{% extends "base.html" %} {% set title="animals html" %} {% block content %}
animals
{% for animal in animals %}{{ animal.name }} has {{ animal.numberOfLegs }} legs. But does this animal have a tail? {{ animal.tail }} - Modify animal - Delete animal :(
{% endfor %} {% endblock content %}


Publications
Shull, Gabriella, Hu, Jay Jia, Buschnyj, Justin, Koon, Henry, Abel, Julianna, and Kodandaramaiah, Suhasa B. “Shape Memory Alloy Actuatable Woven Neural Probes.” Proceedings of the ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies. San Antonio, Texas, USA. September 10–12, 2018. V002T08A011. ASME. https://doi.org/10.1115/SMASIS2018-8169


12 Penalized Linear Models
One of the main weaknesses of the GLM, including all linear models in this chapter, is that the features need to be selected by hand. Stepwise selection helps to improve this process, but fails when the inputs are correlated and often has a strong dependence on seemingly arbitrary choices of evaluation metrics such as using AIC or BIC and forward or backwise directions.
The Bias Variance Tradoff is about finding the lowest error by changing the flexibility of the model. Penalization methods use a parameter to control for this flexibility directly.
Earlier on we said that the linear model minimizes the sum of square terms, known as the residual sum of squares (RSS)
\[ \text{RSS} = \sum_i(y_i - \hat{y})^2 = \sum_i(y_i - \beta_0 - \sum_{j = 1}^p\beta_j x_{ij})^2 \]
This loss function can be modified so that models which include more (and larger) coefficients are considered as worse. In other words, when there are more \(\beta\)’s, or \(\beta\)’s which are larger, the RSS is higher.
12.1 Ridge Regression
Ridge regression adds a penalty term which is proportional to the square of the sum of the coefficients. This is known as the “L2” norm.
\[ \sum_i(y_i - \beta_0 - \sum_{j = 1}^p\beta_j x_{ij})^2 + \lambda \sum_{j = 1}^p\beta_j^2 \]
This \(\lambda\) controls how much of a penalty is imposed on the size of the coefficients. When \(\lambda\) is high, simpler models are treated more favorably because the \(\sum_{j = 1}^p\beta_j^2\) carries more weight. Conversely, then \(\lambda\) is low, complex models are more favored. When \(\lambda = 0\), we have an ordinary GLM.
12.2 Lasso
The official name is the Least Absolute Shrinkage and Selection Operator, but the common name is just “the lasso”. Just as with Ridge regression, we want to favor simpler models; however, we also want to select variables. This is the same as forcing some coefficients to be equal to 0.
Instead of taking the square of the coefficients (L2 norm), we take the absolute value (L1 norm).
\[ \sum_i(y_i - \beta_0 - \sum_{j = 1}^p\beta_j x_{ij})^2 + \lambda \sum_{j = 1}^p|\beta_j| \]
In ISLR, Hastie et al show that this results in coefficients being forced to be exactly 0. This is extremely useful because it means that by changing \(\lambda\), we can select how many variables to use in the model.
Note: While any response family is possible with penalized regression, in R, only the Gaussian family is possible in the library glmnet
, and so this is the only type of question that the SOA can ask.
12.3 Elastic Net
The Elastic Net uses a penalty term which is between the L1 and L2 norms. The penalty term is a weighted average using the mixing parameter \(0 \leq \alpha \leq 1\). The loss fucntion is then
\[\text{RSS} + (1 - \alpha)/2 \sum_{j = 1}^{p}\beta_j^2 + \alpha \sum_{j = 1}^p |\beta_j|\] When \(\alpha = 1\) is turns into a Lasso; when \(\alpha = 1\) this is the Ridge model.
Luckily, none of this needs to be memorized. On the exam, read the documentation in R to refresh your memory. For the Elastic Net, the function is glmnet
, and so running ?glmnet
will give you this info.
Shortcut: When using complicated functions on the exam, use
?function_name
to get the documentation.
12.4 Advantages and disadvantages
Elastic Net/Lasso/Ridge Advantages
- All benefits from GLMS
- Automatic variable selection for Lasso; smaller coefficients for Ridge
- Better predictive power than GLM
Elastic Net/Lasso/Ridge Disadvantages
- All cons of GLMs
| Readings | |
|---|---|
| ISLR 6.1 Subset Selection | |
| ISLR 6.2 Shrinkage Methods |
12.5 Example: Ridge Regression
We will use the glmnet
package in order to perform ridge regression and
the lasso. The main function in this package is glmnet()
, which can be used
to fit ridge regression models, lasso models, and more. This function has
slightly different syntax from other model-fitting functions that we have
encountered thus far in this book. In particular, we must pass in an \(x\)
matrix as well as a \(y\) vector, and we do not use the \(y \sim x\) syntax.
Before proceeding, let’s first ensure that the missing values have been removed from the data, as described in the previous lab.
We will now perform ridge regression and the lasso in order to predict Salary
on
the Hitters
data. Let’s set up our data:
x = model.matrix(Salary~., Hitters)[,-1] # trim off the first column
# leaving only the predictors
y = Hitters %>%
select(Salary) %>%
unlist() %>%
as.numeric()
The model.matrix()
function is particularly useful for creating \(x\); not only
does it produce a matrix corresponding to the 19 predictors but it also
automatically transforms any qualitative variables into dummy variables.
The latter property is important because glmnet()
can only take numerical,
quantitative inputs.
The glmnet()
function has an alpha argument that determines what type
of model is fit. If alpha = 0
then a ridge regression model is fit, and if alpha = 1
then a lasso model is fit. We first fit a ridge regression model:
By default the glmnet()
function performs ridge regression for an automatically
selected range of \(\lambda\) values. However, here we have chosen to implement
the function over a grid of values ranging from \(\lambda = 10^10\) to \(\lambda = 10^{-2}\), essentially covering the full range of scenarios from the null model containing
only the intercept, to the least squares fit.
As we will see, we can also compute
model fits for a particular value of \(\lambda\) that is not one of the original
grid values. Note that by default, the glmnet()
function standardizes the
variables so that they are on the same scale. To turn off this default setting,
use the argument standardize = FALSE
.
Associated with each value of \(\lambda\) is a vector of ridge regression coefficients,
stored in a matrix that can be accessed by coef()
. In this case, it is a \(20 \times 100\)
matrix, with 20 rows (one for each predictor, plus an intercept) and 100
columns (one for each value of \(\lambda\)).
## [1] 20 100
We expect the coefficient estimates to be much smaller, in terms of \(l_2\) norm, when a large value of \(\lambda\) is used, as compared to when a small value of \(\lambda\) is used. These are the coefficients when \(\lambda = 11498\), along with their \(l_2\) norm:
## [1] 11497.57
## (Intercept) AtBat Hits HmRun Runs
##<PHONE_NUMBER>00 0.036957182 0.138180344 0.524629976 0.230701523
## RBI Walks Years CAtBat CHits
## 0.239841459 0.289618741 1.107702929 0.003131815 0.011653637
## CHmRun CRuns CRBI CWalks LeagueN
## 0.087545670 0.023379882 0.024138320 0.025015421 0.085028114
## DivisionW PutOuts Assists Errors NewLeagueN
## -6.215440973 0.016482577 0.002612988 -0.020502690 0.301433531
## [1] 6.360612
In contrast, here are the coefficients when \(\lambda = 705\), along with their \(l_2\) norm. Note the much larger \(l_2\) norm of the coefficients associated with this smaller value of \(\lambda\).
## [1] 705.4802
## (Intercept) AtBat Hits HmRun Runs
## 54.32519950 0.11211115 0.65622409 1.17980910 0.93769713
## RBI Walks Years CAtBat CHits
## 0.84718546 1.31987948 2.59640425 0.01083413 0.04674557
## CHmRun CRuns CRBI CWalks LeagueN
## 0.33777318 0.09355528 0.09780402 0.07189612 13.68370191
## DivisionW PutOuts Assists Errors NewLeagueN
## -54.65877750 0.11852289 0.01606037 -0.70358655 8.61181213
## [1] 57.11001
We can use the predict()
function for a number of purposes. For instance,
we can obtain the ridge regression coefficients for a new value of \(\lambda\), say 50:
## (Intercept) AtBat Hits HmRun Runs
## 4.876610e+01 -3.580999e-01 1.969359e+00 -1.278248e+00 1.145892e+00
## RBI Walks Years CAtBat CHits
## 8.038292e-01 2.716186e+00 -6.218319e+00 5.447837e-03 1.064895e-01
## CHmRun CRuns CRBI CWalks LeagueN
## 6.244860e-01 2.214985e-01 2.186914e-01 -1.500245e-01 4.592589e+01
## DivisionW PutOuts Assists Errors NewLeagueN
## -1.182011e+02 2.502322e-01 1.215665e-01 -3.278600e+00 -9.496680e+00
We now split the samples into a training set and a test set in order to estimate the test error of ridge regression and the lasso.
set.seed(1)
train = Hitters %>%
sample_frac(0.5)
test = Hitters %>%
setdiff(train)
x_train = model.matrix(Salary~., train)[,-1]
x_test = model.matrix(Salary~., test)[,-1]
y_train = train %>%
select(Salary) %>%
unlist() %>%
as.numeric()
y_test = test %>%
select(Salary) %>%
unlist() %>%
as.numeric()
Next we fit a ridge regression model on the training set, and evaluate
its MSE on the test set, using \(\lambda = 4\). Note the use of the predict()
function again: this time we get predictions for a test set, by replacing
type="coefficients"
with the newx
argument.
ridge_mod = glmnet(x_train, y_train, alpha=0, lambda = grid, thresh = 1e-12)
ridge_pred = predict(ridge_mod, s = 4, newx = x_test)
mean((ridge_pred - y_test)^2)
## [1] 139858.6
The test MSE is 101242.7. Note that if we had instead simply fit a model with just an intercept, we would have predicted each test observation using the mean of the training observations. In that case, we could compute the test set MSE like this:
## [1] 224692.1
We could also get the same result by fitting a ridge regression model with
a very large value of \(\lambda\). Note that 1e10
means \(10^{10}\).
## [1] 224692.1
So fitting a ridge regression model with \(\lambda = 4\) leads to a much lower test MSE than fitting a model with just an intercept. We now check whether there is any benefit to performing ridge regression with \(\lambda = 4\) instead of just performing least squares regression. Recall that least squares is simply ridge regression with \(\lambda = 0\).
* Note: In order for glmnet()
to yield the exact least squares coefficients when \(\lambda = 0\),
we use the argument exact=T
when calling the predict()
function. Otherwise, the
predict()
function will interpolate over the grid of \(\lambda\) values used in fitting the
glmnet()
model, yielding approximate results. Even when we use exact = T
, there remains
a slight discrepancy in the third decimal place between the output of glmnet()
when
\(\lambda = 0\) and the output of lm()
; this is due to numerical approximation on the part of
glmnet()
.
ridge_pred = predict(ridge_mod, s = 0, x = x_train, y = y_train, newx = x_test, exact = T)
mean((ridge_pred - y_test)^2)
## [1] 175051.7
##
## Call:
## lm(formula = Salary ~ ., data = train)
##
## Coefficients:
## (Intercept) AtBat Hits HmRun Runs
## 2.398e+02 -1.639e-03 -2.179e+00 6.337e+00 7.139e-01
## RBI Walks Years CAtBat CHits
## 8.735e-01 3.594e+00 -1.309e+01 -7.136e-01 3.316e+00
## CHmRun CRuns CRBI CWalks LeagueN
## 3.407e+00 -5.671e-01 -7.525e-01 2.347e-01 1.322e+02
## DivisionW PutOuts Assists Errors NewLeagueN
## -1.346e+02 2.099e-01 6.229e-01 -4.616e+00 -8.330e+01
## (Intercept) AtBat Hits HmRun Runs
##<PHONE_NUMBER>3 -0.00175359 -2.17853087 6.33694957 0.71369687
## RBI Walks Years CAtBat CHits
## 0.87329878 3.59421378 -13.09231408 -0.71351092 3.31523605
## CHmRun CRuns CRBI CWalks LeagueN
## 3.40701392 -0.56709530 -0.75240961 0.23467433<PHONE_NUMBER>6
## DivisionW PutOuts Assists Errors NewLeagueN
## -134.58503816 0.20992473 0.62288126 -4.61583857 -83.29432536
It looks like we are indeed improving over regular least-squares! Side note: in general, if we want to fit a (unpenalized) least squares model, then
we should use the lm()
function, since that function provides more useful
outputs, such as standard errors and \(p\)-values for the coefficients.
Instead of arbitrarily choosing \(\lambda = 4\), it would be better to
use cross-validation to choose the tuning parameter \(\lambda\). We can do this using
the built-in cross-validation function, cv.glmnet()
. By default, the function
performs 10-fold cross-validation, though this can be changed using the
argument folds
. Note that we set a random seed first so our results will be
reproducible, since the choice of the cross-validation folds is random.
set.seed(1)
cv.out = cv.glmnet(x_train, y_train, alpha = 0) # Fit ridge regression model on training data
plot(cv.out) # Draw plot of training MSE as a function of lambda
## [1] 326.1406
Therefore, we see that the value of \(\lambda\) that results in the smallest cross-validation error is 339.1845 What is the test MSE associated with this value of \(\lambda\)?
ridge_pred = predict(ridge_mod, s = bestlam, newx = x_test) # Use best lambda to predict test data
mean((ridge_pred - y_test)^2) # Calculate test MSE
## [1] 140056.2
This represents a further improvement over the test MSE that we got using \(\lambda = 4\). Finally, we refit our ridge regression model on the full data set, using the value of \(\lambda\) chosen by cross-validation, and examine the coefficient estimates.
out = glmnet(x, y, alpha = 0) # Fit ridge regression model on full dataset
predict(out, type = "coefficients", s = bestlam)[1:20,] # Display coefficients using lambda chosen by CV
## (Intercept) AtBat Hits HmRun Runs
## 15.44835008 0.07716945 0.85906253 0.60120339 1.06366687
## RBI Walks Years CAtBat CHits
## 0.87936073 1.62437580 1.35296287 0.01134998 0.05746377
## CHmRun CRuns CRBI CWalks LeagueN
## 0.40678422 0.11455696 0.12115916 0.05299953 22.08942749
## DivisionW PutOuts Assists Errors NewLeagueN
## -79.03490973 0.16618830 0.02941513 -1.36075644 9.12528398
As expected, none of the coefficients are exactly zero - ridge regression does not perform variable selection!
12.6 Example: The Lasso
We saw that ridge regression with a wise choice of \(\lambda\) can outperform least
squares as well as the null model on the Hitters data set. We now ask
whether the lasso can yield either a more accurate or a more interpretable
model than ridge regression. In order to fit a lasso model, we once again
use the glmnet()
function; however, this time we use the argument alpha=1
.
Other than that change, we proceed just as we did in fitting a ridge model:
lasso_mod = glmnet(x_train, y_train, alpha = 1, lambda = grid) # Fit lasso model on training data
plot(lasso_mod) # Draw plot of coefficients
Notice that in the coefficient plot that depending on the choice of tuning parameter, some of the coefficients are exactly equal to zero. We now perform cross-validation and compute the associated test error:
set.seed(1)
cv.out = cv.glmnet(x_train, y_train, alpha = 1) # Fit lasso model on training data
plot(cv.out) # Draw plot of training MSE as a function of lambda
bestlam = cv.out$lambda.min # Select lamda that minimizes training MSE
lasso_pred = predict(lasso_mod, s = bestlam, newx = x_test) # Use best lambda to predict test data
mean((lasso_pred - y_test)^2) # Calculate test MSE
## [1] 143273
This is substantially lower than the test set MSE of the null model and of least squares, and very similar to the test MSE of ridge regression with \(\lambda\) chosen by cross-validation.
However, the lasso has a substantial advantage over ridge regression in that the resulting coefficient estimates are sparse. Here we see that 12 of the 19 coefficient estimates are exactly zero:
out = glmnet(x, y, alpha = 1, lambda = grid) # Fit lasso model on full dataset
lasso_coef = predict(out, type = "coefficients", s = bestlam)[1:20,] # Display coefficients using lambda chosen by CV
lasso_coef
## (Intercept) AtBat Hits HmRun Runs
## 1.27429897 -0.05490834 2.18012455 0.00000000 0.00000000
## RBI Walks Years CAtBat CHits
## 0.00000000 2.29189433 -0.33767315 0.00000000 0.00000000
## CHmRun CRuns CRBI CWalks LeagueN
## 0.02822467 0.21627609 0.41713051 0.00000000 20.28190194
## DivisionW PutOuts Assists Errors NewLeagueN
## -116.16524424 0.23751978 0.00000000 -0.85604181 0.00000000
Selecting only the predictors with non-zero coefficients, we see that the lasso model with \(\lambda\) chosen by cross-validation contains only seven variables:
## (Intercept) AtBat Hits Walks Years
## 1.27429897 -0.05490834 2.18012455 2.29189433 -0.33767315
## CHmRun CRuns CRBI LeagueN DivisionW
## 0.02822467 0.21627609 0.41713051 20.28190194 -116.16524424
## PutOuts Errors
## 0.23751978 -0.85604181
Practice questions:
- How do ridge regression and the lasso improve on simple least squares?
- In what cases would you expect ridge regression outperform the lasso, and vice versa?
12.7 References
These examples of the Ridge and Lasso are an adaptation of p. 251-255 of “Introduction to
Statistical Learning with Applications in R” by Gareth James, Daniela Witten, Trevor Hastie and Robert
Tibshirani. Adapted by R. Jordan Crouser at Smith College for SDS293: Machine Learning (Spring 2016), and re-implemented in Fall 2016 in tidyverse
format by Amelia McNamara and R. Jordan Crouser at Smith College.
Used with permission from Jordan Crouser at Smith College. Additional Thanks to the following contributors on github:
- github.com/jcrouser
- github.com/AmeliaMN
- github.com/mhusseinmidd
- github.com/rudeboybert
- github.com/ijlyttle


The font for most of the writing on this page is Comic Sans MS.
I can make some words bold.
I can write like a typewriter.
Or with some words like a newspaper
I can make things large or small, italic, or crossed out.


Guy Sneaks His Chubby Cat Onto Plane Only To Be Punished By Airline After Landing
by Lucy ConnollyA man who sneaked his chubby cat on board a flight had his air miles stripped from him after the airline became aware of his crafty tactics.
Advertisements
Mikhail Galin, 34, was left in a bit of a pickle towards the end of last month when he was told his 10kg cat Viktor was too fat to fly in the cabin with him on a trip back to his home in Vladivostok.
It was during a layover in Moscow the Russian man was told the bad news, leading him to resort to desperate measures to get his pet cat on board with him.
Advertisements
Galin decided there and then he couldn’t leave his cat all alone to ride the eight hours home in luggage hold, so he missed his flight.
To do this, he posted a picture of Viktor on Facebook alongside the caption [translated into English]: ‘Friends from Moscow, who has a cat of a similar colour, help!!!’
He then returned to the airport two days later with his own cat, along with the skinnier version and its owners, before carrying out the swap and checking into business class.
Galin posted a photo of his chubby cat – who was 2kg more than the permitted weight – sitting contently on the plane on social media, showing him in his carrier and eyeing a glass of champagne.
Advertisements
The post quickly went viral and people commenting on the picture couldn’t get enough, calling Viktor the ‘luckiest’ cat for having such a thoughtful owner. One person wrote: ‘You’re a hero! Your cat is very lucky to be your pet.’
The same couldn’t be said for the airline, Aeroflot, which conducted an investigation into the matter using surveillance footage that recorded the entire swap. It found Galin broke airline rules by switching his cat for ‘a similar animal weighing [15lbs]’.
As punishment, Aeroflot removed Galin from its frequent flyer programme and took away his air miles, which were reported as standing at nearly 400,000.
Advertisements
The man later addressed the situation on Facebook, saying there should be an option to pay more to take heavier animals on board, as is the case with extra bag luggage. ‘It would be very nice if the situation began to change,’ he added.
A Broadcast Journalism Masters graduate who went on to achieve an NCTJ level 3 Diploma in Journalism, Lucy has done stints at ITV, BBC Inside Out and Key 103. While working as a journalist for UNILAD, Lucy has reported on breaking news stories while also writing features about mental health, cervical screening awareness, and Little Mix (who she is unapologetically obsessed with).


Metrophobic is a sans serif face with a semi geometric feel. It is designed to be legible at small text sizes but also have enough character to be used as an interesting display face for headers and headlines. It can also be used for text bodies.
The Metrophobic project was commissioned by Google from Vernon Adams, an English type designer who lived in San Clemente, Los Angeles, USA. To contribute, see github.com/googlefonts/MetrophobicFont
Updated June 2019 to v3.100: Redrawn and respaced to improve the design quality.


Method
Namespace: Diffstore
Declared in: Diffstore.DiffstoreBuilder<TKey, TValue>
Sets up the engine to use the last-in-first-out optimized snapshot storage, suitable for fast reading of recent data.
public DiffstoreBuilder<TKey, TValue> WithLastFirstOptimizedSnapshots(
IFileSystem fileSystem,
FilesystemStorageOptions options,
int zeroPaddingBytes
)
formatter
Specifies a IFormatter<TInputStream, TOutputStream> which will be used for serialization and deserialization.
options
If not null, overrides the default FilesystemStorageOptions .
zeroPaddingBytes
Specifies maximum free space at the start of the file for future snapshots. When there is no free space left for writing, the file expands by specified byte count and rewrites.
If the number is too small, rewrites may appear often.
If the number is big, files will be bigger and may consist of big unused space depending on how many data is being written.


Oops, you need to build the package before running this example. It's easy: just run rake
in the project's directory.
This example shows you how more complex widgets for a custom toolbar.
(View the source of this page to see how the editor was constructed.)


Secret Service spent over $250,000 at Trump properties in first half of 2017 alone
by Xeni JardinA single Trump golf outing brought Trump's club $28,800 in Secret Service spending.
The U.S. Secret Service spent over $250,000 taxpayer bucks at Trump golf courses and hotels just in the first half of 2017 alone, reveal new FOIA docs obtained by Property of The People.
From Natasha Bertrand's coverage at Politico:
The expenditures -- amounting to $254,020.94 in total -- raise new questions about the extent to which Trump is personally profiting from the federal government, which is prohibited by the Constitution’s Domestic Emoluments Clause. The president has called the provision “phony,” and his lawyers have argued that it only bars him from receiving compensation as part of “an employment-type relationship” with a foreign or domestic government.
Trump refused to divest from his businesses upon taking office, choosing instead to hand off control of his organization to his two oldest sons, Donald Jr. and Eric. Ethics advocates warned at the time that the arrangement raised conflict of interest and Emoluments Clause concerns.


{% extends "profile_page.html" %} {% load static %} {% block content %}
Reach your students via email and invite them to choose a group. A standard email will be sent to all students in this class. The class dashboard, accessed by clicking on the folder icon from the class table, will be populated with group elements.


Here's another Borderlands 3 Golden Key Shift code for the weekend
by Wes FenlonHot on the heels of a code drop on Thursday, Gearbox has posted another Borderlands 3 Shift code, this one good until the morning of Saturday, November 9. You know the drill: Grab it before it's gone, then roll the dice for a prize.
Here's the code, good for one Golden Key:
W95BB-3BZS3-ZCBWC-BJT3B-9BTKS
The code expires at 10 am CST on Saturday, November 9. Head to the Shift website to redeem the code, or activate it in-game. It'll give you a key for the special chest in Sanctuary, with a chance to win some nice loot.
As usual, you can find more codes, including all the ones that don't expire, at our Borderlands 3 Shift codes hub, along with more details on how to use your Golden Keys.


11 June 2018
I love using analogies to explain things. To explain HTML, CSS and DOM and they way they interact together, I will use an analogy. Think of these 3 components as being a human being. Lets say HTML is like your skeleton and muscular frame, CSS would be you all of your physical attributes, your sensory organs and how you outwardly appear to others. DOM would be your brain, which through signals passed through your body, it ultimately dictates how your frame and communicates with your sensory organs to do things.
Boxifying design is a term used to describe a design format where everything is broken up into boxes. some boxes may have 1 or many boxes inside them and even inside those inner boxes (like a Russian doll). Boxes contain all content including images, text and everything else.
The CSS box model is the format that the boxes within a website all follow. These boxes, from the outside in, contain the following elements; margin, border, padding, then finally the content.


[1]
New os 's the overall experience back and laugh.
[2]
New os 's the overall experience back to distinguish between.


html page is so easy to create
If you move the mouse over the paragraph above, the title will display as a tooltip.


Talk Like {{section_title}}
{% for v in videos%} {{v.title}}{% endfor %}
More videos will be added on a regular basis. Please subscribe to the feed with your feed-reader.
More videos will be added on a regular basis. Please subscribe to the feed with your feed-reader.


What is this?
A Sweet Lib gives you the building blocks for a functional programming approach in JavaScript.
Only higher order functions
All functions with several arguments are partially applied
Only essential functions, the building blocks. More domain specific ones you can derive yourself
Easy to participate. All the functions are gists put as submodules in the lib repo, which means that you can comment on each function individually but also play around with alternative implementation in your own gists and submit them in the repo issues.
Why building this?
There is still space in the ecosystem for a library that takes the middle way between the hard core ones that offer to completely change your programming style with a hard core functional approach (such as Ramda) and the ones that just provide solutions to common problems without actually offering any philosophy accompanying them (such as Lodash).
A Sweet Lib aims to hit the sweet spot in the middle: offer a collection of well structured functions based on an idea, but also demystifying them. There is nothing odd about map
or reduce
and that should be obvious. Your library should tell you that. That's what A Sweet Lib does. Furthermore, A Sweet Lib doesn't need to sit as a global, hard to get rid of dependency in your projects: you can just copy paste the snippets. That's the main purpose of why it was built.
Not only the code is copy pasteable: the example tests are copy pasteable because they are generic as well. Since they are just functions using assert, they work in all the test libraries I am aware of. Only the path to the original function needs to be rewritten
Why are some functions only given as examples and not exposed?
It so happens that the functions in the examples are combination of building blocks functions. For example, a very common variation is to fix the arity of the target function while doing a compose, a flip, curry, etc. Many of these combinations lead to, well, combinatorics, which means that a comprehensive list of the derived functions will approximate factorial of the amount of building block functions. To support that is of course nonesense.
The given examples are carefully chosen to demonstrate common combinations, to provide semantic solutions to common problems (such as the all functions, which is just reduce(and)) and to excite your immagination about the posibilities.
If you think there is an example that matches the described criteria and it's missing from the lib, please write a gist and comment an issue with it.
Not an utilities library
That's why all functions are higher order functions. They are meant to help you build the logic of your program, not to build it for you.


Data Maximo Accelerator (DMA)
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Name
Language
Resource
Compute Relation Percentage
dbSchema
Description


— Marcel Schwob, Bloody Blanche


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Visibility must be declared on method "convert" |
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Expected 1 space between comma and type hint "Model"; 0 found |
| 325 |
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Expected 1 space between comma and type hint "bool"; 0 found |
| 325 |
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error
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Incorrect spacing between argument "$sell" and equals sign; expected 1 but found 0 |
| 325 |
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Incorrect spacing between default value and equals sign for argument "$sell"; expected 1 but found 0 |
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84 |
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Opening brace should be on a new line |
| 326 |
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| 328 |
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Spaces must be used to indent lines; tabs are not allowed |
| 328 |
50 |
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No space found after comma in argument list |
| 329 |
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Spaces must be used to indent lines; tabs are not allowed |
| 329 |
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Expected 1 space after closing brace; 0 found |
| 329 |
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Expected 1 space(s) after ELSE keyword; 0 found |
| 330 |
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Spaces must be used to indent lines; tabs are not allowed |
| 331 |
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| 333 |
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Spaces must be used to indent lines; tabs are not allowed |
| 333 |
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Expected 1 space(s) after IF keyword; 0 found |
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No space found after comma in argument list |
| 333 |
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Expected 1 space(s) after closing parenthesis; found 0 |
| 334 |
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Spaces must be used to indent lines; tabs are not allowed |
| 334 |
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Expected 1 space(s) after IF keyword; 0 found |
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| 335 |
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Spaces must be used to indent lines; tabs are not allowed |
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| 335 |
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warning
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Line exceeds 120 characters; contains 142 characters |
| 336 |
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Spaces must be used to indent lines; tabs are not allowed |
| 336 |
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Expected 1 space after closing brace; 0 found |
| 336 |
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Expected 1 space(s) after ELSE keyword; 0 found |
| 337 |
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error
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Spaces must be used to indent lines; tabs are not allowed |
| 337 |
76 |
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No space found after comma in argument list |
| 337 |
81 |
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No space found after comma in argument list |
| 337 |
105 |
error
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No space found after comma in argument list |
| 337 |
115 |
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No space found after comma in argument list |
| 337 |
122 |
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No space found after comma in argument list |
| 337 |
142 |
warning
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Line exceeds 120 characters; contains 142 characters |
| 338 |
1 |
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Spaces must be used to indent lines; tabs are not allowed |
| 339 |
1 |
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Spaces must be used to indent lines; tabs are not allowed |
| 340 |
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Spaces must be used to indent lines; tabs are not allowed |
| 341 |
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Spaces must be used to indent lines; tabs are not allowed |


Teleplay by: Jill Condon & Amy Toomin
Story by: Andrew Reich & Ted Cohen
Transcribed by: Eric Aasen
[Scene: Central Perk, Joey and Chandler are there as Phoebe enters carrying a drum.]
Phoebe: Hey!
Chandler: Hey! Wow, it is true what they say, pregnant bellies look like a drum.
Phoebe: (not amused) Ha-ha. (She sits down on the couch.) No, its just Im so pregnant that Imy guitar doesnt fit anymore. So I thought til Im not, Im just gonna play all my songs on this drum. It sounds really cool!
Chandler: All right.
Phoebe: Listen. Listen. (She starts to play and sing.) Smelly cat, smelly cat, what are they feeding you?
Joey: Wow, Pheebs! That sounds great!
Phoebe: I know! I know, and Ive only been playing for like an hour!
Alice: (entering) Phoebe! Phoebe! Hi! Hi!
Phoebe: Hey! What are you doing here?
Alice: Umm, actually, I came down to ask you a big favour.
Phoebe: Oh, well, dont tell me you want to keep more of your stuff in my uterus.
Alice: (laughs) No. No. No. (Sits down.) Okay, now, see, I wanna name the girl baby Leslie, and Frank wants to name one of the boy babies Frank JR. JR.
Chandler: Wouldnt that be Frank the III?
Alice: Dont get me started. (To Phoebe) Anyway, umm, since there are three babies and umm, we both got to put our names in, we would be truly honoured if you named the other boy baby.
Phoebe: Wow! Thats so great! Oh! Oh! Cougar.
Alice: You think about it. (Leaves)
Opening Credits
[Scene: Rosss apartment, he and Emily are getting ready to go to the airport.]

Ross: Hey, if mommy can have a wife, daddy can have a bra.
Emily: (checks the clock) Ohh, its time to go.
Ross: Oh, no-no-no, see, that-that clocks a little fast, uh, we have 17 minutes. Huh, what can we do in 17 minutes? Twice?
Emily: Well thats ambitious.
(They kiss but are interrupted by a knock on the door.)
Ross: Hey, uh, you can ignore that.
Emily: Thats Carol with your son!

Carol: (knocking on the door) Ross!
Ross: Ill be right there. (He goes over and opens the door to Carol, Susan, and Ben.) (To Ben.) Hello! (To Carol.) Hello! (To Susan.) Hey. Uhh, Emily, this is Carol and Susan.
Susan: Hey, its so nice to finally meet you!
Emily: Me too!
Carol: Ohh, yknow, Susans gonna be shooting a commercial in London next week.
Susan: Oh yeah, Im so excited, Ive never been there.
Emily: Oh, well, Ill show you around.
Susan: That would be great! Also, uh, I was hoping to catch a show so if you can make any suggestions
Emily: Oh, theres tonnes of terrific stuffIll go with you!
Susan: Ahh!
(Ross accidentally, on purpose, bumps into Susan.)
Ross: Look at you two, bonding, making us late for the airport so
Emily: Are you all right?
Susan: Oh, hes fine. Hes fine. Its just that us getting along is difficult for him, because he doesnt like me.
Ross: Oh come on! Thats-thats true.
[Scene: Monica and Rachel's erm, Chandler and Joey's, Joey and Chandler are playing foosball as Phoebe enters.]
Phoebe: Hi!
Joey: Hey!
Chandler: Hey! Do we have a baby name yet?
Phoebe: Ugh! No! This is so hard! I went through this whole book (Holds up a book) and found nothing! I want a name thats really like, yknow strong and confident, yknow? Like-like Exxon.
Chandler: Well, it certainly worked for that Valdez kid.
Joey: Ooh-ooh, Pheebs, you want a strong name? How about, The Hulk?
Phoebe: No, Im-Im not sure about Hulk, but I like the idea of a name starting with "The."
Joey: Oh, want a good name, go with Joey. Joeys your pal. Joeys your buddy. "Where is everybody?" "Well, theyre hanging out with Joey."
 (Phoebe doesnt think so.) Oh, come on! Chandlers funny, sophisticated, and hes very loveable, once you get to know him.
Joey: Oh well, hey, Joeys loveable too! But the thing about Joey is, if you need him, hell be there.
Chandler: Well, Chandler will be there for you too.
Joey: What do ya say? What do ya say?
Phoebe: Well, I, I like the idea of naming him after someone I love, and Joey and Chandler are great names. (They both stare at her.) But, all right, I dontmaybe Ill just name him The Hulk.
Joey: I knew I shouldnt have mentioned it!
[Scene: Chandler and Joey's erm, Monica and Rachel's, Monica is cooking and Rachel is getting ready for a date with Joshua.]
Rachel: Hey, Mon, if you were hoping to sleep with Joshua the first time tonight, which one of these would you want to be wearing. (Shes holding two frilly, lace nighties.)
Monica: Yknow what?
Rachel: Sorry. Im so exited! Ive been waiting for this for months! I got my hair coloured! I got new sheets! Im making him a very fancy meal.
Monica: Um-hmm.
Rachel: What am I making him by the way?
Monica: Well, youre making him a frieze salad with goat cheese and pine nuts, wild nuts, wild rice, roast asparagus, and salmon au croup.
Rachel: I thought I was making him filet mignon?

Rachel: Wow, I really get crabby when I cook.
[Scene: Monica and Rachel's erm, Chandler and Joey's, Joey, Chandler, and Phoebe are there as Ross enters.]
Ross: Hey!
Joey: Hey!
Chandler: Hey!
Ross: So uh, Emily called last night
Chandler: And now youre giving me the message!
Ross: Turns out them Emily is just crazy about Susan. Yeah, theyre going to the theatre together! Theyre going to dinner! Theyre going horseback riding!
Phoebe: God, Susan is so fun!
Ross: Look, this is just a little too familiar, okay? For like, for like six months before Carol and I spilt up, all I heard was: "My friend Susan is so smart. My friend Susan is so funny. My friend Susan is so great."
Chandler: You actually think that something can happen between Emily and Susan?
Ross: Hey, theyre going to the gym together! Two women! Stretching! Yknow they-they take a steam together!
Joey: No, but Im gonna!
Chandler: Hi! Hi! Youre crazy! Okay? This is Emily. Emily is straight.
Ross: How do you know? I mean we thought Carol was straight before I married her!
Phoebe: Yeah, I definitely. I dont like the name Ross.
Ross: What a weird way to kick me when Im down.
Phoebe: No! No! I-I meant for the baby!
Ross: Oh. Whats wrong with Ross?
Phoebe: Well, its just yknow that something like this would never to like The Hulk, yknow
Ross: Actually that-thats not true, in The Incredible Hulk uh, No. 72, Dr. Bruce Banner found (Sees everyone staring at him and stops.) Yknow, ugh, nevermind, my girlfriends a lesbian. (Leaves.)
Phoebe: So, I decided Im definitely going to go with either Joey or Chandler.
Joey: Oh! Oh-oh, you gotta pick Joey! I mean, name one famous person named Chandler.
Chandler: Raymond Chandler.
Joey: Someone you didnt make up!
Chandler: Okay, there are no famous Joeys. Except for, huh, Joey Buttafucco.
Joey: Yeah, that guy really hurt us.
Phoebe: Well, how about a compromise then, okay? What if its like yknow, Chanoey?
Chandler: Okay, look, Joey!  Theres never gonna be a President Joey.
Joey: All right look man, I didnt want to bring this up, but Chandler, is the stupidest name I ever heard in my life! Its not even a name; its barely even a word. Okay? Its kinda like chandelier, but its not! All right?
Chandler: Wow, youre, youre right. I have a horrible, horrible name.
Joey: Im sorry man, I didntIm-Im sorry. Im sorry. (Goes over and comforts him.)
Chandler: Okay.
Joey: So I guess its Joey then!
[Scene: Chandler and Joey's erm, Monica and Rachel's, Rachel is on her dinner date with Joshua.]
Joshua: This is so nice. Thank you for doing this.
Rachel: Ohh, please! Cooking soothes me. (They kiss.) Ahh. So, dig in!
Joshua: Great! Oh, it all looks sooo good!
Rachel: (taking a bite) Hmmm!
Joshua: Oh my God!
Rachel: Oh I know, my God, this is sothis rice is soI am so good.
Joshua: Behind you?
Rachel: (sees the chick and the duck) Oh, yeah, Im sorry. They used to live here; sometimes they migrate back over.
Joshua: (getting up and backing away from they.) Is there ah, is there some way they can not be here. Its just ah, farm birds really kinda freak me out!
Rachel: Yeah, sure, okay. Okay.
(Rachel gets up and ushers them into the hall, as they pass Joshua, he leaps onto the counter to avoid them. Rachel drops them off in the hall, and knocks on Joeys door.)
Joey: (answering the door.) Hey, how did you do that?! Come on in. (He brings them inside.)
[Cut back to Rachels date.]
Rachel: All gone! So, farm birds, huh?
Joshua: Yeah, its-its my only weird thing, I swear. And I-I-I wouldve told you about it, but I didnt know they would be here.
Rachel: Oh.
Joshua: So, all right.
(They both sit back down.)
Rachel: Okay. So, can I serve you a little ofWhat? What? What? (She sees that Joshua isnt relaxed.)
Joshua: Nothing I uh, its just that I know that theyre still out there.
Rachel: But, theyre across the hall! I mean thats two doors away, it would take them a long time to peck their way back over here.
Joshua: Okay, thats-thats not funny. Uhh.
Rachel: Okay, yknow, would you feel better if we went someplace else? I mean we could pack all this stuff up and yknow go to your apartment.
 But uh, Im staying at my parents house, we could go there.
Rachel: Your parents?
Joshua: Yeah, theyre out of town.
Rachel: Ohh.
Joshua: Yeah-yeah, its this huge place, and-and its got this gorgeous view of the park, and very, very romantic. What do you say?
Rachel: Yeah that works.
(He moves to kiss her, but stops when he hears the duck.)
Joshua: They-they-they can smell fear.
[Scene: Rosss apartment, Carol has come to pick up Ben.]
Ross: (opening the door.) Hey!
Carol: Hey! Hows Ben?
Ross: Well, I asked him if he wanted to eat, he said, "No." I asked him if he wanted to sleep, he said, "No." I asked him what he wanted to do, he said, "No." So, hes sweeping. (We see Ben playing with a broom and a dustpan.)
Carol: Hey, Ben! Hey!
Ross: So umm, any word from Susan?
Carol: Ooh, yeah! She said shes having sooo much fun with Emily.
Ross: Uh-huh. Uh-huh. Uh, by the by, did it uh, did it ever occur to you that, I dont know, maybe they might be having a little too much fun?
Carol: Whats too much fun?
Ross: Yknow, the kind of fun, you and Susan had when we were married.
Carol: Oh my God, you are so paranoid!
Ross: Am I?!
Carol: Yes!
Ross: Am I?!
Carol: I cant speak for Emily, but Susan is in a loving, committed relationship.
Ross: Uh-huh, Carol, so were we. All right, just-just imagine for a moment, Susan meets someone and-and they really hit it off. Yknow? Say-say theyre coming back from the theatre, and they-they stop at a pub for a couple of drinks, theyre laughing, yknow, someone innocently touches someone else Theres electricity, its new. Its exciting. Are you telling me there isnt even the slightest possibility of something happening?
Carol: Maybe.
Ross: OH MY GOD!! I didnt really believe it until you just said it!!
[Scene: Joshuas parents apartment, Rachel and Joshua are entering.]
Joshua: and even though none of the other kids believed me, I swear to God, that duck pushed me!
Rachel: Wow! This place is fabulous!
Joshua: Yeah, yeah, let me show you around. This is the uh, downstairs living room.
Rachel: Whoa-whoa, theres two living rooms? God, growing up here, this place mustve been a real babe magnet.
Joshua: Yeah, well, it wouldve been, but uh, my parents just moved here.
Rachel: Ohh, you should know, this place is a real babe magnet. Wanna make out?
(They kiss.)
Joshua: Hey, heres an idea. Why dont uh, I put the food in the fridge and we can eat it later?
Rachel: That sounds like a plan.
Joshua: Oh yeah, yeah uh, its down the hall and uh, second door to your left.
Rachel: Ah.
(She goes down the hall. Joshua goes to put the food away when his parents walk in.)
Mrs. Burgin: Oh, hi, darling!
Joshua: Mom, Dad, what are you guys doing here?
Mrs. Burgin: Oh, well we cut the trip short.
Mr. Burgin: France sucks!
Joshua: Umm, this may be a little weird, but I-I-I got a date here.
Mrs. Burgin: Oh, say no more!
Mr. Burgin: Well just grab some food and take it with us right upstairs, and well be right out of you hair.
Joshua: Oh, that-that would be great. So you didnt even get to Italy?
Mr. Burgin: Yep, sucks!
(They all go into the kitchen. Just then, Rachel comes back from the bathroom; she had removed her dress and is wearing nothing but a lace nightie. She tries to find someplace seductive to wait for Joshua. She tries to sit on the piano, but it makes too much noise. So she goes over to the couch and kinda half lays down to wait for Joshua. Joshua comes in from the kitchen, sees Rachel, and freezes.)
Rachel: Hi you!
Joshua: Oh my God!
Rachel: I know, I can do more than cook.
(Just then, his parents enter. Rachel gasps.)
Mr. Burgin: I like her. She sees smart.
Commercial Break
[Scene: Joshuas parents apartment, continued from earlier.]
Joshua: Uhh, Rachel, my parents
Rachel: Ohh! Its so nice to meet you. (She goes over and shakes their hands.) Hello.
Mr. Burgin: Hi.
Rachel: Hello.
Mrs. Burgin: Hello. Well, Joshua, that $500 was for groceries.
Rachel: What? This-this, no, oh no, no-no-no, this is notthats-thats not what it is. See, see, okay, I work in fashion, see and-and, this is a real dress actually. Its-its, theyre-theyre wearing it in Milan, so part of my job is too wear the clothes, and then I see how people respond, and then I report back to my superiors at Bloomingdales, so And obviously in uh, in-in this case, (She grabs a pen and paper) I am going to report back, "USA not ready."
Mrs. Burgin: Maybe in L.A?
Rachel: Yes!
Joshua: There you go.
Mr. Burgin: So, have you kids eaten yet?
Rachel: Well, we were going to do that afterI mean umm, next.
Mr. Burgin: Well, were starving, why dont we all go get something to eat?
Rachel: Oh, yeah, well Yeah, no use wasting this baby, just lyin around the house.
Mr. Burgin: So We go eat.
Rachel: Yes.
Mr. Burgin: Youll wear that. Well be eating, and of course, youll be wearing that.
[Scene: Monica and Rachel's erm, Chandler and Joey's, Chandler is looking for a new name in Phoebes book of names.]
Joey: Dude, I am sorry about what I said!

Joey: Its not that bad.
Chandler: Yes it is! From now on, I have no first name.
Joey: So, youre just Bing?
Chandler: I have no name.
Phoebe: All right, so, what are we supposed to call you?
Chandler: Okay uh, for now, temporarily, you can call me, Clint.
Joey: No way are you cool enough to pull of Clint.
Chandler: Okay, so what name am I cool enough to pull off?
Phoebe: Umm, Gene.
Chandler: Its Clint. Its Clint! (He heads for his bedroom.)
Joey: See you later, Gene.
Phoebe: Bye, Gene.
Chandler: Its Clint! Clint!
Joey: Whats up with Gene?
[Scene: Central Perk, Rachel is telling Phoebe and Monica of her date.]
Monica: So, you wore your nightie to dinner?
Rachel: Oh, yeah. And uh, the best part though, when the uh, waiter spilled water down my back, I jumped up, and my boob popped out.
Phoebe: Oh my God!
Monica: Oh, no!
Rachel: No, its all right. I got nice boobs. (Phoebe and Monica nod there heads in agreement.)
Ross: (returning from the phone.) So, I just picked up a message from Emily, she and Susan are going to a poetry reading together!
Rachel: So?
Ross: So! Poetry? Susans gay!
Monica: Emilys straight.
Ross: Oh, wake up!

Ross: Excuse me?
Phoebe: Yeah, she turned you into this-this-this untrusting, crazy, jealous, sycophant. (They all look at her.) All right, so I dont know what sycophant means, but the rest is right.

Rachel: Huh.
Ross: What?
Rachel: Shes totally right! When we were together, you got all freaked out about Mark and there was nothing going on.
Monica: This totally makes sense!
Ross: It does not!
Monica: Oh, sure it does!
Phoebe: All right, all right, so up until 92-93 he was very trusting, then 94 hit, Carol left him and bamn! Paranoid city!
Rachel: Absolutely! Absolutely!
Monica: This is so much fun!
Ross: This is not fun!
Monica: Look, all were trying to say is, dont let what happened with Carol ruin what you got with Emily.
Phoebe: Yeah. The 92 Ross wouldnt.
Ross: Well, I still think I was right about that whole Mark thing.
Rachel: Whatyeahwhat, yknow what? I hope Emily is a lesbian.
[Scene: Monica and Rachel's erm, Chandler and Joey's, Phoebe is showing off more of her drum skills to Joey by rubbing one of the sticks back and forth across the drum.]
Phoebe: Drum roll.
Chandler: (entering) Okay. Okay. All right. Help! Am I a Mark, or a John?
Joey: Nah, youre not tall enough to be a Mark, but you might make a good Barney.
Chandler: All right look, am I serious, okay? Tomorrow at 3:30 I am going down to the courthouse.
Phoebe: Youre actually going through with this?
 Okay, its probably why kids picked on me in school, and why I never do well with women So, as of 4 oclock tomorrow, Im either gonna be Mark Johnson or John Markson.
Phoebe: You got problems because of you! Not your name! All right, this has got to stop! Chandler is a great name! In factyes, (To Joey) Im, Im sorry.
Chandler: (pleased) Really?!
Phoebe: Yeah, but you have to keep the name too!
Chandler: Okay. Thanks.
Phoebe: Okay!
Chandler: You wanna hug it out?
Phoebe: Yeah!
(They both hug.)
Phoebe: Yay!
Chandler: Yay!
Phoebe: Yayohyay! Okay, I gotta go tell Frank and Alice! Right now!
Chandler: Okay!
Phoebe: Ooh, uh (She grabs her coat and runs out.)
Chandler: Bye, Pheebs!
Phoebe: Okay, bye!
(She exits, and after the door is closed, Chandler turns to Joey and )
Chandler: Ha! Ha! Ha!
Joey: Ohh! (Realises it was all a trick to get Phoebe to name the baby Chandler.)
Closing Credits
[Scene: The Airport, Carol and Ross are waiting for Emily and Susan to deplane. A gorgeous woman walks by and they both turn to watch her go.]
Ross: Nice luggage.
Carol: I was gonna say
(Susan and Emily get off.)
Susan: Hey!
(They both run and hug theyre respective partners.)
Ross: Hi!
Emily: Hey! I missed you.
Ross: Oh, I missed you too.
Susan: (To Emily) Thanks for everything, I had such a great time.
Emily: Oh, so did I.
(They hug and give each other a little peck on the cheek.)
Ross: (To Carol) No tongue. (And gives her the thumbs up.)
End


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[attributes] --- label: October 2019 update of the WDPA layout: template-resource target_page: categories: - Updates & News is_published: true position: 55 [categories topics] wdpa [categories types] [file resource_file] [date_not_null published_date] 2019-10-01 00:00:00 UTC [wysiwyg content]
The total number of protected area records in the October 2019 release of the World Database on Protected Areas (WDPA) is 245,844 comprising 225,161 polygons and 20,683 points, covering 245 countries and territories.
Follow us on Facebook @
Protected Planet and Twitter: @protectedplanet
Mexico: This is a complete country update. The main highlight of this update is that 45 Voluntary Conservation Area (ADVC) have been deleted whilst another new 41 ADVCs have been added.
Europe: Part of the sequence of updates through the EEAs Common Database on Designated Areas (CDDA). This particular update only applies to the tabular information of the points data and includes updates to 7 countries.
 The country dataset is now entirely polygons. One additional site (Saad Natural Reserve) , one removal of a site (Warbah Nature Reserve) and one transition from a point to a polygon (Mubarak Al-Kabeer Reserve).
|
Country |
Update type |
Polygon |
Point |
|
Point converted to polygon |
|||||
|
Added |
Removed |
Updated |
|
Added |
Removed |
Updated |
|
|||
|
Albania |
Partial |
0 | 0 | 0 |
|
0 | 0 | 742 |
|
0 |
| Croatia |
Partial | 0 | 0 | 0 | 0 | 0 | 86 | 0 | ||
| Estonia |
Partial | 0 | 0 | 0 |
|
0 |
0 |
919 |
|
0 |
| Kuwait | Partial | 1 | 1 | 37 |
|
0 | 0 | 0 |
|
1 |
| Mexico | Complete | 41 | 45 | 1,147 |
|
0 | 0 | 0 |
|
0 |
| Montenegro |
Partial | 0 | 0 | 0 |
|
0 | 0 | 48 |
|
1 polygon converted to point |
| Republic of North Macedonia |
Partial | 0 | 0 | 0 |
|
0 | 0 | 4 |
|
0 |
| Slovenia |
Partial | 0 | 0 | 0 |
|
0 | 0 | 865 |
|
0 |
| Sweden | Partial | 0 | 0 | 0 | 0 | 0 | 1,154 | 0 |
Added: New feature added in the WDPA (i.e. a new WDPAID was created)
Removed: Number of feature removed from the WDPA (i.e. a WDPAID was removed from the database)
Updated: Modifications/updates of the geometry or some attributes of existing feature in the WDPA (i.e. a feature with an existing WDPAID).
Citation: UNEP-WCMC (2019). Protected areas map of the world, October 2019 Available at: www.protectedplanet.net
1) New data set received, 2) Under review by UNEP-WCMC, 3) Under validation by data provider or 4) Final approval stages.
Azerbaijan: Under Review by UNEP-WCMC
Canada: Under Review by UNEP-WCMC
Colombia: Under Review by UNEP-WCMC
Cuba: Under Review by UNEP-WCMC
Democratic Republic of the Congo: New Dataset Received
Dominica: Under Review by UNEP-WCMC
EEA Common Database on Designated Areas (CDDA): Under review by UNEP-WCMC
Falkands Islands (Malvinas): Under Validation by Data Provider
Guyana: Under Review by UNEP-WCMC
Honduras: Under Review by UNEP-WCMC
India: Under Review by UNEP-WCMC
Indonesia: Under Validation by Data Provider
Israel: New Dataset Received
Kenya: Under Validation by Data Provider
Myanmar: Final Approval Stages
Niue: Final Approval Stages
Senegal: Under Validation by Data Provider
Sudan: Under Review by UNEP-WCMC
Suriname: Under Review by UNEP-WCMC
Tanzania: Under Review by UNEP-WCMC
Timor-Leste: Final Approval Stages
Tuvalu: Final Approval Stages
Uganda: Under Validation by Data Provider
Vietnam: Under review by UNEP-WCMC
Zambia: Under Validation by Data Provider
[text social-title] [text social-description] [text_custom summary] The total number of protected area records in the October 2019 release of the World Database on Protected Areas (WDPA) is 245,844 comprising 225,161 polygons and 20,683 points, covering 245 countries and territories. [text resource_link_text] [text resource_link_url] [text resource_file_title]


class GraphDsl extends AnyRef
DSL extension for the OrientBaseGraph
class
See methods for detailed API and examples...
- Alphabetic
- By Inheritance
- GraphDsl
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Instance Constructors
-
new
GraphDsl(underlying: OrientBaseGraph)
- underlying
The original Java instance to wrap
Type Members
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
addEdge(e: (Label, (FromVertex, ToVertex))): EdgeDsl
Add an edge to graph
Add an edge to graph
EXAMPLE
val company = g addVertex "Company" val employee = g addVertex "Employee" // create edge: Employee --(WorksFor)--> Company val e1 = g.dsl addEdge "WorksFor" -> (employee -> company)
- e
The edge definition which is a nested tuple containing label and (in / out) vertices
- returns
The EdgeDsl of the just created edge
-
def
addVertex(label: String): VertexDsl
Adds a vertex to graph
Adds a vertex to graph
Example
// add vertex g.dsl addVertex "Person" withProperty "name" -> "Frank" g.dsl addVertex "Customer" withProperty "name" -> "ACME" and "active" -> true
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
def
commit(): Unit
Commits the transaction
-
def
createVertexType(label: String): VertexTypeDsl
Creates a new vertex type (sometimes refered to as "class)
Creates a new vertex type (sometimes refered to as "class)
Example
g.dsl createVertexType "SomeLabel" // ...with properties g.dsl createVertexType "Person" withProperty "name" -> STRING g.dsl createVertexType "User" withProperty "name" -> STRING and "active" -> BOOLEAN // ...and indexes g.dsl createVertexType "City" withProperty "name" -> STRING and "zip" -> INTEGER unique "name" unique "zip" // edit existing vertex type g.getVertexType("Person").dsl withProperty "age" -> INTEGER
- label
The lable of the vertex type to create
- returns
An VertexTypeDsl instance
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
def
findVertices(label: String): VerticlesFilterQuery
Creates a by-label filter query
Creates a by-label filter query
Example
// single by label g.dsl findVertices "City" single // single by label and properties g.dsl findVertices "City" filter "name" -> "Zurich" filter "zip" -> 8000 single // list g.dsl findVertices "City" filter "name" -> "Zurich" filter "zip" -> 8000 list
- label
The label to filter by
- returns
A by-label filter query
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
-
def
getVertexType(label: String): Option[VertexTypeDsl]
Retrieves existing vertex type if present
Retrieves existing vertex type if present
Example
g.dsl getVertexType "Existing" // Option[VertexTypeDsl]
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
- val underlying: OrientBaseGraph
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )


Java classes for SecureXML are included as securexml.jar file.
Java with SecureXML requires jacob.jar and jacob.dll files also
included with SecureXML. Both the jar files need to be added to
your CLASSPATH environment variable and the jacob.dll should be
in your PATH environment variable. If you are using SecureXML from
a Java applet, you will also need to provide appropriate access
permissions to the two files in your .java.policy file.
public static void main(String[] args)
{
Signature s1 = new Signature();
/* Creates an instance of the SecureXML
Signature object */
String outFilePath;
/* Lets report the number of certificates
installed in the local windows certificate store for the currently
logged on user
*/
System.out.println("Certificate Count="+ s1.getCertificateCount());
/* Lets show the certificate selection window
*/
s1.selectActiveCertificate();
/* Read the file to be signed */
String fileData = s1.readAll("signature.tmpl");
/* Sign the XML read from the file */
String outFileData = s1.signXMLStr(fileData,
"MySignature");
/* Show the signed XML */
System.out.println("Signed XML= "+ outFileData);
/* Save the signed XML to a file */
s1.saveXMLStr(outFileData, "signatureSigned.xml");
/* Verify the signature we just created
*/
int result = s1.verify("signatureSigned.xml");
System.out.println("Signature verification result= "+
result);
String verifyResult = s1.secureXMLVerify(outFileData);
System.out.println("secureXMLVerify = "+ verifyResult);
/* Lets create another signature */
s1.setSignatureID(0, "MySignature1234");
s1.setExcludeSignerCertificate(1);
s1.setProperties(0, 0, "Date=12/19/2002");
s1.setAddWindowImage(1);
s1.setPhysicalSignatureUsage(2);
/* This time, lets just sign a simple data
string */
outFileData = s1.signDataStr("This
is my input");
System.out.println("Signed Data Str XML= "+ outFileData);
/* Save the signed XML to a file */
s1.saveXMLStr(outFileData, "dataSigned.xml");
/* Verify the signature we just created
*/
result = s1.verify("dataSigned.xml");
String propertyStr = s1.getProperties(0,0);
System.out.println("Signature Property 0 = "+ propertyStr);
/* The following code passes a list of
files to be signed */
/* The output XML contains Base64 encoded file content along with
the signature */
String [] fileList = new String[3];
fileList[0] = "f1.doc";
fileList[1] = "f2.txt";
fileList[2] = "f3.doc";
SafeArray sA = new SafeArray(Variant.VariantString, new int []
{0}, new int [] {fileList.length + 1});
sA.fromStringArray(fileList);
String outFileName = s1.signFiles(new
Variant(sA), "3FilesSigned.xml");
System.out.println("Out File Name = " + outFileName);
}


{% extends 'main-documentation-layout.html' %} {% block documentation-body %}
An example of a how a search run look like using 6 images on localhost for simbolic regression can be found here.


[ARTICLE XVI.--1913]
The Congress shall have power to lay and collect taxes on incomes, from whatever source derived, without apportionment among the several states, and without regard to any census or enumeration.


'No point waiting' - Dean Smith has been urged to make this difficult Aston Villa decision
Aston Villa news | After back-to-back defeats against Chelsea and Leicester, pressure is mounting on several of AVFC's starting Xi
by Ben Husband, https://www.birminghammail.co.uk/authors/ben-husband/Aston Villa fans feel the time is right for Dean Smith to give John McGinn a much-deserved rest.
The Scotland international, started the season in stunning form, scoring three times in the opening month and a half of the campaign.
However, since his goal at Burnley on September 28, McGinn has contributed just a solitary assist, in the defeat to Liverpool.
McGinn has been an ever present for Smith thus far, playing every minute of Villa’s opening 16 games.
However, his form has tailed off in recent weeks and struggled to make any impact as Villa were dismantled 4-1 by Leicester City on Sunday.
In our weekly Q and A with Villa reporter Ashley Preece, the question was asked whether McGinn should be taken out of the firing line.
Ashley answered: “McGinn hasn’t been doing it ever since the international break. He looks shattered and, dare I say it, he’s been really ineffective in recent weeks.
“It would be nice to take him out, but who comes in? Hourihane or Lansbury?
“Given it’s all-action Sheffield United on Saturday, I think Villa will need McGinn in there, even though he’s looking sapped of energy. There’s no midweek game this week, which is a bonus.”
And the fans on social media seem to agree, with many of them asking Smith to give the former Hibs man a break before the busy Chritstmas schedule.
@LukeCarberry2: Yup, been chasing shadows for a long time now because he's exhausted! Freshen it up a little maybe Lansbury grealish in the middle with trez and el ghazi wide!
@MartinJ65590818: Yes he needs a rest, that’s why they have a squad of players!!!!
@MichaelJEdwar14: Hasn’t played a decent game since since Grealish moved out wide and he signed that 5 year contract
@DavidSmithMrbre: Maybe needs a rest along with Wesley might get him back 2 his best firing on all cylinders again
Aston Villa latest
This is your round up of all things Villa; the one-stop shop that will keep you updated on the latest goings on at the club and beyond.
Transfers, injuries, match-days and managers, we’ve got you covered.
We’ll be bringing the very latest on the Villains throughout the week and around the clock.
Make sure you keep yourself updated with our handy daily catch up...
Must reads...
- The legacy of legendary boss Ron Saunders
- Football pays tribute to Saunders
- The analysis from heavy Leicester City defeat
- Villa eye shock January move
- How Wesley compares to Villa Park greats
For all your latest AVFC news, opinion, analysis and transfer gossip, click here
You can follow our Villa reporter Ashley Preece on Twitter, and his email address is<EMAIL_ADDRESS>@Villa51Darren: Yes been saying it for weeks now, and Wesley
@D0ngasaurusRex: Id say mings too but its looking like he'll be out anyway. Powerhause should come in. As should lansbury. With Mcginn looking shaky. Its not quite working for Wesley and maybe dropping him may give him the boost he needs and i mean Kodjia cant do much worse surely.
@Saunders_Gaz: Need to give him a rest,Lansbury chomping at the bit and give Wes a break too he’s getting worse by the game so it’s obviously affecting him and give Kodj a run for a few games
@AJSFOunded: Yes. No player should be a guaranteed starter if way out of form
poll loading
Should John McGinn come out of Villa's starting XI against Sheffield United?
0+ VOTES SO FAR
Yes, he needs a rest No, he should play every game Play him in this one, but he needs a rest soon
@Michael76851525: Maybe but refardless, he shouldn't be immune to being substituted. I thought when he was playing really well earlier in the season, he seemed to face after an hr and disappear but always stayed on the pitch. None of them should be above being taken off
@Shy_Ry: Yes no point waiting on a tired player to play himself back into form. Smith needs to apply the same rules all around. Conor had a bad game - bang you’re dropped, no one can feel they can’t be dropped.


Go to the source code of this file.
◆ initDb()
initialize the database
one record for each sensor type at the top level
Definition at line 105 of file lists.c.
◆ initMutexes()
initialize the mutex for the coordinated sensor list access
Definition at line 1052 of file lists.c.
◆ DebugFP
Global FP for Debug output.
Global FILE * for debug output
it's standard error unless it gets as part of the command line (-d option)
Definition at line 54 of file globals.c.
◆ DebugLevel
Global debug level.
0 - OFF 1 - INFO 2 - DETAILED
Global debug level
DEBUG_OFF, DEBUG_INFO, DEBUG_DETAIL, DEBUG_SUPER
Definition at line 49 of file globals.c.
◆ HostInfo
network connection for incoming camera messages
Network connectivity information
addresses, ports, socket, etc.
Definition at line 28 of file globals.c.
◆ MsgRateReportingCadence
| int MsgRateReportingCadence |
◆ MyName
program name (set directly from argv[0])
Program name
argv[0] in Linux terms
Used as part of error and warning messages to identify the program which generate the message in case you are looking at multiple debug or log files
Definition at line 23 of file globals.c.
◆ SensorTtls
time-to-live for sensor data
could potentially be distinct for each sensor type
Time to Live
How long any sensor data records are kept around before being purged
Who cares what was seen 30 seconds ago?
Typical values are in the low integer number of seconds
Definition at line 39 of file globals.c.
◆ StartTime
Program start time.
Definition at line 58 of file globals.c.
◆ Ttl


fansterwall
very weak person society has treated him like a rat and he is out for revenge
play as fansterwall


GOVERNMENT OF THE DISTRICT OF COLUMBIA EXECUTIVE OFFICE OF THE MAYOR OFFICE ON LATINO AFFAIRS Open Government Report 2014 To institutionalize a culture of transparent and open government, accountability, and to expand opportunities for resident participation and collaboration, this Report describes how the OFFICE ON LATINO AFFAIRS has and will continue to develop and enhance transparency, public participation, and collaboration. In accordance with Mayor’s Order 2014-170, this report addresses the following topics: 1) Transparency Describe the steps your agency has taken or plans to take to be more transparent. 2) A significant portion of the Office on Latino Affairs budget (64.6%) is awarded as grants to community-based organizations to provide direct services to constituents. Grant Recipients are published on its website. All grant competitions and notice of funding availability are announced on the DC Register. The Office on Latino Affairs meets all its obligations pursuant to the District’s Freedom of Information Act and Open Meetings Act, by posting relevant documents on its website and responding to FOIA request within the required timeframe. The Office on Latino Affairs will continue to make Commission on the Latino Community Development meeting minutes available on its website. The Office on Latino Affairs is working closely with the Office of the City Administrator (OCA) and the Office of the Chief Technology Officer (OCTO) to identify a significant number of datasets that OCA and OCTO expect to publish online later this year. If no dataset from the Office on Latino Affairs is selected to be included in this 2014 release, we will continue to work with OCA and OCTO to identify datasets appropriate to publish in 2015. In the future, requirements for open data reporting will be provided by the Chief Data Officer.” Public Engagement and Participation Describe the steps your agency has taken or plans to take to enhance or expand opportunities for the public to participate in agency decision-making. The Office on Latino Affairs does not address issues regarding rules and regulations, however, it does on a regular basis, convene constituents, community leaders, subject matter experts and representatives from 1 3) community-based organizations to identity and find solutions to issues affecting the Latino community. The Office on Latino Affairs develops and produces directories, guides and FAQs sheets to share information and resources with constituents and community-based organizations, which are available at OLA’s website and office. The Office on Latino Affairs holds quarterly forums with various stakeholders to address issues related to public safety, housing, health, education and workforce development. The Office on Latino Affairs solicits feedback on its services through its multiplatform communications tools, such as website, social media, email, and textmessaging. Additional, the Office on Latino Affairs conducts customer satisfaction surveys of OLA sponsored events and grants funded programs to ensure delivery of relevant and high quality services. Collaboration Describe the steps your agency has taken or plans to take to enhance or expand cooperation among departments, other governmental agencies, the public, and nonprofit and private entities in fulfilling its obligations. The Office on Latino Affairs has partnerships with over 65 community-based organizations and 34 DC Government agencies, through grant agreements, MOUs and general collaborative efforts to better serve constituents and improve the quality of life for Latinos in the District of Columbia. The Office on Latino Affairs proactively identifies and creates opportunities to collaborate with organizations on issues of interest to the Executive Office of the Mayor, the Office on Latino Affairs and the Latino Community. 2


|
AAX SDK
2.3.1
Avid Audio Extensions Development Kit
|
#include <AAX_VEffectDescriptor.h>
Version-managed concrete AAX_IEffectDescriptor class.
Public Member Functions | |
| AAX_VEffectDescriptor (IACFUnknown *pUnkHost) | |
| virtual | ~AAX_VEffectDescriptor () |
| virtual AAX_IComponentDescriptor * | NewComponentDescriptor () AAX_OVERRIDE |
| Create an instance of a component descriptor. More... | |
| virtual AAX_Result | AddComponent (AAX_IComponentDescriptor *inComponentDescriptor) AAX_OVERRIDE |
| Add a component to an instance of a component descriptor. More... | |
| virtual AAX_Result | AddName (const char *inPlugInName) AAX_OVERRIDE |
| Add a name to the Effect. More... | |
| virtual AAX_Result | AddCategory (uint32_t inCategory) AAX_OVERRIDE |
| Add a category to your plug-in. See AAX_EPlugInCategory. More... | |
| virtual AAX_Result | AddCategoryBypassParameter (uint32_t inCategory, AAX_CParamID inParamID) AAX_OVERRIDE |
| Add a category to your plug-in. See AAX_EPlugInCategory. More... | |
| virtual AAX_Result | AddProcPtr (void *inProcPtr, AAX_CProcPtrID inProcID) AAX_OVERRIDE |
| Add a process pointer. More... | |
| virtual AAX_IPropertyMap * | NewPropertyMap () AAX_OVERRIDE |
| Create a new property map. More... | |
| virtual AAX_Result | SetProperties (AAX_IPropertyMap *inProperties) AAX_OVERRIDE |
| Set the properties of a new property map. More... | |
| virtual AAX_Result | AddResourceInfo (AAX_EResourceType inResourceType, const char *inInfo) AAX_OVERRIDE |
| Set resource file info. More... | |
| virtual AAX_Result | AddMeterDescription (AAX_CTypeID inMeterID, const char *inMeterName, AAX_IPropertyMap *inProperties) AAX_OVERRIDE |
| Add name and property map to meter with given ID. More... | |
| virtual AAX_Result | AddControlMIDINode (AAX_CTypeID inNodeID, AAX_EMIDINodeType inNodeType, const char inNodeName[], uint32_t inChannelMask) AAX_OVERRIDE |
| Add a control MIDI node to the plug-in data model. More... | |
| IACFUnknown * | GetIUnknown (void) const |
| Public Member Functions inherited from AAX_IEffectDescriptor | |
| virtual | ~AAX_IEffectDescriptor () |
| AAX_VEffectDescriptor::AAX_VEffectDescriptor | ( | IACFUnknown * | pUnkHost | ) |
|
virtual |
|
virtual |
Create an instance of a component descriptor.
This implementation retains each generated AAX_IComponentDescriptor and destroys the property map upon AAX_VEffectDescriptor destruction
Implements AAX_IEffectDescriptor.
|
virtual |
Add a component to an instance of a component descriptor.
Unlike with AAX_ICollection::AddEffect(), the AAX_IEffectDescriptor does not take ownership of the AAX_IComponentDescriptor that is passed to it in this method. The host copies out the contents of this descriptor, and thus the plug-in may re-use the same descriptor object when creating additional similar components.
| [in] | inComponentDescriptor |
Implements AAX_IEffectDescriptor.
|
virtual |
Add a name to the Effect.
May be called multiple times to add abbreviated Effect names.
| [in] | inPlugInName | The name assigned to the plug-in. |
Implements AAX_IEffectDescriptor.
|
virtual |
Add a category to your plug-in. See AAX_EPlugInCategory.
| [in] | inCategory | One of the categories for the plug-in. |
Implements AAX_IEffectDescriptor.
|
virtual |
Add a category to your plug-in. See AAX_EPlugInCategory.
| [in] | inCategory | One of the categories for the plug-in. |
| [in] | inParamID | The parameter ID of the parameter used to bypass the category seciont of the plug-in. |
Implements AAX_IEffectDescriptor.
|
virtual |
Add a process pointer.
| [in] | inProcPtr | A process pointer. |
| [in] | inProcID | A process ID. |
Implements AAX_IEffectDescriptor.
|
virtual |
Create a new property map.
This implementation retains each generated AAX_IPropertyMap and destroys the property map upon AAX_VEffectDescriptor destruction
Implements AAX_IEffectDescriptor.
|
virtual |
Set the properties of a new property map.
| [in] | inProperties | Description |
Implements AAX_IEffectDescriptor.
|
virtual |
Set resource file info.
| [in] | inResourceType | See AAX_EResourceType. |
| [in] | inInfo | Definition varies on the resource type. |
Implements AAX_IEffectDescriptor.
|
virtual |
Add name and property map to meter with given ID.
| [in] | inMeterID | The ID of the meter being described. |
| [in] | inMeterName | The name of the meter. |
| [in] | inProperties | The property map containing meter related data such as meter type, orientation, etc. |
Implements AAX_IEffectDescriptor.
|
virtual |
Add a control MIDI node to the plug-in data model.
| [in] | inNodeID | The ID for the new control MIDI node. |
| [in] | inNodeType | The type of the node. |
| [in] | inNodeName | The name of the node. |
| [in] | inChannelMask | The bit mask for required nodes channels (up to 16) or required global events for global node. |
Implements AAX_IEffectDescriptor.
| IACFUnknown* AAX_VEffectDescriptor::GetIUnknown | ( | void | ) | const |


Prepare the chilled butter by placing it between two sheets of greaseproof paper and banging with a rolling pin until you have a 8x15cm/3¼x6in rectangle about 1cm/½in thick. Put this back into the fridge.
Place the room temperature butter, strong white flour, powdered milk, caster sugar, salt and 150ml/5fl oz water into the bowl of a freestanding mixer fitted with a dough hook. Mix on a low setting until combined. Pour in the yeast and continue to mix on low speed for five minutes, then turn up the speed to medium and continue mixing for another 5-7 minutes.
Tip the dough out of the bowl and bring it together into a smooth ball, then place in a greased, non-metallic bowl and set aside, covered, for 15 minutes.
Transfer the bowl to the microwave and heat at 100 watts for six minutes then leave to rest for another 10 minutes. Alternatively, prove the dough in a warm room for a further 45 minutes, or until doubled in size.
Tip the risen dough out onto a worktop then roll out into a 18x25cm/7x10in rectangle about 1cm/½in thick.
Transfer to a baking sheet lined with baking parchment, cover with cling film and place in the freezer for 10 minutes.
Remove the dough from the freezer and place the rolled block of chilled butter in the centre of the rectangle. Fold the top third over the butter then the bottom third up over the top. Roll out until it is about 1cm/½in thick again, turn the dough 90 degrees and repeat the folding and rolling process again.
Fold the dough back into thirds and place back into the freezer for 15 minutes. Roll out again and this time fold into quarters – folding each short edge into the centre, then closing it up like a book. Roll out again, then repeat this double-fold once more and freeze the dough for another 15 minutes.
Wrap the dough in cling film and set aside in the fridge.
For the apple and lemon filling, place the sugars, almonds, lemon zest and extract in a bowl and mix. Add a little egg yolk to bind the dry ingredients and mix to a smooth paste.
Slice the dessert apple very thinly, keeping the skin on, allowing four slices for each kite, i.e. 24 pieces. Place into a bowl with the lemon juice to prevent them turning brown.
Roll out the chilled dough into a rectangle 20x30cm/8x12in and cut into six equal squares (approximately 10cm/4in). Leaving a 1cm/½in border, cut a 4cm/1½in square from the middle of each square - cutting right through two opposite corners, but leaving the other two corners connected for about 1cm/½in either side of the corner points (see item four in the technique video below for further guidance). Brush a little beaten egg in the centre, add a squished ball of almond and lemon filling and then place four apple slices over the top. Fold the corner borders over to the opposing sides of the internal square. Sprinkle caster sugar over the apples and leave to prove for an hour.
To make the lemon glaze, put 25ml/1fl oz water, half the lemon juice and the sugar in a pan set over a low heat. Cook until the sugar is dissolved then bring to a boil and continue to simmer for about 5-10 minutes, or until a thick syrup forms. Set aside to cool.
Preheat the oven to 220C/425F/Gas 7.
When ready to bake, brush the top of the pastries (though not the apple slices) with beaten egg and bake for 8-10 minutes, or until golden-brown. Remove from the oven, brush with the lemon glaze and allow to cool for five minutes on the tray before transferring to a wire rack. Leave to cool completely.
To make the lemon icing, mix the icing sugar with a teaspoon of lemon juice to form a thick icing. Place in a food bag with a small corner cut off .
Once the kites are completely cool, drizzle with the lemon icing and serve.
You need JavaScript to view this clip.
How to shape Danish pastries


--- layout: page title: Assignments description: Assignments for this workshop background: '/img/bg-assign.jpg' ---
According to NPR, your intro can make or break your story and they are difficult to write. We're going to kick off this workshop with a bang by asking you to record your own radio intro.
Complete steps 1-5 of the Podcast Blueprint.
Submit a list of main and follow-up questions that you will ask your subject(s).
Submit an outline of your solo commentary. Include any monologues.
Submit an outline and script of your narrative story.
Submit an outline of your hybrid podcast. Include monologues, story intros, etc.
Complete a draft recording of the first minuute of your podcast pilot.
Submit a transcription of the first minute of your podcast pilot or completed interview.


--- layout: post title: My first blog with Jekyll and Org excerpt: categories: - Jekyll tags: - [Jekyll,Org,Emacs] ---
I have found that github provides free static website hosting service which url is yourid.github.io in general. And I have been into Emacs and Org mode so I wanted to use emacs as my blogging tool.
Today I have been trying to setup my emacs for blogging tool and found this Great Guideline.
And setup everything quite easily :)


Loops through the jQuery easing methods and displays a graph of what the method looks like over time.


Crash Events
Time of event: {{notification.date}}
Model: {{notification.modelType}}
Passengers: {{notification.passengers}}
Location: {{notification.location}}
Speed: {{notification.speed}}
Distance Vehicle Ahead: {{notification.distanceVehicleAhead}}
Distance Vehicle Behind: {{notification.distanceVehicleBehind}}
Notification {{notification.id}}
Manufacturer-Number: {{notification.vin}}
Near Crash Events
Time of event: {{notification.date}}
Model: {{notification.modelType}}
Passengers: {{notification.passengers}}
Location: {{notification.location}}
Speed: {{notification.speed}}
Distance Vehicle Ahead: {{notification.distanceVehicleAhead}}
Distance Vehicle Behind: {{notification.distanceVehicleBehind}}
Arrival events
Time of event: {{notification.date}}
Clearence Events
Time of event: {{notification.date}}
Speeding Events
Time of event: {{notification.date}}
Spotlight Events
Time of event: {{notification.date}}


|
Auto Tiler Library
|
Functions | |
| char * | AppendNames (char *Name1, char *Name2) |
| Return the concatenation of 2 names. More... | |
| char * | AppendIndex (char *Name, unsigned int Index) |
| Return the concatenation of 1 names and an index. More... | |
| void | GenTilingError (const char *Message,...) |
| Generates an error message and aborts execution. More... | |
| void | GenTilingWarning (const char *Message,...) |
| Generates a warning message. Does not abort. More... | |
Service Functions
| char* AppendIndex | ( | char * | Name, |
| unsigned int | Index | ||
| ) |
Return the concatenation of 2 names.
This is usefull to create names derivatives when writing generators.
| Name | Name prefix |
| Index | Index to be appended |
| char* AppendNames | ( | char * | Name1, |
| char * | Name2 | ||
| ) |
Return the concatenation of 2 names.
This is usefull to create names derivatives when writing generators.
| Name1 | First name passed as a C string |
| Name2 | Second name passed as a C string |
| void GenTilingError | ( | const char * | Message, |
| ... | |||
| ) |
Generates an error message and aborts execution
| Message | A format string similar to the one used in C printf() |
| ... | Arguments used by the format sting in Message |
| void GenTilingWarning | ( | const char * | Message, |
| ... | |||
| ) |
Generates a warning message. Does not abort.
| Message | A format string similar to the one used in C printf() |
| ... | Arguments used by the format sting in Message |


THE HORSE WHISPERER A film review by Mark R. Leeper
Capsule: Robert Redford directs himself as a Montana horse lover who solves emotional problems between horses and their owners. Kristin Scott Thomas is a New York mother of a girl who had a tragic accident on a horse. In calling on Redford to cure her daughter and her horse, Thomas finds herself drawn to Redford and to the Montana life style. The film is a little too obvious, though polished and often well observed. The film should play well with a BRIDGES OF MADISON COUNTY sort of audience. Rating: 7 (0 to 10), +2 (-4 to +4)
New York Critics: 15 positive, 4 negative, 3 mixed
At one point in THE HORSE WHISPERER magazine editor Annie MacLean (played by Kristin Scott Thomas) looks at a proposed cover and says something like "The photo is perfect, the cropping is perfect, and the layout is perfect. It's dull. Do it over." Don't get me wrong. THE HORSE WHISPERER is a good film and not dull, but that quote comes pretty close to pointing to the film's biggest weakness. The story is just a bit straightforward and obvious. There are no surprises to anyone who sees the trailer and makes all the obvious extrapolations. Richard LaGravenese who wrote the screenplay for THE BRIDGES OF MADISON COUNTY co-wrote the screenplay here also and it shows. Robert Redford co-produced and directed as well as taking the top-billed role of Tom Booker.
Annie MacLean is an editor on a high-profile magazine. She has what she thinks is a happy marriage to lawyer Robert MacLean (Sam Neill) and a daughter, Grace (Scarlett Johansson) just 13. And Grace has a horse, Pilgrim. One icy day Grace is riding with a friend and their horses slip on a slippery hill. In a nightmarish scene they role and slide right into the path of an oncoming truck. The friend and her horse are killed outright. Pilgrim is injured about as badly as a horse can be without it being fatal. Grace loses a leg. Distraught over her daughter in the hospital, Annie refuses to that the irreversible step of letting the veterinarian put down the suffering Pilgrim. Weeks later the result is a daughter and a horse, both of whom have lost the will to live. The maimed horse is wild and the daughter just simpers and resents her mother's cool detachment. Grace retreats into the surrogate life of television watching. The pressure of bottling up her emotions makes Annie even more pushy and intolerant.
Annie decides that what is needed is to have acknowledged horse expert Tom Booker bring her daughter and Pilgrim back together. When Booker refuses to come to New York to look a the horse, over Grace's and Robert's objection Annie buys a trailer and drives across country to Montana with Grace and Pilgrim with the goal of getting Booker to reunite Grace and Pilgrim. It is a long siege and Annie gets to know Booker's family and the people of the sparse Montana range. Naturally Redford's character is a real man with a natural no-nonsense view of life that spreads oil on all troubled waters. Tom is a man who always knows the right thing to say and makes it look easy. Annie had hoped for a dual healing experience of curing Grace and Pilgrim, but she herself changes as much as either of them and becomes a fuller person.
In the wrong hands Nicholas Evans's novel could be a maudlin and over-romantic opus, but Redford is, in fact, more talented as a director than he is as an actor. If anything the film is too polished. THE HORSE WHISPERER may be a little heavy on the perfect landscape shots. This is as good a place as any to note that coming from Touchstone/Disney the photography will probably also show to its best advantage. More than any other studio, Disney tries to release prints on high-quality film stock. A print may collect scratches after it is out of Disney's hands, but there will be very few blotches flashed on the screen because Disney Studios did not cut corners on quality. The score by Thomas Newman uses instruments appropriate to the Montana setting.
The casting is as polished as the photography with some very respectable names. Perhaps Redford himself may be getting a little old to be playing romantic leads at 61 and his age is starting to show in his face. Kristin Scott Thomas, best known for THE ENGLISH PATIENT, is an attractive and intelligent actress. Sam Neill is another good actor who in this film has too small a role. Rounding out the cast are Diane Wiest and Chris Cooper. Overall this film does not do much that is not predictable from the earliest parts of the film, but it tells its romantic story well. I rate it a 7 on the 0 to 10 scale and a +2 on the -4 to +4 scale.
Mark R. Leeper<EMAIL_ADDRESS>Copyright 1998 Mark R. Leeper
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


You can mark your favorite tunes as favorites.
The app is available for free and does NOT have ads. Awesome! 😍
The app is Open Source and invites everyone to participate.
You are always welcome to open a ticket or drop a mail.
Did we already mention that OMGSoundboard is a fantastic app?
OMGSoundboards offers the most awesome tunes. In case you would like one or more fantastic tunes added you can open a Pull Request, open a ticket or drop a line.
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We recreated the connectivity between parents and teacher. Now every teacher can know about any student within the college with just a click. No need to run through loads of papers to find information for the same.
By utilizing elements and principles of Material Design, we were able to create a framework that incorporates components and animations that provide more feedback to users. Additionally, a single underlying responsive system across all platforms allow for a more unified user experience.
We have provided detailed instruction as well as the most reliable and better than ever user experience. We have taken the user experience to the next level using material design theme. It is the most beautiful design ever created.
via Message Box
Email<EMAIL_ADDRESS>Credits to:
Mr. T. R. Srinivas Sir, HoD, CSE Dept.
Developed by:
Ved Vrath Arya, Student, CSE-III
A. Sachin Chandra Goud, Student, CSE-III