayesian Inference for the Half-Normal and Half- t Distributions}},
	Volume = {37},
	Year = {2008}
}

@article{Zhang2010,
	Author = {Zhang, Hao and El-Shaarawi, A},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Environmetrics/Zhang, El-Shaarawi - 2010 - On spatial skew‐Gaussian processes and applications.pdf:pdf},
	Journal = {Environmetrics},
	Keywords = {em algorithm, matern covariogram, skew-gaussian process,skew-normal distribution,slice},
	Number = {October 2008},
	Pages = {33--47},
	Title = {{On spatial skew-Gaussian processes and applications}},
	Volume = {21},
	Year = {2010}
}

@book{Hollander2014,
	Author = {Hollander, Myles and Wolfe, Douglas A and Chicken, Eric},
	Edition = {3rd},
	Pages = {316----322},
	Publisher = {Wiley},
	Title = {{Nonparametric Statistical Methods}},
	Year = {2014}}

@book{gelfand2010handbook,
	Author = {Gelfand, A E and Diggle, P and Guttorp, P and Fuentes, M},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	Isbn = {9781420072884},
	Publisher = {CRC Press},
	Series = {Chapman {\&} Hall/CRC Handbooks of Modern Statistical Methods},
	Title = {{Handbook of Spatial Statistics}},
	Year = {2010}
}

license: MIT

license_type: permissive

branch_name: refs/heads/master

extension: bib


text:
@inproceedings{soui2017plain,
 author = {Soui, Makram and Chouchane, Mabrouka and Gasmi, Ines and Mkaouer, Mohamed Wiem},
 booktitle = {VISIGRAPP (1: GRAPP)},
 pages = {127--136},
 title = {PLAIN: PLugin for predicting the usAbility of Mobile User INterface.},
 year = {2017}
}

branch_name: refs/heads/main

gha_license_id: MIT

gha_language: TeX


text:
SUMMARY = "Big Buck Bunny Movie - 3840*2160@60fps"
LICENSE = "CC-BY-3.0"
LIC_FILES_CHKSUM = "file://${COREBASE}/meta/files/common-licenses/CC-BY-3.0;md5=dfa02b5755629022e267f10b9c0a2ab7"

SRC_URI = "http://ftp.nluug.nl/pub/graphics/blender/demo/movies/BBB/bbb_sunflower_2160p_60fps_normal.mp4"
SRC_URI[md5sum] = "fb20ba0a7f531eabb345a5abcce33d78"
SRC_URI[sha256sum] = "35db9a007021f1b0066993e1d2c4448c83a8b279f799c97d33cbba73980a8a36"

inherit allarch

do_install() {
    install -d ${D}${datadir}/movies
    install -m 0644 ${WORKDIR}/bbb_sunflower_2160p_60fps_normal.mp4 ${D}${datadir}/movies/
}

FILES_${PN} += "${datadir}/movies"

repository: narrucmot/meta-petalinux

repo_name: narrucmot/meta-petalinux

extension: bb


text:
// +build linux

package kcp

import (
	"net"
	"os"
	"sync/atomic"

	"github.com/pkg/errors"
	"golang.org/x/net/ipv4"
)

func (s *UDPSession) tx(txqueue []ipv4.Message) {
	// default version
	if s.xconn == nil || s.xconnWriteError != nil {
		s.defaultTx(txqueue)
		return
	}

	// x/net version
	nbytes := 0
	npkts := 0
	for len(txqueue) > 0 {
		if n, err := s.xconn.WriteBatch(txqueue, 0); err == nil {
			for k := range txqueue[:n] {
				nbytes += len(txqueue[k].Buffers[0])
			}
			npkts += n
			txqueue = txqueue[n:]
		} else {
			// compatibility issue:
			// for linux kernel<=2.6.32, support for sendmmsg is not available
			// an error of type os.SyscallError will be returned
			if operr, ok := err.(*net.OpError); ok {
				if se, ok := operr.Err.(*os.SyscallError); ok {
					if se.Syscall == "sendmmsg" {
						s.xconnWriteError = se
						s.defaultTx(txqueue)
						return
					}
				}
			}
			s.notifyWriteError(errors.WithStack(err))
			break
		}
	}

	atomic.AddUint64(&DefaultSnmp.OutPkts, uint64(npkts))
	atomic.AddUint64(&DefaultSnmp.OutBytes, uint64(nbytes))
}

repository: xtaci/kcp-go

repo_name: xtaci/kcp-go

gha_language: Go

extension: go


text:
package api

import (
	"context"
	"github.com/virtual-vgo/vvgo/pkg/login"
	"net/http"
	"net/url"
	"strings"
)

const CtxKeyVVGOIdentity = "vvgo_identity"

func IdentityFromContext(ctx context.Context) *login.Identity {
	ctxIdentity := ctx.Value(CtxKeyVVGOIdentity)
	identity, ok := ctxIdentity.(*login.Identity)
	if !ok {
		identity = new(login.Identity)
		*identity = login.Anonymous()
	}
	return identity
}

// Authenticate http requests using the sessions api
// If the request has a valid session or token with the required role, it is allowed access.
type RBACMux struct {
	Basic  map[[2]string][]login.Role
	Bearer map[string][]login.Role
	*http.ServeMux
}

// HandleFunc registers the handler function for the given pattern.
func (auth *RBACMux) HandleFunc(pattern string, handler func(http.ResponseWriter, *http.Request), role login.Role) {
	auth.Handle(pattern, http.HandlerFunc(handler), role)
}

func (auth *RBACMux) Handle(pattern string, handler http.Handler, role login.Role) {
	auth.ServeMux.HandleFunc(pattern, func(w http.ResponseWriter, r *http.Request) {
		ctx := r.Context()
		var identity login.Identity
		switch {
		case auth.readBasicAuth(r, &identity):
			break
		case auth.readBearer(r, &identity):
			break
		case auth.readSession(ctx, r, &identity):
			break
		default:
			identity = login.Anonymous()
		}

		if values := r.URL.Query(); len(values["roles"]) != 0 {
			wantRoles := make([]login.Role, len(values["roles"]))
			for i := range values["roles"] {
				wantRoles[i] = login.Role(values["roles"][i])
			}
			identity = identity.AssumeRoles(wantRoles...)
		}

		if identity.HasRole(role) {
			if role != login.RoleAnonymous {
				logger.WithField("roles", identity.Roles).WithField("path", r.URL.Path).Info("access granted")
			}
			handler.ServeHTTP(w, r.Clone(context.WithValue(ctx, CtxKeyVVGOIdentity, &identity)))
			return
		}
		logger.WithField("roles", identity.Roles).WithField("path", r.URL.Path).Info("access denied")

		if identity.IsAnonymous() {
			values := make(url.Values)
			values.Set("target", r.RequestURI)
			http.Redirect(w, r, "/login?"+values.Encode(), http.StatusFound)
			return
		}
		unauthorized(w)
	})
}

func (auth *RBACMux) readBasicAuth(r *http.Request, dest *login.Identity) bool {
	if auth.Basic == nil {
		return false
	}

	user, pass, _ := r.BasicAuth()
	gotRoles, ok := auth.Basic[[2]string{user, pass}]
	if !ok {
		return false
	}

	*dest = login.Identity{
		Kind:  login.KindBasic,
		Roles: gotRoles,
	}
	return true
}

func (auth *RBACMux) readBearer(r *http.Request, dest *login.Identity) bool {
	if auth.Bearer == nil {
		return false
	}

	bearer := strings.TrimSpace(r.Header.Get("Authorization"))
	if !strings.HasPrefix(bearer, "Bearer ") {
		return false
	}
	bearer = bearer[len("Bearer "):]

	gotRoles, ok := auth.Bearer[bearer]
	if !ok {
		return false
	}

	*dest = login.Identity{
		Kind:  login.KindBearer,
		Roles: gotRoles,
	}
	return true
}

func (auth *RBACMux) readSession(ctx context.Context, r *http.Request, dest *login.Identity) bool {
	return login.NewStore(ctx).ReadSessionFromRequest(ctx, r, dest) == nil
}

repository: Ranzha/vvgo

repo_name: Ranzha/vvgo


text:
SUMMARY = "Common files for LXQt"
LICENSE = "CC-BY-SA-3.0"
LIC_FILES_CHKSUM = "file://README.md;beginline=9;endline=9;md5=1468be559d088410c64a6db10364f77c"

inherit lxqt gtk-icon-cache

DEPENDS += "liblxqt"

SRC_URI += " \
    file://0001-startlxqt-enable-starting-from-console.patch \
    file://0002-slightly-change-theme-defaults.patch \
"
SRCREV = "e6e6a97d01482497d4fd45c68e1eee0aaed458a4"
PV = "0.11.2"

FILES_${PN} += " \
    ${datadir}/kdm \
    ${datadir}/xsessions \
    ${datadir}/desktop-directories \
    ${datadir}/lxqt \
"

RDEPENDS_${PN} += "xinit"
RRECOMMENDS_${PN} += "oxygen qtsvg-plugins"


text:
SUMMARY = "Collection of plug-ins and tools for the GStreamer multimedia framework specifically geared towards transcoding and recording purposes"
HOMEPAGE = "http://gentrans.sourceforge.net/"
SECTION = "multimedia"
DEPENDS = "gstreamer1.0 gstreamer1.0-plugins-base orc"

LICENSE = "LGPLv2"
LIC_FILES_CHKSUM = "file://COPYING;md5=a6f89e2100d9b6cdffcea4f398e37343"

inherit autotools pkgconfig gettext

require recipes-multimedia/gstreamer/gst-plugins-package.inc

SRC_URI = "git://git.code.sf.net/p/gentrans/code"
SRCREV = "8afe0b652ffaafcdfc105a61d59fdde31a88eca3"

PV = "1.0.3"

S = "${WORKDIR}/git"

do_configure_prepend() {
	touch ${S}/ABOUT-NLS
}

LIBV = "1.0"

PACKAGES_DYNAMIC = "^${PN}-.*"

repository: qtec/meta-qtec-core

repo_name: qtec/meta-qtec-core


text:
SUMMARY = "Auto Time Zone"
LICENSE = "Pegatron"
COMPATIBLE_MACHINE = "(alt|gose|yose|lager)"
LIC_FILES_CHKSUM = "file://COPYING;md5=d41d8cd98f00b204e9800998ecf8427e"

#inherit autotools pkgconfig
#inherit autotools
#inherit pkgconfig
PACKAGES = "${PN} "

#S = "${TOPDIR}/../source/autotz"
S = "${WORKDIR}/autotz"

#TARGET_YOSEUI = "${TOPDIR}/../source/yoseui"

INHIBIT_PACKAGE_STRIP = "1"

addtask do_src before do_patch after do_unpack

do_src() {
    cp -rfp ${TOPDIR}/../source/autotz/ ${WORKDIR}/
}

#do_compile () {
#	oe_runmake
#}

do_install_append() {
#	rm -rf ${S}/patches

    #/usr/share/autotz_1.0/
    install -d ${D}${datadir}/yoseui-1.0

    #copy execution
    install -m 0755 ${B}/autotz ${D}${datadir}/yoseui-1.0
    #cp -rfp ${WORKDIR}/autotz/citieslist.txt /data/apps/yoseui/citieslist.txt
}

FILES_${PN} += " \
    ${datadir}/${P}/* \
    "

repository: ericosur/myqt

repo_name: ericosur/myqt


text:
package distribution

import (
	"github.com/iov-one/weave"
	"github.com/iov-one/weave/coin"
	"github.com/iov-one/weave/errors"
	"github.com/iov-one/weave/migration"
	"github.com/iov-one/weave/orm"
	"github.com/iov-one/weave/x"
)

const (
	newRevenueCost                 = 0
	distributePerDestinationCost   = 0
	resetRevenuePerDestinationCost = 0
)

// RegisterQuery registers feedlist buckets for querying.
func RegisterQuery(qr weave.QueryRouter) {
	NewRevenueBucket().Register("revenues", qr)
}

// CashController allows to manage coins stored by the accounts without the
// need to directly access the bucket.
// Required functionality is implemented by the x/cash extension.
type CashController interface {
	Balance(weave.KVStore, weave.Address) (coin.Coins, error)
	MoveCoins(weave.KVStore, weave.Address, weave.Address, coin.Coin) error
}

// RegisterRoutes registers handlers for feedlist message processing.
func RegisterRoutes(r weave.Registry, auth x.Authenticator, ctrl CashController) {
	r = migration.SchemaMigratingRegistry("distribution", r)
	bucket := NewRevenueBucket()
	r.Handle(&CreateMsg{}, &createRevenueHandler{
		auth:   auth,
		bucket: bucket,
		ctrl:   ctrl,
	})
	r.Handle(&DistributeMsg{}, &distributeHandler{
		auth:   auth,
		bucket: bucket,
		ctrl:   ctrl,
	})
	r.Handle(&ResetMsg{}, &resetRevenueHandler{
		auth:   auth,
		bucket: bucket,
		ctrl:   ctrl,
	})
}

type createRevenueHandler struct {
	auth   x.Authenticator
	bucket orm.ModelBucket
	ctrl   CashController
}

func (h *createRevenueHandler) Check(ctx weave.Context, db weave.KVStore, tx weave.Tx) (*weave.CheckResult, error) {
	if _, err := h.validate(ctx, db, tx); err != nil {
		return nil, err
	}
	return &weave.CheckResult{GasAllocated: newRevenueCost}, nil
}

func (h *createRevenueHandler) Deliver(ctx weave.Context, db weave.KVStore, tx weave.Tx) (*weave.DeliverResult, error) {
	msg, err := h.validate(ctx, db, tx)
	if err != nil {
		return nil, err
	}

	key, err := revenueSeq.NextVal(db)
	if err != nil {
		return nil, errors.Wrap(err, "cannot acquire ID")
	}
	_, err = h.bucket.Put(db, key, &Revenue{
		Metadata:     &weave.Metadata{},
		Admin:        msg.Admin,
		Destinations: msg.Destinations,
		Address:      RevenueAccount(key),
	})
	if err != nil {
		return nil, errors.Wrap(err, "cannot store revenue")
	}
	return &weave.DeliverResult{Data: key}, nil
}

func (h *createRevenueHandler) validate(ctx weave.Context, db weave.KVStore, tx weave.Tx) (*CreateMsg, error) {
	var msg CreateMsg
	if err := weave.LoadMsg(tx, &msg); err != nil {
		return nil, errors.Wrap(err, "load msg")
	}
	return &msg, nil
}

type distributeHandler struct {
	auth   x.Authenticator
	bucket orm.ModelBucket
	ctrl   CashController
}

func (h *distributeHandler) Check(ctx weave.Context, db weave.KVStore, tx weave.Tx) (*weave.CheckResult, error) {
	msg, err := h.validate(ctx, db, tx)
	if err != nil {
		return nil, err
	}
	var rev Revenue
	if err := h.bucket.One(db, msg.RevenueID, &rev); err != nil {
		return nil, errors.Wrap(err, "cannot load revenue from the store")
	}

	res := weave.CheckResult{
		GasAllocated: distributePerDestinationCost * int64(len(rev.Destinations)),
	}
	return &res, nil
}

func (h *distributeHandler) Deliver(ctx weave.Context, db weave.KVStore, tx weave.Tx) (*weave.DeliverResult, error) {
	msg, err := h.validate(ctx, db, tx)
	if err != nil {
		return nil, err
	}

	var rev Revenue
	if err := h.bucket.One(db, msg.RevenueID, &rev); err != nil {
		return nil, errors.Wrap(err, "cannot load revenue from the store")
	}
	if err := distribute(db, h.ctrl, rev.Address, rev.Destinations); err != nil {
		return nil, errors.Wrap(err, "cannot distribute")
	}
	return &weave.DeliverResult{}, nil
}

func (h *distributeHandler) validate(ctx weave.Context, db weave.KVStore, tx weave.Tx) (*DistributeMsg, error) {
	var msg DistributeMsg
	if err := weave.LoadMsg(tx, &msg); err != nil {
		return nil, errors.Wrap(err, "load msg")
	}
	if err := h.bucket.Has(db, msg.RevenueID); err != nil {
		return nil, errors.Wrap(err, "cannot load revenue from the store")
	}
	return &msg, nil
}

type resetRevenueHandler struct {
	auth   x.Authenticator
	bucket orm.ModelBucket
	ctrl   CashController
}

func (h *resetRevenueHandler) Check(ctx weave.Context, db weave.KVStore, tx weave.Tx) (*weave.CheckResult, error) {
	msg, err := h.validate(ctx, db, tx)
	if err != nil {
		return nil, err
	}

	var rev Revenue
	if err := h.bucket.One(db, msg.RevenueID, &rev); err != nil {
		return nil, errors.Wrap(err, "cannot load revenue from the store")
	}
	// Reseting a revenue cost is counterd per destination, because this is a
	// distribution operation as well.
	res := weave.CheckResult{
		GasAllocated: resetRevenuePerDestinationCost * int64(len(rev.Destinations)),
	}
	return &res, nil
}

func (h *resetRevenueHandler) Deliver(ctx weave.Context, db weave.KVStore, tx weave.Tx) (*weave.DeliverResult, error) {
	msg, err := h.validate(ctx, db, tx)
	if err != nil {
		return nil, err
	}

	var rev Revenue
	if err := h.bucket.One(db, msg.RevenueID, &rev); err != nil {
		return nil, errors.Wrap(err, "cannot load revenue from the store")
	}
	// Before updating the revenue all funds must be distributed. Only a
	// revenue with no funds can be updated, so that destinations trust us.
	// Otherwise an admin could change who receives the money without the
	// previously selected destinations ever being paid.
	if err := distribute(db, h.ctrl, rev.Address, rev.Destinations); err != nil {
		return nil, errors.Wrap(err, "cannot distribute")
	}
	rev.Destinations = msg.Destinations
	if _, err := h.bucket.Put(db, msg.RevenueID, &rev); err != nil {
		return nil, errors.Wrap(err, "cannot save")
	}
	return &weave.DeliverResult{}, nil
}

func (h *resetRevenueHandler) validate(ctx weave.Context, db weave.KVStore, tx weave.Tx) (*ResetMsg, error) {
	var msg ResetMsg
	if err := weave.LoadMsg(tx, &msg); err != nil {
		return nil, errors.Wrap(err, "load msg")
	}
	return &msg, nil
}

// distribute split the funds stored under the revenue address and distribute
// them according to destinations proportions. When successful, revenue account
// has no funds left after this call.
//
// It might be that not all funds can be distributed equally. Because of that a
// small leftover can remain on the revenue account after this operation.
func distribute(db weave.KVStore, ctrl CashController, source weave.Address, destinations []*Destination) error {
	var chunks int64
	for _, r := range destinations {
		chunks += int64(r.Weight)
	}

	// Find the greatest common division for all weights. This is needed to
	// avoid leaving big fund leftovers on the source account when
	// distributing between many destinations. Or when there is only one
	// destination with a high weight value.
	var weights []int32
	for _, r := range destinations {
		weights = append(weights, r.Weight)
	}
	div := findGcd(weights...)

	chunks = chunks / int64(div)

	balance, err := ctrl.Balance(db, source)
	switch {
	case err == nil:
		balance, err = coin.NormalizeCoins(balance)
		if err != nil {
			return errors.Wrap(err, "cannot normalize balance")
		}
	case errors.ErrNotFound.Is(err):
		// Account does not exist, so there is are no funds to split.
		return nil
	default:
		return errors.Wrap(err, "cannot acquire revenue account balance")
	}

	// For each currency, distribute the coins equally to the weight of
	// each destination. This can leave small amount of coins on the original
	// account.
	for _, c := range balance {
		// Ignore those coins that have a negative value. This
		// functionality is supposed to be distributing value from
		// revenue account, not collect it. Otherwise this could be
		// used to charge the destinations instead of paying them.
		if !c.IsPositive() {
			continue
		}

		// Rest of the division can be ignored, because we transfer
		// funds to each destinations separately. Any leftover will be
		// left on the destinations account.
		one, _, err := c.Divide(chunks)
		if err != nil {
			return errors.Wrap(err, "cannot split revenue")
		}

		for _, r := range destinations {
			amount, err := one.Multiply(int64(r.Weight / div))
			if err != nil {
				return errors.Wrap(err, "cannot multiply chunk")
			}
			// Chunk is too small to be distributed.
			if amount.IsZero() {
				continue
			}
			if err := ctrl.MoveCoins(db, source, r.Address, amount); err != nil {
				return errors.Wrap(err, "cannot move coins")
			}
		}
	}

	return nil
}

// findGcd returns greatest common division for any number of numbers.
func findGcd(values ...int32) int32 {
	switch len(values) {
	case 0:
		return 0
	case 1:
		return values[0]
	}

	res := values[0]
	for i := 1; i < len(values); i++ {
		res = gcd(res, values[i])
	}
	return res
}

// gcd returns greatest common division of two numbers.
func gcd(a, b int32) int32 {
	for b != 0 {
		t := b
		b = a % b
		a = t
	}
	return a
}

repository: sahith-narahari/weave

repo_name: sahith-narahari/weave


text:
@article{ericson2017performance,
  title={On the performance of network parallel training in artificial neural networks},
  author={Ericson, Ludvig and Mbuvha, Rendani},
  journal={arXiv preprint arXiv:1701.05130},
  year={2017}
}

repository: rendanim/stie

repo_name: rendanim/stie


text:
package main

import (
	"bytes"
	"fmt"
	"log"
	"os"
	"os/exec"

	"github.com/brutella/hc"
	"github.com/brutella/hc/accessory"
	"github.com/webview/webview"
)

/*
#cgo darwin CXXFLAGS: -DWEBVIEW_COCOA -std=c++11
#cgo darwin LDFLAGS: -framework WebKit

#include <CoreGraphics/CoreGraphics.h>
#include <objc/objc-runtime.h>

extern void webview_set_fullscreen(void *m_window) {
	((void (*)(id, SEL, id))objc_msgSend)((id)m_window, sel_registerName("toggleFullScreen:"), (id)0);
}
*/
import "C"

func main() {
	// create an accessory
	info := accessory.Info{Name: "Fireplace"}
	ac := accessory.NewSwitch(info)
	w := webview.New(true)
	w.SetSize(800, 600, webview.HintNone)
	C.webview_set_fullscreen(w.Window())
	if dir, err := os.Getwd(); err != nil {
		panic(err)
	} else {
		w.Navigate("file://" + dir + "/index.html")
	}

	defer func() {
		if w != nil {
			w.Destroy()
		}
	}()

	ac.Switch.On.OnValueRemoteUpdate(func(on bool) {
		if on {
			if err := exec.Command("caffeinate", "-u", "-t", "1").Run(); err != nil {
				fmt.Println(err)
			}
			w.Dispatch(func() {
				w.Eval("video.play()")
			})
		} else {
			w.Dispatch(func() {
				w.Eval("video.pause()")
			})
			if err := exec.Command("pmset", "displaysleepnow").Run(); err != nil {
				fmt.Println(err)
			}
		}
		ac.Switch.On.SetValue(on)
	})

	ac.Switch.On.OnValueRemoteGet(func() bool {
		output, err := exec.Command("pmset", "-g", "powerstate", "IODisplayWrangler").CombinedOutput()
		if err != nil {
			fmt.Println(err)
		}
		return bytes.Contains(output, []byte("USEABLE"))
	})

	// configure the ip transport
	config := hc.Config{Pin: "00102003"}
	t, err := hc.NewIPTransport(config, ac.Accessory)
	if err != nil {
		log.Panic(err)
	}

	hc.OnTermination(func() {
		w.Terminate()
		<-t.Stop()
	})

	go t.Start()

	w.Run()
}

repository: ConradIrwin/fireplace

repo_name: ConradIrwin/fireplace


text:
SRC_URI = "http://wl500g.dyndns.org/loader/loader-${PV}.tar.gz \
	file://toolchain.patch"

COMPATIBLE_HOST = '(mips|mipsel.*)'

S = "${WORKDIR}/${P}"

do_compile() {
	make CROSS_COMPILE=${TARGET_PREFIX}
}

do_install() {
	install ${S}/loader.gz ${DEPLOY_DIR}/images/
}
SRC_URI[md5sum] = "c0b7c36232d3910c425d03e56d0f532b"
SRC_URI[sha256sum] = "6f11fc04a1f3f04ebb996723fc86ca363c3959ca1ef76d1057db9db3ea5a98d0"

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD


text:
package sbp

import (
	"time"

	"github.com/aergoio/aergo/config"
	"github.com/aergoio/aergo/consensus"
	"github.com/aergoio/aergo/consensus/impl/dpos/param"
	"github.com/aergoio/aergo/consensus/util"
	"github.com/aergoio/aergo/pkg/component"
	"github.com/aergoio/aergo/pkg/log"
	"github.com/aergoio/aergo/types"
)

const (
	slotQueueMax = 100
)

var logger *log.Logger

func init() {
	logger = log.NewLogger(log.SBP)
}

// SimpleBlockFactory implments a simple block factory which generate block each cfg.Consensus.BlockInterval.
//
// This can be used for testing purpose.
type SimpleBlockFactory struct {
	*component.ComponentHub
	jobQueue         chan interface{}
	blockInterval    int64
	maxBlockBodySize int
	onReorganizing   util.BcReorgStatus
	txOp             util.TxOp
	quit             chan interface{}
}

// New returns a SimpleBlockFactory.
func New(cfg *config.Config, hub *component.ComponentHub) (*SimpleBlockFactory, error) {
	s := &SimpleBlockFactory{
		ComponentHub:     hub,
		jobQueue:         make(chan interface{}, slotQueueMax),
		blockInterval:    cfg.Consensus.BlockInterval,
		maxBlockBodySize: util.MaxBlockBodySize(),
		onReorganizing:   util.BcNoReorganizing,
		quit:             make(chan interface{}),
	}

	s.txOp = util.NewCompTxOp(
		util.NewBlockLimitOp(s.maxBlockBodySize),
		func(txIn *types.Tx) error {
			select {
			case <-s.quit:
				return util.ErrQuit
			default:
				return nil
			}
		},
	)

	return s, nil
}

// Ticker returns a time.Ticker for the main consensus loop.
func (s *SimpleBlockFactory) Ticker() *time.Ticker {
	return time.NewTicker(param.BlockInterval)
}

// QueueJob send a block triggering information to jq.
func (s *SimpleBlockFactory) QueueJob(now time.Time, jq chan<- interface{}) {
	if b := util.GetBestBlock(s); b != nil {
		jq <- b
	}
}

// IsTransactionValid checks the onsensus level validity of a transaction
func (s *SimpleBlockFactory) IsTransactionValid(tx *types.Tx) bool {
	// SimpleBlockFactory has no tx valid check.
	return true
}

// IsBlockValid checks the consensus level validity of a block.
func (s *SimpleBlockFactory) IsBlockValid(block *types.Block) error {
	// SimpleBlockFactory has no block valid check.
	return nil
}

// QuitChan returns the channel from which consensus-related goroutines check
// when shutdown is initiated.
func (s *SimpleBlockFactory) QuitChan() chan interface{} {
	return s.quit
}

// IsBlockReorganizing reports whether the blockchain is currently under
// reorganization.
func (s *SimpleBlockFactory) IsBlockReorganizing() bool {
	return util.OnReorganizing(&s.onReorganizing)
}

// SetReorganizing sets dpos.onReorganizing to 'OnReorganization.'
func (s *SimpleBlockFactory) SetReorganizing() {
	util.SetReorganizing(&s.onReorganizing)
}

// UnsetReorganizing sets dpos.onReorganizing to 'NoReorganization.'
func (s *SimpleBlockFactory) UnsetReorganizing() {
	util.UnsetReorganizing(&s.onReorganizing)
}

// BlockFactory returns s itself.
func (s *SimpleBlockFactory) BlockFactory() consensus.BlockFactory {
	return s
}

// Start run a simple block factory service.
func (s *SimpleBlockFactory) Start() {
	for {
		select {
		case e := <-s.jobQueue:
			if prevBlock, ok := e.(*types.Block); ok {
				txs, err := util.GatherTXs(s, s.txOp)
				if err != nil {
					return
				}

				block := types.NewBlock(prevBlock, txs)
				logger.Infof("block produced: no=%d, hash=%v",
					block.GetHeader().GetBlockNo(), block.ID())

				util.ConnectBlock(s, block)
			}
		case <-s.quit:
			return
		}
	}
}

// JobQueue returns the queue for block production triggering.
func (s *SimpleBlockFactory) JobQueue() chan<- interface{} {
	return s.jobQueue
}


text:
@article{Gauch-2020-Rainfall–Runoff,
    title = "Rainfall{--}Runoff Prediction at Multiple Timescales with a SingleLong Short-Term Memory Network",
    author = "Gauch, Martin  and
      Kratzert, Frederik  and
      Klotz, Daniel  and
      Nearing, Grey  and
      Lin, Jimmy  and
      Hochreiter, Sepp",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-3001",
    doi = "10.5194/hess-2020-540",
}
@article{Aukes-2020-Size-Based,
    title = "Size-Based Characterization of Freshwater Dissolved Organic Matter finds Similarities within a Water Body Type across Different Canadian Ecozones",
    author = "Aukes, Pieter J. K.  and
      Schiff, Sherry L.  and
      Venkiteswaran, Jason J.  and
      Elgood, Richard J.  and
      Spoelstra, John",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-3002",
    doi = "10.1101/2020.04.03.024174",
}
@article{Pradhananga-2020-Hydrometeorological,,
    title = "Hydrometeorological, glaciological and geospatial research data from the Peyto Glacier Research Basin in the Canadian Rockies",
    author = "Pradhananga, Dhiraj  and
      Pomeroy, John W.  and
      Aubry-Wake, Caroline  and
      Munro, D. Scott  and
      Shea, Joseph  and
      Demuth, Michael N.  and
      Kirat, Nammy Hang  and
      Menounos, Brian  and
      Mukherjee, Kriti",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-3003",
    doi = "10.5194/essd-2020-219",
}
@article{Essery-2020-Snow,
    title = "Snow cover duration trends observed at sites and predicted bymultiple models",
    author = "Essery, Richard  and
      Kim, Hyungjun  and
      Wang, Libo  and
      Bartlett, Paul  and
      Boone, Aaron  and
      Brutel-Vuilmet, Claire  and
      Burke, Eleanor  and
      Cuntz, Matthias  and
      Decharme, Bertrand  and
      Dutra, Emanuel  and
      Fang, Xing  and
      Gusev, Yeugeniy  and
      Hagemann, Stefan  and
      Haverd, Vanessa  and
      Kontu, Anna  and
      Krinner, Gerhard  and
      Lafaysse, Matthieu  and
      Lejeune, Yves  and
      Marke, Thomas  and
      Marks, Danny  and
      Marty, Christoph  and
      Menard, Cecile B.  and
      Nasonova, Olga  and
      Nitta, Tomoko  and
      Pomeroy, John  and
      Schaedler, Gerd  and
      Semenov, Vladimir  and
      Smirnova, Tatiana  and
      Swenson, Sean  and
      Turkov, Dmitry  and
      Wever, Nander  and
      Yuan, Hua",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-3004",
    doi = "10.5194/tc-2020-182",
}
@article{Vionnet-2020-Multi-scale,
    title = "Multi-scale snowdrift-permitting modelling of mountain snowpack",
    author = "Vionnet, Vincent  and
      Marsh, Christopher B.  and
      Menounos, Brian  and
      Gascoin, Simon  and
      Wayand, Nicholas E.  and
      Shea, Joseph  and
      Mukherjee, Kriti  and
      Pomeroy, John W.",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-3005",
    doi = "10.5194/tc-2020-187",
}
@article{Brunner-2020-Flood,
    title = "Flood hazard and change impact assessments may profit from rethinking model calibration strategies",
    author = "Brunner, Manuela I.  and
      Melsen, Lieke A.  and
      Wood, Andrew W.  and
      Rakovec, Oldrich  and
      Mizukami, Naoki  and
      Knoben, Wouter J. M.  and
      Clark, Martyn P.",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-3006",
    doi = "10.5194/hess-2020-192",
}
@article{Gharari-2020-Flexible,
    title = "Flexible vector-based spatial configurations in land models",
    author = "Gharari, Shervan  and
      Clark, Martyn P.  and
      Mizukami, Naoki  and
      Knoben, Wouter J. M.  and
      Wong, Jefferson S.  and
      Pietroniro, Alain",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-3007",
    doi = "10.5194/hess-2020-111",
}
@article{Nitze-2020-The,
    title = "The catastrophic thermokarst lake drainage events of 2018 in northwestern Alaska: Fast-forward into the future",
    author = "Nitze, Ingmar  and
      Cooley, Sarah  and
      Duguay, Claude  and
      Jones, Benjamin M.  and
      Grosse, Guido",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-3008",
    doi = "10.5194/tc-2020-106",
}
@article{Radcliffe-2020-Virtual,
    title = "Virtual Barriers: Unpacking the Sustainability Implications of Online Food Spaces and the Yellowknife Farmers Market{'}s Response to COVID-19",
    author = "Radcliffe, Josalyn  and
      Skinner, Kelly  and
      Spring, Andrew  and
      Picard, Lise  and
      Benoit, France  and
      Dodd, Warren",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-3009",
    doi = "10.21203/rs.3.rs-40591/v1",
}
@article{Yassin-2020-Hydrologic-Land,
    title = "Hydrologic-Land Surface Modelling of a Complex System under Precipitation Uncertainty: A Case Study of the Saskatchewan River Basin, Canada",
    author = "Yassin, Fuad  and
      Wong, Jefferson  and
      Razavi, Saman  and
      Pietroniro, Al  and
      Wheater, Howard",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-3010",
    doi = "10.1002/essoar.10501699.1",
}
@article{Sun-2020-Shallow,
    title = "Shallow groundwater inhibits soil respiration and favors carbon uptake in a wet alpine meadow ecosystem",
    author = "Sun, Shaobo  and
      Che, Tao  and
      Gentine, Pierre  and
      Chen, Qiting  and
      Wang, Lichun  and
      Yan, Zhifeng  and
      Chen, Baozhang  and
      Song, Zhaolong",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-75001",
    doi = "10.22541/au.158880248.84807120",
}
@article{Tang-2020-EMDNA:,
    title = "EMDNA: Ensemble Meteorological Dataset for North America",
    author = "Tang, Guoqiang  and
      Clark, Martyn P.  and
      Papalexiou, Simon Michael  and
      Newman, Andrew J.  and
      Wood, Andrew W.  and
      Brunet, Dominique  and
      Whitfield, Paul H.",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-75002",
    doi = "10.5194/essd-2020-303",
}
@article{Horak-2020-A,
    title = "A process-based evaluation of the Intermediate Complexity Atmospheric Research Model (ICAR) 1.0.1",
    author = "Horak, Johannes  and
      Hofer, Marlis  and
      Gutmann, Ethan  and
      Gohm, Alexander  and
      Rotach, Mathias W.",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-75003",
    doi = "10.5194/gmd-2020-317",
}
@article{Alonso-González-Gutmann-2020-Snowpack-dynamics,
    title = "Snowpack dynamics in the Lebanese mountainsfrom quasi-dynamically downscaled ERA5reanalysis updated by assimilating remotely-sensedfractional snow-covered area",
    author = "Alonso-Gonz{\'a}lez, Esteban  and
      Gutmann, Ethan  and
      Aalstad, Kristoffer  and
      Fayad, Abbas  and
      Gascoin, Simon",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-75004",
    doi = "10.5194/hess-2020-335",
}
@article{Kim-2020-Snow,
    title = "Snow Ensemble Uncertainty Project (SEUP): Quantification of snowwater equivalent uncertainty across North America via ensemble landsurface modeling",
    author = "Kim, Rhae Sung  and
      Kumar, Sujay  and
      Vuyovich, Carrie  and
      Houser, Paul  and
      Lundquist, Jessica  and
      Mudryk, Lawrence  and
      Durand, Michael  and
      Barros, Ana  and
      Kim, Edward J.  and
      Forman, Barton A.  and
      Gutmann, Ethan D.  and
      Wrzesien, Melissa L.  and
      Garnaud, Camille  and
      Sandells, Melody  and
      Marshall, Hans-Peter  and
      Cristea, Nicoleta  and
      Pflug, Justin M.  and
      Johnston, Jeremy  and
      Cao, Yueqian  and
      Mocko, David  and
      Wang, Shugong",
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-75005",
    doi = "10.5194/tc-2020-248",
}
@article{Matiu-2020-Observed,
    title = "Observed snow depth trends in the European Alps 1971 to 2019",
    author = {Matiu, Michael  and
      Crespi, Alice  and
      Bertoldi, Giacomo  and
      Carmagnola, Carlo Maria  and
      Marty, Christoph  and
      Morin, Samuel  and
      Sch{\"o}ner, Wolfgang  and
      Cat Berro, Daniele  and
      Chiogna, Gabriele  and
      De Gregorio, Ludovica  and
      Kotlarski, Sven  and
      Majone, Bruno  and
      Resch, Gernot  and
      Terzago, Silvia  and
      Valt, Mauro  and
      Beozzo, Walter  and
      Cianfarra, Paola  and
      Gouttevin, Isabelle  and
      Marcolini, Giorgia  and
      Notarnicola, Claudia  and
      Petitta, Marcello  and
      Scherrer, Simon C.  and
      Strasser, Ulrich  and
      Winkler, Michael  and
      Zebisch, Marc  and
      Cicogna, Andrea  and
      Cremonini, Roberto  and
      Debernardi, Andrea  and
      Faletto, Mattia  and
      Gaddo, Mauro  and
      Giovannini, Lorenzo  and
      Mercalli, Luca  and
      Soubeyroux, Jean-Michel  and
      Su{\v{s}}nik, Andrea  and
      Trenti, Alberto  and
      Urbani, Stefano  and
      Weilguni, Viktor},
    journal = "",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-75006",
    doi = "10.5194/tc-2020-289",
}

repository: gwf-uwaterloo/gwf-publications

repo_name: gwf-uwaterloo/gwf-publications

gha_language: Python


text:
# Copyright (C) 2016 - 2017 Randy Li <ayaka@soulik.info>
# Released under the GNU GENERAL PUBLIC LICENSE Version 2
# (see COPYING.GPLv2 for the terms)
include rockchip-mpp.inc

# TAG = "release_20171218"
SRCREV = "develop"
SRC_URI = "git://github.com/rockchip-linux/mpp.git;branch=develop"
# SRC_URI = "git://github.com/rockchip-linux/mpp.git;tag=${TAG};nobranch=1"

MPP_VERSION = "1.3.1"

# PV = "${MPP_VERSION}+${TAG}"
PV = "${MPP_VERSION}+"

S = "${WORKDIR}/git"


text:
package lc

import (
	"bytes"
	"strings"
)

// Time: O(n)
// Benchmark: 0ms 2.1mb | 100%

func reorderSpaces(text string) string {
	var spaceCount int
	for _, ch := range text {
		if ch == ' ' {
			spaceCount++
		}
	}

	words := strings.Fields(text)
	var spacesBetween, spacesAfter int
	if len(words) <= 1 {
		spacesAfter = spaceCount
	} else {
		spacesBetween = spaceCount / (len(words) - 1)
		spacesAfter = spaceCount % (len(words) - 1)
	}

	var buf bytes.Buffer
	for i, w := range words {
		buf.WriteString(w)
		if i == len(words)-1 {
			break
		}
		for j := 0; j < spacesBetween; j++ {
			buf.WriteString(" ")
		}
	}

	for i := 0; i < spacesAfter; i++ {
		buf.WriteString(" ")
	}
	return buf.String()
}

repository: phea/leetcode-go

repo_name: phea/leetcode-go


text:
/*
Package fcradminmsg - stores all the admin messages.
*/
package fcradminmsg

/*
 * Copyright 2020 ConsenSys Software Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 * the License. You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 * an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
 * specific language governing permissions and limitations under the License.
 *
 * SPDX-License-Identifier: Apache-2.0
 */

import "encoding/json"

// listFilesResponseJson represents the response of listing files.
type listFilesResponseJson struct {
	Files     []string `json:"files"`
	CIDs      []string `json:"cids"`
	Sizes     []int64  `json:"sizes"`
	Published []bool   `json:"published"`
	Frequency []int    `json:"frequency"`
}

// EncodeListFilesResponse is used to get the byte array of listFilesResponseJson
func EncodeListFilesResponse(
	files []string,
	cids []string,
	sizes []int64,
	published []bool,
	frequency []int,
) ([]byte, error) {
	return json.Marshal(&listFilesResponseJson{
		Files:     files,
		CIDs:      cids,
		Sizes:     sizes,
		Published: published,
		Frequency: frequency,
	})
}

// DecodeListFilesResponse is used to get the fields from byte array of listFilesResponseJson
func DecodeListFilesResponse(data []byte) (
	[]string, // files
	[]string, // cids
	[]int64, // sizes
	[]bool, // published
	[]int, // frequency
	error, // error
) {
	msg := listFilesResponseJson{}
	err := json.Unmarshal(data, &msg)
	if err != nil {
		return nil, nil, nil, nil, nil, err
	}
	return msg.Files, msg.CIDs, msg.Sizes, msg.Published, msg.Frequency, nil
}

repository: wcgcyx/fc-retrieval

repo_name: wcgcyx/fc-retrieval


text:
@inproceedings{marta2016EncDTW,
 author = {M. Gomez-Barrero and J. Galbally and J. Fierrez},
 booktitle = {Proc. Int. Workshop on Biometrics and Forensics (IWBF)},
 month = {06},
 owner = {Biometrics},
 timestamp = {2015.07.17},
 title = {Variable-length Template Protection Based on Homomorphic Encryption with Application to Signature Biometrics},
 year = {2016}
}

repository: mgbarrero/academic-kickstart

repo_name: mgbarrero/academic-kickstart


text:
SECTION = "libs"

inherit native

require ${@bb.data.getVar('P', d , 1).replace('-native-', '_')}.bb


text:
#
# Base recipe: meta/recipes-graphics/xorg-lib/libx11.inc
# Base branch: daisy
#

SUMMARY = "Xlib: C Language X Interface library"

DESCRIPTION = "This package provides a client interface to the X Window \
System, otherwise known as 'Xlib'.  It provides a complete API for the \
basic functions of the window system."

require xorg-lib-common.inc

PR = "${INC_PR}.0"

inherit siteinfo gettext

PROVIDES = "virtual/libx11"

LICENSE = "MIT & MIT-style & BSD"
LIC_FILES_CHKSUM = "file://COPYING;md5=172255dee66bb0151435b2d5d709fcf7"

DEPENDS += "xproto xextproto xtrans libxcb kbproto inputproto"
DEPENDS += "xproto-native"

# Follow Debian/rules
EXTRA_OECONF += "--disable-specs --without-fop --disable-silent-rules"

# The location of keysymdef.h
EXTRA_OECONF += "--with-keysymdefdir=${STAGING_INCDIR}/X11/"

PACKAGECONFIG ??= "xcms"
PACKAGECONFIG[xcms] = "--enable-xcms,--disable-xcms"

# src/util/makekeys is built natively but needs -D_GNU_SOURCE defined.
CPPFLAGS_FOR_BUILD += "-D_GNU_SOURCE"

PACKAGES =+ "${PN}-xcb ${PN}-xcb-dev ${PN}-data"

FILES_${PN} += "${datadir}/X11/XKeysymDB ${datadir}/X11/XErrorDB ${datadir}/X11/Xcms.txt"
FILES_${PN}-xcb += "${libdir}/libX11-xcb.so.*"
FILES_${PN}-xcb-dev += "${includedir}/X11/Xlib-xcb.h ${libdir}/libX11-xcb.so \
		${libdir}/pkgconfig/x11-xcb.pc ${datadir}/man/man3/XGetXCBConnection.3 \
		${datadir}/man/man3/XSetEventQueueOwner.3"
FILES_${PN}-data += "${datadir}/X11/locale ${libdir}/X11/locale ${datadir}/X11/XErrorDB"

# Multiple libx11 derivatives from from this file and are selected by virtual/libx11
# A world build should only build the correct version, not all of them.
EXCLUDE_FROM_WORLD = "1"

BBCLASSEXTEND = "native nativesdk"

# applying debian patch by quilt
DEBIAN_PATCH_TYPE = "quilt"

# Correct the package name follow Debian
DEBIANNAME_${PN}-dbg = "${PN}6-dbg"

repository: nghiaht-tsdv/meta-debian

repo_name: nghiaht-tsdv/meta-debian

branch_name: refs/heads/jethro

gha_language: BitBake


text:
require automake.inc
PR = "${INC_PR}.2"

SRC_URI += "file://0001-py-compile-compile-only-optimized-byte-code.patch \
		file://automake-dont-execute-perl.patch"

SRC_URI[automake.md5sum] = "c2972c4d9b3e29c03d5f2af86249876f"
SRC_URI[automake.sha256sum] = "5b159d3c0e0a1f87de71b68bcb9f1a1c49e9e71749c9b723f17e2e1e0295c7ae"


text:
SUMMARY = "Radeon-GPUProfiler"
DESCRIPTION = "The Radeon GPU Profiler (RGP) is a ground-breaking \
              low-level optimization tool from AMD.  It provides \
              detailed timing information on Radeon Graphics \
              using custom, built-in, hardware thread-tracing, \
              allowing the developer deep inspection of GPU workloads. \
              This package merely deploys the remote profiling service \
              on the target so a host can collect and display profiling \
              data."

LICENSE = "Proprietary"
LIC_FILES_CHKSUM = "file://License.rtf;md5=5441ae9fb95849e3aacd0f330710f9fa"

inherit systemd

RDEPENDS_${PN} += "connman-wait-online"

SRC_URI = "file://License.rtf \
           file://RadeonDeveloperServiceCLI \
           file://${BOOT_SERVICE}"

S = "${WORKDIR}"
BOOT_SERVICE = "rds-cli.service"
SYSTEMD_SERVICE_${PN} = "${BOOT_SERVICE}"
SYSTEMD_AUTO_ENABLE = "enable"

# Skip configure and compile
do_configure[noexec] = "1"
do_compile[noexec] = "1"

do_install () {
    # Install the binary for RDS CLI
    install -d ${D}${bindir}
    install -m 0755 ${S}/RadeonDeveloperServiceCLI ${D}${bindir}/

    # Install the systemd service so we can kick start on boot
    install -d ${D}${systemd_unitdir}/system
    install -m 644 ${WORKDIR}/${BOOT_SERVICE} ${D}${systemd_unitdir}/system/
}


text:
require recipes/images/console-base-image.bb

DISTRO_SSH_DAEMON = "openssh"

DEPENDS += "task-base-extended \
	   "

IMAGE_INSTALL += " \
  task-base-extended \
  task-proper-tools \
  task-usrp-embedded \
  ti-dsplink \
  ti-dsplink-examples \
"

export IMAGE_BASENAME = "console-e1xx-devel-image"

repository: mmoselhy/meta-ettus

repo_name: mmoselhy/meta-ettus


text:
package main

import (
	"encoding/json"
	"os"
	"strings"
	"syscall"

	"github.com/concourse/docker-image-resource/cmd/print-metadata/passwd"
)

type imageMetadata struct {
	User string   `json:"user"`
	Env  []string `json:"env"`
}

var blacklistedEnv = map[string]bool{
	"HOSTNAME": true,
}

func main() {
	err := json.NewEncoder(os.Stdout).Encode(imageMetadata{
		User: username(),
		Env:  env(),
	})
	if err != nil {
		panic(err)
	}
}

func username() string {
	users, err := passwd.ReadUsers("/etc/passwd")
	if err != nil {
		panic(err)
	}

	name, found := users.NameForID(syscall.Getuid())
	if !found {
		panic("could not find user in /etc/passwd")
	}

	return name
}

func env() []string {
	var envVars []string
	for _, e := range os.Environ() {
		parts := strings.SplitN(e, "=", 2)
		name := parts[0]

		if !blacklistedEnv[name] {
			envVars = append(envVars, e)
		}
	}

	return envVars
}

repository: hpcloud/docker-image-resource

repo_name: hpcloud/docker-image-resource


text:
DESCRIPTION = "Linux Kernel for x86 compatible machines"
SECTION = "kernel"
LICENSE = "GPL"
PR = "r2"

SRC_URI = "${KERNELORG_MIRROR}/pub/linux/kernel/v2.6/linux-${PV}.tar.bz2 \
           file://defconfig"

S = "${WORKDIR}/linux-${PV}"

inherit kernel

COMPATIBLE_HOST = "i.86.*-linux"

do_configure_prepend() {
	install -m 0644 ${WORKDIR}/defconfig ${S}/.config
}


text:
// Copyright 2017 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.

package main

import (
	"flag"
	"fmt"
	"io/ioutil"
	"net/url"
	"os"
	"strings"

	"go.chromium.org/luci/common/errors"
	"go.chromium.org/luci/common/proto"
)

var defPath = flag.String(
	"path",
	"",
	"path to the file with text proto Template message, see template.proto")

var queryEscape = flag.Bool(
	"query-escape",
	false,
	"escape the URL as a query string parameter")

// Label creates the appropriate priority label.
func (p Priority) Label() string {
	if p == 0 {
		return ""
	}
	return "Pri-" + strings.TrimPrefix(p.String(), "P")
}

// Label creates the appropriate bug type label.
func (t Type) Label() string {
	if t == 0 {
		return ""
	}
	return "Type-" + t.String()
}

func cleanStrings(slice []string) []string {
	// keep it simple
	clean := make([]string, 0, len(slice))
	for _, s := range slice {
		s = strings.TrimSpace(s)
		if s != "" {
			clean = append(clean, s)
		}
	}
	return clean
}

func run() error {
	flag.Parse()
	if len(flag.Args()) > 0 {
		return fmt.Errorf("unexpected args: %q", flag.Args())
	}

	contents, err := ioutil.ReadFile(*defPath)
	if err != nil {
		return errors.Annotate(err, "failed to read template file").Err()
	}

	var template Template
	if err := proto.UnmarshalTextML(string(contents), &template); err != nil {
		return errors.Annotate(err, "failed to parse template file").Err()
	}
	template.Labels = append(template.Labels, template.Pri.Label(), template.Type.Label())

	params := url.Values{}
	// Display an appropriate default template in the form to avoid confusion.
	params.Set("template", "Build Infrastructure")
	params.Set("summary", template.Summary)
	params.Set("comment", template.Description)
	params.Set("cc", strings.Join(cleanStrings(template.Cc), ","))
	params.Set("components", strings.Join(cleanStrings(template.Components), ","))
	params.Set("labels", strings.Join(cleanStrings(template.Labels), ","))
	params.Set("blocking", strings.Join(cleanStrings(template.Blocking), ","))
	ret := "https://bugs.chromium.org/p/chromium/issues/entry?" + params.Encode()
	if *queryEscape {
		ret = url.QueryEscape(ret)
	}
	fmt.Println(ret)
	return nil
}

func main() {
	if err := run(); err != nil {
		fmt.Fprintln(os.Stderr, err)
		os.Exit(1)
	}
}


text:
//  Copyright 2020 Google Inc. All Rights Reserved.
//
//  Licensed under the Apache License, Version 2.0 (the "License");
//  you may not use this file except in compliance with the License.
//  You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
//  Unless required by applicable law or agreed to in writing, software
//  distributed under the License is distributed on an "AS IS" BASIS,
//  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//  See the License for the specific language governing permissions and
//  limitations under the License.

package ospatch

import (
	"context"
	"os"
	"os/exec"
	"testing"

	"github.com/GoogleCloudPlatform/osconfig/packages"
	utilmocks "github.com/GoogleCloudPlatform/osconfig/util/mocks"
	"github.com/golang/mock/gomock"
)

func TestRunYumUpdateWithSecurity(t *testing.T) {
	data := []byte(`
	=================================================================================================================================================================================
	Package                                      Arch                           Version                                              Repository                                Size
    =================================================================================================================================================================================
    Upgrading:
      foo                                       noarch                         2.0.0-1                           BaseOS                                   361 k
    blah
`)
	ctx := context.Background()

	if os.Getenv("EXIT100") == "1" {
		os.Exit(100)
	}

	cmd := exec.CommandContext(context.Background(), os.Args[0], "-test.run=TestRunYumUpdateWithSecurity")
	cmd.Env = append(os.Environ(), "EXIT100=1")
	err := cmd.Run()

	mockCtrl := gomock.NewController(t)
	defer mockCtrl.Finish()

	mockCommandRunner := utilmocks.NewMockCommandRunner(mockCtrl)
	packages.SetCommandRunner(mockCommandRunner)
	checkUpdateCall := mockCommandRunner.EXPECT().Run(ctx, utilmocks.EqCmd(exec.Command("/usr/bin/yum", []string{"check-update", "--assumeyes"}...))).Return([]byte("stdout"), []byte("stderr"), err).Times(1)
	// yum install call to install package
	mockCommandRunner.EXPECT().Run(ctx, utilmocks.EqCmd(exec.Command("/usr/bin/yum", []string{"install", "--assumeyes", "foo"}...))).After(checkUpdateCall).Return([]byte("stdout"), []byte("stderr"), nil).Times(1)

	packages.SetPtyCommandRunner(mockCommandRunner)
	mockCommandRunner.EXPECT().Run(ctx, utilmocks.EqCmd(exec.Command("/usr/bin/yum", []string{"update", "--assumeno", "--cacheonly", "--color=never", "--security"}...))).Return(data, []byte("stderr"), nil).Times(1)

	err = RunYumUpdate(ctx, YumUpdateMinimal(false), YumUpdateSecurity(true))
	if err != nil {
		t.Errorf("did not expect error: %+v", err)
	}
}

func TestRunYumUpdateWithSecurityWithExclusives(t *testing.T) {
	data := []byte(`
	=================================================================================================================================================================================
	Package                                      Arch                           Version                                              Repository                                Size
	=================================================================================================================================================================================
	Installing:
      kernel                                    x86_64                         2.6.32-754.24.3.el6                                  updates                                   32 M
	    replacing kernel.x86_64 1.0.0-4
	Upgrading:
	  foo                                       noarch                         2.0.0-1                                              BaseOS                                   361 k
	  bar                                       x86_64                         2.0.0-1                                              repo                                      10 M
	Obsoleting:
	  baz                                       noarch                         2.0.0-1                                              repo                                      10 M
`)
	ctx := context.Background()
	exclusivePackages := []string{"foo", "bar"}
	if os.Getenv("EXIT100") == "1" {
		os.Exit(100)
	}

	cmd := exec.CommandContext(context.Background(), os.Args[0], "-test.run=TestRunYumUpdateWithSecurityWithExclusives")
	cmd.Env = append(os.Environ(), "EXIT100=1")
	err := cmd.Run()

	mockCtrl := gomock.NewController(t)
	defer mockCtrl.Finish()

	mockCommandRunner := utilmocks.NewMockCommandRunner(mockCtrl)
	packages.SetCommandRunner(mockCommandRunner)
	checkUpdateCall := mockCommandRunner.EXPECT().Run(ctx, utilmocks.EqCmd(exec.Command("/usr/bin/yum", []string{"check-update", "--assumeyes"}...))).Return([]byte("stdout"), []byte("stderr"), err).Times(1)
	// yum install call to install package, make sure only 2 packages are installed.
	mockCommandRunner.EXPECT().Run(ctx, utilmocks.EqCmd(exec.Command("/usr/bin/yum", []string{"install", "--assumeyes", "foo", "bar"}...))).After(checkUpdateCall).Return([]byte("stdout"), []byte("stderr"), nil).Times(1)

	packages.SetPtyCommandRunner(mockCommandRunner)
	mockCommandRunner.EXPECT().Run(ctx, utilmocks.EqCmd(exec.Command("/usr/bin/yum", []string{"update", "--assumeno", "--cacheonly", "--color=never", "--security"}...))).Return(data, []byte("stderr"), nil).Times(1)

	err = RunYumUpdate(ctx, YumUpdateMinimal(false), YumUpdateSecurity(true), YumExclusivePackages(exclusivePackages))
	if err != nil {
		t.Errorf("did not expect error: %+v", err)
	}
}

repository: GoogleCloudPlatform/osconfig

repo_name: GoogleCloudPlatform/osconfig


text:
IF(NOT DEMI_FIND_FRAMEWORKS_INCLUDED)
  SET(DEMI_FIND_FRAMEWORKS_INCLUDED 1)
  MACRO(DEMI_FIND_FRAMEWORKS fwk)
    IF(APPLE)
      SET(${fwk}_FRAMEWORKS)

      SET(DEMI_FRAMEWORK_PATH
    	~/Library/Frameworks
    	/Library/Frameworks
    	/System/Library/Frameworks
    	/Network/Library/Frameworks
	  )
	  FOREACH(dir ${DEMI_FRAMEWORK_PATH})
	    SET(fwkpath ${dir}/${fwk}.framework)
	    IF(EXISTS ${fwkpath})
          SET(${fwk}_FRAMEWORKS ${${fwk}_FRAMEWORKS} ${fwkpath})
        ENDIF(EXISTS ${fwkpath})
      ENDFOREACH(dir)
    ENDIF(APPLE)
  ENDMACRO(DEMI_FIND_FRAMEWORKS)
ENDIF(NOT DEMI_FIND_FRAMEWORKS_INCLUDED)

extension: cmake


text:
package classfile

import "github.com/PandaBlame/Gava/java"

/*
ConstantValue_attribute {
    u2 attribute_name_index;
    u4 attribute_length;
    u2 constantvalue_index;
}
*/
type ConstantValueAttributeV8 struct {
	constantValueIndex uint16
}

func (self *ConstantValueAttributeV8) Read(reader *java.ClassReader) (err error) {
	self.constantValueIndex, err = reader.ReadUint16()
	return
}

func (self *ConstantValueAttributeV8) ConstantValueIndex() uint16 {
	return self.constantValueIndex
}


text:
@Article{goudey2015high,
  title = {High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies},
  author = {Benjamin Goudey and Mani Abedini and John L Hopper and Michael Inouye and Enes Makalic and Daniel F Schmidt and John Wagner and Zeyu Zhou and Justin Zobel and Matthias Reumann},
  journal = {Health information science and systems},
  volume = {3},
  number = {1},
  pages = {1--11},
  year = {2015},
  publisher = {BioMed Central},
  date = {2015-01-01},
}

repository: bwgoudey/starter-academic

repo_name: bwgoudey/starter-academic


text:
package database

import (
	"database/sql/driver"
	"encoding/json"
  "fmt"
  "time"

	"github.com/google/uuid"
	"gorm.io/gorm"
)

type JSONB map[string]interface{}

func (j JSONB) Value() (driver.Value, error) {
	valueString, err := json.Marshal(j)
	return string(valueString), err
}

func (j *JSONB) Scan(value interface{}) error {
	if err := json.Unmarshal([]byte(fmt.Sprintf("%v", value)), &j); err != nil {
		return err
	}
	return nil
}

type PrimaryKeyUUID struct {
	ID uuid.UUID `json:"id" gorm:"primaryKey;type:uuid;default:gen_random_uuid()"`
}

type TimeFields struct {
	CreatedAt time.Time      `json:"createdAt"`
	UpdatedAt time.Time      `json:"updatedAt"`
	DeletedAt gorm.DeletedAt `gorm:"index" json:"deletedAt"`
}

repository: globash/frontend

repo_name: globash/frontend