Spark is cool. It's like writing code and never having to worry about parallelism. This is nice if you have a cluster but also if your local machine has multiple cores and sufficient memory. No need for extra packages or workarounds, the api will handle the scaling automatically.
Show of force
Suppose I run the following parallel spark code;
val count = spark.parallelize(1 to 100000000).map{i =>
val x = Math.random()
val y = Math.random()
if (x*x + y*y < 1) 1 else 0
}.reduce(_ + _)
The moment that I run this code, the top
command in my terminal will show that I'm almost reaching the full 800% of 8-core mac cpu power.
That's 8x more computing power that's available to you as a programmer just by following the standard spark api. No multicore library needed.
More numbers
To show the potential gain in performance I've set up two benchmarks to compare local spark to local python. One will count bins from a uniform distribution through and the other will aggregate a large data file. I will use the ipython notebook %%time
magic and the following scala function to measure the performance:
def time[A](f: => A) = {
val s = System.nanoTime
val ret = f
println("time: "+(System.nanoTime-s)/1e6+"ms")
ret
}
Binning random numbers
Python Code 3min 23s
The quickest way for python without extra hassle involves using vectorized numpy.
[len(i) for i in pd.cut(np.random.uniform(0,1,10000000),100)]
Scala Spark Code 25.2s
Notice that the scala spark code hardly has to be verbose. You only need to appreciate the functional method chaining.
sc.parallelize(1 to 100000000)
.map(_ => scala.util.Random.nextDouble())
.map(x => (x - x % 0.01, 1))
.reduceByKey( (a,b) => a + b )
.collect()
Fast Scala Spark Code
Spark can even be quicker. The documentation is not entirely up to par but there is support for creating random variables instead of casting random variables as an intermediate .map
step. It's like using numpy
instead of normal python but in spark with scala.
import org.apache.spark.SparkContext
import org.apache.spark.mllib.random.RandomRDDs._
uniformRDD(sc, 100000000, 10).sum()
Handling large files
I took a 1.1Gb .csv file from my local drive to do some benchmarks as well. Here's a small hint at performance for just opening the file and doing a linecount:
Python 13.3 s
df = pd.read_csv("/some/path/largefile.csv")
df.shape
Scala Spark 1.8s
val txtfile = sc.textFile("/some/path/largefile.csv")
txtfile.count()
Bash 1.0s
$ time wc -l /some/path/largefile.csv
real 0m1.002s
user 0m0.786s
sys 0m0.213s
Aggregation
Aggregation would make a better indication of performance in a real life scenario. Here I aggregate the sum on the third column of a file and show the top 100.
Python 11.1s
For the python code I excluded the 13.3s needed to load in the file.
df.groupby(['col3']).count().sort("col3").head(100)['col3']
Scala Spark 4.6s
val txtfile = sc.textFile("/some/path/largefile.csv")
txtfile.map(line => line.split(" ")(3)).map( x => (x, 1) ).reduceByKey((a,b) => a + b)
.map(item => item.swap)
.sortByKey(false, 1)
.map(item => item.swap)
.take(100)
Conclusion
Benchmarks are always stenchmarks because there are ways to get more juice out of pandas. Still, the numpy/pandas stack of python is one of the most performant pieces of python and with some basic examples spark seems to be outperforming without any trouble.
The api of numpy and pandas still seems more convenient for middle sized data. For bigger datasets, spark provides an easy enough api that is fast and flexible. I've only scratched the surface here as spark also offers scalable machine learning algorithms as well as graph analysis tools.
Did I mention you can also run all of this on a Hadoop cluster? Profit.


iPath Series B Bloomberg Cotton Subindex Total Return ETN (NYSEARCA:BAL) Stock Price Down 1%
by Amy SteeleiPath Series B Bloomberg Cotton Subindex Total Return ETN (NYSEARCA:BAL) was down 1% during mid-day trading on Monday . The company traded as low as $38.71 and last traded at $38.71, approximately 130 shares changed hands during mid-day trading. A decline of 97% from the average daily volume of 4,426 shares. The stock had previously closed at $39.12.
The stock’s 50 day moving average price is $39.21 and its two-hundred day moving average price is $38.51.
See Also: Trading Ex-Dividend Strategy


This Horizontal Progress bar doesn't use the built-in adjusting but uses a custom click
event listener (which you can see below) in order to make the adjusting
animated.
<script src="RGraph.common.core.js"></script> <script src="RGraph.hprogress.js"></script>Put this where you want the chart to show up:
<canvas id="cvs" width="600" height="100"> [No canvas support] </canvas>This is the code that generates the chart - it should be placed AFTER the canvas tag(s):


Preparation time
over 2 hours
Cooking time
1 to 2 hours
Serves
Serves 6-8
Dietary
Chocolate truffles seem so indulgent, but are simple to make. Try them with cinder toffee, also known as honeycomb toffee, for a truly sweet treat.
For the chocolate truffles, place the cream into a saucepan and bring to the boil.
Put the chocolate into a bowl, then when the cream is boiling pour it over the chocolate and mix well until the mixture is smooth.
Set aside to cool, then cover and place into the fridge to chill and become firm for about two hours.
Sift the cocoa powder onto a plate and, using a teaspoon, curl balls of chocolate and drop straight into the cocoa and then roll to coat them
Place on a plate to firm up, then serve, or keep in a sealed container in the fridge until ready to serve.
For the cinder toffee, line a baking tray with baking parchment.
Place the sugar, honey, glucose and 50ml/1¾fl oz of water into a saucepan and bring to a boil.
Continue to cook until the temperature reaches 160C/320F on a sugar thermometer.
Remove from the heat, allow to cool for 30 seconds so that the bubbles disperse, then quickly beat in the bicarbonate of soda, stirring constantly.
Pour onto the lined baking tray and leave to cool for 30 minutes.
Break into shards and store in a sealed container until you want to serve it.


“He went home as a foster to Doug, one of the two men who found him on the side of the highway,” The Animal Foundation tweeted Thursday. “Doug plans to adopt (him) after Dudley recovers to provide him with a loving furever home!”
The dog was found bleeding from his mouth around noon Sunday on U.S. Highway 95 near Paiute Drive, according to the Nevada Highway Patrol. Dudley, as he was named, was taken to The Animal Foundation, at 655 N. Mojave Road, where vets determined he had likely been shot in the face.
Animal Foundation veterinarian Dr. Ken Sieranski said Dudley had surgery Wednesday to remove bullet fragments and to adjust fractured teeth. The dog is now in recovery mode.
NHP troopers said Friday morning that efforts to find the responsible person are ongoing.
“Animal cruelty is a felony in Nevada, and these perpetrators need to be brought to justice,” Trooper Jason Buratczuk said Tuesday in a news release.
Doug and another person gave Dudley a blanket and a bowl of water, which was already stained red with blood when a trooper arrived.
“It’s unthinkable that someone would do something like this to an animal,” Buratczuk said.
“We don’t know who the owner of this dog is,” he said. “If (Dudley) was shot, this is a crime.”
How to help
Anyone with information about Dudley’s owners may call the Highway Patrol at<PHONE_NUMBER> or The Animal Foundation at<PHONE_NUMBER>. To leave an anonymous tip, call Crime Stoppers at<PHONE_NUMBER>.
The Animal Foundation has been accepting donations to help pay for Dudley’s surgery and care. To donate, go here.


{-| Vinyl is a general solution to the records problem in Haskell using type level strings and other modern GHC features, featuring static structural typing (with a subtyping relation), and automatic row-polymorphic lenses. All this is possible without Template Haskell. Let's work through a quick example. We'll need to enable some language extensions first: >>> :set -XDataKinds >>> :set -XPolyKinds >>> :set -XTypeApplications >>> :set -XTypeOperators >>> :set -XTypeFamilies >>> :set -XFlexibleContexts >>> :set -XFlexibleInstances >>> :set -XNoMonomorphismRestriction >>> :set -XGADTs >>> :set -XTypeSynonymInstances >>> :set -XTemplateHaskell >>> :set -XStandaloneDeriving >>> import Data.Vinyl >>> import Data.Vinyl.Functor >>> import Control.Applicative >>> import Control.Lens hiding (Identity) >>> import Control.Lens.TH >>> import Data.Char >>> import Test.DocTest >>> import Data.Singletons.TH (genSingletons) >>> import Data.Maybe Let's define a universe of fields which we want to use. First of all, we need a data type defining the field labels: >>> data Fields = Name | Age | Sleeping | Master deriving Show Any record can be now described by a type-level list of these labels. The @DataKinds@ extension must be enabled to autmatically turn all the constructors of the @Field@ type into types. >>> type LifeForm = [Name, Age, Sleeping] Now, we need a way to map our labels to concrete types. We use a type family for this purpose. Unfortunately, type families aren't first class in Haskell. That's why we also need a data type, with which we will parametrise 'Rec'. We also generate the necessary singletons for each field label using Template Haskell. >>> :{ type family ElF (f :: Fields) :: * where ElF Name = String ElF Age = Int ElF Sleeping = Bool ElF Master = Rec Attr LifeForm newtype Attr f = Attr { _unAttr :: ElF f } makeLenses ''Attr genSingletons [ ''Fields ] instance Show (Attr Name) where show (Attr x) = "name: " ++ show x instance Show (Attr Age) where show (Attr x) = "age: " ++ show x instance Show (Attr Sleeping) where show (Attr x) = "sleeping: " ++ show x instance Show (Attr Master) where show (Attr x) = "master: " ++ show x :} To make field construction easier, we define an operator. The first argument of this operator is a singleton - a constructor bringing the data-kinded field label type into the data level. It's needed because there can be multiple labels with the same field type, so by just supplying a value of type @ElF f@ there would be no way to deduce the correct "f". >>> :{ let (=::) :: sing f -> ElF f -> Attr f _ =:: x = Attr x :} Now, let's try to make an entity that represents a human: >>> :{ let jon = (SName =:: "jon") :& (SAge =:: 23) :& (SSleeping =:: False) :& RNil :} Automatically, we can show the record: >>> print jon {name: "jon", age: 23, sleeping: False} And its types are all inferred with no problem. Now, make a dog! Dogs are life-forms, but unlike humans, they have masters. So, let’s build my dog: >>> :{ let tucker = (SName =:: "tucker") :& (SAge =:: 9) :& (SSleeping =:: True) :& (SMaster =:: jon) :& RNil :} Now, if we want to wake entities up, we don\'t want to have to write a separate wake-up function for both dogs and humans (even though they are of different type). Luckily, we can use the built-in lenses to focus on a particular field in the record for access and update, without losing additional information: >>> :{ let wakeUp :: (Sleeping ∈ fields) => Rec Attr fields -> Rec Attr fields wakeUp = rput $ SSleeping =:: False :} Now, the type annotation on @wakeUp@ was not necessary; I just wanted to show how intuitive the type is. Basically, it takes as an input any record that has a 'Bool' field labelled @sleeping@, and modifies that specific field in the record accordingly. >>> let tucker' = wakeUp tucker >>> let jon' = wakeUp jon >>> tucker' ^. rlens @Sleeping sleeping: False >>> tucker ^. rlens @Sleeping sleeping: True >>> jon' ^. rlens @Sleeping sleeping: False We can also access the entire lens for a field using the rLens function; since lenses are composable, it’s super easy to do deep update on a record: >>> let masterSleeping = rlens @Master . unAttr . rlens @Sleeping >>> let tucker'' = masterSleeping .~ (SSleeping =:: True) $ tucker' >>> tucker'' ^. masterSleeping sleeping: True A record @Rec f xs@ is a subtype of a record @Rec f ys@ if @ys ⊆ xs@; that is to say, if one record can do everything that another record can, the former is a subtype of the latter. As such, we should be able to provide an upcast operator which "forgets" whatever makes one record different from another (whether it be extra data, or different order). Therefore, the following works: >>> :{ let upcastedTucker :: Rec Attr LifeForm upcastedTucker = rcast tucker :} The subtyping relationship between record types is expressed with the '<:' constraint; so, 'rcast' is of the following type: > rcast :: r1 <: r2 => Rec f r1 -> Rec f r2 Also provided is a "≅" constraint which indicates record congruence (that is, two record types differ only in the order of their fields). In fact, 'rcast' is actually given as a special case of the lens 'rsubset', which lets you modify entire (possibly non-contiguous) slices of a record! Consider the following declaration: > data Rec :: (u -> *) -> [u] -> * where > RNil :: Rec f '[] > (:&) :: f r -> Rec f rs -> Rec f (r ': rs) Records are implicitly parameterized over a kind @u@, which stands for the "universe" or key space. Keys (inhabitants of @u@) are then interpreted into the types of their values by the first parameter to 'Rec'<EMAIL_ADDRESS>An extremely powerful aspect of Vinyl records is that you can construct natural transformations between different interpretation functors @f,g@, or postcompose some other functor onto the stack. This can be used to immerse each field of a record in some particular effect modality, and then the library functions can be used to traverse and accumulate these effects. Let\'s imagine that we want to do validation on a record that represents a name and an age: >>> type Person = [Name, Age] We\'ve decided that names must be alphabetic, and ages must be positive. For validation, we\'ll use 'Maybe' for now, though you should use a left-accumulating @Validation@ type (the module @Data.Either.Validation@ from the @either@ package provides such a type, though we do not cover it here). >>> :{ let goodPerson :: Rec Attr Person goodPerson = (SName =:: "Jon") :& (SAge =:: 20) :& RNil :} >>> :{ let badPerson = (SName =:: "J#@#$on") :& (SAge =:: 20) :& RNil :} We\'ll give validation a (rather poor) shot. >>> :{ let validatePerson :: Rec Attr Person -> Maybe (Rec Attr Person) validatePerson p = (\n a -> (SName =:: n) :& (SAge =:: a) :& RNil) <$> vName <*> vAge where vName = validateName $ p ^. rlens @Name . unAttr vAge = validateAge $ p ^. rlens @Age . unAttr validateName str | all isAlpha str = Just str validateName _ = Nothing validateAge i | i >= 0 = Just i validateAge _ = Nothing :} Let\'s try it out: >>> isJust $ validatePerson goodPerson True >>> isJust $ validatePerson badPerson False The results are as expected (@Just@ for @goodPerson@, and a @Nothing@ for @badPerson@); but this was not very fun to build. Further, it would be nice to have some notion of a partial record; that is, if part of it can\'t be validated, it would still be nice to be able to access the rest. What if we could make a version of this record where the elements themselves were validation functions, and then that record could be applied to a plain one, to get a record of validated fields? That\'s what we’re going to do. >>> type Validator f = Lift (->) f (Maybe :. f) Let\'s parameterize a record by it: when we do, then an element of type @a@ should be a function @Identity a -> Result e a@: >>> :{ let lift f = Lift $ Compose . f validateName (Attr str) | all isAlpha str = Just (Attr str) validateName _ = Nothing validateAge (Attr i) | i >= 0 = Just (Attr i) validateAge _ = Nothing vperson :: Rec (Validator Attr) Person vperson = lift validateName :& lift validateAge :& RNil :} And we can use the special application operator '<<*>>' (which is analogous to '<*>', but generalized a bit) to use this to validate a record: >>> let goodPersonResult = vperson <<*>> goodPerson >>> let badPersonResult = vperson <<*>> badPerson >>> isJust . getCompose $ goodPersonResult ^. rlens @Name True >>> isJust . getCompose $ goodPersonResult ^. rlens @Age True >>> isJust . getCompose $ badPersonResult ^. rlens @Name False >>> isJust . getCompose $ badPersonResult ^. rlens @Age True So now we have a partial record, and we can still do stuff with its contents. Next, we can even recover the original behavior of the validator (that is, to give us a value of type @Maybe (Rec Attr Person)@) using `rtraverse`: >>> :{ let mgoodPerson :: Maybe (Rec Attr Person) mgoodPerson = rtraverse getCompose goodPersonResult :} >>> let mbadPerson = rtraverse getCompose badPersonResult >>> isJust mgoodPerson True >>> isJust mbadPerson False -} {-# OPTIONS_GHC -fno-warn-unused-imports #-} module Data.Vinyl.Tutorial.Overview where import Data.Vinyl.Core import Data.Vinyl.Functor import Data.Vinyl.Lens


Start Bootstrap has everything you need to get your new website up and running in no time! All of the templates and themes on Start Bootstrap are open source, free to download, and easy to use. No strings attached!
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Photos: Ileana D'Cruz flaunts her sexy abs in a three-piece suit and fans say 'Hottie'!
by TIMESOFINDIA.COMIleana D’Cruz is busy dishing out one lewk after another, case in point being her latest three-piece suit ensemble. The actress was on fleek with her striped suit and we are in awe of her sartorial choice. The custom made suit perfectly complimented her sexy abs and envious figure. As soon as the actress shared her OOTD on Instagram, fans flooded her post with comments of ‘hotness’ and ‘hottie’ and Ileana perfectly justified the praises in her sexy avatar.
Ileana D’Cruz enjoys massive popularity on social media and is not just hitting headlines for her glamorous photoshoots, but she was also in the news for parting ways from beau Andrew Kneebone. The actress had told a news portal that after her break-up she went to a therapist and while she is emerging positive from the heart-break, Ileana stated that she is not ready for another relationship.
The actress is about town busy promoting her upcoming film ‘Pagalpanti’. Directed by Anees Bazmee, the film also features John Abraham, Anil Kapoor, Kriti Kharbanda, Pulkit Samrat, Arshad Warsi, Saurabh Shukla and others. ‘Pagalpanti’ is scheduled to hit the screens on November 22.
Image Courtesy: Ileana D'Cruz Instagram
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| add_stat {gtsummary} | R Documentation |
The function allows a user to add a new column with a custom, user-defined statistic.
add_stat( x, fns, fmt_fun = NULL, header = "**Statistic**", footnote = NULL, new_col_name = NULL )
x |
tbl_summary object |
fns |
list of formulas indicating the functions that create the statistic |
fmt_fun |
for numeric statistics, |
header |
Column header of new column. Default is |
footnote |
Footnote associated with new column. Default is no footnote (i.e. NULL) |
new_col_name |
name of new column to be created in |
The custom functions passed in fns=
are required to follow a specified
format. Each of these function will execute on a single variable from tbl_summary()
.
Each function must return a single scalar or character value of length one.
Each function may take the following arguments: foo(data, variable, by, tbl)
data=
is the input data frame passed to tbl_summary()
variable=
is a string indicating the variable to perform the calculation on
by=
is a string indicating the by variable from tbl_summary=
, if present
tbl=
the original tbl_summary()
object is also available to utilize
The user-defined does not need to utilize each of these inputs. It's
encouraged the user-defined function accept ...
as each of the arguments
will be passed to the function, even if not all inputs are utilized by
the user's function, e.g. foo(data, variable, by, ...)
Example 1
Example 2
# Example 1 ---------------------------------- # this example replicates `add_p()` # fn returns t-test pvalue my_ttest <- function(data, variable, by, ...) { t.test(data[[variable]] ~ as.factor(data[[by]]))$p.value } add_stat_ex1 <- trial %>% select(trt, age, marker) %>% tbl_summary(by = trt, missing = "no") %>% add_p(test = everything() ~ t.test) %>% # replicating result of `add_p()` with `add_stat()` add_stat( fns = everything() ~ my_ttest, # all variables compared with with t-test fmt_fun = style_pvalue, # format result with style_pvalue() header = "**My p-value**" # new column header ) # Example 2 ---------------------------------- # fn returns t-test test statistic and pvalue my_ttest2 <- function(data, variable, by, ...) { tt <- t.test(data[[variable]] ~ as.factor(data[[by]])) # returning test statistic and pvalue stringr::str_glue( "t={style_sigfig(tt$statistic)}, {style_pvalue(tt$p.value, prepend_p = TRUE)}" ) } add_stat_ex2 <- trial %>% select(trt, age, marker) %>% tbl_summary(by = trt, missing = "no") %>% add_stat( fns = everything() ~ my_ttest2, # all variables will be compared by t-test fmt_fun = NULL, # fn returns and chr, so no formatting function needed header = "**Treatment Comparison**", # column header footnote = "T-test statistic and p-value" # footnote ) # Example 1 ----------------------------------


Blocs, procs and lambdas in Ruby
Can you repeat please?
July 21st, 2015
Blocks, procs and lambdas are different types of closures in Ruby. In human language it simply refers to ways of repeating code. Blocks are usually well understood but procs and lambdas can be more mysterious. Hopefully this little explanation will help.
Blocs
They allow us to apply a specific code to a series of elements. They are recognizable under 2 forms, using {} or do…end.
array = [1, 2, 3, 4] array.cycle do |n| n*2 end # => [1, 4, 6, 8]
So, what happens here?
- First, we send the cycle method to an Array with a block of code (you remember the cycle method, right?)
- The code block interacts with a variable used within the cycle method (n in this case) and multiple it by 2.
- Each element inside the array is now doubled.
Procs
Blocs are great but what if we want to use the same bloc in several parts of a well-respected DRY code? We use procs! Procs are like blocks but can be saved and reused later in the code.
array = [1, 2, 3, 4] p = Proc.new { |n| puts n*2 } array.each(&p) # => [1, 4, 6, 8]
So, what happens here?
- First, we create a proc using Proc.new and write the block to be saved.
- The proc is then called on a method using &p.
- Each element inside the array is now doubled.
Lambdas
Procs and lambdas are very similar and are often used interchangeably. There are 2 main differences between both. First, procs do not check for the number of arguments when lambdas do, here is how it looks:
lam = lambda { |x| puts x } proc = Proc.new { |x| puts x } lam.call(1) # prints out 1 proc.call(1) # prints out 1 lam.call # ArgumentError: wrong number of arguments (0 for 1) proc.call # returns nil lam.call(1,2,3) # ArgumentError: wrong number of arguments (3 for 1) proc.call(1,2,3) # prints out 1 and forgets about the extra arguments
The second difference is that procs and lambdas treat return differently. While proc return will only work at the first method and return the value provided, lambdas will return their value and continue through the rest of the code.
def lambda lambda { return "Block string" }.call return "method is finished" end def proc Proc.new { return "Block string"}.call return "method is finished" end puts lambda # => Block string puts proc # => method is finished
This second difference makes procs more difficult to work with than lambdas, Ruby experts usually recommend working with the latter. I’m writing about these concepts as I’m learning about them, so as usual any feedback is more than welcome!


org.apache.spark.sql.execution
Interfaces
BinaryNode
Command
LeafNode
RunnableCommand
UnaryNode
Classes
AddExchange
Aggregate
AggregateEvaluation
BatchPythonEvaluation
BigDecimalSerializer
CacheTableCommand
DescribeCommand
Distinct
EvaluatePython
Except
Exchange
ExecutedCommand
ExistingRdd
ExplainCommand
ExternalSort
ExtractPythonUdfs
Filter
Generate
GeneratedAggregate
HyperLogLogSerializer
IntegerHashSetSerializer
Intersect
KryoResourcePool
Limit
LogicalRDD
LongHashSetSerializer
OpenHashSetSerializer
OutputFaker
PhysicalRDD
Project
PythonUDF
RDDConversions
Sample
SetCommand
Sort
SparkLogicalPlan
SparkPlan
SparkSqlSerializer
SparkStrategies
TakeOrdered
UncacheTableCommand
Union
Exceptions
QueryExecutionException


Pega Certified Senior System Architect (CSSA) - Web Developer - Full Stack Developer
Experienced Application Developer with a demonstrated history of working in the insurance industry. Skilled in Pega PRPC, Full Stack Web Development (HTML5, CSS3, JavaScript, jQuery, Java, Bootstrap, Express.js, React.js, Node.js, SQL), Moble Development, and Test Automation.
Pega Certified Senior Architect (CSSA) working at Genworth Financial as an Application Developer. Strong background in QA Testing, Test Automation and Automation Frameworks using Selenium and Katalon.


| Name | Kind | Description |
| targetIntervals | relation | |
| targetTransition | attribute | |
| targetType | attribute | |
| targetTypes | attribute | |
| TemperatureCelsius | attribute | |
| threshold | attribute | |
| time | attribute | |
| TimeOfDayData | artifact | |
| TimeOfDayData | class | |
| TimeOfDayData | operation | |
| timestamp | attribute | |
| TimeToWalk | operation | |
| ToastBroadcastReceiver | class | |
| ToastBroadcastReceiver | artifact | |
| toString | operation | |
| trigger | relation | |
| Trigger | operation | |
| Trigger | class | Triggers basically just link conditions to actions. If data relevant to the condition changes, the Trigger checks if the condition is satisfied, and if so executes the action. |
| Trigger | artifact | |
| triggerList | relation | |
| TriggerNotConnectedException | class | |
| TriggerNotConnectedException | artifact | |
| TriggerNotConnectedException | operation | |
| triggers | package | |
| triggers | class view | |
| triggers | deployment view | |
| turnOffCalendarManager | operation | |
| turnOffLocationManagers | operation |


I'm currently taking a deep learning course, which used learning the XOR function as its first example of feedforward networks. The XOR function has the following truth table
| \(x\) | \(y\) | \(x \oplus y\) |
|---|---|---|
| 0 | 0 | 0 |
| 0 | 1 | 1 |
| 1 | 0 | 1 |
| 1 | 1 | 0 |
which when graphed, is not linearly separable (1s cannot be separated from the 0s by drawing a line)
So if a linear model won't work, I guess that means we need a nonlinear one. We can do this by using the \(relu(x)\) activation function on the outputs of our neurons. \(relu(x)\) is defined as
\[relu(x) = \max\{0, x\}\]
and graphed below.
However, since \(relu(x)\) has sharp corners, it is not differentiable at \(x = 0\), so gradient based learning methods won't work as well. So we use the \(softplus(x)\) function instead, which is a softened version of \(relu(x)\) defined as
\[softplus(x) = \log(1 + \exp(x))\]
shown below.
We begin with your usual imports
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
Then define the inputs and expected outputs of the neural network
inputs = np.array([[0, 0],
[0, 1],
[1, 0],
[1, 1]])
xor_outputs = np.array([0, 1, 1, 0])
Next, we define the structure of the neural network. Note that I had to increase the learning rate from the default value.
XOR = Sequential()
XOR.add(Dense(2, activation='softplus', input_dim=2))
XOR.add(Dense(1, activation='sigmoid'))
# Make the model learn faster (take bigger steps) than by default.
sgd = SGD(lr=0.1)
XOR.compile(loss='binary_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
This defines the network
where the hidden layer activation function is \(softplus(x)\) and the output layer activation function is the traditional sigmoid function used to output a number between 0 and 1, indicating the probability of the output being a logical 0 or a logical 1. Note that Keras does not require us to explicitly form the input layer.
Now we actually train the network.
XOR.fit(inputs, xor_outputs, epochs=5000, verbose=0)
cost, acc = XOR.evaluate(inputs, xor_outputs, verbose=0)
print(f'cost: {cost}, acc: {acc * 100}%')
print(XOR.predict(inputs))
which outputs
cost: 0.007737404201179743, acc: 100.0%
[[0.00496492]
[0.9978434 ]
[0.98019916]
[0.00380662]]
Training the network on other boolean functions work exactly the same way, so much so that the only difference is using a different output array.
This was my first experience with a neural network, so here are some things that I learned for your amusement:
Note that boolean functions are bad functions for neural networks to learn. This is because their domain and ranges are discrete and (typically) small. Learning the function takes more time and space than simply listing a truth table.


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Personal Boundaries
Personal Boundaries refer to the rules and limits that we set for ourselves in regard to our relationships with others.
You can think of boundaries as a spectrum with porous boundaries on one side and rigid boundaries on the other.
Healthy boundaries fall somewhere in between these 2 extremes.
Healthy boundaries include being able to say “no” to others when you want or need to, but also being comfortable opening up to others and forming close relationships.
Someone with rigid boundaries keeps themselves at a distance from others while someone with porous boundaries gets too involved with others.
People are multi-faceted, so someone may have porous boundaries in one area of their lives while having rigid boundaries in another.
Most people have different boundaries depending on the situation.
A healthy range for boundaries would be to set them at 20% on the low end and 80% on the high end, depending on your personality and the specific situation.
How To Achieve Healthy Boundaries Within a Community
Healthy boundaries are essential to maintaining peace within a community or workplace.
Being completely porous and emotionally raw can be as problematic as being rigid and closed off from others.
We can create healthy boundaries by being mindful of our emotional state and setting intentions.
Mindfulness:
Being mindful and aware of our emotional state is a key element for successfully working with others.
For example, if we are having an emotionally raw day we need to first be aware of this in order to manage it so that we don’t explode and cause problems within the community.
Setting intentions and managing your emotional state:
We need to manage our emotional states to successfully work with others and maintain peace.
We can’t always wear our hearts on our sleeves or be distant and unapproachable when working with others.
Before entering a situation, you can focus your mind to set your personal shield strength.
Imagine your personal walls and either build them or take them down depending on your current emotional state.
If you are feeling emotionally raw then build your walls to guard yourself.
If you are feeling overly guarded, then take down your walls in order to process the needs and concerns of others.
If you find that you aren’t able to successfully manage your emotional state in this way then it is best to take a break if possible.
It is not necessarily easy and it takes some work, but creating healthy boundaries is essential in order to successfully work with other community members and to ultimately maintain peace.
This page is synchronized from the post: ‘The Peace Academy: Curriculum on Personal Boundaries (Part 1)’


You have two options to run and use coal, by downloading an OS optimized release, or by building from source.
First, install or build Coal using the instructions below. Next, take a look at examples and read up on the Command-line interface (CLI), Layouts, Pages, and Assets
By downloading a release binary, you do not need Rust installed. Find Coal for your OS on the releases page here.
Once installed using Operating System specific directions below, use Coal as normal, see CLI for more info.
Copy the binary into a directory of your choice, below we copy into /usr/local/bin/
.
sudo cp coal /usr/local/bin/coal
Copy the coal.exe into a folder that is already on the PATH
, or add it to a new folder and register
that new folder to your PATH
.
sudo cp coal /usr/local/bin/coal
You will need to have Rust 2018+ stable installed, and Cargo (which is probably likely if you have Rust).
The crate for Coal can be installed via the Cargo interface, and will be installed to ~/.cargo/bin/
.
If you have Rust installed then this folder is already part of your PATH
.
$ cargo install coal
Check your installed coal version like so:
$ coal --version
You can update Coal to a new release with the --force
flag.
# check your coal version like so: $ coal --version $ cargo install coal --force
You will need to have Rust 2018+ stable installed, we recommend version 1.47.0
or higher, but it
should work on any 2018+ version.
The source code for Coal can be downloaded from the git repo on GitHub.
$ git clone git@github.com:camsjams/rust-coal.git
You can run Coal directly from the project by using cargo run [MODE] [ARGS]
. See CLI for more info.
# Use Coal's local development server $ cargo run serve my_source # Build your Coal project into static HTML for deployment $ cargo run build my_source my_destination
You can compile Coal into a binary for use on your machine.
$ cargo build --release
A binary for Coal will be built in target/release/coal
.
Copy this binary into a directory of your choice, below we copy into /usr/local/bin/
.
sudo cp target/release/coal /usr/local/bin/coal
Use Coal as normal, see CLI for more info.
# Use Coal's local development server $ coal serve my_source # Build your Coal project into static HTML for deployment $ coal build my_source my_destination


Facelet compiler package, not for extension.
{@link org.apache.myfaces.view.facelets.compiler.Compiler Compilers} are used in conjunction with a
{@link org.apache.myfaces.view.facelets.FaceletFactory FaceletFactory} implementation.
You may register multiple {@link org.apache.myfaces.view.facelets.tag.TagLibrary TagLibraries} and
{@link jakarta.faces.view.facelets.TagDecorator TagDecorators} in order to define how the
document will be parsed with the Compiler instance. TagLibraries packaged with Facelets
will be loaded for you automatically and bound to their appropriate namespaces.
Compiler compiler = new SAXCompiler();
compiler.setTrimmingWhitespace(true);
compiler.addTagLibrary(new MyTagLibrary());
String alias = "index.html";
URL url = faces.getExternalContext().getResource(alias);
FaceletHandler handler = compiler.compile(url, alias);
A Compiler instance may be used multiple times and is thread safe for compilation.


WELCOME
Do you know the original Nokia 3310 Snake game?
This is quite similar but with some additional ruels:
1. you must pick up the light colored apples
2. do not pick up the dark colored apples
3. avoid hitting the wall or the black apples
START
Score:
0


name:
surename:
date of birth:
gender:
woman
man
OK
name
surename
date of birth
remove
all
women
men
show age


Decorator setter @Input
Decorator stops undefined
values if they were passed
into setter input. If it gets undefined
, setter will
not be called and it will show an error message about the problem in
console in dev mode.
See console. Min value is 5
You can also pass contract function of type
TuiBooleanHandler<T>
with the component instance
as this
. If check is unsuccessful, it shows an error
message about the problem in console in dev mode.
Warning: Angular Compiler does not allow
arrow functions in decorators with
strictMetadataEmit
(by default). Add
// @dynamic
comment right above your
@Component
decorator to use them
Add @tuiRequiredSetter
to setter input of your
component


vr-components/templates examples
Boxes
(inlined)
Boxes
(imported)
Lots of objects
(inlined)
Lots of objects
(imported)


Email:
Password:
Weekly Notification
Calendar Notification
Activity Notification
GiftCardcard_giftcard:
GiftCardcard_giftcard Pin:
RewardCardcard_membership:
Zip:
Phonelocal_phone:
Carrier: T-Mobile Verizon Sprint AT&T Other
Name:
Date:
Location: Lehi - Thanksgiving Point Vineyard - Geneva Sandy - Jordan Commons South Jordan - The District
IMDB:
Title:
MovieId:


How do you succeed?
1. Know what you want to acheive. Set Goals
2. Realize what prvents you from getting it. Analize Problems
3. Find Solutions


MemHandle MemAlloc( word byteSize, /* Size of block in bytes */ HeapFlags hfFlags, /* Type of block */ HeapAllocFlags haFlags); /* How to allocate block */
This routine allocates a global memory block and creates an entry for it in the global handle table. The properties of the block are determined by the
HeapFlags
record passed; the way the block should be allocated is determined by the
HeapAllocFlags
record. Both sets of flags are described below. The routine returns the block's handle. If it could not allocate the block, it returns a null handle. The block allocated may be larger than the size requested, as the block size is rounded up to the next even paragraph (one paragraph equals sixteen bytes).
HeapFlags
are stored in the block's handle table entry. They can be retrieved with the routine
MemGetInfo()
; some of them can be changed with the routine
MemModifyFlags()
. The following flags are available:
LMemInitHeap()
and
VMAllocLMem()
; applications should not need to set this flag.
HeapAllocFlags
indicate how the block should be allocated and initialized. They are not stored and can not be retrieved. Some of the flags can be passed with
MemReAlloc()
. The following flags are available:
MemReAlloc()
to cause new memory to be zero-initialized.
MemDeref()
. This flag may be passed to
MemReAlloc()
.
MemReAlloc()
.If necessary, this routine will automatically compact the heap to find enough space to allocated the desired sized block.
Include: heap.h
See Also: MemAllocSetOwner()
,
MemReAlloc()
,
MemDeref()
.
MemHandle MemAllocLMem( LMemType type, /* type of LMem block */ word headerSize); /* size of header structure */
This routine allocates and initializes a local memory block; it can be used to simplify this procedure from the two-step process of
MemAlloc()
followed by
LMemInitHeap()
. Pass an LMem type indicating what will be stored in the block, along with the size of the header structure to use. If the block is to have the standard header, pass zero in
headerSize
.
This routine returns the handle of the unlocked, newly allocated block. The block will contain two LMem handles and 64 bytes allocated for the LMem heap.
Include: lmem.h
See Also: LMemInitHeap()
.
MemHandle MemAllocSetOwner( GeodeHandle owner, /* Handle of block's owner */ word byteSize, /* Size of block in bytes */ HeapFlags hfFlags, /* Type of block */ HeapAllocFlags haFlags); /* How to allocate block */
This routine is the same as
MemAlloc()
except that you can specify the owner of the global memory block created.
Include: heap.h
See Also: MemAlloc()
.
void MemDecRefCount( MemHandle mh); /* handle of affected block */
This routine decrements the reference count of a global memory block (the reference count is stored in HM
_otherInfo
). If the reference count reaches zero,
MemDecRefCount()
will free the block.
Warnings: This routine assumes that a reference count is stored in HM
_otherInfo
. You may only use this routine if the block has had a reference count set up with
MemInitRefCount()
.
Include: heap.h
void * MemDeref( MemHandle mh); /* Handle of locked block to dereference */
This routine takes one argument, the handle of a global memory block; it returns the address of the block on the global heap. If the block has been discarded, or if the handle is not a memory handle, it returns a null pointer. It gets this information by reading the block's handle table entry; it does not need to actually access the block.
Note that if the handle is of an unlocked, moveable block,
MemDeref()
will return the block's address with out any warning; however, the address will be unreliable, since the memory manager can move the block at any time.
Include: heap.h
Tips and Tricks: This is very useful when you allocate a fixed or locked block, and need to get the block's address without calling
MemLock()
.
Warnings: This routine, if given an unlocked, moveable block, will return the pointer without a warning, even though that block may move at any time.
See Also: MemGetInfo()
,
MemModifyFlags()
.


Neelam Valley
Neelam Valley is a 144 km long bow-formed valley in Azad Kashmir Region. The Valley is arranged at the North and North-East of Muzaffarabad (The Capital of Azad Kashmir).
Neelam Valley is a 144 km long bow-formed valley in Azad Kashmir Region. The Valley is arranged at the North and North-East of Muzaffarabad (The Capital of Azad Kashmir).
07
mar
The Biafo Glacier is a 67 km (42 mi) long glacier in the Karakoram Mountains of Gilgit Baltistan, Pakistan which meets the 49 km (30 mi) long Hispar Glacier at an altitude of 5,128 m (16,824 ft) at Hispar La (Pass) to create the world's longest glacial system outside the polar.
06
mar
Saiful Muluk (جھیل سیف الملوک) is a mountainous lake located at the northern end of the Kaghan Valley, near the town of Naran. It is in the north east of Mansehra District in the Khyber-Pakhtunkhwa province, Pakistan.
04
mar
The Cold Desert , Katpana Desert at Skardu, Gilgit-Baltistan in Pakistan, is the world's highest desert.
01
mar
Welcome to the remarkable Pakistan. Pakistan is one of a kind! Situated in the heart of the South Asian sub-landmass, it is a nation with its own particular history and social legacy, intriguing in its own particular right.
16
mar


Oops!
I think you lost buddy!
We are really sorry, but we can't successfully proceed your request. But there is option you you can back to the rail buddy. Yeay!
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My name is Shobhit Jain
I am currently a third year student 🎓 persuing my bachelors in Computer Science and Engineering and doing my intern at All Friends under platform relibility team.
I like to spend most of my time building cool things and pondering over potential billion dollar ideas. I am currently building Magverse 📚 - a Digital Magazine Maker and Viewer. Some of the things I have made in the past include Voila✨ - a web application to match personalised tourists and tourguides, Seat Management System✨ - a drag and drop platform for designing prototypes of furniture of offices,schools etc.
I love participating in hackathons and in my recent hackathon my team was selected in Top 8 finalists.
There is something about building new things and trying out new ideas in a very short
period of time makes me really excited.
Few things I made in the past include
Covid-19 Sentiment Analysis🦸♂
- which shows what people's sentiments during Covid Phase with relevant plots,
Medistory🙏
- an Interactive Web app which helps hospitals to digitalize Medical Records .
I love opensource development and have contributed in a platform named
CircuitVerse 🔌
- Online platform to Simulate and Explore Digital Circuits.
My Major contribution include :
I also write sometimes. It is really motivatitng to discover your blogs were able to help someone.
When we write even a small blog and people read it and reach out to say thank you.
It feels really nice.
Read my latest blog here :
I love sleeping more than everything else so you can find me sleeping if I'm not doing coding or watching movies🙈.
I would really love to talk🤠 to you if you are a budding programmer like me so please reach out to me via twitter


From 29 January to 4 February four new confirmed cases were reported in the ongoing Ebola virus disease (EVD) outbreak in the Democratic Republic of the Congo. All four cases were reported in Beni Health Zone, North Kivu Province. Three cases had epidemiological links to the transmission chain which originated in Aloya Health Area, Mabalako Health Zone, and their most likely exposure was through nosocomial infection in traditional practitioner facilities. The fourth and most recent case reported in Beni Health Zone was not a known contact at the time of detection, stayed in the community for nine days after symptom onset, and passed away in the community on 4 February. Confirmed cases who spend time in the community increase the risk of transmission of EVD to contacts.
In the past 21 days (15 January to 4 February January 2020), 18 confirmed cases, including three community deaths, were reported from three health areas within two active health zones in North Kivu Province (Figure 1, Figure 2, Table 1): Beni (n=17) and Mabalako (n=1). The reduction of geographic spread of EVD cases and the declining trend in case incidence observed in the past 21 days are encouraging. In addition, monthly surveillance indicators have overall improved since October 2019. These indicators include an increase in proportion of cases registered as contacts, a decrease in the proportion of community deaths among new reported cases, a decrease in delay to isolation, and a decrease in the monthly case fatality ratio.
These improvements are fragile and should not be interpreted as an indication that response efforts can be reduced. Among the 18 people confirmed with EVD in the past 21 days, nine cases were isolated and provided care three or more days after symptom onset, which increases the possibility of community transmission. The four most recent cases reported in Beni Health Zone in the past seven days were not under surveillance at the time of detection. Therefore, heightened vigilance of the response is critical, including continued identification of cases and contacts, follow up of contacts, and continuing to improve infection prevention and control in health care facilities.
As of 4 February, a total of 3429 EVD cases were reported, including 3306 confirmed and 123 probable cases, of which 2251 cases died (overall case fatality ratio 66%) (Table 1). Of the total confirmed and probable cases, 56% (n=1920) were female, 28% (n=967) were children aged less than 18 years, and 5% (n=172) of all reported cases were health care workers.
*3429 confirmed and probable cases, reported as of 4 February 2020. Excludes n=169 cases for whom onset dates not reported. Data in recent weeks are subject to delays in case confirmation and reporting, as well as ongoing data cleaning. Other health zones include: Alimbongo, Ariwara, Biena, Bunia, Goma, Kayna, Komanda, Kyondo, Lolwa, Lubero, Mandima, Manguredjipa, Masereka, Mutwanga, Mwenga, Nyakunde, Nyiragongo, Oicha, Pinga, Rwampara, Tchomia, and Vuhovi.
**Total cases and areas affected during the last 21 days are based on the initial date of case alert and may differ from date of confirmation and daily reporting by the Ministry of Health.
For further information about public health response actions by the Ministry of Health, WHO, and partners, please refer to the latest situation reports published by the WHO Regional Office for Africa:
WHO continuously monitors changes to the epidemiological situation and context of the outbreak to ensure that support to the response is adapted to the evolving circumstances. The last assessment concluded that the national and regional risk levels remain very high, while global risk levels remain low.
WHO advises against any restriction of travel to, and trade with, the Democratic Republic of the Congo based on the currently available information. Any requirements for certificates of Ebola vaccination are not a reasonable basis for restricting movement across borders or the issuance of visas for travellers to/from the affected countries. WHO continues to closely monitor and, if necessary, verify travel and trade measures in relation to this event. Currently, no country has implemented travel measures that significantly interfere with international traffic to and from the Democratic Republic of the Congo. Travellers should seek medical advice before travel and should practise good hygiene. Further information is available in the WHO recommendations for international traffic related to the Ebola Virus Disease outbreak in the Democratic Republic of the Congo.
For more information, please see:


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I have a question about controlling a mouse pointer in Linux (i.e. in Webbrowser) by C++ Code in Eclipse. So my question is which libraries do I need for this? I found some code in the Internet but only for Windows.
There is no standard or platform-independent way, you need to look up how to do it for your operating system. Be aware that getting and/or setting the actual absolute position of the mouse cursor is something that antivirus software hate.
In *nix, the way you accomplish this depends entirely on the windowing system being used. There might not even be a windowing system though.


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We don't just build technologies, we create and accomplish Missions. We do this by partnering with Mission Partners in various ways
Contracting Partnership
This form of partnership generally occurs when the Mission objective originates from a partner's need or paint point. In this scenario, Trillium will work with the partner to define a set of Mission objectives, budget, and development roadmap. If necessary, Trillium may contribute it's own technologies, expertise, and/or Partner Network to help accomplish the Mission goals.
While we focus our systems development in the domains of ocean, space, and climate change our expertise built in these areas also allows us to accept contracting partnerships across a broad scope related to innovation : Health, Insurance, Entrepreneurship, Finance, etc.
Mission Partnership
Once a Mission objective for a Trillium system has been identified, the next step is to begin identifying key Mission Partners. These Mission Partners typically have the same or similar Mission objectives as our own and thus a partnership is formed in which there may be an exchange of IP, financial resources, know-how, or extended partnership relations.
A common example of this type of partnership is one in which a technology provider and/or a PR agency form a partnership with Trillium to demonstrate how their products are used in one of the Trillium Missions.


Docs
Disks and Volumes
Making persistent storage in the cluster (volumes) accessible to VMs consists of three parts. First, volumes are specified in spec.volumes
. Second, disks are added to the VM by specifying them in spec.domain.devices.disks
. Finally, a refererence to the specified volume is added to the disk specification by name.
Disks
Like all other vmi devices a spec.domain.devices.disks
element has a mandatory name
, and furthermore, it has a mandatory volumeName
entry which references a volume inside spec.volumes
.
A disk can be made accessible via four different types:
- disk
- cdrom
- floppy
- lun
All possible configuration options are available in the Disk API Reference.
All types, with the exception of floppy, allow you to specify the bus
attribute. The bus
attribute determines how the disk will be presented to the guest operating system. floppy disks don’t support the bus
attribute: they are always attached to the fdc
bus.
lun
A lun
disk will expose the volume as a LUN device to the VM. This allows the VM to execute arbitrary iSCSI command passthrough.
A minimal example which attaches a PersistentVolumeClame
named mypvc
as a lun
device to the VM:
metadata:
name: testvmi-lun
apiVersion: kubevirt.io/v1alpha2
kind: VirtualMachineInstance
spec:
domain:
resources:
requests:
memory: 64M
devices:
disks:
- name: mypvcdisk
volumeName: mypvc
# This makes it a lun device
lun: {}
volumes:
- name: mypvc
persistentVolumeClaim:
claimName: mypvc
disk
A disk
disk will expose the volume as an ordinary disk to the VM.
A minimal example which attaches a PersistentVolumeClame
named mypvc
as a disk
device to the VM:
metadata:
name: testvmi-disk
apiVersion: kubevirt.io/v1alpha2
kind: VirtualMachineInstance
spec:
domain:
resources:
requests:
memory: 64M
devices:
disks:
- name: mypvcdisk
volumeName: mypvc
# This makes it a disk
disk: {}
volumes:
- name: mypvc
persistentVolumeClaim:
claimName: mypvc
You can set the disk bus
type, overriding the defaults, which in turn depends on the chipset the VM is configured to use:
metadata:
name: testvmi-disk
apiVersion: kubevirt.io/v1alpha2
kind: VirtualMachineInstance
spec:
domain:
resources:
requests:
memory: 64M
devices:
disks:
- name: mypvcdisk
volumeName: mypvc
# This makes it a disk
disk:
# This makes it exposed as /dev/vda, being the only and thus first
# disk attached to the VM
bus: virtio
volumes:
- name: mypvc
persistentVolumeClaim:
claimName: mypvc
floppy
A floppy
disk will expose the volume as a floppy drive to the VM.
A minimal example which attaches a PersistentVolumeClame
named mypvc
as a floppy
device to the VM:
metadata:
name: testvmi-floppy
apiVersion: kubevirt.io/v1alpha2
kind: VirtualMachineInstance
spec:
domain:
resources:
requests:
memory: 64M
devices:
disks:
- name: mypvcdisk
volumeName: mypvc
# This makes it a floppy
floppy: {}
volumes:
- name: mypvc
persistentVolumeClaim:
claimName: mypvc
cdrom
A cdrom
disk will expose the volume as a cdrom drive to the VM. It is read-only by default.
A minimal example which attaches a PersistentVolumeClame
named mypvc
as a floppy
device to the VM:
metadata:
name: testvmi-cdrom
apiVersion: kubevirt.io/v1alpha2
kind: VirtualMachineInstance
spec:
domain:
resources:
requests:
memory: 64M
devices:
disks:
- name: mypvcdisk
volumeName: mypvc
# This makes it a cdrom
cdrom:
# This makes the cdrom writeable
readOnly: false
# This makes the cdrom be exposed as SATA device
bus: sata
volumes:
- name: mypvc
persistentVolumeClaim:
claimName: mypvc
Volumes
Supported volume sources are
- cloudInitNoCloud
- ephemeral
- persistentVolumeClaim
- registryDisk
- emptyDisk
All possible configuration options are available in the Volume API Reference.
cloudInitNoCloud
Allows attaching cloudInitNoCloud
data-sources to the VM. If the VM contains a proper cloud-init setup, it will pick up the disk as a user-data source.
A simple example which attaches a Secret
as a cloud-init disk
datasource may look like this:
metadata:
name: testvmi-cloudinitnocloud
apiVersion: kubevirt.io/v1alpha2
kind: VirtualMachineInstance
spec:
domain:
resources:
requests:
memory: 64M
devices:
disks:
- name: mybootdisk
volumeName: mypvc
lun: {}
- name: mynoclouddisk
volumeName: mynocloudvolume
disk: {}
volumes:
- name: mypvc
persistentVolumeClaim:
claimName: mypvc
- name: mynoclouddisk
cloudInitNoCloud:
secretRef:
name: testsecret
persistentVolumeClaim
Allows connecting a PersistentVolumeClaim
to a VM disk.
Use a PersistentVolumeClain when the VirtualMachineInstance’s disk needs to persist after the VM terminates. This allows for the VM’s data to remain persistent between restarts.
For KubeVirt to be able to consume the disk present on a PersistentVolume’s filesystem, the disk must be named disk.img
and be placed in the root path of the filesystem. Currently the disk is also required to be in raw format.
Important: The disk.img
image file needs to be owned by the user-id 107
in order to avoid permission issues.
A simple example which attaches a PersistentVolumeClaim
as a disk
may look like this:
metadata:
name: testvmi-pvc
apiVersion: kubevirt.io/v1alpha2
kind: VirtualMachineInstance
spec:
domain:
resources:
requests:
memory: 64M
devices:
disks:
- name: mypvcdisk
volumeName: mypvc
lun: {}
volumes:
- name: mypvc
persistentVolumeClaim:
claimName: mypvc
ephemeral
An ephemeral volume is a local COW (copy on write) image that uses a network volume as a read-only backing store. With an ephemeral volume, the network backing store is never mutated. Instead all writes are stored on the ephemeral image which exists on local storage. KubeVirt dynamically generates the ephemeral images associated with a VM when the VM starts, and discards the ephemeral images when the VM stops.
Ephemeral volumes are useful in any scenario where disk persistence is not desired. The COW image is discarded when VM reaches a final state (e.g., succeeded, failed).
Currently, only PersistentVolumeClaim
may be used as a backing store of the ephemeral volume.
Up-to-date information on supported backing stores can be found in the KubeVirt API.
metadata:
name: testvmi-ephemeral-pvc
apiVersion: kubevirt.io/v1alpha2
kind: VirtualMachineInstance
spec:
domain:
resources:
requests:
memory: 64M
devices:
disks:
- name: mypvcdisk
volumeName: mypvc
lun: {}
volumes:
- name: mypvc
ephemeral:
persistentVolumeClaim:
claimName: mypvc
registryDisk
The Registry Disk feature provides the ability to store and distribute VM disks in the container image registry. Registry Disks can be assigned to VMs in the disks section of the VirtualMachineInstance spec.
No network shared storage devices are utilized by Registry Disks. The disks are pulled from the container registry and reside on the local node hosting the VMs that consume the disks.
When to use a registryDisk
Registry Disks are ephemeral storage devices that can be assigned to any number of active VirtualMachineInstances. This makes them an ideal tool for users who want to replicate a large number of VM workloads that do not require persistent data. Registry Disks are commonly used in conjunction with VirtualMachineInstanceReplicaSets.
When Not to use a registryDisk
Registry Disks are not a good solution for any workload that requires persistent disks across VM restarts, or workloads that require VM live migration support. It is possible Registry Disks may gain live migration support in the future, but at the moment live migrations are incompatible with Registry Disks.
registryDisk Workflow Example
Users push VM disks into the container registry using a KubeVirt base image designed to work with the Registry Disk feature. The latest base container image is kubevirt/registry-disk-v1alpha.
Using this base image, users can inject a VirtualMachineInstance disk into a container image in a way that is consumable by the KubeVirt runtime. Disks placed into the base container must be placed into the /disk directory. Raw and qcow2 formats are supported. Qcow2 is recommended in order to reduce the container image’s size.
Example: Inject a VirtualMachineInstance disk into a container image.
cat << END > Dockerfile
FROM kubevirt/registry-disk-v1alpha
ADD fedora25.qcow2 /disk
END
docker build -t vmidisks/fedora25:latest .
Example: Upload the RegistryDisk container image to a registry.
docker push vmidisks/fedora25:latest
Example: Attach the RegistryDisk as an ephemeral disk to a VM.
metadata:
name: testvmi-registrydisk
apiVersion: kubevirt.io/v1alpha2
kind: VirtualMachineInstance
spec:
domain:
resources:
requests:
memory: 64M
devices:
disks:
- name: registrydisk
volumeName: registryvolume
disk: {}
volumes:
- name: registryvolume
registryDisk:
image: vmidisks/fedora25:latest
Note that a registryDisk
is file-based and therefore cannot be attached as a lun
device to the VM.
emptyDisk
An emptyDisk
works similar to an emptyDir
in Kubernetes. An extra sparse qcow2
disk will be allocated and it will live as long as the VM. Thus it will survive guest side VM reboots, but not a VM re-creation. The disk capacity
needs to be specified.
Example: Boot cirros with an extra emptyDisk
with a size of 2GiB
:
apiVersion: kubevirt.io/v1alpha2
kind: VirtualMachineInstance
metadata:
name: testvmi-nocloud
spec:
terminationGracePeriodSeconds: 5
domain:
resources:
requests:
memory: 64M
devices:
disks:
- name: registrydisk
volumeName: registryvolume
disk:
bus: virtio
- name: emptydisk
volumeName: emptydiskvolume
disk:
bus: virtio
volumes:
- name: registryvolume
registryDisk:
image: kubevirt/cirros-registry-disk-demo:latest
- name: emptydiskvolume
emptyDisk:
capacity: 2Gi
When to use an emptyDisk
Ephemeral VMs very often come with read-only root images and limited tmpfs space. In many cases this is not enough to install application dependencies and provide enough disk space for the application data. While this data is not critical and thus can be lost, it is still needed for the application to function properly during its lifetime. This is where an emptyDisk
can be useful. An emptyDisk is often used and mounted somewhere in /var/lib
or /var/run
.


|
Jinx
|
Performance stats. More...
#include <Jinx.h>
Public Attributes | |
| uint64_t | compilationTimeNs |
| Total compilation time of all scripts in nanoseconds. | |
| uint64_t | executionTimeNs |
| Total execution time of all scripts in nanoseconds. | |
| uint64_t | perfTimeNs |
| Performance sample time in nanoseconds. | |
| uint64_t | scriptCompilationCount |
| Number of scripts compiled. | |
| uint64_t | scriptExecutionCount |
| Number of scripts executed. | |
| uint64_t | scriptCompletionCount |
| Number of scripts completed. | |
| uint64_t | instructionCount |
| Number of instructions executed. | |
Performance stats.
Struct that contains several performance stats of scripts since the beginning of the runtime object creation or the last time IRuntime::GetScriptPerformanceStats() was called with the cleared flag set.


---
layout: layouts/post.njk
title: The Intimidation Barrier
permalink: /intimidation-barrier/
date: '2014-11-09'
comments: true
tags:
- link
- virtual machines
- craft
- cms
- internship
- viget
subtitle: A closer look at one of the reasons why designers don't participate in the open source community. (This links out to designopen.org)
link-out: http://designopen.org/blogging_about/barriers-for-designers/
---
If you are not redirected, please visit this think!


3.2. fmas.propagation_constant¶
The provided module implements a convenience class for performing simple calculations for a user-defined propagation constant.
Convenience class for working with propagation constants. |
|
Propagation constant for ZBLAN fiber. |
|
Propagation constant for an ESM PCF. |
|
Propagation constant for NLPM750 PCF. |
|
Helper function for propagation constant. |
|
Helper function for propagation constant. |
|
Helper function for propagation constant. |
-
class
fmas.propagation_constant.
PropConst
(beta_fun)¶ Convenience class for working with propagation constants.
Implements methods that provide convenient access to recurrent tasks involving propagation constants.
- Parameters
beta_fun (
callable
) – Function implementing a propagation constant.
-
beta_fun
¶ Function implementing a propagation constant.
- Type
callable
-
dw
¶ Angular frequency increment used for calculating derivatives.
- Type
int
-
c0
¶ Speed of light (default = 0.29970 micron/fs).
- Type
float
-
CD
(w)¶ Chromatic dispersion.
- Parameters
w (
numpy array
or float) – Angular frequency for which to compute chromatic dispersion.- Returns
Chromatic dispersion.
- Return type
numpy.ndarray
or float
-
beta
(w)¶ Propagation constant.
- Parameters
w (
numpy.ndarray
) – Angular frequency for which to compute propagation constant.- Returns
Propagation constant.
- Return type
numpy.ndarray
or float
-
beta1
(w)¶ Group delay.
- Parameters
w (
numpy.ndarray
) – Angular frequency for which to compute group delay.- Returns
Group delay.
- Return type
numpy.ndarray
or float
-
beta2
(w)¶ Group velocity dispersion (GVD).
- Parameters
w (
numpy.ndarray
) – Angular frequency for which to compute GVD.- Returns
Group velocity dispersion.
- Return type
numpy.ndarray
or float
-
beta3
(w)¶ Third order dispersion.
- Parameters
w (
numpy.ndarray
) – Angular frequency for which to compute 3rd order dispersion.- Returns
Group velocity dispersion.
- Return type
numpy.ndarray
or float
-
compute_expansion_coefficients
(w0, n_max=5)¶ Obtain Taylor expansion coefficients at given frequency.
Note
Uses scipy.misc.derivative and scipy.misc.factorial for computing the expansion coefficients.
- Parameters
w0 (
float
) – Frequency value about which to expand.n_max (
int
) – Number of expansion coefficients to compute (default = 5).
- Returns
Taylor expansion coefficients.
- Return type
numpy.ndarray
-
find_match_beta1
(w0, w_min, w_max)¶ Determine group velocity matched partner frequency.
Attempts to find a group-velocity matched partner frequency for \(\omega_0\) in the interval from \(\omega_{\mathrm{min}}\) to \(\omega_{\mathrm{max}}\).
Note
Helper method for analysis of dispersion profile
Uses scipy.optimize.minimize_scalar for bracketed minimization
If no group velocity matched frequency is contained in the supplied interval, the output should not be trusted. Check the resulting frequency by assessing whether beta1(res)==beta1(w0).
- Parameters
w0 (
float
) – Frequency for which group velocity matched partner frequency will be computed.w_min (
float
) – Lower bound for root finding procedurew_max (
float
) – Upper bound for root finding procedure
- Returns
Group-velocity matched partner frequency of w0.
- Return type
float
-
find_root_beta2
(w_min, w_max)¶ Determine bracketed root of 2nd order dispersion profile.
Attempts to find a root of the 2nd order dispersion profile in the interval from \(\omega_{\mathrm{min}}\) to \(\omega_{\mathrm{max}}\).
Note
Helper method for analysis of dispersion profile
Uses scipy.optimize.bisect for bracketed root finding
- Parameters
w_min (
float
) – lower bound for root finding procedurew_max (
float
) – upper bound for root finding procedure
- Returns
root of 2nd order dispersion profile in bracketed interval
- Return type
float
-
lam
(w)¶ Angular frequency to wavelength conversion.
- Parameters
w (
numpy array
or float) – Angular frequency for which to compute wavelengths.- Returns
Wavelength values.
- Return type
numpy.ndarray
or float
-
local_coeffs
(w0, n_max=5)¶ Obtain Taylor expansion coefficients at given frequency.
Note
Uses scipy.misc.derivative and scipy.misc.factorial for computing the expansion coefficients.
- Parameters
w0 (
float
) – Frequency value about which to expand.n_max (
int
) – Number of expansion coefficients to compute (default = 5).
- Returns
Taylor expansion coefficients.
- Return type
numpy.ndarray
-
v_sol_corr
(t0, w)¶ Corrected soliton velocity.
Computes corrected soliton velocity
\[v_{g}^\prime(\omega_0, t_0) = \left[ \beta_1(\omega_0) - \beta_2(\omega_0)/(\omega_0 t_0^2) + \beta_3(\omega_0)/(6t_0^2) \right]^{-1},\]defined in [PBA2016].
- Parameters
t0 (
float
) – Soliton duration.w (
numpy array
or float) – Angular frequency for which to compute group-velocity.
- Returns
corrected soliton velocity.
- Return type
numpy.ndarray
or float
- PBA2016
S. Pickartz, U. Bandelow, Sh. Amiranashvili, Adiabatic theory of solitons fed by dispersive waves, Phys. Rev. A 94 (2016) 033811, https://doi.org/10.1103/PhysRevA.94.033811.
-
vg
(w)¶ Group velocity profile.
- Parameters
w (
numpy array
or float) – Angular frequency for which to compute group-velocity.- Returns
Group velocity.
- Return type
numpy.ndarray
or float
-
fmas.propagation_constant.
define_beta_fun_ESM
()¶ Propagation constant for an ESM PCF.
Enclosing function returning a closure implementing a rational Pade-approximant of order [8/8] for the refractive index of a endlessly single mode (ESM) nonlinear photonic crystal fiber (PCF), see [SK2007].
- Returns
Propagation constant for ESM PCF.
- Return type
callable
- SK2007
J.M. Stone, J.C. Knight, Visibly ‘white’ light generation in uniform photonic crystal fiber using a microchip laser , Optics Express 16 (2007) 2670.
-
fmas.propagation_constant.
define_beta_fun_NLPM750
()¶ Propagation constant for NLPM750 PCF.
Enclosing function returning a closure implementing a rational Pade-approximant of order [4/4] for the refractive index of a NL-PM-750 nonlinear photonic crystal fiber (PCF), see [NLPM750].
- Returns
Propagation constant for NL-PM-750 PCF.
- Return type
callable
- NLPM750
NL-PM-750 Nonlinear Photonic Crystal Fiber, www.nktphotonics.com.
-
fmas.propagation_constant.
define_beta_fun_PCF_Ranka2000
()¶ Helper function for propagation constant.
Enclosing function returning a closure implementing a polynomial expansion of order 11 for the propagation constant of [RWS2000] given in [DGC2006].
- Returns
Propagation constant.
- Return type
callable
- RWS2000
J. K. Ranka, R. S. Windeler, A. J. Stentz, Visible continuum generation in air-silica microstructure optical fibers with anomalous dispersion at 800 nm, Opt. Lett. 25 (2000) 25, https://doi.org/10.1364/OL.25.000025.
- DGC2006
J. M. Dudley, G. Genty, S. Coen, Supercontinuum generation in photonic crystal fiber, Rev. Mod. Phys. 78 (2006) 1135, http://dx.doi.org/10.1103/RevModPhys.78.1135
-
fmas.propagation_constant.
define_beta_fun_ZBLAN
()¶ Propagation constant for ZBLAN fiber.
Enclosing function returning a closure implementing a rational Pade-approximant of order [4/4] for the refractive index of a ZBLAN fiber (PCF).
- Returns
Propagation constant for NL-PM-750 PCF.
- Return type
callable
-
fmas.propagation_constant.
define_beta_fun_fluoride_glass_AD2010
()¶ Helper function for propagation constant.
Enclosing function returning a closure implementing a rational Pade-approximant of order [5/5] for the refractive index of a fluorid glass fiber given in [AD2010].
- Returns
Propagation constant for NL-PM-750 PCF.
- Return type
callable
- AD2010
Sh. Amiranashvili, A. Demircan, Hamiltonian structure of propagation equations for ultrashort optical pulses, Phys. Rev. A, 82 (2010) 013812
-
fmas.propagation_constant.
define_beta_fun_slot_waveguide_Zhang2012
()¶ Helper function for propagation constant.
Enclosing function returning a closure implementing a polynomial expansion of order 8 for the propagation constant of [Z2012].
- Returns
Propagation constant.
- Return type
callable
- Z2012
Zhang et al., Silicon waveguide with four zero-dispersion wavelengths and its application in on-chip octave spanning supercontinuum generation, Opt. Express 20 (2012) 1685.


{% extends "base.html" %} {% block title %}
Medieval catalogues > CARTHUSIANS > London > Loans to Beauvale, 1510
{% endif %}6 identified entries found.
All data was derived from the List of Identifications by Professor Richard Sharpe.
{% if not printing %}
A key to codes used in the List is available (opens in new tab).{% endif %}


DUPLOCALE
Section: Linux Programmer's Manual (3)
Updated: 2014-03-12
Index
Return to Main Contents
NAME
duplocale - duplicate a locale object
SYNOPSIS
#include <locale.h>
locale_t duplocale(locale_t locobj);
Feature Test Macro Requirements for glibc (see
feature_test_macros(7)):
duplocale():
-
- Since glibc 2.10:
-
_XOPEN_SOURCE >= 700
- Before glibc 2.10:
-
_GNU_SOURCE
DESCRIPTION
The
duplocale()
function creates a duplicate of the locale object referred to by
locobj.
If
locobj
is
LC_GLOBAL_LOCALE,
duplocale()
creates a locale object containing a copy of the global locale
determined by
setlocale(3).
RETURN VALUE
On success,
duplocale()
returns a handle for the new locale object.
On error, it returns
(locale_t) 0,
and sets
errno
to indicate the cause of the error.
ERRORS
- ENOMEM
-
Insufficient memory to create the duplicate locale object.
VERSIONS
The
duplocale()
function first appeared in version 2.3 of the GNU C library.
CONFORMING TO
POSIX.1-2008.
NOTES
Duplicating a locale can serve the following purposes:
- *
-
To create a copy of a locale object in which one of more categories
are to be modified (using
newlocale(3)).
- *
-
To obtain a handle for the current locale which can used in
other functions that employ a locale handle, such as
toupper_l(3).
This is done by applying
duplocale()
to the value returned by the following call:
loc = uselocale((locale_t) 0);
-
This technique is necessary, because the above
uselocale(3)
call may return the value
LC_GLOBAL_LOCALE,
which results in undefined behavior if passed to functions such as
toupper_l(3).
Calling
duplocale()
can be used to ensure that the
LC_GLOBAL_LOCALE
value is converted into a usable locale object.
See EXAMPLE, below.
Each locale object created by
duplocale()
should be deallocated using
freelocale(3).
EXAMPLE
The program below uses
uselocale(3)
and
duplocale()
to obtain a handle for the current locale which is then passed to
toupper_l(3).
The program takes one command-line argument,
a string of characters that is converted to uppercase and
displayed on standard output.
An example of its use is the following:
$ ./a.out abc
ABC
Program source
#define _XOPEN_SOURCE 700
#include <ctype.h>
#include <stdio.h>
#include <stdlib.h>
#include <locale.h>
#define errExit(msg) do { perror(msg); exit(EXIT_FAILURE); \
} while (0)
int
main(int argc, char *argv[])
{
locale_t loc, nloc;
char *p;
if (argc != 2) {
fprintf(stderr, "Usage: %s string\n", argv[0]);
exit(EXIT_FAILURE);
}
/* This sequence is necessary, because uselocale() might return
the value LC_GLOBAL_LOCALE, which can't be passed as an
argument to toupper_l() */
loc = uselocale((locale_t) 0);
if (loc == (locale_t) 0)
errExit("uselocale");
nloc = duplocale(loc);
if (nloc == (locale_t) 0)
errExit("duplocale");
for (p = argv[1]; *p; p++)
putchar(toupper_l(*p, nloc));
printf("\n");
freelocale(nloc);
exit(EXIT_SUCCESS);
}
SEE ALSO
freelocale(3),
newlocale(3),
setlocale(3),
uselocale(3),
locale(5),
locale(7)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- RETURN VALUE
-
- ERRORS
-
- VERSIONS
-
- CONFORMING TO
-
- NOTES
-
- EXAMPLE
-
- Program source
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:55:14 GMT, September 18, 2014


About
Floyd Warshall is an algorithm to find out all pair shortest paths. This project will visualize the algorithm in action in user friendly graphics. In this project, I wanted to try out MVC so I used JavaFX this time. You're welcome to explore the project for more.
Development Tools
Netbeans, Gluon Scene Builder
Project Screenshot(s)
1. Graph Viewer
2. Table Viewer
Project Screencast
Project Features
- Matrix Input
- Manual Input
- Distance Table
- Sequence Table
- Auto Play
- Graph Simulation
Notes
- THIS PROJECT IS DEVELOPED ON LINUX ENVIRONMENT USING NETBEANS IDE 8.2, JAVAFX
- INCLUDED JAR FILES SHOULD BE IMPORTED TO IDE(Location: /lib)
- RED COLORED EDGE SIGNIFIES BIDIRECTIONAL RELATIONSHIP
- ANY OTHER COLOR SIGNIFIES THAT EDGE IS STARTING FROM THE SAME COLORED VERTEX TO THE OTHER VERTEX (FOR - - - EXAMPLE: THE EDGE BETWEEN B, E INDICATES A DIRECTION FROM B TO E IN FIRST SAMPLE PICTURE)
- BUGS COULD BE FOUND
Repository
If you're interested in this project, you may browse the github repository of the project by clicking here.
Go Back to Projects Next Project


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- reinvent frictionless e-commerce
- productize cross-platform markets
- empower cross-platform portals
- incubate seamless users
- scale bleeding-edge networks
- architect open-source architectures
- strategize granular e-commerce
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- revolutionize frictionless methodologies
- extend back-end paradigms
- reinvent cutting-edge functionalities
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- productize innovative infomediaries


Interested in Contributing?
- Check out available resources.
- Create an account and start submitting your own systems.
Scored Systems
| System | Submitter | System Notes | Constraint | Run Notes | BLEU | BLEU-cased | TER | BEER 2.0 | CharactTER |
|---|---|---|---|---|---|---|---|---|---|
| uedin-nmt-ensemble (Details) | rsennrich University of Edinburgh |
BPE neural MT system with monolingual training data (back-translated). ensemble of 4 L2R and 4 R2L models. | yes |
28.9 |
28.3 |
0.612 |
0.592 |
0.518 |
|
| UniMelb-NMT-Transformer-BT (Details) | vhoang The University of Melbourne, Australia |
NMT with Transformer architecture (medium size, 4 heads + 4 encoder/decoder layers) enhanced with WMT'16 back-translation data. (decoding with single best system) | yes |
27.8 |
27.3 |
0.612 |
0.585 |
0.540 |
|
| en_de_transformer_test (Details) | hpulfc hpulfc |
transformer base system | yes |
27.7 |
27.2 |
0.627 |
0.583 |
0.538 |
|
| en_de_transformer_test (Details) | hpulfc hpulfc |
transformer base system | yes |
27.7 |
27.1 |
0.626 |
0.584 |
0.540 |
|
| LMU-nmt-reranked-wmt17-en-de (Details) | Matthias.Huck LMU Munich |
NMT, single model plus r2l reranking, linguistically motivated target word segmentation | yes |
27.9 |
27.1 |
0.618 |
0.583 |
0.538 |
|
| SYSTRAN-single (Details) | jmcrego SYSTRAN |
OpenNMT + BPE + backtranslated monolingual data + hyperspecialization | yes |
28.0 |
26.7 |
0.611 |
0.580 |
0.542 |
|
| xmu-ensemble (Details) | Zhixing Tan Xiamen University |
ensemble 4 models + bpe + backtranslation | yes |
27.2 |
26.7 |
0.622 |
0.580 |
0.540 |
|
| lium-nmt-backtrans-ensemble-ftuned (Details) | ozancaglayan LIUM - Le Mans University |
Ensemble of 2 backtranslated augmented finetuned-NMT trained with nmtpy | yes |
27.2 |
26.6 |
0.633 |
0.581 |
0.544 |
|
| LMU-nmt-single-wmt17-en-de (Details) | Matthias.Huck LMU Munich |
NMT, single model (contrastive), linguistically motivated target word segmentation | yes |
27.3 |
26.6 |
0.626 |
0.579 |
0.544 |
|
| uedin-nmt-single (Details) | rsennrich University of Edinburgh |
BPE neural MT system with monolingual training data (back-translated). single model. (contrastive) | yes |
27.2 |
26.6 |
0.627 |
0.582 |
0.539 |
|
| fbk-nmt-combination (Details) | Mattia Di Gangi Fondazione Bruno Kessler - FBK |
OpenNMT + bpe + backtranslations + system combination | yes |
26.9 |
26.3 |
0.636 |
0.574 |
0.592 |
|
| KIT-cG-mA-single (Details) | thanhleha KIT |
yes |
26.9 |
26.3 |
0.631 |
0.578 |
0.547 |
||
| xmu-single-backtrans (Details) | Zhixing Tan Xiamen University |
single model on preprocessed data + bpe + backtranslation (contrastive) | yes |
26.7 |
26.1 |
0.628 |
0.576 |
0.544 |
|
| KIT primary (Details) | eunah.cho KIT |
NMT, BPE, rescore using five models | yes |
26.7 |
26.1 |
0.641 |
0.577 |
0.546 |
|
| RWTH NMT (Details) | jtp RWTH Aachen University |
Ensemble of 3 using backtranslated data and BPE | yes |
26.8 |
26.0 |
0.628 |
failed |
failed |
|
| KIT correct BT single (Details) | thanhleha KIT |
yes |
26.5 |
25.9 |
0.628 |
0.577 |
0.568 |
||
| xmu-single (Details) | Zhixing Tan Xiamen University |
single model on preprocessed data + bpe (contrastive) | yes |
26.3 |
25.7 |
0.639 |
0.570 |
0.562 |
|
| uedin-nmt-2016 (Details) | rsennrich University of Edinburgh |
single system of WMT16 (uedin-nmt-single). Contrastive. | yes |
25.5 |
24.9 |
0.649 |
0.571 |
0.559 |
|
| fbk-nmt-single (Details) | Mattia Di Gangi Fondazione Bruno Kessler - FBK |
OpenNMT + bpe + backtranslations (contrastive) | yes |
25.3 |
24.8 |
0.644 |
0.569 |
0.576 |
|
| C-3MA (Details) | mphi University of Tartu |
Nematus + filtered monolingual back-translated data + NE forcing + ngram deduplication | yes | NeuralMonkey + filtered monolingual back-translated data + NE forcing + ngram deduplication |
23.2 |
22.7 |
0.669 |
0.553 |
0.598 |
| Moses Phrase-Based, word clusters (Details) | jhu-smt Johns Hopkins University |
Moses, use of word clusters. Preliminary run (full run may not finish): use of word clusters in OSM, LM. | yes | Moses phrase-based, word cluster LM |
22.2 |
21.6 |
0.709 |
0.557 |
0.596 |
| TALP-UPC (Details) | cescolano TALP-UPC |
Character to character NMT system with additional monolingual training data (backtranslated) and rescoring using the inverse language pair model. | yes | Character to character NMT system with extra corpus and rescoring using the inverse language pair model. |
21.9 |
21.2 |
0.685 |
0.548 |
0.587 |
| BaseNematusEnDe (Details) | m4t1ss Tilde |
yes | This should be right |
21.4 |
21.0 |
0.706 |
0.544 |
0.599 |
|
| ParFDA (Details) | bicici |
en-de ParFDA Moses phrase-based SMT system | yes | en-de (after the deadline) |
19.1 |
18.5 |
0.749 |
0.533 |
0.647 |
| PROMT Rule-based (Details) | Alex Molchanov PROMT LLC |
no |
17.0 |
16.6 |
0.752 |
0.527 |
0.602 |


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At the time when she was found as a stray, it was deemed she was a pit bull-type dog and was due to be euthanised after a magistrate issued a destruction order in February 2017.
Now, a group of campaigners are desperately trying to save Olive, saying she isn’t a pit bull-type and shouldn’t be put down but be a given to a good home instead.
Dog trainer Alexis Atchison, who helped launch the campaign, said:
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We want her to live a normal life like any other dog. This is just ridiculous.
A loving home awaits her but she’s not allowed to go to that loving home. She’s a great dog, a perfect dog. She’s been held prisoner for over two years.
The 36-year-old added:
Olive is the nation’s dog. She’s not just one person’s dog. She is loved by so many.
She’s got an army behind her, there are so many people fighting for her.
Solicitor James Parry is representing the campaigners and he believes the dog may have been used before breeding before being abandoned.
The case recently reached the High Court, where it was agreed that the campaigners should be allowed to be considered Olive’s new keeper, after two years of going through the courts.
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Campaigners managed to raise more than £16,000 through crowdfunding by setting up a Facebook group called Olive’s Fight Against BSL.
Alexis said the group have already spent £10,000 on the initial round of legal bill as well as Olive’s vet costs.
It’s impossible to comprehend why anyone would want to mistreat and abandon a beautiful creature like Olive, but it’s fair to say my faith in humanity is somewhat restored by the hard work and dedication the campaigners have put into saving her.
Here’s to hoping she gets the loving and caring home she deserves.
Emma Rosemurgey is an NCTJ trained Journalist at UNILAD. She graduated from the University of Central Lancashire in Preston and started her career in regional newspapers before joining the LADbible Group team in 2017.


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