text:
Carolyn Kaster, Associated Press
WASHINGTON — Government agents in 74 countries demanded information on about 38,000 Facebook users in the first half of this year, with about half the orders coming from authorities in the United States, the company said Tuesday.
The social-networking giant is the latest technology company to release figures on how often governments seek information about its customers. Microsoft and Google have done the same.
As with the other companies, it's hard to discern much from Facebook's data, besides the fact that, as users around the globe flocked to the world's largest social network, police and intelligence agencies followed.
Facebook and Twitter have become organizing platforms for activists and, as such, have become targets for governments. During anti-government protests in Turkey in May and June, Turkish Prime Minister Recep Tayyip Erdogan called social media "the worst menace to society."
At the time, Facebook denied it provided information about protest organizers to the Turkish government.
Data released Tuesday show authorities in Turkey submitted 96 requests covering 173 users. Facebook said it provided some information in about 45 of those cases, but there's no information on what was turned over and why.
"We fight many of these requests, pushing back when we find legal deficiencies and narrowing the scope of overly broad or vague requests," Colin Stretch, Facebook's general counsel company said in a blog post. "When we are required to comply with a particular request, we frequently share only basic user information, such as name."
Facebook spokeswoman Sarah Feinberg said the company stands by its assertions that it gave no information regarding the Turkey protests.
"The data included in the report related to Turkey is about child endangerment and emergency law enforcement requests," she said.
Facebook and other technology companies have been criticized for helping the National Security Agency secretly collect data on customers. Federal law gives government the authority to demand data without specific warrants, and while companies can fight requests in secret court hearings, it's an uphill battle.
Facebook turned over some data in response to about 60 percent of those requests.
It's not clear from the Facebook data how many of the roughly 26,000 government requests on 38,000 users were for law-enforcement purposes and how many were for intelligence gathering.
Technology and government officials have said criminal investigations are far more common than national security matters as a justification for demanding information from companies.
The numbers are imprecise because the federal government forbids companies from revealing how many times they've been ordered to turn over information about their customers. Facebook released only a range of figures for the United States.
The company said it planned to start releasing these figures regularly.
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The onetime "Daily Show" host offered up his raw reaction to Donald Trump's RNC speech during Thursday's live episode.
Trump's part in the filmmaker's "Wall Street: Money Never Sleeps" was left on the cutting-room floor.
The cult-classic remake premieres on Fox in October.
A "Muggle Mob" whips up anticipation for the upcoming book version of the new "Harry Potter" play.
Elise Archer fell for the character two decades ago, but she isn't keen on the "Suicide Squad" version.
The billionaire appeared as a guest on Wednesday's episode of "The Late Show."
The comedian joined 'SNL' castmates Colin Jost and Michael Che Wednesday night in Cleveland.
Missy Elliott also joined the first lady and the "Late Late Show" host for the ride.
50 fans who watched the world premiere of the new "Star Trek" film weigh in.
God is a delusion, Elliot Alderson tells us. So is Mr. Robot, for that matter, but it doesn't keep him from interfering in the affairs of man.
The Seattle supergroup is celebrating the 25th anniversary of its self-titled album release.
"It's essentially 'The Big Chill' set in the world of an improv theater," he says.
The "Tonight Show" host added some Michael Jackson splash, while the "Late Show" comedian went with shadow puppets.
A new study shows a stark partisan divide when it comes to TV favorites.
Toddler Moana makes friends with the ocean waves in this whimsical clip from the animated film.
"I'm still wrapping my head around all of the spectacle," said the "Daily Show" host.
The Tony Award winner injected Charles Dickens, Dr. Seuss and the Beastie Boys into her apology speech.
The television and film icon behind "Happy Days" and "Pretty Woman" died Tuesday at age 81.
The actress also discusses her recent appearance at the White House press secretary podium.
The first episode of 'Batman -- The Telltale Series" will be available Aug. 2.

subset: commoncrawl


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Compliance Officer Salaries in Jacksonville, FL
The average salary for Compliance Officer jobs near Jacksonville, FL is $51,107*.
Similar Locations
Popular Companies
* Please note that all salary figures are approximations based upon third party submissions to Simply Hired. These figures are given to Simply Hired users for the purpose of generalized comparison only. Minimum wage may differ by jurisdiction and you should consult the employer for actual salary figures.
Top Compliance Officer jobs near Jacksonville, FL
Precious Metals Trader
Deutsche Bank - US - Jacksonville, FL
TS Compliance officers review the firm's activities daily to identify possible violations of regulatory rule based models and firm policies....
Associate Director, Regulatory Reporting - Opportunity in Jacksonville, FL
Macquarie Group Limited - Florida
Experience with statutory reporting, financial accounting and compliance. Acting Chief Financial Officer for US regulated broker-dealer and futures clearing...
Medical Technologist (Reserve)
U.S. Navy - United States
For current or former Navy Officers (NAVET):. For current or former Officers of military branches other than the Navy (OSVET), as well as for Officer candidates...
Licensed Mortgage Loan Originator
R P Funding - Jacksonville, FL
We do not offer any type of "Net Branching" or "Self Sourcing" Loan Officer Positions. The Loan Officer conducts interviews with prospective borrowers in order...
Grant Accountant
Kelly Services - Jacksonville, FL
PROJECT ACCOUNTING OFFICER. PROJECT ACCOUNTING OFFICER NEEDED IN DOWNTOWN JACKSONVILLE! Monitors project expenditures to ensure compliance with applicable rules...


text:
Here's a little puzzle that might shed some light on some apparently confusing behaviour by missing values (NAs) in R:
What is NA^0 in R?
You can get the answer easily by typing at the R command line:
[1] 1
But the interesting question that arises is: why is it 1? Most people might expect that the answer would be NA, like most expressions that include NA. But here's the trick to understanding this outcome: think of NA not as a number, but as a placeholder for a number that exists, but whose value we don't know.
Now think of all of the numbers that could replace NA in the expression NA^0. Any positive number to the power zero is 1. Same goes for any negative number. Even zero to the power zero is defined by mathematicians to be 1 (for reasons I'm not going to go into here). So that means whatever number you substitute for NA in the expression NA^0, the answer will be 1. And so that's the answer R gives.
There are a few other instances where using the indeterminate NA in an expression can lead to a specific non-NA result. Consider this example:
Here. the NA is holding the place of a logical value1, so it could be representing only TRUE or FALSE. But whatever it represents, the answer will be the same:
By the same token, any(x) can return TRUE even if the logical vector includes NAs, as long as x includes at least one TRUE value. Similarly, NA && FALSE is always FALSE.
There are a few other examples as well (if you know some, share them in the comments). But always remember: if you're ever confused by the behaviour of NA in R, think about what values it might contain, and if changing them changes the outcome. That might explain what's going on. For more on how R handles NAs, see the R Language Definition.
1Footnote: I'm deliberately ignoring the storage mode of NA, which can come in logical, integer, double and character flavours. In all the examples above, it gets coerced to the type of the other elements in the expression.
by Joseph Rickert
Over the years I have seen several excellent tutorials at useR!conferences that were not only very satisfying "you had to be there" experiences but were also backed up with meticulously prepared materials of lasting value. This year, quite a few useR!20i6 tutorials measure up to this level of quality. My take on why things turned out this way is that GitHub, Markdown, and Jupyter notebooks have been universally adopted as workshop / tutorial creation tools, and that having the right tools encourages creativity and draws out one's best efforts.
Jenny Bryan's tutorial Using Git and GitHub with R, Rstudio, and R Markdown and the tutorial by Andrie de Vries and Micheleen Harris: Using R with Jupyter notebooks for reproducible research are two superb, Escheresque self-referencing examples of what I am talking about. Bryan's tutorial which uses GitHub and R Markdown to teach GitHub and R Markdown is an impressive introduction to these two essential resources. And, the tutorial by de Vries and Harris makes very effective use of GitHub and Jupyter Notebooks. Moreover, this tutorial sets the gold standard for how to set up a system for interactive user participation. Harris and de Vries staged their tutorial on Microsoft's Azure Data Science VM. The Linux version of this VM comes provisioned with JupyterHub, a set of processes that enables a multi-user Jupyter Notebook server. Once the VM is loaded with the training materials, its only a matter of giving students a username and password to grant them immediate access to the interactive workshop materials. Have a look at notebook 06 to see how to set all of this up.
After seeing this, and comparing it to other tutorials where instructors wasted the better part of an hour trying to get students up and running with local copies of their course materials I can't see why everyone wouldn't opt for a cloud solution to this problem. When word gets out, the Data Science VM is going to be the standard for delivering technical workshops.
Ledell is also a gifted teacher who anticipates where her audience may have difficulties. Her historical approach to understanding gradient boosting machines provides an opportunity to clarify the differences between various versions of the boosting algorithms. Sometimes understanding how something came to be is halfway towards understanding how it works.
The bar for presenting lectures, tutorials and workshops has been set pretty high. Anyone who is serious about delivering a high quality education probably needs to develop some skills with GitHub, Markdown and Notebooks. Studying the tutorial materials from useR! 2016 is a good place to start.
Many newcomers to R got their start learning the language with Computerworld's Beginner's Guide to R, a 6-part introduction to the basics of the language. Now, budding R users who want to take their skills to the next level have a new guide to help them: Computerword's Advanced Beginner's Guide to R. Written by Sharon Machlis, author of the prior Beginner's guide and regular reporter of R news at Computerworld, this new 72-page guide dives into some trickier topics related to R: extracting data via API, data wrangling, and data visualization.
On the data wrangling front, the guide provides some recipes for handling messy data. You'll learn how to transform data and add the resulting data as a new column to a data frame. There's also an extended look at restructuring data: transforming "wide" data to "long" data, and vice versa.
For visualizing data, there's a basic intro to the ggplot2 package and its grammar of graphics. There's also an in-depth tutorial on creating choropleths: geographics maps with regions shaded by data values.
And as an in-dept example of importing data, you'll learn how to use the Google Analytics API to download and prepare data on traffic to your website.
Finally, don't miss the comprehensive index of R packages for data import, data wrangling, and visualization.
To download the 72-page PDF (free by providing your email address) visit Computerworld at the link below.
Computerworld Crash Course: Advanced Beginner's Guide to R
by Joseph Rickert
Just about two and a half years ago I wrote about some resources for doing Bayesian statistics in R. Motivated by the tutorial Modern Bayesian Tools for Time Series Analysis by Harte and Weylandt that I attended at R/Finance last month, and the upcoming tutorial An Introduction to Bayesian Inference using R Interfaces to Stan that Ben Goodrich is going to give at useR! I thought I'd look into what's new. Well, Stan is what's new! Yes, Stan has been under development and available for some time. But somehow, while I wasn't paying close attention, two things happened: (1) the rstan package evolved to make the mechanics of doing Bayesian in R analysis really easy and (2) the Stan team produced and/or organized an amazing amount of documentation.
My impressions of doing Bayesian analysis in R were set in the WinBUGS era. The separate WinBUGs installation was always tricky, and then moving between the BRugs and R2WinBUGS packages presented some additional challenges. My recent Stan experience was nothing like this. I had everything up and running in just a few minutes. The directions for getting started with rstan are clear and explicit about making sure that you have the right tool chain in place for your platform. Since I am running R 3.3.0 on Windows 10 I installed Rtools34. This went quickly and as expected except that C:\Rtools\gcc-4.x-y\bin did not show up in my path variable. Not a big deal: I used the menus in the Windows System Properties box to edit the Path statement by hand. After this, rstan installed like any other R package and I was able to run the 8schools example from the package vignette. The following 10 minute video by Ehsan Karim takes you through the install process and the vignette example.
The Stan documentation includes four major components: (1) The Stan Language Manual, (2) Examples of fully worked out problems, (3) Contributed Case Studies and (4) both slides and video tutorials. This is an incredibly rich cache of resources that makes a very credible case for the ambitious project of teaching people with some R experience both Bayesian Statistics and Stan at the same time. The "trick" here is that the documentation operates at multiple levels of sophistication with entry points for students with different backgrounds. For example, a person with some R and the modest statistics background required for approaching Gelman and Hill's extraordinary text: Data Analysis Using Regression and Multilevel/Hierarchical Models can immediately beginning running rstan code for the book's examples. To run the rstan version of the example in section 5.1, Logistic Regression with One Predictor, with no changes a student only needs only to copy the R scripts and data into her local environment. In this case, she would need the R script: 5._LogisticRegressionWithOnePredictor. R, the data: nes1992_vote.data.R and the Stan code: nes_logit.stan. The Stan code for this simple model is about as straightforward as it gets: variable declarations, parameter identification and the model itself.
|data {
|int<lower=0> N;
|vector[N] income;
|int<lower=0,upper=1> vote[N];
|parameters {
|vector[2] beta;
|model {
|vote ~ bernoulli_logit(beta[1] + beta[2] * income);
Running the script will produce the iconic logistic regression plot:
I'll wind down by curbing my enthusiasm just a little by pointing out that Stan is not the only game in town. JAGS is a popular alternative, and there is plenty that can be done with unaugmented R code alone as the Bayesian Inference Task View makes abundantly clear.
If you are a book person and new to Bayesian statistics, I highly recommend Bayesian Essentials with R by Jean-Michel Marin and Christian Robert. The authors provide a compact introduction to Bayesian statistics that is backed up with numerous R examples. Also, the new book by Richard McElreath, Statistical Rethinking: A Bayesian Course with Examples in R and Stan looks like it is going to be an outstanding read. The online supplements to the book are certainly worth a look.
Finally, if you are a Bayesian or a thinking about becoming one and you are going to useR!, be sure to catch the following talks:
by Lourdes O. Montenegro
Lourdes O. Montenegro is a PhD candidate at the Lee Kuan Yew School of Public Policy, National University of Singapore. Her research interests cover the intersection of applied data science, technology, economics and public policy.
Many of us now find it hard to live without a good quality internet connection. As a result, there is growing interest in characterizing and comparing internet performance metrics. For example, when planning to switch internet service providers or considering a move to a new city or country, internet users may want to research in advance what to expect in terms of download speed or latency. Cloud companies may want to provision adequately for different markets with varying levels of internet quality. And governments may want to benchmark their communications infrastructure and invest accordingly. Whatever the purpose, a consortium of research, industry and public interest organizations called Measurement Lab has made available the largest open and verifiable internet performance dataset in the planet. With the help of a combination of packages, R users can easily query, explore and visualize this large dataset at no cost.
In the example that follows, we use the bigrquery package to query and download results from the Network Diagnostic Tool (NDT) used by the U.S. FCC. The bigrquery package provides an interface to Google BigQuery which hosts NDT results along with several other Measurement Lab (M-Lab) datasets. However, R users will need to first set-up a BigQuery account and join the M-Lab mailing list to authenticate. Detailed instructions are provided on the M-Lab website. Once done, SQL-like queries can be run from within R. The results are saved as a dataframe on which further analysis can be performed. Aside from the convenience of working within the R environment, the bigrquery package has another advantage: the only limitation to the size of the query results that can be saved for further exploration is the amount of available RAM. In contrast, the BigQuery web interface only allows export to .csv format of query results which are at or below 16,000 rows.
The following R script gives us the average download speed (in Mbps) per country in 2015.[1] The SQL-like query can be modified to return other internet performance metrics that may be of interest to the R user such as upload speed, round-trip time (latency) and packet re-transmission rates.
# Querying average download speed per country in 2015 require(bigrquery) downquery_template <- "SELECT connection_spec.client_geolocation.country_code AS country, AVG(8 * web100_log_entry.snap.HCThruOctetsAcked/ (web100_log_entry.snap.SndLimTimeRwin + web100_log_entry.snap.SndLimTimeCwnd + web100_log_entry.snap.SndLimTimeSnd)) AS downloadThroughput, COUNT(DISTINCT test_id) AS tests, FROM plx.google:m_lab.ndt.all WHERE IS_EXPLICITLY_DEFINED(web100_log_entry.connection_spec.remote_ip) AND IS_EXPLICITLY_DEFINED(web100_log_entry.connection_spec.local_ip) AND IS_EXPLICITLY_DEFINED(web100_log_entry.snap.HCThruOctetsAcked) AND IS_EXPLICITLY_DEFINED(web100_log_entry.snap.SndLimTimeRwin) AND IS_EXPLICITLY_DEFINED(web100_log_entry.snap.SndLimTimeCwnd) AND IS_EXPLICITLY_DEFINED(web100_log_entry.snap.SndLimTimeSnd) AND project = 0 AND IS_EXPLICITLY_DEFINED(connection_spec.data_direction) AND connection_spec.data_direction = 1 AND web100_log_entry.snap.HCThruOctetsAcked >= 8192 AND (web100_log_entry.snap.SndLimTimeRwin + web100_log_entry.snap.SndLimTimeCwnd + web100_log_entry.snap.SndLimTimeSnd) >= 9000000 AND (web100_log_entry.snap.SndLimTimeRwin + web100_log_entry.snap.SndLimTimeCwnd + web100_log_entry.snap.SndLimTimeSnd) < 3600000000 AND IS_EXPLICITLY_DEFINED(web100_log_entry.snap.CongSignals) AND web100_log_entry.snap.CongSignals > 0 AND (web100_log_entry.snap.State == 1 OR (web100_log_entry.snap.State >= 5 AND web100_log_entry.snap.State <= 11)) AND web100_log_entry.log_time >= PARSE_UTC_USEC('2015-01-01 00:00:00') / POW(10, 6) AND web100_log_entry.log_time < PARSE_UTC_USEC('2016-01-01 00:00:00') / POW(10, 6) GROUP BY country ORDER BY country ASC;" downresult <- query_exec(downquery_template, project="measurement-lab", max_pages=Inf)
Once we have the query results in a dataframe, we can proceed to visualize and map average download speeds for each country. To do this, we can use the rworldmap package which offers a relatively simple way to map country level and gridded user datasets. Mapping is done mainly through two functions: (1) joinCountryData2Map joins the query results with shapefiles of country boundaries; (2) mapCountryData plots the chloropleth map. Note that the join is best effected using either two or three-letter ISO country codes, although the rworldmap package also allows join columns filled with country names.
In order to make the chloropleth map prettier and more comprehensible, we can augment with a combination of the classInt package to calculate natural breaks in the range of download speed results and the RColorBrewer package for a wider selection of color schemes. In the succeeding R script, we specify the Jenks method to cluster download speed results in such a way that minimizes deviation from the class mean within each class but maximizes deviation across class means. Compared to other methods for clustering download speed results, the Jenks method draws a sharper picture of countries clocking greater than 25 Mbps on average.
require(rworldmap) require(classInt) require(RColorBrewer) downloadmap <- joinCountryData2Map(downresult , joinCode='ISO2' , nameJoinColumn='country' , verbose='TRUE') par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i") #getting class intervals using a 'jenks' classification in classInt package classInt <- classInt::classIntervals( downloadmap[["downloadThroughput"]], n=5, style="jenks") catMethod = classInt[["brks"]] #getting a colour scheme from the RColorBrewer package colourPalette <- RColorBrewer::brewer.pal(5,'RdPu') mapParams <- mapCountryData(downloadmap, nameColumnToPlot="downloadThroughput", mapTitle="Download Speed (mbps)", catMethod=catMethod, colourPalette=colourPalette, addLegend=FALSE) do.call( addMapLegend, c(mapParams, legendWidth=0.5, legendLabels="all", legendIntervals="data", legendMar = 2))
Looking at the map, we see that the UK, Japan, Romania, Sweden, Taiwan, The Netherlands, Denmark and Singapore (if we squint!) are the best places to be for internet speed addicts. Until further investigation, we can safely discount the suspiciously high results for North Korea since the number of observations are too low. In contrast, average download speeds in South Korea might be grossly underestimated when measured from foreign servers, as may be the case with NDT results, since most Koreans access locally hosted content. There are, of course, a number of caveats worth mentioning before drawing any conclusions regarding the causes of varying internet performance between countries. Confounding factors such as distance from the client to the test server, the client's operating system, and the proportion of fixed broadband to wireless connections will need to be controlled for. Despite these caveats, this tentative exploration already reveals interesting patterns in global internet performance that is worth a closer look.
[1] Thanks to Stephen McInerney and Chris Ritzo for code advice.
by Max Kuhn: Director, Nonclinical Statistics, Pfizer
Many predictive and machine learning models have structural or tuning parameters that cannot be directly estimated from the data. For example, when using K-nearest neighbor model, there is no analytical estimator for K (the number of neighbors). Typically, resampling is used to get good performance estimates of the model for a given set of values for K and the one associated with the best results is used. This is basically a grid search procedure. However, there are other approaches that can be used. I'll demonstrate how Bayesian optimization and Gaussian process models can be used as an alternative.
To demonstrate, I'll use the regression simulation system of Sapp et al. (2014) where the predictors (i.e.
x's) are independent Gaussian random variables with mean zero and a variance of 9. The prediction equation is:
x_1 + sin(x_2) + log(abs(x_3)) + x_4^2 + x_5*x_6 + I(x_7*x_8*x_9 < 0) + I(x_10 > 0) + x_11*I(x_11 > 0) + sqrt(abs(x_12)) + cos(x_13) + 2*x_14 + abs(x_15) + I(x_16 < -1) + x_17*I(x_17 < -1) - 2 * x_18 - x_19*x_20
The random error here is also Gaussian with mean zero and a variance of 9. This simulation is available in the
caret package via a function called
SLC14_1. First, we'll simulate a training set of 250 data points and also a larger set that we will use to elucidate the true parameter surface:
> library(caret)
> set.seed(7210)
> train_dat <- SLC14_1(250)
> large_dat <- SLC14_1(10000)
We will use a radial basis function support vector machine to model these data. For a fixed epsilon, the model will be tuned over the cost value and the radial basis kernel parameter, commonly denotes as
sigma. Since we are simulating the data, we can figure out a good approximation to the relationship between these parameters and the root mean squared error (RMSE) or the model. Given our specific training set and the larger simulated sample, here is the RMSE surface for a wide range of values:
There is a wide range of parameter values that are associated with very low RMSE values in the northwest.
A simple way to get an initial assessment is to use random search where a set of random tuning parameter values are generated across a "wide range". For a RBF SVM,
train function defines wide as cost values between
2^c(-5, 10) and
sigma values inside the range produced by the
sigest function in the
kernlab package. This code will do 20 random sub-models in this range:
> rand_ctrl <- trainControl(method = "repeatedcv", repeats = 5,
+ search = "random")
> set.seed(308)
> rand_search <- train(y ~ ., data = train_dat,
+ method = "svmRadial",
+ ## Create 20 random parameter values
+ tuneLength = 20,
+ metric = "RMSE",
+ preProc = c("center", "scale"),
+ trControl = rand_ctrl)
> rand_search
Support Vector Machines with Radial Basis Function Kernel
250 samples
20 predictor
Pre-processing: centered (20), scaled (20)
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 226, 224, 224, 225, 226, 224, ...
Resampling results across tuning parameters:
sigma C RMSE Rsquared
0.01161955 42.75789360 10.50838 0.7299837
0.01357777 67.97672171 10.71276 0.7212605
0.01392676 828.08072944 10.75235 0.7195869
0.01394119 0.18386619 18.56921 0.2109284
0.01538656 0.05224914 19.33310 0.1890599
0.01711920 228.59215128 11.09522 0.7047713
0.01790202 0.78835920 16.78597 0.3217203
0.01936110 0.91401289 16.45485 0.3492278
0.02023763 0.07658831 19.03987 0.2081059
0.02690269 0.04128731 19.33974 0.2126950
0.02780880 0.64865483 16.52497 0.3545042
0.02920113 974.08943821 12.22906 0.6508754
0.02963586 1.19350198 15.46690 0.4407725
0.03370625 31.45179445 12.60653 0.6314384
0.03561750 0.04970422 19.23564 0.2306298
0.03752561 0.06592800 19.07130 0.2375616
0.03783570 398.44599747 12.92958 0.6143790
0.04534046 3.91017571 13.56612 0.5798001
0.05171719 296.65916049 13.88865 0.5622445
0.06482201 47.31716568 14.66904 0.5192667
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were sigma = 0.01161955 and C
= 42.75789.
> ggplot(rand_search) + scale_x_log10() + scale_y_log10()> getTrainPerf(rand_search)
TrainRMSE TrainRsquared method
1 10.50838 0.7299837 svmRadial
There are other approaches that we can take, including a more comprehensive grid search or using a nonlinear optimizer to find better values of cost and
sigma. Another approach is to use Bayesian optimization to find good values for these parameters. This is an optimization scheme that uses Bayesian models based on Gaussian processes to predict good tuning parameters.
Gaussian Process (GP) regression is used to facilitate the Bayesian analysis. If creates a regression model to formalize the relationship between the outcome (RMSE, in this application) and the SVM tuning parameters. The standard assumption regarding normality of the residuals is used and, being a Bayesian model, the regression parameters also gain a prior distribution that is multivariate normal. The GP regression model uses a kernel basis expansion (much like the SVM model does) in order to allow the model to be nonlinear in the SVM tuning parameters. To do this, a radial basis function kernel is used for the covariance function of the multivariate normal prior and maximum likelihood is used to estimate the kernel parameters of the GP.
In the end, the GP regression model can take the current set of resampled RMSE values and make predictions over the entire space of potential cost and
sigma parameters. The Bayesian machinery allows of this prediction to have a distribution; for a given set of tuning parameters, we can obtain the estimated mean RMSE values as well as an estimate of the corresponding prediction variance. For example, if we were to use our data from the random search to build a GP model, the predicted mean RMSE would look like:
The darker regions indicate smaller RMSE values given the current resampling results. The predicted standard deviation of the RMSE is:
The prediction noise becomes larger (e.g. darker) as we move away from the current set of observed values.
GPfit package was used to create these models.)
To find good parameters to test, there are several approaches. This paper (pdf) outlines several but we will use the confidence bound approach. For any combination of cost and
sigma, we can compute the lower confidence bound of the predicted RMSE. Since this takes the uncertainty of prediction into account it has the potential to produce better directions to take the optimization. Here is a plot of the confidence bound using a single standard deviation of the predicted mean:
Darker values indicate better conditions to explore. Since we know the true RMSE surface, we can see that the best region (the northwest) is estimated to be an interesting location to take the optimization. The optimizer would pick a good location based on this model and evaluate this as the next parameter value. This most recent configuration is added to the GP's training set and the process continues for a pre-specified number of iterations.
Yachen Yan created an R package for Bayesian optimization. He also made a modification so that we can use our initial random search as the substrate to the first GP used. To search a much wider parameter space, our code looks like:
> ## Define the resampling method
> ctrl <- trainControl(method = "repeatedcv", repeats = 5)
> ## Use this function to optimize the model. The two parameters are
> ## evaluated on the log scale given their range and scope.
> svm_fit_bayes <- function(logC, logSigma) {
+ ## Use the same model code but for a single (C, sigma) pair.
+ txt <- capture.output(
+ mod <- train(y ~ ., data = train_dat,
+ method = "svmRadial",
+ preProc = c("center", "scale"),
+ metric = "RMSE",
+ trControl = ctrl,
+ tuneGrid = data.frame(C = exp(logC), sigma = exp(logSigma)))
+ )
+ ## The function wants to _maximize_ the outcome so we return
+ ## the negative of the resampled RMSE value. `Pred` can be used
+ ## to return predicted values but we'll avoid that and use zero
+ list(Score = -getTrainPerf(mod)[, "TrainRMSE"], Pred = 0)
+ }
> ## Define the bounds of the search.
> lower_bounds <- c(logC = -5, logSigma = -9)
> upper_bounds <- c(logC = 20, logSigma = -0.75)
> bounds <- list(logC = c(lower_bounds[1], upper_bounds[1]),
+ logSigma = c(lower_bounds[2], upper_bounds[2]))
> ## Create a grid of values as the input into the BO code
> initial_grid <- rand_search$results[, c("C", "sigma", "RMSE")]
> initial_grid$C <- log(initial_grid$C)
> initial_grid$sigma <- log(initial_grid$sigma)
> initial_grid$RMSE <- -initial_grid$RMSE
> names(initial_grid) <- c("logC", "logSigma", "Value")
> ## Run the optimization with the initial grid and do
> ## 30 iterations. We will choose new parameter values
> ## using the upper confidence bound using 1 std. dev.
> library(rBayesianOptimization)
> set.seed(8606)
> ba_search <- BayesianOptimization(svm_fit_bayes,
+ bounds = bounds,
+ init_grid_dt = initial_grid,
+ init_points = 0,
+ n_iter = 30,
+ acq = "ucb",
+ kappa = 1,
+ eps = 0.0,
+ verbose = TRUE)
20 points in hyperparameter space were pre-sampled
elapsed = 1.53 Round = 21 logC = 5.4014 logSigma = -5.8974 Value = -10.8148
elapsed = 1.54 Round = 22 logC = 4.9757 logSigma = -5.0449 Value = -9.7936
elapsed = 1.42 Round = 23 logC = 5.7551 logSigma = -5.0244 Value = -9.8128
elapsed = 1.30 Round = 24 logC = 5.2754 logSigma = -4.9678 Value = -9.7530
elapsed = 1.39 Round = 25 logC = 5.3009 logSigma = -5.0921 Value = -9.5516
elapsed = 1.48 Round = 26 logC = 5.3240 logSigma = -5.2313 Value = -9.6571
elapsed = 1.39 Round = 27 logC = 5.3750 logSigma = -5.1152 Value = -9.6619
elapsed = 1.44 Round = 28 logC = 5.2356 logSigma = -5.0969 Value = -9.4167
elapsed = 1.38 Round = 29 logC = 11.8347 logSigma = -5.1074 Value = -9.6351
elapsed = 1.42 Round = 30 logC = 15.7494 logSigma = -5.1232 Value = -9.4243
elapsed = 25.24 Round = 31 logC = 14.6657 logSigma = -7.9164 Value = -8.8410
elapsed = 32.60 Round = 32 logC = 18.3793 logSigma = -8.1083 Value = -8.7139
elapsed = 1.86 Round = 33 logC = 20.0000 logSigma = -5.6297 Value = -9.0580
elapsed = 0.97 Round = 34 logC = 20.0000 logSigma = -1.5768 Value = -19.2183
elapsed = 5.92 Round = 35 logC = 17.3827 logSigma = -6.6880 Value = -9.0224
elapsed = 18.01 Round = 36 logC = 20.0000 logSigma = -7.6071 Value = -8.5728
elapsed = 114.49 Round = 37 logC = 16.0079 logSigma = -9.0000 Value = -8.7058
elapsed = 89.31 Round = 38 logC = 12.8319 logSigma = -9.0000 Value = -8.6799
elapsed = 99.29 Round = 39 logC = 20.0000 logSigma = -9.0000 Value = -8.5596
elapsed = 106.88 Round = 40 logC = 14.1190 logSigma = -9.0000 Value = -8.5150
elapsed = 4.84 Round = 41 logC = 13.4694 logSigma = -6.5271 Value = -8.9728
elapsed = 108.37 Round = 42 logC = 19.0216 logSigma = -9.0000 Value = -8.7461
elapsed = 52.43 Round = 43 logC = 13.5273 logSigma = -8.5130 Value = -8.8728
elapsed = 39.69 Round = 44 logC = 20.0000 logSigma = -8.3288 Value = -8.4956
elapsed = 5.99 Round = 45 logC = 20.0000 logSigma = -6.7208 Value = -8.9455
elapsed = 113.01 Round = 46 logC = 14.9611 logSigma = -9.0000 Value = -8.7576
elapsed = 27.45 Round = 47 logC = 19.6181 logSigma = -7.9872 Value = -8.6186
elapsed = 116.00 Round = 48 logC = 17.3060 logSigma = -9.0000 Value = -8.6820
elapsed = 2.26 Round = 49 logC = 14.2698 logSigma = -5.8297 Value = -9.1837
elapsed = 64.50 Round = 50 logC = 20.0000 logSigma = -8.6438 Value = -8.6914
Best Parameters Found:
Round = 44 logC = 20.0000 logSigma = -8.3288 Value = -8.4956
Animate the search!
The final settings were found at iteration 44 with a cost setting of 485,165,195 and
sigma=0.0002043. I would have never thought to evaluate a cost parameter so large and the algorithm wants to make it even larger. Does it really work?
We can fit a model based on the new configuration and compare it to random search in terms of the resampled RMSE and the RMSE on the test set:
> set.seed(308)
> final_search <- train(y ~ ., data = train_dat,
+ method = "svmRadial",
+ tuneGrid = data.frame(C = exp(ba_search$Best_Par["logC"]),
+ sigma = exp(ba_search$Best_Par["logSigma"])),
+ metric = "RMSE",
+ preProc = c("center", "scale"),
+ trControl = ctrl)
> compare_models(final_search, rand_search)
One Sample t-test
data: x
t = -9.0833, df = 49, p-value = 4.431e-12
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-2.34640 -1.49626
sample estimates:
mean of x
> postResample(predict(rand_search, large_dat), large_dat$y)
RMSE Rsquared
10.1112280 0.7648765
> postResample(predict(final_search, large_dat), large_dat$y)
RMSE Rsquared
8.2668843 0.8343405
Much Better!
Thanks to Yachen Yan for making the
rBayesianOptimization package.
by Joseph Rickert
The model table on the caret package website lists more that 200 variations of predictive analytics models that are available withing the caret framework. All of these models may be prepared, tuned, fit and evaluated with a common set of caret functions. All on its own, the table is an impressive testament to the utility and scope of the R language as data science tool.
For the past year or so xgboost, the extreme gradient boosting algorithm, has been getting a lot of attention. The code below compares gbm with xgboost using the segmentationData set that comes with caret. The analysis presented here is far from the last word on comparing these models, but it does show how one might go about setting up a serious comparison using caret's functions to sweep through parameter space using parallel programming, and then used synchronized bootstrap samples to make a detailed comparison.
After reading in the data and dividing it into training and test data sets, caret's trainControl() and expand.grid() functions are used to set up to train the gbm model on all of the combinations of represented in the data frame built by expand.grid(). Then train() function does the actual training and fitting of the model. Notice that all of this happens in parallel. The Task Manager on my Windows 10 laptop shows all four cores maxed out at 100%.
After model fitting, predictions on the test data are computed and an ROC curve is drawn in the usual way. The AUC for gbm was computed to be 0.8731. Here is the ROC curve.
Next, a similar process for xgboost computes the AUC to be 0.8857, a fair improvement. The following plot shows how the ROC measure behaves with increasing tree depth for the two different values of the shrinkage parameter.
The final section of code shows how to caret can be used to compare the two models using the bootstrap samples that were created in the process of constructing the two models. The boxplots show xgboost has the edge although the gbm has a tighter distribution.
The next step, which I hope to take soon, is to rerun the analysis with more complete grids of tuning parameters. For a very accessible introduction to caret have a look at Max Kuhn's 2013 useR! tutorial.
#COMPARE XGBOOST with GBM ### Packages Required library(caret) library(corrplot) # plot correlations library(doParallel) # parallel processing library(dplyr) # Used by caret library(gbm) # GBM Models library(pROC) # plot the ROC curve library(xgboost) # Extreme Gradient Boosting ### Get the Data # Load the data and construct indices to divied it into training and test data sets. data(segmentationData) # Load the segmentation data set dim(segmentationData) head(segmentationData,2) # trainIndex <- createDataPartition(segmentationData$Case,p=.5,list=FALSE) trainData <- segmentationData[trainIndex,-c(1,2)] testData <- segmentationData[-trainIndex,-c(1,2)] # trainX <-trainData[,-1] # Pull out the dependent variable testX <- testData[,-1] sapply(trainX,summary) # Look at a summary of the training data ## GENERALIZED BOOSTED RGRESSION MODEL (BGM) # Set up training control ctrl <- trainControl(method = "repeatedcv", # 10fold cross validation number = 5, # do 5 repititions of cv summaryFunction=twoClassSummary, # Use AUC to pick the best model classProbs=TRUE, allowParallel = TRUE) # Use the expand.grid to specify the search space # Note that the default search grid selects multiple values of each tuning parameter grid <- expand.grid(interaction.depth=c(1,2), # Depth of variable interactions n.trees=c(10,20), # Num trees to fit shrinkage=c(0.01,0.1), # Try 2 values for learning rate n.minobsinnode = 20) # set.seed(1951) # set the seed # Set up to do parallel processing registerDoParallel(4) # Registrer a parallel backend for train getDoParWorkers() gbm.tune <- train(x=trainX,y=trainData$Class, method = "gbm", metric = "ROC", trControl = ctrl, tuneGrid=grid, verbose=FALSE) # Look at the tuning results # Note that ROC was the performance criterion used to select the optimal model. gbm.tune$bestTune plot(gbm.tune) # Plot the performance of the training models res <- gbm.tune$results res ### GBM Model Predictions and Performance # Make predictions using the test data set gbm.pred <- predict(gbm.tune,testX) #Look at the confusion matrix confusionMatrix(gbm.pred,testData$Class) #Draw the ROC curve gbm.probs <- predict(gbm.tune,testX,type="prob") head(gbm.probs) gbm.ROC <- roc(predictor=gbm.probs$PS, response=testData$Class, levels=rev(levels(testData$Class))) gbm.ROC$auc #Area under the curve: 0.8731 plot(gbm.ROC,main="GBM ROC") # Plot the propability of poor segmentation histogram(~gbm.probs$PS|testData$Class,xlab="Probability of Poor Segmentation") ##---------------------------------------------- ## XGBOOST # Some stackexchange guidance for xgboost # http://stats.stackexchange.com/questions/171043/how-to-tune-hyperparameters-of-xgboost-trees # Set up for parallel procerssing set.seed(1951) registerDoParallel(4,cores=4) getDoParWorkers() # Train xgboost xgb.grid <- expand.grid(nrounds = 500, #the maximum number of iterations eta = c(0.01,0.1), # shrinkage max_depth = c(2,6,10)) xgb.tune <-train(x=trainX,y=trainData$Class, method="xgbTree", metric="ROC", trControl=ctrl, tuneGrid=xgb.grid) xgb.tune$bestTune plot(xgb.tune) # Plot the performance of the training models res <- xgb.tune$results res ### xgboostModel Predictions and Performance # Make predictions using the test data set xgb.pred <- predict(xgb.tune,testX) #Look at the confusion matrix confusionMatrix(xgb.pred,testData$Class) #Draw the ROC curve xgb.probs <- predict(xgb.tune,testX,type="prob") #head(xgb.probs) xgb.ROC <- roc(predictor=xgb.probs$PS, response=testData$Class, levels=rev(levels(testData$Class))) xgb.ROC$auc # Area under the curve: 0.8857 plot(xgb.ROC,main="xgboost ROC") # Plot the propability of poor segmentation histogram(~xgb.probs$PS|testData$Class,xlab="Probability of Poor Segmentation") # Comparing Multiple Models # Having set the same seed before running gbm.tune and xgb.tune # we have generated paired samples and are in a position to compare models # using a resampling technique. # (See Hothorn at al, "The design and analysis of benchmark experiments # -Journal of Computational and Graphical Statistics (2005) vol 14 (3) # pp 675-699) rValues <- resamples(list(xgb=xgb.tune,gbm=gbm.tune)) rValues$values summary(rValues) bwplot(rValues,metric="ROC",main="GBM vs xgboost") # boxplot dotplot(rValues,metric="ROC",main="GBM vs xgboost") # dotplot #splom(rValues,metric="ROC")
by John Mount Ph. D.
Data Scientist at Win-Vector LLC
In her series on principal components analysis for regression in R, Win-Vector LLC's Dr. Nina Zumel broke the demonstration down into the following pieces:
In the earlier parts Dr. Zumel demonstrates common poor practice versus best practice and quantifies the degree of available improvement. In part 3, she moves from the usual "pick the number of components by eyeballing it" non-advice and teaches decisive decision procedures. For picking the number of components to retain for analysis there are a number of standard techniques in the literature including:
Dr. Zumel shows that the last method (designing a formal statistical test) is particularly easy to encode as a permutation test in the y-aware setting (there is also an obvious similarly good bootstrap test). This is well-founded and pretty much state of the art. It is also a great example of why to use a scriptable analysis platform (such as R) as it is easy to wrap arbitrarily complex methods into functions and then directly perform empirical tests on these methods. The following "broken stick" type test yields the following graph which identifies five principal components as being significant:
However, Dr. Zumel goes on to show that in a supervised learning or regression setting we can further exploit the structure of the problem and replace the traditional component magnitude tests with simple model fit significance pruning. The significance method in this case gets the stronger result of finding the two principal components that encode the known even and odd loadings of the example problem:
In fact that is sort of her point: significance pruning either on the original variables or on the derived latent components is enough to give us the right answer. In general, we get much better results when (in a supervised learning or regression situation) we use knowledge of the dependent variable (the "y" or outcome) and do all of the following:
The above will become much clearer and much more specific if you click here to read part 3.
by Joseph Rickert
R / Finance 2016 lived up to expectations and provided the quality networking and learning experience that longtime participants have come to value. Eight years is a long time for a conference to keep its sparkle and pizzazz. But, the conference organizers and the UIC have managed to create a vibe that keeps people coming back. The fact that invited keynote speakers (e.g. Bernhard Pfaff 2012, Sanjiv Das 2013, and Robert McDonald 2014) regularly submit papers in subsequent years is a testimony to the quality and networking importance of the event. My guess is that the single track format, quality presentations, intense compact schedule and pleasant venues comprise a winning formula.
Since I have recently written about the content of this year's conference in preparation for the event, and since most of the presentations are already online for you to examine directly I'll just present a few personal highlights here.
My favorite single visual from the conference is Bryan Lewis' depiction of corporate "Big Data" architectures as a manifestation of the impulse for completeness, control and dominance that once drove Soviet style central planning. (If you don't read Russian, run google translate on the text in the first panel.)
In his presentation, R in practice, at scale, Bryan presents a lightweight, R-centric architecture built around Redis that is adequate fro many "big data" tasks.
Matt Dziubinski's talk on Getting the Most out of Rcpp, High-Performance C++ in Practice, is probably not a talk I would have elected to attend in a multi-track conference, and I would have missed seeing a virtuoso performance. Matt got through over 120 of his 153 prepared slides in a single, lucid stream of clear (but loud) explanations in only 20 minutes. Never stopping to pause, he gave a mini-course in computer science performance evaluation (both hardware and software aspects) that addressed the Why, What and How of it all.
Ryan Hafen's presentation, Interactively Exploring Financial Trades in R, showed how to use a tool chain built around Tessera and the NxCore R package to perform exploratory data analysis on a large NxCore data set containing approximately 1.25 billion records of 47 variables without leaving the R environment. The following slide provides an example of the kinds of insights that are possible.
In his presentation, Quantitative Analysis of Dual Moving Average Indicators in Automated Trading, Douglas Service showed how to use stochastic differential equations and the Itô calculus to derive a closed form solution for expected Log returns under the Luxor trading strategy and a baseline set of simplifying assumptions. If you like seeing the Math you will be pleased to see that Doug provides all of the details.
Michael Kane (glmnetlib: A Low-Level Library for Regularized Regression) discussed the motivations for continuing to improve linear models and showed the progress he is making on re-implementing glmnet which, although very efficient, does not support arbitrary link family combinations or out of memory calculations and is written in the obscure Mortran flavor of Fortran. Kane's goal with his new package (renamed pirls: Penalized, Iteratively Reweighted Least Squares Regression) is to rectify these deficiencies while producing something fast enough to use.
In his presentation Community Finance Teaching Resources with R/Shiny, Matt Brigida showed off some opensource resources for teaching quantitative finance that are based on the new paradigm of GitHub as the place for tech savvy people to hang out and Shiny as the teaching / presentation tool for making calculations come alive. Check out some of Matt's 5 minute mini lessons. Here is an example from his What is Risk module:
There is much more than I have presented here on the R / Finance conference site. If you are interested in deep Finance and not just the tools I have highlighted, be sure to check out the presentations by Sanjiv Das, Bernhard Pfaff, Matthew Dixon and others. There is plenty of useful R code to be mined in these presentations too.
I would be remiss without mentioning Patrick Burns' keynote presentation which was highly entertaining, novel and thought provoking on many levels: everything a keynote should be. Pat launched his talk by referring to the Sapir-Worf hypothesis which posits that language controls how we think and assigned a similar role to model building. He went on to describe his Agent inspired R simulation model and showed how he calibrated this model to provide a useful tool for investigating ideas such as risk parity, variance targeting and strategies for taxing market pollution. The code for Pat's model is available here, but since his slides are not up on the conference site, and I was apparently too mesmerized to take useful notes, we will have to wait for Pat to post more on his website. (Pat's slides should be available soon.)
Finally, I would like to note that Doug Service and Sanjiv Das won the best paper prizes. This is the second year in a row for Sanjiv to win an R / Finance award. Congratulations to both Doug and Sanjiv!
by John Mount Ph. D.
Data Scientist at Win-Vector LLC
In part 2 of her series on Principal Components Regression Dr. Nina Zumel illustrates so-called y-aware techniques. These often neglected methods use the fact that for predictive modeling problems we know the dependent variable, outcome or y, so we can use this during data preparation in addition to using it during modeling. Dr. Zumel shows the incorporation of y-aware preparation into Principal Components Analyses can capture more of the problem structure in fewer variables. Such methods include:
This recovers more domain structure and leads to better models. Using the foundation set in the first article Dr. Zumel quickly shows how to move from a traditional x-only analysis that fails to preserve a domain-specific relation of two variables to outcome to a y-aware analysis that preserves the relation. Or in other words how to move away from a middling result where different values of y (rendered as three colors) are hopelessly intermingled when plotted against the first two found latent variables as shown below.
Dr. Zumel shows how to perform a decisive analysis where y is somewhat sortable by the each of the first two latent variable and the first two latent variables capture complementary effects, making them good mutual candidates for further modeling (as shown below).
Click here (part 2 y-aware methods) for the discussion, examples, and references. Part 1 (x only methods) can be found here.


text:
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On My Bookshelf: Weird Life
David Toomey's smart and sassy book, Weird Life is a quite enjoyable read. I was paging through it about the same time I was reading The Hydrogen Sonata. Fascinating contrast. I'll get to that. The author takes the reader through a … Continue reading
Posted in On My Bookshelf, Science Tagged extra-terrestrials, popular science, speculation, weird life Leave a comment
On My Bookshelf: The King of Infinite Space
When I was a senior in college, I took two upper level math courses to satisfy a degree requirement for General Science. I was in way over my head in Number Theory. But I survived. Two of us were enrolled … Continue reading
Posted in On My Bookshelf, Science Tagged geometry, popular science Leave a comment
On My Bookshelf: Are We Being Watched?
I just finished Paul Murdin's 2013 book, Are We Being Watched? The subtitle gives it away as a science book, not conspiracy theory: The Search for Life in the Cosmos. An astronomer pens a book that amasses planetary science, geology, … Continue reading
Posted in Astronomy, On My Bookshelf, Science Tagged astronomy, life in the universe, Mars, popular science 1 Comment


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Jayant Patel faces another trial in Australia
Melbourne: Indian-origin surgeon Jayant Patel was on Tuesday charged in a Brisbane court with grievous bodily harm of a patient in a case related to a surgery the doctor performed in 2004.
The 62-year-old former Bundaberg-based surgeon was charged at the court hearing in Brisbane, though he was not present when the indictment was formally presented to the Chief District Court Judge.
Patel has been accused of unnecessarily removing the colon of 65-year-old Ian Vowles during a surgery in 2004, when he was at the Bundaberg Hospital, an AAP report said.
Media reports said that Queensland prosecutors have filed proceedings for a new trial against Patel.
Prosecutor David Meredith presented the indictment in the Brisbane District Court.
The trial will begin in late September and is expected to run for three weeks, with both sides to call expert medical witnesses.
Prosecutors will claim Vowles` bowel showed no sign of cancer when the surgeon decided to operate, causing health complications for the patient.
In a separate case, a Supreme Court jury on March 13 had found Patel not guilty of the manslaughter of patient Mervyn Morris, who died on June 14, 2003, three weeks after the doctor removed part of his bowel to treat rectal bleeding.
It was the second time the India-born US citizen stood for the manslaughter trial of 75-year-old Morris. He was convicted of killing Morris and two other patients in June 2010.
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Watch the Bellator 159: Caldwell vs. Taimanglo weigh-in video replay.
Go inside the lives and training camps of 18-time
Jul 21, 2016
Holly Holm considers Cris "Cyborg" Justino a friend, but
Jul 21, 2016
Jose Aldo said he used a spy to figure
Jul 20, 2016
The voices were actually pretty good…except for Dana's
Article source: http://www.mmaweekly.com/watch-animated-versions-of-conor-mcgregor-and-rocky-balboa-bar-fight-with-nate-diaz-and-dana-white]]>
UFC fighter George Sullivan has been pulled from Saturday's UFC on FOX 20 fight card due to a potential compliance issue with the UFC Anti-Doping Policy.
Sullivan was scheduled to fight Hector Urbina on the UFC Fight Pass early prelims. UFC officials on Thursday announced his removal from the card, while the United States Anti-Doping Agency conducts an investigation into information that Sullivan voluntarily disclosed, which could be in conflict with the anti-doping policy.
TRENDING Dana White's RNC Speech: 'I Stand with Donald Trump'
Urbina will still weigh-in for Saturday's card as a back-up should the need arise for him to step in for anyone in either of the other two welterweight bouts on the fight card: Kamaru Usman vs. Alexander Yakovlev and Alex Oliveira vs. James Moontasri.
The UFC released the following statement on Thursday:
"UFC competitor George Sullivan voluntarily disclosed information to the U.S. Anti-Doping Agency (USADA) that could pose a potential compliance issue with the UFC Anti-Doping Policy. Out of an abundance of caution, Sullivan has been removed from his scheduled bout on Saturday night against Hector Urbina while USADA conducts an investigation into the matter. Urbina will still weigh in as a potential backup opponent should there be a need for him to compete in another bout on Saturday. Otherwise, he will be re-booked for another card in the near future."
UFC on FOX 20 features Holly Holm vs. Valentina Shevchenko in a five-round women's bantamweight main event on Saturday at the United Center in Chicago.
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Cris "Cyborg" Justino believes she has earned the right to a UFC featherweight division, calls for equal opportunity for women to compete in the Octagon.
Emmanuel Sanchez will look to pick up his fourth
Jul 21, 2016
Watch the Bellator 159: Caldwell vs. Taimanglo weigh-in video
Jul 21, 2016
Should Conor McGregor lose to Nate Diaz at UFC
Jul 21, 2016
Article source: http://www.mmaweekly.com/ufc-on-fox-20-fighter-pulled-from-saturdays-card-over-potential-usada-conflict]]>
Sneaky, sneaky, Jose Aldo.
The newly crowned UFC interim featherweight champion used some straight-up 007-style techniques to figure out what Frankie Edgar was going to do in their fight at UFC 200.
According to Aldo, he had spies scoping out Edgar during the week leading up to UFC 200. And you know what? It doesn't seem like he thinks he did anything wrong.
Aldo talking to reporters in Brazil:
"We had some spies over on his side, where he was training all week. They came and told us what he was doing. So I had to hold off on the kicks and on the left hand since he was looking to counter on that … Of course, there was (spies). Why wouldn't I kick? That's my own instinct! We always place a spy. We send someone to watch their training and tell us, so we know what they're training. Thankfully, it always happens. Brazilians in the other corners. So when I get there to train, I already look at the list of cornermen. If there's a Brazilian on the other side, we look for him right away. We ask them to have a look there because it helps us out a lot."
Is that cheating? I don't know. The fight game can be a dirty one, after all, but I can't help to avoid remembering the NFL's New England Patriots and their deep dive into "Spygate." Should we call this "Cornergate?" You make the call.
In the meantime, Jose Aldo is a master spy and should be cast as the next James Bond.
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Justin Bieber seems to think he can fight like Floyd Mayweather Jr.
Mark Hunt led the charge as fighters blasted Brock
Jul 16, 2016
How does Michael Bisping prepare for the wrestling of
Jul 15, 2016
The response Eddie Alvarez gave to people calling him
Jul 14, 2016
Article source: http://www.mmaweekly.com/jose-aldo-said-he-had-some-spies-scoping-frankie-edgar-ahead-of-ufc-200]]>
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After Brock Lesnar was flagged for a second potential anti-doping violation, his UFC 200 opponent, Mark Hunt, is planning the formation of a fighter union.
Having last fought for the UFC and being under
Jul 20, 2016
As distracting as Jon Jones' recent anti-doping violation might
Jul 20, 2016
Undefeated Darrion Caldwell returns to the Bellator cage on
Jul 20, 2016
Article source: http://www.mmaweekly.com/holly-holm-would-fight-cris-cyborg-if-the-time-is-right-video]]>
Tune in Wednesday, July 20, at 6 p.m. EST, for Knockout Radio LIVE! Randy Harris and the crew talk to UFC champ Robbie Lawler, Kamaru Usman, and several RFC fighters, as well as previewing this weekend's UFC on FOX 20.
As always, Knockout Radio has the best giveaways, prizes, surprises, and celebrity guests!
The phone lines will be open from 6 to 8 p.m. EST on our call-in number: 888-69-BUBBA (888-692-8222).
The radio show player goes live below from 6 to 8 p.m. EST. Refresh the page at that time to load the player.
Be sure to Follow @KnockoutRadio on Twitter.
KnockoutRadio is part of the Bubba Radio Army!
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After Brock Lesnar was flagged for a second potential anti-doping violation, his UFC 200 opponent, Mark Hunt, is planning the formation of a fighter union.
USADA announced on Wednesday that Chad Mendes has received
Jul 20, 2016
Emil Meek has been pulled from his UFC debut
Jul 20, 2016
Lightweight contender Gilbert Melendez talks about the roots of
Jul 20, 2016
Article source: http://www.mmaweekly.com/knockout-radio-live-ufc-champ-robbie-lawler-kamaru-usman-and-ufc-on-fox-20-preview]]>
In an interview with his website, MarkHunt.tv, The "Super Samoan" criticized the UFC for not caring about fighters.
"The way I see it, the Brock Lesnar doping thing is just another reason why we need a fighter's association," Hunt said. "These guys are just making up the rules as they go.
"First, the Reebok thing, then Brock's 4 month-testing exemption. Conor (McGregor) gets pulled off a card for not going to a press conference that me or Brock didn't go to anyway. Work that out. There's probably a heap of others."
"These guys are just making (expletive) it up as they go," he added. "Yet, fighters refuse to support other fighters when they (expletive) us over."
TRENDING Brock Lesnar Allegedly Tested Positive for Same Estrogen Blocker as Jon Jones
Hunt questioned the anti-doping procedures and believes they're in place to put the fight promotion in a favorable light with a $4 billion sale pending.
"They say they are cleaning up the sport, yet I've risked my health two times against guys cheating since this USADA thing started. I was told Brock was gonna be tested when I took the fight. He comes in looking fricking juicy as hell and then I find out he's popped after the PPV has already been sold.
"It's ridiculous. The fact that they haven't even bothered to make contact with me since all of this happened just proves they don't care.
"I don't even know how he made 265 pounds, that guy; he looked about 500 kilograms. Look at the pictures and tell me this guy passed the smell test that Jeff (Novitsky) guy talks about. I mean, even the other passport stuff, which is supposed to be important for testing, they would have none of that from Brock because of the exemption," said Hunt.
"The whole point of the four-month testing period should be to make sure someone doesn't pop back in, juiced to the gills, and beat the crap out of the guys who are clean and have been clean and tested this whole time," he added. "I'm pretty sure all of this stuff is just there to look good for the sale."
Hunt's rant didn't stop there. He continued to blast the UFC about fighter pay and benefits.
"These guys have lined their pockets with our blood. They deserve to get paid, no doubt. They took the UFC from nothing into what it is today, but come on, most of the guys fighting get paid nothing and have no benefits," he said.
"There needs to be a system where they at least run things past the fighters before making these decisions and the fighters can look out for each other. We need an association where we can have our voices heard. We are a massive reason why fans watch the sport, and we risk our health to do it."
TRENDING Dana White's RNC Speech: 'I Stand with Donald Trump'
Speaking out against the organization could have repercussions, but Hunt's not concerned about that.
"I'm not worried about the UFC," he said. "I've been fighting my whole life. Fighting to survive in my home, to the ring, and now the cage. These guys didn't even want me in the UFC, but here I am. I'm not scared of them.
"I'm not sure what it's like in the States, but in Australia workers stick together to make sure their voice is heard. Lots of guys have talked about this, but someone needs to make this happen and I'm happy for it to be me."
Hunt called for fighters to contact him about forming a fighter association. He's more than willing to spearhead the effort.
"I'm not an expert on all this, but me and my team have been talking to the experts about how to make this a reality," he said.
"To the fighters listening to this, I want them to get in touch with me via my Facebook page and we can get things going. I won't name anyone until we sort it all out, but get in touch and let's make this happen."
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Article source: http://www.mmaweekly.com/mark-hunt-puts-ufc-on-blast-plans-on-forming-a-fighter-association]]>
UFC officials confirmed the fight to MMAWeekly.com following an initial tweet by Canadian reporter Neil Davidson.
Condit (30-9) hasn't fought since losing his bid to take the title from Robbie Lawler at UFC 195 in January. After losing a split decision to the champ, Condit contemplated retirement, saying, "I have to see if I can continue to do this."
TRENDING Brock Lesnar's UFC 200 Drug Test Positive for Same Substance as Out-of-Competition Test
Obviously, he decided that he could continue to fight, at least one more time, and will do so against Maia at UFC 202.
The last few years, however, have been rocky for Condit, who has won the interim version of the welterweight title, then lost a unification bout to Georges St-Pierre, and bounced between wins and losses in his most recent bouts.
Maia (23-6) has recently been streaking towards the top of the division, passing Condit in the UFC's official rankings along the way. He has won his last five bouts, including an impressive submission victory over Matt Brown at UFC 198 in May. A victory over Condit would make it difficult to deny him a shot at the welterweight crown.
Also confirmed for UFC on FOX 21: Maia vs. Condit: Anthony Pettis vs. Charles Oliveira, Paige VanZant vs. Bec Rawlings, Jim Miller vs. Joe Lauzon, Enrique Barzola vs. Kyle Bochniak, Chad Laprise vs. Thibault Gouti, Sam Alvey vs. Kevin Casey, Shane Campbell vs. Felipe Silva, Ryan Janes vs. Adam Hunter, and Garreth McLellan vs. Alessio Di Chirico.
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Article source: http://www.mmaweekly.com/carlos-condit-vs-demian-maia-shifted-from-ufc-202-to-ufc-on-fox-21-main-event]]>
Though the nature of the potential violation of the June 28 drug test was not initially disclosed, UFC officials confirmed that it was a substance and not a method in Tuesday's statement. The initial positive was from a sample was collected on June 28, 2016, but the United States Anti-Doping Agency did not receive the test results from the World Anti-Doping Agency accredited UCLA Olympic Analytical Laboratory until the evening of Thursday, July 14.
The timing being what it was, Lesnar fought at UFC 200 on July 9, where he defeated Mark Hunt via unanimous decision in the night's co-main event in Las Vegas.
In Tuesday's statement, UFC officials announced that Lesnar also failed his in-compeitition drug test stemming from July 9.
"The UFC organization was notified today that the U.S. Anti-Doping Agency (USADA) has informed Brock Lesnar that his in-competition sample collection from July 9, 2016, at UFC 200, has tested positive for the same substance as his previously announced out-of-competition collection on June 28, 2016," read the statement.
"USADA, the independent administrator of the UFC Anti-Doping Policy, will handle the results management and appropriate adjudication of this case. It is important to note that, under the UFC Anti-Doping Policy, there is a full fair legal review process that is afforded to all athletes before any sanctions are imposed. The Nevada State Athletic Commission also retains jurisdiction over this matter as the sample collection was performed at UFC 200 in Las Vegas.
RELATED WWE Releases Statement Regarding Brock Lesnar's Potential Anti-Doping Violation
"Consistent with all previous potential anti-doping violations, additional information will be provided at the appropriate time as the process moves forward."
Hunt has been vocal on social media and in interviews, claiming that this is the third time that he's fought someone caught for a potential anti-doping violation. He initially demanded half of Lesnar's $2.5 million salary as compensation for the infraction, then changed his tune and asked for 100-percent of Lesnar's pay. Hunt has threatened to leave the UFC if he isn't compensated.
Lesnar and his camp has said little, but told the Associated Press, "We will get to the bottom of this."
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Article source: http://www.mmaweekly.com/brock-lesnars-ufc-200-drug-test-positive-for-same-substance-as-out-of-competition-test]]>
Kickboxer: Retaliation picks up where Kickboxer: Vengeance leaves off with Kurt Sloan (Moussi) back in Thailand fending for his life against a new formidable foe, Mongkut portrayed by Hafþór Júlíus Björnsson (The Mountain on Game of Thrones). Nelson, who is a trained mixed martial artist and former professional rugby player, is set to appear in the film alongside previously announced fighting champions Mike Tyson, Georges St-Pierre and Fabricio Werdum, worldwide soccer phenom Ronadlinho and Jean Claude Van Damme. He'll portray an inmate who is part of a group of prisoners being held with Kurt Sloan and training him to fight for his life.
TRENDING Evangelista Cyborg Suffers Fractured Skull from Michael Page's Flying Knee
"Roy's determination during our Vegas shoot, as he worked alongside Mike Tyson and Ronaldinho, showed us that he obviously has a genuine love for the screen," said producer Rob Hickman. "His intriguing performance has led our team to expand his role in the film and we believe this could open further doors, ultimately being a cross over opportunity for him. Roy will now be heading to Thailand to work with the rest of the cast," producer Rob Hickman.
Kickboxer: Retaliation is currently filming principal photography on location in Thailand with additional filming taking place in California and Nevada. Dimitri Logothetis, (Stephen King's Sleepwalkers), who himself holds two black belts in martial arts, serves as writer and director on the film.
Logothetis' Acme Rocket Fuel has teamed with Our House Films as the production houses behind Kickboxer: Retaliation with Hickman and Logothetis producing and Larry Nealy, Jay Strommen and Steven Swadling serving as executive producers.
The first film in the trilogy, Kickboxer: Vengeance, stars Jean Claude Van Damme, Alain Moussi, Dave Bautista and Gina Carano and hits theaters and VOD in September of this year.
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Article source: http://www.mmaweekly.com/roy-nelson-joins-georges-st-pierre-mike-tyson-and-gina-carano-in-kickboxer-retaliation]]>
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Roy Nelson has joined Georges St-Pierre, Mike Tyson, and Gina Carano as part of the cast of the upcoming Kickboxer: Retaliation.
The Nevada Athletic Commission on Monday confirmed two substances
Jul 18, 2016
Listen in as UFC on FOX 20 headliners Holly
Jul 18, 2016
UFC officials on Monday revealed that a lightweight bout
Jul 18, 2016
Article source: http://www.mmaweekly.com/holly-holm-moves-ahead-of-conor-mcgregor-as-most-drug-tested-ufc-fighter-video]]>


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You can still navigate around this archive, but know that no new mails
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Subject: [OMPI users] With IMPI works fine,With OMPI fails
From: Sangamesh B (forum.san_at_[hidden])
Date: 2009-10-28 13:39:41
Hi all,
The compilation of a fortran application - CPMD-3.13.2 - with OpenMP +
OpenMPI-1.3.3 + ifort-10.1 + MKL-10.0 is failing with following error on a
Rocks-5.1 Linux cluster:
/lib/cpp -P -C -traditional -D__Linux -D__PGI -DFFT_DEFAULT -DPOINTER8
-DLINUX_IFC -DPARALLEL -DMYRINET ./potfor.F ./potfor.f
/opt/mpi/openmpi/1.3.3/intel/bin/mpif90 -c -pc64 -O3 -xT -unroll -openmp
./potfor.f -o ./potfor.o
fortcom: Severe: **Internal compiler error: segmentation violation signal
raised** Please report this error along with the circumstances in which it
occurred in a Software Problem Report. Note: File and line given may not
be explicit cause of this error.
compilation aborted for ./potfor.f (code 3)
make: *** [potfor.o] Error 3
But if the potfor.f is compiled by removing -openmp option, then it compiles
successfully. But while running it again failed with "Aborted"
The interesting thing is, the same application compiles and runs fine with
IntelMPI on another cluster with exactly the same environment.
What's here the issue with Open MPI?


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Sasha invites you to sign a new petition to the HHS to protect patients against the PACE trial, CBT and GET ...
Discuss the article on the Forums.
Is it safe for someone with dysfunctional mitochondria to fast for a few days?
Discussion in 'Detox: Methylation; B12; Glutathione; Chelation' started by lizw118, May 15, 2011.


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This artwork is not for sale.
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Art Description
Painting: Acrylic on Canvas and Wood.
Acrylic, pencil and chalk pastel on canvas
Keywords: birds, colorful, female, figure, nude
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Comments (2)
Ken Omom
4 years ago
Great work and very strong like it very beautiful work
Ynes Guevara
3 years ago
Yes, great work!! Love it!


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Who wouldn't want this bit of Swiss cheese/chocolate (you choose) spread on their sheets! Drop by her profile and say hello ...
So very sexy its great to see an Aussie lady on here cant wait to see more of you ;)
What a beauty ! So sexy
awesome pictures very hot
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Language translator Jobs:
|Price (USD)
|Price (USD)
|Add Functions to WordPress Website + New Design but you will add Woocommerce section
|http://allseasondeals.com/demo/ I create this website Add Functions to WordPress Website + New Design but you will add Woocommerce section 1- Design Change to be like and must support RTL https://www.theentertainerme.com and add Multi Currency and Multi Language.
|Website Design, Wordpress, WooCommerce
|Jul 29, 2016
|Today6d 23h
|Build an Online Store -- 2
|hello i had an online shop i buy it from ebay its good but need to improve the arabic language becouse some show in English and few miner things
|Website Design, Shopping Carts, eCommerce, HTML
|Jul 29, 2016
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|Social Media Marketing Manager -- 2
|We are in need of a Social Media Manager that can perform the following tasks each month: - Post our store's weekly events on Facebook and Twitter. Promote them throughout the week for the days preceding the events on the page and within associated groups. Events are already created in Facebook Events and will need to be copied and images, date/time updated as needed. - Create 1-2 d...
|Social Networking, Twitter, Facebook Marketing, Social Engine, Social Media Marketing
|Jul 29, 2016
|Today6d 23h
|Write some code for a web
|It is developing a web with 2 parts, shop for sports shoes. There will be two distinct parts: customers and clerks. The customer will use one or more screens in the store to order a shoe and try it. The clerk will have a backend to control such requests centrally. Customer Zone: - Interface to be made full screen on production and emulating appearance of touch mobile system (work in touch mo...
|PHP, Javascript, CSS, MySQL, HTML
|Jul 29, 2016
|Today2d 23h
|WIX website: add multiple languages feature
|I have a WIX website in english and need someone to create multi languages features (German - French). No need to translate pages, just need the layout and interconnectivity to do be done. Add language button but keep added language pages inactive. Should be a straight forward, fast and easy job for a WIX specialist. Please do not apply if you have no WIX multiple language experience. Th...
|Jul 29, 2016
|Today6d 22h
|Professional Mosque Guide
|We have collected data from about 85% of all mosques in our country and now want to set up a professional outstanding mosque guide web site which allows mosque goers to search according to different criteria such as: Place, Language, Ratings, Availability of certain services etc. The guide must offer the option to search on a google map, to select a certain mosque and directly use the navigation f...
|PHP, Website Design, Graphic Design, MySQL, HTML
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|Today6d 22h
|Design a Logo
|Please design a logo and cover page photo for delivery service project. The project will be a site and applications provides delivery service for home business work. please use Arabic and English languages in logo. Twseeel is the name of project in Arabic language " توصيل " Twseeel.com Note: House work / home business work examples ( Cook, sew clothes, gifts processing, c...
|Graphic Design, Logo Design
|Jul 28, 2016
|Today9d 22h
|Translate Hindi Certificate
|Hi, I have a few certificates to translate from Hindi to English. If you are a qualified translator, please write to us. Regards, aes.
|Translation, Hindi
|Jul 28, 2016
|Today6d 21h
|Search the Web for Something
|I want to write a code(using any language) which can do the following: a) Pull data from any website(using credentials) b) select filters based on an excel sheet that will be provided c) run the report on the website d) dump the data into an excel sheet e) run steps b to d for all the filter conditions in the excel sheet in step b f) Combine all the excel sheets into one
|Excel, Web Scraping, Web Search
|Jul 28, 2016
|Today6d 20h
|Write an iPhone application
|I am looking for someone to program and design a mobile app for my store. The used language would be both English and Arabic. please contact me with prices, similar projects and deadline if you are interested
|Mobile Phone, iPhone, Android
|Jul 28, 2016
|Today6d 20h
|Manage my Search Marketing
|i want to build google campaigns ( retargeting ) for visitors who visit the site before i prefer who have knowledge in Middle East and Arabic language its shopping site
|Internet Marketing, SEO, Google Adwords, Google Analytics, Search Engine Marketing
|Jul 28, 2016
|Today20h 26m
|Farsi/Persian to English
|To whom it may concern: It is obvious for preventing spammer I have to write my description in Persian language since I'd like to find someone who is willing to translate a Persian text to English, so please read the following text to know what I'm looking for... موضوع تخصصی نیست • تنها دوصفحه متن فارسی با فونت کتابی ست برای ترجمه • نیاز هست که احساس و فکر پشت انتخاب هر ...
|Translation, English (US)
|Jul 28, 2016
|Today6d 19h
|Data Pull from Website
|I want to write a code(using any language) which can do the following: a) Pull data from any website(using credentials) b) select filters based on an excel sheet that will be provided c) run the report on the website d) dump the data into an excel sheet e) run steps b to d for all the filter conditions in the excel sheet in step b f) Combine all the excel sheets into one
|Excel, Anything Goes
|Jul 28, 2016
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|SEO für meine Website
|create data feed for google shopping in german, french, english, italien spanish then upload each to google merchant so that all products from my site www.2-phones-in-1.com will be shown ein google shopping in each language. EAN numbers are availabl
|Internet Marketing, SEO, Link Building, Marketing
|Jul 28, 2016
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|English to Bengali Translator - 4 -- 3
|Please only bid if you are willing to undergo a short Unpaid assessment. This job will include translation of our site from English to Bengali and proofreading content. This is an hourly project not per word. Project Duration: Indefinite
|Translation, Proofreading, Bengali
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|Please only bid if you are willing to undergo a short Unpaid assessment. This job will include translation of our site from English to Norwegian and proofreading content. This is an HOURLY project NOT per word. Project Duration: Indefinite
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|We are looking for a native Japanese to English translator to translate about 14000 Japanese characters to English. I assume it will be around 6000-7000 English words after translating into English. Need 100% accurate and human translation. Google or Software works are not allowed. Need this to be done within 2 to 3 days. Looking forward to your bids.
|Translation, English (UK), Japanese, English (US)
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|Altcoin/Bitcoin buy sell trading Platform -- 2
|I want similar site like yobit.net or alcurex.com It need to be similar feature as those site similar like: Features: Trade any coin pair (BTC/LTC, BTC/DRK, BTC/PPC, BTC/DOGE or LTC/DOGE, LTC/BTC, etc.) You can easily add or edit wallet/coin pairs in the admin panel Coin Volume, Order Depth Coin Voting System JSON RPC Wallet connections User Registration User Login User Balances, Deposit, Withdraw...
|PHP, Website Design, Graphic Design, HTML, Bitcoin
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|We have few lessons which needs to be translated to Filipino from ANY of these languages Arabic/ Chinese/ Portuguese/ Spanish/ Indonesia/ Malay/ Turkish, which is designed in a way that it enables Philippines speakers to learn English. The objective is to create lessons that enables Philippines speakers to learn English. There are around 300 short lessons. This is a long term project, ...
|English (UK), Filipino
|Jul 28, 2016
|Today6d 16h
|Need a native Mayan Translator.
|Need a native Mayan Translator. only native speakers
|Translation, Maya
|Jul 28, 2016
|Today6d 16h
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Re: As an American, I am confused....
How can I view entire discussion thread on a single page?
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