The Rules
There are 5 questions, you have 50 seconds to answer all 5 questions.
1. What is the Simpsons family dogs name?
2. Who's catchphrase is 'up and atom'?
3. Who was Lisa's first kiss from?
4. What color is the Marge's necklace?
4. Who blocked out the sun over Springfield?
Results!


Chapter 3: Heidi: our first adv3Lite game
In this chapter we'll write our first adv3Lite game together.
Each of the three games in this tutorial is created step by step; you’ll get most benefit (especially to begin with) if you take an active part, typing in the source code on your computer. Our first game, described in this chapter and the two which follow, is borrowed directly from the "Inform Beginner's Guide" by Roger First and Sonja Kesserich. As they describe it, it tells this sentimental little story:
“Heidi lives in a tiny cottage deep in the forest. One sunny day, standing before the cottage, she hears the frenzied tweeting of a baby bird; its nest has fallen from the tall tree in the clearing! Heidi puts the bird into the nest, and then climbs the tree to place the nest back on its branch.”
It’s a very simple tale, but even so we’ll cover quite a lot of ground before we have a finished adv3Lite game. We’ll get there in stages, first making a very rough approximation of the story, and then successively refining the details until it’s good enough for an initial attempt (there’s time later for more advanced stuff).


Since the network contains feedback cycles, the usual backpropagation algorithm won't work very well. Furthermore, relying on handcrafted labels that are applied to the output of the network is very error-prone and it creates a large distance between our training signal and the weights that we would like to adjust. Hence, we would like to train our neurons more locally without a reliance on an external signal source. This is where the Shannon entropy framework comes quite in handy. If we consider the example of a word pattern again, we can measure the amount of information for each letter — that is, the number of bits required to represent this letter in a message — by calculating the Shannon entropy. We can then look at the word pattern neuron as a way of compressing the information given by the individual letter neurons. This compression can be formalized using mutual information, which can then be used to derive an objective function for our training algorithm. The resulting derivative function exhibits a very interesting and unexpected behavior with regard to when the synapse weights and neuron bias values increase or decrease. Before we go on, let's first have a look at the formula for calculating entropy: $$H(X) = -\sum_{i=1}^n {\mathrm{P}(x_i) \log \mathrm{P}(x_i)}$$ This formula is actually comprised of two parts. The first involves the Shannon information or information content or surprisal part: $$S(x) = -\log_b{\left(P\right)}$$ The second involves the calculation of the expected value of the surprisal: $$\operatorname{E}[X] =\sum_{i=1}^n x_i\,p_i$$ The Shannon information can be interpreted as quantifying the level of "surprise" of a particular outcome. So, events with a high probability have a low surprisal value and events with a low probability have a high surprisal value.
Now that we have an understanding of the basics of Shannon entropy, we can see what information gain is
and how it is calculated. Information gain quantifies the "amount of information" obtained about one random
variable through the observation of another random variable. In our example, the observed random variable would be that of
the letter, and the other that of the word. The formula for information gain is as follows:
$$I(X;Y) = \sum_{y \in \mathcal Y} \sum_{x \in \mathcal X}
{ p_{(X,Y)}(x, y) \log{ \left(\frac{p_{(X,Y)}(x, y)}{p_X(x)\,p_Y(y)} \right) }} $$
To get a better understanding of this formula, we can take a closer look at its components.
It actually uses the concept of Kullback-Leibler divergence to calculate the divergence between the
joint distribution of \(X\) and \(Y\) and the product of the marginals.
$$D_\text{KL}(P \parallel Q) = \sum_{x\in\mathcal{X}} P(x) \log\left(\frac{P(x)}{Q(x)}\right)$$
In other words, we are using KL divergence to compare the joint distribution of \(X\) and \(Y\) to the
special case in which these occur independently of each other.
Before going forward, we would like to introduce a slightly different notion of information gain:
$$I(X_i;Y) = \sum_{y \in \mathcal Y} \sum_{x_i \in \mathcal X_i}
{ p_{(X_i,Y)}(x_i, y) \left( \log{p_{(X_i,Y)}(x_i, y)} - \log{p_{X_i}(x_i)} - \log{p_Y(y)} \right) } $$
As you can see, the surprisal values of different probability distributions now lie at the core of this formula.
The outer part is simply the calculation of the expected value of the inner part.
To clarify the variables and indices, we would like to give a brief overview:
The set \(\mathcal Y\) contains the positive and negative or fired and non-fired states of the current neuron. Hence, \(p_Y(y)\) and \(p_Y(\neg y)\) are
the probabilities of these two states. The same holds for the set \(\mathcal X_i\) that simply contains the positive and negative states of the input neuron
of the synapse \(i\). This means that, if we consider the probabilities of the synapse \(i\), we get four different states: \(p(x_i,y), p(\neg x_i,y), p(x_i,\neg y), p(\neg x_i,\neg y)\)
Note that there is a strong asymmetry between the positive and negative cases. If you take, for example, the probability distribution for the neuron representing a very common word like “the,” the probability for the negative case will still be far greater than that for the positive case. Since the surprisal is inversely related to the probability, the contribution of the surprisal of the negative case can often be neglected.
To figure out how the objective function may look, we need to take a closer look at the neuron and its input synapses. Let us consider the synapses as information consumers and the neuron itself as an information producer. The synapses or information consumers can now be modeled using the information gain \(I\) and the neuron itself can be modeled using the entropy \(H\). $$G = H(Y) - \sum \limits _{i} I(X_i, Y)$$ In this model, we make the implicit assumption that the probability distributions of the input synapses are independent of each other, except in the case that they are all activated together. In that case, the neuron would fire, and we can use it as a compressed representation of its inputs.
Since the calculation of the entropy relies on probability distributions, we need to figure how we can determine these. This part may sound easier than it actually is, since there are some pitfalls to consider. A simple way to estimate the probability for a neuron would look like this: $$P(neuron) = \frac{\text{number of fired activations}}{\text{size of the sample space}}$$ Counting the number of fired activations for a neuron is easy, but questions remain: 1) how do we determine the sample space? and 2) is the sample space equal for all the neurons? The answer to the latter question is “no”. The size of the sample space depends on the space that the activations cover in the input data set. For example, an activation representing a single letter requires less space than does an activation representing a whole word. In the case of a neuron representing a single letter, we could determine the size of the sample space \(N\) by summing up the number of characters of all the training documents. For neurons whose activations cover a larger space within the document, we need to divide the total number of characters by the average covered area of these neurons’ activations. $$N_{chars} = \text{number of characters over all training examples}$$ $$c = \text{average space covered by activations of a neuron}$$ $$N = N_{chars} \cdot c$$
Another problem with estimating probability distributions is that we might not have enough training instances for a reliable statistic after inducing a new neuron or a new synapse. Yet, we still want to be able adjust the weights of this neuron. Hence, we need a conservative estimate of what we already know for certain about the distribution. Considering that the surprisal (\(-log(p)\)) only gets large when the probability gets close to zero, we can use cumulative beta-distribution in order to determine what the maximum probability is that can be explained by the observed frequencies. Lets consider an example wherein the frequency \(f\) is 10 and our sample space \(N\) is 100. Then we need to choose the parameters \(\alpha\) and \(\beta\) in the following manner. $$\alpha = f + 1$$ $$\beta = (N - f) + 1$$ The probability density function \(f(x;\alpha,\beta)\) of the beta-distribution then looks as follows:
Here, we can see how likely each probability estimate is given our measured frequency. In order to choose a reliable estimated probability we need to look at the cumulative distribution function:
If we invert this function and select a threshold, say 5%, then we can estimate an upper bound for our true probability. In other words, we are now 95% certain that the true probability distribution underlying the measurement is lower than our estimate. Consequently, we are now able to compute a lower bound value for the surprisal value.
Since we would like to optimize the compression rate within the network, we need to find a way to adjust the synapse weights and the neuron biases such that the network requires less information to encode the message of the input data. To do so we will need an objective function that we can convert to its first derivative. With this derivative function we are then able to use the gradient decent method to adjust the synapse weights and bias values.
Before going into more detail, we would like to break up the calculation of the derivative function into three more easily comprehensible parts. The first, or outer part, includes the derivative of the information gain and entropy functions. The second consists of the derivative of the activation function, while the third, or inner part, consists of the derivative of the weighted sum.
To calculate the outer part for our derivative function we start out with equation 7. This first part is fairly simple and can be rewritten using the sum rule: $$G' = H'(Y) - \sum \limits _{i} I'(X_i, Y)$$
Now, before we go on to calculate the derivative of the information-gain function, we need to make the simplifying assumptions that the surprisal parts of the equation are considered constants. So, if we take equation 6, we can split it into two simpler parts and consider the \(S(x_i, y)\) part as constant. $$S(x_i,y) = \log{p(x_i, y)} - \log{p(x_i)} - \log{p(y)} $$ $$I(X_i;Y) = \sum_{y \in \mathcal Y} \sum_{x_i \in \mathcal X_i} { p_{(X_i,Y)}(x_i, y) S(x_i, y) } $$ The only variable left that we need to consider while calculating the derivative is the probability \(p_{(X,Y)}(x, y)\). This can be computed using the frequency \(f_{(X_i,Y)}(x_i, y)\) and the number of trainings instances \(N\). $$p_{(X_i,Y)}(x_i, y) = \frac{f_{(X_i,Y)}(x_i, y)}{N}$$ Where the frequency \(f_{(X_i,Y)}(x_i, y)\) is just the sum of all fired activations.
Now we have a slight problem arising. \(p_{(X_i,Y)}(x_i, y)\) is a distribution over the discrete random variable \(\mathcal Y\) and \(\mathcal X_i\). Unfortunately, we need a continuous function in order to calculate its derivative. That is the reason that we use \(x_{il} y_l\) as a model for counting a single trainings instance \(l\). $$f_{(X_i,Y)}(x_i, y) = \sum_{l}^N x_{il} y_l $$ For the negative cases \(\neg y\) and \(\neg x_i\) we use \((1 - y)\) and \((1 - x_i)\) respectively.
Another assumption that we need to make is, that only the most recent training example is considered a variable. All other training examples are considered constants and, since they are part of a summation, they can be simply removed from the equation. Hence, the derivative function of \(f_{(X_i, Y)}\) is as follows: $$ f_{(X_i,Y)}'(x_i, y) = x_i y'$$
Now, let us go back to equation 16, which describes information gain and calculates the gradient for it. Remember, the surprisal part of the equation is considered a constant. So, if we put the whole equation together and derive it with respect to \(y\), it is as follows: $$I'(X_i;Y) = \frac{1}{N} \sum_{y \in \mathcal Y} \sum_{x_i \in \mathcal X_i} {x_i y' S(x_i, y)}$$
The derivative of the entropy function can be stated very similarly: $$H'(Y) = \frac{1}{N} \sum_{y \in \mathcal Y} {y' S(y)}$$
To obtain the derivative of the activation function $$ y = \varphi (net) $$ we can simply apply the chain rule to obtain $$ y' = net' \varphi' (net) $$ Since the activation function \(\varphi (x)\) is given by \[\varphi(x) = \Bigg \{ {0 \atop \tanh(x)} {: x \leq 0 \atop : x > 0}\] the derivative of it is \[\varphi'(x) = \Bigg \{ {0 \atop 1 - \tanh^2(x)} {: x \leq 0 \atop : x > 0}\] To get a better understanding, the following graph shows the outer derivative of the activation function.
As you can see, there is a discontinuity at \(x = 0\) where the activation function exceeds the activation threshold of the neuron.
Now, remember that the weighted sum \(net\) of the neuron is calculated in the following way: $$net = {b + \sum\limits_{j=0}^N{x_j w_{j}}}$$ To adjust the parameters of this model, we need to distinguish three different cases: the positive case, the negative case, and the bias case. Then we need to calculate the derivative functions for them. The positive and the negative case stem from the fact that the set \(\mathcal X_i\) contains these two cases which are modeled as \(x_i\) and \((1 - x_i)\). If we only model the positive case, the learn rule would be unable to learn from examples in which the input \(x_j\) is zero.
The positive case can be derived relatively easy from equation 26. The sum can be dropped, because all cases where \(j \ne l\) become constant and therefore 0. $$\dfrac{\partial net}{\partial w_l} = x_l $$
To describe the negative case, we need to split up the bias into two components: the actual bias \(b_c\) and a set of counterweights \(b_j\) for each synapse. $$b = b_c + \sum\limits_{j=0}^N{-b_j}$$ If we combine the equation 26 and 28, we get: $$net = {b_c + \sum\limits_{j=0}^N{x_j w_{j} - b_j}}$$ If we then chose the value of these counterweight biases to be equal to the synapse weights, that is \(b_j = w_j\) we get the following equation: $$net = {b_c + \sum\limits_{j=0}^N{-w_{j} (1 - x_j)}}$$ For the negative case we need to take into account that we have assumed \(b_j\) to be equal to \(w_j\) and therefore needs to be adjusted at the same time. $$\dfrac{\partial net}{\partial w_l} = \dfrac{\partial net}{\partial b_l} = 1 - x_l $$ Basically, in the negative case, the \(b_l\) part of the bias needs to be adjusted at the same time as \(w_l\).
For the bias case, we start with equation 29 and consider everything except the bias \(b_c\) a constant. $$\dfrac{\partial net}{\partial b_c} = 1 $$
The following diagram illustrates, how the gradient is propagated through the network.
Another problem is concept drift. Since we are using the distribution to adjust the synapse weights, changes will occur in the activation patterns and, therefore, so will changes in the distributions.


Systems: If your system is defective, it can be returned or exchanged within two weeks from date of purchase.
Games: Defective games can be returned or exchanged within 1 week.
New Items: New items cannot be returned if they have been opened.
Buyer's Remorse: If you decide you just don't want what you bought nearly as much as you thought you did, items can be returned within two days.
If you are unable to get to the store within the return time, please give us a call (within the eligible return period) and we'll try to work something out.
Short answer: No.
Long answer: Due to the hit-or-miss nature of system repair, we do not offer this as a service. We DO repair our own systems to make sure they are fully functional.
We also sell a small number of replacement parts (like N64 joysticks or NES 72 Pin connectors) if you're feeling adventurous enough to attempt your own repairs.
We will pay cash or offer store credit for your new or used games and systems.
We offer more for store credit than for cash.
You must be 18 or older to sell/trade and have an ID with you at the time of trade.
Every item has a different price, based on factors like popularity, rarity, and demand.
For a quote on the items you wish to sell/trade, please call us. If you have a long list of items, please email it to us, and we will get back to you as soon as we can.
Yes!
We will replace the internal battery in your cartridge games (which is the typical cause of "corrupted" saved games).
Short answer: Probably.
Long answer: We have the best disc-resurfacer available, but it can't do magic (although, it comes close sometimes).
Our machine can repair your average wear-and-tear scratches, but not deep gouges or disc-warping.
We'd be happy to look at your disc(s) and let you know whether or not we think your disc can be repaired.
We charge $2.50 per disc
Short answer: 10% of your total purchase will be added to your rewards card, which can be used as store credit.
Long answer: Every dollar you spend at our store is equal to one "point". You must accrue 20 points before it becomes useable as store credit.
10 Points = $1 in store credit.

Long answer: When the first Shaq-Fu was traded in, we decided to frame it. And although Shaq-Fu isn't a particularly rare game, we didn't see it very often.
Every time a new Shaq-Fu was traded in, we framed it.
At some point, friends and customers began bringing in Shaq paraphernalia to add to the collection. Now, our Shaq collection has evolved into an amalgam of random items from Shaq Soda to Shaq action figures (Shaqtion Figures).
Shorter answer: Shaq is great.
Probably.
We generally have it in stock, but Battletoads does sell out from time to time.
Nope.
Nope!
But we CAN add you to a list and let you know when we eventually get it in stock!
Almost all of our inventory comes from what we buy from our customers.
Due to the random nature of buying and trading, we don't know how long it will take us to get a particular item in stock. It may be weeks, it may be months.
5 North Broadway
Salem, NH 03079
(603) 458-5791


But One Day It Will Be Your
It Hurts Now,
More Info
A class in education has a variety of related.
For Your Friends & Family.
Weeks
8
See More
A class in education has a variety of
related meanings.
of Your LifeStyle.
Your Body is Reflection
HEALTHY LIFESTYLE
Prevent Cancer


Job Descriptions:
• Ensure excellent quality of customer service is provided;
• Ensure a proper closure of all calls;
• Provide general administrative support;
• Handling reception and front-line activities;
• Resolve complaints/ feedback and escalate to the various levels;
• Follow up with customers on issues and feedback;
• Perform clerical and other ad hocs duties
Requirements:
• Candidate must possess Diploma in any discipline
• Preferably with 1 – 2 years experience in customer service
• Proficiency in MS Office applications is a must
Pleasant personality with good interpersonal skills
Good written and communication skills
Ability to work independently with minimum supervision
• Good attitude and responsible.
Remuneration will be commensurate with experience and qualifications.
About the company:
Sembcorp Development is a subsidiary of Sembcorp Industries Ltd, a public-listed company on the Singapore Stock Exchange. We are a leading Asian developer with over 20 years’ experience undertaking master planning, land preparation and infrastructure development, and transforming raw land into large scale urban developments. We own, develop, market and manage urban developments comprising industrial parks as well as business, commercial and residential space in Vietnam, China and Indonesia. If you are highly motivated, self-driven and dynamic professionals, we invite you to make a strategic move to strengthen our present team in meeting the challenges of the millennium.


{% extends 'inheritanceClassworkApp/base.html' %} {% block header %}
CodeCrew Gallery EXPERIENCE THE HIGHLIGHTS OF WHAT WE DO
{% end block header %}88 Union Avenue, 2nd Floor, Memphis, TN 38103 |<PHONE_NUMBER> |<EMAIL_ADDRESS>| #GritGrindCode
{% end block footer %}


Mum transforms her kitchen just £200 and reveals how she did it
The 26-year-old didn't have to splash out a ton of cash
by James Andrews, Christopher HarperA money-saving mum has revealed how she managed to completely transform her kitchen without spending a ton of money.
Charlotte Greedy, 26, from Pontyclun, used ingenuity without splashing out.
"I came up with the idea when I desperately wanted a new kitchen but just couldn’t afford to buy a brand new one," Charlotte told money-saving community LatestDeals.co.uk .
"I then had the idea of painting it all to mimic a new kitchen. I bought stick on tiles from a company called Tic Tac Tiles on Amazon to cover my existing tiles and bought Good Home North pole paint from B&Q and painted my worktops!"
But she didn't stop there, reports the Mirror.
"I then painted my cupboards using Good Home Durable Delware Satin Multi-Surface paint," she explained.
"I used a mini gloss roller from Poundstretcher to apply both the worktop and cupboard paint, and only used a little brush for the hard to get to parts!
"I changed all my cupboard handles, again purchased from B&Q, and I changed my own plug sockets from white to chrome.
"I bought my flooring off eBay and used a pen knife and spray glue to fit it, and changed my light fitting to a brighter light. It cost around £200 to completely change the kitchen.”
And Charlotte, who posts shots of her home renovations on Instagram at @missgreedyshome , is made up with her kitchen transformation, saying it even makes cooking for her two boys better.
"I am absolutely in love with the transformation and it's made my kitchen look brand new," she said.
"Once the kitchen was complete I felt like I could be proud of my kitchen, something I never was.
"Whenever I go in there now I absolutely love it instead of loathing to go and make dinner!"


Boustead Plantations post net loss of RM34.31m in Q3
KUALA LUMPUR, Nov 21 — Boustead Plantations Bhd’s net loss had widened to RM34.31 million in the third quarter ended Sept 30, 2019 from RM21.89 million in the same quarter last year, mainly due to higher amortisation and finance costs.
Its revenue, however, rose to RM139.2 million from RM131.1 million previously due to the significant increase in sales volume of palm oil and kernel, which had more than offset the decline in prices of palm products, it said in a filing with Bursa Malaysia today.
Meanwhile, in a separate statement, Boustead Plantation said average crude palm oil (CPO) selling price for the first nine-month of 2019 was RM2,011 per metric tonne (MT), down by 16 per cent compared with the same period last year, while average palm kernel price declined by 39 per cent to RM1,179 per MT.
“For fresh fruit bunches production for the period, it grew by 10 per cent to 727,771 MT and average oil extraction rate increased to 21.6 per cent, while the average kernel extraction rate remained stable at 4.4 per cent,” it said.
Moving forward, the group expects the rest of the year to be driven by CPO price, crop production and its transformation programme.
“The transformation programme will focus on yield improvement, prudent cost management and operational efficiency to enhance profitability,” it added. — Bernama


[gecode-users] how to explore domain variables with Gist
Guido Tack
tack at ps.uni-sb.de
Tue May 8 18:55:04 CEST 2007
Hi!
Alberto Delgado wrote:
> I downloaded the source code and compiled gecode gist [...]
> Now i'd like to explore the variables in each nodes, i mean the
> domain of the variables. I tried double-click and it didn't work
> and the menu displayed by right-click doesn't include any action
> related with the variables.
The file qtgist/gist.hh defines a class Gecode::Gist::Inspector. You
have to inherit from this class, and override the virtual function
void inspect(Space* s). The you create an object of your new
inspector and pass it to the explore function. The inspector is then
registered with gist, and double-clicking a node will invoke the
inspect function. You just have to static_cast the Space* into the
specific type of your constraint problem, and output the variables in
any way you like.
A simple example (not tested, but you'll get the idea):
using namespace Gecode;
class A : public Space {
public:
IntVarArray x;
// the rest of your code defining your constraint problem
};
class I : public Gist::Inspector {
public:
virtual void inspect(Space* s) {
A* a = static_cast<A*>(s);
for (int i=0; i<a.x.size(); i++) std::cout << a.x[i] << std::endl;
}
};
int main(void) {
A a = new A();
I i = new I();
explore(a, i);
return 0;
}
Cheers,
Guido
--
Guido Tack
Programming Systems Lab, Saarland University, Germany
http://www.ps.uni-sb.de/~tack
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Donald Trump travels to Russia to explore building a hotel in coordination with Politburo officials. “He met with a lot of the economic and financial advisers in the Politburo,” said Trump spokesman Dan Klores.


Statistical data set
Apprenticeship and education and training national achievement rates tables.
The release of the 2014 to 2015 apprenticeship national achievement rates tables replaces the data for 2011 to 2012 to 2013 to 2014 published in 2015. The data used in this release is a cascade of the information first made available in the March 2016 statistical first release supplementary tables.
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The release of the 2014 to 2015 education and training national achievement rates tables replaces the data for 2013 to 2014 published in 2015. The data used in this release is a cascade of the information first made available in the March 2016 statistical first release supplementary tables.
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National Achievement Rate Tables are produced annually in arrears of the production of the qualification achievement rate reports.
Their purpose is to give providers and the sector as a whole summary indicators of overall performance against which individual providers can contextualise their own targets and actual performance.
Published: 18 May 2016
Updated: 20 May 2016


This package lets you access Matba Rofex API for trading using R. This will enable you to integrate Matba Rofex’ data easily into R workflows.
You can find the complete documentation under https://apihub.primary.com.ar/
At the moment rRofex
is only available through GitHub using devtools
.
devtools
# 1. In R install the package `devtools`. install.packages("devtools") # 2. Once `devtools` is installed, install `rRofex` library(devtools) devtools::install_github("gruporofex/rRofex")
We provide two libraries for ingesting data from Matba Rofex:
These are the currently available methods:
Available environments:
library(rRofex) # Once you have cretencials, you'll be able to get a token when you login conn <- trading_login(username = XXX, password = XXX, base_url ='http://api.remarkets.primary.com.ar') # You can get a complete Reference Data list with details trading_instruments(connection = conn, request = "securities", sec_detailed = T) # Real Time Prices using the REST APP trading_md(connection = conn, symbol = "DODic20") # Historical Trades trading_mdh(connection = conn, symbol = "DOJul20", date = "2020-02-06")
Development of this software was driven by Primary as part of an Open Source initiative of Matba Rofex.


|
tan 0.0.1
|
Parse, Analyze, and compile a list of tan source files. The compilation consists of multiple stages, performed using the CompilerAction interface. More...
#include <compiler.h>
Public Member Functions | |
| Compiler (const vector< str > &files) | |
| void | parse () |
| Parse the corresponding source file, and build AST. | |
| void | analyze () |
| Perform Semantic Analysis. | |
| llvm::Value * | codegen (CompilationUnit *cu, bool print_ir) |
| Generate LLVM IR. | |
| void | emit_object (CompilationUnit *cu, const str &out_file) |
| Compile to object files. | |
| void | dump_ast () const |
| Pretty-print AST. | |
| const vector< CompilationUnit * > & | get_compilation_units () const |
Static Public Member Functions | |
| static llvm::TargetMachine * | GetDefaultTargetMachine () |
| Get information about the current machine. | |
| static vector< str > | resolve_import (const str &callee_path, const str &import_name) |
| Get a list of possible files that corresponds to an import. | |
Static Public Attributes | |
| static vector< str > | import_dirs {} |
| Import search directories. | |
| static TanCompilation | compile_config {} |
| Current compile configuration. | |
Parse, Analyze, and compile a list of tan source files. The compilation consists of multiple stages, performed using the CompilerAction interface.
Definition at line 23 of file compiler.h.
|
explicit |
Definition at line 23 of file compiler.cpp.
| Compiler::~Compiler | ( | ) |
Definition at line 21 of file compiler.cpp.
| void Compiler::analyze | ( | ) |
Perform Semantic Analysis.
Definition at line 91 of file compiler.cpp.
| Value * Compiler::codegen | ( | CompilationUnit * | cu, |
| bool | print_ir | ||
| ) |
Generate LLVM IR.
Definition at line 52 of file compiler.cpp.
| void Compiler::dump_ast | ( | ) | const |
Pretty-print AST.
Definition at line 64 of file compiler.cpp.
| void Compiler::emit_object | ( | CompilationUnit * | cu, |
| const str & | out_file | ||
| ) |
Compile to object files.
Resulting *.o files are stored in the current working directory
Definition at line 47 of file compiler.cpp.
| const vector< CompilationUnit * > & Compiler::get_compilation_units | ( | ) | const |
Definition at line 131 of file compiler.cpp.
|
static |
Get information about the current machine.
Definition at line 104 of file compiler.cpp.
| void Compiler::parse | ( | ) |
Parse the corresponding source file, and build AST.
Definition at line 68 of file compiler.cpp.
|
static |
Get a list of possible files that corresponds to an import.
Suppose there's an import statement import "../parent.tan"
, in a file at "./src.tan", the call to resolve_import should be like Compiler::resolve_import("./src.tan", "../parent.tan")
| callee_path | The path to the file which the import statement is in |
| import_name | The filename specified by the import statement |
Definition at line 109 of file compiler.cpp.
|
inlinestatic |
Current compile configuration.
Definition at line 39 of file compiler.h.
|
inlinestatic |
Import search directories.
This is set by compile_files() in libtanc.h
Definition at line 34 of file compiler.h.


--- layout: post date: 2012-02-27 time: "10:00:00" authors: ["Greg Wilson"] title: Frustration (continued) tags: ["Content"] ---
It's been a frustrating couple of days. To recap, I want to convert our material from PowerPoint to HTML5 to make it easier for people to fork and merge, to make things easier to re-style, because it's an open format, and so on. David Seifried has welded an HTML5 audio player to Caleb Troughton's deck.js to create a display tool, which I'm very pleased with, but the content is killing me. Seriously. Here are three slides taken from our first episode on the Unix shell:
How can I translate those into HTML? The shell session transcripts are straightforward enough—a

here, a
there—but what about the explanatory comment in blue in the third slide? Or the filesystem diagram in the second? Or the stuff (I can't think of a better term) in the first? As I see it, the options are:
What I mean by "painful to work with" is that a lot of careful manual editing would be needed to do things like add elements incrementally in sync with a transcript. The result would also be largely unintelligible to search engines, and good luck copying and pasting it.
Back to the drawing board...


we learned from the basics from electrical components, to AC and DC current, to understanding Ohm’s Law. V = I R . We were then introduced to TinkerCAD, that allows creation of simple 3D designs.
TinkerCAD circuits – allows simulation of
Counting with a switch with states. Switch is press,
We learn to programme up a 555 timer circuit. Here is a component list of the circuit.


{% extends "base.html" %} {% block title %}TrendImages : About{% endblock %} {% block page_css %}{% endblock %} {% block page_js %}{% endblock %} {% block breadcrumb %}
TrendImages is a simple mashup site, taking interesting terms (currently gathered via the Twitter API) and creating galleries of images (from the Flickr API) using them. It loads the list of current trending search terms every "period of time" and creates galleries from them.
The purpose of the site was to give the author (Robert Seeger) a chance to learn (more) about Python and development on a PaaS environment (Google App Engine). Along the way, I managed to pick up some knowledge about the Django platform, which was also nice.
The code for this site (and partial implementations using other languages/platforms) can be found on Sourceforge, as can some of my other projects. In order to learn platforms/languages, I have a habit of implementing something using it. For a language, that tends to be a quick little program that solves a game. For web frameworks, I implement this site. Recently, I decided to start putting this work on Sourceforge for a couple of reasons:
If you have any other questions, they might be answered in the FAQ. If not, there's a link on that page to the recommended support page on Sourceforge.
{% endblock %}


| Defining URL: | http://www.fhir.org/guides/test4/ValueSet/blah-codes |
| Version: | 0.0.0 |
| Name: | BlahCodes |
| Status: | draft |
| Definition: | A bunch of example codes |
| Copyright: | markdown link: American Medical Association markdown new paragraph: All rights reserved. This value set includes content from SNOMED CT, which is copyright © 2002+ International Health Terminology Standards Development Organisation (IHTSDO), and distributed by agreement between IHTSDO and HL7. Implementer use of SNOMED CT is not covered by this agreement ## header2 ### header3 ### header4 emphasis bold markdown list: - list 1 - list 2 1. list1 1. list2 |
| Source Resource: | XML / JSON / Turtle |


Type in some sequences to pairwise align.
First, a '1' value is shown in all cells with matching letters.
Second, the matrixed is "walked" from bottom-right to top-left.
Third, we trace the best scores from top-left to bottom-right.
Lastly, we print the best alignment!


public class Player
extends java.util.Observable
| 构造器和说明 |
|---|
Player(java.lang.String name,
java.awt.Color color,
GameState game)
Constructor method
|
Player(java.lang.String name,
java.awt.Color color,
GameState game,
Strategy strategy)
Constructor method
|
| 限定符和类型 | 方法和说明 |
|---|---|
void |
addAllCard(java.util.ArrayList<Card> onhand)
Add all cards of the given player to receive player
|
void |
addCard(Card cards)
Add a certain type of card to player
|
void |
addCountry(Country c)
To add a country to the player's countries list
|
void |
addInfantry(Country c)
To add an infantry for target country of current player
|
void |
attack()
Method to attack by implementing the selected strategy
|
void |
changed()
Method to specify the state changes
|
boolean |
checkIfCanAttack()
This class will take in a country as well as the country it is going to
attack.
|
Country |
checkIfCanMove(Country origin,
java.lang.String countryName)
To check the selected country is able to move the troop to another
|
Country |
checkIfOwned(java.lang.String countryName)
To check if the country is belong to the player
|
boolean |
checkWinGame()
Method to check if the player wins the game
|
void |
fortify()
Method to fortify by implementing the selected strategy
|
int |
getBonus()
To get the initial army bonus in the setup phase
|
int |
getBonusAndChangeCard()
To get the initial army bonus in the setup phase
|
java.awt.Color |
getColor()
To get the player's color
|
java.util.ArrayList<Country> |
getCountries()
To get the player's countries list
|
GameState |
getGame()
To get the game state
|
java.util.ArrayList<CountryDecorator> |
getHand()
To get the player's hand cards list
|
int |
getInitTroop()
To get the init troop nums
|
java.lang.String |
getName()
To get the player name
|
java.util.ArrayList<Card> |
getOnHand()
To get the player's hand cards list
|
javax.swing.JFormattedTextField |
getPlayerTextName()
To get the playerName in the text
|
Strategy |
getStrategy()
Method to get strategy
|
void |
giveCards()
To give the player the hand cards
|
boolean |
isConquered() |
void |
isConquered(boolean _isCQ)
Indicate whether player conquered at least one country
|
boolean |
isHasMoved()
To check whether the player has moved the troop or not
|
void |
moveTroops(Country c1,
Country c2,
int toMove)
To perform move troop action
|
java.util.Map<java.lang.String,java.lang.Object> |
originalAttack(Country c1,
Country c2,
int decision1,
int decision2)
To perform attack action
|
void |
reinforce()
Method to reinforce by implementing the selected strategy
|
void |
removeCountry(Country c)
To remove a country from the player's countries list
|
void |
setColor(java.awt.Color color)
To set the player's color
|
void |
setCountries(java.util.ArrayList<Country> countries)
To set the countries list to belong to the player
|
void |
setgame(GameState g)
To set the game state
|
void |
setHasMoved(boolean hasMoved)
To set the status that the player has moved the troop or not
|
void |
setInitTroop(int initTroop)
To set the init troop nums
|
void |
setName(java.lang.String name)
To set the player name
|
void |
setPlayerTextName(javax.swing.JFormattedTextField playerTextName)
To set the playerTextName
|
void |
setStrategy(Strategy strategy)
Plugs in a specific strategy to be used
|
public Player(java.lang.String name, java.awt.Color color, GameState game, Strategy strategy)
name
- the player name with String typecolor
- the color of the player with Color typegame
- the game state with GameState typepublic Player(java.lang.String name, java.awt.Color color, GameState game)
name
- the player name with String typecolor
- the color of the player with Color typegame
- the game state with GameState typepublic Strategy getStrategy()
public void setStrategy(Strategy strategy)
strategy
- The operator to be appliedpublic boolean isHasMoved()
public void setHasMoved(boolean hasMoved)
hasMoved
- the status that the player has moved the troop or not with
boolean typepublic java.lang.String getName()
public int getInitTroop()
public void setInitTroop(int initTroop)
initTroop
- the init troop numspublic void setName(java.lang.String name)
name
- the desired the player name that want to be set with String
typepublic java.awt.Color getColor()
public void setColor(java.awt.Color color)
color
- the desired the player color that want to be set with Color
typepublic java.util.ArrayList<Country> getCountries()
public void setCountries(java.util.ArrayList<Country> countries)
countries
- the countries list desired to be set to belong to the player
with ArrayList typepublic java.util.ArrayList<CountryDecorator> getHand()
public void addInfantry(Country c)
c
- the target countrypublic java.util.ArrayList<Card> getOnHand()
public void addCard(Card cards)
cards
- public void addAllCard(java.util.ArrayList<Card> onhand)
onhand
- public void isConquered(boolean _isCQ)
_isCQ
- true if conquered one or more countriespublic boolean isConquered()
public GameState getGame()
public void setgame(GameState g)
g
- the desired game state wanted to be set with Gamestate typepublic Country checkIfOwned(java.lang.String countryName)
countryName
- the country name with String typepublic boolean checkIfCanAttack()
public Country checkIfCanMove(Country origin, java.lang.String countryName)
origin
- the selected country with Country typecountryName
- the country name with String typepublic java.util.Map<java.lang.String,java.lang.Object> originalAttack(Country c1, Country c2, int decision1, int decision2) throws java.lang.Exception
c1
- the selected country to perform attack action with Country
typec2
- the selected target to be attacked with Country typejava.lang.Exception
public void reinforce()
public void fortify()
public void attack()
public void moveTroops(Country c1, Country c2, int toMove)
c1
- the selected country to perform move troops action with
Country typec2
- the selected target country to receive the moved troop with
Country typetoMove
- the number of the troops that need to be movedpublic boolean checkWinGame()
public void giveCards()
public int getBonus()
public int getBonusAndChangeCard()
public void addCountry(Country c)
c
- a country that need to be added with Country typepublic void removeCountry(Country c)
c
- a country that need to be removed with Country typepublic javax.swing.JFormattedTextField getPlayerTextName()
public void setPlayerTextName(javax.swing.JFormattedTextField playerTextName)
playerTextName
- the playerTextName need to be setpublic void changed()


--- layout: page title: About header-img: "img/about-bg.png" ---
I live in Brooklyn and work full time at Rescuing Leftover Cuisine, a nonprofit food rescue organization. I love reading, crossword puzzles, and the St. Louis Cardinals, and I'm excited to learn to code!


Team diversity and generational differences are one of the novel keys to developing a global business model.
Your iGOAL-readiness Score:
{{ assessment.score }} / 100
iGOAL-Rubric Reports
Your iGOAL-readiness Score:
{{ assessment.score }} / 100
Classification:
{{ assessment.text }}
Analysis
Readiness Analysis based on innovation process
| Global Innovation Process | Ideation process | Matching process | Design and Development | Commercialization |
|---|---|---|---|---|
| Readiness percentage (%) | {{ assessment.averageIdeation }} | {{ assessment.averageMatching }} | {{ assessment.averageDesign }} | {{ assessment.averageCommercial }} |
| Group of competence | ||||
| {{ report.label }} | {{ data }} |
Readiness Charts
Ideation
Matching Process
Design & Development
Commercialization


Invalid / Unknown SCE context
$sce
Attempted to trust a value in invalid context. Context: {0}; Value: {1}
The context enum passed to $sce.trustAs was not recognized.
$sce.trustAs
Please consult the list of supported Strict Contextual Escaping (SCE) contexts.
supported Strict Contextual Escaping (SCE) contexts


About Pythonizr
Pythonizr is here to kick-start the development of your new Python project. It generates necessary boilerplate code to get started with. Also, provided a few helpers to add in a project.
Based on other *izr
This project is heavily inspired by Initializr by Jonathan Verrecchia and Extensionizr by Alex Wolkov
What next?
Let's add as much boilerplate and helper code as possible and find quality time for coffee!
Contact
Email at<EMAIL_ADDRESS>or tweet me @akashp1712.


History of Changes
Version Java 1.2.1 (February 2005)
- Clean unused jar (xmlParserAPI.jar,etc) and check and stored new versions. (RB)
- Clean unused build*.xml files. (RB)
- Generated the dist jar with version (i.e. xmlsec-1.2.1.jar instead of plain xmlsec.jar) (RB)
- Fix a memory leak when using xpath or using ResourceResolver and not hitting getElementByIdUsingDOM() (RB) Thanks to Sylvain Dusart. Fixes 32836.
- Fix erroneous creation/verification when using XPath2Filter and inclusive c14n.(RB)
- Library now throws an exception when asked to sign/verify an inexistent fragment.(RB) Thanks to Raymond Wong. Fixes 23554.
- Restore reset behaviour as default when reusing Canonicalizers(but an append one can still be used).(RB)
- Fix a bug when using base64transformation and external resources.(RB) Thanks to Sean Mullan. Fixes 33393.
- Fix a bug when passing XMLsignatureInput(InputStream) streams that don't acknowledge reset() as expected. (RB)
- Added i14n Base64 error message. (RB) Thanks to Sean Mullan. Fixes 32996.
Version Java 1.2 (December 2004)
- Rework the canonicalization for speed-up common cases (RB)
- General memory footprint improvements (RB)
- General speed optimizations (RB)
- Update the JCE algorith mechanism (VM)
Version C++ 1.10 (March 2004)
-
- Bug fixes for signature code
- Beta implementation of XML Encryption
- Initial implementation of pluggable algorithm handlers
Version C++ 1.00 (July 2003)
-
- First stable release
- Support for FreeBSD, NetBSD and Cygwin builds
- All KeyInfo elements now available
- Various bug fixes
Version C++ 0.20 (May 2003)
-
- Windows Crypto API interface
- Basic functions to extract information from signature objects
- Various bug fixes
Version Java 1.0.5 (unreleased)
-
Ported the docs to Forrest
(KW)
Version C++ 0.10 (unreleased)
-
First release of a Beta for the C++ library.
(BL)
Version Java 1.0.4 (15 July 2002)
-
Java - People who did not install Xalan properly under JDK 1.4.0 now get a more specific error message.
(CGP) -
Java - We use the most recent version of the BouncyCastle JCE now.
(CGP)
Version Java 1.0.3 (unknown)
-
Java - Added support Exclusive XML Canonicalization Version 1.0, W3C Recommendation 18 July 2002 . (There is no interop to test vector Y4 because of a problem in Xalan)
Canonicalization is written completely new: it's about 5-80 times faster than the implementation in version 1.0.2. It's highly recommended to upgrade to the new version.
(CGP) -
Java - Added support for XML-Signature XPath Filter 2.0, W3C Candidate Recommendation 18 July 2002
(CGP)


Join Nomad Pass Community
To create your Nomad Pass we will use your name, profile picture, job title, and geolocation from your Linked-In profile.
Example:
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class: center, middle # Hello, Terraform part I .footnote[by Marcin Chwedczuk] --- # Infrastructure as code According to Wikipedia: > **Server provisioning** is a set of actions to prepare a server with appropriate systems, data and software, and make it ready for network operation. > Typical tasks when provisioning a server are: select a server from a pool of available servers, load the appropriate software and appropriately customize and configure the system and the software. --- # Infrastructure as code Currently we have three types of solutions that try to address IaC problems: * Configuration Management Tools: Ansible, Chef, Puppet * Server Templating Tools: Docker, Packer * Server Provisioning Tools: Terraform, Cloud Formation, Heat We also have container management platforms like Kubernetes and Rancher (started as an independent solution, but becomes just a nice UI for Kubernetes). * Terraform - an open source product, created by HashiCorp (mostly known by its other tools like Vagrant and Consul) * Cutting edge technology (started just 3 years ago), written in Go, crossplatform --- # Terraform "Hello, world!" * Infrastructure is described as a set of `.tf` files * HCL (HashiCorp Configuration Language) is used to describe a new state of the infrastructure * Terraform (TF) figures out what changes must applied to the current state of the system to transition it into the new state Let's start with a simple TF script that creates a file on your local machine: ```yaml # This is a comment /* This is a multiline * comment */ provider "local" {} # resource RESOURCE_TYPE TF_RESOURCE_NAME resource "local_file" "hello_world" { content = "Hello, World!" # ${path.module} - directory containing this .tf file filename = "${path.module}/hello_world" } ``` --- # Terraform "Hello, world!" When running terraform for the first time or after adding a new provider you must execute: ```bash $ terraform init ``` This will download all necessary providers to `.terraform` directory. ```bash $ terraform plan ``` Will return a list of changes that TF is going to apply to the infrastructure Finally, you can apply the changes by running: ```bash $ terraform apply ``` The results of all these: ```bash $ ls hello_world local_files.tf terraform.tfstate ``` --- # Terraform "Hello, world!" `terraform.tfstate` file is used to keep current snapshot of the infrastructure. Do NOT edit or modify this file. The same rule applies to the infrastructure. When you are using TF you must NOT modify infrastructure manually. Otherwise it will get out of sync with TF state and you may expect some really horrendous problems. To rollback all changes just invoke: ```bash $ terraform destroy ``` Bonus! You may use `terraform fmt` to perttify all `.tf` files before commit. There is also VSCode extension for editing TF files. Yay! --- # HCL Basics - Providers * `provider`s are responsible for managing resources. For example `azurerm` will allow us to manage Azure cloud resources * `local` provider used in "hello world" example manages local machine resources like files * Often you must configure provider before you can use it, e.g. ```yaml # Configure the AWS Provider provider "aws" { access_key = "ABCD1234ABCD" secret_key = "ABCDABCDABCD" region = "us-east-1" } ``` --- # HCL Basics - Resources * `resource`s are parts of our infrastructure like servers, load balancers, service bus topics etc. * To define resource we use syntax: ```yaml resource "
" "
" { resource configruation } ``` * Resources can depend on each other (more on that later) --- # HCL Basics - Variables * TF allow us to define input and output variables. Input variables may be e.g. number of servers to create. Output variable e.g. DNS CNAME records for created servers ```yaml variable "file_suffix" { description = "Suffix of the file with dot, e.g. `.txt`." # No extension by default default = "" # type - string by default, but can also be list or map type = "string" } provider "local" {} resource "local_file" "hello_world" { content = "Hello, World!" filename = "${path.module}/hello_world${var.file_suffix}" } output "create_file_name" { description = "Full path of the created file name." value = "${local_file.hello_world.filename}" } ``` --- # HCL Basics - Variables * Variables can be set via command line parameters: ```bash $ terraform apply -var 'file_suffix=.txt' ``` * Or read from a `terraform.tfvars` file (in current working directory): ```yaml file_suffix = ".txt" ``` * Output variables are written to stdout: ``` create_file_name = /.../examples/002_variables/hello_world.txt ``` * You may access variable value in the code using syntax: ```yaml "${var.variable_name}" ``` * Also resources export various properties that can be later accessed using syntax: ```yaml "${resource_type.tf_resource_name.property}" # for example: "${local_file.hello_world.filename}" ``` --- # HCL Basics - Data * TF providers allow us to query our infrastructure * An Example: Looking for a suitable Amazon Machine Image (AMI): ```yaml data "aws_ami" "ubuntu" { owners = ["099720109477"] filter { name = "name" values = ["ubuntu/images/hvm-ssd/ubuntu-xenial-16.04-amd64-server-*"] } filter { name = "virtualization-type" values = ["hvm"] } most_recent = true } # Later usage resource "aws_instance" "app_server" { ami = "${data.aws_ami.ubuntu.id}" ... } ``` --- # HCL Basics - Data * TF data query can also call external programs and scripts * TF data can only parse JSON format (no free text format) ```yaml provider "local" {} data "external" "current_user_name" { program = ["bash", "${path.module}/get_user_name.sh"] query = {} } resource "local_file" "hello_user" { content = "Hi ${data.external.current_user_name.result.username}!" filename = "${path.module}/hello_${data.external.current_user_name.result.username}" provisioner "local-exec" { # Make file readonly command = "chmod a-wx ${local_file.hello_user.filename}" } } ``` ```bash # get_user_name.sh #!/bin/bash echo "{ \"username\": \"$(id -u -n)\" }"; ``` --- # HCL Basics - Lists * `count` meta-attribute allow us to create multiple copies of the resource * We can use `count` with list variables thank to two auxiliary functions `length(list)` and `element(list,index)` * Properties of "multi-resources" can be accessed using syntax `${local_file.hello_world.123.filename}` or if you want to get a list of values `${local_file.hello_world.*.filename}` ```yaml variable "list_of_files" { description = "List of file names like [\"foo\",\"bar\"]." type = "list" } provider "local" {} resource "local_file" "hello_world" { count = "${length(var.list_of_files)}" content = "Hello file ${element(var.list_of_files, count.index)}!" filename = "${path.module}/hello_world_${count.index}.txt" } output "list_of_created_files" { value = "${local_file.hello_world.*.filename}" } ``` --- # HCL Basics - Dependencies * TF automatically detects dependencies between resources, varaibles, datas etc. * If resource `machine.service_a` has inside its definition a reference to value `${machine.service_b.some_prop}`, then resource `machine.service_a` is dependent on resource `machine.service_b` and will be created after `service_b` * TF will try to perform as much parallel operations as possible without breaking dependencies (mostly to speed things up, e.g. creating a simple database on AWS can take up to 10 minutes) * `terraform graph` will generate graph of dependencies in DOT format * You can use GraphViz library or sites like https://dreampuf.github.io/GraphvizOnline/ to generate image from DOT description --- # HCL Basics - Dependencies .center[![Dependency graph](dependency_graph.png)] --- # HCL Basics - Modules * Modules reside in separate directories or subdirectories (TF can even load modules from remote git repository) * You should tread modules as units of reusability (they are like functions in imperative programming) ```yaml variable "filename_prefix" {} # Module input data "external" "current_user_name" { program = ["bash", "${path.module}/get_user_name.sh"] query = {} } resource "local_file" "hello_user" { content = "Hi ${data.external.current_user_name.result.username}!" filename = "${var.filename_prefix}_${data.external.current_user_name.result.username}" provisioner "local-exec" { command = "chmod a-wx ${local_file.hello_user.filename}" } } output "filename" { # Module output value = "${local_file.hello_user.filename}" } ``` --- # HCL Basics - Modules * `source` property contains relative path to module directory * Modules can be read from many sources. For example they can be read from Terraform Registry (something like Docker Hub) * Production grade modules should be versioned * You must run `terraform get -update` otherwise TF will not notice updates made to module code ```yaml provider "local" {} module "hello_current_user" { source = "hello_user" filename_prefix = "hello" # Pass input to module } module "witaj_current_user" { source = "hello_user" filename_prefix = "witaj" } output "created_files" { # Get output from module value = ["${module.hello_current_user.filename}", "${module.witaj_current_user.filename}"] } ``` --- # HCL Basics - Other nice things * `create_before_destroy` resource property allows us to perform infrastructure updates without downtime * `prevent_destroy` resource property may protect resources from `terraform destroy` * `terraform console` opens interpreter that allows you to play with `${...}` expressions * TF provides a lot of useful functions like `file(file_name)` to read contents of file or `uuid()` to create a new UUID/GUID --- # Docker and Postgres Examples * We will create two Docker containers and a network between them * Check `examples\006_docker` directory * We will create database and role in that database * Check `examples\007_db_provisioning` --- # Problem with shared state * So far we allow TF to write infrastructure state snapshot to current directory (`terraform.tfstate` file) * When working in teams state must be shared between team members * HashiCorp offers a commercial solution called Terraform Enterprise that can store state snapshot (also offers private module registry) * State can also be keept in AWS S3 buckets, Azure Storage or in Consul (Distributed configuration server) * This solves only half of the problem. Even with shared state two users may try to perform two different operations on infrastructure. Locking also is needed * Open source project called `terragrunt` offers simple locking solution --- # How to organize team work around Terraform * Blog: https://blog.gruntwork.io/how-to-use-terraform-as-a-team-251bc1104973 * Book: https://www.terraformupandrunning.com/ (short book, about 200 pages with plenty of images) * General idea: DevOps'es prepare reusable modules that are stored in a separate repository and are versioned * All team members use above modules to define infrastructure * For every environment we have a separate folder with the definition of insfrastructure for that environment (all in the same repo) * How to integrate this approach with existing CI/CD? --- # Terraform - Summary Pros: * A lot of examples (mostly for AWS and Azure) on GitHub * Can provision several cloud providers at once * Declarative language (you describe desired state of your infrastructure, not changes that should be made) Cons: * Poor documentation and not too many answers on StackOverflow (just 1.5k, huge number of them without answers) * Written in Go (looking at the source code of the providers may be more time consuming) * Syntax errors in configuration files may cause strange error messages * Do not tolerates delays when cloud processes infrastructure change requests async (confirmed on AWS). From user point of view this means you must run `terraform apply` several times * Configuration depends on cloud provider (move to another cloud provider = rewrite configuration)


Web development
Large B2B marketplace for construction companies in Germany. I was the sole front-end developer on this project, from design to launch.
An app for amateur astronomers that tells you if the weather conditions in your area are suitable for sky viewing. It gives a graph of cloud cover, humidity, and wind speed for the next 5 nights.
A React app that calculates profit or loss from trading bitcoin. It takes a date and the amount of bitcions bought, and returns the percentage and the total dollar value.
Hydrodynamical simulation of a planet spinning on its own axis.


Have reached tipping point, still can change course: Prince Charles on climate change
"We have abused nature, exploited her and given her nothing back in return," he told an audience at Christchurch's Lincoln University
Prince Charles warned Friday that time is running out to address the impact of climate change as he prepares to visit one of the Pacific island nations most affected by global warming.
As he wrapped up a six-day trip to New Zealand ahead of three days in the Solomon Islands, Charles said the need for climate action was urgent.
"We have reached a tipping point and we still have the ability to change course, but only 10 years," said the first in line to the British throne, who has been a passionate environmentalist for decades.
He said climate change was a scientific fact, rejecting suggestions from sceptics that "scaremongering" was dominating the debate.

"Nothing is sacred anymore, we are reaping a loss of biodiversity and experiencing the impacts of climate changes. We urgently need to pay the mounting debt."
He and wife Camilla, Duchess of Cornwall, will have the chance to see the impact of climate change at close quarters when they make their first visit to the Solomon Islands on Saturday.
Some communities in the impoverished archipelago have already had to move because of rising seas and the entire population of about 660,000 has been affected by increasingly extreme weather, including wild cyclones.


--- layout: post title: A full shelf permalink: /a-full-shelf date: 2012-04-25 15:33:10.000000000 -05:00 comments: true categories: - photos published: true --- I got this photo of a bookshelf at the CFEngine office in Palo Alto. Looks awesome, doesn't it? :)


Namespace: |
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xsd:duration |
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simple |
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locally within InterchangeTransferDurationGroup group in siri_datedVehicleJourney-v2.0.xsd; see XML source |
XML Representation Summary |
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<MinimumTransferTime> |
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Content: |
{ xsd:duration } |
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</MinimumTransferTime> |
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<xsd:annotation>
<xsd:documentation>
</xsd:annotation>
Minimum transfer duration for INTERCHANGE. SIRI v2,0
</xsd:documentation>
</xsd:element>
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This XML schema documentation has been generated with DocFlex/XML 1.9.5 using DocFlex/XML XSDDoc 2.8.1 template set.
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Once having only such a license, you will be able to run the fully-featured XML schema documentation generator both with DocFlex/XML (Full Edition) and with DocFlex/XML RE, which is a reduced free edition containing only the template interpretor / output generator. No other licenses will be required!
But this is not all. In addition to it, a commercial license for "DocFlex/XML SDK" will allow you to modify the XSDDoc templates themselves as much as you want. You will be able to achieve whatever was impossible to do with the template parameters only.
And, of course, you could develop any template applications by your own!
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you will also make an important investment in its future development, the results of which you could enjoy later by yourself.

To purchase a license, please follow this link: http://www.filigris.com/shop/
|


Introduction
In the last chapter we had a glimpse of Pandas. In this chapter we will learn resampling methods and DataFrame manipulating with real financial data. DataFrame is a very powerful tool for data analysis, and we will cover some commonly used methods dealing with financial data.
Here we use the data from yahoo_finance API.
import quandl
quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'
aapl_table = quandl.get('WIKI/AAPL')
aapl = aapl_table['Adj. Close']['2017']
Resampling
Last time we discussed how to access to series data by date index. Let's start from here using real financial time series data. aapl is a series who's values are Apple stock's daily close price, and the index is date. We will introduce how to obtain this series later.
print aapl
We have already known that we can access to a specific data point using series['yyyy-mm-dd']. We can also get access to the data in a specific month by simply using series['yyyy-mm'].
print aapl['2017-3']
Or in some consecutive months:
aapl['2017-2':'2017-4']
seires.head() and series.tail() are also useful method to quickly get access to the first or last N elements.
print aapl.head(5)
print aapl.tail(10)
Now we introduce a very powerful method: resample(). series.resample(freq) is a class called 'resampler'. The Argument freq tells the resampler the frequency for aggregating data. series.resample.mean() is a complete statement that aggregate the data by mean by a certain frequency. For example, if we want to aggregate the daily data into monthly data by mean:
by_month = aapl.resample('M').mean()
print by_month
We can also aggregate the data by week:
by_week = aapl.resample('W').mean()
print by_week
Virtually we can use almost any frequency we want by simply use the format 'nf', where 'n' is an integer and 'f' is M for month, W for week and D for day.
three_day = aapl.resample('3D').mean()
two_week = aapl.resample('2W').mean()
two_month = aapl.resample('2M').mean()
We can also change the aggregating method. By default we can use mean(), std(), max(), min(), which are mean, standard deviation, maximum value and minimum value respectively.
std = aapl.resample('W').std()
max = aapl.resample('W').max()
min = aapl.resample('W').min()
However, most of the time we don't want to aggregate in these methods. For example, if we want to calculate monthly return of the stock, we may just want to access the data for the last day of a month. Fortunately, we can customize the aggregating method. We write our function in series.resample.agg().
last_day = aapl.resample('M').agg(lambda x: x[-1])
print last_day
Or directly calculate the monthly rates of return using the data for the first day and the last day:
monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)
print monthly_return
Series object also provides us some convenient methods to do some quick calculation.
print monthly_return.mean()
print monthly_return.std()
print monthly_return.max()
We can also check the statistical summaries of a series.
print aapl.describe()
Another two frequently used method is series.diff() and series.pct_change(). This first method calculates the different between consecutive elements, and the second one calculates the percentage changes.
print last_day.diff()
print last_day.pct_change()
You may noticed that we induced a NaN value while calculating the percentage change, or rate or return.
When dealing with NaN, we usually either removing the data point or fill it with a specific value. Here we fill it with value 0:
daily_return = last_day.pct_change()
print daily_return.fillna(0)
We fill it with the next fitted value. This is called 'backward fill', or 'bfill' in short:
daily_return = last_day.pct_change()
print daily_return.fillna(method = 'bfill')
As you expected, since there is a 'backward fill' method, there must be a 'forward fill' method, or 'ffill' in short. However we can't use it here because the NaN is the first value.
We can also simply remove the NaN data point by series.dropna().
daily_return = last_day.pct_change().dropna()
print daily_return
DataFrame
After the long introduction to Series, finally we are going to discuss the most commonly used Pandas data structure: Pandas DataFrame. A DataFrame is essentially a group of series, thus it's not hard to understand how a DataFrames works if we master the series.
DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think it like a spreadsheet or SQL table. Like series, DataFrame accepts many different kinds of input: dictionary, 2-D numpy.ndarray, a series and other DataFrame.
Create DataFrames
The most commonly used method to create a DataFrame is passing a data dictionary.
dict = {'AAPL': [143.5, 144.09, 142.73, 144.18, 143.77],'GOOG':[898.7, 911.71, 906.69, 918.59, 926.99],
'IBM':[155.58, 153.67, 152.36, 152.94, 153.49]}
data_index = pd.date_range('2017-07-03',periods = 5, freq = 'D')
df = pd.DataFrame(dict, index = data_index)
print df
Manipulating DataFrames
We can access to the values in a DataFrame by columns and index. Each columns in a DataFrame is essentially a pandas series. We can access to a column by square brackets df['column_name']. The columns names are also properties of the DataFrame, so we can also use df.column_name to get the columns. In the previous demonstration, we used exactly this method to access to the Apple stock close price series.
df = aapl_table
print df.Close.tail(5)
print df['Adj. Volume'].tail(5)
All the methods we apply to a series index such as iloc[], loc[], and resampling methods, can also be applied to a DataFrame:
aapl_2016 = df['2016']
aapl_month = aapl_2016.resample('M').agg(lambda x: x[-1])
print aapl_month
It's easy to access to multiple columns to form a subset of the original DataFrame:
aapl_bar = aapl_month[['Open', 'High', 'Low', Close']]
print aapl_bar
We can also combined the methods above using loc[ ]. We separate the index and columns by comma to specify the range of data we want:
print aapl_month.loc['2016-03':'2016-06',['Open', 'High', 'Low', 'Close']]
The subset methods in DataFrame is very powerful. By writing logical statements in square brackets, we can make customized subsets:
above = aapl_bar[aapl_bar.Close > np.mean(aapl_bar.Close)]
print above
Data Validation
As we mentioned, all the series methods can be apply to a DataFrame in the same way. Here we create a new column from an existing DataFrame:
aapl_bar['rate_return'] = aapl_bar.Close.pct_change()
print aapl_bar
Here the calculation introduced a NaN value. If the DataFrame is large, we would not be able to observe it. isnull() provides a convenient way to check abnormal values.
missing = aapl_bar.isnull()
print missing
print '------------------ separate line ------------'
print missing.describe()
Note that the only column with value 2 in row 'unique' is 'rate_return'. This indicate that there are at least one missing value in the column 'rate_return'. We can even know the number of missing values by comparing 'count' and 'freq'. There are 12 counts and 11 'False', we know there is one 'True', which is the missing value.
We can also find the rows with missing values easily:
print missing[missing.rate_return == True]
Usually when dealing with missing data, we either delete the whole row or fill it with some value. As we introduced in the series chapter, the same method df.dropna() and df.fillna() can be applied to a DataFrame.
drop = aapl_bar.dropna()
print drop
print '---------------------- separate ----------------------'
fill = aapl_bar.fillna(0)
print fill
DataFrame Concat
We have already known how to extract a series from a dataFrame. Now we need to consider how to merge a series or a DataFrame into another one.
In Pandas, the function concat handles almost all of the merge tasks.
We can merge multiple series into a DataFrame:
s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')
s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')
data_frame = pd.concat([s1,s2], axis = 1)
print data_frame
joining two DataFrames by axis:
log_price = np.log(aapl_bar.Close)
log_price.name = 'log_price'
print log_price
print '\n---------------------- separate line--------------------\n'
concat = pd.concat([aapl_bar, log_price], axis = 1)
print concat
When we joining a DataFrame into another, we can indicate whether we want to merge by index.
df_volume = aapl_table.loc['2016-10':'2017-04',['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])
print df_volume
print '\n---------------------- separate line--------------------\n'
df_2017 = aapl_table.loc['2016-10':'2017-04',['Open', 'High', 'Low', 'Close']].resample('M').agg(lambda x: x[-1])
print df_2017
Using the methods we have already learned, we subset two DataFrames from the original one. Now we are going to merge the them with our DataFrame 'aapl_bar'
concat = pd.concat([aapl_bar,df_volume],axis = 1)
print concat
By default the the DataFrame are joined with all of the data. This default options results in zero information loss. We can also merge them by intersection, this is called 'inner join':
concat = pd.concat([aapl_bar,df_volume],axis = 1, join = 'inner')
print concat
Only the intersection part was left if use 'inner join' method.
Now let's try to append a DataFrame to another one:
append = aapl_bar.append(df_2017)
print append
'Append' is essentially to concat two DataFrames by axis = 0, thus here is an alternative way to append:
concat = pd.concat([aapl_bar, df_2017], axis = 0)
print concat
Please note that if the two DataFrame have some columns with the same column names, these columns are considered to be the same and will be merged. It's very important to have the right column names. If we change a column names here:
df_2017.columns = ['Change', 'High','Low','Close']
concat = pd.concat([aapl_bar, df_2017], axis = 0)
print concat
Since the column name of 'Open' has been changed, the new DataFrame has an new column named 'Change'.
Summary
Hereby we introduced the most import part of python: resampling and DataFrame manipulation. We only introduced the most commonly used method in Financial data analysis. There are also many methods used in data mining, which are also beneficial. You can always check the Pandas official documentations for help.


--- layout: page title: "The Little Book on CoffeeScript" prev: OEBPS/pr01s06.html next: OEBPS/ch01s02.html book_path: books/alex-maccaw-the-little-book-on-coffeescript-_oeb/ --- {% include JB/setup %} {% raw %}
Firstly, before we get any further into this section, I want to
reiterate that while CoffeeScript’s
syntax is often identical with JavaScript’s, it’s not a superset, and
therefore some JavaScript keywords,
such as function
and var
, aren’t permitted, and will throw syntax
errors. If you’re writing a CoffeeScript file, it needs to be pure CoffeeScript; you can’t intermingle the two
languages.
Why isn’t CoffeeScript a superset? Well, the very fact that white space is significant in CoffeeScript programs prevents it from being a superset. And, once that decision’s been made, the team decided you might as well go the full hog and deprecate some JavaScript keywords and features in the name of simplicity and in an effort to reduce many commonly occurring bugs.
What I find mind-blowing, in a meta sort of way, is that the CoffeeScript interpreter itself is actually written in CoffeeScript. It looks like the chicken or egg paradox has finally been solved!
Right, so firstly let’s tackle the basic stuff. There are no semicolons in CoffeeScript, it’ll add them automatically for you upon compilation. Semicolons were the cause of much debate in the JavaScript community, and behind some weird interpreter behavior. Anyway, CoffeeScript resolves this problem for you by simply removing semicolons from its syntax, adding them as needed behind the scenes.
Comments are in the same format as Ruby comments, starting with a hash character:
# A comment
Multiline comments are also supported, and are brought forward to the generated JavaScript. They’re enclosed by three hash characters:
### A multiline comment, perhaps a LICENSE. ###
As you’re going through this book’s examples, it may be worth pasting the CoffeeScript into the online compiler showing you the generated JavaScript.
As I briefly alluded to, white space is significant in CoffeeScript.
In practice, this means that you can replace curly brackets ({}
) with a tab. This takes inspiration from
Python’s syntax, and has the excellent side effect of ensuring that your
script is formatted in a sane manner; otherwise it won’t even
compile!
CoffeeScript fixes one of the major bugbears with JavaScript, global
variables. In JavaScript, it’s all
too easy to accidentally declare a global variable by forgetting to
include var
before the variable
assignment. CoffeeScript solves this by simply removing global variables.
Behind the scenes, CoffeeScript wraps up scripts with an anonymous
function, keeping the local context, and automatically prefixes all
variable assignments with var
. For
example, take this simple variable assignment in CoffeeScript:
myVariable = "test"
As you can see, the variable assignment is kept completely local; it’s impossible to accidentally create a global variable. CoffeeScript actually takes this a step further, and makes it difficult to shadow a higher-level variable. This goes a great deal to prevent some of the most common mistakes developers make in JavaScript.
However, sometimes it’s useful to create global variables. You can
either do this by directly setting them as properties on the global object
(window
in browsers), or with the
following pattern:
exports = this exports.MyVariable = "foo-bar"
In the root context, this
is
equal to the global object, and by creating a local exports
variable you’re making it really obvious
to anyone reading your code exactly which global variables a script is
creating. Additionally, it paves the way for CommonJS modules, which we’re going to cover later in
the book.


--- layout: default ---
Siyuan's current research focuses on machine learning and computational vision. He also had much research experience in Data visualization. Now he is doing lab rotation within the department and open to all kinds of challenges.
Read More...B.Sc., Math and Applied Math
2012 - 2016, Zhejiang University, China
Ph.D., Biomedical Engineering
2016 - , Yale University, U.S.A
Email<EMAIL_ADDRESS>Address: 51 Prospect Street
New Haven, CT 06511-8937


PARADISE LOST: THE CHILD MURDERS AT ROBIN HOOD HILLS A film review by Steve Rhodes Copyright 1996 Steve Rhodes
RATING (0 TO ****): ****
"God, please help us through this. God, please help us through this," wails John Mark Byers. His eight year old stepson has been brutally tortured, raped, and murdered and his grief is understandably overwhelming him. Viewers will be whispering the same sentiments to themselves as they sit through the movie.
PARADISE LOST: THE CHILD MURDERS AT ROBIN HOOD HILLS is one of the most realistically tragic documentaries ever. Directed and edited by brilliant documentarians Joe Berlinger and Bruce Sinofsky, who worked together previously on BROTHER'S KEEPER, the show is a tour de force. Rarely has the cinema been this powerful, this engrossing, and this painful to watch.
In 1993 three second graders, Steven Branch, Christopher Byers, and Michael Moore were viciously killed at Robin Hood Hills near West Memphis, Arkansas. Soon thereafter the police picked up three teenagers: Jessie Lloyd Misskelly, age 17; Damien Wayne Echols, age 18; and Charles Jason Baldwin, age 16 and charged them with the murder.
The police find satanic books with key passages underlined in Damien Echols's bedroom, and they get a full confession from Jessie Misskelly that implicates all three of them. Misskelly weaves a macabre tale of satanic rituals that were performed on the hapless little boys. A television reporter asks Chief Inspector Gary Gitchell, "On a scale of one to ten how solid do you think the case is?" Gitchell confidently replies, "Eleven."
The filmmakers were afforded a unique opportunity that they used to maximum effect. They were present during almost every part of the trial and its preparations. This includes attorney strategy sessions and interviews with the defendants and the witnesses.
The editing of the film is flawless. It serves only to enlighten and never to confuse. If there is a mistake made in the film, it is that the filmmakers are somewhat more sympathetic to the defendant's arguments than to the prosecution's, but it is on whole, a well-balanced documentary.
Although the film is unrated, it is NC-17 material. The shocking video and still pictures of the crime scene are essential to showing graphically how heinous were the actions of the killers, but they are suitable for viewing only by mature adults. Even hardened critics like I frequently have to look away. In the clips that we are shown the boys have turned blue and are mutilated. Sickening. Sickening. And yet, I am glad the show was this explicit. The truth is horrible, and should not be censored. People should know how bad it is.
In the press kit, the filmmakers argue that the reason they feel their approach is not gratuitous is that "our society has become so desensitized to news reports of violent crime that we felt it was important that the viewer really feels and emotionally recognizes the horrific nature of the crime." I concur.
Because of the confession, the trials are separated with Misskelly's trial going first. Although there are many surprises at the trials, I will not give anything away other than that necessary to set up the cases. Suffice it to say, that you would not like to have not been called to serve on either jury. If you had been, the phrase "reasonable doubt" and the pictures of the little boys would be indelibly marked on your brain and would forever making your sleeping difficult.
The parents of the victims and of the accused take center stage in this real life drama. This is backwoods Arkansas. The people in the film are uniformly poor and naive. Each is convinced that they know all the answers and that god is on their side.
In an alternatively moving and chilling sequence, John Mark Byers sings "That old rugged Cross" in church. After that he goes outside and shows off a big gun he has that he claims has untraceable bullets. He shoots round after round into an exploding pumpkin and tells the camera how he will blow off the heads of those teenagers if the courts ever let them go.
Fundamentalist Christianity is prominently featured in the film. One of the signs on the church proclaims that "Satan has no unemployment problems." People babble biblical phrases continually in nonsensical arguments for their cause.
The heart of the first trial is whether Jessie Misskelly, who has an IQ of 72, gave his confession willingly.  Before you decide if you think this is a potentially valid line of reasoning, see the trial first. It is not as simple as it sounds.
As I watched the film, I thought how I would have walked out had it been a fictional show. People like these do not exist, and the actors are shamelessly overacting. Except this is exactly what happened and as it happened.
In the second trial the evidence looks as if it may be mainly circumstantial, and the big question is whether the prosecution can get Jessie Misskelly to testify. Both defense and prosecution have major aces up their sleeves. A parent of one of the victims gives a Christmas present to a member of the documentary crew, and it turns out that it may or may not be a key piece of evidence.
During one of the trial's recesses, Damien complains that, "West Memphis is sort of like Second Salem because whatever happens is blamed on Satanism."
Rather than evoking tears, the film is more likely to make your emotional systems rupture because it is so hard to take in all that is arrayed before you. Clearly one of the best films of the year, and certainly the hardest of them all to endure.
PARADISE LOST: THE CHILD MURDERS AT ROBIN HOOD HILLS runs 2:30, and I would not want one a minute of it deleted. It is unrated but would be NC-17.  Do not let your teenagers go. Period. I give the film my absolute top rating of **** and say people should see it if they are up to it.
**** = One of the top few films of this or any year. A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show.  Totally unbearable.
REVIEW WRITTEN ON: December 2, 1996
Opinions expressed are mine and not meant to reflect my employer's.
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Maharashtra govt formation: Anything can happen in cricket and politics, says Gadkari
by PTIUnion Minister Nitin Gadkari said on Saturday that “anything can happen in cricket and politics”, and expressed the confidence that the new BJP-led government in Maharashtra would pass the floor test.
The BJP’s Devendra Fadnavis on Saturday returned as Maharashtra Chief Minister for a second term, propped up by NCP leader Ajit Pawar, who took oath as Deputy Chief Minister.
Asked for his reaction to the dramatic developments, Mr. Gadkari told the media: “As I had commented earlier, anything can happen in cricket and politics. Now you would have understood the importance of my statement. I congratulate Devendra Fadnavis and Ajit Pawar. They will prove the majority in the House in the period given by the Governor.”
Maharashtra would get a stable government under the leadership of Mr. Fadnavis and Mr. Ajit Pawar and witness further development, he added.


Ingredients
Graham Cracker Crust
-
1 and 1/2 cups (150g) graham cracker crumbs (about 10 full sheet graham crackers)
-
5 Tablespoons (70g) unsalted butter, melted
-
1/4 cup (50g) granulated sugar
Cheesecake
-
four 8-ounce blocks (904g) full-fat cream cheese, softened to room temperature
-
1 cup (200g) granulated sugar
-
1 cup (240g) full-fat sour cream, at room temperature
-
1 teaspoon pure vanilla extract
-
2 teaspoons fresh lemon juice (optional, but recommended)
-
3 large eggs, at room temperature
-
topping suggestions: salted caramel, lemon curd, strawberry topping, chocolate ganache, red wine chocolate ganache, fresh fruit, whipped cream, or raspberry sauce (recipe in notes)
Instructions
1
Adjust the oven rack to the lower-middle position and preheat oven to 350°F (177°C).
2
Make the crust: Using a food processor, pulse the graham crackers into crumbs. Pour into a medium bowl and stir in sugar and melted butter until combined. (You can also pulse it all together in the food processor.) Mixture will be sandy. Press firmly into the bottom and slightly up the sides of a 9-inch or 10-inch springform pan. No need to grease the pan first. I use the bottom of a measuring cup to pack the crust down tightly. Pre-bake for 8 minutes.  The foil will wrap around the pan for the water bath in step 4. Allow crust to slightly cool as you prepare the filling.
3
Make the filling: Using a handheld or stand mixer fitted with a paddle attachment, beat the cream cheese and granulated sugar together on medium-high speed in a large bowl until the mixture is smooth and creamy, about 2 minutes. Add the sour cream, vanilla extract, and lemon juice then beat until fully combined. On medium speed, add the eggs one at a time, beating after each addition until just blended. After the final egg is incorporated into the batter, stop mixing. To help prevent the cheesecake from deflating and cracking as it cools, avoid over-mixing the batter as best you can.
4
Prepare the simple water bath (see note) Boil a pot of water. You need 1 inch of water in your roasting pan for the water bath, so make sure you boil enough. I use an entire kettle of hot water. As the water is heating up, wrap the aluminum foil around the springform pan. Pour the cheesecake batter on top of the crust. Use a rubber spatula or spoon to smooth it into an even layer. Place the pan inside of a large roasting pan. Carefully pour the hot water inside of the pan and place in the oven. (Or you can place the roasting pan in the oven first, then pour the hot water in. Whichever is easier for you.)
5
Bake cheesecake for 55-70 minutes or until the center is almost set. When it’s done, the center of the cheesecake will slightly wobble if you gently shake the pan. Turn the oven off and open the oven door slightly. Let the cheesecake sit in the oven in the water bath as it cools down for 1 hour. Remove from the oven and water bath, then cool cheesecake completely at room temperature. Then refrigerate the cheesecake for at least 4 hours or overnight.
6
Use a knife to loosen the chilled cheesecake from the rim of the springform pan, then remove the rim. Using a clean sharp knife, cut into slices for serving. For neat slices, wipe the knife clean and dip into warm water between each slice.
7
Serve cheesecake with desired toppings. Cover and store leftover cheesecake in the refrigerator for up to 5 days.


Copyright © 2010-2011 ashbb
Except:
* hh/static/(all).png (c) 2008 why the lucky stiff * lib/ext/hpricot/(all) (c) 2008 why the lucky stiff * lib/ext/projector/(all).rb (c) 2010 MIZUTANI Tociyuki * samples/akatsukiface.png (c) 2010 MIZUTANI Tociyuki * samples/class-book.yaml (c) 2008 why the lucky stiff * samples/splash-hand.png (c) 2008 why the lucky stiff * samples/loogink.png (c) 2008 Anita Kuno * samples/cy.png (c) 2008 Anita Kuno * static/Coolvetica.ttf (c) 1999 Ray Larabie * static/Lacuna.ttf (c) 2003 Glashaus, designed by Peter Hoffman * static/gshoes-icon.png (c) 2010 Zachary Scott * static/gshoes-heading-icon.png (c) 2010 Zachary Scott * static/code_highlighter.js (c) 2005 Dan Webb * static/code_highlighter_ruby.js (c) 2008 why the lucky stiff * static/manual.css (c) 2008 why the lucky stiff
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


OOTD
HOLY MOTHER OF SHOES!
White shoes + the skinny jeans.
It will go with almost anything. Love it!
Wear it without any dress and I bet it will still look good.
White shoes was never this good!
Totally recommended.
I want mooooore!.
Its the new SWAAAG. Hail Stan Smith
Its not waterproof but I love it!
This is good for work and for casual attires.


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Gecode::Set::GLBndSet Class Reference
Inherits Gecode::Set::BndSet.Detailed Description
Growing sets of integers.These sets provide operations for monotonically growing the set. Growing sets are used for implementing the greatest lower bound of set variables.
Definition at line 245 of file var.icc.
Constructors and initialization | |
| GLBndSet (Space *=NULL) | |
| Default constructor. Creates an empty set. | |
| GLBndSet (Space *home, int i, int j) | |
| Initialize as the set . | |
| GLBndSet (Space *home, const IntSet &s) | |
| Initialize as the set represented by s. | |
| void | init (Space *home) |
| Initialize as the empty set. | |
Update operations | |
| bool | include (Space *home, int i, int j) |
| Include the set in this set. | |
| template<class I> | |
| bool | includeI (Space *home, I &i) |
| Include the set represented by i in this set. |
Constructor & Destructor Documentation
|
|
Default constructor. Creates an empty set.
Definition at line 380 of file integerset.icc. |
|
|
Initialize as the set .
Definition at line 383 of file integerset.icc. |
|
|
Initialize as the set represented by s.
Definition at line 387 of file integerset.icc. |
Member Function Documentation
|
|
Initialize as the empty set.
Definition at line 391 of file integerset.icc. |
|
|
Include the set in this set.
Definition at line 394 of file integerset.icc. |
|
|
Include the set represented by i in this set.
Definition at line 409 of file integerset.icc. |
The documentation for this class was generated from the following files:
- gecode/set/var.icc (Revision: 3568)
- gecode/set/var/integerset.cc (Revision: 3188)
- gecode/set/var/integerset.icc (Revision: 3413)


Creating a Terrain Input File
Preliminaries
Install or verify that the following is installed:
Create FDS Input File
- Open Google Earth and identify a rectangular region of interest in the USA (elevation data not available at web site used below).
- Record the lat/lon coordinates of two opposite corners of this region.
- Create an image for this region using a screen capture program. Name this image casename.png where casename is the 'CHID' parameter in the FDS input file
- Create a file named casename.in containing the following line:
longitude_begin longitude_end n_longitudes latitude_begin latitude_end n_latitudes
- Google Earth gives latitudes and longitudes as d=degrees, m=minutes and s=seconds.
The coordinates in casename.in are decimal i.e. d + m/60 + s/3600
- Note that longitudes in the United States are negative.
- Type the command:
dem2fds casename < casename.in
This command generated a series of files named casename_longlats_xxx.csv . Each file contains 400 longitude/latitude pairs
- Go to http://viewer.nationalmap.gov/theme/elevation/ and click on bulk
- Click on Choose Files and select a file for each casename_longlats_xxx.csv generated in step 5
- Click on Get Elevations
- After that web site retrieved all the data requested save the file
- Concatenate all elevation files generated in step 6 after removing the header line in each file.
Call this file casename_elevs.csv . The first line of casename_elevs.csv should be
longitude_begin longitude_end n_longitudes latitude_begin latitude_end n_latitudes zmin zmax nz
- Creating an FDS input file
- Using obstacles (&OBST lines):
dem2fds -o casename < casename_elevs.csv
- Using geometry (&GEOM lines):
dem2fds -e casename < casename_elevs.csv


These test results were generated by the OpenID Foundation conformance suite.
By themselves, they are not proof that a deployment is conformant nor that it
meets the requirements for certification. For a list of certified deployments,
see https://openid.net/certification/
- to be added to this list follow
the certification instructions.