1. Working With Custom Actions
An action is technically a class, derived from the AnAction class. To update the state of the action, the method AnAction.update() is periodically called by IDEA. The object of type AnActionEvent passed to this method carries the information about the current context for the action, and in particular, the specific presentation which needs to be updated.
1.1. Creating actions
To create a new we need to extend AnAction class:
public class SimpleAction extends AnAction {
}
1.2. Overriding actionPerformed()
The only method of an inheritor of
AnAction
which needs to be overridden is public void actionPerformed(AnActionEvent anActionEvent);
, and it should contain a part of code to be executed after the action has been invoked.
In this case the action does nothing.
public class SimpleAction extends AnAction {
@Override
public void actionPerformed(AnActionEvent anActionEvent) {
}
}
1.3. Registering actions
To register a newly created action, <action>
attribute should be added to the <actions>
section of the plugin configuration file
plugin.xml.
IntelliJ IDEA has an embedded inspection that spots unregistered actions.
To register the action and set up it’s attributes press Alt + Enter while the caret is placed on the action’s declaration.
Fill the New Action form to set up action’s parameters such as: action’s name and description, a UI component the action is bound to, visual position of the menu item the action is bound to, and a shortcut for invoking the action. In our case the action will be available in the Tools Menu, it will be placed on top, and will have no shortcuts.
After filling the New Action form and applying the changes <actions>
section of our
plugin.xml
file will look like this:
<actions>
<!-- Add your actions here -->
<action id="org.jetbrains.tutorials.actions.SimpleAction" class="org.jetbrains.tutorials.actions.SimpleAction"
text="Simple Action" description="IntelliJ Action System Demo">
<add-to-group group-id="ToolsMenu" anchor="first"/>
</action>
</actions>
1.4. Setting attributes manually
Full list of action’s attributes can also be set manually in plugin.xml configuration file like the following code sample shows:
<actions>
<!-- Add your actions here -->
<!-- The <action> element defines an action to register.
The mandatory "id" attribute specifies an unique identifier for the action.
The mandatory "class" attribute specifies the full-qualified name of the class implementing the action.
The mandatory "text" attribute specifies the text of the action (tooltip for toolbar button or text for menu item).
The optional "use-shortcut-of" attribute specifies the ID of the action whose keyboard shortcut this action will use.
The optional "description" attribute specifies the text which is displayed in the status bar when the action is focused.
The optional "icon" attribute specifies the icon which is displayed on the toolbar button or next to the menu item. -->
<action id="org.jetbrains.tutorials.actions.SimpleAction" class="org.jetbrains.tutorials.actions.SimpleAction"
text="Simple Action" description="IntelliJ Action System Demo">
<!-- The <keyboard-shortcut> node specifies the keyboard shortcut for the action. An action can have several keyboard shortcuts.
The mandatory "first-keystroke" attribute specifies the first keystroke of the action. The key strokes are specified according to the regular Swing rules.
The optional "second-keystroke" attribute specifies the second keystroke of the action.
The mandatory "keymap" attribute specifies the keymap for which the action is active. IDs of the standard keymaps are defined as
constants in the com.intellij.openapi.keymap.KeymapManager class. -->
<keyboard-shortcut first-keystroke="control alt A" second-keystroke="C" keymap="$default"/>
<!-- The <mouse-shortcut> node specifies the mouse shortcut for the action. An action can have several mouse shortcuts.
The mandatory "keystroke" attribute specifies the clicks and modifiers for the action. It is defined as a sequence of words separated by spaces:
"button1", "button2", "button3" for the mouse buttons; "shift", "control", "meta", "alt", "altGraph" for the modifier keys;
"doubleClick" if the action is activated by a double-click of the button.
The mandatory "keymap" attribute specifies the keymap for which the action is active. IDs of the standard keymaps are defined as
constants in the com.intellij.openapi.keymap.KeymapManager class. -->
<mouse-shortcut keystroke="control button3 doubleClick" keymap="$default"/>
<!-- The <add-to-group> node specifies that the action should be added to an existing group. An action can be added to several groups.
The mandatory "group-id" attribute specifies the ID of the group to which the action is added.
The group must be implemented by an instance of the DefaultActionGroup class.
The mandatory "anchor" attribute specifies the position of the action in the group relative to other actions. It can have the values
"first", "last", "before" and "after".
The "relative-to-action" attribute is mandatory if the anchor is set to "before" and "after", and specifies the action before or after which
the current action is inserted. -->
<add-to-group group-id="ToolsMenu" anchor="first"/>
</action>
</actions>
After performing the steps described above we need to compile and run the plugin to the the newly created action available as a Tools Menu item:
1.5. Performing an action
In order to make the action do something we need to implement it’s public void actionPerformed(AnActionEvent anActionEvent);
method.
In the following example action invokes a dialog that shows information about a selected Project View Item and has no icon and any pre-selected default option:
@Override
public void actionPerformed(AnActionEvent anActionEvent) {
Object navigatable = anActionEvent.getData(CommonDataKeys.NAVIGATABLE);
if (navigatable != null) {
Messages.showDialog(navigatable.toString(), "Selected Element:", new String[]{"OK"}, -1, null);
}
}
1.6. Setting up action’s visibility and availability
To manipulate with action’s visibility and availability we need to override it’s public void update(@NotNull AnActionEvent e);
Default implementation of this method does nothing. Override this method to provide the ability to dynamically change action’s state and(or) presentation depending on the context.
public class SimpleAction extends AnAction {
@Override
public void actionPerformed(AnActionEvent anActionEvent) {
//...
}
@Override
public void update(AnActionEvent anActionEvent) {
}
}
The following code sample illustrates how to make the action visible and available only when the following conditions are met: there’s a project available and there’s an item you can navigate to selected in the project view pane:
public class SimpleAction extends AnAction {
@Override
public void actionPerformed(AnActionEvent anActionEvent) {
//...
}
@Override
public void update(AnActionEvent anActionEvent) {
final Project project = anActionEvent.getData(CommonDataKeys.PROJECT);
if (project == null)
return;
Object navigatable = anActionEvent.getData(CommonDataKeys.NAVIGATABLE);
anActionEvent.getPresentation().setEnabledAndVisible(navigatable != null);
}
}
Parameter anActionEvent carries information on the invocation place and data available.
Note This method can be called frequently, for instance, if an action is added to a toolbar, it will be updated twice a second. This means that this method is supposed to work really fast, no real work should be done at this phase. For example, checking selection in a tree or a list, is considered valid, but working with a file system is not. If you cannot understand the state of the action fast you should do it in the AnActionEvent method and notify the user that action cannot be executed if it’s the case.
After compiling and running the plugin project and invoking the action, the dialog will pop up:


Black to Play 22
b1-w3/5
pips Black=125 White=132
(CorrectAnswer= 0/0, ErrorScore= 0)
1. 9/5 6/2* eq:+0.457 51.13% (G:21.71% B:3.21%) 48.87% (G:19.71% B:3.66%) 2. 13/11 7/5 6/2* eq:+0.418 (-0.039) 49.75% (G:21.15% B:2.71%) 50.25% (G:22.87% B:4.68%) 3. 9/5 4/2*(2) eq:+0.387 (-0.070) 48.28% (G:21.08% B:2.43%) 51.72% (G:15.19% B:2.42%) 4. 13/11 7/5 4/2*(2) eq:+0.385 (-0.072) 47.63% (G:21.63% B:2.49%) 52.37% (G:17.79% B:2.56%) 5. 13/11(2) 6/2* eq:+0.384 (-0.073) 49.69% (G:19.50% B:2.79%) 50.31% (G:19.21% B:3.76%)


---
layout: index
title: Home
---
Hi,
I'm Ruben Wiersma, a PhD student at the TU Delft. My research topics include computer graphics, geometry processing, and machine learning.
On this website, you can find out more about me, my publications, a selection of my creative projects and news.


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About Me
Hola! my name is Melvin Emmanuelli. I am an aspiring Full Stack Developer, curentlly studying at the University of Central Florida (UCF). I startted in the Information & Technology field about 12 years ago in Puerto Rico, where I became a Computer Repair Technician.
I've being always driven by the passion of learning the inner workings of computers and devices alike. I am exited about what lies ahead in the path of development, as it opens endles posibilities.
This is certainly a challenge, but one I am willing to take without hesitation. Full Stack Developement will become a cornerstone to build my self upon, in order to achieve the goal of becoming Software Engineer.


|
libomexmeta
1.2.13
Annotations for models in systems biology
|
A Triples object is a collection of Triple objects. More...
#include <Triples.h>
Public Member Functions | |
| Triples (int size) | |
| create an empty Triples object big enough for More... | |
| Triples (Triple &triple) | |
| construct a Triples object from a single triple More... | |
| Triples (std::vector< Triple > triples) | |
| construct a Triples object directly from a std::vector<Triple> | |
| bool | operator== (const Triples &rhs) const |
| bool | operator!= (const Triples &rhs) const |
| void | moveBack (Triple &triple) |
| move a Triple to the back of the Triples object. Analogous to push back but with move semantics. More... | |
| void | emplace_back (UriHandler &uriHandler, LibrdfNode subject, const PredicatePtr &predicatePtr, const LibrdfNode &resource) |
| construct a Triple object from the necessary components and move it to the back of Triples More... | |
| void | emplace_back (UriHandler &uriHandler, const LibrdfNode &subject, const LibrdfNode &predicate, const LibrdfNode &resource) |
| void | emplace_back (UriHandler &uriHandler, LibrdfNode subject, const Predicate &predicate, const LibrdfNode &resource) |
| construct a Triple object from the necessary components and move it to the back of Triples More... | |
| std::vector< std::string > | getSubjectsStr () |
| get all subjects as strings More... | |
| std::vector< std::string > | getPredicates () |
| get all predicates as strings More... | |
| std::vector< std::string > | getResources () |
| get all resources as strings More... | |
| int | size () const |
| get the number of triples in the Triples object More... | |
| TripleVector::iterator | begin () |
| the begin iterator for a Triples object More... | |
| TripleVector::iterator | end () |
| the end iterator for a Triples object More... | |
| void | freeTriples () |
| Triple | pop () |
| moves one triple of the end of triples More... | |
| Triple & | operator[] (int index) |
| getter operator for Triples object More... | |
| const Triple & | operator[] (int index) const |
| bool | isEmpty () |
| indicates whether Triples object is empty. More... | |
| int | capacity () |
| move the from Triple object out of the Triples object More... | |
A Triples object is a collection of Triple objects.
the implementation of Triples is hampered by the inability to clone Triple objects. Instead triples must use move semantics to get Triples in or out of a Triples object. Under the hood, the Triples object is just a std::vector<Triple>.
|
explicit |
|
explicit |
construct a Triples object from a single triple
| triple | The triple to put into triples |
The triple is moved into element 0 of a new Triples object. The Triple must be passed by reference so that the triple is moved directly, instead of copied and then moved into the Triples object (which is a memory leak).
| TripleVector::iterator omexmeta::Triples::begin | ( | ) |
the begin iterator for a Triples object
this method is simply forwarded on to std::vector::begin()
| int omexmeta::Triples::capacity | ( | ) |
| void omexmeta::Triples::emplace_back | ( | UriHandler & | uriHandler, |
| LibrdfNode | subject, | ||
| const Predicate & | predicate, | ||
| const LibrdfNode & | resource | ||
| ) |
| void omexmeta::Triples::emplace_back | ( | UriHandler & | uriHandler, |
| LibrdfNode | subject, | ||
| const PredicatePtr & | predicatePtr, | ||
| const LibrdfNode & | resource | ||
| ) |
| TripleVector::iterator omexmeta::Triples::end | ( | ) |
the end iterator for a Triples object
this method is simply forwarded on to std::vector::end()
| std::vector< std::string > omexmeta::Triples::getPredicates | ( | ) |
get all predicates as strings
we do not return the predicate itself because Predicate objects cannot be copied.
| std::vector< std::string > omexmeta::Triples::getResources | ( | ) |
get all resources as strings
we do not return the LibrdfNode itself because LibrdfNode objects cannot be copied.
| std::vector< std::string > omexmeta::Triples::getSubjectsStr | ( | ) |
get all subjects as strings
we do not return the subject itself because subjects cannot be copied, which complicates matters.
| bool omexmeta::Triples::isEmpty | ( | ) |
| void omexmeta::Triples::moveBack | ( | Triple & | triple | ) |
| Triple & omexmeta::Triples::operator[] | ( | int | index | ) |
getter operator for Triples object
index
A copy is made of the Triple you want to return. To get a reference without copying see Triples::pop(). Remember that iterating over a Triples object and making copies will cause complications with freeing the objects.
| Triple omexmeta::Triples::pop | ( | ) |
moves one triple of the end of triples
No copies are made. This is deliberate as it reduces risk of memory issues. When you pop, you remove from the end of the Triples object and return it. This means that responsibility for freeing the returned Triple switches to the caller.
| int omexmeta::Triples::size | ( | ) | const |


Gina Lucy
About
Portfolio
Contact
Portfolio
Hangman
RPG Game
Trivia Game
Rutgers Info Widget
Rock Paper Scissors


#include <db.h> int DBC->db_stream(DBC *dbc, DB_STREAM **dbs, u_int32_t flags);
The DBcursor->db_stream()
method points to a
key/value pair where the data item is an external file.
Use the flags parameter
to indicate whether the stream is to be opened for reading, or for
reading and writing.
Once the stream is opened, you read it using DB_STREAM->read(), and you write to it using DB_STREAM->write().
Close this stream using DB_STREAM->close().
If the data item is not an external file, this method returns an error.
Unless otherwise specified, the
DBcursor->db_stream()
method returns a non-zero error value on failure and 0 on success.
The flags parameter must be set by bitwise inclusively OR'ing together one or more of the following values:
Indicates that the stream is to be opened for read-only access.
Indicates that the stream is to be opened for read write access. The stream is sync'd to disc when the stream is closed.
Indicates that the stream is to be opened for read and write access. The stream is sync'd to disc after each write, instead of when the stream is closed.
The DBcursor->db_stream()
method may fail and return one of the following non-zero errors:


Chapter 11 Bayesian - 1
The following is the part of the class note that I took from the online course of “Bayesian Statistics: From Concept to Data Analysis.” (https://www.coursera.org/learn/bayesian-statistics/home/welcome)
Important note: All the notes here are just for my own study purpose. I do not clain any copyright. You can use it for study purpose as well, but not for any business purposes.
11.1 Frequentist perspective
\[\theta = \{ fair , loaded \}\] \[x \sim Bin (5, \theta)\] \[\begin{aligned} f(x|\theta) &=\begin{cases} \binom{5}{x} (\frac{1}{2})^5 & if \; \theta=fair \\ \binom{5}{x} (0.7)^x(0.3)^{5-x} & if \; \theta=loaded \end{cases} \\ &= \binom{5}{x} (\frac{1}{2})^5 I_{\{\theta=fair \}}+\binom{5}{x} (0.7)^x(0.3)^{5-x}I_{\{\theta=loaded \}}\end{aligned}\]
When \(x=2\)
\[f(\theta | x=2)=\begin{cases} \binom{5}{x} (\frac{1}{2})^5 = 0.3125& if \; \theta=fair \\ \binom{5}{x} (0.7)^x(0.3)^{5-x} = 0.1323& if \; \theta=loaded \end{cases}\] Thus, based on MLE, it suggests that it should be “fair”, since it has a greater probablity if we observe 2 head out of 5 trials.
However, we can not know the following probability: given that we observe \(x=2\), what is the probability that \(\theta\) is fair?
\[P(\theta=fair | X=2)\] From the frequentist’s perspective, the coin is the fixed coin. And thus, the probablity of \(P(\theta=fair|x=2)\) is equal to \(P(\theta=fair)\).
\[P(\theta=fair|x=2)=P(\theta=fair)\] As,
\[P(\theta=fair) \in C(0,1) (i.e., either \; 0 \; or \; 1)\]
11.2 Bayesian perspective
Prior \(P(loaded)=0.6\)
\[\begin{aligned} f(\theta | X) &= \frac{f(x|\theta) f(\theta)}{\sum_{\theta} f(x|\theta)f(\theta)} \\ &=\frac{\binom{5}{x} [(\frac{1}{2})^5 \times 0.4 \times I_{\{\theta=fair \}}+ (0.7)^x(0.3)^{5-x} \times 0.6 \times I_{\{\theta=loaded \}}]}{\binom{5}{x} [(\frac{1}{2})^5 \times 0.4 + (0.7)^x(0.3)^{5-x} \times 0.6]} \end{aligned}\]
\[\begin{aligned} f(\theta |X=2) &=\frac{0.0125 I_{\{\theta=fair \}}+0.0079 I_{\{\theta=loaded \}} }{0.0125+0.0079} \\ &= 0.612 I_{\{\theta=fair \}} + 0.388 I_{\{\theta=loaded \}} \end{aligned}\]
Thus, we can say that:
\[P(\theta=loaded | X=2)=0.388\] We can change the prior, and get different posterior probabilities:
\[P(\theta=loaded)=\frac{1}{2} \rightarrow P(\theta=loaded | X=2)=0.297\] \[P(\theta=loaded)=\frac{9}{10} \rightarrow P(\theta=loaded | X=2)=0.792\]
11.3 Continous parameters
In the examples above, \(\theta\) is discrete. In contrast, the examples below use continous \(\theta\).
\[f(\theta |y)=\frac{f(y|\theta) f(\theta)}{f(y)}=\frac{f(y|\theta) f(\theta)}{\int f(y|\theta)f(\theta)d\theta}=\frac{likelihood \times prior}{normalizing-constant} \propto likelihood \times prior\]
Note that, the posterior is a PDF of \(\theta\), which is not in the function of \(f(y)\). Thus, removing the denominator (i.e., the normalizing constant) does not change the form of the posterior.
11.3.1 Uniform
Suppose that \(\theta\) is the probablity of a coin getting head. We could assign a uniform distribution.
\[\theta \sim U[0,1]\]
\[f(\theta)=I_{ \{0 \leqq \theta \leqslant 1 \}}\] (It is interesting to see how to write the pdf for uniform distribution.)
\[f(\theta | Y=1)= \frac{\theta^1(1-\theta)^0 I_{\{0 \leqq \theta \leqslant 1\}}}{\int_{-\infty}^{+\infty} \theta^1(1-\theta)^0 I_{\{0 \leqq \theta \leqslant 1\}} d\theta}=\frac{\theta I_{\{0 \leqq \theta \leqslant 1 \}}}{\int_0^1 \theta d\theta}=2\theta I_{ \{0 \leqq \theta \leqslant 1\}}\]
If we ignore the normalizing constant, we will get
\[f(\theta | Y=1) \propto \theta^1(1-\theta)^0 I_{ \{0 \leqq \theta \leqslant 1\} }=\theta I_{ \{0 \leqq \theta \leqslant 1\} }\]
Thus, we can see that with vs. without the noramlizing constant is the “2”.
11.3.2 Uniform: prior versus posterior
When \(\theta\) follows uniform distribution:
Prior
\[P(0.025 <\theta<0.975)=0.95\] \[P( 0.05< \theta )=0.95\] Posterior
\[P(0.025<\theta<0.975)=\int_{0.025}^{0.975} 2\theta d\theta=0.95\] \[P(0.05<\theta)=1-P(\theta <0.05)=\int_{0}^{0.05} 2\theta d\theta=1-0.05^2=0.9975\]
Thus, we can see that, while \(P(0.025<\theta<0.975)\) is the same for prior and posterior, \(P(0.05<\theta)\) is not the same.
11.3.3 Uniform: equal tailed versus HPD
Equal tailed
\[P(\theta < q|Y=1)=\int_0^q 2\theta d\theta=q^2\] \[P(\sqrt{0.025}<\theta<\sqrt{0.975}|Y=1)=P(0.158<\theta<0.987)=0.95\] We can say that: there’s a 95% probability that \(\theta\) is in between 0.158 and 0.987.
Highest Posterior Density
\[P(\theta > \sqrt{0.05}|Y=1)=P(\theta >0.224|Y=1)=0.95\]
11.4 Bernoulli/binomial likelihood with uniform prior
\[\begin{aligned} f(\theta | Y=1) &= \frac{\theta^{\sum y_i}(1-\theta)^{\sum n-y_i} I_{\{0 \leqq \theta \leqslant 1\}}}{\int_{-\infty}^{+\infty} \theta^{\sum y_i}(1-\theta)^{n-\sum y_i} I_{\{0 \leqq \theta \leqslant 1\}} d\theta} \\ &=\frac{\theta^{\sum y_i}(1-\theta)^{\sum n-y_i} I_{\{0 \leqq \theta \leqslant 1\}}}{\frac{\Gamma(\sum y_i+1)\Gamma(n-\sum y_i+1)}{\Gamma(n+2)} \int_{-\infty}^{+\infty} \frac{\Gamma(n+2)}{\Gamma(\sum y_i+1)\Gamma(n-\sum y_i+1)} \theta^{\sum y_i}(1-\theta)^{n-\sum y_i} I_{\{0 \leqq \theta \leqslant 1\}} d\theta} \\ &= \frac{\Gamma(n+2)}{\Gamma(\sum y_i+1)\Gamma(n-\sum y_i+1)}\theta^{\sum y_i}(1-\theta)^{\sum n-y_i} I_{\{0 \leqq \theta \leqslant 1\}} \end{aligned} \]
Thus,
\[\theta | y \sim Beta (\sum y_i+1, n-\sum y_i +1)\]
Side note: \(Beta(1,1)=Uniform(0,1)\):
\(Beta(\alpha,\beta)=\frac{x^{\alpha-1}(1-x)^{\beta-1}}{B(\alpha,\beta)} I_{\{0 \leqq x \leqslant 1\}}\)
Thus, we can get the following since the support for beta distribution is \([0,1]\):
\(Beta(1,1)=\frac{x^0(1-x)^0}{B(\alpha,\beta)}=1\times I_{\{0 \leqq x \leqslant 1\}}\)
11.5 Conjugate priors
As noted above, beta prior (or, Uniform) leads to beta posterior. In a more general sense, Beta prior always leads to beta posterior.
For instance,
\[\begin{aligned} f(\theta |y) \propto f(y|\theta)f(\theta)&=\theta^{\sum y_i}(1-\theta)^{\sum n-y_i}\frac{\theta^{\alpha-1}(1-\theta)^{\beta-1}}{B(\alpha,\beta)} I_{\{0 \leqq \theta \leqslant 1\}} \\ &=\frac{1}{B(\alpha, \beta)}\theta^{\sum y_i+\alpha-1}(1-\theta)^{\sum n-y_i+\beta-1} I_{\{0 \leqq \theta \leqslant 1\}} \\ &\propto \theta^{\sum y_i+\alpha-1}(1-\theta)^{\sum n-y_i+\beta-1} I_{\{0 \leqq \theta \leqslant 1\}} \end{aligned}\] Thus,
\[f(\theta |y) \sim Beta(\alpha+\sum y_i,\beta+\sum n-y_i)\] Conjugate prior: prior and posterior share the same distribution. As we can see, both the data and the prior contribute to the posterior.
For the prior of \(Beta(\alpha, \beta)\), the mean is
\[Mean_{prior}=\frac{\alpha}{\alpha+\beta}\]
Posterior mean is,
\[\begin{aligned} Mean_{posterior}&= \frac{\alpha+\sum y_i}{\alpha+\sum y_i+\beta+n-\sum y_i} \\ &=\frac{\alpha+\sum y_i}{\alpha+\beta+n} \\ &= \frac{\alpha+\beta}{\alpha+\beta+n}\frac{\alpha}{\alpha+\beta}+\frac{n}{\alpha+\beta+n}\frac{\sum y_i}{n} \\ &= Weight_{prior} \times Mean_{prior}+Weight_{data} \times Mean_{data}\end{aligned}\]
Side Note
- Binomial proportion confidence interval:
\[\hat{p} +/- 1.96 \sqrt{\frac{\hat{p}(1-\hat{p})}{n}}\] (2) Mean of Beta distribution:
\[\frac{\alpha}{\alpha+\beta}\] (3) Plot of Beta distribution
11.6 Poisson distribution
Pmf of Poisson distribution:
\[Pois (\lambda) \sim \frac{\lambda^k e^{-\lambda}}{k!}\]
We can replace \(k\) with the notation of \(y\), and assume that we observe \(n\) \(y_i\): \[y_i \sim \frac{\lambda^{y_i} e^{-\lambda}}{y_i!}\]
\[f(y|\lambda)=\frac{\lambda^{\sum y_i} e^{- n \lambda}}{\prod_{i=1}^n y_i !}\]
We assume that \(\lambda\) follows Gamma distribution (i.e.,Gamma prior):
\[\lambda \sim \Gamma(\alpha, \beta)\] The pdf for Gamma distribution is:
\[\frac{\beta^{\alpha}}{\Gamma(\alpha)}x^{\alpha-1}e^{-\beta x}\]
Thus, the posterior is as follows:
\[\begin{aligned} f(\lambda | y) &\propto f(y|\lambda)f(\lambda) \\ &=\frac{\lambda^{\sum y_i} e^{- n \lambda}}{\prod_{i=1}^n y_i !} \times \frac{\beta^{\alpha}}{\Gamma(\alpha)}\lambda^{\alpha-1}e^{-\beta \lambda} \\ &\propto \lambda^{\sum y_i}e^{- n \lambda} \times \lambda^{\alpha-1}e^{-\beta \lambda} \\ &=\lambda^{(\alpha+\sum y_i)-1}e^{- (\beta+n) \lambda} \end{aligned}\] Thus, the posterior is:
\[\Gamma(\alpha + \sum y_i, \beta+n)\]
As we know that, the mean of prior for Gamma is \(\frac{\alpha}{\beta}\). Thus, we can get the mean for the posterior for Gamma is:
\[\begin{aligned} &=\frac{\alpha+\sum y_i}{\beta+n} \\ &= \frac{\beta}{\beta+n} \frac{\alpha}{\beta}+\frac{n}{\beta+n} \frac{\sum y_i}{n} \end{aligned}\] To determine the prior of \(\alpha\) and \(\beta\):
- Prior mean \(\frac{\alpha}{\beta}\)
- Prior std. dev. \(\frac{\sqrt \alpha}{\beta}\)
- Effective sample size \(\beta\)
- Vague prior Small \(\varepsilon >0\): \(\Gamma (\varepsilon,\varepsilon)\) . Thus, the posterior mean is primarily driven by the data:
\[\frac{\varepsilon + \sum y_i}{\varepsilon + n} \approx \frac{ \sum y_i}{n} \]
As we know, beta prior lead the Bernoulli trial to a beta posterior. That is, we know \(f(\theta|y)=\frac{f(y|\theta) f(\theta)}{f(y)}\). What is the prior predictive distribution of \(f(y)\)?
If Beta is Beta (3,3), what is the prior predictive probablity that we wil observe \(y=0\) in the next trial?
11.7 Exponential data
For instance, suppose that on average you need to wait for 10 minutes for a fast food delivery, and thus we can assume that \(y \sim Exp(\lambda)\). Furthermore, we assume that the prior \(\lambda\) follows Gamma distribution \(Gamma(\alpha, \beta)\), thus it is with a mean of \(\frac{\alpha}{\beta}=\frac{1}{10}\).
\[if \; \; \Gamma (100, 1000)\] (Note that, it has a mean of \(\frac{100}{1000}=\frac{1}{10}\)).
Thus, we can get:
\[\begin{aligned} f(\lambda | y) &\propto f(y|\lambda) f(\lambda) \\ &\propto \lambda e^{-\lambda y} \lambda^{\alpha-1}e^{-\beta \lambda} \\ &\propto \lambda^{(\alpha+1)-1} e^{-(\beta+y)\lambda } \end{aligned}\] Thus, we get
\[\lambda |y \sim \Gamma (\alpha+1,\beta+y)\] Thus, if we observe a data point that we need to wait for 12 minutes for a fast food delivery, we can update the posterior:
\[\lambda |y \sim \Gamma (101,1012)\] Thus, the mean for the posterior is
\[\frac{101}{1012}=\frac{1}{10.02}\]
Note:
Typically, we know that the pdf of Gamma is \(\frac{\beta^{\alpha}}{\Gamma(\alpha)}x^{\alpha-1}e^{-\beta x}\). We replace \(x\) with \(\lambda\) since now the random variable of \(x\) is to represent the parameter \(\lambda\) in the exponential distribution.
In the above, we drop the constant part (\(\frac{\beta^{\alpha}}{\Gamma(\alpha)}\)) in the Gamma distribution as long as it does not include \(x\) (i.e., \(\lambda\)).
Suppose that you have 4 observations in total, then
\[\begin{aligned} f(\lambda | y) &\propto f(y_1|\lambda)f(y_2|\lambda)f(y_3|\lambda)f(y_4|\lambda) f(\lambda) \\ &\propto \lambda e^{-\lambda y_1} \lambda e^{-\lambda y_2}\lambda e^{-\lambda y_3} \lambda e^{-\lambda y_4} \lambda^{\alpha-1}e^{-\beta \lambda} \\ &\propto \lambda^{(\alpha+4)-1} e^{-(\beta+\sum_{i=1}^4 y_i)\lambda } \end{aligned}\] Thus, the generalized form is as follows:
\[\lambda^{(\alpha+n)-1} e^{-(\beta+\sum_{i=1}^n y_i)\lambda }\]
11.8 Normal likelihood
11.8.1 When variance is known
\[x_i \sim N(\mu, \sigma_0^2)\] (\(\sigma_0\) is assumed to be known. Thus, the only unknown parameter is \(\mu\).)
The conjugate prior for normal distribution is normal distribution itself.
\[f(\mu |x) \sim f(x|\mu) f(\mu)\]
Assume that \[\mu \sim N(m_0,s_0^2)\]
\[\mu|x \sim N(\frac{\frac{n \bar{x}}{\sigma_o^2}+\frac{m_o}{S_0^2}}{\frac{n}{\sigma_0^2}+\frac{1}{s_0^2}},\frac{1}{\frac{n}{\sigma_0^2}+\frac{1}{s_0^2}})\]
Thus, where
\[\begin{aligned} \frac{\frac{n \bar{x}}{\sigma_o^2}+\frac{m_o}{S_0^2}}{\frac{n}{\sigma_0^2}+\frac{1}{s_0^2}}&=\frac{\frac{n \bar{x}}{\sigma_o^2}}{\frac{n}{\sigma_0^2}+\frac{1}{s_0^2}}+\frac{\frac{m_o}{S_0^2}}{\frac{n}{\sigma_0^2}+\frac{1}{s_0^2}}\\ &=\frac{n}{n+\frac{\sigma_0^2}{s_0^2}} \bar{x}+\frac{\frac{\sigma_0^2}{S_0^2}}{n+\frac{\sigma_0^2}{s_0^2}}m_o \end{aligned}\] Note:
As we can see, the posterior mean is a weighted mean – a combination of prior mean and sample mean.
When n is larger, the sample mean \(\bar{x}\) gets more weight.
For the prior mean \(m_0\), the smaller the prior variance \(s_0^2\) is, the prior mean gets more weight. If the prior variance \(s_0^2\) is big, the prior mean will get less weight in the final posterior mean.
11.8.2 When variance is unknown
\[x_i | \mu, \sigma^2 \sim N (\mu, \sigma^2)\]
\[\mu | \sigma^2 \sim N(m, \frac{\sigma^2}{w})\]
Side note：
\(w=\frac{\sigma^2}{\sigma_{\mu}^2}\) effective sample size
\[\sigma^2 \sim \Gamma^{-1}(\alpha, \beta)\]
Thus, we can get that,
\[\sigma^2 | x\sim \Gamma^{-1}(\alpha+\frac{n}{2}, \beta+\frac{1}{2} \sum_{i=1}^{n}(x_i-\bar{x})^2+\frac{nw}{2(n+w)}(\bar{x}-m)^2)\]
\[\mu| \sigma^2,x \sim N(\frac{n \bar{x}+wm}{n+w},\frac{\sigma^2}{n+w})\]
Where,
\(\frac{n \bar{x}+wm}{n+w} = \frac{w}{n+w}m+\frac{n}{n+w}\bar{x}\)
\[\mu |x \sim t - distribution \]
11.9 Non-informative priors
11.9.1 Bernoulli
\(Y_i \sim B(\theta)\)
\[\theta \sim U[0,1]= Beta (1,1)\] (Effective sample size is 1+1=2)
If we get \(Beta(\frac{1}{2},\frac{1}{2})\) and \(Beta(0.001, 0.001)\) have less impact on the posterior.
Improper prior, for instance, THe prior \[Beta (0,0)\] \[f(\theta) \propto \theta ^{-1}(1-\theta)^{-1}\]
In this case,
\[f(\theta| y) \propto \theta^{y-1}(1-\theta)^{n-y-1} \sim Beta(y, n-y)\]
Posterior mean: \(\frac{y}{n}=\hat{\theta}\)
11.9.2 Gaussian
\[Y_i \sim N(\mu, \sigma^2)\]
Vague prior:
\[\mu \sim N(0, 1000000^2)\] or,
\[f(\mu) \sim 1\]
\[\begin{aligned} f(\mu | y) &\propto f(y|\mu)f(\mu) \\ &\propto exp(-\frac{1}{2 \sigma^2} \sum (y_i-\mu)^2) \times 1 \\ &\propto exp(-\frac{1}{2 \frac{\sigma^2}{n}} \sum (y_i-\bar{y})^2) \end{aligned}\]
Thus,
\[\mu | y \sim N(\bar{y}, \frac{\sigma^2}{n})\]
This is exactly the same as the esitimate from MLE estimate.
NOTE
In case that the variance is unknown,
\[f(\sigma^2) \propto \frac{1}{\sigma^2}\]
This is equivalent to the following:
\[\Gamma ^{-1}(0,1)\] Thus the posteiro for \(\sigma^2\)
\[\sigma^2|y \sim \Gamma ^{-1} (\frac{n-1}{2},\frac{1}{2} \sum (y_i-\bar{y})^2)\]
11.10 Jeffreys Priors
Jeffreys Prior
\[f(\theta) \propto \sqrt{I(\theta)}\] For instance,
11.10.1 Gaussian
\[Y_i \sim (\mu, \sigma^2) \rightarrow f(\mu) \propto 1, f(\sigma^2)\propto \frac{1}{\sigma^2}\]
11.10.2 Bernoulli
\[Y_i \sim B(\theta) \rightarrow f(\theta) \propto \theta ^{-\frac{1}{2}}(1-\theta)^{-\frac{1}{2}}\sim Beta(\frac{1}{2},\frac{1}{2})\]
11.10.3 Side Note: Fisher Information
The Fisher information (for one paramter):
\[?(\theta)=E[(\frac{d}{d\theta} log(f(X|\theta)))^2]\] The Expectation is with respect to \(X\) with a PDF \(f(x|\theta)\).
For instance, suppose the \(X|\theta \sim N(\theta,1)\)
\[\begin{aligned} f(x|\theta) &=\frac{1}{\sqrt{2 \pi}} exp[-\frac{1}{2}(x-\theta)^2] \\ log(f(x|\theta)) &= log(\frac{1}{\sqrt{2 \pi}})-\frac{1}{2}(x-\theta)^2 \\ \frac{d}{d\theta}log(f(x|\theta)) &=x-\theta \\ (\frac{d}{d\theta}log(f(x|\theta)))^2 &=(x-\theta)^2 \\E[(\frac{d}{d\theta}log(f(x|\theta)))^2]&=(x-\theta)^2=Var(X)=1 \end{aligned}\]
11.10.4 Prior predictive distribution
Prior distribution vs. prior predictive distribution
Stochastic Processes
11.11 Linear regression
11.11.1 Review
For single covariate of \(x\):
\[E[y]=\beta_0+\beta_1 x\] Thus,
\[Y \sim N(\beta_0+\beta_1 x, \sigma^2)\] For multiple covariates,
\[E[y]=\beta_0+\beta_1 x_1+\beta_2 x_2 + ...+ \beta_kx_k\] Side Note It is interesting to think \(y\) as the expected value of of the combination of \(\beta x\).
11.11.2 When \(\sigma^2\) is known
If we use a Jeffreys prior and assume \(\sigma^2\) is known, we will get \(\beta\) that has the same mean as the standard OLS. Assuming we only has one covariate:
\[\beta_1|y \sim N(\frac{\sum_{i=1}^n (x_i-\bar{x})(y_i-\bar{y})}{\sum_{i=1}^n(x_i-\bar{x})^2},\frac{\sigma^2}{\sum_{i=1}^n(x_i-\bar{x})^2})\]
For mutiple covariates, we can use matrix notation,
\[\beta|y \sim N((X^tX)^{-1}X^ty,(X^tX)^{-1}\sigma^2)\]
11.11.3 When \(\sigma^2\) is unknown
When both \(\beta\) and \(\sigma^2\) are unknown, the standard prior is the non-informative Jeffreys prior:
\[f(\beta, \sigma^2) \propto \frac{1}{\sigma^2}\]
The posterior mean for \(\beta\) is the same as standard OLS estimates. The posterior for \(\beta\) conditional on \(\sigma^2\) is the same normal distribution when \(\sigma^2\) is known. However, the marginal posteior distribution for \(\beta\), with \(\sigma^2\) integrated out, is a \(t\) distribution.
The \(t\) distribution has the mean of \((X^tX)^{-1}X^ty\) and variance matrix, \(s^2(X^tX)^{-1}\), where \(s^2=\sum_{i=1}^n(y_i-\hat{y_i})^2/(n-k-1)\)
The variance of \(\sigma^2\) is an inverse gamma distribution
\[\sigma^2|y \sim \Gamma^{-1}(\frac{n-k-1}{2},\frac{n-k-1}{2}s^2)\]


Course Outline: Function and structure of a computer, Functional components of a computer, Interconnection of components, Performance of a computer; Number representation and arithmetic: Binary, octal, and hexadecimal numbers, One’s and two’s complements and other representations, Addition and subtraction; Digital logic and integrated circuits: Boolean algebra and truth tables, Boolean functions (Gates, Functions, Simplification), Integrated circuits ( Combinational circuits - adders, shifters, decoders, multiplexers and ROM’s; Flip-flops; Sequential circuits - registers, counters and RAM); Representation of Instructions: Machine instructions, Operands, Addressing modes, Instruction formats, Instruction sets, Instruction set architectures - CISC and RISC architectures; Introduction to Assembly Language: Programming with Assembly language, The assembly process, Linking and loading, Register-level debugging, Processing Unit: Organization of a processor - Registers, ALU and Control unit, Data path in a CPU, Instruction cycle, Organization of a control unit - Operations of a control unit, Hardwired control unit, Micro-programmed control unit; Memory Subsystem: Semiconductor memories, Memory cells - SRAM and DRAM cells, Internal Organization of a memory chip, Organization of a memory unit, Error correction memories, Interleaved memories, Cache memory unit - Concept of cache memory, Mapping methods, Organization of a cache memory unit, Fetch and write mechanisms, Memory management unit - Concept of virtual memory, Address translation, Hardware support for memory management; Input/Output Subsystem: Access of I/O devices, I/O ports, I/O control mechanisms - Program controlled I/O, Interrupt controlled I/O, and DMA controlled I/O, I/O interfaces - Serial port, Parallel port, PCI bus, SCSI bus, USB bus, Firewall and InfiniBand, I/O peripherals - Input devices, Output devices, Secondary storage devices; Multiprocessing Systems: Shared memory multiprocessor, Message-passing multiprocessor, Hardware multithreading.


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This is the fourteenth part of a series of posts looking at the PCI recommendations for container security as they apply to Kubernetes environments. This time we’re looking at the Version Management section. An index of the posts in this series that I’ve written so far can be found here.
This one is a slightly interesting one as it’s not really a security issue, but more of a best practice, but there are some considerations which are specific to containerization. The main one is the move to “Infrastructure As Code”, where the setup of Kubernetes clusters and their applications are stored in formats like YAML and HCL, and processed by tools like Helm and Terraform. Having all of this information stored in files lends itself to improved version management practices as, in theory anyway, everything which is neeeded to re-create the environment is stored in the files.
Of course, in addition to the tools, we need an approach to managing their versioning and storage if we want to achieve our goals. One approach which lends itself to this is gitops which uses git as the source of truth for the environment.
These tools and approaches are not specific to Kubernetes, but they are a natural fit for it, and so it’s worth considering them when looking at the PCI requirements.
In terms of the PCI requirements there’s one in this section.
Section 14.1
Threat - Without proper control and versioning of container orchestration configuration files, it may be possible for an attacker to make an unauthorized modification to an environment’s setup
Best Practice - a. Version control should be used to manage all non-secret configuration files. b. Related objects should be grouped into a single file. c. Labels should be used to semantically identify objects.
Details - Reviewing a cluster for this recommendation would likely start with speaking to the operators to understand how they control the configuration files, and then checking the management of key elements such as the helm charts and terraform files (or alternative tools if they are being used).
Conclusion
This is a relatively short post as the PCI recommendations are relatively straightforward and Kubernetes doesn’t introduce any very specific concerns, however it’s another one to add to the list of things to consider.


Gecode::Int::BinPacking::ConflictGraph Class Reference
Graph containing conflict information. More...
#include <bin-packing.hh>
Classes | |
| class | Clique |
| Clique information. More... | |
| class | Node |
| Class for node in graph. More... | |
| class | Nodes |
| Iterator over node sets. More... | |
| class | NodeSet |
| Sets of graph nodes. More... | |
Public Member Functions | |
| ConflictGraph (Home &home, Region &r, const IntVarArgs &b, int m) | |
| Initialize graph. | |
| void | edge (int i, int j, bool add=true) |
| Add or remove an edge between nodes i and j (i must be less than j). | |
| bool | adjacent (int i, int j) const |
| Test whether nodes i and j are adjacent. | |
| ExecStatus | post (void) |
| Post additional constraints. | |
| IntSet | maxclique (void) const |
| Return maximal clique found. | |
| ~ConflictGraph (void) | |
| Destructor. | |
Protected Member Functions | |
| int | nodes (void) const |
| Return number of nodes. | |
Protected Attributes | |
| Home & | home |
| Home space. | |
| const IntVarArgs & | b |
| Bin variables. | |
| unsigned int | bins |
| Number of bins. | |
| Node * | node |
| The nodes in the graph. | |
Managing cliques | |
|
| |
| Clique | cur |
| Current clique. | |
| Clique | max |
| Largest clique so far. | |
| ExecStatus | clique (void) |
| Report the current clique. | |
| ExecStatus | clique (int i) |
| Found a clique of node i. | |
| ExecStatus | clique (int i, int j) |
| Found a clique of nodes i and j. | |
| ExecStatus | clique (int i, int j, int k) |
| Found a clique of nodes i, j, and k. | |
Routines for Bosch-Kerbron algorithm | |
|
| |
| int | pivot (const NodeSet &a, const NodeSet &b) const |
| Find a pivot node with maximal degree from a or b. | |
| ExecStatus | bk (NodeSet &p, NodeSet &x) |
| Run Bosch-Kerbron algorithm for finding max cliques. |
Detailed Description
Graph containing conflict information.
Definition at line 182 of file bin-packing.hh.
Constructor & Destructor Documentation
| Gecode::Int::BinPacking::ConflictGraph::ConflictGraph | ( | Home & | home, | |
| Region & | r, | |||
| const IntVarArgs & | b, | |||
| int | m | |||
| ) | [inline] |
Initialize graph.
Definition at line 139 of file conflict-graph.hpp.
| Gecode::Int::BinPacking::ConflictGraph::~ConflictGraph | ( | void | ) | [inline] |
Destructor.
Definition at line 152 of file conflict-graph.hpp.
Member Function Documentation
| int Gecode::Int::BinPacking::ConflictGraph::nodes | ( | void | ) | const [inline, protected] |
Return number of nodes.
Definition at line 42 of file conflict-graph.hpp.
| int Gecode::Int::BinPacking::ConflictGraph::pivot | ( | const NodeSet & | a, | |
| const NodeSet & | b | |||
| ) | const [inline, protected] |
Find a pivot node with maximal degree from a or b.
Definition at line 175 of file conflict-graph.hpp.
| ExecStatus Gecode::Int::BinPacking::ConflictGraph::bk | ( | NodeSet & | p, | |
| NodeSet & | x | |||
| ) | [protected] |
Run Bosch-Kerbron algorithm for finding max cliques.
Definition at line 38 of file conflict-graph.cpp.
| ExecStatus Gecode::Int::BinPacking::ConflictGraph::clique | ( | void | ) | [inline, protected] |
Report the current clique.
Definition at line 201 of file conflict-graph.hpp.
| ExecStatus Gecode::Int::BinPacking::ConflictGraph::clique | ( | int | i | ) | [inline, protected] |
Found a clique of node i.
Definition at line 218 of file conflict-graph.hpp.
| ExecStatus Gecode::Int::BinPacking::ConflictGraph::clique | ( | int | i, | |
| int | j | |||
| ) | [inline, protected] |
Found a clique of nodes i and j.
Definition at line 227 of file conflict-graph.hpp.
| ExecStatus Gecode::Int::BinPacking::ConflictGraph::clique | ( | int | i, | |
| int | j, | |||
| int | k | |||
| ) | [inline, protected] |
Found a clique of nodes i, j, and k.
Definition at line 240 of file conflict-graph.hpp.
| void Gecode::Int::BinPacking::ConflictGraph::edge | ( | int | i, | |
| int | j, | |||
| bool | add = true | |||
| ) | [inline] |
Add or remove an edge between nodes i and j (i must be less than j).
Definition at line 156 of file conflict-graph.hpp.
| bool Gecode::Int::BinPacking::ConflictGraph::adjacent | ( | int | i, | |
| int | j | |||
| ) | const [inline] |
Test whether nodes i and j are adjacent.
Definition at line 169 of file conflict-graph.hpp.
| ExecStatus Gecode::Int::BinPacking::ConflictGraph::post | ( | void | ) | [inline] |
Post additional constraints.
Definition at line 256 of file conflict-graph.hpp.
| IntSet Gecode::Int::BinPacking::ConflictGraph::maxclique | ( | void | ) | const [inline] |
Return maximal clique found.
Definition at line 330 of file conflict-graph.hpp.
Member Data Documentation
Home& Gecode::Int::BinPacking::ConflictGraph::home [protected] |
Home space.
Definition at line 185 of file bin-packing.hh.
const IntVarArgs& Gecode::Int::BinPacking::ConflictGraph::b [protected] |
Bin variables.
Definition at line 187 of file bin-packing.hh.
unsigned int Gecode::Int::BinPacking::ConflictGraph::bins [protected] |
Number of bins.
Definition at line 189 of file bin-packing.hh.
Node* Gecode::Int::BinPacking::ConflictGraph::node [protected] |
The nodes in the graph.
Definition at line 240 of file bin-packing.hh.
Clique Gecode::Int::BinPacking::ConflictGraph::cur [protected] |
Current clique.
Definition at line 294 of file bin-packing.hh.
Clique Gecode::Int::BinPacking::ConflictGraph::max [protected] |
Largest clique so far.
Definition at line 296 of file bin-packing.hh.
The documentation for this class was generated from the following files:
- gecode/int/bin-packing.hh
- gecode/int/bin-packing/conflict-graph.cpp
- gecode/int/bin-packing/conflict-graph.hpp


|
SimpleITK
1.1.0
|
SimpleITK: a simplified layer build on top of the Insight Toolkit (ITK), intended to facilitate its use in rapid prototyping, education and interpreted languages.
Welcome to the National Library of Medicine Insight Segmentation and Registration Toolkit (ITK). ITK is an open-source, cross-platform system that provides developers with an extensive suite of software tools for image analysis. Among them, SimpleITK is a simplified layer built on top of ITK, intended to facilitate its use in rapid prototyping, education, interpreted languages. SimpleITK has the following main characteristics:
The Home Page of the SimpleITK can be found at :
The Home Page of the Insight toolkit can be found at :
This documentation describes the C++ API of the SimpleITK interface to the Insight Toolkit. The wrapped languages have a very simular interface, that should only be trivially different. The Related Pages link presents interface conventions, user and developer information. Additionally the Example tab provides a variety of example of SimpleITK with different language bindings.


You can add, replace, and remove the organization's credit card.
Follow these steps to add a credit card:
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Follow these steps to change the credit card:
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Follow these steps to remove the credit card:
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Grayscale is a free Bootstrap 3 theme created by Start Bootstrap. It can be yours right now, simply download the template on the preview page. The theme is open source, and you can use it for any purpose, personal or commercial.
This theme features stock photos by Gratisography along with a custom Google Maps skin courtesy of Snazzy Maps.
Grayscale includes full HTML, CSS, and custom JavaScript files along with LESS files for easy customization.
^([0-9])?(\.[0-9]+)?(e[-+]?[0-9]+(\.[0-9]+)?)?$
(\d)(?=(?:\d\d\d){2,}(?!\d))|(\d\d)(?=(?:\d\d\d)(?!\d))/$&,/g
replace 123123123 to 123,123,123, but not: 2034
4324534 ->
13245345443 ->
3132 ->
@async function () { var b = 45; var c = 23; var g <= mysql.query("sel * from bananas where b = ?", [b]); var d = await mysql.query("sel * from ss where c = ?", [c]); //alias return g.merge(d); }
// will be: function(cb) { var b = 45; var c = 23; mysql.query("sel * from bananas where b = ?", [b], function(err, g) { if (err) cb(err) else { mysq.query("self * from ss where c = ?", [c], function(err, d) { if (err) cb(err) else { cb(null, g.merge(d); } }); } }); }
@route('/user/:uid') @get function(uid) { @send {a: uid} } @post function(uid) { @send {b: uid * 4} }
app.get('/user', function(req, res) { var uid = ...; res.send(JSON.strungify({a: uid})); } app.post('/user', function(req, res)) { var uid = ...; res.send(JSON.stringify({b: uid})); }


TLDR: Want to build your own puzzles, use the JSON-api to get generated puzzles, just add an UI.
I love playing puzzle-games, I can do them on paper, but I prefer an friendly UI that helps me keep track of clues.
Simon G. Tatham (SGT) Puzzle collection have allot of good puzzles, and often a good UI.
I also plays nonograms(/pattern) with non-random images, and Zebra-puzzles
In this project I used Simons code to generate puzzle-states, and then converting them to JSON-format, and then addin my own UI.
In the API-folder you find a few php-script, that genereates a game-state in JSON-format.
Thus can be used to implement your own UI, or to build a solver.
So far I have implemented an JSON-format for 15 of the puzzles.
Here is the source-code for all parts of the project:
Here is a list of all games I'm implemented so far:
Here is the Games I'm started to implement, I'll move them up above when they are playable.
Here is the Games I planning to implement, when I get the time:


{% extends "inc/layout.html" %} {% block title %}Internal Server Error{% endblock %} {% block content %}
Something broke inside. A report has been sent to the server’s administrator. Apologies for the inconvenience.
If you got this when attempting to login with twitter, could you say hello to us at @hasgeek on Twitter? We’re having occasional problems with some Twitter accounts and need your help sorting it out. Or, you may want to login with Google for now.
{% endblock %}


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| Jul 05, 2017 | 250.00 USD | Pool View | 0.00 USD | 273.00 USD |
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| Balcony | 0.00 USD | |||
| Low Floor Room | 13.00 USD | |||
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| 1 Night(s) per room | 250.00 USD | 58.00 USD | 273.00 USD |
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| Total Price with tax and Fees for 1 night(s) | 324.16 USD |
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| Dates | Base Price | Attribute Prices | Total Price | |
|---|---|---|---|---|
| Jul 05, 2017 | 280.00 USD | Pool View | 0.00 USD | 285.00 USD |
| 2 Queen Beds | 10.00 USD | |||
| Balcony | 0.00 USD | |||
| Low Floor Room | 13.00 USD | |||
| Near Elevator | 0.00 USD | |||
| 1 Night(s) per room | 280.00 USD | 58.00 USD | 285.00 USD |
| Review_CityTax (for stay) | 10.00 USD |
| Review_RollAwayTax (for stay) | 5.26 USD |
| Review_CountryTax (Jul 05) | 5.90 USD |
| Resort Fee (Jul 05) | 20.00 USD |
| State Cost Recovery Fee (Jul 05) | 10.00 USD |
| Total Price with tax and Fees for 1 night(s) | 329.16 USD |
Rate Breakdown
| Dates | Base Price | Attribute Prices | Total Price | |
|---|---|---|---|---|
| Jul 05, 2017 | 300.00 USD | Pool View | 0.00 USD | 305.00 USD |
| 2 Queen Beds | 10.00 USD | |||
| Balcony | 0.00 USD | |||
| Low Floor Room | 13.00 USD | |||
| Near Elevator | 0.00 USD | |||
| 1 Night(s) per room | 300.00 USD | 58.00 USD | 305.10 USD |
| Review_CityTax (for stay) | 10.00 USD |
| Review_RollAwayTax (for stay) | 5.26 USD |
| Review_CountryTax (Jul 05) | 5.90 USD |
| Resort Fee (Jul 05) | 20.00 USD |
| State Cost Recovery Fee (Jul 05) | 10.00 USD |
| Total Price with tax and Fees for 1 night(s) | 335.16 USD |
Rate Breakdown
| Dates | Base Price | Attribute Prices | Total Price | |
|---|---|---|---|---|
| Jul 05, 2017 | 290.00 USD | Pool View | 0.00 USD | 320.00 USD |
| 2 Queen Beds | 10.00 USD | |||
| Balcony | 0.00 USD | |||
| Low Floor Room | 13.00 USD | |||
| Near Elevator | 0.00 USD | |||
| 1 Night(s) per room | 290.00 USD | 58.00 USD | 320.00 USD |
| Review_CityTax (for stay) | 10.00 USD |
| Review_RollAwayTax (for stay) | 5.26 USD |
| Review_CountryTax (Jul 05) | 5.90 USD |
| Resort Fee (Jul 05) | 20.00 USD |
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| Dates | Base Price | Attribute Prices | Total Price | |
|---|---|---|---|---|
| Jul 05, 2017 | 350.00 USD | Pool View | 0.00 USD | 365.00 USD |
| 2 Queen Beds | 10.00 USD | |||
| Balcony | 0.00 USD | |||
| Low Floor Room | 13.00 USD | |||
| Near Elevator | 0.00 USD | |||
| 1 Night(s) per room | 350.00 USD | 58.00 USD | 365.00 USD |
| Review_CityTax (for stay) | 10.00 USD |
| Review_RollAwayTax (for stay) | 5.26 USD |
| Review_CountryTax (Jul 05) | 5.90 USD |
| Resort Fee (Jul 05) | 20.00 USD |
| State Cost Recovery Fee (Jul 05) | 10.00 USD |
| Total Price with tax and Fees for 1 night(s) | 387.10 USD |
Rate Breakdown
| Dates | Base Price | Attribute Prices | Total Price | |
|---|---|---|---|---|
| Jul 05, 2017 | 350.00 USD | Pool View | 0.00 USD | 373.00 USD |
| 2 Queen Beds | 10.00 USD | |||
| Balcony | 0.00 USD | |||
| Low Floor Room | 13.00 USD | |||
| Near Elevator | 0.00 USD | |||
| 1 Night(s) per room | 350.00 USD | 58.00 USD | 373.00 USD |
| Review_CityTax (for stay) | 10.00 USD |
| Review_RollAwayTax (for stay) | 5.26 USD |
| Review_CountryTax (Jul 05) | 5.90 USD |
| Resort Fee (Jul 05) | 20.00 USD |
| State Cost Recovery Fee (Jul 05) | 10.00 USD |
| Total Price with tax and Fees for 1 night(s) | 405.00 USD |
Rate Breakdown
| Dates | Base Price | Attribute Prices | Total Price | |
|---|---|---|---|---|
| Jul 05, 2017 | 360.00 USD | Pool View | 0.00 USD | 429.00 USD |
| 2 Queen Beds | 10.00 USD | |||
| Balcony | 0.00 USD | |||
| Low Floor Room | 13.00 USD | |||
| Near Elevator | 0.00 USD | |||
| 1 Night(s) per room | 360.00 USD | 58.00 USD | 429.00 USD |
| Review_CityTax (for stay) | 10.00 USD |
| Review_RollAwayTax (for stay) | 5.26 USD |
| Review_CountryTax (Jul 05) | 5.90 USD |
| Resort Fee (Jul 05) | 20.00 USD |
| State Cost Recovery Fee (Jul 05) | 10.00 USD |
| Total Price with tax and Fees for 1 night(s) | 452.00 USD |
Lowest Available Rates
Day: 473.00 USD
Stay: 491.00 USD
View RatesThe following 3 products are available for your travel plans.
Rate Breakdown
| Dates | Base Price | Attribute Prices | Total Price | |
|---|---|---|---|---|
| Jul 05, 2017 | 450.00 USD | Pool View | 0.00 USD | 473.00 USD |
| 2 Queen Beds | 10.00 USD | |||
| Balcony | 0.00 USD | |||
| Low Floor Room | 13.00 USD | |||
| Near Elevator | 0.00 USD | |||
| 1 Night(s) per room | 450.00 USD | 58.00 USD | 473.00 USD |
| Review_CityTax (for stay) | 10.00 USD |
| Review_RollAwayTax (for stay) | 5.26 USD |
| Review_CountryTax (Jul 05) | 5.90 USD |
| Resort Fee (Jul 05) | 20.00 USD |
| State Cost Recovery Fee (Jul 05) | 10.00 USD |
| Total Price with tax and Fees for 1 night(s) | 491.00 USD |
Rate Breakdown
| Dates | Base Price | Attribute Prices | Total Price | |
|---|---|---|---|---|
| Jul 05, 2017 | 480.00 USD | Pool View | 0.00 USD | 541.00 USD |
| 2 Queen Beds | 10.00 USD | |||
| Balcony | 0.00 USD | |||
| Low Floor Room | 13.00 USD | |||
| Near Elevator | 0.00 USD | |||
| 1 Night(s) per room | 480.00 USD | 58.00 USD | 541.00 USD |
| Review_CityTax (for stay) | 10.00 USD |
| Review_RollAwayTax (for stay) | 5.26 USD |
| Review_CountryTax (Jul 05) | 5.90 USD |
| Resort Fee (Jul 05) | 20.00 USD |
| State Cost Recovery Fee (Jul 05) | 10.00 USD |
| Total Price with tax and Fees for 1 night(s) | 545.40 USD |
Rate Breakdown
| Dates | Base Price | Attribute Prices | Total Price | |
|---|---|---|---|---|
| Jul 05, 2017 | 550.00 USD | Pool View | 0.00 USD | 573.00 USD |
| 2 Queen Beds | 10.00 USD | |||
| Balcony | 0.00 USD | |||
| Low Floor Room | 13.00 USD | |||
| Near Elevator | 0.00 USD | |||
| 1 Night(s) per room | 550.00 USD | 58.00 USD | 573.00 USD |
| Review_CityTax (for stay) | 10.00 USD |
| Review_RollAwayTax (for stay) | 5.26 USD |
| Review_CountryTax (Jul 05) | 5.90 USD |
| Resort Fee (Jul 05) | 20.00 USD |
| State Cost Recovery Fee (Jul 05) | 10.00 USD |
| Total Price with tax and Fees for 1 night(s) | 585.10 USD |


|
Ocular Engine
|
Generic buffer for storing and binding of shader uniforms. More...
#include <UniformBuffer.hpp>
Public Member Functions | |
| UniformBuffer (UniformBufferType type) | |
| virtual void | bind () |
| virtual void | unbind () |
| void | setFixedData (UniformPerFrame const &data) |
| void | setFixedData (UniformPerCamera const &data) |
| void | setFixedData (UniformPerObject const &data) |
| void | setUniform (Uniform const &uniform) |
| Uniform const * | getUniform (std::string const &name) const |
| Uniform const * | getUniform (uint32_t registerIndex) const |
| std::vector< Uniform > const * | getUniforms () const |
| uint32_t | getNumUniforms () const |
| uint32_t | getUniformsSize () const |
Protected Attributes | |
| uint32_t | m_Type |
| bool | m_IsDirty |
| std::vector< Uniform > | m_Uniforms |
| User-friendly copy of Uniform data that is easily modifiable. Strictly CPU-only. | |
| float * | m_UniformData |
| Raw, packed (16-byte) uniform data. Only modify when m_IsDirty is true and not in use by GPU. | |
| uint32_t | m_UniformDataSize |
| Size of the raw uniform data. | |
Generic buffer for storing and binding of shader uniforms.
Typically, the UniformBuffer class is only to be used internally by the engine. For most cases, per-Material uniform access is sufficient (see the family of Material::setUniform and Material::getUniform methods).
There is normally four different instances of UniformBuffers in use at any time by the Ocular engine. These are as follows:
Per-Frame (global values, lights, etc.) Per-Camera (view and projection matrices, eye position, etc.) Per-Object (world matrix, etc.) Per-Material (any user defined values)
All buffers except Material buffers are treated as 'fixed' while Materials are 'dynamic'. What this means is that fixed buffers take in full structs as data, while dynamic buffers can take in individual uniform values and registers. So a fixed uniform is rigid and the structure of it's data can never change.
See the manual section on Shaders for more information.
| uint32_t Ocular::Graphics::UniformBuffer::getNumUniforms | ( | ) | const |
| Uniform const * Ocular::Graphics::UniformBuffer::getUniform | ( | std::string const & | name | ) | const |
| Uniform const * Ocular::Graphics::UniformBuffer::getUniform | ( | uint32_t | registerIndex | ) | const |
| uint32_t Ocular::Graphics::UniformBuffer::getUniformsSize | ( | ) | const |
| void Ocular::Graphics::UniformBuffer::setUniform | ( | Uniform const & | uniform | ) |


Backchat.IO is hiring!
BackChat.io is easiest to describe as grep for real-time data. We’re busy developing a platform that makes sense of the data you send it (in real-time).
For the social web that means that you can start following certain users on a network or define a search term and you get those in a unified activity streams format.
But for something more arcane, like say, log data, that means you can send it lines of text and have it extract out fields on which you can define filters. All of this happens in real-time and changes are reflected virtually immediately in open connections.
Our system is designed to allow for easy definition of data sources. Sources that currently feed into our system are Twitter, Facebook, LinkedIn, SMTP, RSS, ATOM, PubSubHubbub, and more. By next year we hope to provide dozens, if not 100’s, of API implementations.
In the longer term we hope to provide more intelligent stuff like real-time reporting by allowing you to define aggregates like SUM, enhance data with secondary information, and many more crazy ideas. We’re aiming to build our system exclusively on existing standards described in RFC’s or the activity streams spec. However not everything we do is described in standards and that’s where we’ll try to extend an existing standard. Our use cases are wide, ranging from niche collaboration or social media apps, to communication/PR/marketing agencies, to telecommunications… We operate at an abstraction on a much lower level than most similar services; so many tools you see and use on the web today can be built with only a few lines of code in your favourite language on our platform.
The entire system is built on dogfooding and practicing what we preach. So where it makes sense we rely on our own service, like with log processing. Our entire website is built using the API methods we expose to our customers. Our API integration language will be shared with our customers too.
How do you guys work?
We use a mix of technologies like scala, akka, mongodb, lucene, zeromq, dropbox, flowdock, skype, …. We apply agile principles to our development process, but this doesn’t mean we follow a doctrine that is being sold because some consultant wants to make some money. To us Agile development means: design for change. Which also means that the process we use is adapted to the people in the process and not vice versa. It’s about being productive and creating an environment where you want to work. At the moment, all of us work from home. We’re a team of 3, looking for a fourth team-member to start in September. We don’t care where or how you work, just that you deliver 40 hours worth of output, regular github pushes (so new code can be reviewed before it hits our integration branch) and, most importantly, passion!
Great! What are you guys looking for?
We’re looking for a passionate developer. This person ideally:
knows about concurrent and distributed programming
is well-versed in Scala
can accept a team decision, and understands that communication is the most important part of development as a team.
is polyglot (the ability to work with C#, node.js/javascript, ruby, python and/or php)
preferably knows XMPP beyond using the smack library
has used mongodb
knows Lucene
regular expressions have little or no secrets for you
does not believe in sugar-coating things and voices reservations early
No! Try not. Do, or do not. There is no try. (Some of us hope you’re a Star Wars fan too)
It’s a plus if you’ve done some of the following:
BIG data
Language parsing and development
Natural language processing
Statistics
knows how to express himself in simple english and not academic language
Netty server development / Java NIO
Jetty customization
Understands the problems communication agencies and social-media developers face
What we’re not looking for is someone who has little or no experience, community zealots and big framework lovers (I’m looking at you rails, lift, spring, …). We need doers!
You do not have to live in the UK to work with/for us. We are firm believers in smarter remote-working, and our cloud-based infrastructure is designed to allow us to work from where we want.
Open-Source?
We use a lot of open source, and we believe in being good citizens and contribute back where we can. Some of the projects we use: casbah, akka, scalatra, lift-mongodb-record, lift-json, codahale metrics, databinder dispatch, … So far most of our contributions were to scalatra and peripheral to akka. If we come up with stuff that we all agree on should really be in an open source project then we will actually take the necessary steps to make that happen.
Any open-source library we produce is licensed as MIT and uses as few dependencies as possible (be nice to people with complicated projects).
What’s the vision?
To build the best damned developer platform for social-media and real-time data! Quickly!
Our service is primarily aimed at developers. We provide a metered, utility billing model. Depending on which bits and pieces of our infrastructure a developer uses we calculate a price per message for each incoming channel. At the end of the month they get a bill. This billing is updated in real-time so everybody knows exactly where they stand. For corporate customers we provide a behind-firewall-install license. This license fee may or may not include bespoke development or integration into their systems.
Our founding customer has been O2/Telefonica. They’re using our platform in cool ways, but we can’t talk about that just yet (ssshh).
Who are the company?
Adam Burmister is the CEO of the company and has previously worked as team lead for the bbc.com homepage. He has also done assorted javascript libraries and was nominated for a webby award for his work at xero.com. He’s a Kiwi, so be warned, he sounds funny.
Ivan Porto Carrero is the CTO and has previously almost published a book on IronRuby and has been an MVP for the last 2 years. Worked as an architect/consultant in the .NET sphere and met Adam while he was working as a senior development at xero.com It helps to know that Ivan is from Belgium, as an extremely low bullshit tolerance and it’s best to feed him at regular times with pre-approved snacks. Also, his english sounds funny.
Stefan De Boey was our very first helper and we haven’t managed to scare him off, he’s actually happy with his job, but won’t say so because he’s Belgian and compliments make people suspicious where he’s from (not to mention the thought of making a government). Stefan typically starts his day between 11am and 12:30 and finishes around 20:00. Also, his english sounds funny.
Ok, I’m sold. How do I apply?
Great! We’re looking forward to meeting you.
If you could, please start by sending us your LinkedIn profile and preferably some recent open source projects you worked on.


import "go/importer"
Package importer provides access to export data importers.
Default returns an Importer for the compiler that built the running binary. If available, the result implements types.ImporterFrom.
For returns an Importer for the given compiler and lookup interface, or nil. Supported compilers are "gc", and "gccgo". If lookup is nil, the default package lookup mechanism for the given compiler is used. BUG(issue13847): For does not support non-nil lookup functions.
type Lookup func(path string) (io.ReadCloser, error)
A Lookup function returns a reader to access package data for a given import path, or an error if no matching package is found.
☞ For does not support non-nil lookup functions.


( ( person-group | collab )*, ( article-title | trans-title )*, source?, patent?, trans-source?, year?, ( ( month?, day?, time-stamp? ) | season? ), access-date?, volume?, edition?, conf-name?, conf-date?, conf-loc?, ( issue | supplement )*, publisher-loc?, publisher-name?, ( fpage?, lpage? )*, page-count?, series?, comment*, pub-id*, annotation? )
May Contain:


Always Developing
Substance
Playground
Tech A
Tech B
Expose
Real Code
Clear Sight
Sustenance
Playground
Tech A
Tech B
Expose
Real Code
Clear Sight
Sam's Playground
Welcome to my playground page. This is where I will mess around with some techniques as I learn them and when I have time to play.
This is a button
placeholder
placeholder2
placeholder3
placeholder4
placeholder5
placeholder6
placeholder7
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Honorary Co-Chairs
- Laura Olhasso Current City Councilwoman, former Mayor of La Cañada Flintridge
- Pat Anderson Current President & CEO of the La Cañada Flintridge Chamber of Commerce and Community Association
- Steve Del Guercio Past City Councilman and Mayor of La Cañada Flintridge
Campaign Committee
- Wes Seastrom Campaign Manager
- John Landrum Treasurer
- Caroline Craven Social Media Coordinator
- Deborah Weirick Public Relations and School Board Liaison
- Kay Linden
- Rick Gunter
- Woody Walker
Events
-
John Doe


Stored Procedure Attacks
It is often believed that stored procedures are not vulnerable to SQL injection attacks, but the reality is totally different. In fact, stored procedures and PL/SQL can be vulnerable to SQLIA. This article covers the different situations where stored procedures can be exploited. If you are looking to secure you code against this security flaw, consider reading the article about writing secure stored procedures.
Vulnerable Code
Many database management systems support user defined stored procedures. Even though the syntax may slightly vary from a DBMS to another, the principle stays the same. When a stored procedure integrates user supplied input into a dynamically built query, we encounter exactly the same SQL injection vulnerability we presented in the tutorial. There is basically two ways to create a vulnerable stored procedure. Microsoft SQL Server syntax is used in examples, but you could adapt it could be converted to almost any DBMS syntax with minimal changes. Let’s take a look at the code behind those security flaws.
Vulnerable EXEC statement
The most common mistake with stored procedures is to misuse the EXEC statement. If the user input was not sanitized before integrating it in the query, a SQL injection is created.
Vulnerable stored procedure using EXEC statement.
CREATE PROCEDURE getDescription
@vname VARCHAR(50)
AS
EXEC('SELECT description FROM products WHERE name = '''+@vname+ '''')
RETURN
As you can guess, the previous example will execute any correctly crafted user input as if it was part of the query.
Vulnerable Dynamic Cursors
Dynamic cursors are not as frequently used as EXEC statements in stored procedures. However, that does not make them less vulnerable. In fact, it might represent a little advantage for the attacker since security flaws in dynamic cursors are less likely to be identified in a code review (article available soon). Let’s take a look at an insecure use of dynamic cursors.
Vulnerable stored procedure using dynamic cursor.
CREATE PROCEDURE printDescriptions
@vname VARCHAR(100)
AS
DECLARE @vdesc VARCHAR(1000)
DECLARE @vsql VARCHAR(4000)
SET @vsql = 'SELECT description FROM products WHERE name='''+@vname+''''
DECLARE cur CURSOR FOR EXEC @vsql
OPEN cur
FETCH NEXT FROM cur INTO @vdesc
WHILE @@FETCH_STATUS = 0
BEGIN
PRINT @vdesc
FETCH NEXT FROM cur INTO @vdesc
END
CLOSE cur
DEALLOCATE cur
RETURN
Here again, the user input is integrated directly in the query. Let’s now see how the stored procedure could be manipulated by the attacker.
Attacking Stored Procedures
In both cases the attack is quite straight forward.  Below is an example that could be applied to successfully attack vulnerable procedures presented earlier in this article.
Malicious user input.
z' AND 'a'='b' UNION SELECT password FROM members WHERE username='admin
Query generated in both cases.
SELECT description FROM products WHERE name ='z' AND 'a'='b' UNION SELECT password FROM members WHERE username='admin'
When the stored procedure is executed, it returns the password of the administrator.
Limitations
There is no specific limitation when attacking stored procedures, although you need to keep in mind that the injected code will be limited to the stored procedure permissions (available soon). For example, the attack will be voided if you attempt to inject an INSERT statement in a stored procedure which has only read acces. In addition, DBMS/API specific limitations (available soon) are still taking effect.
Advanced Attacks
Many advanced attacks such as privilege escalation (available soon), SQL injection buffer overflow (available soon) and advanced information gathering (available soon) rely on stored procedures. Those are not covered here since they habitually only apply to a specific (unpatched) version of database management system. For more information about those advanced techniques (available soon), refer to mentioned articles.


{% extends 'base.html' %} {% block main %} {% load i18n %} {% load static %}
{% blocktrans %} This project is collaboration between the Interaction Data Lab and the Peer-Produced Research Lab of CRI Paris. {% endblocktrans %}
{% blocktrans %} In this study, we provide a replicable, data-driven approach to investigate a potential second wave of the COVID pandemic. In an open science spirit, we are particularly interested in how citizens can take part in research projects at all levels, from crowdsourcing to machine learning algorithms. {% endblocktrans %}
{% blocktrans %} This is why we tried to open the products of this study as much as possible, from downloadable labeling data to directly editable notebooks through Binder. {% endblocktrans %}
{% blocktrans %} We collected tweets specifically from users in Île-de-France. We first used the Streaming API to identify users in the Paris area, and then collected the historic data from these users. This dashboard presents our analyses, based on 30,000 Twitter users, for a total of about 33 million tweets from December 2019 (17 million without retweets). {% endblocktrans %}
{% blocktrans %} For the number of passages to emergencies, we used public data from Santé Publique France about emergencies and SOS Médecins data related to COVID. {% endblocktrans %}
{% blocktrans %}
This website does not store any personal identifiable information.
If you are annotating tweets, the only thing being stored is a random but
unique identifier tied to your current browser that will be associated with your annotations.
{% endblocktrans %}
{% blocktrans %}
Additionally, this site's hoster Heroku stores and gives us access to the IP addresses of visitors
and the pages visited on this site. These logs are stored by them for up to a week.
{% endblocktrans %}
{% blocktrans %} Team leader of the Interaction Data Lab, CRI Research Fellow and Director of Research for Just One Giant Lab. {%endblocktrans%}
Learn more{% blocktrans %} Team leader of the Peer-Produced Research Lab, CRI Research Fellow and Director of Research for Open Humans. {%endblocktrans%}
Learn more{% blocktrans %} ENSAE Student in Data Science and Social Science. Intern at Interaction Data Lab {%endblocktrans%}
Learn more{% blocktrans %} Data Analytics and Tools team at the World Bank, visiting researcher at NYU Computer Science, and fellow at the MIT Medialab. {%endblocktrans%}
Learn more


Overview
Classes: Header, ICMPClient, ICMPEventArgs, ICMPPacket, ICMPPacketImpl, ICMPSocket, ICMPSocketImpl, ICMPv4PacketImpl
Classes
struct Header
class ICMPClient
This class provides ICMP Ping functionality.
class ICMPEventArgs
The purpose of the ICMPEventArgs class is to be used as template parameter to instantiate event members in ICMPClient class.
class ICMPPacket
This class is the ICMP packet abstraction.
class ICMPPacketImpl
This is the abstract class for ICMP packet implementations.
class ICMPSocket
This class provides an interface to an ICMP client socket.
class ICMPSocketImpl
This class implements an ICMP socket.


The Mighty (1998) Elden Henson, Kieran Culkin, Sharon Stone, Gena Rowlands, Harry Dean Stanton, Gillian Anderson, James Gandolfini, Meat Loaf, Joe Perrino. Screenplay by Charles Leavitt, based on the novel "Freak The Mighty" by Rodman Philbrick. Directed by Peter Chelsom. 106 minutes. Rated PG-13, 3 stars (out of five stars)
Review by Ed Johnson-Ott, NUVO Newsweekly www.nuvo-online.com Archive reviews at http://us.imdb.com/M/reviews_by?Edward+Johnson-ott To receive reviews by e-mail at no charge, send subscription requests to<EMAIL_ADDRESS>Both "Simon Birch" and "The Mighty" feature sweet tales of friendship between two boys. Both films are also quite flawed, albeit in different ways, and your tolerance of those flaws will likely depend on whether or not the central storyline strikes an emotional chord with you.
Like "Simon Birch," "The Mighty" deals with a pair of young male outcasts who bond together. Kevin Dillon (Kieran Culkin) is unusually small and can walk only with braces, the result of a progressive, degenerative disease called Morquio syndrome. The bright kid spends most of his time in the world of books and the imagination, where, unencumbered by his physical limitations, he can slay dragons and rescue damsels in distress as a knight in King Arthur's court.
Maxwell Kane (Elden Henson) is a stocky, quiet and very lonely illiterate. The boy lives in his grandparents' basement, hiding from the cruel taunts of classmates and his own painful memories of one horrific night that tore his family apart. When Kevin (also known as "Freak") and his loving, fiercely protective mother (Sharon Stone) move in next door, everything changes. The 13-year-olds eventually become friends, each making up for the others limitations. With Kevin perched atop Max's shoulders, they become a powerful single unit. As Max puts it, "Freak was the brain, and I was the legs." Together, they venture forth confidently as a symbiotic knight in urban Cincinnati.
Based on "Freak The Mighty," the beloved Rodman Philbrick novel, "The Mighty" is a solid, often moving story about the pain of being ostracized and the healing power of friendship. Unlike "Simon Birch," which used an overbearing score and contrived situations to emotionally bully the viewer, "The Mighty" avoids overt manipulation. The central storyline is strong enough to play without orchestration, although a subplot involving Max's dad and his miscreant friends is heavy-handed, to put it mildly.
With a creation like this, chemistry is everything and the producers of "The Mighty" made some interesting casting decisions. 21-year-old Elden Henson is perfect as 13-year-old Max, despite the startling age gap. He captures the tortured young man's bottled-up pain splendidly, projecting strong emotions with an economy of expression. Henson is so good that he nearly makes up for Kieran Culkin's deficits.
Reportedly, "Simon Birch's" Ian Michael Smith, who has Morquio syndrome in real life, was turned down for the role of Kevin, and Culkin is simply too big to play the part. Beyond the physical incongruity, Culkin projects an extremely arrogant, smarmy air. While I empathized with the character, I didn't like him. Despite his distasteful smugness, though, there is an undeniable chemistry between the boys.
Sharon Stone is quite strong as Kevin's devoted mother, giving one of the best performances of her career, and Gena Rowlands and Harry Dean Stanton are solid, if underused, as Max's grandparents. Two other supporting performances must be noted. Meat Loaf plays Max's father and as an actor, he's a good singer. Finally, Gillian Anderson is embarrassingly bad as a drunken floozy, coming off like a bush-league version of Faye Dunaway in "Mommie Dearest."
Had the filmmakers cast Ian Michael Smith as Kevin and given the heave-ho to some of the film's secondary elements (including Gillian Anderson), "The Mighty" would have been a much more satisfying production. The film still resonates as is, thanks to its universal themes and Elden Henson's fine work, but, like "Simon Birch," "The Mighty" ultimately plays as a charming portrait of friendship trapped in an unexceptional movie.
© 1998 Ed Johnson-Ott
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{%extends 'base.html'%}
{% block head %}condition: {{conditions_detail.conditions}}
number replicates: {{conditions_detail.no_replicates}}
Update Delete{{ conditions_detail.conditions}} Samples:
{% for sample in conditions_detail.samples.all %}Library Type: {{sample.libtype}}
Read 1: {{sample.read_1}}
Read 2: {{sample.read_1}}
{% endfor %}{% endif %} {% if not conditions_detail.samples.all %}
{{ conditions_detail.conditions}} Samples:
No samples associated with this condition.
{% endif %} Add Sample


{% extends 'base.html' %} {% block content %}
If an account exists with the email you entered then, you should receive a reset link shortly.
If you don't receive an email, please make sure you've entered the address that is registered with Time Tracker and check your spam folder.
{% endblock %}


Neal Ardley launches defence of Notts County players as he urges them to rediscover their swagger
Magpies are back in action against Boreham Wood on Tuesday
by Leigh CurtisNeal Ardley has launched a spirited defence of his Notts County players as he urged them to rediscover their swagger.
The Magpies entertain Boreham Wood at Meadow Lane on Tuesday night from their defeat to Aldershot at the weekend as the club suffered back to back defeats for the first time this season.
With four points separating them from the top seven, Notts fans are eager for the club to get back to winning ways in an incredibly congested National League table.
And Ardley is eager for his players not to throw the baby out with the bath water as a result of their last defeats.
“We had a bit of a swagger about us until a certain point and it’s important we don’t let doubt creep in,” said the boss.
“My message to the players is ‘don’t stop believing, let’s not become a team that scraps for points in a way that evens the playing field’.
“We were on a run of three defeats in 19 games – that shows we were doing something right and we can’t come away from that just because we’ve lost a couple.
“We’ve done well and can’t press the panic button because we’ve had a couple of indifferent results.
“We’re not on the verge of a crisis. We’ve had a bad week but, by the end of this one, we can turn it into a good one.”
Notts have fitness doubts over winger Sean Shields who has an ankle problem, while Damian McCrory has fractured his knuckles.
Alex Lacey also has a tight hamstring. Mitch Rose is not suspended despite claims he would miss tomorrow night after picking up a fifth booking against Aldershot.
One of those yellows came in the FA Cup and does not count towards the National League.


Class
PatternHolds the needle and preprocessing data (depends on algorithm).
Holds the needle and preprocessing data (depends on algorithm).
| All Subcl's | AbndmAlgoPattern, AhoCorasickPattern, BfamPattern, BndmAlgoPattern, DPSearchPattern, HammingSimplePattern, HierarchicalPexPattern, HorspoolPattern, MultiBfamPattern, MultipleShiftAndPattern, MyersPattern, NonHierarchicalPexPattern, OracleBfamPattern, OracleMultiBfamPattern, PexPattern, PigeonholePattern, SetHorspoolPattern, ShiftAndPattern, ShiftOrPattern, SimplePattern, SwiftLocalPattern, SwiftPattern, SwiftSemiGlobalPattern, TrieBfamPattern, WildShiftAndPattern, WuManberPattern |
|---|---|
| Defined in | <seqan/find.h> |
| Signature |
template <typename TNeedle[, typename TSpec]>
class Pattern;
|
Template Parameters
TNeedle |
The needle type. Types: TextConcept. |
|---|---|
TSpec |
A tag that specifies the online algorithm to use for the search. Defaults to the result of DefaultPattern. |
Interface Function Overview
-
TScoreValue getBeginScore(pattern);
Score of the last match found by findBegin during approximate searching. -
THost host(pattern);
Query a Pattern for its host. -
TNeedle needle(pattern);
Returns the needle of a Pattern object (not implemented for some online-algorithms). -
TPosition position(pattern);
Return the position of the last match in the pattern. -
TScoringScheme scoringScheme(pattern);
The scoring scheme used for finding or aligning. -
void setNeedle(pattern, needle);
Sets the needle of a Pattern object and optionall induces preprocessing. -
void setScoringScheme(pattern, score);
Sets the scoring scheme used for finding or aligning.
Interface Metafunction Overview
-
Container<TPattern>::Type;
Returns the needle type of the pattern. -
Difference<TPattern>::Type;
Returns the difference type of the underlying pattern. -
Host<TPattern>::Type;
Returns the host type of the pattern. -
Needle<TPattern>::Type;
Returns the needle type of a Pattern type. -
PexMultiFinder<Pattern<TNeedle,Pex<TVerification, TMultiFinder> > >::Type;
Determines the multiple exact string matching algorithm used by the Pex algorithm. -
Position<TPattern>::Type;
Returns the position type of the underlying pattern. -
ScoringScheme<TPattern>::Type;
Returns the scoring scheme type of an approximate search algorithm. -
Size<TPattern>::Type;
Returns the size type of the underlying pattern. -
Value<TPattern>::Type;
Returns the value type of the underlying pattern.
Detailed Description
If Needle is a StringSet then position(pattern) returns a Pair with the index of the currently matching needle and the position in the needle.
Interface Functions Detail
TScoreValue getBeginScore(pattern);
Score of the last match found by findBegin during approximate searching.
Parameters
pattern
|
A Pattern that can be used for approximate searching. |
|---|
Returns
TScoreValue |
The score of the lst match found using pattern. The value is set after successfully call of findBegin. If no match was found, the value is undefined. |
|---|
Data Races
Thread safety unknown!
THost host(pattern);
Query a Pattern for its host.
Parameters
pattern
|
The Pattern to query for its host. |
|---|
Returns
THost |
Reference to the host. |
|---|
Data Races
Thread safety unknown!
TNeedle needle(pattern);
Returns the needle of a Pattern object (not implemented for some online-algorithms).
Parameters
pattern
|
The Pattern to query for its needle. |
|---|
Returns
TNeedle |
Reference of the needle object. |
|---|
TNeedle is the result of the Needle metafunction of TPattern. This is an alias to the function host.
Data Races
Thread safety unknown!
TPosition position(pattern);
Return the position of the last match in the pattern.
Parameters
pattern
|
The Pattern to query for its position. |
|---|
Returns
TPosition |
The position of the last match in the pattern. |
|---|
Data Races
Thread safety unknown!
TScoringScheme scoringScheme(pattern);
The scoring scheme used for finding or aligning.
Parameters
pattern
|
The Pattern to query for its scoring scheme. |
|---|
Returns
TScoringScheme |
The scoring scheme of the pattern. |
|---|
Data Races
Thread safety unknown!
void setNeedle(pattern, needle);
Sets the needle of a Pattern object and optionall induces preprocessing.
Parameters
pattern
|
The pattern to set the needle for. |
|---|---|
needle
|
The needle to set. |
Data Races
Thread safety unknown!
void setScoringScheme(pattern, score);
Sets the scoring scheme used for finding or aligning.
Parameters
pattern
|
The pattern to set the scoring scheme for. |
|---|---|
score
|
The scoring scheme to set. |
Data Races
Thread safety unknown!


The banner at the top of the optimization results page displays the performance statistics of the optimization job.
The banner updates in real-time as the optimization job progresses on our servers. The following table describes the runtime statistics:
| Statistic | Description |
|---|---|
| Completed | The number of backtests that have successfully completed |
| Failed | The number of backtests that have failed during execution |
| Running | The number of backtests that are currently running |
| In Queue | The number of backtests that are waiting to start |
| Average Length | The average amount of time to complete one of the backtests |
| Total Runtime | The total runtime of the optimization job |
| Total | The total number of backtests run in the optimization job |
| Consumed | The amount of QuantConnect Credit that was used to perform the optimization |


TO DIE FOR A film review by Ben Hoffman Copyright 1995 Ben Hoffman
As Jimmy Durante used to say, "Everyone wants to get into the act." TO DIE FOR is the story of Suzanne Stone (Nicole Kidman) who wanted the limelight so bad she let nothing stand in her way ... and by nothing, we mean just that.
Suzanne is married to Larry (Matt Dillon) and live in a small town. He is madly in love with her, as who would not be; she is absolutely beautiful. On the other hand, Suzanne's desire to be "somebody" moves into an obsession and Larry has become an obstacle.
Managing by coyness, aggressiveness, and her beauty, she talks a local TV station into giving her a job as their weather-person. From there she embarks on the making of a documentary at a school where the high school students hear her speak about the world of TV and the opportunities available to anyone who wants to really go for it. The purpose of her talking to her students is in fact to select some losers to do her a favor: get rid of her husband.
I must say that I was surprised by Nicole Kidman. That she was pretty was obvious but in the film, when she turns on all her sexual charms, she is stunning. Her acting in the film is right on target. That, in addition to the screenplay by the brilliant Buck Henry, make for a very entertaining film.
The students Suzanne inveigles into her scheme are played by Joaquin Phoenix, Casey Affleck and Alison Folland.
Directed by Gus Van Sant.
2.75 bytes 4 Bytes = Superb 3 Bytes = Too good to be missed. 2 Bytes = So so. 1 Byte = Save your money.
-- Ben Hoffman
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


library(polySimIBD)
library(ggplot2)
library(tidygraph) # useful for manipulations & visaulizations
#>
#> Attaching package: 'tidygraph'
#> The following object is masked from 'package:stats':
#>
#> filter
We extend the discrete-loci, discrete-time structured Wright Fisher model for space by considering spatial demes as a collection of hosts. Hosts move between demes based on a migration matrix M. In the spirit of the Wright-Fisher, the number of hosts within the deme (i.e. deme size) does not change over time. As a result, there are two levels of “structure”: (1) hosts in order to model complexity of infection; (2) Discrete locations that contain a collection of hosts (i.e. spatial deme) in order to model spatial relationships.
The full mathematical formulation of the nonspatial model can be found in the Supplementary Section of Verity, Aydemir, Brazeau et al. Nat Comms 2020 (PMC7192906). The spatial extension to the model is described in Brazeau et al. Biorxiv.
As a brief lay summary, we assume that each deme (hereafter macrodeme) is a subpopulation within a very large population (\(\iota \in \nu\), where \(\iota\) is an individual macrodeme and \(\nu\) is the total number of macrodemes in the entire population) and that each macrodeme represents a collection of individual hosts. Then, as before, let each individual host be represented by a deme (hereafter macrodeme), or a subpopulation within this large macrodeme (\(i \in N_{\iota}\), where \(i\) is an individual host and \(N_{\iota}\) is the total host population for that macrodeme).
We then allow the \(J\) parasites (that reside within the host microdeme) to mate at random with the previous generation of parasites (\(t_{1-}\)) and produce a large number of parasite progeny. During mating, genetic recombination has the potential to occur based on the length of the genome and the recombination rate, \(\rho\). Progeny are then allowed to move to a new macrodeme or stay in the current macrodeme based on the migration matrix, \(\mathbf{M}\). Within the macrodemes, the progeny are then allowed migrate to a new or the same host microdeme with a probability of \(\frac{m}{N_{\iota}}\). Progeny are then culled down to a smaller number of parasites per host by drawing from a Poisson distribution with a mean COI. In essence, the non-spatial and spatial simulator only differ by an additional hierarchical level of spatial “demes” (macrodemes versus microdemes).
As this is a Wright-Fisher model, in each generation we are drawing a new set of parents. As such, the migration matrix is parameterized to look where parents originated versus where progeny may move, or put more simply the migration matrix represents “where one came from” versus “where one is headed”. As such, this is a “to-from” migration matrix, with the origin specified as columns and destination in rows
A separate important detail to note is that the if you are using the migration matrix as a probability matrix, rows must sum to 1 (valid marginal probability); otherwise, values will be assumed to be rates and converted to probabilities. The default value (1x1 matrix) of 1 indicates non-spatial model.
Here, we will make a dummy case of two island populations (population A vs. B) that are seperated by a barrier to gene flow (i.e. no connections between populations A and B).
Run the simulation:
# vectors must be ordered for population A and B
demesizes <- c(10, 15)
coimeans <- c(1.25, 1.25)
m <- c(0.25, 0.25)
migr_dist_mat <- matrix(0, ncol = 2, nrow = 2)
diag(migr_dist_mat) <- 100
swf <- polySimIBD::sim_swf(pos = sort(sample(1:1e3, size = 50)),
N = demesizes,
m = m,
mean_coi = coimeans,
migr_mat = migr_dist_mat,
rho = 1e-2,
tlim = 10)
#> completed in 0.000459 seconds
Pull out the ARG and then calculate the pairwise IBD.
ARG <- polySimIBD::get_arg(swf)
#> completed in 0.000361 seconds
# here we are going to get all the pairwise IBD combinations for our hosts
adj_graph <- t(combn(x = c(1:sum(demesizes)), m = 2)) %>%
tibble::as_tibble(., .name_repair = "minimal") %>%
magrittr::set_colnames(c("smpl1", "smpl2")) %>%
dplyr::mutate(pairwiseIBD = purrr::map2_dbl(.x = smpl1, .y = smpl2, .f = function(x, y){
polySimIBD:::quiet(
polySimIBD::get_pairwise_bv_ibd(swf = swf, host_index = c(x,y))
)
}))
Let’s visualize our simple scenarios
adj_graph %>%
ggplot() +
geom_tile(aes(x = smpl1, y = smpl2, fill = pairwiseIBD)) +
scale_fill_viridis_c("Pairwise IBD") +
xlab("Sample 1") + ylab("Sample 2") +
labs(caption = "Samples 1:10 are on island A; Samples 11:25 are on island B") +
theme_minimal()
As can be seen from the figure, samples 1-10, which reside on island A have no relatedness to samples 11-25 that reside on island by B as they have been completely isolated through our migration matrix.


Version 1.2.0.RELEASE
1. Features
- Add new functionalities:
- Add “Card creation” screen
- Add “Notification reception configuration” screen
- Update existing functionalities:
- “Card feed” screen:
- Display multiple bubbles in timeline for notifications with errors and/or warnings
- “Archives” screen:
- Update filtering tags
- Add export functionality
- “RSC KPI report” screen:
- Add yearly granularity
- Add regional selection
- Add new graph types
- Update the JSON generic format
- Make Let’s Coordinate compatible with the version 2.3.0.RELEASE of OperatorFabric
- Improve the getting started process and documentation
- Improve the security of the Let’s Coordinate application
- Improve the code quality by integrating the Sonar tool
- Update online documentation for release 1.2.0.RELEASE
2. Tasks
- [LC-208] Display many bubbles in timeline for notifications that have errors and warnings
- [LC-242] Add yearly view and region concept to RSC KPI report
- [LC-408] Upgrade the version of OperatorFabric to 2.3.0.RELEASE
- [LC-258] Integrate the “Card creation” screen
- [LC-262] Integrate the “Notification Reception Configuration” screen
- [LC-189] Improve the getting started process and documentation
- [LC-191] Improve the security of the backend and frontend modules
- [LC-254] Change rules for displaying cards in feed
- [LC-255] Split validation tags in the archive screen
- [LC-256] Change National Grid’s EIC Code
- [LC-260] Improve the validator of JSON and Excel files
- [LC-264] Split granularity and period selection in RSC KPI config screen
- [LC-267] Add new fields to the JSON generic format and update the implementation guide
- [LC-284] Specify the list of messageTypeNames recognized by the letsco API
- [LC-285] Upgrade Letsco OS frameworks
- [LC-287] Update TSOs, RSCs and regions mapping
- [LC-316] Use joinGraph attribute for joining RSC KPI graphs
- [LC-333] Improve the authentication for the RSC KPi report screen
- [LC-371] Change RSC KPI bar graphs to stacked bar graphs when joinGraph flag equals true
- [LC-378] Update CCRs/Regions EicCodes
- [LC-150] Integrate sonar to check the test coverage
- [LC-363] Scan code with Fossology
- [LC-403] Add unit tests
3. Bugs
- [LC-350] Fix VPN/Proxy access problems
- [LC-373] Fix the publisher name displayed on the bottom of the free message card details
- [LC-315] Fix tags in the archive screen
- [LC-209] Identify fields that may have long values and increase their size in database
- [LC-450] Fix “invalid date” displayed in validation notifications
- [LC-407] Fix bugs and vulnerabilities reported by Sonar


{% extends "base.html" %} {% block body %}
Successfully created payload with UUID: {{errors.uuid}}. This payload can be executed as follows:


Sheffield Benefit Centre Handovers queue - Imogen Johnson
| Name & NINO | Date/time raised | Time left to clear | Benefit type | Handover type | Handover Status | Callback status | ||
|---|---|---|---|---|---|---|---|---|
Adam Brown |
8 February 2018 |
Expired |
ESA |
1 |
Claim closure |
Paused |
First call pending |
|
Grace Harris |
27 February 2018 13:06 |
58mins |
SF |
7 |
New Claim | Not allocated |
First call pending |


| DOSE: |
20 g |
oral |
Nutmeg |
(powder / crystals) |
I had haerd about the pschoactive effects of nutmeg and decided to try it out. I'd previously tried smoking and drinking a tea from it with no effect so decided to try eating it.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Gardens Reception Hall
Getting married and need a venue for the big day? Look no further. The Temple Inn Suites has and elligant reception hall that everyone is sure to remember! We do not only offer space, but catering service as well as a bundt cake bakery! Get everything done in one place to reduce hassel. Call to reserve your date today!
We don't just give you a space to remember. We cater as well! Let us do everything for you. We have several menus that our catering service provides. Set an appointment to meet with a consultant, look over menus, and sample food and cake. There is not other way to tell if the food is right for your big day unless you try it first. We set up, and take everything down so you can relax and enjoy.


{% extends "mapApp/base.html" %} {% block title %}Terms and Conditions{% endblock %} {% block body %}
Letter of information for implied consent:
You are invited to participate in a study entitled Citizen Based Web-Mapping of Bike Accidents and Near Misses (BikeMaps).
As part of a UVic research project, we are interested in obtaining information regarding your cycling experiences. In particular we would like to know about any incidents that you have had. The data collected is completely anonymous and answers are multiple choice containing no identifiable information. The survey should take approximately 5 minutes.
By adding a geographic marker onto to this website and answering questions, you are consenting to participate in a study conducted by researchers from the Spatial Pattern Analysis and Research (SPAR) Lab in the Department of Geography at the University of Victoria. All information gathered will be completely anonymous. Volunteers must be 13 years of age or older to participate. Parents or guardians may log the details for participants under the age of 13. Please be advised that once a volunteer participant has submitted their survey, it is impossible to withdraw this data at a later date given that it is fully anonymous. Also, the data may be used in additional future studies. Please be aware that data provided in this study could be linked to data collected in other studies (for e.g., hospital ER intake surveys).
The data collected from this research will enable SPAR Lab researchers and graduate students to study the patterns of bike accidents and near misses in the Capital Regional District. Understanding the geographical patterns to risk for cyclists will enable better decisions to be made regarding future transportation plans for the region.
Funding for this research project has been provided by an NSERC Engage grant to Dr. Trisalyn Nelson. Other project partners providing in kind support include: the Captial Regional District (CRD), Pedalheads, KidicalMassyyj; the Canadian Automobile Association; Strava.com, and the Traffic Injury Research Foundation (TIRF). If you have any questions regarding this research, please contact Dr. Trisalyn Nelson<EMAIL_ADDRESS>In addition, you may verify the ethical approval of this study, or raise any concerns you might have, by contacting the Human Research Ethics Office at the University of Victoria<PHONE_NUMBER> or ethics@uvic.ca).