Abstract
In this vignette, we go through creating balanced partitions for
train/test sets and balanced folds for cross-validation with the R
package groupdata2
. We write a simple cross-validation
function and use it on some linear regression models.
The purpose of the vignette is to give a basic understanding of
cross-validation. We recommend using the cvms
package in
combination with groupdata2
for actual cross-validation
tasks.
groupdata2
is a set of methods for easy grouping,
windowing, folding, partitioning and splitting of data.
For a more extensive description of groupdata2
, please see
Description of groupdata2
Contact author at<EMAIL_ADDRESS>In this vignette, we will train a couple of linear regression models on some data. We will use cross-validation with balanced folds to find the best model, and then test that model on a subset of the original data that we haven’t used for training the model.
Our first task will be to split the dataset into a training set and a
test set using partition()
. Then, we will create folds
using the fold()
function. We will code up a simple
cross-validation function and put some models to the test!
While we will be writing our own code for cross-validation, it is
recommended to use the cvms
package for this functionality, when working with linear or logistic
regression models.
partition() creates (optionally) balanced partitions (e.g. train/test sets) from given group sizes. It can balance partitions on one categorical variable (e.g. diagnosis) and/or one numerical variable (e.g. age). It is also able to keep all datapoints with a shared ID (e.g. participant_id) in the same partition.
fold() creates (optionally) balanced folds for cross-validation. It can balance folds on one categorical variable (e.g. diagnosis) and/or one numerical variable (e.g. age). It is also able to keep all datapoints with a shared ID (e.g. participant_id) in the same fold. If we want to run repeated cross-validation, it can create multiple unique fold columns at once.
The essence of cross-validation is to test a model against data that it hasn’t been trained on, i.e. estimating out-of-sample error. It is done by first dividing the data into groups called folds.
Say we choose to divide the data into 5 folds. Then, in the first iteration, we train a model on the first four folds and test it on the fifth fold. In the second iteration, we then train on folds 2,3,4,5 and test on fold 1. We continue changing which fold is the test fold until all folds have been test folds (i.e. we train and test 5 times in total). In the end we get the average performance of the models and compare these to other cross-validated models.
The model with the lowest average error is believed to be the best at predicting unseen data from the same population(s) and thus chosen for further interpretation / use.
This is a great technique, and fold()
makes it even more
powerful.
Even with cross-validation, we have a risk of overfitting our models to our data. That is, while we get really good predictions for our current data, it won’t generalize to new data that we might gather in the future.
To test if this is the case, we store some of the data and don’t touch it before we feel confident that we have found the best model. Then we use the model to predict the targets / dependent variable in that data. If the results are much worse on the test set, it likely means that our model is suffering from overfitting! Then we might go back and adjust the models, until we’re pretty confident again. We might also change the number of folds or run repeated cross-validation instead.
Generally though, we should use the test set as few times (>1) as possible, as running this cycle too many times could accidentally find a model that is a good predictor of the specific datapoints in the test set, but not in general.
If the test set results are instead somewhat similar to the cross-validation results, these are the results that we report (possibly along with the cross-validation results).
Let’s say, we have scored 10 participants with either of two
diagnoses 'a'
and 'b'
on a very interesting
task, that you are free to call ‘the task’.
# Attach some packages
library(groupdata2)
library(dplyr)
library(knitr) # kable()
library(lmerTest) #lmer()
# Create data frame
<- data.frame("participant" = factor(as.integer(
df rep(c('1','2', '3', '4', '5',
'6', '7', '8', '9', '10'), 3))),
"age" = rep(c(20,23,27,21,32,31,43,21,34,32), 3),
"diagnosis" = factor(rep(c('a', 'b', 'a', 'b', 'b',
'a', 'a', 'a', 'b', 'b'), 3)),
"score" = c(10,24,15,35,24,14,11,16,33,29, # for 1st session
24,40,30,50,54,25,35,32,53,55, # for 2nd session
45,67,40,78,62,30,41,44,66,81)) # for 3rd session
# Order by participant
<- df %>% arrange(participant)
df
# Remove index
rownames(df) <- NULL
# Add session info
$session <- as.integer(rep(c('1','2', '3'), 10))
df
# Show the data frame
kable(df, align = 'c')
| participant | age | diagnosis | score | session |
|---|---|---|---|---|
| 1 | 20 | a | 10 | 1 |
| 1 | 20 | a | 24 | 2 |
| 1 | 20 | a | 45 | 3 |
| 2 | 23 | b | 24 | 1 |
| 2 | 23 | b | 40 | 2 |
| 2 | 23 | b | 67 | 3 |
| 3 | 27 | a | 15 | 1 |
| 3 | 27 | a | 30 | 2 |
| 3 | 27 | a | 40 | 3 |
| 4 | 21 | b | 35 | 1 |
| 4 | 21 | b | 50 | 2 |
| 4 | 21 | b | 78 | 3 |
| 5 | 32 | b | 24 | 1 |
| 5 | 32 | b | 54 | 2 |
| 5 | 32 | b | 62 | 3 |
| 6 | 31 | a | 14 | 1 |
| 6 | 31 | a | 25 | 2 |
| 6 | 31 | a | 30 | 3 |
| 7 | 43 | a | 11 | 1 |
| 7 | 43 | a | 35 | 2 |
| 7 | 43 | a | 41 | 3 |
| 8 | 21 | a | 16 | 1 |
| 8 | 21 | a | 32 | 2 |
| 8 | 21 | a | 44 | 3 |
| 9 | 34 | b | 33 | 1 |
| 9 | 34 | b | 53 | 2 |
| 9 | 34 | b | 66 | 3 |
| 10 | 32 | b | 29 | 1 |
| 10 | 32 | b | 55 | 2 |
| 10 | 32 | b | 81 | 3 |
As we can see, there are 5 participants for each diagnosis, and they all seem to have gotten better at the task throughout the three sessions they went through.
In this step, we will split our data into two subsets called
train_set
and test_set
. The main point will
be, that we must avoid leakage between the two sets, before it is of any
use to us.
If we were to split the data randomly, with 80 percent going to the training set, and 20 percent going to the test set, we would likely have some of the participants in both the training set AND the test set. But our motivation for having a test set is that we want to know how well our model predicts new, future participants, not how well it knows the ones it saw during training. Furthermore, if our model is overfitted to those participants, our test set might not warn us of this. With this approach, we could get a really low error on both the test set and the training set even though our model is useless outside of the data, we’re working with.
So, how do we deal with this? In this case, it’s as simple as making
sure each participant is only in one of the data sets. We can do this
with partition()
and the id_col
argument.
::set_test_seed(1) # For reproducibility
xpectr
# Split data in 20/80 (percentage)
partition(df, p = 0.2, id_col = "participant") %>%
1] %>% # See only the test set
.[kable() # Pretty tables :)
|
The above shows the test set. It contains 2 participants (10 and 5). They both have all 3 sessions in this subset, meaning they are not in the training set! Perfect!
But ehmm.. if we look at the diagnosis column, they both have the
diagnosis 'b'
. If we would like to know how well our future
model classifies both of the diagnoses, this won’t do. It would be nicer
if we have somewhat the same balance of both diagnoses in both the
training set and the test set. This is luckily what the
cat_col
argument is for.
More specifically (behind the scenes), it first subsets the full
dataset by each class in the categorical column. Then, it creates
partitions in each subset and merges the partitions in the end. When
using both the id_col
and cat_col
arguments,
it first subsets by class, then partitions by the unique values in
id_col
. This means, that the final partition sizes might
not always be exactly as specified, depending on the data. Therefore, it
is good practice to look at the distribution of participants, classes,
etc. in the final partitions, which we will do after trying out that
cat_col
argument to get our final test and train sets!
::set_test_seed(1) # For reproducibility
xpectr
# Split data in 20/80 (percentage)
<- partition(df, p = 0.2, id_col = "participant", cat_col = 'diagnosis')
parts
<- parts[[1]]
test_set <- parts[[2]]
train_set
# Show test_set
%>% kable() test_set
| participant | age | diagnosis | score | session |
|---|---|---|---|---|
| 8 | 21 | a | 16 | 1 |
| 8 | 21 | a | 32 | 2 |
| 8 | 21 | a | 44 | 3 |
| 10 | 32 | b | 29 | 1 |
| 10 | 32 | b | 55 | 2 |
| 10 | 32 | b | 81 | 3 |
Now, our test set contains 2 participants (8 and 10), and the diagnosis column contains 50 percent a’s and 50 percent b’s. Let’s count the diagnoses in the training set as well.
%>%
train_set count(diagnosis) %>%
kable(align = 'c')
| diagnosis | n |
|---|---|
| a | 12 |
| b | 12 |
There are 12 rows for each diagnosis in the training set. In this case, we know that the rest of the participants are all “fully” in this dataset. But let’s count them anyway, so we will remember to do it in the future.
%>%
train_set count(participant) %>%
kable(align = 'c')
| participant | n |
|---|---|
| 1 | 3 |
| 2 | 3 |
| 3 | 3 |
| 4 | 3 |
| 5 | 3 |
| 6 | 3 |
| 7 | 3 |
| 9 | 3 |
In this section, we will create balanced folds for cross-validation.
The thought process resembles that in the previous section.
fold()
basically just creates a number of similarly sized
partitions. Where partition()
returned a list
of data frame
s (this is optional, btw.),
fold()
will return the entire training set with a new
column called ".folds"
. This will be used directly in the
cross-validation function to subset the data on each iteration.
Remember, that the folds take shifts at being the cross-validation test
set (not to be confused with the test set we created in the previous
step).
As we’re basically recreating the training / test set scenario that we discussed previously, we want to avoid leakage between the folds. We would also like somewhat balanced distributions of the diagnoses between the folds, though this depends on the context. Let’s create our balanced folds:
::set_test_seed(1) # For reproducibility
xpectr
<- fold(train_set, k = 4, cat_col = 'diagnosis', id_col = 'participant')
train_set
# Order by .folds
<- train_set %>% arrange(.folds)
train_set
%>% kable() train_set
| participant | age | diagnosis | score | session | .folds |
|---|---|---|---|---|---|
| 7 | 43 | a | 11 | 1 | 1 |
| 7 | 43 | a | 35 | 2 | 1 |
| 7 | 43 | a | 41 | 3 | 1 |
| 2 | 23 | b | 24 | 1 | 1 |
| 2 | 23 | b | 40 | 2 | 1 |
| 2 | 23 | b | 67 | 3 | 1 |
| 1 | 20 | a | 10 | 1 | 2 |
| 1 | 20 | a | 24 | 2 | 2 |
| 1 | 20 | a | 45 | 3 | 2 |
| 5 | 32 | b | 24 | 1 | 2 |
| 5 | 32 | b | 54 | 2 | 2 |
| 5 | 32 | b | 62 | 3 | 2 |
| 6 | 31 | a | 14 | 1 | 3 |
| 6 | 31 | a | 25 | 2 | 3 |
| 6 | 31 | a | 30 | 3 | 3 |
| 4 | 21 | b | 35 | 1 | 3 |
| 4 | 21 | b | 50 | 2 | 3 |
| 4 | 21 | b | 78 | 3 | 3 |
| 3 | 27 | a | 15 | 1 | 4 |
| 3 | 27 | a | 30 | 2 | 4 |
| 3 | 27 | a | 40 | 3 | 4 |
| 9 | 34 | b | 33 | 1 | 4 |
| 9 | 34 | b | 53 | 2 | 4 |
| 9 | 34 | b | 66 | 3 | 4 |
The training set now contains the ".folds"
column. As
fold()
groups the output data frame
by this
column, we can count the number of rows for each diagnosis and
participant per fold like so:
%>%
train_set count(participant, diagnosis) %>%
kable(align = 'c')
| .folds | participant | diagnosis | n |
|---|---|---|---|
| 1 | 2 | b | 3 |
| 1 | 7 | a | 3 |
| 2 | 1 | a | 3 |
| 2 | 5 | b | 3 |
| 3 | 4 | b | 3 |
| 3 | 6 | a | 3 |
| 4 | 3 | a | 3 |
| 4 | 9 | b | 3 |
Fold 1 contains participants 2 and 7 with 3 rows each. Participant 2
has the diagnosis 'b'
and participant 7 has the diagnosis
'a'
. This pattern is the same for all folds. This dataset
was created for the purpose of showing these functions, so we should
expect real world data to be less perfectly distributed and remember to
run some checks of the created folds and partitions. Also be aware, that
the maximum number of folds is restricted by the number of participants
in the classes, if using the id_col
and the
cat_col
arguments together. Remember, that the function
first subsets the dataset by cat_col
and then creates
groups (folds) from the unique values in id_col
in each
subset, before merging the subsets. So if you only have 3 participants
in one of the classes, this will be the maximum number of folds you can
create.
What’s left, you ask? Well, now we get to have fun, training and
comparing models using cross-validation! If you just came for
partition()
and fold()
, you can skip this
part. Personally, I will move on and walk you (you won’t let me walk
alone, will you? pleaase?) through a simple cross-validation function
for testing a set of models. The models will be predicting the scores of
the participants.
We have 4 folds in our training set, so we want to train our models on 3 of the folds and test on the last fold. All the folds should be used as test fold once. While there are often faster alternatives to a for loop, we will use it to illustrate the process. We will create a simple cross-validation function where we can specify the model to test, and whether it has random effects. The performance of the model will be measured with RMSE (Root Mean Square Error).
# RMSE metric
<- function(predictions, targets){
rmse sqrt(mean((predictions - targets)^2))
}
<- function(data, k, model, dependent, random = FALSE){
crossvalidate # 'data' is the training set with the ".folds" column
# 'k' is the number of folds we have
# 'model' is a string describing a linear regression model formula
# 'dependent' is a string with the name of the score column we want to predict
# 'random' is a logical (TRUE/FALSE); do we have random effects in the model?
# Initialize empty list for recording performances
<- c()
performances
# One iteration per fold
for (fold in 1:k){
# Create training set for this iteration
# Subset all the datapoints where .folds does not match the current fold
<- data[data$.folds != fold,]
training_set
# Create test set for this iteration
# Subset all the datapoints where .folds matches the current fold
<- data[data$.folds == fold,]
testing_set
## Train model
# If there is a random effect,
# use lmer() to train model
# else use lm()
if (isTRUE(random)){
# Train linear mixed effects model on training set
<- lmer(model, training_set, REML = FALSE)
model
else {
}
# Train linear model on training set
<- lm(model, training_set)
model
}
## Test model
# Predict the dependent variable in the testing_set with the trained model
<- predict(model, testing_set, allow.new.levels = TRUE)
predicted
# Get the Root Mean Square Error between the predicted and the observed
<- rmse(predicted, testing_set[[dependent]])
RMSE
# Add the RMSE to the performance list
<- RMSE
performances[fold]
}
# Return the mean of the recorded RMSEs
return(c('RMSE' = mean(performances)))
}
We could have a hypothesis that people with the diagnosis
'b'
tend to be better at the experiment. This could be
tested by a simple linear model.
lm(score ~ diagnosis, df) %>%
::tidy()
broom#> # A tibble: 2 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 27.5 4.07 6.74 0.000000253
#> 2 diagnosisb 22.6 5.76 3.92 0.000515
The linear model supports the hypothesis, as the average score of
participants with diagnosis 'b'
is larger than that of
participants with diagnosis 'a'
, and as this difference is
statistically significant, we can reasonably reject the null-hypothesis
that there is no difference between the two diagnoses.
To improve our model, we might want to include the information we
have about age
and session
. Perhaps, the older
participants do better than the younger? And perhaps, participants with
the diagnosis 'b'
are better at learning over time
(session
)? By including such information in our model, we
might be better at predicting the score. We could also use
participant
as a random effect, to factor out the personal
differences.
Let’s list a bunch of possible model formulas that we can then compare with cross-validation. The cross-validation function needs the model to be passed in the format below (a string). Instead of looking at summaries for each model, we will find the best model with cross-validation and only look at the summary for that one. Notice that when we want to compare models, we want to keep the same random effects for all the models, so we are only comparing the combination of fixed effects.
<- 'score ~ 1 + (1 | participant)'
m0 <- 'score ~ diagnosis + (1 | participant)'
m1 <- 'score ~ diagnosis + age + (1 | participant)'
m2 <- 'score ~ diagnosis + session + (1 | participant)'
m3 <- 'score ~ diagnosis * session + (1 | participant)'
m4 <- 'score ~ diagnosis * session + age + (1 | participant)' m5
Now, let’s cross-validate the 6 models. If you get a message saying
"boundary (singular) fit"
, it’s because the model is too
complex for our small dataset. To avoid this message, try removing the
random effects from the models.
m0#> [1] "score ~ 1 + (1 | participant)"
crossvalidate(train_set, k = 4, model = m0, dependent = 'score', random = TRUE)
#> RMSE
#> 18.27222
m1#> [1] "score ~ diagnosis + (1 | participant)"
crossvalidate(train_set, k = 4, model = m1, dependent = 'score', random = TRUE)
#> RMSE
#> 14.7661
m2#> [1] "score ~ diagnosis + age + (1 | participant)"
crossvalidate(train_set, k = 4, model = m2, dependent = 'score', random = TRUE)
#> RMSE
#> 15.28533
m3#> [1] "score ~ diagnosis + session + (1 | participant)"
crossvalidate(train_set, k = 4, model = m3, dependent = 'score', random = TRUE)
#> RMSE
#> 6.176477
m4#> [1] "score ~ diagnosis * session + (1 | participant)"
crossvalidate(train_set, k = 4, model = m4, dependent = 'score', random = TRUE)
#> RMSE
#> 5.950014
m5#> [1] "score ~ diagnosis * session + age + (1 | participant)"
crossvalidate(train_set, k = 4, model = m5, dependent = 'score', random = TRUE)
#> RMSE
#> 7.004665
The model m4
has the lowest error on average and so, we
assume that it is the best predictor of out-of-sample data. To make sure
it doesn’t overfit the data, let’s look at the error on the test
set.
# Training the model on the full training set
<- lmer(m4, train_set, REML = FALSE)
model_m4
# Predict the dependent variable in the test set with the trained model
<- predict(model_m4, test_set, allow.new.levels = TRUE)
predicted
# Get the Root Mean Square Error between the predicted and the observed
<- rmse(predicted, test_set[['score']])
RMSE
RMSE#> [1] 6.418155
6.42 is a bit higher than the cross-validated average, but it’s so close that we’re not afraid of overfitting. Be aware that the scale of the RMSE is dependent on the scale of the dependent variable in our dataset, so you might find some models with much higher RMSEs than this one. What matters is the relative difference between models, and that the best model will have the lowest error.
Let’s look at its summary:
%>%
model_m4 summary()
#> Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
#> method [lmerModLmerTest]
#> Formula: m4
#> Data: train_set
#>
#> AIC BIC logLik deviance df.resid
#> 157.0 164.1 -72.5 145.0 18
#>
#> Scaled residuals:
#> Min 1Q Median 3Q Max
#> -1.8723 -0.6998 -0.0137 0.7042 1.6556
#>
#> Random effects:
#> Groups Name Variance Std.Dev.
#> participant (Intercept) 3.714 1.927
#> Residual 21.398 4.626
#> Number of obs: 24, groups: participant, 8
#>
#> Fixed effects:
#> Estimate Std. Error df t value Pr(>|t|)
#> (Intercept) 0.1667 3.6621 22.2764 0.046 0.9641
#> diagnosisb 9.4167 5.1790 22.2764 1.818 0.0825 .
#> session 13.2500 1.6355 16.0000 8.102 4.71e-07 ***
#> diagnosisb:session 6.3750 2.3129 16.0000 2.756 0.0141 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Correlation of Fixed Effects:
#> (Intr) dgnssb sessin
#> diagnosisb -0.707
#> session -0.893 0.632
#> dgnssb:sssn 0.632 -0.893 -0.707
In this model, we have a significant interaction between diagnosis and session. The interpretation of this result would be quite different from that of the first model, we tried.
Notice that the cross-validated m3
model is so close to
the cross-validated m4
model that we can’t be completely
sure which is better. The observed differences might as well stem from
the randomness in the partition()
or fold()
steps. In such a case, we could turn to repeated
cross-validation, where we create multiple fold columns (by setting
num_fold_cols
in fold()
) and average their
cross-validated results. If the models are still very close, we might
consider reporting all three models or at least checking if the
interpretations of the three models differ. These things are highly
dependent on the context.
When working with linear and logistic regression, the cvms
package contains functions for (repeated) cross-validation and is
specifically designed to work well with groupdata2
.
Well done, you made it to the end of this introduction to
groupdata2
! If you want to know more about the various
methods and arguments, you can read the Description of groupdata2.
If you have any questions or comments to this vignette (tutorial) or
groupdata2
, please send them to me at
<EMAIL_ADDRESS>or open an issue on the github
page https://github.com/LudvigOlsen/groupdata2 so I can make
improvements.


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I would like to thank the hard working staff at the Minor Programming of the University of Amsterdam for their great help en patience. Also Jitte Waagen and the rest of the team behind the Sacred Landscape Project for letting me use their data in this way.
J. Pelgrom & T. D. STEK, A landscape archaeological perspective on the functioning of a rural cult place in Samnium: field surveys around the sanctuary of S. Giovanni in Galdo (Molise), 2010


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Jenson Button spent "17 hours" in desert after Baja 1000 breakdown
by Jack BenyonJenson Button claims he spent 17 hours stranded in the desert during his off-road racing debut, after his truck's differential broke during the Baja 1000.
The 2009 Formula 1 world champion was contesting the multi-class off-road classic in a spec Trophy Truck, sporting similar colours to those used by Brawn GP during his title-winning F1 campaign.
Button hit trouble in the Mexican event when he and his co-driver Terry Madden heard a "loud clatter" and lost all drive.
"We come away from the Baja 1000 with a story to tell, not the one we wanted but a great Baja story all the same," Button posted on Instagram.
"[We] started well, passed five moving trucks and then another seven/eight broken or crashed, found a really good rhythm of looking after the truck when needed and hammering the fast flowing sections.
"At mile 235 Terry Madden my co-driver and I heard a really loud clatter and lost drive, we had broken the differential."
Button said the breakdown was peculiar because "all temps were good" and that he and Madden had taken care of the truck, but added "this happens".
"The problem was we had found the most remote place to breakdown which meant we spent the whole night and a total of 17 hours under the stars," he continued.
"The only people we saw were other racers asking us to wipe their dirty light bars clean.
"Fuelled by energy bars and a fire that needed constant help as all we could find was twigs, Terry and I could just enjoy the moment of peacefulness by telling stories which always ended with "it's Baja".
"A life experience but not the one I expected. But I'll take it."
Since retiring from F1 following a long stint with McLaren, Button has contested Super GT in Japan, winning the title in 2018, and also took part in the '18/19 World Endurance Championship with the SMP Racing LMP1 outfit.
He has ended both programmes, but said earlier this month he plans to return to the WEC when the top-class hypercar rules are introduced, and that he wants to run in more one-off events.
Last year's IndyCar runner-up Alexander Rossi also contested the Baja 1000, won by motorcycle rider Justin Morgan, and led before a minor roll stunted his progress.
He was able to continue, though he and co-drivers Jeff Proctor and Pat Dailey ultimately retired.


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There is a new version of BeatzKlaud available.
Installation of the latest version is requires, as it ensures you are always able to stream music and have the best experience.
If you do not want to receive updates, create a file called NOUPDATE.txt in the www directory.


I am working on a project that is a calculator. I have the project done, but am trying to tweak the code due to one problem. When asked for a number, you put in a letter and it closes the program with erroneous data. I am trying to figure out how to do something similar to as follows.
Input number = program works as intended
Input letter = program recognizes this is not correct and asks to input a number only.
what I am using for the variable now is:
1 2 3
double weight = 0;
cout << "Enter your weight in pounds to one decimal point: ";
cin >> weight;
Using this, it acts as explained above. What can i add to make sure it recognizes its not a number and asks for a number?
Use operator bool instead of good() member function. .good() should be only used to check stream readiness for further inputs, not for checking success of already happened input
in your code:


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@article{zeng2018higher, title = {Higher-dimensional quantum hypergraph-product codes}, author = {Zeng, Weilei and Pryadko, Leonid P}, journal = {arXiv preprint arXiv:1810.01519}, year = {2018} }
@article{zeng2019quantum, title = {Quantum convolutional data-syndrome codes}, author = {Zeng, Weilei and Ashikhmin, Alexei and Woolls, Michael and Pryadko, Leonid P}, journal = {arXiv preprint arXiv:1902.07395}, year = {2019} }
@article{zeng2019higher, title = {Higher-Dimensional Quantum Hypergraph-Product Codes with Finite Rates}, author = {Zeng, Weilei and Pryadko, Leonid P}, journal = {Physical Review Letters}, volume = {122}, number = {23}, pages = {230501}, year = {2019}, publisher = {American Physical Society} }
This file was generated by bibtex2html 1.96.


badblocks
Search a device for bad blocks.
Some usages of badblocks can cause destructive actions, such as erasing all data on a disk, including the partition table.
More information: home page
- Search a disk for bad blocks by using a non-destructive read-only test:
sudo badblocks /dev/sdX
- Search an unmounted disk for bad blocks with a non-destructive read-write test:
sudo badblocks -n /dev/sdX
- Search an unmounted disk for bad blocks with a destructive write test:
sudo badblocks -w /dev/sdX
- Search an unmounted disk for bad blocks with a destructive write test and show verbose status:
sudo badblocks -svw /dev/sdX
- Search an unmounted disk in destructive mode and output found blocks to a file:
sudo badblocks -o path/to/file -w /dev/sdX
- Search an unmounted disk in destructive mode with improved speed using 4K block size and 64K block count:
sudo badblocks -w -b 4096 -c 65536 /dev/sdX


Review your application
Before submitting your application please check what you've told us below. If you see any mistakes you can go back and correct them.
About you
- Name
- Mr Christopher Howard Dennett
- National Insurance number
- ZZ 55 55 55 A
- Date of birth
- 26 June 1903
- Address
- 10 King road, Kingston, Kingside K13 GSS
- Date of birth
- 26 June 1903
About you
- Name
- Mr Christopher Howard Dennett
- National Insurance number
- ZZ 55 55 55 A
- Date of birth
- 26 June 1903
- Address
- 10 King road, Kingston, Kingside K13 GSS
- Date of birth
- 26 June 1903
About you
- Name
- Mr Christopher Howard Dennett
- National Insurance number
- ZZ 55 55 55 A
- Date of birth
- 26 June 1903
- Address
- 10 King road, Kingston, Kingside K13 GSS
- Date of birth
- 26 June 1903
About you
- Name
- Mr Christopher Howard Dennett
- National Insurance number
- ZZ 55 55 55 A
- Date of birth
- 26 June 1903
- Address
- 10 King road, Kingston, Kingside K13 GSS
- Date of birth
- 26 June 1903
Employment
- Jobs
-
- Firefighter
- Superhero
- Supermarket Assistant
- King of the world
- National Insurance number
- ZZ 55 55 55 A
- Date of birth
- 26 June 1903
- Address
- 10 King road, Kingston, Kingside K13 GSS
- Date of birth
- 26 June 1903


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{% macro topic_subtopic(topics) %}
Topic is the new name for Specialist Sector.
Adding a topic makes content appear on topic pages.
Preview how your document will appear on the topic pages you've tagged to:
The following topics are curated:
Read the guidance on how to get content added to curated topics.


Speech
The Minister for Schools talks about greater freedoms for primary schools to teach Latin as part of the National Curriculum reforms.
I am delighted to be here today. Sheila Lawlor and Politeia have been and remain hugely influential in steering public policy debate gently in a right of centre direction, particularly in social policy areas such as education.
I studied Latin at secondary school in the state sector, to O level - the grade you don’t need to know. But I thoroughly enjoyed it. It equipped me for life. And it is for this reason, that the decimation of the teaching of Latin in the state sector over the last few decades is so alarming.
So I thank you for putting on today’s conference - about how schools can take advantage of the new freedoms that the Government is giving to teachers, to bring Latin to more state schools and to primary schools in particular.
And I thank Professor Pelling and Dr Morgan for their pamphlet and for the passion of their arguments and the growing groundswell of support for Latin all of you are leading.
Latin is important.
Ed Hirsch talks about the importance of cultural literacy and the importance of knowledge in building upon knowledge. Latin is so prevalent in our culture, in our political and legal systems; in our religious and spiritual institutions and thinking; in medicine, botany and horticulture; and in our art and architecture. The Roman Empire is around us every day - from the way our towns are laid out to the literature we read. Virgil and Ovid should be seen as the start of a great tradition of Western literature leading to Chaucer, Shakespeare, Keats and Eliot. Latin gives us a direct link to our own past - and dare I say it, an insight into how politics and power have always worked.
And Latin shows us how the mechanics of language works. The English we speak today descends in part from the Vulgar Latin spoken by workers, merchants and legionaries. English is so riddled with exceptions to the rule that we need Latin to bring sense, order and structure to grammar. Latin gives us the skills to learn not just Romance languages like Italian, Portuguese, Spanish and French - but the aptitude and confidence to learn new tongues beyond Western Europe.
So when people urge schools to teach a modern language rather than Latin, there need not be an either/or. Learning an ancient language equips you to learn a modern language and vice versa. And learning any language, new or old, helps give young people the academic hunger, thirst and confidence to keep on exploring the world around them.
That’s what makes the decline in the studying of languages at GCSE-level such a tragedy.
The numbers studying Latin at GCSE in state schools, remain pitifully small - just 2,868 this year. Overall there were just 9,360 GCSE entries for Latin - 70% of them taken in the independent sector, where just seven per cent of pupils are educated.
And the proportion studying a modern language overall has fallen from 79% in 2000 to just 44% in 2009 - and when you take out the independent sector that 44% falls to 39%.
This is all at the precise moment when globalization is demanding that we need to keep up with the rest of the world. Business continues to complain about the paucity of foreign language skills amongst school leavers and graduates. Ignorance of languages breeds insularity and it means an integral part of the brain’s intellectual function remains undeveloped.
That’s something we’re determined to put right.
Our white paper has a clear vision at its heart - that high quality teaching is the single biggest determinant of a pupil’s achievement.
All the evidence from different education systems around the world shows that the most important factor in determining how well children do is the quality of teachers and teaching.
And the latest McKinsey report just published, entitled ‘Capturing the Leadership Premium’ - about how the world’s top school systems are building leadership capacity - cites a number of studies from North America, including one saying that:
… nearly 60% of a school’s impact on student achievement is attributable to principal and teacher effectiveness. These are the most important in-school factors driving school success, with principals accounting for 25% and teachers 33% of a school’s total impact on achievement.
This is why, when you read the White Paper, you will see that its constant theme is the importance of the profession and helping to liberate that profession from over-centralised initiatives, from over-prescription and from too much bureaucracy and red tape.
And in that liberty lies opportunity for those who believe in promoting Latin.
You will already have noted our determination to increase the autonomy of schools through expanding the academies programme and giving teachers and headteachers more control over their own destiny. The OECD cites autonomy combined with rigorous and objective external accountability as the two key factors that high performing jurisdictions have in common. Again, in that autonomy lies opportunity - as the proposed West London Free School is doing. It intends to make Latin compulsory for all at age 11-14 - exactly the sort of freedoms which these reforms have opened up.
But also in the six months since the new coalition government was formed, we’ve already begun to take forward a series of reforms to bear down on unnecessary burdens and bureaucracy - granting schools greater freedoms; extending teachers’ powers to enforce discipline; more classroom autonomy; rigorous qualifications, valued by universities and employers; and the right targets and measures, which don’t create perverse incentives to shy away from academic subjects.
These are the freedoms that should assist professionals who wish to reintroduce Latin into the curriculum.
We will slim down the national curriculum. At present the national curriculum has in it too much that is not essential or which is unclear and there is too much prescription about how to teach.
We need a new approach to the National Curriculum so it specifies a tighter, more rigorous model of the knowledge which every child should be expected to master in core subjects at every key stage.
It is our view that in a school system that moves towards a greater degree of autonomy, the National Curriculum will increasingly become a benchmark against which schools can be judged rather than a prescriptive straitjacket into which education is squeezed - a straitjacket which has been squeezing Latin out.
We will be launching the Curriculum Review very shortly - but it will, of course, be looking at languages in primary schools as well as secondary schools. We made it very clear when we announced that we would not be implementing the recommendations in the Rose Review that those primary schools that had made preparations for the introduction of languages at Key Stage 2, or were already teaching languages, should continue to do so. Languages are hugely important and under this government will become more so, not less.
We are also introducing the new English Baccalaureate, to recognise pupils who achieve good GCSEs in English; maths; science; a humanity, such as history or geography; and a foreign language - modern or ancient.
Reintroducing the importance of a broad range of academic subjects as a measure of standards in our schools will provide an incentive for schools to refocus on encouraging more young people to study a language. And since we include ancient languages in that measure, this is a real opportunity for the Latin lobby to promote the teaching of Latin in schools.
One of the overriding objectives of the Government is to close the attainment gap between those from wealthier and poorer backgrounds.
The fact that the opportunity to learn Latin is so rare in the state sector is one of a range of factors that has led to the width of that gap. Spreading these opportunities is part and parcel of closing that attainment gap and helping to create a more equal society.
So when people say that Latin is an elitist subject that shouldn’t be taught in the state sector they are contributing to the widening of that gap and to the very elitism they rail against.
Published: 30 November 2010


finally
blocks. Empty finally
blocks
usually indicate coding errors or could be left over after refactoring the code and can be removed.
This inspection does not report in JSP files.
Example:
try { Files.readString(Paths.get("in.txt")); } catch (IOException e) { throw new RuntimeException(e); } finally { }
After the quick fix is applied the result looks like:
try { Files.readString(Paths.get("in.txt")); } catch (IOException e) { throw new RuntimeException(e); }


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 6
This is one of those quick, comforting, homemade suppers that should be taken to the sofa with a glass of red wine.
Heat two tablespoons of olive oil in a heavy stainless steel saucepan over a gentle heat and add the onion and garlic. Cover and sweat for four minutes, until soft and a little golden. Allow to cool.
In a bowl, mix the minced beef with the cold sweated onion and garlic. Add the herbs and the beaten egg. Season the mixture with salt and pepper.
Fry a tiny bit to check the seasoning and adjust if necessary. Divide the mixture into approximately 24 round balls. Cover the meatballs and refrigerate until required.
Meanwhile, make the tomato sauce. Heat the oil in a stainless steel saucepan.
Add the sliced onion and the crushed garlic, toss until coated, cover, and sweat on a gentle heat until soft and pale golden.
Slice the canned tomatoes and add, with all the juice, to the onion mixture.
Season the contents with sugar, salt and freshly ground pepper.
Cook the tomatoes uncovered for approximately 30 minutes or until the tomato softens (while this is cooking make the meatballs).
Heat a frying pan and cook the meatballs for about 10 minutes in about three tablespoons of olive oil.
When they are cooked, put them into an ovenproof dish with the tomato sauce and top with the grated mozzarella. Place under a preheated grill until the cheese has melted.
Serve with spaghetti.


HTTP connection manager¶
- HTTP connection manager architecture overview.
- HTTP protocols architecture overview.
{
"name": "http_connection_manager",
"config": {
"codec_type": "...",
"stat_prefix": "...",
"rds": "{...}",
"route_config": "{...}",
"filters": [],
"add_user_agent": "...",
"tracing": "{...}",
"http1_settings": "{...}",
"http2_settings": "{...}",
"server_name": "...",
"idle_timeout_s": "...",
"drain_timeout_ms": "...",
"access_log": [],
"use_remote_address": "...",
"forward_client_cert": "...",
"set_current_client_cert": "...",
"generate_request_id": "..."
}
}
- codec_type
(required, string) Supplies the type of codec that the connection manager should use. Possible values are:
- http1
- The connection manager will assume that the client is speaking HTTP/1.1.
- http2
- The connection manager will assume that the client is speaking HTTP/2 (Envoy does not require HTTP/2 to take place over TLS or to use ALPN. Prior knowledge is allowed).
- auto
- For every new connection, the connection manager will determine which codec to use. This mode supports both ALPN for TLS listeners as well as protocol inference for plaintext listeners. If ALPN data is available, it is preferred, otherwise protocol inference is used. In almost all cases, this is the right option to choose for this setting.
- stat_prefix
- (required, string) The human readable prefix to use when emitting statistics for the connection manager. See the statistics documentation for more information.
- rds
- (sometimes required, object) The connection manager configuration must specify one of rds or route_config. If rds is specified, the connection manager’s route table will be dynamically loaded via the RDS API. See the documentation for more information.
- route_config
- (sometimes required, object) The connection manager configuration must specify one of rds or route_config. If route_config is specified, the route table for the connection manager is static and is specified in this property.
- filters
- (required, array) A list of individual HTTP filters that make up the filter chain for requests made to the connection manager. Order matters as the filters are processed sequentially as request events happen.
- add_user_agent
- (optional, boolean) Whether the connection manager manipulates the user-agent and x-envoy-downstream-service-cluster headers. See the linked documentation for more information. Defaults to false.
- tracing
- (optional, object) Presence of the object defines whether the connection manager emits tracing data to the configured tracing provider.
- http1_settings
(optional, object) Additional HTTP/1 settings that are passed to the HTTP/1 codec.
- allow_absolute_url
- (optional, boolean) Handle http requests with absolute urls in the requests. These requests are generally sent by clients to forward/explicit proxies. This allows clients to configure envoy as their http proxy. In Unix, for example, this is typically done by setting the http_proxy environment variable.
- http2_settings
(optional, object) Additional HTTP/2 settings that are passed directly to the HTTP/2 codec. Currently supported settings are:
- hpack_table_size
- (optional, integer) Maximum table size (in octets) that the encoder is permitted to use for the dynamic HPACK table. Valid values range from 0 to<PHONE_NUMBER> (2^32 - 1) and defaults to 4096. 0 effectively disables header compression.
- max_concurrent_streams
- (optional, integer) Maximum concurrent streams allowed for peer on one HTTP/2 connection. Valid values range from 1 to<PHONE_NUMBER> (2^31 - 1) and defaults to<PHONE_NUMBER>.
- initial_stream_window_size
(optional, integer) Initial stream-level flow-control window size. Valid values range from 65535 (2^16 - 1, HTTP/2 default) to<PHONE_NUMBER> (2^31 - 1, HTTP/2 maximum) and defaults to 268435456 (256 * 1024 * 1024).
NOTE: 65535 is the initial window size from HTTP/2 spec. We only support increasing the default window size now, so it’s also the minimum.
This field also acts as a soft limit on the number of bytes Envoy will buffer per-stream in the HTTP/2 codec buffers. Once the buffer reaches this pointer, watermark callbacks will fire to stop the flow of data to the codec buffers.
- initial_connection_window_size
- (optional, integer) Similar to initial_stream_window_size, but for connection-level flow-control window. Currently , this has the same minimum/maximum/default as initial_stream_window_size.
These are the same options available in the upstream cluster http2_settings option.
- server_name
- (optional, string) An optional override that the connection manager will write to the server header in responses. If not set, the default is envoy.
- idle_timeout_s
- (optional, integer) The idle timeout in seconds for connections managed by the connection manager. The idle timeout is defined as the period in which there are no active requests. If not set, there is no idle timeout. When the idle timeout is reached the connection will be closed. If the connection is an HTTP/2 connection a drain sequence will occur prior to closing the connection. See drain_timeout_ms.
- drain_timeout_ms
- (optional, integer) The time in milliseconds that Envoy will wait between sending an HTTP/2 “shutdown notification” (GOAWAY frame with max stream ID) and a final GOAWAY frame. This is used so that Envoy provides a grace period for new streams that race with the final GOAWAY frame. During this grace period, Envoy will continue to accept new streams. After the grace period, a final GOAWAY frame is sent and Envoy will start refusing new streams. Draining occurs both when a connection hits the idle timeout or during general server draining. The default grace period is 5000 milliseconds (5 seconds) if this option is not specified.
- access_log
- (optional, array) Configuration for HTTP access logs emitted by the connection manager.
- use_remote_address
- (optional, boolean) If set to true, the connection manager will use the real remote address of the client connection when determining internal versus external origin and manipulating various headers. If set to false or absent, the connection manager will use the x-forwarded-for HTTP header. See the documentation for x-forwarded-for, x-envoy-internal, and x-envoy-external-address for more information.
- forward_client_cert
(optional, string) How to handle the x-forwarded-client-cert (XFCC) HTTP header. Possible values are:
- sanitize: Do not send the XFCC header to the next hop. This is the default value.
- forward_only: When the client connection is mTLS (Mutual TLS), forward the XFCC header in the request.
- always_forward_only: Always forward the XFCC header in the request, regardless of whether the client connection is mTLS.
- append_forward: When the client connection is mTLS, append the client certificate information to the request’s XFCC header and forward it.
- sanitize_set: When the client connection is mTLS, reset the XFCC header with the client certificate information and send it to the next hop.
For the format of the XFCC header, please refer to x-forwarded-client-cert.
- set_current_client_cert_details
- (optional, array) A list of strings, possible values are Subject and SAN. This field is valid only when forward_client_cert is append_forward or sanitize_set and the client connection is mTLS. It specifies the fields in the client certificate to be forwarded. Note that in the x-forwarded-client-cert header, Hash is always set, and By is always set when the client certificate presents the SAN value.
- generate_request_id
- (optional, boolean) Whether the connection manager will generate the x-request-id header if it does not exist. This defaults to true. Generating a random UUID4 is expensive so in high throughput scenarios where this feature is not desired it can be disabled.
Tracing¶
{
"tracing": {
"operation_name": "...",
"request_headers_for_tags": []
}
}
- operation_name
- (required, string) Span name will be derived from operation_name. “ingress” and “egress” are the only supported values.
- request_headers_for_tags
- (optional, array) A list of header names used to create tags for the active span. The header name is used to populate the tag name, and the header value is used to populate the tag value. The tag is created if the specified header name is present in the request’s headers.
Filters¶
HTTP filter architecture overview.
{
"name": "...",
"config": "{...}"
}
- name
- (required, string) The name of the filter to instantiate. The name must match a supported filter.
- config
- (required, object) Filter specific configuration which depends on the filter being instantiated. See the supported filters for further documentation.


Cool Hacks
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28. Audit Log Events¶
As Cog processes requests, both HTTP API requests, as well as command pipelines, it emits events throughout the processing lifecycle. These are recorded in Cog’s event log as JSON maps. Many of the keys are shared across events, but some will be specific to a particular event type. The events that Cog currently emits, as well as a description of their structure, are enumerated here.
28.1. API Events¶
These events are emitted in the process of handling an HTTP request.
28.1.1. API Event Types¶
- api_request_started
- api_request_authenticated
- api_request_finished
28.1.2. Common Fields¶
All API events contain the following fields, regardless of type.
- request_id
- The unique identifier assigned to the request. All events emitted in the processing of the request will share the same ID.
- event
- Label indicating which API request lifecycle event is being recorded.
- timestamp
- When the event was created, in UTC, as an ISO-8601 extended-format string (e.g. “2016-01-07T15:08:00.000000Z”).
- elapsed_microseconds
- Number of microseconds elapsed since beginning of request processing to the creation of this event.
- http_method
- The HTTP method of the request being processed as an uppercase string.
- path
- The path portion of the request URL.
28.1.3. API Event-specific Data¶
In addition to the common fields, each individual API event type can have additional type-specific fields.
- api_request_started Event
- No extra fields
- api_request_authenticated Event
- user
- The Cog username of the authenticated requestor. Note that this is not a chat handle.
- api_request_finished Event
- status
- The HTTP status code of the response.
28.2. Pipeline Events¶
Whenever Cog processes a command pipeline, whether it arrives from a chat client or was initiated using a trigger, it generates a number of events.
28.2.1. Pipeline Event Types¶
- pipeline_initialized
- command_dispatched
- pipeline_succeeded
- pipeline_failed
28.2.2. Common Fields¶
All pipeline events share the following fields:
- pipeline_id
- The unique identifier of the pipeline emitting the event. Can be used to correlate events from the same pipeline instance.
- event
- Label indicating which pipeline lifecycle event is being recorded.
- timestamp
- When the event was created, in UTC, as an ISO-8601 extended-format string (e.g. “2016-01-07T15:08:00.000000Z”).
- elapsed_microseconds
- Number of microseconds elapsed since beginning of pipeline execution to the creation of this event.
28.2.3. Pipeline Event-specific Data¶
In addition to the common fields, each individual pipeline event type can have additional type-specific fields.
- pipeline_initialized Event
- command_text
- The text of the entire pipeline, as typed by the user. No variables will have been interpolated or bound at this point.
- provider
- The chat provider being used
- handle
- The provider-specific chat handle of the user issuing the command.
- cog_user
- The Cog-specific username of the invoker of issuer of the command. May be different than the provider-specific handle.
- command_dispatched Event
- command_text
- The text of the command being dispatched to a Relay. In contrast to pipeline_initialized above, here, variables _have_ been interpolated and bound. If the user submitted a pipeline of multiple commands, a command_dispatched event will be created for each discrete command.
- relay
- The unique identifier of the Relay the command was dispatched to.
- cog_env: JSON string
- The calling environment sent to the command. The value is presented formally as a string, not a map.
- pipeline_succeeded Event
- result: JSON string
- The JSON structure that resulted from the successful completion of the entire pipeline. This is the raw data produced by the pipeline, prior to any template application. The value is presented formally as a string, not a list or map.
- pipeline_failed Event
- error
- A symbolic name of the kind of error produced.
- message
- Additional information and detail about the error.


I have never seen any girl in this university who is as amazing as you are. Thank you for being so good to me. Happy birthday.
Aminah Darins
Girl with Swag and BrainsAug | 10 | 2020
Starting at 12:00Mini Skirt as I fondly call her is one of best people have ever met in the entirey of my life. She amazes me in ways you can't even imagine. She's a very good person with an heart of gold
As her name implies Olaitan, her wealth never finishes. Gbogbo biz girls. She's so rich intelectually, spiritually, kindly, and also 'foodly'. She doesn't joke with food
One interesting about her is that she doesn't joke with her well being.  They are one of the best friends have ever seen and the got each other's back
She's one of the most friendly and jovial person you'll ever mett. She so down to earth and her humility is out of this world
She's a kind of a person who doesn't forget about her own people, and she tries as much to create time for them
I have never seen any girl in this university who is as amazing as you are. Thank you for being so good to me. Happy birthday.
Aminah Darins
Girl with Swag and BrainsMy time in this university has been a wonderful experience thanks to a loving friend like you. On this day, I wish you all the happiness and beauty that this world of ours has to offer.
Aminah Darins
Girl with Swag and BrainsHappy birthday, my dear! It is my hope and prayer that you will continue to excel academically and graduate with honors. God bless you.
Aminah Darins
Girl with Swag and BrainsYou deserve an award for being such a wonderful friend and classmate to me. Happy birthday.
Aminah Darins
Girl with Swag and BrainsThanks to a wonderful friend like you, my childhood years were some of the best years of my life. I will forever be grateful to you for that. Have a wonderful birthday, my dear.
Aminah Darins
Girl with Swag and BrainsMonday, 10, 2020
00:00 am-10:00 am
S5/900B, Kunfayakun Area Academy, Ibadan, Nigeria
Monday, 10, 2020
10:00 am-02:00 pm
S5/900B, Kunfayakun Area Academy, Ibadan, Nigeria
Monday, 10, 2020
02:00 pm-10:00 pm
S5/900B, Kunfayakun Area Academy, Ibadan, Nigeria


A channel to discuss trading. For useful links and more info click the pinned messages to the right.
100 messages
Before 05-Dec-19 12:00 AM
FishyGuts20-Feb-19 09:38 PM
And I wont even sell
Tek20-Feb-19 09:38 PM
yeah I wouldnt either
FishyGuts20-Feb-19 09:38 PM
Im dumb like that
Makes up for all th $5 KMD I bought last year
Also a true story
Tek20-Feb-19 09:39 PM
I found KMD during the bear market when I was looking for solid projects
I have Dash as BTC and KMD as ETH
yorklab20-Feb-19 09:40 PM
@FishyGuts I can relate.
Markus20-Feb-19 09:40 PM
i heard about kmd from one dude at work....he held through ath 2017 xD(edited)
linuxcaptain20-Feb-19 09:41 PM
1 minute of silence for him
Markus20-Feb-19 09:41 PM
yea poor dude
Tek20-Feb-19 09:42 PM
well kmd notso bad
imagine not selling AION at the top
and holding back to where its now
or REQ lol
Markus20-Feb-19 09:42 PM
cant say i was better though, i held btc through ath also....bought at 4-6k tho, then i decided to take serious step into learn trading during 2018 and well im in profits
Tek20-Feb-19 09:43 PM
I tried trading but now I just buy and wait then I sell
trading too stressy for me
Markus20-Feb-19 09:43 PM
try swing trading, less stressful
focus on 4h and 1 day charts
FishyGuts20-Feb-19 09:45 PM
I dunnoi about swing trading. I like my girlfriend.
1
That would feel weird.
Sorry obscure reference. Back to geting REKT buying KMD last year.
Literally my first buy was $5 KMD.
Glad I didn't find it sooner or I would be more rekt.... probbaly.
Tek20-Feb-19 09:46 PM
my first alt was OMG
i got out of it 2 months later though
luckely
i'd be so rekt
FishyGuts20-Feb-19 09:46 PM
I bought some OMG. thanksfully sold it for Z9 Minis
I got the better end there. Same with EOS at $18. Sold it ALL for z9 minis.

Tek20-Feb-19 09:47 PM
hes meeming
FishyGuts20-Feb-19 09:47 PM
Markus20-Feb-19 09:47 PM
actually made profit hehe
FishyGuts20-Feb-19 09:47 PM
You didn't lose your monmey.... well...technically....
You lost your money
1
Markus20-Feb-19 09:48 PM
Tek20-Feb-19 09:48 PM
OPPAAAAAAAAAAAAAAAAAAAAAAAAA
coingirl8720-Feb-19 09:59 PM
Where i can buy chips?
ComputerGenie 👴👴👴20-Feb-19 10:01 PM
at the bodega on the corner, they are next to the pretzels
@SHossain if @blackjok3r had moved it to vh like he said he would, it would be much more alive (edited)
SHossain20-Feb-19 10:09 PM
yes, in that case i wouldn't have to run a T9+ 24/7 and be done with few CPU cores
@coingirl87 when pangea goes live and generates any fee, a portion will be rewarded to PANGEA holders
coingirl8720-Feb-19 10:13 PM
Ok thank you
Cryptoon20-Feb-19 10:16 PM
There are a couple big chips holders here, yassin among others iirc, not sure if they are selling any big chunks though
blackjok3r20-Feb-19 10:20 PM
Might be able to sell some chips at right price.
I want to buy chips for 1000 kmd for the right price(edited)
blackjok3r20-Feb-19 10:23 PM
21 million chips in total
Tek20-Feb-19 10:24 PM
;o
blackjok3r20-Feb-19 10:24 PM
I have I think about 10,000 or less that might be able to sell but need to track them down. Been holding a long time lol
ComputerGenie 👴👴👴20-Feb-19 10:53 PM
shill much?
shillers be shillin'
coingirl8720-Feb-19 11:03 PM
Mhhh...its worth a look
1
ComputerGenie 👴👴👴20-Feb-19 11:03 PM
@Deleted User when shill ARRR?
I mean, Pirate was the first Komodo project that performed a successful z only transaction chain, and delivers instead of just promising. Super good investment.
2
1
ComputerGenie 👴👴👴20-Feb-19 11:16 PM
It's not bait, it's sarcasm
It you're going to shill one project, then you should shill the rest
I just pointed out that you don't even have to change much of the words to do so


Boeing Starliner Crew spacecraft heads to pre-launch processing
by Staff WritersBoeing's CST-100 Starliner spacecraft is transported from NASA's Kennedy Space Center to a facility at Cape Canaveral Air Force Station on Thursday, Nov. 21, 2019. Later in the day it was placed atop a United Launch Alliance Atlas V rocket ahead of Boeing's uncrewed Orbital Flight Test to the International Space Station.
Boeing's uncrewed flight test, which is targeted for Dec. 17, will provide valuable data on the end-to-end performance of the rocket, spacecraft and ground systems, as well as, in-orbit and landing operations. The data will be used toward certification of Boeing's crew transportation system for carrying astronauts to and from the space station.
NASA's Commercial Crew Program is working with the American aerospace industry through public-private partnerships to launch astronauts on American rockets and spacecraft from American soil for the first time since 2011. The goal of the program is safe, reliable and cost-effective human space transportation to and from the International Space Station.
This could allow for additional research time aboard the station and increase the opportunity for discovery aboard humanity's testbed for exploration, which includes sending astronauts to the Moon and Mars.


The observation aimpoint is defined as the position on the focal plane where an on-axis target is located, assuming that the SIM offset is zero and both Y Offset and Z Offset (aimpoint offset) are zero. Over the course of the mission the mean aimpoint has drifted over 30 arcsec, due primarily due to the steadily increasing temperature of the Aspect Camera Assembly (ACA). See the Details and References sections for further information.
In addition to a mean drift there has been a steady increase in the scatter on time scales of days. This term is directly related to the absolute pointing accuracy which describes the positional accuracy with which a target can be placed on the detector. Starting around 2015 the amplitude of this scatter has been large enough to potentially impact "pointing-sensitive" observations in which an absolute pointing error might affect science or instrument safety. This includes ACIS windowed or subarray observations, or grating observations of unusually bright sources that must be kept just off the detector.
The mean aimpoint drift adds to pointing uncertainty by introducing a discrepancy between the predicted median aimpoint (used in the planning process) and the actual median aimpoint around the time of observation. The predicted aimpoint for each detector fixed at a preset value since cycle 18 (2017) as stated in the Proposers' Observatory Guide. The temperature-dependent ACA alignment shift is compensated by means of a dynamical pointing offset that is introduced into the planning process. See the FDB transition to dynamic aimpoints for an overview and the Aimpoint transition plan for implementation details.
The trend of ACA housing temperature is shown below, and one easily sees the qualitative similarity
with the aimpoint trending data.


Nigeria based Web Developer
CustomerPayMe helps business owners manage daily transactions/debt collection.
Technology Used: #Laravel #Javascript #MongoDB
A mobile web application that creates room for people to be vocal about things that affects their community.
Technology Used: #Laravel #Javascript #mySQL
Innovation Lab Company Website - Renaissance Innovation Labs
Technology Used: #Apollo GraphQL #Docker #Firebase #Prisma #React
Logistics Company Website - Sevenstar Logistics Limited
Technology Used: #HTML/CSS #Firebase #Javascript
Jeremiah Favour Iromaka (Jeremiah Iro), is a business management graduate with great passion for
technology and innovation.
He is an innovative web developer committed to creating and enhancing interactive/user-friendly
applications.
Jeremiahiro has an extraordinary relational abilities, and fantastic collaboration capacities.
A few tasks he is permitted to show off can be found in his
Github Repository.
He is open to learning and enjoys progressive challenges.


At Salmon Cookies, we pride ourselves in providing the best possible cookie made out of Salmon that you've ever had.  You're friends will mock you at first, and then think that they need to be odd to be cool so they start to embrace it. We are a millenial company, for a millenial crowd, and we exploit millenial insecurities. Try one and you'll see what we mean.
I was a skeptic. But then I tried them and realized how cool I was. Now I'm an addict.-Xtina
These things are gross, but I felt so cool having something so obscure.-Hipster Pete
© Salmon Cookies


Filler Dropping (
processes)
99PCTL:
Max:
Entities:
AD Processor Dropping (
processes)
99PCTL:
Max:
Spans:
AD Writer Dropping (
processes)
99PCTL:
Max:
Spans:
CPU User (
hosts)
CPU System (
hosts)
CPU Wait (
hosts)


Self Hosting WordPress
Brian Retterer
<EMAIL_ADDRESS>@bretterer
https://joind.in/talk/36446
WordPress.com
vs.
WordPress.org
Google Domains
https://domains.google.com
Hover
https://hover.com
Digital Ocean
https://digitalocean.com
Rackspace
https://rackspace.com
Amazon AWS
https://aws.amazon.com
Backing Up Data From WordPress.com
Tools -> Export
Paid Or Free
Export All Data
Download WordPress Core
Extract Zip
Laravel Forge
Server Setup
Site Setup
FTP
Database Setup
WordPress Setup
WordPress Setup
WordPress Setup
WordPress Setup
WordPress Setup
WordPress Setup
WordPress Setup
Tools -> Import
Install Importer
Run importer
Choose File
Map Authors, Download Files
Import Done
Secure Your Site
Lets Encrypt
Lets Encrypt
Lets Encrypt
Secure Site
Resources
- WordPress.org
- Laravel Forge - https://forge.laravel.com
- Digital Ocean - https://digitalocean.com
- Google Domains - https://domains.google.com
- Lets Encrypt - https://letsencrypt.org
- Slides - https://github.com/bretterer-talks/self-hosting-wordpress
- Review - https://joind.in/talk/36446


Quickstart¶
To get started, simply import and call one of the implemented algorithms. According to Astrakhantsev 2016, combo_basic is the most precise of the five algorithms, though basic and cvalues is not too far behind (see Precision). The same study shows that PU-ATR and KeyConceptRel have higher precision than combo_basic but are not implemented and PU-ATR take significantly more time since it uses machine learning.
from pyate import combo_basic
# source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1994795/
string = """Central to the development of cancer are genetic changes that endow these “cancer cells” with many of the
hallmarks of cancer, such as self-sufficient growth and resistance to anti-growth and pro-death signals. However, while the
genetic changes that occur within cancer cells themselves, such as activated oncogenes or dysfunctional tumor suppressors,
are responsible for many aspects of cancer development, they are not sufficient. Tumor promotion and progression are
dependent on ancillary processes provided by cells of the tumor environment but that are not necessarily cancerous
themselves. Inflammation has long been associated with the development of cancer. This review will discuss the reflexive
relationship between cancer and inflammation with particular focus on how considering the role of inflammation in physiologic
processes such as the maintenance of tissue homeostasis and repair may provide a logical framework for understanding the U
connection between the inflammatory response and cancer."""
print(combo_basic(string).sort_values(ascending=False))
""" (Output)
dysfunctional tumor 1.443147
tumor suppressors 1.443147
genetic changes 1.386294
cancer cells 1.386294
dysfunctional tumor suppressors 1.298612
logical framework 0.693147
sufficient growth 0.693147
death signals 0.693147
many aspects 0.693147
inflammatory response 0.693147
tumor promotion 0.693147
ancillary processes 0.693147
tumor environment 0.693147
reflexive relationship 0.693147
particular focus 0.693147
physiologic processes 0.693147
tissue homeostasis 0.693147
cancer development 0.693147
dtype: float64
"""
If you would like to add this to a spacy pipeline, simply use add spaCy’s add_pipe method.
import spacy
from pyate.term_extraction_pipeline import TermExtractionPipeline
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe(TermExtractionPipeline())
doc = nlp(string)
print(doc._.combo_basic.sort_values(ascending=False).head(5))
""" (Output)
dysfunctional tumor 1.443147
tumor suppressors 1.443147
genetic changes 1.386294
cancer cells 1.386294
dysfunctional tumor suppressors 1.298612
dtype: float64
"""
TermExtractionPipeline.__init__
is defined as follows
__init__(
self,
func: Callable[..., pd.Series] = combo_basic,
*args,
**kwargs
)
where func
is essentially your term extracting algorithm that takes in a corpus (either a string or iterator of strings) and outputs a Pandas Series of term-value pairs of terms and their respective termhoods. func
is by default combo_basic
. args
and kwargs
are for you to overide default values for the function, which you can find by running help.


A Note on 'London Town' (UK)(1946) Alternative title 'My Heart Goes Crazy' (USA) By Roger Mellor
The critical and financial failure of the extravagant LONDON TOWN (1946), Britain's first major Technicolor musical (there were one or two attempts in the thirties, but not on this scale), is part of British film legend. Lavishly funded, and produced by the Rank Organization in association with the American director Wesley Ruggles at a time of rationing and shortages of materials (perhaps this explains why so many of the chorus girls costumes are muddy green or khaki!), it was filmed in the shell of 'Sound City Shepperton' (later renamed the London Film Studios by Korda, and later 'Shepperton Studios') which had just been made available as a film studio after being requisitioned during the war as a factory for aircraft parts. According to reports, part of the studio was still being used for this purpose during the production of LONDON TOWN. It was unusual for Rank to produce a film here, but perhaps Denham was fully booked! (Pinewood was still occupied by the goverment, not being released for film production, until well after the war).
Sid Field was the talk of London town at the end of the war, having just conquered and cheered up wartime London audiences with his enormously successful stage variety shows, and so he could more or less dictate his own tems for the film. He insisted on having an American director, as was of the opinion that no British director was capable of making a good musical (hadn't he heard of Victor Saville?). J.Arthur Rank, flush with funds (Cinema admissions in 1945 were at an all time high), was happy to spend large sums of money to bring over a fading American comedy director of the 1930's with little experience of musicals, whose career was, frankly, on the slide in Hollywood, and give him lots of money and a financial stake through his own production company, mainly funded by Rank. Top US songwriters were comissioned, musicians were placed under "long term contracts", and the stud io also had to be re-equipped from the ground up! Rank was confident that, even though he was wasting money left, right and centre, the cinema business was so boyant at this time, that he would still make a healthy profit. And financial Controls were slack, to say the least......
The movie became a legendary turkey! It was not that UK audiences stayed away in droves from "London Town", just that the film needed to perform spectacularly well just to break even at the box office. Critics were dismissive, calling it "Tacky and Tasteless". In hindsight, and especially for 1940's nostalgia fans, these aspects make it absolutely fascinating! Apart from the kitsch elements, the film is also interesting historically. After Britain's victory in the war, it can be seen as a tribute to London and its citizens, and as a celebration of popular cockney culture, especially music hall. It also, in a way in which the makers almost certainly did't intend, celebrates the sense of community, the "all classes in harmony" as seen in the Riverboat sequence, filmed as a day out on the River Thames, below Windsor Castle. The symbolism of this is fascinating, as all are included, from the very high, Royalty in the Castle, to an Eton Schoolboy, to the cockney folk, all living in harmony in this "English Utopia". With its emphasis on "Community Values", it could almost be an election commercial for Clement Atlee's Labour government, which swept to power in the same year!
The Songs in 'London Town' include: 'You Can't Keep a Good Dreamer Down', 'The 'Ampstead Way', 'Any Way the Wind Blows' 'So Do I ' (performed by 1940's Dance Band singer Beryl Davis), 'My Heart Goes Crazy', and a medley of cockney songs: 'Knock 'em in the Old kent Road/Any Old Iron/(My Old Man said) Follow the Van'
The musical director was Tutti Camerata, from Decca Records, and many of the musicians who played on the Soundtrack went on to form the Ted Heath band. The film orchestra is, in effect, the Ted Heath band of 1945 in embryonic form. (Ted Heath was the "orchestral contractor" for the film, which may have given him a taste for band leadership!). The music and lyrics were by Bing Crosby's regular collaborators,Jimmy Van Heusen and Johnny Burke, and the film is not without interest. Much of the material for the comedy sketches comes from comedian Sid Field's two West End wartime variety revues, Strike a New Note(Prince of Wales,1943) and Strike it Again (Prince of Wales,1944), which were highly successful with the public (followed by Piccadilly Hayride (Prince of Wales,1946). Strike it Again featured the golf sketch which is seen in LONDON TOWN. Thus the film provides a valuable historical record of 1940's variety, with comic sketches and musical production numbers, and all in Technicolor. Among those featured are Kay Kendall singing 'The 'ampstead way', and with 'The Dozen and One Girls' (also known as 'The London Lovelies', as they are billed in the film's press book) 'My heart goes crazy': fourteen year old Petula Clark appears in a non singing role, and Tessie O'Shea, later to appear in Noel Coward's The Girl Who came to Supper (Broadway Theatre NY, 1963) demonstrates her ability to steal the show, in the 'Pearly Kings and Queens' sequence. LONDON TOWN was released in the US in a truncated form by United Artists in 1953 under the title MY HEART GOES CRAZY.
Roger Mellor If you have any comments on this review, or the movie, you can e mail me at<EMAIL_ADDRESS>Check out the British Musical Movies site at: http://britmusicalfilm.webjump.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


View record
| Title: | Mr |
| Initial: | J |
| Surname: | Smith |
| Age: | 34 |
| National Insurance number: | JC628596A |
| Mobile number: |<PHONE_NUMBER>8 |
| Benefit type: | Jobseekers Allowance |
| Status: | Live |
| Initial date of claim: | 14/04/2015 |
| Job Centre: | Worcester |
| Work coach: | amanda.wright |
| Next appointment: | None |


Magister Hanellin:
Where are the other pieces? I distinctly remember asking you to bring me 10 pieces of White Fabric soaked in blood... You haven't thrown them away or given them to someone else, have you? Oh, well, nothing can be done about it now. Since you've done the work, I'll pay you, but I must say that I'm very disappointed... I thought you were trustworthy! Oh, well, live and learn. Goodbye.


Functions are reusable pieces of code. They can be named, like variables, and called when you need them.
function chihuahuaFacts() {
console.log("Chihuahuas are the smallest of all dogs.")
console.log("Chihuahuas can be 'apple-headed' or 'deer-headed.'")
}
Functions can accept one or more input values, called "arguments."
function callDog(dogName){
console.log("Come here, " + dogName + "!");
}
callDog("Charlie");
// "Come here, Charlie."
Write a function to calculate someone's age and output the result to the console in a string, like "You are 52 years old!".
It should accept a single argument: a year of birth.
Functions can return a value, allowing them to be used for common calculations.
function miles2Kilometers(miles) {
return miles * 1.60934;
}
var kilometers = miles2Kilometers(3)
console.log(kilometers);
// 4.82802
Functions can have multiple return values. The first one to occur ends the function.
function letterGrade(score) {
if (score >= 90) {
return "A";
} else if (score >= 80) {
return "B";
} else if (score >= 70) {
return "C";
} else if (score >= 60) {
return "D";
}
return "F";
}
Write a function that converts temperature from Fahrenheit to Celsius.
To convert Fahrenheit to Celsius, deduct 32, then multiply by 5, then divide by 9.
Try writing this code by editing this Glitch program.
Arrays are like lists for multiple values. Values are listed inside square brackets ( []) and separated by commas.
var emptyArray = [];
var arrayOfNumbers = [2, 4, 6, 8];
var arrayOfStrings = ["cats", "dogs", "hamsters"];
var arrayOfDifferentStuff = [23, null, "octopus", true];
var arrayOfArrays = [arrayOfNumbers, arrayOfStrings];
Find out how many values are in an array:
var colors = ["red", "green", "blue"];
colors.length
You can also retrieve values from an array:
var firstColor = colors[0];
var lastColor = colors[2];
Write a shopping list of the foods you need to make your favorite recipe as an array.
Can you access the second and third items in your list and output them to the console?
A loop is a code structure that lets us repeat a task until a certain condition is met.
For loops are commonly used to count a certain number of times (iterations) to repeat a task.
for ([initial value]; [condition]; [final expression]) {
// do this thing
}
// count 1 to 10
for (var i = 1; i <= 10; i++) {
console.log(i);
}
Run the code from the preceeding slide in your console.
Experiment with changing parts of the expressions.
Try writing a for loop that will output the first twelve squares to the console.
(e.g., 1 2 = 1 through 12 2 = 144)
When we put loops and arrays together, we can perform operations on the entire array.
// Print each student
var students = ["Dale", "Cadence", "Alex", "Nevada"];
for (var index = 0; index < students.length; index++) {
console.log(students[index]);
}
Write a for loop that will take an array of numbers, like so:
var numbers = [7, 2, -1, 18];
The loop should print the square of each number.
Try turning this into a function.
We can use functions and loops together to create new arrays or extract a piece of information from an array.
// Return the average of an array.
function average(numbers) {
var sum = 0;
for (var i = 0; i < numbers.length; i++) {
sum += numbers[i];
}
return sum / numbers.length;
}
An object in JavaScript is a more complex data structure that can store pairs of keys and values. It is a little like a dictionary.
var anObject = {
key: value,
key: value
};
var aboutMe = {
hometown: "New York, NY",
almaMater: "UNC",
likes: ["avocadoes", "chihuahuas", "code"],
zodiacSign: "Scorpio"
};
You can retrieve values using "dot notation"
var aboutMe = {
hometown: "New York, NY",
almaMater: "UNC",
likes: ["avocadoes", "chihuahuas", "code"],
zodiacSign: "Scorpio"
};
var myHometown = aboutMe.hometown;
...or using "bracket notation" (like arrays)
var mySign=aboutMe['zodiacSign'];
Create an object in your JS file that holds information about a book. It should have properties for:
Add other properties if you like.
Functions, arrays, loops, and objects are the fundamental parts of programming. There are more things to learn, but with these tools, you can write almost any program you want. When you combine them, you can do very powerful things.
var students = [{name: "Trinidad", age: 27},
{name: "Scout", age: 53},
{name: "Kai", age: 31}];
function getAges(people) {
var ages = [];
for (var i = 0; i < people.length; i++) {
ages.push(people[i].age);
}
return ages;
}
getAges(students); // [27, 53, 31]
Take the code from the previous example. Make a function called getAverageAge that takes an array of students and returns the average age of the students.
Create more book objects and store them in an array.
Write a loop that iterates through the array of books and logs each book title to the console.
BONUS: Write a function that creates a reading list from of books you haven't read and outputs that list to the console.
But wait, there's more
Write a function that converts a Fahrenheit temperature to a color. You can determine the colors.
Call this function
fahrenheitToColor
and add it to the Glitch project for your
fahrenheitToCelsius
function.


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| Packages that use HostConfiguration | |
| org.apache.commons.httpclient | Classes and interfaces supporting the client side of the HTTP protocol. |
| org.apache.commons.httpclient.params | HttpClient preferences framework. |
| Uses of HostConfiguration in org.apache.commons.httpclient |
| Fields in org.apache.commons.httpclient declared as HostConfiguration | |
static HostConfiguration |
HostConfiguration.ANY_HOST_CONFIGURATION
A value to represent any host configuration, instead of using something like null . |
| Methods in org.apache.commons.httpclient that return HostConfiguration | |
HostConfiguration |
HttpMethod.getHostConfiguration()
Deprecated. no longer applicable |
HostConfiguration |
ProxyClient.getHostConfiguration()
Returns the host configuration associated with the
ProxyClient. |
HostConfiguration |
HttpMethodBase.getHostConfiguration()
Deprecated. no longer applicable |
HostConfiguration |
HttpClient.getHostConfiguration()
Returns the host configuration associated with the
HttpClient. |
| Methods in org.apache.commons.httpclient with parameters of type HostConfiguration | |
void |
ProxyClient.setHostConfiguration(HostConfiguration hostConfiguration)
Assigns the host configuration to use with the
ProxyClient. |
void |
HttpMethodBase.setHostConfiguration(HostConfiguration hostconfig)
Deprecated. no longer applicable |
HttpConnection |
MultiThreadedHttpConnectionManager.getConnection(HostConfiguration hostConfiguration)
|
HttpConnection |
MultiThreadedHttpConnectionManager.getConnectionWithTimeout(HostConfiguration hostConfiguration,
long timeout)
|
HttpConnection |
MultiThreadedHttpConnectionManager.getConnection(HostConfiguration hostConfiguration,
long timeout)
Deprecated. Use #getConnectionWithTimeout(HostConfiguration, long) |
int |
MultiThreadedHttpConnectionManager.getConnectionsInPool(HostConfiguration hostConfiguration)
Gets the total number of pooled connections for the given host configuration. |
int |
MultiThreadedHttpConnectionManager.getConnectionsInUse(HostConfiguration hostConfiguration)
Deprecated. Use MultiThreadedHttpConnectionManager.getConnectionsInPool(HostConfiguration) |
int |
HttpClient.executeMethod(HostConfiguration hostConfiguration,
HttpMethod method)
Executes the given HTTP method using custom
host configuration . |
int |
HttpClient.executeMethod(HostConfiguration hostconfig,
HttpMethod method,
HttpState state)
Executes the given HTTP method using the given custom
host configuration with the given custom
HTTP state . |
void |
HttpClient.setHostConfiguration(HostConfiguration hostConfiguration)
Assigns the host configuration to use with the
HttpClient. |
HttpConnection |
HttpConnectionManager.getConnection(HostConfiguration hostConfiguration)
Gets an HttpConnection for a given host configuration. |
HttpConnection |
HttpConnectionManager.getConnection(HostConfiguration hostConfiguration,
long timeout)
Deprecated. Use #getConnectionWithTimeout(HostConfiguration, long) |
HttpConnection |
HttpConnectionManager.getConnectionWithTimeout(HostConfiguration hostConfiguration,
long timeout)
Gets an HttpConnection for a given host configuration. |
HttpConnection |
SimpleHttpConnectionManager.getConnection(HostConfiguration hostConfiguration)
|
HttpConnection |
SimpleHttpConnectionManager.getConnectionWithTimeout(HostConfiguration hostConfiguration,
long timeout)
|
HttpConnection |
SimpleHttpConnectionManager.getConnection(HostConfiguration hostConfiguration,
long timeout)
Deprecated. Use #getConnectionWithTimeout(HostConfiguration, long) |
| Constructors in org.apache.commons.httpclient with parameters of type HostConfiguration | |
HttpConnection(HostConfiguration hostConfiguration)
Creates a new HTTP connection for the given host configuration. |
|
HostConfiguration(HostConfiguration hostConfiguration)
Copy constructor for HostConfiguration |
| Uses of HostConfiguration in org.apache.commons.httpclient.params |
| Methods in org.apache.commons.httpclient.params with parameters of type HostConfiguration | |
void |
HttpConnectionManagerParams.setMaxConnectionsPerHost(HostConfiguration hostConfiguration,
int maxHostConnections)
Sets the maximum number of connections to be used for the given host config. |
int |
HttpConnectionManagerParams.getMaxConnectionsPerHost(HostConfiguration hostConfiguration)
Gets the maximum number of connections to be used for a particular host config. |
|
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| PREV NEXT | FRAMES NO FRAMES |


In these sectioncs, We will dicuss about the the key definition of Artificial Intelligence, Machine Learning and Mathematics which related to those domain. In addition, the writter's pratical experience and lessons will be shared during the blog series


This tool appears in the course navigation area in Canvas, and can be used by course administrators to add a link to student locations for the course.
To configure this tool in Canvas, use the XML configuration here: XML Configuration


Introduction
Authors
Installation
Get the CCured sources
Configure and Compile CCured
Test CCured
CCured Tutorial
CCured Attributes
SAFE
pointers
Checks for
SAFE
Pointers
Checks for Returning Pointers
Checks for Writing Pointers
SEQ
uence Pointers
FSEQ
Pointers
WILD
Pointers
Split Metadata
Invoking CCured
Command-line options
Controlling error handling at run time
CCured Type Inference
Using the pointer browser
Using CCured
Example:
mathopd
HTTP server
Example: UCSPI TCP Suite
Example: PING
Example: THTTPD Server
How to Eliminate WILD Pointers
Polymorphism
User-defined memory allocators
Pointers with Run-Time Type Information
Specifying Trusted Code
Writing Wrappers
Name Mangling
Writing Simple Wrappers
Writing Complex Wrappers
Wrappers for data
Wrapper Helper Functions
Final Notes on Wrappers
Advanced CCured Issues
Function Pointers
The
main
Function
Global Initialization
Casting Integers to Pointers
The
sizeof
Issue
Variable argument functions
Tagged Unions
Annotated Lengths
Memory Management
CCured Pragmas
CCured Warnings and Errors
Merging
Inference
Curing
Linking
Running the Cured Code
License
Bug reports
Changes
Inference Results
Type Names
A Tour of the Source Code
Old Tutorials
ftpd
Writing Wrappers Manually
Apache Modules
Using the Regression Tester
Running the regression
Analyzing the results
Configuring the regression
An alternative interface
The Automated Regression Tester
Debugging support
Experimental Features
This document was translated from L
A
T
E
X by
H
E
V
E
A
.


I've had the pleasure of partnering with some of Corpus Christi's finest businesses, including:
- Water Street Oyster Bar
- Gill Landscape Nursery
- WKMC Architects
- Harbor Playhouse
- Executive Surf Club
- Corpus Christi Symphony Orchestra
- Art Museum of South Texas
- Texas Surf Museum
- Dorsey & Clower Real Estate Appraisal
- Gignac & Associates Architecture
- Pearl Restaurant Group, (SF, CA)
- Stand Out! Photo Forum, (NY, NY)
- Harwin Camera (NY, NY)
- Concrete Street Amphitheater
- Rockit’s Whiskey Bar
- Sterling Personnel + Sterling HR
- Ironwood Finance
- Industrial Fabricators Corpus Christi


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GPGPU-Sim
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#include <credit.hpp>
Public Member Functions | |
| void | Reset () |
| void | Free () |
Static Public Member Functions | |
| static Credit * | New () |
| static void | FreeAll () |
| static int | OutStanding () |
Public Attributes | |
| set< int > | vc |
| bool | head |
| bool | tail |
| int | id |
Private Member Functions | |
| Credit () | |
| ~Credit () | |
Static Private Attributes | |
| static stack< Credit * > | _all |
| static stack< Credit * > | _free |
Definition at line 34 of file credit.hpp.
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private |
Definition at line 39 of file credit.cpp.
|
inlineprivate |
Definition at line 56 of file credit.hpp.
| void Credit::Free | ( | ) |
Definition at line 65 of file credit.cpp.
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static |
Definition at line 69 of file credit.cpp.
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static |
Definition at line 52 of file credit.cpp.
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static |
Definition at line 77 of file credit.cpp.
| void Credit::Reset | ( | ) |
Definition at line 44 of file credit.cpp.
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staticprivate |
Definition at line 52 of file credit.hpp.
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staticprivate |
Definition at line 53 of file credit.hpp.
| bool Credit::head |
Definition at line 41 of file credit.hpp.
| int Credit::id |
Definition at line 42 of file credit.hpp.
| bool Credit::tail |
Definition at line 41 of file credit.hpp.
| set<int> Credit::vc |
Definition at line 38 of file credit.hpp.


--- layout: post title: Make it faster! categories: [] tags: dotnet-sdk status: publish type: post published: true meta: {} ---
Recently I spent several hours chasing 280 milliseconds that I was convinced should not have been there. The ServiceAPI URL I was calling looked like this:
http://localhost/ServiceAPI/Record?q=container:9000002536&format=json&excludecount=false&pagesize=100&properties=recordtitle,recordowner,recordcontainer,recorddateregistered,recordassignee,recorddatecreated,recordcreator,recordnumber
After hours tuning the ServiceAPI code I managed to tweak a 20 millisecond improvement, still I was sure there was more. I looked once more at the URL and made one small improvement, to this:
http://localhost/ServiceAPI/Record?q=container:9000002536&format=json&excludecount=false&pagesize=100&properties=recordtitle,recordownerlocation,recordcontainer,recorddateregistered,recordassignee,recorddatecreated,recordcreator,recordnumber
Suddenly I had eliminated my extra 260 milliseconds!
Can you spot the difference? Previously I had an incorrect property name, RecordOwner should have been RecordOwnerLocation. Why is this a problem? If the ServiceAPI does not recognise a property it checks to see if it is a valid 'additional field' for this Record. Multiply the time to check this by 100 (the page size) and there is the 260 milliseconds.
260 milliseconds is not a lot but in a web service environment it adds up so be careful to to include invalid property names in the properties parameter.
Now I am off to see if the 'additional field' code can be optimised.


About Me
Hi, I am Nur Irsyad!
I am currently a student at Singapore Polytechnic. My course in SP is Mechanical Engineering.
My hobbies are to read books such as to kill the mockingbird, the curious incident of the dog in the night-time and etc. I also like to spend my time studying and revising for my lessons.
Contact
Email: NIBA.20@ichat.sp.edu.sg


| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more | |
| 3 | * contributor license agreements. See the NOTICE file distributed with | |
| 4 | * this work for additional information regarding copyright ownership. | |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 | |
| 6 | * (the "License"); you may not use this file except in compliance with | |
| 7 | * the License. You may obtain a copy of the License at | |
| 8 | * | |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 10 | * | |
| 11 | * Unless required by applicable law or agreed to in writing, software | |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 14 | * See the License for the specific language governing permissions and | |
| 15 | * limitations under the License. | |
| 16 | */ | |
| 17 | ||
| 18 | package org.apache.commons.math3.analysis.function; | |
| 19 | ||
| 20 | import org.apache.commons.math3.analysis.FunctionUtils; | |
| 21 | import org.apache.commons.math3.analysis.UnivariateFunction; | |
| 22 | import org.apache.commons.math3.analysis.DifferentiableUnivariateFunction; | |
| 23 | import org.apache.commons.math3.analysis.differentiation.DerivativeStructure; | |
| 24 | import org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction; | |
| 25 | import org.apache.commons.math3.util.FastMath; | |
| 26 | ||
| 27 | /** | |
| 28 | * Natural logarithm function. | |
| 29 | * | |
| 30 | * @since 3.0 | |
| 31 | * @version $Id: Log.java 1383441 2012-09-11 14:56:39Z luc $ | |
| 32 | */ | |
| 33 | public class Log implements UnivariateDifferentiableFunction, DifferentiableUnivariateFunction { | |
| 34 | /** {@inheritDoc} */ | |
| 35 | public double value(double x) { | |
| 36 | 1 | return FastMath.log(x); |
| 37 | } | |
| 38 | ||
| 39 | /** {@inheritDoc} | |
| 40 | * @deprecated as of 3.1, replaced by {@link #value(DerivativeStructure)} | |
| 41 | */ | |
| 42 | @Deprecated | |
| 43 | public UnivariateFunction derivative() { | |
| 44 | 1 | return FunctionUtils.toDifferentiableUnivariateFunction(this).derivative(); |
| 45 | } | |
| 46 | ||
| 47 | /** {@inheritDoc} | |
| 48 | * @since 3.1 | |
| 49 | */ | |
| 50 | public DerivativeStructure value(final DerivativeStructure t) { | |
| 51 | 1 | return t.log(); |
| 52 | } | |
| 53 | ||
| 54 | } | |
Mutations | ||
| 36 |
replaced return of double value with -(x + 1) for org/apache/commons/math3/analysis/function/Log::value : KILLED -> org.apache.commons.math3.analysis.integration.gauss.LegendreTest.testInverse(org.apache.commons.math3.analysis.integration.gauss.LegendreTest) |
|
| 44 |
mutated return of Object value for org/apache/commons/math3/analysis/function/Log::derivative to ( if (x != null) null else throw new RuntimeException ) : NO_COVERAGE |
|
| 51 |
mutated return of Object value for org/apache/commons/math3/analysis/function/Log::value to ( if (x != null) null else throw new RuntimeException ) : KILLED -> org.apache.commons.math3.analysis.FunctionUtilsTest.testComposeDifferentiable(org.apache.commons.math3.analysis.FunctionUtilsTest) |


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