A scripting language that is often used by software developers to
add programmability to their applications, such as engineering-analysis tools or animation software.
C is used to write software where speed and flexibility is
important, such as in embedded systems or high-performance computing.
Designed to allow the creation of programs that can run on different
platforms with little or no modification, Java is a popular choice for Web applications.
Essentially a version of C with built-in support for
"objects"--self-contained modules of code and data--C++ proved to be a natural fit for software driven by
graphical user interfaces.
Created by Microsoft for developing software for the .NET framework,
which supports many applications running on Windows.
Designed for programming statistical analysis and data-mining
applications.
A scripting language used primarily to add functionality to Web
browsers on the fly, allowing the creation of complex Web pages.
A scripting language primarily designed to support dynamic
websites.
Created by Google, Go has built-in support for programs that need to
exchange information while running concurrently on different computers.
Apple's latest language for creating iOS and OS X software. The
language is compatible with existing Objective-C code and was open sourced this year to increase adoption among
coders.
Based on Processing, the native language for the Arduino
microcontroller has become the basis of a huge number of home-brew and prototype devices.
A scripting language designed to offer greater support for objects
than other such languages.
Writing assembly code requires considerable expertise, but it allows
the creation of high-speed software that can run directly on a computer processor.
Programs written in Scala, an alternative to Java, can run on the
same "virtual machine" as Java programs.
A scripting language for MathWorks' Matlab computing environment,
Matlab is designed for numerical computing tasks, such as engineering or scientific simulations.
A specialized language for describing the appearance and content of
Web pages.
A catchall term for a number of frameworks that allow scripting of
operating system command-line instructions.
A scripting language often used as the "glue" in complex systems.
Microsoft created Visual Basic to allow rapid development of Windows
applications but no longer supports it. A descendant, Visual Basic.NET, which is still supported, works with the
more modern .NET framework.
Created by graphics-chip maker Nvidia, Cuda allows programmers to
tap the power of GPUs for general-purpose computing.
A scripting language often used to make other applications
programmable, especially in the animation and gaming domains.
With its first stable version released this year, Rust is designed
to make concurrent systems easier to program reliably.
A specialized language designed to query and manipulate databases.
Originating at MIT, Processing has been developed to make it easy
for artists, designers, and hobbyists to generate visualizations.
Designed for applications where reliability is important.
Adding support for objects--self-contained modules of code and
data--to the C language, Objective-C was Apple's favored language for writing applications for OS X or iOS, but
this may change with the introduction of Swift.
A derivative of Pascal used for Windows applications, owned by the
Embarcadero Delphi company. Open-source implementations under the rubric of Open Pascal exist for other platforms.
Created in the 1950s, Fortran is designed for high-performance
scientific and engineering computing.
Intended as a successor to C++, D is still a relatively new
language.
A hardware description language used in the creation and analysis of
electronic circuits.
Julia is designed for statistical and scientific computing while
retaining compatibility with legacy libraries written in Fortran or C.
Originating in the artificial intelligence community, Prolog is
primarily used as a teaching and research tool.
Created by National Instruments, LabView is designed for data
acquisition and industrial control.
A hardware description language used in the creation and analysis of
electronic circuits.
Created in the 1950s, Lisp became widely used by artificial
intelligence researchers, but it has seen a decline in more recent years.
Created by Ericsson for embedded telephony applications, the release
of Erlang as an open-source language in 1998 boosted its popularity among programmers developing applications that
have to handle many concurrent tasks.
A data- and statistical-analysis language.
Originally designed for the U.S. Department of Defense, Ada is used
for applications where reliability is critical, such as aerospace control systems.
A business programming language created by SAP.
Created in the late 1950s for business and financial applications,
COBOL software still drives critical systems in many large institutions.
An attempt to revive Lisp by making it compatible with the same
"virtual machine" that supports Java programs.
Originally a teaching language, this variant of Lisp is still used
for applications such as the App Inventor for Android, which allows the rapid development of mobile applications.
A scripting language intended for rapid prototyping and supporting
the Tk graphical user interface used primarily with Unix systems.
Primarily used for analytics and mathematical programming.
An object-oriented language with an emphasis on supported software
reliability.
Intended for the development of industrial programmable logic
controllers.
Originally designed to control radio telescopes, Forth is still used
for applications such as boot loaders and other firmware.
Primarily used for writing programs for Adobe's Web and mobile
multimedia Flash platform.


Who shall then say, Wherefore hast thou done so?


A 500 ERROR OCCURS WHEN SOMETHING BLOWS UP WITH OUR CODE.

IN FACT YOU'RE LIKELY PISSED OFF AT THE MOMENT.
TAKE SOLACE, TOP MENS ARE NOW AWARE


Insert random numbers, strings, and UUIDs using an IntelliJ action.
To insert random data, press Alt + R (⌥R) and choose the type of data you want to insert.
A different value will be inserted at each caret.
🕸 Data Types
There are five types of data that can be inserted:
- Integers, such as 7,826,922, in any base from binary to hexatrigesimal.
- Decimals, such as 8,816,573.10, using customisable separators.
- Strings, such as "PaQDQqSBEH", with custom symbol lists.
- Words, such as "Bridge", with custom word lists.
- UUIDs, such as 0caa7b28-fe58-4ba6-a25a-9e5beaaf8f4b, with or without dashes.
In addition to these data types, it's also possible to generate entire arrays of a data type.
For example, an array of integers might look like [978, 881, 118, 286, 288].
⚙ Settings
The way the data is generated can be adjusted to your demands.
You can customise the smallest integer to generate, the quotation marks to surround strings with, the number of elements
to put in an array, the decimal separator to use, the capitalisation of strings and UUIDs, and much more.
👀 Previews
To help you decide what settings to choose, a preview of the data that is generated with your current settings is
shown at the bottom of the settings window.
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Instead of using up all your shortcuts, Randomness only uses the Alt + R (or ⌥R) shortcut by
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However, you have the option to assign a unique shortcut to each individual data type and each settings window to
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add your own symbols, with support for Chinese symbols and emoji.
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Randomness is bundled with a small English dictionary from which it chooses random words.
However, you may want to add random words from another language or insert random movie quotes.
You can create your own dictionaries by creating a text file and putting one option on each line, and saving the
file with the .dic extension.
Empty lines and lines that start with a # are ignored.


One of the questions that I love to answer is, “What is the difference between Mathematical Planning and Machine Learning?” This is an excellent question. The fields are close to one another and solutions often involve both techniques. The way I differentiate is based on what question they are meant to answer. Mathematical Planning is primarily concerned with answering the question, “What should we do?” Machine Learning answers the question, “What is most likely?” We often ask these questions at the same time which is why the techniques can become conflated.
I want to provide an example of a real-world problem that involves the marriage of these two techniques. Due to the amount of material to cover, I decided to break it into several posts. This first post will setup the problem, the Food Cart Packing Problem, and go over the tools we will use to evaluate the quality of different strategies. In the next post we will formulate a Mathematical Planning model to find a better strategy than the simple heuristic we start within this post. Finally, we will implement a Machine Learning model to make predictions on demand trends and feed that into the Mathematical Planning model for even more profitable strategies.
Note: To see all the code for this post, go here
The Food Cart Problem
An example problem that I frequently use is the Food Cart Problem. It is easy for people to conceptualize so we can focus on the techniques. We are running a Food Cart and we want to know what items to pack at the beginning of the day to maximize our revenue. In this case we sell Burgers, Pizza, and Tacos (it’s an eclectic food cart). Each food takes up a certain amount of pantry space, fridge space, and weight. Our food cart has recently been downsized so we must be purposeful about what we pack. We have kept track of how much we sell each day when we didn’t used to run out of inventory.
We decide to start with a simple heuristic. On average, we sell 600 Burgers per day, 900 pizzas, and 700 tacos when we have excess inventory. Due to the downsize we are more restricted on space. We only have $3.0 m^3$ of storage, $2.0 m^3$ of fridge space, and can only carry $1,000 kg$ of weight. We have our same parking spot, so we expect to see the same demand.
Below is a breakdown of the space and weight requirements per serving of the food we provide.
| Burger | Pizza | Taco | Available | |
|---|---|---|---|---|
| Storage Space | $700.0 cm^3$ | $950.0 cm^3$ | $800.0 cm^3$ | $3.0 m^3$ |
| Fridge Space | $900.0 cm^3$ | $940.0 cm^3$ | $850.0 cm^3$ | $2.0 m^3$ |
| Weight Capacity | $550.0 gm$ | $800.0 gm$ | $600.0 gm$ | $1,000 kg$ |
A Simple Heuristic
We decide to prioritize pizza because it makes the most money, then tacos, then burgers. We pack for 900 pizza orders because that’s the average demand. With the remaining space, we pack tacos until we meet the average daily demand. If there is any room leftover, we will pack some burgers. Given the dimensions of our food, the result comes to 900 pizzas, 466 tacos, and 0 burgers. This makes sense to us since we have much less space than we did before, and we thought we may have to drop a food item from the menu. If we run the numbers, we expect to make $2,668.50 per day… or do we?
The important nuance to this problem is that this plan was made using the average demand.  On days where the demand for pizza is greater than 900, we would not be able to capture that additional revenue anymore because we do not have enough inventory. At the same time, when the demand for pizza is below 900, we lose out on the possibility we could have sold more of other foods had they been in stock. We would like to get some idea of what the actual revenue value we should expect. Fortunately there is a tool for this, Monte Carlo Simulation.
Monte Carlo Simulation
Monte Carlo simulation is conceptually simple, you can get an idea for the shape of a distribution (in our case expected revenue) by sampling from it many times and then computing descriptive statistics. Our hope is to find what the average revenue is expected to be.
Note: I encourage people to read up on Monte Carlo. I do not have the space here to provide an exhaustive explanation. To my Statistician readers, I know I am glossing over details. I am trying to provide an engaging example without intimidating readers with mathematical rigor.
Now, if you can remember your course on probability distributions, you may remember that the arrival rate of customers can be modeled by a Poisson Distribution. We will assume that this is a valid distribution to model the demand that we see at our food cart. Part of what makes this distribution easy to work with is that it takes a single parameter, $\lambda$. The $\lambda$ is easy to calculate, it is the mean of the data. Therefore, we say that pizza demand is a Poisson distribution with a $\lambda$ of 900.0 or in math notation:
$$\text{PizzaDemand} \sim Poisson(\lambda = 900 )$$
$\lambda$ can be thought of as the average rate of arrivals or in our case the overage number of orders in a day for a given food.
Now we need to do some domain modeling and setup our little simulation. We will start with some types to describe our domain.
type Food = Food of string
type Lambda = Lambda of float
The Food
type differentiates between which food item we are referring to. The Lambda
is the average demand we have observed and is the input for our Poisson distributions. We will use Units of Measure to track the units in our calculations. This will protect us from making silly conversion errors.
[<Measure>] type USD
[<Measure>] type cm
[<Measure>] type gm
[<Measure>] type serving
USD
stands for United States Dollars. cm
and gm
are the SI units for volume and mass. The serving
measure is for the quantity of servings we are packing. We will be using the Math.NET
library for performing calculations with distributions. We need a function for taking a plan and generating a random outcome for our revenue model. We will call this function many, many times to get an idea of the distribution of revenue.
let sample
(foodDemands: seq<Food * Lambda>)
(revenue: SMap<Food, float<USD/serving>>)
(plan: Map<Food, float<serving>>)
(rng: System.Random) =
let evaluteSoldQuantity planAmount (Lambda lambda) rng =
// Generate a random sample from the Poisson distribution and take the lesser
// of the planned inventory or of the random Demand value that was generated
let actualQuantity = Math.Min (float planAmount, Sample.poisson lambda rng |> float)
// Multiply by 1.0<serving> to get the correct units on the result
actualQuantity * 1.0<serving>
foodDemands
|> Seq.map (fun (food, demandRate) -> food, (evaluteSoldQuantity plan[food] demandRate rng))
|> Seq.sumBy (fun (food, soldQuantity) -> soldQuantity * revenue[food])
What sample
does is take the plan and perform a single simulation. It generates a random demand value for the given food and compares that to what we planned to have in inventory. It takes the lesser of our planned quantity or the random demand that was generated which becomes the actual amount sold. It takes the quantity of each food that was sold and multiplies it by the revenue of the food. It sums this across all the foods which gives us our revenue for that single simulation.
sample
performs a single simulation. We need to run many to calculate statistics on the results. We create an evaluate
function which will call the sample
function many times and gather the results. evaluate
then computes the descriptive statistics and the revenues that were generated in our simulations.
let evalute
(foodDemands: seq<Food * DemandRate>)
(revenue: SMap<Food, float<USD/serving>>)
(plan: Map<Food, float<serving>>)
(rng: System.Random)
(numberSamples: int) =
let samples =
seq {
for _ in 1..numberSamples ->
sample foodDemands revenue plan rng
|> float
} |> Array.ofSeq
DescriptiveStatistics samples
We provide an argument to evaluate
called numberSamples
which controls the number of simulations that we perform. DescriptiveStatistics
from Math.NET
allows us to compute the descriptive statistics. This provides us with the sample mean, variance, and standard deviation.
Evaluating Our Simple Heuristic
Now that we have the ability to simulate the effects of different plans on our revenue, let’s see how well our simple heuristic does. First, we define the data for the parameters of our model.
let burger = Food "Burger"
let pizza = Food "Pizza"
let taco = Food "Taco"
let foods =
[
burger
pizza
taco
]
let revenue =
[
burger, 1.3<USD/serving>
pizza, 1.6<USD/serving>
taco, 1.4<USD/serving>
] |> SMap
let storage =
[
burger, 700.0<cm^3/serving>
pizza, 950.0<cm^3/serving>
taco, 800.0<cm^3/serving>
] |> SMap
let fridgeSpace =
[
burger, 900.0<cm^3/serving>
pizza, 940.0<cm^3/serving>
taco, 850.0<cm^3/serving>
] |> SMap
let weight =
[
burger, 550.0<gm/serving>
pizza, 800.0<gm/serving>
taco, 600.0<gm/serving>
] |> SMap
let demandRates =
[
burger, DemandRate 600.0
pizza, DemandRate 900.0
taco, DemandRate 700.0
]
let maxItems = 1_000
let maxWeight = 1_000_000.0<gm>
let maxStorage = 3_000_000.0<cm^3>
let maxFridge = 2_000_000.0<cm^3>
We then define some functions which implement our simple heuristic for prioritizing pizza, then tacos, then burgers.
// A function to call Math.Floor on floats with Units of Measure
let floor (x: float<'Measure>) =
Math.Floor (float x)
|> FSharp.Core.LanguagePrimitives.FloatWithMeasure<'Measure>
// A function to call Math.Min on floats with Units of Measure
let min (a: float<'Measure>, b: float<'Measure>) =
Math.Min (float a, float b)
|> FSharp.Core.LanguagePrimitives.FloatWithMeasure<'Measure>
// We packe the average daily demand of pizzas
let pizzaQuantity = 900.0<serving>
let tacoQuantity =
// The number of possible tacos based on space
let tacosBasedOnSpace =
List.min [
(maxStorage - (pizzaQuantity * storage[pizza])) / storage.[taco]
(maxFridge - (pizzaQuantity * fridgeSpace[pizza])) / fridgeSpace.[taco]
(maxWeight - (pizzaQuantity * weight[pizza])) / weight.[taco]
] |> floor
// The min of taco demand and the space available
min (700.0<serving>, tacosBasedOnSpace)
let burgerQuantity =
// The number of possible burgers based on space
let burgersBasedOnSpace =
List.min [
(maxStorage - (pizzaQuantity * storage[pizza]) - (tacoQuantity * storage[taco])) / storage[taco]
(maxFridge - (pizzaQuantity * fridgeSpace[pizza]) - (tacoQuantity * fridgeSpace[taco])) / fridgeSpace[taco]
(maxWeight - (pizzaQuantity * weight[pizza]) - (tacoQuantity * weight[taco])) / weight[taco]
] |> floor
// The min of burgers demand and the space available
min (600.0<serving>, burgersBasedOnSpace)
let plan =
[
burger, burgerQuantity
pizza, pizzaQuantity
taco, tacoQuantity
] |> Map
Now let’s analyze the result of performing 100 simulations and analyze the distribution of the revenue.
let rng = System.Random ()
let stats_100Runs = Simulation.evalute demandRates revenue plan rng 100
I like to print things out in tables, and I am loving the Spectre.Console
project. Here is a table with the results of our simulations.
┌──────────────┬─────────┬──────────┬────────┐
│ NumberOfRuns │ Mean │ Variance │ StdDev │
├──────────────┼─────────┼──────────┼────────┤
│ 100 │ 2075.50 │ 605.37 │ 24.60 │
└──────────────┴─────────┴──────────┴────────┘
Now we need to ask a critical question, “How confident are we in the answer?” Our initial run shows that on average, we appear to make $2,075.50. What happens if we run this experiment again?
┌──────────────┬─────────┬──────────┬────────┐
│ NumberOfRuns │ Mean │ Variance │ StdDev │
├──────────────┼─────────┼──────────┼────────┤
│ 100 │ 2071.54 │ 926.30 │ 30.44 │
└──────────────┴─────────┴──────────┴────────┘
We get a slightly different answer, \$2,071.54. Well, this is interesting. Our numbers are making sense based on our earlier thought experiment. We are no longer carrying extra inventory, so we do not benefit from days where demand is exceptionally high. We would have to have excessive demand for pizza and tacos to sell everything and achieve a revenue of \$2,668.50.
What we need to focus on answering now is how well do we know the Mean, the expected revenue. Fortunately, there is a useful statistical tool called the Confidence Interval. It allows us to put bounds on where we think the true expected revenue is. The formula for the Confidence Interval is:
$$ CI = \bar{x} \pm z \frac{s}{\sqrt{n}} $$
The Confidence Interval ($CI$) is defined as a lower and upper bound. $\bar{x}$ is the average of the data, $s$ is the standard deviation, and $n$ is the number of samples that were taken. $z$ is a parameter you choose based on how confident you want to be that the true mean of the distribution is between the lower and upper bound. If we want to be 95% confident that the true average revenue is between the lower and upper bound, we will use a $z$ of 1.960. If we wanted to be 99% percent sure, we would use a $z$ of 2.567. If you would like to know where the values of $z$ are coming from, look up the t-distribution and t-distribution Confidence Intervals.
Note: For my stats friends. I am assuming that the error is normally distributed and has a mean of 0. Normally I would plot this to verify but the blog series is already turning into 3 posts, so I have to cut some details somewhere.
Let’s add the confidence interval for 95% and 99% to our output table and see what we get.
┌──────────────┬─────────┬──────────┬────────┬──────────────────┬──────────────────┐
│ NumberOfRuns │ Mean │ Variance │ StdDev │ 95% CI │ 99% CI │
├──────────────┼─────────┼──────────┼────────┼──────────────────┼──────────────────┤
│ 100 │ 2064.35 │ 997.13 │ 31.58 │ 2058.16, 2070.54 │ 2056.25, 2072.46 │
└──────────────┴─────────┴──────────┴────────┴──────────────────┴──────────────────┘
This gives us a much clearer understanding of what we can expect out of our revenue. This is how we should interpret these Confidence Intervals. There is a 95% probability that the true average revenue is between 2058.16 and 2070.54. There is a 99% probability that the true average revenue is between 2056.25 and 2072.46. Notice that the bounds of the 99% CI are wider than the 95% CI. The more confident we want to be that we have captured the true average revenue, the wider we must make the bounds.
The average revenue of 2064.35 we observed in this most recent run is only the average of the data we simulated. It is more valuable to understand where the true revenue average actually is. Let’s increase the number of simulations that we perform and see how that affects the Confidence Intervals.
┌──────────────┬─────────┬──────────┬────────┬──────────────────┬──────────────────┐
│ NumberOfRuns │ Mean │ Variance │ StdDev │ 95% CI │ 99% CI │
├──────────────┼─────────┼──────────┼────────┼──────────────────┼──────────────────┤
│ 100 │ 2073.74 │ 735.74 │ 27.12 │ 2068.43, 2079.06 │ 2066.78, 2080.71 │
│ 1,000 │ 2071.96 │ 895.10 │ 29.92 │ 2070.11, 2073.81 │ 2069.53, 2074.39 │
│ 10,000 │ 2073.31 │ 776.40 │ 27.86 │ 2072.77, 2073.86 │ 2072.60, 2074.03 │
│ 100,000 │ 2073.34 │ 773.98 │ 27.82 │ 2073.16, 2073.51 │ 2073.11, 2073.56 │
│ 1,000,000 │ 2073.20 │ 778.17 │ 27.90 │ 2073.14, 2073.25 │ 2073.13, 2073.27 │
└──────────────┴─────────┴──────────┴────────┴──────────────────┴──────────────────┘
We see that the mean, variance, and standard deviation are not changing significantly as we increase the number of simulations. What we do see though is that the Confidence Intervals are getting tighter. This is because as we gather more data, we become more confident of where the true average revenue is. We can see that by the time we get to 1,000,000 samples the CI for 95% is only 0.011 wide.
Our model for revenue does not have many random variables so it shouldn’t surprise us that it is easy to get a tight bound on the CIs. As models becomes larger with more random variables though, the number of iterations to achieve tight bound goes up significantly. There is a cost trade off. The tighter you want the bounds, the more computational effort that is required.
Next Steps
We’ve covered quite a bit of ground in this post. We have introduced a new problem, the Food Cart Problem.  We also introduced the idea of a Confidence Interval for understanding how good our estimates are. Next time we will show how to use Mathematical Planning to find a better plan for packing the Food Cart that achieves a higher expected revenue which is also more reliable.


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Scene 3rd
Study and Laboratory of The Black Crook.
(in 2) Hertzog discovered L.H., seated at
table poring over a large cabalistic book.
Greppo pinched and starved asleep on stool
R.C. before a retort furnace in R.H. flat.
An antique lamp illuminates the characters
of the book
upon the table, upon on
which
is a skull and hourglass--Door on L.H.
flat. Transparent window in R.H. flat.
Music () at opening of scene.
Hertzog
(Rising in pain and with difficulty he closes
the book and comes forward)
Vain, vain! Some subtile spell is
hovering in the air, that mocks my power
and makes the charms that once were potent,
a jabbering, idle sound. And shall I yield
to this invisible? I, Hertzog, the Crook, whom
men call Sorcerer. I, at whose name the
strong man trembles and the weak grows
faint. I whose life of long, laborious
years hath well nigh run its course, gleaning
dark knowledge in forbidden paths,
shall I now seek the light? My eyes
are old and dim and could not brave the
glare. No, no, I'll work new mines, new
mines, and plumb the depths of darker
mysteries still.
Hertzog
(Turning)
How now, knave?
Greppo
(Starts from his sleep and uses the bellows
rapidly at the mouth of the furnace)
Hertzog
Come hither, varlet.
Greppo
Yes, master.
(puts down
bellows and comes forward, yawning)
Hertzog
What, drone, sluggard, drowsing again?
Greppo
N-n-n-no good master, no.
Hertzog
Out, lying knave, did I not hear thee
snore?
Greppo
Snore? Aye, granted, but 'twas in thy service,
master. Much fasting and long
watching caused this left, rebellious eye
to wink, and so I snored to wake it up
again.
Hertzog
Bah! shuffling loon, thy drowsing comes of overfeeding.
Greppo
(Viewing himself)
Overfeeding? Look I if I were
overfed? A scanty chopin of weak sour beer
with one poor groschen's worth of musty beans
is all the banquet this shrunk belly knows
from week to week.
Hertzog
What, rogue, dost grumble?
Greppo
No, good master, no.
(Rumbling thunder)
Music () Tremolo piano
Hertzog
Ha, the night grows foul. 'Tis all the better,
Bring me my cloak and staff
#Thunder#
Greppo
Bringing forward crutch stick and short,
black hooded cloak from nail on flat
They are here.
(Places cloak on Hertzog's
shoulders
Hertzog
(Drawing the hood over his head)
So, now
thine own.
Greppo
My cloak, I have none, Master.
Greppo
Oh, Lord, what a night,
(brings his cap
from peg beside the furnace)
Hertzog
Bring with thee yonder brazen casket.
Greppo
Th-th-the brazen casket?
Hertzog
Aye, echoing fool!
#(Thunder)#
Greppo
(Tremblingly goes to table and takes casket)
(Aside)
The devil's tool chest
(Aloud)
Whither
go we, dread master?
Hertzog
To the Serpents' Glen.
(#Loud thunder)#
Greppo
(Starting)
Oh Lord! The Serpents Glen!
Beelzebub's favorite chapel! Surely, good
master, you would not, and above all
on such a night as this?
(#Thunder and lightning,# the latter showing
itself in transparent window on
R.H. flat)
Hark how the tempest howls. Strong
pines are toppling down the mountain
sides.
Hertzog
Silence and follow.
(Goes toward door.
#lightning and loud thunder#)
Greppo
(Who has made a movement to follow,
starts back trembling violently.)
Master, I-I cannot!
Hertzog
(Turning fiercely)
What, ingrate, do you
rebel?
Greppo
No, master, no, my spirit's willing, but
my legs are weak.
Hertzog
Wretch, did I not snatch thee drowning
from the whirling gulf, bind thy torn
limbs with rare medicaments and stanch
the current of thy ebbing life that fast
was running out?
Greppo
You did, good master, you did.
(Aside)
Out of the water into the fire.
Herzog
Begone!
#Music forte#() Throws open the door in L.H. flat. Loud
crashing thunder and vivid lightning. Greppo starts back.
Fool, begone, I say!
(Seizes and hurls him toward the open door. Heavy thunder and
lightning. Greppo in doorway entreats. Hertzog raises
his staff)
Away, away!
Thunder and lightning--Exeunt door in L.H. flat
Close in


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layout: default
---
-
小川 雄大 / OGAWA Katsuhiro
-
a.k.a. fivestar
-
Software Engineer, Technical Book Writing, Engineering Advising
-
Work at Mercari Inc.
-
Born on in Saitama, Japan
-
Live in Tokyo
-
Avatars ...
-
KANA
-
Japanese "hiragana/katakana" and "zenkaku/hankaku" transformer.
-
Timer
-
Simple web countdown timer.
-
Resozyme
-
Resource-oriented framework for building the RESTful web application in Go.
-
CrocosSecurityBundle
-
Symfony bundle provides a simple way to manage auth with annotation.
-
memberid
-
ID management/converting utility for helping your team members management.
-
Navy
-
GitHub web-hooked event handling framework.


Gecode::Set::Int::ChannelBool< View > Class Template Reference
[Set propagators]
Propagator for channelling between set variable and its characteristic function More...
#include <int.hh>
Classes | |
| class | IndexAdvisor |
| Advisor storing a single index More... | |
Public Member Functions | |
| virtual Actor * | copy (Space &home, bool) |
| Copy propagator during cloning. | |
| virtual PropCost | cost (const Space &home, const ModEventDelta &med) const |
| Cost function (defined as PC_QUADRATIC_LO). | |
| virtual size_t | dispose (Space &home) |
| Delete propagator and return its size. | |
| virtual ExecStatus | propagate (Space &home, const ModEventDelta &med) |
| Perform propagation. | |
| virtual ExecStatus | advise (Space &home, Advisor &a, const Delta &d) |
| Give advice to propagator. | |
Static Public Member Functions | |
| static ExecStatus | post (Home home, ViewArray< Gecode::Int::BoolView > &x, View y) |
| Post propagator for . | |
Protected Types | |
| typedef MixNaryOnePropagator < Gecode::Int::BoolView, Gecode::Int::PC_BOOL_VAL, View, PC_GEN_NONE > | Super |
Protected Member Functions | |
| ChannelBool (Space &home, bool share, ChannelBool &p) | |
| Constructor for cloning p. | |
| ChannelBool (Home home, ViewArray< Gecode::Int::BoolView > &, View) | |
| Constructor for posting. | |
Protected Attributes | |
| Council< IndexAdvisor > | co |
| Council for managing advisors. | |
| SetDelta | delta |
| Accumulated delta information. | |
| GLBndSet | zeros |
| Accumulated zero Booleans. | |
| GLBndSet | ones |
| Accumulated one Booleans. | |
| bool | running |
| Flag whether propagation is currently running. |
Detailed Description
template<class View>
class Gecode::Set::Int::ChannelBool< View >
Propagator for channelling between set variable and its characteristic function
Implements channelling constraints between BoolVar and a SetVar. For BoolVars and SetVar it propagates the constraint .
Requires
#include <gecode/set/int.hh>
Definition at line 346 of file int.hh.
Member Typedef Documentation
typedef MixNaryOnePropagator<Gecode::Int::BoolView, Gecode::Int::PC_BOOL_VAL, View,PC_GEN_NONE> Gecode::Set::Int::ChannelBool< View >::Super [protected] |
Constructor & Destructor Documentation
| Gecode::Set::Int::ChannelBool< View >::ChannelBool | ( | Space & | home, | |
| bool | share, | |||
| ChannelBool< View > & | p | |||
| ) | [inline, protected] |
Constructor for cloning p.
Definition at line 109 of file channel-bool.hpp.
| Gecode::Set::Int::ChannelBool< View >::ChannelBool | ( | Home | home, | |
| ViewArray< Gecode::Int::BoolView > & | x0, | |||
| View | y0 | |||
| ) | [inline, protected] |
Constructor for posting.
Definition at line 83 of file channel-bool.hpp.
Member Function Documentation
| Actor * Gecode::Set::Int::ChannelBool< View >::copy | ( | Space & | home, | |
| bool | share | |||
| ) | [inline, virtual] |
Copy propagator during cloning.
Implements Gecode::Actor.
Definition at line 139 of file channel-bool.hpp.
| PropCost Gecode::Set::Int::ChannelBool< View >::cost | ( | const Space & | home, | |
| const ModEventDelta & | med | |||
| ) | const [inline, virtual] |
Cost function (defined as PC_QUADRATIC_LO).
Reimplemented from Gecode::MixNaryOnePropagator< Gecode::Int::BoolView, Gecode::Int::PC_BOOL_VAL, View, PC_GEN_NONE >.
Definition at line 125 of file channel-bool.hpp.
| size_t Gecode::Set::Int::ChannelBool< View >::dispose | ( | Space & | home | ) | [inline, virtual] |
Delete propagator and return its size.
Reimplemented from Gecode::MixNaryOnePropagator< Gecode::Int::BoolView, Gecode::Int::PC_BOOL_VAL, View, PC_GEN_NONE >.
Definition at line 131 of file channel-bool.hpp.
| ExecStatus Gecode::Set::Int::ChannelBool< View >::propagate | ( | Space & | home, | |
| const ModEventDelta & | med | |||
| ) | [inline, virtual] |
Perform propagation.
Implements Gecode::Propagator.
Definition at line 145 of file channel-bool.hpp.
| ExecStatus Gecode::Set::Int::ChannelBool< View >::advise | ( | Space & | home, | |
| Advisor & | a, | |||
| const Delta & | d | |||
| ) | [inline, virtual] |
Give advice to propagator.
Reimplemented from Gecode::Propagator.
Definition at line 195 of file channel-bool.hpp.
| ExecStatus Gecode::Set::Int::ChannelBool< View >::post | ( | Home | home, | |
| ViewArray< Gecode::Int::BoolView > & | x, | |||
| View | y | |||
| ) | [inline, static] |
Post propagator for .
Definition at line 116 of file channel-bool.hpp.
Member Data Documentation
Council<IndexAdvisor> Gecode::Set::Int::ChannelBool< View >::co [protected] |
SetDelta Gecode::Set::Int::ChannelBool< View >::delta [protected] |
GLBndSet Gecode::Set::Int::ChannelBool< View >::zeros [protected] |
GLBndSet Gecode::Set::Int::ChannelBool< View >::ones [protected] |
bool Gecode::Set::Int::ChannelBool< View >::running [protected] |
The documentation for this class was generated from the following files:
- gecode/set/int.hh (Revision: 11973)
- gecode/set/int/channel-bool.hpp (Revision: 10387)


Poet
Lead Singer
Guitarist
H_art the band is a super talented band made of three versatile artists Mordecai Kimeu, Wachira Gatama, and Kenneth Muya. The band is well known for how they interact with a wide variety of musical instruments, their musical poetic rhymes with smooth, danceable tunes and futuristic dress code. They have worked enough to roar their band products in Kenya and East Africa.
The band was formed when the three gentlemen met in their acting career at the Kenya National Theatre in late 2012. This was after they learned that they share a similar passion for music. They later joined Penya Africa’s prestigious artist development program at Sauti Academy where they learned more about music from writing, recording, music business. They graduated in April 2014; following a year of hard work, determination, perseverance, and performing at almost every venue and street corner in Nairobi, Kenya thus building an ever-growing steady reputation and prominence with various audiences.
The band is defined by African cultured theme which is observed from their dress code all through to their music. Their music speaks to generations from young to old since it is inspired by day-to-day experiences. Mordecai and Wachira have flourish delivery in African poetry while Kenneth compliments their rhymes with smooth African tunes. Mordecai also beautifies their jams with his heavenly vocals, thus creating a fresh genre they like to call Afro Poetry.


Kathleen Hobbs
Account Settings
Sign Out
Home
Camera Recording
Heating
Lighting
Notification Log
Security
Home
Floor 1
Floor 2
Home - Floor1
Bathroom
Foyer
Hallway
Laundry
Dining Room
Garage
Kitchen
Living Room


Tottenham 'identify three-man shortlist' to replace Mauricio Pochettino
Tottenham boss Mauricio Pochettino is under increasing pressure after a poor start to the season, with his side languishing in 14th in the table
by Irishmirror.ieTottenham have reportedly lined up three managers to replace boss Mauricio Pochettino.
The Spurs boss' future is growing increasingly unclear after a disappointing start to the season left the Champions League finalists in 14th in the Premier League.
Tottenham take on West Ham this weekend and speculation continues that Pochettino could well be on his way out of the club.
The Mail reports there are fears in the boardroom over the current malaise and it now appears a case of when, not if, Pochettino will leave the club.
It is claimed Tottenham have drawn up a shortlist of managers to come in, although they accept it will be difficult to land them mid-season.
Bournemouth boss Eddie Howe is believed to be a contender with his side having enjoyed a decent start to the season.
Former Chelsea boss Carlo Ancelotti is also well-liked within the Spurs boardroom but there are doubts over whether he would leave Napoli at this stage.
Ancelotti, 60, has reportedly been identified as a potential manager for David Beckham's MLS side Inter Miami next season.
RB Leipzig boss Julian Nagelsmann is another name tipped to be in contention to replace Pochettino.
The 32-year-old has done an excellent job in Germany and has been reportedly earmarked as a future Bayern Munich boss.
Tottenham could fall as low as 17th if they lose to West Ham this weekend, although a win would see them climb considerably up the table.


Support
Our Support Policy
ThemeRex technical support is always there to assist their users with all the technical questions regarding ThemeRex products.
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- We try to answer your questions within 1-10 hours. However, if any delay occurs, it doesn’t mean that we forgot about you. Some of the issues require testing and analyzing, so we can resolve it effectively.
- Please note! We do not provide support, if you do not have the purchase code.
- Item support does not include:
- Customization and installation.
- Support for third party software and plugins.
Support
How to use our Support System
ThemeRex technical support is always there to assist their users with all the technical questions regarding ThemeRex products.
- In the ‘Item Details’ there is a clickable banner “Support Sys”, which leads you to tickets.
- Or you may simply follow this link: themerex.ticksy.com.
- Enter your purchase key and register in the system.
- To avoid a confusion, please use your own ThemeForest login in Support System.
- Fill out the ticket form.
Support
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ThemeREX offers only the best products and services and recommends them to their customers. One of the most important services is HOSTING. Unlike any cheap hostings that usually have limited resources which causes many problems to the site owners, quality hosting gives a client a fair amount of resources sufficient for any website and any task. By using quality hosting you can rest assured that your site will work smoothly. If you happen to use such hosting with limited resources (e.g. GoDaddy!), you may experience issues with “one-click install” feature. In that case, you are always welcome to contact our support team to get qualified help.
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Dynamic Feature Modules 101
At Zola Electric, we have started the process of splitting our app into dynamic features. As a result of this, we’ve been forced to have serious conversations about the structure of our app’s code and question a lot of our design decisions.
The first step of the process - determining what features we wanted - was a bit easy for us because the code had already been grouped by features, but we found areas where we needed to rethink some of our implementation. This article (Dagger Multibinding in Dynamic Feature Modules) by Annyce explains one of such areas, where we had to update our process of handling Multibinding with Android Jobs.
I’m going to walk through the steps involved with converting one of the modules and all the pain points experienced. This does not take into account all the decisions we had to make but gives a high level overview of the process for anyone looking to get started.
Create and configure the module
To get started, right click on the root folder and select New
, Module
and then the Dynamic Feature Module
option as displayed above.
The module needs a name and a package name. Ensure that the base package name is the same as the app’s package name.
An optional title may be added to the module. Here, you also determine whether the module should be available on-demand, and whether to install the module on devices without the appropriate support.
A couple of changes are made in the project after the Finish button is clicked and a new package is added to the project’s root file.
The build.gradle
file
A custom build.gradle
file with a dependency on the selected base application module is created along with the new module. The new module will automatically be added to the existing settings.gradle
file in the root folder like so include ':app', ':dynamic_feature'
. The module will also be added to the list of dynamic features in the app level build.gradle
file.
dynamicFeatures = [":dynamic_feature"]
In our app, I updated this build.gradle file to include only the required dependencies for the classes specific to the feature. We have a .gradle
file in the root project that houses all the dependencies needed for tests and it was imported into the build.gradle
file.
apply from: '../core_test_dependencies.gradle'
The Layout resources
If the module was given a title when it was created, a strings.xml
file with that title would have been created in the res
folder of the new module. I had to relocate the respective layout files for the feature into the res
folder too.
The AndroidManifest.xml
file
An AndroidManifest.xml
file is generated for the new module, but we had to update it. All the required permissions needed for the feature’s functionality were included as usual. We haven’t enabled instant modules or the on demand delivery system yet, so we set those values to false.
<dist:module
dist:instant="false"
dist:onDemand="false"
dist:title="@string/title_dynamic_feature">
<dist:fusing dist:include="true" />
</dist:module>
I also moved the feature specific Activities
from the app’s AndroidManifest.xml
file to the AndroidManifest.xml
file in the newly created dynamic feature module. Everything remained the same except for the entry activity into that module which was called from the App module. Here, we had to create an activity with an intent filter.
<activity
android:name=".DynamicFeatureActivity">
<intent-filter>
<action android:name="android.intent.action.VIEW" />
</intent-filter>
</activity>
The Classes
I moved the related classes for the feature from the app module to the feature module. Since our app already followed a convention which we used for the other dynamic feature modules, all I had to do was ensure that this module conformed to it.
We had to decide what classes to leave in the app module and what to move over. For example, we left all the repositories in the app module because before this, we had already split the database into its own feature module and only the app module depended on it. We also left the associated dagger module class, the events class and a couple of other helper classes in the app module.
The Unit Tests
We migrated the unit tests and everything ran out of the box, except for a few snags. The first thing to note is that we needed to include a resources
folder in the test package where we added the MockMaker
file because this ensures that static classes can still be mocked. Some tests will fail if this is not done.
There was also this issue where JVM unit tests in the dynamic feature module were failing at runtime with a NoClassDefFoundError
when run from the terminal, but they passed when run directly in Android Studio.
The Distribution
Our app is now distributed via bundles
on Google Play instead of .apk
files as is required of apps with dynamic feature modules, and we’re reaping the benefits already as the app size is already so much smaller. However, because of the regions we build for, we found that sometimes to save data costs the app files were distributed manually among our users and this led to crashes. This would have worked fine with the .apk
files, but since bundles strip out all the unused resources before creating a device specific apk file, it means a different device would not have all the required resources needed to run the app.
We also needed to update our CI configuration to generate universal and Armeabi apk files needed for QA testing.
What’s next? Instrumentation tests?
We’re currently working on creating a shared test module that will host all the instrumentation tests. To achieve this, we needed to extract a new core feature module from the base app module. This will act as the new base module and depend only on the database module. The app module and the shared test module will depend on the core module. We went through each file trying to determine if it should be migrated to the core module or remain in the app module.
Final thoughts
This is a lot of work, the kind that’s usually very important but not urgent. In the short term, we have to explain to the rest of the team that we were actually hard at work even though there’s no visible change in the app. In the long term however, we know that this is a huge step in the right direction as it is giving us the rare opportunity to go through our entire codebase and clean up legacy code.
We’re grateful to have resources from the community to guide us in the right direction - the Plaid app and articles written by the Android Dev Rel team and other members of the community.
Do you have any comments or suggestions? Please reach out on Twitter.


{% extends "post_sequence.html" %}
{% block page_name %}#{{ hash_tag }}{% endblock page_name %}
{% block sequence_header %}Posts with #{{ hash_tag }}{% endblock sequence_header %}
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I am a Master's Student in Computer Science at Duke University. I completed my undergrad in Computer Science and Engineering from Jawaharlal Nehru Technological University, India. My interests include Software Development, Machine Learning and Data Science. I love Competitive programming and watching Tennis, especially when Roger Federer plays.
Bring to the table win-win survival strategies to ensure proactive domination. At the end of the day, going forward, a new normal that has evolved from generation X is on the runway heading towards a streamlined cloud solution. User generated content in real-time will have multiple touchpoints for offshoring.
Capitalize on low hanging fruit to identify a ballpark value added activity to beta test. Override the digital divide with additional clickthroughs from DevOps. Nanotechnology immersion along the information highway will close the loop on focusing solely on the bottom line.
Podcasting operational change management inside of workflows to establish a framework. Taking seamless key performance indicators offline to maximise the long tail. Keeping your eye on the ball while performing a deep dive on the start-up mentality to derive convergence on cross-platform integration.
Collaboratively administrate empowered markets via plug-and-play networks. Dynamically procrastinate B2C users after installed base benefits. Dramatically visualize customer directed convergence without revolutionary ROI.
Collaboratively administrate empowered markets via plug-and-play networks. Dynamically procrastinate B2C users after installed base benefits. Dramatically visualize customer directed convergence without revolutionary ROI.
Apart from being a web developer, I enjoy most of my time being outdoors. In the winter, I am an avid skiier and novice ice climber. During the warmer months here in Colorado, I enjoy mountain biking, free climbing, and kayaking.
When forced indoors, I follow a number of sci-fi and fantasy genre movies and television shows, I am an aspiring chef, and I spend a large amount of my free time exploring the latest technolgy advancements in the front-end web development world.


Below is an interactive display of the data that is returned. By default, my own profile data is provided, but feel free to enter your own user name to see the data and the structure that is returned.
This API will cache values, meaning counts are only updated once per 24 hours per user.


These are some of the ways that one can use boomerang. This will help us make sure the library actually supports all possible use cases.
We need the ability to measure the time the user thinks it took to load a page. This is typically the time between the user clicking a link (or entering a URL into the browser) and the page becoming usable.
While it's easy enough to note the time when a user clicked a link if the link was on a page you control, it's not easy to tell the exact moment when a user enters a URL into a browser and hits enter. We'll therefore only concentrate on link clicks on pages that we control.
One unknown is the time when a page becomes usable. For most pages, this is when the onload event fires, however, there may be pages on which the onload event fires before the page is actually usable (eg, a lot of content loaded via javascript), or when the page becomes usable before the onload event fires (eg, hidden resources downloaded via javascript at the bottom of the page). In both these cases, the developer of the page has a better idea of when their page has become usable, so they should have the ability to tell the library when to fire this event.
Breaking this into four separate use cases:
Many websites might load content dynamically. For example, images for a slide show or content for tabs may be loaded via javascript. A stock ticker may periodically refresh itself from a back end web service using XHR, a webmail service may check with the server for new messages or download the selected message using javascript, etc.
In all these cases the browser may not fire an event for when the download was initiated or completed and the library will need to expose methods/events that the web developer can invoke when needed.
See HOWTO #2.
Since users browse the web using different types of internet connections, it's not always possible to aggregate page load times for multiple users to get an indication of a number that's statistically representative for all users. Knowing the user's bandwidth, however, allows us to aggregate data from similar users and use these numbers as representative for users with that type of network connection.
See HOWTO #3.
Many sites may be composed of separate components loaded from different back end services. For example, on my home page, I have widgets from dopplr, upcoming, twitter, delicious and flickr. Now while it's important to have the overall page load time, which is what users perceive as my page's performance, it can also be useful and instructive to measure the load time of each component separately, but as part of the measurement for the entire page. This allows us to then cut up a page's load time into the load time for each of its components, and carry out statistical analysis of each component in the context of its containing page.
To do this, the library needs to expose additional timers that the page developer can set. These timers should be beaconed back along with the overall page load time.
See HOWTO #4.
Network Latency is one of the largest contributors to page download time. HTTP latency is typically higher than simple ping time because it requires a few to-and-fro packets to estabilish a TCP connection and make a HTTP request. There's also overhead involved in HTTP request and response headers that don't contribute to actual page content.
Measuring a user's HTTP latency provides a good indication of the impact that parallelising component downloads can have on overall page load time.
See HOWTO #3.
When doing performance analysis, it is often useful to run an A/B test that compares the performance of two or more different page designs. Since these pages might all have the same URL, it becomes necessary to add some additional information to the data beaconed back that identifies the page from a series of tests.
The library should provide a method for the page developer to tag each page with additional information.
See HOWTO #5.
DNS latency tells us how long it takes to make a DNS request. This may be affected by various factors including your web server's DNS configuration and TTL, and how an end user's ISP's DNS servers are configured. Measuring this latency gives us an indication of how many domains we can safely lookup on a single page. There's a trade-off between the number of DNS lookups we can make and the parallelisability that multiple domains bring us. HTTP latency and DNS latency together can tell us where this point lies for our users.
See HOWTO #8
For high traffic sites, it's useful to only measure a random sample of requests rather than every request that comes in. We could either do the random sampling on the server side and only serve the javascript on a subset of requests, or we could simplify the server, and sample on the client side.
Client side sampling won't be as accurate as server side sampling since we don't share state across clients, however we can still get a decently random sample.
See TODO #1.
If you have a URL entry point, chances are that someone will abuse it, either intentionally or accidentally.
For example, you design a site that you're happy with, and you include the boomerang javascript in your code to measure performance. The javascript beacons back to your server. Now someone comes along and really likes your design, so goes ahead and copies your HTML and sticks it on their own server. They change some of the text and images, but leave the javascript still beaconing to your server. You end up getting their beacons, which not only increase the load on your server, but also messes with your measurements.
The second possibility is that some script kiddie notices the beacon URL on your site and starts hitting it with a whole bunch of different combinations to try and exploit your server, or if nothing else, to try and DoS you.
boomerang should be able to protect against these problems.
See HOWTO #7
The latest code and docs is available on github.com/yahoo/boomerang


|
JavaTM 2 Platform Standard Ed. 5.0 |
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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object java.util.concurrent.locks.LockSupport
public class LockSupport
Basic thread blocking primitives for creating locks and other synchronization classes.
This class associates with each thread that uses it, a permit
(in the sense of the Semaphore
class). A call to park will return immediately
if the permit is available, consuming it in the process; otherwise
it may block. A call to unpark makes the permit
available, if it was not already available. (Unlike with Semaphores
though, permits do not accumulate. There is at most one.)
Methods park and unpark provide efficient means of blocking and unblocking threads that do not encounter the problems that cause the deprecated methods Thread.suspend and Thread.resume to be unusable for such purposes: Races between one thread invoking park and another thread trying to unpark it will preserve liveness, due to the permit. Additionally, park will return if the caller's thread was interrupted, and timeout versions are supported. The park method may also return at any other time, for "no reason", so in general must be invoked within a loop that rechecks conditions upon return. In this sense park serves as an optimization of a "busy wait" that does not waste as much time spinning, but must be paired with an unpark to be effective.
These methods are designed to be used as tools for creating higher-level synchronization utilities, and are not in themselves useful for most concurrency control applications.
Sample Usage. Here is a sketch of a First-in-first-out non-reentrant lock class.
class FIFOMutex { private AtomicBoolean locked = new AtomicBoolean(false); private Queue<Thread> waiters = new ConcurrentLinkedQueue<Thread>(); public void lock() { boolean wasInterrupted = false; Thread current = Thread.currentThread(); waiters.add(current); // Block while not first in queue or cannot acquire lock while (waiters.peek() != current || !locked.compareAndSet(false, true)) { LockSupport.park(); if (Thread.interrupted()) // ignore interrupts while waiting wasInterrupted = true; } waiters.remove(); if (wasInterrupted) // reassert interrupt status on exit current.interrupt(); } public void unlock() { locked.set(false); LockSupport.unpark(waiters.peek()); } }
| Method Summary | |
|---|---|
static void |
park()
Disables the current thread for thread scheduling purposes unless the permit is available. |
static void |
parkNanos(long nanos)
Disables the current thread for thread scheduling purposes, for up to the specified waiting time, unless the permit is available. |
static void |
parkUntil(long deadline)
Disables the current thread for thread scheduling purposes, until the specified deadline, unless the permit is available. |
static void |
unpark(Thread thread)
Make available the permit for the given thread, if it was not already available. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Method Detail |
|---|
public static void unpark(Thread thread)
thread
- the thread to unpark, or null, in which case
this operation has no effect.public static void park()
If the permit is available then it is consumed and the call returns immediately; otherwise the current thread becomes disabled for thread scheduling purposes and lies dormant until one of three things happens:
interrupts
the current
thread; or
This method does not report which of these caused the method to return. Callers should re-check the conditions which caused the thread to park in the first place. Callers may also determine, for example, the interrupt status of the thread upon return.
public static void parkNanos(long nanos)
If the permit is available then it is consumed and the call returns immediately; otherwise the current thread becomes disabled for thread scheduling purposes and lies dormant until one of four things happens:
interrupts
the current
thread; or
This method does not report which of these caused the method to return. Callers should re-check the conditions which caused the thread to park in the first place. Callers may also determine, for example, the interrupt status of the thread, or the elapsed time upon return.
nanos
- the maximum number of nanoseconds to waitpublic static void parkUntil(long deadline)
If the permit is available then it is consumed and the call returns immediately; otherwise the current thread becomes disabled for thread scheduling purposes and lies dormant until one of four things happens:
interrupts
the current
thread; or
This method does not report which of these caused the method to return. Callers should re-check the conditions which caused the thread to park in the first place. Callers may also determine, for example, the interrupt status of the thread, or the current time upon return.
deadline
- the absolute time, in milliseconds from the Epoch, to
wait until
|
JavaTM 2 Platform Standard Ed. 5.0 |
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Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


ShopFast.com
ShopFast.com is an American fashion and lifestyle e-coomerce portal. It offers apparel,footwears,accessories,fragrances, beauty products,home accessories and other fashion and lifestyle products. It also offers shoe,home decor and furniture. The e-store at present carries over 4,000 brands and over 60,000 products.
MEN
CLOTHINGS
SHOES
ACCESSORIES
WOMEN
CLOTHINGS
SHOES
ACCESSORIES
KIDS
CLOTHINGS
SHOES
ACCESSORIES


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#include <fcntl.h>
#include <termios.h>
#include <stdio.h>
#include <string.h>
#include <unistd.h>
#include <errno.h>
#include <sys/types.h>
#include <dirent.h>
#include "TeleoInterface.h"
Go to the source code of this file.
Data Structures | |
| struct | TeleoInterfaceInt |
| Teleo Interface Internal The private structure used for the Interface. More... | |
Functions | |
| TeleoError | TI_FindUsbSerialDevice (cchar **dest, int index) |
| Function to find the device representing the Teleo USB Translator Mac OS X only. | |
| int | matchUsbSerialDevice (struct dirent *tryThis) |
| Function to return true if an individual directory entry should be selected; returns false otherwise. | |
| TeleoError | TI_Create (cchar *name, TI_InterfaceType interfaceType, TeleoInterface **ti) |
| Creates and initializes the interface structure. | |
| TeleoError | TI_nameGet (TeleoInterface *ti, cchar **name) |
| Allows the user to retrieve the interface name. | |
| TeleoError | TI_typeGet (TeleoInterface *ti, cchar **type) |
| Allows the user to get the short name - i.e. | |
| TeleoError | TI_blockingSet (TeleoInterface *ti, bool blocking) |
| Control whether the interface does blocking IO or not. | |
| TeleoError | TI_open (TeleoInterface *ti) |
| Open the port. | |
| TeleoError | TI_close (TeleoInterface *ti) |
| Close the port. | |
| TeleoError | TI_get (TeleoInterface *ti, uint8 *cp, int countIn) |
| Get characters. | |
| TeleoError | TI_put (TeleoInterface *ti, uint8 *cp, int count) |
| Put characters. | |
| TeleoError | TI_SleepMs (long ms) |
| Sleep for the specified ms. |
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Function to return true if an individual directory entry should be selected; returns false otherwise.
Definition at line 299 of file TeleoInterfaceMacOSX.c. |
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Control whether the interface does blocking IO or not.
Definition at line 137 of file TeleoInterfaceMacOSX.c. References TeleoInterfaceInt::blocking, and TeleoError. |
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Close the port.
Definition at line 191 of file TeleoInterfaceMacOSX.c. References TeleoInterfaceInt::deviceHandle, TELEO_OK, and TeleoError. |
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Creates and initializes the interface structure. Does not open the device, since there maybe be configuration to be done first. The name may be omitted. If this happens, the default name for the supplied interface type will be used.
Definition at line 55 of file TeleoInterfaceMacOSX.c. References TeleoInterfaceInt::blocking, TeleoInterfaceInt::interfaceType, TeleoInterfaceInt::name, TELEO_E_ALLOC, TELEO_OK, TeleoError, TI_FindUsbSerialDevice(), TI_USB, TI_USB0, TI_USB1, and TI_USB2. |
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Function to find the device representing the Teleo USB Translator Mac OS X only.
Definition at line 268 of file TeleoInterfaceMacOSX.c. References TELEO_E_ALLOC, and TeleoError. |
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Get characters. Currently only permits a single character at a time.
Definition at line 215 of file TeleoInterfaceMacOSX.c. References TeleoInterfaceInt::blocking, TeleoInterfaceInt::deviceHandle, and TeleoError. |
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Allows the user to retrieve the interface name.
Definition at line 100 of file TeleoInterfaceMacOSX.c. References TeleoInterfaceInt::name, TELEO_E_NULL_POINTER, TELEO_OK, and TeleoError. |
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Open the port.
Definition at line 146 of file TeleoInterfaceMacOSX.c. References TeleoInterfaceInt::blocking, TeleoInterfaceInt::deviceHandle, TeleoInterfaceInt::name, TELEO_OK, TeleoError, TeleoInterfaceInt::terminalSettings, and TeleoInterfaceInt::terminalSettingsOld. |
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Put characters.
Definition at line 246 of file TeleoInterfaceMacOSX.c. References TeleoInterfaceInt::deviceHandle, TELEO_E_IO, TELEO_OK, and TeleoError. |
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Sleep for the specified ms.
Definition at line 262 of file TeleoInterfaceMacOSX.c. References TELEO_OK, and TeleoError. |
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Allows the user to get the short name - i.e. "ser", "usb", "net", etc.
Definition at line 112 of file TeleoInterfaceMacOSX.c. References TeleoInterfaceInt::interfaceType, TELEO_E_NULL_POINTER, TELEO_OK, TeleoError, TI_NETWORK, TI_SERIAL, and TI_USB. |
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copyright © 2002, 2003 MakingThings LLC |


Constructor # new SeedUI(seedGroup, uid) Parameters: Name Type Description seedGroup SeedUI group to which this seed belongs to uid number unique hole uid View Source ui_entities/SeedUI.ts, line 29 Members Type: SeedGroupUI # group the group to which this seed belongs to View Source ui_entities/SeedUI.ts, line 86 Type: Vector # originalPos initial position for the seed before it was dragged View Source ui_entities/SeedUI.ts, line 92 Methods # randomizePosition() Randomizes the position of the seed in the hole - seed group container View Source ui_entities/SeedUI.ts, line 50


This is a wider card with supporting text below as a natural lead-in to additional content. This content is a little bit longer.
This card has supporting text below as a natural lead-in to additional content.
This is a wider card with supporting text below as a natural lead-in to additional content. This card has even longer content than the first to show that equal height action.
This is a wider card with supporting text below as a natural lead-in to additional content. This card has even longer content than the first to show that equal height action.
This is a template for a simple marketing or informational website. It includes a large callout called the hero unit and three supporting pieces of content. Use it as a starting point to create something more unique.


The Georgian Lari has depreciated by 22.35% in value against the U.S. Dollar (USD) in the three months between November 2014 and February 2015.
24 November 2014: 1 USD = 1.79 Lari
24 February 2015: 1 USD = 2.19 Lari
According to Forbes, Bidzina Ivanishvili, Georgia’s richest person and former Prime Minister, has a net worth of 5,200,000,000 USD. If he had his money saved in Lari, his net worth would have decreased by 949,771,689.50 USD during these three months.
How has the Lari depreciation affected your net worth?
Lari
24 November 2014
USD
24 February 2015
USD
USD
How Does This Work?
It is pretty simple, actually.
The calucalator divides your net worth by each of the exchange rates (1.79 and 2.19) to convert your Lari into USD.
Then the difference is taken between the two numbers to see how much your net worth had decreased.
That's it!
Play with the newest calculator!
This depreciation calculator was made using exchange rates between November 2014 and February 2015. A more recent version of the calculator is available as part of our Lari Explorer application that also includes over 40 currency exchange rates and 20 bank exchange rates.