Users who switch from PHP to C++ often ask whether it is more difficult to manage a system if you use C++ code instead of PHP code. We must be honest here: working with PHP is easier than working with C++. To activate a PHP script, you for example don't need root access, and you can simply copy the script to the web server. Deploying a native C++ extension requires more work than that: you need to stop the webserver first, compile the extension, install it, and then restart the web server.
On top of that, when an extension is deployed it is immediately active for all websites that are hosted on the webserver. A deployed PHP script only changes the behavior of a single website, but a deployed C++ extension affects all sites. It is not really possible to activate an extension for only specific sites, or to test a new version of an extension for just a single website, as the extensions are shared by all PHP processes. If you really want to use different extensions for different sites, you need multiple servers that all have their own configuration.
Or you can use dynamic loading.
PHP has a builtin dl() function that you can use to load extensions on the fly. This allows you to call the 'dl("myextension.so")' function from a PHP script to load an extension, so that an extension only works for a specific site. This builtin 'dl()' function has some restrictions because of security considerations (it would otherwise allow users to run arbitrary native code), but if you're the only one who is in charge of a system, or when a server is not shared by multiple organizations, you can use PHP-CPP to create a function similar to 'dl()' that does not have this restriction.
The dl() function is restricted because of security issues. When you use dl(), you can only load extensions that are stored in the system wide extensions directory, and not for loading extensions that users have placed in other locations. A call to dl("/home/user/myextension.so") will therefore fail, because "/home/user" is not the official extensions directory. Why this restriction?
To understand this, one must first realize that in a normal PHP installation PHP scripts are edited by users who do not have root access. On shared hosting environments different users all run their own website on the same system. In such a setup, it is an absolute no-go if one user can write a script or program that has access to the data of others. With an unrestricted dl() function however, exactly this would be possible. An unrestricted dl() call would allow PHP programmers to write a native extension, store that in their home directory or in the /tmp directory, and have it loaded by the webserver process. They could then execute arbitrary code, and possibly install loggers or other malicious code inside other people's websites. By only allowing extensions from the system wide extensions directory to be loaded, PHP ensures every dynamically loaded extension must at least have been installed by the system administrator.
When you write your own extensions however - either directly on top of the Zend API, or by using the PHP-CPP library - you already are in a position to write and execute arbitrary code anyway. No need for security checks here. From you C/C++ code you can do whatever you want. Would it not be cool if you could dynamically load an extension based on the requirements of a site? One website needs to be stable, and loads the well tested version 1.0 of your extension, while a second website is more experimental, and loads version 2.0. You could be running two versions of your extension on the same machine.
Imagine you're writing your own extension "MyExtension" that has many different classes and functions, and for which you plan to bring out new releases all the time. You do not want to deploy a new release in a "big bang" style, but you want to roll out new versions slowly, one customer or one website at a time. How would you do this?
You start by developing a thin loader extension: the ExtensionLoader extension. This extension has only one function: enable_my_extension() - which takes the version number of your actual extension, and dynamically loads that version of your extension. This is a simple extension (it only has one function) that you install globally, and that you will probably never have to update.
/**
* Function to load an extension by its version number
*
* It takes one argument: the version number of your extension,
* and returns a boolean to indicate whether the extension was
* correctly loaded.
*
* @param params Vector of parameters
* @return boolean
*/
Php::Value enable_my_extension(Php::Parameters ¶ms)
{
// get version number
int version = params[0];
// construct pathname to your extension (this is for example
// /path/to/MyExtension.so.1 or /path/to/MyExtension.so.2)
std::string path = "/path/to/MyExtension.so." + std::to_string(version);
// load the extension
return Php::dl(path);
}
/**
* Switch to C context to ensure that the get_module() function
* is callable by C programs (which the Zend engine is)
*/
extern "C" {
/**
* Startup function that is called by the Zend engine
* to retrieve all information about the extension
* @return void*
*/
PHPCPP_EXPORT void *get_module() {
// create static instance of the extension object
static Php::Extension myExtension("ExtensionLoader", "1.0");
// the extension has one method
myExtension.add("enable_my_extension", enable_my_extension, {
Php::ByVal("version", Php::Type::Numeric)
});
// return the extension
return myExtension;
}
}
The above code holds the full source code of the ExtensionLoader extension. You can install this extension on your system, by copy'ing it to the global php extensions directory and updating the php.ini files.
After you've installed this thin loader extension, you can write your actual big extension full with classes and functions, and compile this extension into *.so files: the first version you compile into MyExtension.so.1, and later versions into MyExtension.so.2, MyExtension.so.3, and so on. For every new release you introduce a new version number, and you copy these shared objects to the /path/to directory (the same path hardcoded in the 'loader' extension showed above). And although this is not the official PHP extensions directory, such extensions can nevertheless be loaded byt the enable_my_extension() function.
You do not have to copy the extensions into the PHP extension directory, nor do you have to update the php.ini configuration. To activate an extension, you simply need to call the enable_my_extension() function that was introduced but he thin loader:
<?php
// enable version 2 of the extension (this will load MyExtension.so.2)
if (!enable_my_extension(2)) die("Version 2 of extension is missing");
// from now on we can use classes and functions from version 2 of the extension
$object = new ClassFromMyExtension();
$object->methodFromMyExtension();
// you get the idea...
?>
The thin loader that we showed above is still pretty secure. It is not possible to run arbitrary code, or to open arbitrary *.so files. The worst thing that can happen is that someone opens an extension with a wrong version number - which is not disastrous at all.
But if you really trust the users on your system, you can easily adjust the thin loader extension to allow other types of parameters too. In the most open scenario, you could even write a function that allows users to literally open every possible shared object file:
/**
* Function to load every possible extension by pathname
*
* It takes one argument: the filename of the PHP extension, and returns a
* boolean to indicate whether the extension was correctly loaded.
*
* This function goes further than the original PHP dl() fuction, because
* it does not check whether the passed in extension object is stored in the
* right directory. Literally every possible extension, also local ones
* created by end users, can be loaded.
*
* @param params Vector of parameters
* @return boolean
*/
Php::Value dl_unrestricted(Php::Parameters ¶ms)
{
// get extension name
std::string pathname = params[0];
// load the extension
return Php::dl(pathname);
}
/**
* Switch to C context to ensure that the get_module() function
* is callable by C programs (which the Zend engine is)
*/
extern "C" {
/**
* Startup function that is called by the Zend engine
* to retrieve all information about the extension
* @return void*
*/
PHPCPP_EXPORT void *get_module() {
// create static instance of the extension object
static Php::Extension myExtension("load_extension", "1.0");
// the extension has one method
myExtension.add("dl_unrestricted", dl_unrestricted, {
Php::ByVal("pathname", Php::Type::String)
});
// return the extension
return myExtension;
}
}
The above code would allow PHP scripts to dynamically load PHP extensions, no matter where they are stored on the system:
<?php
// load the C++ extension stored in the same directory as this file
if (!dl_unrestricted(__DIR__.'/MyExtension.so')) die("Extension could not be loaded");
// from now on we can use classes and functions from the extension
$object = new ClassFromMyExtension();
$object->methodFromMyExtension();
?>
The dl_unrestricted() function is an awesome function, but be careful here: if you're the administrator of a shared hosting platform, you definitely do not want to install it!
A dynamically loaded extension is automatically unloaded at the end of the page view. If a subsequent pageview also dynamically loads the same extension, it will start with a completely new and fresh environment. If you want to write an extension that uses static data or static resources (think of a persistent database connection, or a worker thread that processes tasks) this may not always be the desired behavior. You want to keep the database connection active, or the thread running, also after the extension is unloaded.
To overcome this, the Php::dl() function comes with a second boolean parameter that you can use to specify whether you want to load the extension persistently, or only for that specific pageview.
Notice that if you set this parameter to true, the only thing that persists is the data in an extension. In subsequent pageviews you will still have to load the extension to activate the functions and classes in it, even if you had already loaded the extension persistently in an earlier pageview. But because the extension was already loaded before, the static data in it (like the database connection or the thread) is preserved.
The dl_unrestricted() function that we demonstrated above can be modified to include this persistent parameter:
/**
* Function to load every possible extension by pathname
*
* It takes two arguments: the filename of the PHP extension, and a boolean to
* specify whether the extension data should be kept in memory. It returns a
* boolean to indicate whether the extension was correctly loaded.
*
* This function goes further than the original PHP dl() fuction, because
* it does not check whether the passed in extension object is stored in the
* right directory, and because it allows persistent loading of extensions.
* Literally every possible extension, also local ones created by end users,
* can be loaded.
*
* @param params Vector of parameters
* @return boolean
*/
Php::Value dl_unrestricted(Php::Parameters ¶ms)
{
// get extension name
std::string pathname = params[0];
// persistent setting
bool persistent = params.size() > 1 ? params[1].boolValue() : false;
// load the extension
return Php::dl(pathname, persistent);
}
/**
* Switch to C context to ensure that the get_module() function
* is callable by C programs (which the Zend engine is)
*/
extern "C" {
/**
* Startup function that is called by the Zend engine
* to retrieve all information about the extension
* @return void*
*/
PHPCPP_EXPORT void *get_module() {
// create static instance of the extension object
static Php::Extension myExtension("load_extension", "1.0");
// the extension has one method
myExtension.add("dl_unrestricted", dl_unrestricted, {
Php::ByVal("pathname", Php::Type::String),
Php::ByVal("persistent", Php::Type::Bool, false)
});
// return the extension
return myExtension;
}
}
And this extension allows us to dynamically load extensions, while preserving persistent data inside the extension:
<?php
// load the C++ extension stored in the same directory as this file, the
// extension is persistently loaded, so it may use persistent data like
// database connections and so on.
if (!dl_unrestricted(__DIR__.'/MyExtension.so', true)) die("Extension could not be loaded");
// from now on we can use classes and functions from the extension
$object = new ClassFromMyExtension();
$object->methodFromMyExtension();
?>


THE DREAM TEAM A film review by Randy Parker Copyright 1996 Randy Parker
RATING: *** (out of ****)
(Review written in 1989)
THE DREAM TEAM is a thoroughly entertaining comedy featuring four loveable characters who just happen to be slightly insane. Billy, played by Michael Keaton, is an extremely temperamental mental ward patient whose short fuse and violent tantrums tend to get him in trouble. Christopher Lloyd portrays Henry, an obsessive-compulsive who requires that everything be neat, orderly, on schedule, and by the books. He thinks he's a doctor so he walks around wearing a suit and carrying a clipboard on which he constantly scribbles notes and files reports. Jack, played by Peter Boyle, thinks he's Jesus. And finally, there's Stephen Furst as Albert, a chubby catatonic who speaks only in baseball and television phrases.
THE DREAM TEAM follows the misadventures that beset the four-some when their psychiatrist, Dr. Weitzman, takes them out of the hospital for a field trip to Yankee Stadium. During a pit stop, they get separated from Weitzman and are left to fend for themselves in Manhattan.
The movie's charm derives mostly from the camaraderie and chemistry between its delightful cast. The four leads in the film play off each other quite effectively; they never miss an opportunity to argue, bicker, or insult one another. Henry and Jack, for example, fight over who's going to get to ride in the front passenger seat on the way to the game.
The actors in THE DREAM TEAM have a lot of fun with their characters and with each other. Michael Keaton is well cast as Billy; he gives his cynical character considerable complexity. As the divinely Jack, Peter Boyle succeeds in keeping his one-dimensional character funny and fresh. Although Stephen Furst's catatonic character is cliched, the actor makes him sympathetic and endearing.
While Keaton, Boyle, and Furst are good, it's Christopher Lloyd who really makes the movie special. Lloyd gives yet another outstanding comic performance.
THE DREAM TEAM is a solid comedy with more depth and drama than you might expect from its simple premise. The characters are so appealing that you can excuse the film for its often outlandish and unbelievable plot.
----------- Randy Parker<EMAIL_ADDRESS>http://www.shoestring.org
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
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Related links: index of all rec.arts.movies.reviews reviews


Adventure Guildsman:
Welcome, my friend! Some astonishingly macabre adventures have just become available!
Quest Information
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Use Life Crystals
Quest


| DOSE: |
3 hits |
smoked |
Cannabis |
(plant material) |
Let me start by saying that I have been a heavy smoker for a couple years, although I have stopped now. This is the first time I have experienced something like this.
I have been going through an extremely painful breakup with my girlfriend lately. Very messy.  Around when this started, but while we were ostensibly still together, I went through a small episode of cutting myself, thinking that I would see if she really cared the way I did when she cut herself before. Needless to say, this was a bad idea. I had never done anything like that before to myself, and it felt insanely bad. And to top it all off, she didn't really care when I told her on the phone right afterwards.
I fell asleep at this point, then woke up at about 2 AM and hear that a close friend of mine had come over and was downstairs smoking.  This is mostly because I was using weed as a crutch at this point, and was mentally addicted. I acted like everything was normal and fine just so I could hit the weed. We smoked out of my new bong, which hits very hard, and I was high as hell. Something felt really wrong, but I ignored it and made small talk with my friend and acted happy (when I was actually anything but).
I started feeling higher & higher at this point, with no real ceiling to it. I began to believe that I had lost too much blood when I cut myself and that my legs wouldn't work. I also thought that the razor blades had given me an infection because I didn't disinfect them. I still hid it from my friend, and eventually wobbled my way back upstairs and laid down in bed.  I was hallucinating, my whole world was crashing down, I thought I was going to die. My only hope was to clutch the phone and think that I could call an ambulance at any moment if I needed to. But I was too afraid to do that, and was letting myself 'die'. I called my girlfriend in tears and begged for her help, because she had always been the only one who could help me with something like this.  I rode it out and survived, of course, but it was extremely difficult and horrible.
I see now that I had hinged my mental stability and happiness on her so heavily that I could only think to call her when this was happening. I didn't realize that it was happening because she had not cared about my cutting and my mind had crashed. I can safely say that it is not a good idea to depend on someone to that extent, no matter how much you trust them not to betray you.   It can be severely damaging mentally. This experience messed my head up for a long time. I'm still recovering slowly, but I have gone through some severe suicidal episodes, especially once my girlfriend cheated on me.
I have smoked weed one more time since then, and once again it was horrible. Much easier to get through, though, especially since it was a much smaller dose. Right now I am looking at bowl of weed packed in a bong and am fighting to not smoke it. I know that if I do, there is a very good chance I would kill myself. But maybe that's what I really want, especially since I have been very weak mentally today.  I know that if I went through with it, she would understand.
I guess I just have to hold on and stay far away from cannabis. I hope that anyone reading this will think twice before repeating my mistakes. It's really not worth it to get high when the consequences can be so bad.
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.


Package serializers
source code
Serializer module with a slightly improved versions of the Turtle and
Pretty XML serializers.
Both are, actually, almost verbatim copy of the RDFLib Turtle and
pretty XML serializer module, respectively. For more detailed on how
serializers work and are registered by RDFLib, please refer to the RDFLib
descriptions and source code.
The differences, v.a.v. the original, for the Pretty XML
serializer:
-
there were bugs in the original in case an rdf:List was generated
with Literal content
-
added a pretty print branch for Seq/Alt/Bag
-
removal of the CDATA sections when generating an XML Literal (it is a
cautionary support in RDFLib but, because XML literals can be
generated under very controlled circumstances only in the case of
RDFa, it is unnecessary).
-
use the character " instead of ' for namespace declarations (to
make it uniform with the way attributes are handled
-
increased the initial depth for nesting (to 8)
The differences, v.a.v. the original, for the Turtle serializer:
-
there was a bug in the syntax of the @prefix setting
-
the original Turtle serializer insisted on creating prefixes for all
URI references, ending up with a large number of fairly unnecessary
prefixes that made the output a bit unreadable. This has been changed
so that only the 'registered' prefixes are used, ie, those that the
original RDFa source contains
-
the original Turtle had a bug and did not generate the shorhands for
lists
-
changed the indentation rules for anonymous ('squared') blank nodes
Obviously, these serializers can be used directly, too. Here is a way
to do it (for the turtle one):
from rdflib.plugin import register
from rdflib.syntax import serializer, serializers
register("my-turtle",serializers.Serializer,"RDFClosure.serializers.TurtleSerializer","TurtleSerializer")
...
graph.serialize(format="my-turtle")
|
|
__author__ = ' Ivan Herman '
|
|
|
__license__ = u' W3C® SOFTWARE NOTICE AND LICENSE, http://www.w ...
|
|
|
__package__ = None
|
__license__
- Value:
u' W3C® SOFTWARE NOTICE AND LICENSE, http://www.w3.org/Consortium/Legal
/2002/copyright-software-20021231 '
|
|


Bloodkill was formed in late 2016 with Vishwas and Yash being the founding members. The band started off as a typical thrash metal outfit influenced by the 80’s era of Metallica, Slayer, Megadeth, Kreator, Exodus, etc. However, with a few lineup changes the dimensions of Bloodkill’s music broadened as they added elements of sub genres like Heavy and Groove metal.
Genre: Heavy/Thrash Metal
Location: Mumbai, India
Release Date: Unreleased
Album Credits: Produced by Keshav Dhar (Skyharbor) at Illusion studio. Album artwork and Layout by Acid toad.
Band Lineup:
Anirudh Gollapudi (vocals),
Vishwas Shetty (Rhythm Guitars),
Shubham Khare (Lead Guitars),
Yash Wadkar (Bass) and
Jay Patil (Drums).
The band released their first single ‘3B’ in 2017, followed by ‘Horrorscope’ in 2018. Bloodkill is set to release their debut album titled ‘Throne Of Control’.
The album contains an eclectic range of songs to offer, from the Old school thrash metal goodness of the Title-track and ‘Visions’, the mid-tempo thrash with a good dose of old school heavy metal in tracks ‘False Face’
and ‘Unite and Conquer’, to the melodic and groovy ‘For I am the Messiah’. The record also consists of reimagined rendition of ‘3B’ with the new lineup along with a remixed and remastered version of ‘Horrorscope’.
Track Listing:
Sample track:
Contact us
Email<EMAIL_ADDRESS>Phone: +91 73879 46802 / +91 96192 69775
|
Designed and Developed by Swapnil Khare


MTV
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Welcome, you can order your pizza here!
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library(readr) # read_csv()
library(dplyr) # mutate(), filter()
library(lubridate)
library(ggplot2)
<- read_csv('../../raw_data/SPY.csv') |>
df_spy select(date, adjClose) |>
arrange(date) |>
mutate(return = log(adjClose / lag(adjClose))) |>
filter(date > '2018-01-01')
<- read_csv('../../raw_data/AMD.csv') |>
df_amd select(date, adjClose) |>
arrange(date) |>
mutate(return = log(adjClose / lag(adjClose))) |>
filter(date > '2018-01-01')
This post is about summarizing the various statistical moments when doing quantitative finance. The focus is on the asset returns. From a previous post, we already know that financial asset returns do not follow a normal distribution (too peaked at the mean and fat tails).
We’ll show these parameters using both R and python.
We’ll use the SPY as a low-ish vol asset and AMD as an equity with higher vol. We only use the last 5 years of data (from 2018 and beyond)
Let’s first load our libraries and the 2 data frame worth of prices.
Mean
The mean is our first moment. We’ll cons
Mathematically speaking, we define the mean as \[\sum^{n}_{i=1} \frac{r_i}{n}\]
- \(r_i\) is the return of the i-th observation
- using log return
- \(n\) is the number of observation
Standard Deviation
Standard deviation is the second moment.
\[\sigma = \sqrt{\frac{\sum(x_i - \mu)^2}{n}}\] In the case of a sample: \(s = \sqrt{\frac{(x_i - \bar{x})^2}{n-1}}\)
The variance is the square of the standard deviation. There is this neat little alternative expression of variance (I like the way it sounds).
Variance is the difference between the mean of the square and the square of the mean.
Here is how it goes:
\[\sigma^2 = \frac{\sum(x_i-\mu)^2}{n} = \frac{\sum(x_i)^2}{n} - 2 \frac{\sum(x_i \mu)}{n} + \frac{\sum(\mu)^2}{n}\]
Considering
- \(\frac{\sum(x_i \mu)}{n} = \mu \frac{\sum(x_i)}{n} = \mu \mu=\mu^2\)
- and considering \(\frac{\sum(\mu)^2}{n} = n \frac{\mu^2}{n} = \mu^2\), we can re-write our variance like this
\[\sigma^2 = \frac{\sum(x_i)^2}{n} - 2 \mu^2 + \mu^2 = \frac{\sum(x_i)^2}{n} - \mu^2\]
Coefficient of variation
The coefficient of variation is not a statistical moment, but considering it is the ratio of the first 2 moments (ratio of sd to the mean), we include it here as well. It allows to compare together 2 different distributions (that have different mean and sd).
\[CV = \frac{\sigma}{\mu}\]
Or in the case of a sample \(CV = \frac{s}{\bar{x}}\)
Skewness
In a sense, skewness is quantifying for us how far is the median from the mean.
Mathematically, we define skewness as
\[\frac{\frac{1}{n} \sum(x_i - \bar{x})^3}{\sqrt{ \left( \frac{1}{n} \sum(x_i - \bar{x})^2 \right)^3}} \tag{1}\]
In the case of a sample, we’ll multiply Equation 2 by a factor of \(\frac{\sqrt{n(n-1)}}{n-2}\)
- skenwess = 0 ==> normallly distributed
- $ -0.5 skewness $ ==> moderately skew
- $$
Kurtosis
Because, we raised the difference of a data point to its mean to the 4th power, it is really the data points far away from the mean that do participate to the kurtosis.
“increasing kurtosis is associated with the “movement of probability mass from the shoulders of a distribution into its center and tails.” (Someone important who got published and knew his stuff!)
Mathematically, we define kurtosis as
\[\frac{\frac{1}{n} \sum(x_i - \bar{x})^4}{ \left( \frac{1}{n} \sum(x_i - \bar{x})^2 \right)^2} \tag{2}\]
Some statistical packages are providing excess kurtosis by subtracting 3 to the kurtosis value. So for a data set that is perfectly normally distributed, we expect the excess kurtosis to be 0.
There are 3 categories of kurtosis: leptokurtic (positive excess kurtosis), mesokurtic (aka normal distribution), platykurtic.
- A kurtosis greater than 3 => leptokurtic
- A kurtosis around 3 => mesokurtic
- A kurtosis less than 3 => platykurtic
A Student’s T distribution with degree of freedom 4 has infinite kurtosis (huge peak and tails + narrow shoulders)
Most equities display a leptokurtic behavior (skinny at the mean - most returns are clustered around the mean) and narrow shoulders and fat tails.
Using Python
import numpy as np
import pandas as pd
= pd.read_csv('../../raw_data/AMD.csv')
amd
= amd['adjClose']
x
= np.log(x / x.shift(21)).dropna()
returns_21d
= np.mean(returns_21d)
mean_21dret = np.std(returns_21d)
std_21dret
print("The mean rolling 21 days return is: %s" % round(mean_21dret, 5))
The mean rolling 21 days return is: -0.00594
print("The standard deviation of the rolling 21 days return is: %s" %round(std_21dret, 5))
The standard deviation of the rolling 21 days return is: 0.1832
The standard deviation is quite bigger than the mean. An histogram of the returns will confirm that.
import matplotlib.pyplot as plt
= 'rice', label = 'Rolling 21-days return') plt.hist(returns_21d, bins
(array([ 2., 0., 3., 4., 5., 26., 31., 56., 66., 117., 192.,
269., 357., 420., 607., 637., 644., 567., 464., 380., 206., 157.,
109., 98., 73., 31., 28., 14., 10., 8., 2., 1., 0.,
3., 2., 1.]), array([-0.7537718 , -0.70724263, -0.66071346, -0.6141843 , -0.56765513,
-0.52112596, -0.47459679, -0.42806762, -0.38153845, -0.33500928,
-0.28848011, -0.24195095, -0.19542178, -0.14889261, -0.10236344,
-0.05583427, -0.0093051 , 0.03722407, 0.08375324, 0.1302824 ,
0.17681157, 0.22334074, 0.26986991, 0.31639908, 0.36292825,
0.40945742, 0.45598659, 0.50251575, 0.54904492, 0.59557409,
0.64210326, 0.68863243, 0.7351616 , 0.78169077, 0.82821994,
0.8747491 , 0.92127827]), <BarContainer object of 36 artists>)
plt.show()
We are seeing a larger left tail with indeed the mean looking around 0. An other to visualize this and putting emphasis on the outliers would be to plot the returns on a box-and-whiskers plot.
= ['Rolling 21-days return']) plt.boxplot(returns_21d, labels
{'whiskers': [<matplotlib.lines.Line2D object at 0x7fe94921c430>, <matplotlib.lines.Line2D object at 0x7fe94921c790>], 'caps': [<matplotlib.lines.Line2D object at 0x7fe94921ca60>, <matplotlib.lines.Line2D object at 0x7fe94921cbe0>], 'boxes': [<matplotlib.lines.Line2D object at 0x7fe94921c2b0>], 'medians': [<matplotlib.lines.Line2D object at 0x7fe94921cfa0>], 'fliers': [<matplotlib.lines.Line2D object at 0x7fe94921d240>], 'means': []}
plt.show()
from scipy.stats import skew, skewtest
skew(returns_21d)
0.21432056577181507
The skew value is quite large and negative which confirms the fat left tail we saw on the histogram
skewtest(returns_21d)
SkewtestResult(statistic=6.477804949429641, pvalue=9.306654969735504e-11)
Very small p-value. We reject the null-hypothesis. The distribution is not symetrical.


Clan Trader Mulia:
Ah, you are the one who leads the clan. Your excellent reputation precedes you! What do you wish from me?
Buy clan items.
Ask about clan reputation.
View information about the lord and tax rate.
Quest


Document Information
Preface
Part I Introduction
1. Overview
2. Using the Tutorial Examples
Part II The Web Tier
3. Getting Started with Web Applications
4. JavaServer Faces Technology
5. Introduction to Facelets
6. Expression Language
7. Using JavaServer Faces Technology in Web Pages
8. Using Converters, Listeners, and Validators
9. Developing with JavaServer Faces Technology
10. JavaServer Faces Technology: Advanced Concepts
11. Using Ajax with JavaServer Faces Technology
12. Composite Components: Advanced Topics and Example
13. Creating Custom UI Components and Other Custom Objects
14. Configuring JavaServer Faces Applications
15. Java Servlet Technology
16. Java Servlet Technology: Advanced Topics
17. Internationalizing and Localizing Web Applications
Part III Web Services
18. Introduction to Web Services
19. Building Web Services with JAX-WS
20. Building RESTful Web Services with JAX-RS
21. JAX-RS: Advanced Topics and Example
Part IV Enterprise Beans
22. Enterprise Beans
23. Getting Started with Enterprise Beans
24. Running the Enterprise Bean Examples
25. A Message-Driven Bean Example
26. Using the Embedded Enterprise Bean Container
27. Using Asynchronous Method Invocation in Session Beans
Part V Contexts and Dependency Injection for the Java EE Platform
28. Introduction to Contexts and Dependency Injection for the Java EE Platform
29. Running the Basic Contexts and Dependency Injection Examples
30. Contexts and Dependency Injection for the Java EE Platform: Advanced Topics
31. Running the Advanced Contexts and Dependency Injection Examples
Part VI Persistence
32. Introduction to the Java Persistence API
33. Running the Persistence Examples
34. The Java Persistence Query Language
35. Using the Criteria API to Create Queries
36. Creating and Using String-Based Criteria Queries
37. Controlling Concurrent Access to Entity Data with Locking
38. Improving the Performance of Java Persistence API Applications By Setting a Second-Level Cache
Part VII Security
39. Introduction to Security in the Java EE Platform
40. Getting Started Securing Web Applications
41. Getting Started Securing Enterprise Applications
42. Java EE Security: Advanced Topics and Example
Part VIII Java EE Supporting Technologies
43. Introduction to Java EE Supporting Technologies
44. Transactions
45. Resources and Resource Adapters
46. Running the Resource Adapter Example
47. Java Message Service Concepts
48. Java Message Service Examples
49. Bean Validation: Advanced Topics
50. Using Java EE Interceptors
Part IX Case Studies
51. Duke's Bookstore Case Study Example
52. Duke's Tutoring Case Study Example
53. Duke's Forest Case Study Example
Index
|
|
N
- naming contexts, Java Naming and Directory Interface API
- naming environments, Java Naming and Directory Interface API
- navigation model, Navigation Model
- action attribute
- Using Command Component Tags for Performing Actions and Navigation
- Referencing a Managed Bean Method
- Referencing a Method That Performs Navigation
- Understanding the Facelets Page
- ActionEvent class, Referencing a Method That Handles an Action Event
- action methods
- Writing a Method to Handle Navigation
- Configuring Navigation Rules
- configuring navigation rules, Configuring Navigation Rules
- logical outcome
- Writing a Method to Handle Navigation
- Configuring Navigation Rules
- NavigationHandler class, Navigation Model
- referencing methods that perform navigation
- Referencing a Method That Performs Navigation
- Writing a Method to Handle Navigation
- writing a managed bean method to perform navigation processing, Writing a Method to Handle Navigation
- NetBeans IDE, NetBeans IDE
- NON_PERSISTENT delivery mode, Specifying Message Persistence
- non-repudiation, Characteristics of Application Security
O
P
- package-appclient tool, GlassFish Server Tools
- parameters, extracting, Extracting Request Parameters
- path parameters
- JAX-RS
- Extracting Request Parameters
- Extracting Path Parameters
- path templates, JAX-RS, The @Path Annotation and URI Path Templates
- permissions, security policy, Securing the GlassFish Server
- persistence
- BLOBs, BLOB and CLOB Database Types in the order Application
- cascade operations, Cascade Operations in the order Application
- CLOBs, BLOB and CLOB Database Types in the order Application
- collections, Using Collections in Entity Fields and Properties
- configuration, Persistence Units
- context, Managing Entities
- embeddable classes, Embeddable Classes in Entities
- entities, Entities
- examples, Running the Persistence Examples
- JMS example, An Application That Uses the JMS API with an Entity
- JMS messages, Specifying Message Persistence
- many-to-many, The Many-To-Many Relationship in roster
- maps, Using Collections in Entity Fields and Properties
- one-to-many, One-to-Many Relationship Mapped to Overlapping Primary and Foreign Keys
- one-to-one, One-to-One Relationships
- overview, Introduction to the Java Persistence API
- persistence units, Persistence Units
- persistent fields, Persistent Fields
- primary keys
- Primary Keys in Entities
- Generated Primary Keys
- Compound Primary Keys
- properties, Persistent Properties
- queries
- Introduction to the Java Persistence API
- Querying Entities
- Using Queries
- Creating Queries Using the Java Persistence Query Language
- creating, Creating a Criteria Query
- Criteria, Using the Criteria API to Create Queries
- dynamic, Creating Queries Using the Java Persistence Query Language
- executing, Executing Queries
- expressions
- The Expression Interface Methods
- Expression Methods in the CriteriaBuilder Interface
- joins, Querying Relationships Using Joins
- parameters
- Named Parameters in Queries
- Positional Parameters in Queries
- path navigation, Path Navigation in Criteria Queries
- See also query language
results
- Restricting Criteria Query Results
- Managing Criteria Query Results
- static, Creating Queries Using the Java Persistence Query Language
- typesafe, Using the Criteria API to Create Queries
query language, Queries and Relationship Direction
- relationships, Entity Relationships in the order Application
- scope, Persistence Units
- self-referential relationships, Self-Referential Relationships
- temporal types, Temporal Types in the order Application
- persistence units
- query language
- The Java Persistence Query Language
- Identification Variables
- PERSISTENT delivery mode, Specifying Message Persistence
- pluggable audit modules, Securing the GlassFish Server
- pluggable authorization providers, Securing the GlassFish Server
- point-to-point messaging domain, Point-to-Point Messaging Domain
- See also queues
- POJOs, Java EE 6 Platform Highlights
- policy files, Java SE Security Mechanisms
- primary keys, One-to-Many Relationship Mapped to Overlapping Primary and Foreign Keys
- compound, Compound Primary Keys
- defined, Primary Keys in Entities
- examples, Primary Keys in Entities
- generated, Generated Primary Keys
- principal, Some Other Terminology
- PrintWriter class, Constructing Responses
- priority levels, for messages, Setting Message Priority Levels
- JMSPriority message header field, Message Headers
- producer fields
- CDI, Using Producer Methods and Fields in CDI Applications
- example, The producerfields Example: Using Producer Fields to Generate Resources
- producer methods
- CDI
- Injecting Objects by Using Producer Methods
- Using Producer Methods and Fields in CDI Applications
- example, The producermethods Example: Using a Producer Method To Choose a Bean Implementation
- programmatic security
- Overview of Java EE Security
- Using Programmatic Security
- Overview of Web Application Security
- Securing Enterprise Beans
- example, Example: Securing an Enterprise Bean with Programmatic Security
- programming model, JMS, The JMS API Programming Model
- providers, JMS, JMS API Architecture
- proxies, Building Web Services with JAX-WS
- public key certificates, Client Authentication
- public-key cryptography, Working with Digital Certificates
- publish/subscribe messaging domain
- durable subscriptions, Creating Durable Subscriptions
- introduction, Publish/Subscribe Messaging Domain
- See also topics
Copyright © 2012, Oracle and/or its affiliates. All rights reserved. Legal Notices
|


{% load static %}
{% block title %} {% endblock %} {% url 'homepage_view' as homepage %} {% url 'investigatie_view' as investigatie %} {% url 'login_view' as login %} {% url 'logout_view' as logout %} {% url 'notifications_view' as notifications %} {% url 'notifications_detail_page_view' as motifications_details %} {% url 'alege_view' as alege_medici %} {% url 'calendar_view' as calendar %}


Sigiriya Fortress
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Coastal Area
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.


Hello Fiddle
What is Fiddle, btw?
We always dreamt of working together on ideas and bringing its fruits to the world, however we think world was not yet ready for it :) But now, with an excuse to celebrate Naseem’s first medal of recognition, fiddle is our first entry to the wall of fame.
Actually, fiddle is our lifebook to pin places which we’ve ever visited in our lifetime, all around the world & the universe (I hope somebody will visit to Mars soon :p). We’ve recorded those events with some nostalgic instances that we'll cherish whenever we open this book again and again in time.
And last but not the least, A big cheers for Naseem for realizing fiddle and make his boss shine in office and to Shivam for finding this opportunity for Naseem!!
Guys it's dedicated to you!!
Shivam, Naseem, Ashish, Tanuj, Vikas, Preeti & Anjali
Release Date: 16 Aug, 2014.
Release version: 1.0
Actually, fiddle is our lifebook to pin places which we’ve ever visited in our lifetime, all around the world & the universe (I hope somebody will visit to Mars soon :p). We’ve recorded those events with some nostalgic instances that we'll cherish whenever we open this book again and again in time.
And last but not the least, A big cheers for Naseem for realizing fiddle and make his boss shine in office and to Shivam for finding this opportunity for Naseem!!
Guys it's dedicated to you!!
Shivam, Naseem, Ashish, Tanuj, Vikas, Preeti & Anjali
Release Date: 16 Aug, 2014.
Release version: 1.0


{% extends 'land/base.html' %} {% block central_content %}
Out company provides best video convertors in the universe!.


example.Rmd
The likelihood and prior distributions that go into drjacoby can be specified by the user either as R functions or as C++ functions. This vignette demonstrates a basic MCMC implementation using both the R and C++ methods, and compares the two in terms of speed.
We need the following elements to run drjacoby:
Starting with the data, let’s assume that our observations consist of a series of draws from a normal distribution with a given mean (mu_true
) and standard deviation (sigma_true
). We can generate some random data to play with:
# set random seed
set.seed(1)
# define true parameter values
mu_true <- 3
sigma_true <- 2
# draw example data
x <- rnorm(10, mean = mu_true, sd = sigma_true)
For our example MCMC we will assume that we know the correct distribution of the data (i.e. we know the data is normally distributed), and we know that the mean is no smaller than -10 and no larger than 10, but otherwise the parameters of the distribution are unknown. Parameters within drjacoby are defined in dataframe format, where we specify minimum, maximum, and initial values of all parameters:
# define parameters dataframe
df_params <- data.frame(name = c("mu", "sigma"),
min = c(-10, 0),
max = c(10, Inf),
init = c(9, 10))
print(df_params)
#> name min max init
#> 1 mu -10 10 9
#> 2 sigma 0 Inf 10
In this example we have one parameter (mu
) that occupies a finite range [-10, 10], and another parameter (sigma
) that can take any positive value. drjacoby deals with different parameter ranges using reparameterisation, which all occurs internally meaning we don’t need to worry about these constraints affecting our inference.
Next, we need a likelihood function. This must have two input arguments: 1) a vector of parameters, 2) a vector of data, and these must be input in that order. It also must return a single value for the likelihood in log space. These constraints on the format of the likelihood function might seem a bit restrictive, but they are needed in order for drjacoby to know how to use the function internally. The issue of taking logs is particularly important, as the MCMC will still run even if we forget to take logs, but the results produced will be nonsense!
Inside the likelihood function we can extract individual parameter values from the input vector, and then use these values to calculate the probability of the data. In our example, the likelihood function is quite simple thanks to the dnorm()
function which can return the density of the normal distribution already in log space:
# define log-likelihood function
r_loglike <- function(params, x) {
# extract parameter values
mu <- params[1]
sigma <- params[2]
# calculate log-probability of data
ret <- sum(dnorm(x, mean = mu, sd = sigma, log = TRUE))
# return
return(ret)
}
Finally, we need a prior function. This must take a single vector of parameters as input, and it must return a single value for the prior probability of those parameters in log space. Again, this strict format is required for drjacoby to know how to use the prior internally. In our case we will assume a uniform prior on mu
, and a log-normal prior on sigma
:
# define log-prior function
r_logprior <- function(params) {
# extract parameter values
mu <- params[1]
sigma <- params[2]
# calculate log-prior
ret <- dunif(mu, min = -10, max = 10, log = TRUE) +
dlnorm(sigma, meanlog = 0, sdlog = 1.0, log = TRUE)
# return
return(ret)
}
Be careful to ensure that your prior is defined over the same range as specified in the df_params
dataframe. For example, here our uniform prior for mu
ranges from -10 to 10, and our log-normal prior for sigma
ranges from 0 to infinity.
Once we have all the elements above it is straightforward to run a basic MCMC. We simply input the four elements listed above, along with the number of burn-in and sampling iterations By default drjacoby prints progress bars to the console to keep you updated on the progress of the MCMC. When running in R markdown we can use the option pb_markdown = TRUE
to print progress bars in a markdown-friendly way, although you will probably want to leave this option turned off when running interactively (simply delete this argument).
# run MCMC
r_mcmc_out <- run_mcmc(data = x,
df_params = df_params,
loglike = r_loglike,
logprior = r_logprior,
burnin = 1e3,
samples = 1e3,
pb_markdown = TRUE)
#> MCMC chain 1
#> burn-in
#>
|
|======================================================================| 100%
#> acceptance rate: 22.7%
#> sampling phase
#>
|
|======================================================================| 100%
#> acceptance rate: 23.7%
#>
#> completed in 0.161213 seconds
#> MCMC chain 2
#> burn-in
#>
|
|======================================================================| 100%
#> acceptance rate: 22.7%
#> sampling phase
#>
|
|======================================================================| 100%
#> acceptance rate: 24%
#>
#> completed in 0.121806 seconds
#> MCMC chain 3
#> burn-in
#>
|
|======================================================================| 100%
#> acceptance rate: 23%
#> sampling phase
#>
|
|======================================================================| 100%
#> acceptance rate: 23.3%
#>
#> completed in 0.131105 seconds
#> MCMC chain 4
#> burn-in
#>
|
|======================================================================| 100%
#> acceptance rate: 23.1%
#> sampling phase
#>
|
|======================================================================| 100%
#> acceptance rate: 23.6%
#>
#> completed in 0.136317 seconds
#> MCMC chain 5
#> burn-in
#>
|
|======================================================================| 100%
#> acceptance rate: 23%
#> sampling phase
#>
|
|======================================================================| 100%
#> acceptance rate: 23.9%
#>
#> completed in 0.129409 seconds
The output returned by the MCMC function has three parts: 1) an “output” dataframe containing raw posterior draws and other key elements at each iteration of the MCMC, 2) a “diagnostics” object containing useful summaries such as the effective sample size of each parameter, 3) a “parameters” object containing a record of the exact parameters used to run the MCMC. We can take a peek at the first of these outputs:
head(r_mcmc_out$output)
#> chain rung iteration stage logprior loglikelihood mu sigma
#> 1 chain1 rung1 1 burnin -8.868205 -33.96977 9.000000 10.000000
#> 2 chain1 rung1 2 burnin -8.868205 -33.96977 9.000000 10.000000
#> 3 chain1 rung1 3 burnin -7.845730 -29.58273 5.341293 7.220550
#> 4 chain1 rung1 4 burnin -7.845730 -29.55180 5.262133 7.220550
#> 5 chain1 rung1 5 burnin -7.845730 -29.55180 5.262133 7.220550
#> 6 chain1 rung1 6 burnin -7.667840 -29.26141 5.750924 6.797553
Before we can draw any conclusions from our MCMC results there are some basic checks that we should carry out. First, we can examine trace plots of all parameters to see how they are moving. This can be done using the plot_par()
function, shown here for the burn-in phase:
plot_par(r_mcmc_out, show = "mu", phase = "burnin")
plot_par(r_mcmc_out, show = "sigma", phase = "burnin")
Notice that for all five chains both mu
and sigma
move quickly from their initial values to stable levels at around 3 and 2. This is a visual indication that the MCMC has burned in for an appropriate number of iterations.
Next we can look at trace plots from the sampling phase:
plot_par(r_mcmc_out, show = "mu")
plot_par(r_mcmc_out, show = "sigma")
Here we can see that the MCMC continues to move freely, and that all chains appear to exploring the same part of the parameter space. The final plot shows the autocorrelation of the chains, which in this case falls off fairly rapidly with samples being approximately independent at around 5 lags. This is another indication that the MCMC is mixing well. The marginal histogram shows a single clear peak, although this is still a bit rough so we may want to re-run the MCMC with a larger number of sampling iterations to get a smoother result. We can also explore this by looking at the effective sample size (ESS), which is stored within the MCMC diagnostics:
r_mcmc_out$diagnostics$ess
#> mu sigma
#> 851.5148 550.7152
We can see that despite running the MCMC for 1000 sampling iterations, the actual number of effectively independent samples accounting for autocorrelation is much lower. When doing any calculation that relies on the number of samples, we should use the ESS rather than the raw number of sampling iterations. For example if we want to know the standard error of our estimate of mu
we should do sd(mu)/sqrt(ESS)
. But just as importantly, when producing any summary of the posterior that does not make direct use of this number - for example when producing posterior histograms - we should use all posterior samples, and we should certainly not thin samples to reduce autocorrelation. A histogram produced from all samples is more accurate than once produce from thinned samples, even if the samples are autocorrelated.
The final question is how can we be confident that our MCMC has actually explored the space well? Although the trace plot above looks good, it is possible to get results that look like this from very pathological MCMC runs. This is a more complex problem that is dealt with to some extent in another vignette.
Although drjacoby is an R package, under the hood it is running C++ through Rcpp. When we pass an R likelihood function to run_mcmc()
, as in the example above, the code is forced to jump out of C++ into R to evaluate the likelihood before jumping back into C++. This comes with a computational overhead, which can be avoided by specifying functions directly within C++.
To use C++ functions within drjacoby we simply write them directly within R as character strings. As before, there are some constraints on what this function must look like. First, it must take as input a std::vector<double>
of parameters followed by a std::vector<double>
of data. Data must be input as doubles, and so data consisting of integer or boolean values should be dealt with as though they are continuous values (for example TRUE = 1.0
, FALSE = 0.0
). Second, the function must return an object of class SEXP
. The easiest way to achieve this is to calculate the raw return value as a double, and to use the Rcpp::wrap()
function when returning to transform to SEXP
. As before, the value returned should be the likelihood evaluated in log space.
Even though we are workin in C++, we still have access to most of R’s nice distribution functions through the R::
namespace. For example, the dnorm()
function can be accessed within C++ using R::dnorm()
. Details of the available functions can be found here. The r_loglike()
function defined above can be re-written in C++ as follows:
# define cpp log-likelihood function as character string
cpp_loglike <- "SEXP loglike(std::vector<double> params, std::vector<double> x) {
// extract parameters
double mu = params[0];
double sigma = params[1];
// sum log-likelihood over all data
double ret = 0.0;
for (size_t i = 0; i < sizeof(x); ++i) {
ret += R::dnorm(x[i], mu, sigma, true);
}
// return as SEXP
return Rcpp::wrap(ret);
}"
Similarly, we can define the prior as a C++ function. This function must take as input a std::vector<double>
of parameters, and must output a single SEXP
value representing the prior probability of the parameters in log space. The r_logprior()
function defined above can be re-written in C++ as follows:
# define cpp logprior function
cpp_logprior <- "SEXP logprior(std::vector<double> params){
// extract parameters
double sigma = params[1];
// calculate logprior
double ret = -log(20.0) + R::dlnorm(sigma, 0.0, 1.0, true);
// return as SEXP
return Rcpp::wrap(ret);
}"
With these two functions defined we can run the MCMC exactly the same as before, passing in the new functions:
# run MCMC
r_mcmc_out <- run_mcmc(data = x,
df_params = df_params,
loglike = cpp_loglike,
logprior = cpp_logprior,
burnin = 1e3,
samples = 1e3,
pb_markdown = TRUE)
#> MCMC chain 1
#> burn-in
#>
|
|======================================================================| 100%
#> acceptance rate: 26.3%
#> sampling phase
#>
|
|======================================================================| 100%
#> acceptance rate: 39.6%
#>
#> completed in 0.004309 seconds
#> MCMC chain 2
#> burn-in
#>
|
|======================================================================| 100%
#> acceptance rate: 22.5%
#> sampling phase
#>
|
|======================================================================| 100%
#> acceptance rate: 22.5%
#>
#> completed in 0.004318 seconds
#> MCMC chain 3
#> burn-in
#>
|
|======================================================================| 100%
#> acceptance rate: 0%
#> sampling phase
#>
|
|======================================================================| 100%
#> acceptance rate: 72.1%
#>
#> completed in 0.004037 seconds
#> MCMC chain 4
#> burn-in
#>
|
|======================================================================| 100%
#> acceptance rate: 35.1%
#> sampling phase
#>
|
|======================================================================| 100%
#> acceptance rate: 24.3%
#>
#> completed in 0.095583 seconds
#> MCMC chain 5
#> burn-in
#>
|
|======================================================================| 100%
#> acceptance rate: 22.2%
#> sampling phase
#>
|
|======================================================================| 100%
#> acceptance rate: 17.8%
#>
#> completed in 0.004097 seconds
You should see that this MCMC runs considerably faster than the previous version that used R functions. There is a short delay initially while the strings are compiled into functions, but this only occurs the first time a function is compiled. On the other hand, writing C++ functions in this way is more error prone and difficult to debug mistakes. In summary, if efficiency is your goal then C++ versions of both the likelihood and prior should be used, whereas if ease of programming is more important then R versions of these functions should suffice.
This was a very easy problem, and so required no fancy MCMC tricks. The next vignette demonstrates how drjacoby can be applied to more challenging problems.


MotoGP, Lorenzo: “I would have liked to continue and win a title with Ducati“
“Right now, retirement is my definitive decision, but never say never. Unfortunately I lost the motivation I need to continue and have been unable to find it again. Marquez? He'd win with any bike”
by Riccardo GugliemettiToday's news is Jorge Lorenzo's retirement. The spotlight is on the Majorcan at Valencia who, after announcing his decision during a special press conference, spoke to Sky journalists Guido Meda and Antonio Boselli.
“Of course I thank everyone who has supported me over the years, as well as everyone here at Valencia today - begins #99 – there's a part of me that's sad at having to leave racing, as this has been my world for so many years, becoming a sort of family. At the same time, there's another part of me that now feels more free. In the end, I've done what I loved, or rather race, for a long time and on Monday I'll start to think about my future”.
There is of course regret as we consider a grid without Jorge come 2020.
“Without the Assen crash, I would have continued for another year. The fact is that, after that accident, I no longer had the motivation to give it my all and the moment I lost that motivation was key for me. After Assen, I was able to talk to Puig, and I told him that I might have found the motivation to get back to being 100% but in the end that wasn't the case”.
Another moment contributed to the Majorcan's eventual decision.
“I was also able to talk to Marc and Cal in recent months, and they told me the new 2020 bike wouldn't offer any big improvements with respect to the current one. My 2020 season would have been very long".
One experience that remains on the Majorcan's mind is his time in Ducati. A pity that farewell came too soon.
“Ducati? It's easy to say certain things after the event. We each make decisions in life and these can turn out to be right or wrong. If I'd won before Mugello, maybe my adventure with Ducati would have turned out differently. Deep down, I wanted to stay with Ducati and win the title. 2018 was a complicated year, as I would start the race strong but then couldn't keep it up to the line. They'd already made their decision before Mugello though, but there are no regrets about leaving Ducati".
The Majorcan turns to the more recent Honda experience.
“On a personal level, they've always treated me very well. Alberto Puig was the first to believe in me. He has always supported me and understood, especially in tough times. The same goes for Takeo, who always told me to remember I'm Lorenzo. The fact is that the 2019 bike didn't suit my qualities.”
How about Marquez?
“Marc has won with this bike, he takes priority and they of course follow him. I was the point of reference in Yamaha and Ducati in the past, but not in Honda. The manufacturer focuses on him, and rightly so, as he's the most successful. I think that Marc could win with any bike, though the Honda is the one that best suits his style”.
There is mention of Valentino Rossi, his Yamaha team-mate for many years.
“Rossi and I love each other when we're far apart– he jokes - when we're together, things change. I think it's normal, as when we were in Yamaha together we both wanted to fight for the win and so the rivalry between us was only natural”.
Lorenzo also talks about his life away from the track.
“I'm a person with many passions. Inside, I think I'm tough, perhaps not very sensitive. And I don't like being alone. At the same time, I'd say I'm honest and transparent. If something's not working with someone, I like to have it out, I'm not scared to voice my opinion, even if it's not the same as everyone else's”.
It will definitely be strange not to see Lorenzo on the grid next season.
“Right now, I see my decision to stop as a definitive one, but you should never say never in life. I am 99% sure of my decision to stop, though there's that 1%... Fears? I don't know. My life will change as of Monday of course, I'll definitely be calmer and more relaxed than I've been recently”.


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Good Newzz Trailer Launch: Kareena Kapoor Khan’s Yellow Dress Or Kiara Advani’s Pink Mini Dress; Who Is Your Pick?
Whose look did you like more? Let us know in the comments section below.
by Ananya SwaroopGood Newwz starring Kareena Kapoor Khan, Akshay Kumar, Kiara Advani and Diljit Dosanjh has been all over the news for its hilarious trailer. The team gathered together to unveil the trailer to the fans and Kareena and Kiara made sure they made heads turn with their sexy ensembles.
Kiara went for a blush pink, mini one-shoulder dress for the event. The dress had a puffy sleeve and Kiara looked like an absolute diva in it. The upper part of the dress perfectly highlighted the curves of the actress while the bottom had overlapping details giving the dress an asymmetrical hemline. Kiara ditched wearing any jewellery with the sexy dress.
Talking about her makeup, Kiara went for a dewy base, bushy eyebrows and kohl eyes. She used a nude shade for her lips and accentuated her eyes with a pink shade of eyeshadow.
Talking about the fashion diva Kareena Kapoor Khan, she opted for a lemon-coloured dress for the Good Newzz trailer launch. Kareena flaunted her washboard abs in the dress by the Dion Lee. The dress was a drape dress with a plunging neckline, long sleeve and it had pearls on the chest and the waist to give it more detail. She paired the dress with a pair of clear heels. Kareena added a pair of silver rings and earrings to complete the look.
Talking about her makeup, Kareena opted for lenses, kohl eyes and a no-makeup makeup look. She went for a nude lipstick and smokey eye shadow to complete the look. She let her hair open in light waves and added a pair of vintage sunnies to the look.
Whose look did you like more? Let us know in the comments section below.
Talking about Good Newzz, the film is a hilarious story of two couples ( Kareena-Akshay & Diljit-Kiara) who want a baby. The trailer shows how Akshay and Diljit’s sperms get exchanged and what happens next is going to be a rib-tickling experience.
Good Newzz hit the theatres on December 6.


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RE: TPAMI-2012-12-0973, "Large (Brain) Graph Matching via Fast Approximate Quadratic Programming"
Manuscript Type: Regular
Dear Dr. Joshua Vogelstein,
We have completed the review process of the above referenced paper that was submitted to the IEEE Transactions on Pattern Analysis and Machine Intelligence. Enclosed are your reviews. We hope that you will find the editor's and reviewers’ comments and suggestions helpful.
I regret to inform you that based on the reviewer feedback, we could not recommend publishing your paper. Final decisions on acceptance are based on the referees' reviews and such factors as restriction of space, topic, and the overall balance of articles.
We hope that this decision does not deter you from submitting to us again. Thank you for your interest in the IEEE Transactions on Pattern Analysis and Machine Intelligence.
Sincerely,
Dr. David Forsyth,
EIC
IEEE Transactions on Pattern Analysis and Machine Intelligence
***********
Editor Comments
Editor
Comments to the Author:
The reviewers criticize that related work is not adequately covered. They also point out a lack of care in the experiments (e.g. missing baselines), and a lack of quality in writing and presentation. The lack of care in the experiments is decisive, as the method is a heuristics and not theoretically justified.
In its current state and with its limited degree of novelty, this article does not provide a substantial enough contribution to the field to warrant publication in TPAMI.
***********************
Reviewer Comments
Reviewer: 1
Recommendation: Author Should Prepare A Major Revision For A Second Review
Comments:
Strong points:
-- Good empirical evaluation
Weak points:
-- Not clear exactly what the novelty is compared to similar approaches
-- Overly informal writing style and presentation
Detailed comments:
I find the writing of the paper could be substantially improved. The style is way too conversational and familiar. e.g. "that FAQ was not always the best was annoying to us." Try to write the paper using standard academic voice. I'd also like to see more focus on the introduction and technical content, rather than focusing the paper so strongly on experimental results as you do now.
I'm not really sure why the authors chose to focus so much on brain graphs, even to the point of promoting it to the title. It seems like this is really a paper about proposing a novel approach to quadratic assignment algorithms, and brain graphs is simply one possible domain. Therefore I'd change the title be more general.
I'd also change the abstract: it tells me you have good results, but does little to say why your method is novel or important. The fact that you have good results should be mentioned, but should hardly be the main focus of your abstract in my opinion.
Section 5.1 doesn't add much. The complexity of Linear Assignment has been studied elsewhere, and Erdos Renyi graphs may not have representative performance of the models typical complexity.
In short, while the results of this paper seem to be good, I don't know that this algorithm warrants full publication in a journal. I'd think it'd be better suited to a shorter format -- unless more technical details, or a better explanation of its novelty are given.
Additional Questions:
1. Which category describes this manuscript?: Research/Technology
2. How relevant is this manuscript to the readers of this periodical? If you answer Not very relevant or Irrelevant please explain your rating under Public Comments below.: Relevant
1. Please evaluate the significance of the manuscript’s research contribution.: Fair - Even with the recommended changes, the contribution of this paper is unlikely be significant enough for publication in TPAMI.
2. Please explain how this manuscript advances this field of research and/or contributes something new to the literature. : The paper proposes a novel solution to the problem of graph matching, via a reduction to quadratic assignment. However, the paper doesn't do a great job of explaining exactly what the novelty is. Briefly the approach in this paper is to:
1) Phrase graph matching as a QAP (which is standard)
2) Relax the binary constraints of the resulting QAP (also standard)
3) Solve the relaxed QP using standard approaches
4) Project on to the feasible region
To me, there seem to be several similar works. Consider for example the seminar work by Rangarajan et al. (softassign). It follows essentially the same programme, which many others have built upon. Although the authors give good evidence that their algorithm works well in practice on standard benchmark datasets, it's not really clear to me exactly what the novelty is compared to myriad other approaches.
3. Is the manuscript technically sound? In the Public Comments section, please provide detailed explanations to support your assessment: Yes
4. How thorough is the experimental validation (where appropriate)? Please discuss any shortcomings in the Public Comments section.: Compelling experiments; clearly state of the art
1. Are the title, abstract, and keywords appropriate? If not, please comment in the Public Comments section.: No
2. Does the manuscript contain sufficient and appropriate references? Please comment and include additional suggested references in the Public Comments section.: References are sufficient and appropriate
3. Does the introduction state the objectives of the manuscript in terms that encourage the reader to read on? If not, please explain your answer in the Public Comments section.: Could be improved
4. How would you rate the organization of the manuscript? Is it focused? Please elaborate with suggestions for reorganization in the Public Comments section.: Could be improved
5. Please rate the readability of the manuscript. Explain your rating under Public Comments below. : Easy to read
6. How is the length of the manuscript? If changes are suggested, please make explicit recommendations in the Public Comments section.: Too long – the manuscript should be shortened significantly (e.g., if a regular paper, should be reduced to a short paper).
7. Should the supplemental material be included? (Click on the Supplementary Files icon to view files): Does not apply, no supplementary files included
8. If yes to 7, should it be accepted: As is
Please rate the manuscript overall. Explain your choice.: Fair
Reviewer: 2
Recommendation: Revise and resubmit as “new”
Comments:
The key insight behind the approach is to allow the assignment matrix
to be continuous during optimization. During the optimization, the
objective is linearized (akin to convex concave procedure) and then
solved by the Hungarian method.
I am surprised that this technique is new given that the Lagrangian
relaxation technique has been used quite extensively for integer
programs. A google search leads me to the following link:
http://www.opt.math.tu-graz.ac.at/~cela/papers/qap_bericht.pdf
And there are a few linearization techniques described there. It'd be
great for the readers to know how is this technique compared to those
techniques.
The paper claims that the method is a "fast approximation" but the
results seem to be quite speculative. In particular, the speed
comparison in Figure 3 does not look impressive at all. Results in 5.8
need a baseline. Without any improvement in speed nor theoretical
guarantees, it's quite hard to acknowledge the contributions of this
work.
Additional Questions:
1. Which category describes this manuscript?: Research/Technology
2. How relevant is this manuscript to the readers of this periodical? If you answer Not very relevant or Irrelevant please explain your rating under Public Comments below.: Relevant
1. Please evaluate the significance of the manuscript’s research contribution.: Fair - Even with the recommended changes, the contribution of this paper is unlikely be significant enough for publication in TPAMI.
2. Please explain how this manuscript advances this field of research and/or contributes something new to the literature. : This paper proposes an approximation solver for the Quadratic Assignment Problem. Quadratic Assignment Problem is an NP-hard problem that has several interesting applications in computer vision (such as
3D point correspondence, or Brain graph matching).
3. Is the manuscript technically sound? In the Public Comments section, please provide detailed explanations to support your assessment: Appears to be - but didn't check completely
4. How thorough is the experimental validation (where appropriate)? Please discuss any shortcomings in the Public Comments section.: Insufficient; clearly inferior to state of the art, or necessary tests are absent
1. Are the title, abstract, and keywords appropriate? If not, please comment in the Public Comments section.: Yes
2. Does the manuscript contain sufficient and appropriate references? Please comment and include additional suggested references in the Public Comments section.: Important references are missing; more references are needed
3. Does the introduction state the objectives of the manuscript in terms that encourage the reader to read on? If not, please explain your answer in the Public Comments section.: Yes
4. How would you rate the organization of the manuscript? Is it focused? Please elaborate with suggestions for reorganization in the Public Comments section.: Satisfactory
5. Please rate the readability of the manuscript. Explain your rating under Public Comments below. : Readable - but requires some effort to understand
6. How is the length of the manuscript? If changes are suggested, please make explicit recommendations in the Public Comments section.: About right
7. Should the supplemental material be included? (Click on the Supplementary Files icon to view files): Yes, as part of the digital library for this submission if accepted
8. If yes to 7, should it be accepted: After revisions. Please include explanation under Public Comments below.
Please rate the manuscript overall. Explain your choice.: Fair
Reviewer: 3
Recommendation: Revise and resubmit as “new”
Comments:
Graph matching is a famously computational difficult problem. It’s clearly meaningful to design a fast algorithm for it. The manuscript proposed a fast approximate quadratic assignment problem algorithm (FAQ) for graph matching problem. The method showed some interesting results. The manuscript is well written and easy to follow. However, I have some major concerns.
Major concerns
1. No theoretical foundation for the algorithm is given in the manuscript. It seems that the manuscript only propose a heuristic algorithm, and does not present why the algorithm can solve the graph matching problem. Can the algorithm guarantee convergence? If the algorithm converges, is it convergent to the solution of the original problem? What are the error bounds of FAQ?
2. In section 5.1, the manuscript states that the FAQ algorithm is in cubic time based on experiments. A theoretical explanation should be presented.
3. Some synthetic data can be generated to show the performance of the algorithm, since the 137 benchmarks have limitations in the number of vertices. It might be interesting to see how the methods perform for graphs with specific properties (e.g. scale-free).
4. From Figure 4, although the linear fits showed FAQ is better than PATH, The time spent by FAQ has much larger variance than PATH. This figure shows running time by FAQ is rather unstable and unpredictable. From the figure, it is also unconvincing to say FAQ might be better when the number of vertices increase. The readers might want to see the experimental results which can prove this point even based on synthetic data.
5. In table 1, why not show the results by methods U and QCV?
6. In the left panel of figure 5, the algorithm U performs strangely bad (almost 100% error). Some explanation should be provided.
Minors:
1. ‘Table ??’ in the caption in Table 2.
2. P7,line8, something wrong with this sentence ‘If FAQ ….than FAQ’.
3. Page 11, line 17, ‘error is the fraction of vertices correctly matched’?
Additional Questions:
1. Which category describes this manuscript?: Research/Technology
2. How relevant is this manuscript to the readers of this periodical? If you answer Not very relevant or Irrelevant please explain your rating under Public Comments below.: Relevant
1. Please evaluate the significance of the manuscript’s research contribution.: Fair - Even with the recommended changes, the contribution of this paper is unlikely be significant enough for publication in TPAMI.
2. Please explain how this manuscript advances this field of research and/or contributes something new to the literature. : Graph matching is a famously computational difficult problem. It’s clearly meaningful to design a fast algorithm for it. The manuscript proposed a fast approximate quadratic assignment problem algorithm (FAQ) for graph matching problem. The method showed some interesting results. The manuscript is well written and easy to follow. However, I have some major concerns.
3. Is the manuscript technically sound? In the Public Comments section, please provide detailed explanations to support your assessment: Appears to be - but didn't check completely
4. How thorough is the experimental validation (where appropriate)? Please discuss any shortcomings in the Public Comments section.: Lacking in some respects; some cases of interest not tested
1. Are the title, abstract, and keywords appropriate? If not, please comment in the Public Comments section.: Yes
2. Does the manuscript contain sufficient and appropriate references? Please comment and include additional suggested references in the Public Comments section.: References are sufficient and appropriate
3. Does the introduction state the objectives of the manuscript in terms that encourage the reader to read on? If not, please explain your answer in the Public Comments section.: Could be improved
4. How would you rate the organization of the manuscript? Is it focused? Please elaborate with suggestions for reorganization in the Public Comments section.: Satisfactory
5. Please rate the readability of the manuscript. Explain your rating under Public Comments below. : Easy to read
6. How is the length of the manuscript? If changes are suggested, please make explicit recommendations in the Public Comments section.: About right
7. Should the supplemental material be included? (Click on the Supplementary Files icon to view files): Does not apply, no supplementary files included
8. If yes to 7, should it be accepted: As is
Please rate the manuscript overall. Explain your choice.: Fair
|


Some days have passed, and our players have settled down at the inn. Last night Elli was talking about some strange noises coming from below her school.
Elli: Grat thinks there's probably a monster down there.
Orin (teasing): I bet it has all sorts of tentacles.
Elli (rolling her eyes): Da-aaad...
Gwynnip: Well, maybe our new guests could check it out. Elli's teacher is an old friend of yours. I bet she'd let them take a look. Don't cellars make great places for starting adventurers ?
They do, of course. And so now our players find themselves in front of Elli's school.
You stand at the bottom of some steps to weathered double doors. The sign above the doors says "School of Illumination".
When inspecting the sign more closely, the players can see that the text has been painted over an older sign. A successful DC 18 Intelligence (Investigation) check reveals the old markings, which read: "Church of Transfiguration".
Beyond the double doors lies a somewhat dark entrance hall. The floor is made of patterned black and white tile, which has seen a lot of traffic. A door into the left wall has a sign identifying it as the teacher's lounge. On the wall on the right hangs a board for notices which, currently, only has a parchment showing the school's schedule.
 She apologizes that her husband, the Headmaster, could not receive them personally, as he is out on business.
Mrs. Lindenbrook will extend the players every courtesy, allowing them free reign of the school, provided they do not disturb the students or the classes. When asked about the noises she will say that some of the kitchen staff have heard them, but she herself has not.
A long wooden table, propped up on one side by a book, stands in the middle of the teacher's lounge. On it are a ceramic pitcher and a variety of mugs.
Items. The book propping the table up is an old textbook on Dwarvish.
Many narrow windows line the street side of this room, and let in a lot of light. A halfling-sized desk sits at far end, with a matching office chair behind it. Many bookcases line the wall to the right, though their shelves are far from filled. A ladder leans against one of them.
This is the office of Headmaster Alton Lindenbrook. You won't find him at the school as he's "out on business".
Locked door. A locked door leads, via a stairway, down to the Headmaster's storage room. The lock is well-maintained and requires a successful DC 20 Dexterity (Sleight of Hand) check to open it.
Several tables with simple wooden chairs fill this room. Windows on one side look out over the inner courtyard, while windows on the other side show a view of a walled garden.
This place fills at noon lunchbreak, when the students crowd the tables and the kitchen staff serves lunch.
This is the domain of Davgeon and Belice, the school's cooks. In the mornings they are very busy preparing lunch for both students and teachers. The calm returns in the afternoon, when it becomes all about cleaning up.
When asked about the noises Belice will confirm she has heard them, down in the cellars. A pitter-pattering, a scratching, a wailing,... it gave her the shivers. She no longer ventures far into the cellar, and gets Davgeon to fetch whatever she needs from there. For his part, Davgeon has not heard anythig sinister, but will defend Belice and state that if she says she heard something then that is good enough for him.
Creatures. Three Violet Fungus [MM, p. 138] will start attacking any disturbance within five feet of them.
Items. An old pickaxe, sledgehammer, and random bits of junk.
Crooked Mirror. When looking into the mirror, this is what the players see:
Coming from the hall behind you you see a man in robes carrying a heavy bin. He turns into a passage in the east wall and disappears from view.
This man can only be see in the mirror. The passage, too, is presently missing. But closer inspection of the place where the man supposedly entered the wall reveals that it has been filled in with stone.
Hidden passage. The hidden passage can be uncovered by brute strength (target DC 20), by attacking the wall with a blunt weapon (target DC 17), or by means of the sledge hammer and/or pickaxe found in another room. When broken down, the entrance opens up to stairs leading to the crypt.
You enter a stuffy, rectangular chamber.  Your footsteps echo, and are soon joined by the rattling of bones...
Creatures. Three skeletons [MM, p. 272], one zombie [MM, p. 316] and two crawling claws [MM, p. 44] lay in wait. The noise from breaking down the wall to the crypt will have warned them.
Sarcophagus. This stone sarcophagus lies silent, and covered with dust. Cursory inspection reveals an inscription on the lid. It says: "A Truant Inmost" (which is an anagram of "Transmutation").
If the players remove the lid they find the corpse of a man beneath a shield. This man is the same one seen in the mirror before. It looks like he was badly burnt.
Embossed on the shield are two symbols (the alchemical symbols of lead and gold).
Markings on the floor. Inscribed into the stone tile are a collection of strange symbols. These are a variety of alchemical symbols. One of them is the symbol for lead. Another is the symbol for gold.
When the players place the bin with lead on the symbol for lead, and an empty bin on the symbol for gold, the the empty bin will start filling with gold, causing the ground to crack and break open, and revealing a tunnel which leads to the reactor room.
If the players have trouble solving the puzzle, they can ask the teachers for help. (Check the schedule to figure out where they might be.) Nienna can reveal to them that the inscription is an anagram (if she is in class, have her write it on the classroom board, and explain it with the help of her students). Wilven can explain what transmutation is (he will ask his favourite student to show off).
The walls of this perfectly hexagonal room have been charred by intense fire. Through the webbing, littering pretty much all available space, you can see the outlines of a pedestal. Before you can get much closer, though, fresh webbing reaches out to find you...
Creatures. Two Swarm of Insects (Spiders) [MM, p. 338], and one Giant Spider [MM, p. 328]. The first character to enter the room gets webbed.
Shattered Pedestal. The remains of this pedestal are inscribed with the same archaic alchemical symbols seen in the crypt. Closer inspection will tell that something, probably some experiment in transmutation, went horribly wrong here and resulted in an intense blaze of fire cracking the pedestal and charring the room.
Pit covered with broken bars. In one corner of the room is a pit covered in iron bars, some of which have been bent forcibly at odd angles. You suspect this is how the spiders found their way in.
The bottom of the pit lies hidden in darkness. If the players manage to throw get some light all the way down to the bottom, they see something glimmering back at them. To try and get at this object will require bending the bars further (a successful DC 18 Strength check), and somehow making it down the tight, slippery pit (a succesful DC 20 Acrobatics or Athelitics check).
Crystal d20. This magic item lies in dirty water at the bottom of the pit. If rolled when the d20 is clear of colour (and somewhat luminescent) it shows a scene from the current location’s past, present of future. The scene as the d20 turns black. It takes 24 hours before it becomes clear again.
Headmaster Lindenbrook is a greying Halfling [PHB, p. 26], who has been working at the School of Illumination all his life. He started out as teacher, but has long since taken on the arduous task of managing the school and keeping it afloat. Somehow he has managed to keep classes going.
He is aware of the school's former history as a church to some kind of cult. But he has no idea that beneath the old chapel lies a crypt and trouble.
Anne Lindenbrook teaches mathematics and physics at the school. She is the wife of the Headmaster, and as such something of an unofficial spokesperson while he is out.
An ex-adventurer who settled down with his love, Gwynnip Copperfist, and their daughter, Elli. Together they maintain the "Marthammor's Du'Inn", an inn welcoming adventurers of all ilk in need of a warm meal, hearty ale, or a soft bed.
Armor Class 18 (Chain Mail and Shield)
Hit Points 45 (
Speed 25ft.
14 (+2)
8 (-1)
15 (+2)
10 (+0)
18 (+4)
12 (+1)
Dwarven Resilience. Orin has advantage on saving throws against poison, and resistance against poison damage.
Multiattack. Orin can make two attacks.
Warhammer. Melee Weapon Attack: +5 to hit, reach 5ft., one target. Hit: 6 (
[MM] Wizards RPG Team, "Monster Manual, 5th edition", Wizards of the Coast, September 30, 2014, ISBN: 978-0-7869-6561-8
[PHB] Wizards RPG Team, "Player's Handbook, 5th edition", Wizards of the Coast, August 19, 2014, ISBN: 978-0-7869-4867-1


Ionic is the framework that I use for all of my projects, and I'm not sure I can give a bigger compliment than that. The innate sense of style and sophistication built into Ionic is awesome and makes building professional-level mobile applications a snap.
Ionic will dramatically change the way mobile apps are built. Their choice of AngularJS means building functionality is a now breeze and it feels near-native. Your developers and your users will thank you.
I was so inspired when I found Ionic. It fills a gap that’s missing when building for mobile and solves many complexities that otherwise require multiple libraries, keeping your code cleaner. Overall, it just makes mobile development fun and fast, so you can build more!


{% extends "base.html" %} {% import "bootstrap/wtf.html" as wtf %} {% block app_content %}
Welcome to rpi.tv.
Click things and stuff to do stuff and things.


{% extends "console/pages/clients/clients.html" %} {% block section %} {% include "console/pages/clients/components/records_table.html" with records=records %} {% endblock %}


Sorry, the page or resource you are looking for does not exist. When you create a new resource, for example an App or Schema it might take some seconds until it is available.


name
request
url
method
GET
POST
PUT
DELETE
HEAD
OPTIONS
headers
data
response
processing
status
headers
debuglink
data


{% extends "barriers/edit/base.html" %} {% load static %} {% block page_title %}{{ block.super }} - Add initial economic assessment{% endblock %} {% block back_link %} Back {% endblock %} {% block page_content %}
The automated assessment uses codes up to HS6 so results will be based on this level. Higher HS codes can be entered but the automated assessment will ignore any figure above HS6.
{% else %}No HS commodity codes have been added.
{% endif %}


The same procedures in Service Binpath Hijack and SERVICE Unquoted Path Hijack apply to scheduled tasks.
If a service has unquoted paths, attempt to check for write access along the search path
If a service has quoted paths, attempt to check the binary itself for write a access
If the service itself is configurable, just change the task to run


I'm a Computer Science senior student currently enrolled both in Pace University and UFF - Universidade Federal Fluminense because of the BSMP (Brazilian Scientific Mobility Program). I like Artificial Intelligence, especially the Neural Networks, Deep Learning and Natural Language Processing. I pretend to pursue a masters and doctorals degrees.


Wanderlust: A deep uncontrollable desire to hit the road and travel, by whatever means desired, to explore and enjoy the world.


Plotters WebAssembly Demo
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32
33 34 35
36 package ti.uia.runtime;
37
38 /*!
39 * ======== EventHdr ========
40 * Module defines the header format to be used when logging an event
41 *
42 * Each event's first word includes the type, length of the event and
43 * sequence number. The length includes the first word and is in bytes.
44 *
45 * The following is the desciption of the first word.
46 *
47 * @p(code)
48 * EventHdr
49 * 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0
50 * 1 0 9 8 7 6 5 4 3 2 1 0 9 8 7 6 5 4 3 2 1 0 9 8 7 6 5 4 3 2 1 0
51 * |---------------------------------------------------------------|
52 * |H H H H H|L L L L L L L L L L L|S S S S S S S S S S S S S S S S|
53 * |---------------------------------------------------------------|
54 *
55 * H = HdrType (5-bits)
56 * L = Event Length (11-bits)
57 * S = Sequence Number (16-bits)
58 * @p
59 *
60 * There are currently 4 different types of events supported in UIA. The
61 * following discusses the format of each one of the types.
62 *
63 * @p(code)
64 * HdrType_Event
65 * word0: EventHdr
66 * word1: event Id (top 16 bits) & module Id (bottom 16 bits)
67 *
68 * HdrType_EventWithTimestamp
69 * word0: EventHdr
70 * word1: Timestamp lower 32 bits
71 * word2: Timestamp upper 32 bits
72 * word3: event Id (top 16 bits) & module Id (bottom 16 bits)
73 *
74 * HdrType_EventWithSnapshotId
75 * word0: EventHdr
76 * word1: event Id (top 16 bits) & module Id (bottom 16 bits)
77 * word2: filename pointer
78 * word3: linenum
79 * word4: snapshotId
80 * word5: address where the data was located
81 * word6: total length of data (top 16-bits)
82 * length for this record (bottom 16 bits)
83 * word7: format pointer
84 * data: the rest of the record contains the data
85 *
86 * HdrType_EventWithSnapshotIdAndTimestamp:
87 * word0: EventHdr
88 * word1: Timestamp lower 32 bits
89 * word2: Timestamp upper 32 bits
90 * word3: event Id (top 16 bits) & module Id (bottom 16 bits)
91 * word4: filename pointer
92 * word5: linenum
93 * word6: snapshotId
94 * word7: address where the data was located
95 * word8: total length of data (top 16-bits)
96 * length for this record (bottom 16 bits)
97 * word9: format pointer
98 * data: the rest of the record contains the data
99 * @p
100 *
101 * Snapshot events can span multiple records. The snapshotId is used to
102 * correlate the records when this occurs.
103 * The length field (word6 or word8) contains two lengths. The bottom 16 bits
104 * length of data for this record. The top 16 bits is the sum of all
105 * lengths for this specific snapshotId.
106 *
107 * The address field points to the location of the data. When the snapshot
108 * spans multiple records, the address field is updated accordingly. Therefore
109 * this field can be used to detect dropped records. Note the snapshot
110 * event data is contiguous.
111 *
112 * EventHdr for HdrType_EventWithPrevLen* and HdrType_SnapshotEventWithPrevLen*
113 * 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0
114 * 1 0 9 8 7 6 5 4 3 2 1 0 9 8 7 6 5 4 3 2 1 0 9 8 7 6 5 4 3 2 1 0
115 * |---------------------------------------------------------------|
116 * |H H H H H|L L L L L L L L L L L|P P P P P P P P P P P|S S S S S|
117 * |---------------------------------------------------------------|
118 *
119 * H = HdrType (5-bits)
120 * L = Event Length in bytes (11-bits: # of bytes in current event)
121 * P = Number of Bytes in Previous Event (5-bits: # of bytes in prev. event)
122 * S = Sequence Number (5-bits)
123 */
124 @CustomHeader
125 module EventHdr {
126 /*!
127 * ======== HdrType ========
128 * Enumeration of the various types of events headers
129 *
130 * Stored in a 5 bit bitfield (b31-b27) of the first word in the event.
131 */
132 enum HdrType {
133 HdrType_Event = 0, /*! Event with no timestamp or other optional parameters */
134 HdrType_EventWithTimestamp = 1, /*! Event with 64 bit Timestamp */
135 HdrType_EventWithSnapshotId = 2, /*! Snapshot event */
136 HdrType_EventWithSnapshotIdAndTimestamp = 3, /*! Snapshot event with 64 bit Timestamp */
137 HdrType_EventWith32bTimestamp = 4, /*! Event with 32 bit Timestamp */
138 HdrType_EventWithTimestampAndEndpointId = 5,
139 HdrType_EventWithSnapshotIdAnd32bTimestamp = 6, /*! Snapshot event with 32 bit Timestamp */
140 HdrType_EventWithSnapshotIdAndTimestampAndEndpointId = 7,
141 HdrType_EventWithPrevLenWithNoTimestamp = 8, /*! reverse param event with prev. length field */
142 HdrType_EventWithPrevLenWith32bTimestamp = 9, /*! reverse param event with prev. length field */
143 HdrType_EventWithPrevLenWith64bTimestamp = 10, /*! reverse param event with prev. length field */
144 HdrType_SnapshotEventWithPrevLen = 11, /*! Snapshot event with no timestamp*/
145 HdrType_SnapshotEventWithPrevLenAnd64bTimestamp = 12, /*! Snapshot event with 64 bit Timestamp */
146 HdrType_Reserved13 = 13, /*! reserved for future use */
147 HdrType_Reserved14 = 14, /*! reserved for future use */
148 HdrType_Reserved15 = 15, /*! reserved for future use */
149 HdrType_Reserved16 = 16, /*! reserved for future use */
150 HdrType_Reserved17 = 17, /*! reserved for future use */
151 HdrType_Reserved18 = 18, /*! reserved for future use */
152 HdrType_Reserved19 = 19, /*! reserved for future use */
153 HdrType_Reserved20 = 20, /*! reserved for future use */
154 HdrType_Reserved21 = 21, /*! reserved for future use */
155 HdrType_Reserved22 = 22, /*! reserved for future use */
156 HdrType_Reserved23 = 23, /*! reserved for future use */
157 HdrType_Reserved24 = 24, /*! reserved for future use */
158 HdrType_Reserved25 = 25, /*! reserved for future use */
159 HdrType_Reserved26 = 26, /*! reserved for future use */
160 HdrType_Reserved27 = 27, /*! reserved for future use */
161 HdrType_Reserved28 = 28, /*! reserved for future use */
162 HdrType_Reserved29 = 29, /*! reserved for future use */
163 HdrType_Reserved30 = 30, /*! reserved for future use */
164 HdrType_Reserved31 = 31 /*! reserved for future use */
165 };
166
167 /*!
168 * ======== getHdrType ========
169 * Gets the message header type from the first word of the event header
170 *
171 * @param(eventWord1) first word of the event header
172 *
173 * @a(returns) HdrType of the event
174 */
175 @Macro HdrType getHdrType(UInt32 eventWord1);
176
177 /*!
178 * ======== setHdrType ========
179 * Sets the header type in the event header
180 *
181 * @param(eventWord1) the first word of the event header to be updated
182 * @param(HdrType hdrType) the new header type
183 */
184 @Macro Void setHdrType(UInt32 eventWord1, EventHdr.HdrType hdrType);
185
186 /*!
187 * ======== getLength ========
188 * gets the event length (in bytes) from the event header
189 *
190 * @param(eventWord1) first word of the event header
191 */
192 @Macro SizeT getLength(UInt32 eventWord1);
193
194 /*!
195 * ======== setLength ========
196 * sets the event length (in bytes) in the event header
197 *
198 * @param(eventWord1) first word of the event header
199 * @param(eventLength) the new event length
200 */
201 @Macro Void setLength(UInt32 eventWord1, SizeT eventLength);
202
203 /*!
204 * ======== getSeqCount ========
205 * gets the sequence count from the message header
206 *
207 * @param(eventWord1) first word of the event header
208 */
209 @Macro UInt16 getSeqCount(UInt32 eventWord1);
210
211 /*!
212 * ======== setSeqCount ========
213 * sets the sequence count in the message header
214 *
215 * @param(eventWord1) first word of the event header
216 * @param(seqCount) the new message sequence count
217 */
218 @Macro Void setSeqCount(UInt32 eventWord1, UInt16 seqCount);
219
220 /*!
221 * ======== genEventHdrWord1 ========
222 * generates the first word to use in an Event header
223 *
224 * @param(numBytesInEvent) number of bytes in the event
225 * @param(seqCount) sequence count number to tag the event with
226 * @param(eventHdrType) type of event header for this event
227 */
228 @Macro UInt32 genEventHdrWord1(SizeT numBytesInEvent, UInt16 seqCount,
229 EventHdr.HdrType eventHdrType);
230
231 /*!
232 * ======== genEventWithPrevLenHdrWord1 ========
233 * generates the first word to use in a header that contains the length of the previous event
234 *
235 * @param(numBytesInEvent) number of bytes in the event
236 * @param(numBytesInPrevEvent) number of bytes in the previous event
237 * @param(seqCount) sequence count number to tag the event with
238 * @param(eventHdrType) type of event header for this event
239 */
240 @Macro UInt32 genEventWithPrevLenHdrWord1(SizeT numBytesInEvent, SizeT numBytesInPrevEvent, UInt16 seqCount,
241 EventHdr.HdrType eventHdrType);
242
243 /*!
244 * @_nodoc
245 * ======== getEventHdrWithPrevLenSeqCount ========
246 * Extract the sequence number from an EventHdr with previous length.
247 * For testing.
248 */
249 @Macro UInt32 getEventHdrWithPrevLenSeqCount(UInt32 word);
250
251 /*!
252 * @_nodoc
253 * ======== getPrevLength ========
254 * Extract the length of the previous event from an EventHdr with previous
255 * length. For testing.
256 */
257 @Macro UInt32 getPrevLength(UInt32 word);
258 }