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Files and Filehandles
Note: this article deals with files and filehandles; getting the
output of programs, and using directories and dirhandles will be covered in
a later article.
Email comments to
<EMAIL_ADDRESS>The Basics
While many people know what files and filehandles are in Perl, there are
many times that people forget exactly how they should be used. Too often
people open files incorrectly, causing data to be lost, or they expect a
file to exist, when it really doesn't, and their program lacks the proper
error reporting to alert them. Let's fix these problems up first.
The open() Function
The most common way of opening files in Perl is to use the open()
function. Here are the most common ways files are opened:
open FH, "filename"; # for reading
open FH, "filename"; # create, for writing
open FH, ">>filename"; # for appending
Let's be sure we have some terms straight. "Reading" means that you can
get the contents of the file. "Writing" means that you are placing content
in the file. "Appending" means that you are writing, starting at the
end of the file; if the file does not exist, Perl attempts to create it
for you. "Create" means the file is brought into existence if it does not
exist, and clobbered (the contents are erased) if it does.
If you have a filename stored in a variable, and you're opening the file for
reading, it isn't necessary to put the variable in quotes:
open FOO, $file or die "can't open $file: $!";
And, while it isn't common practice, you can include the symbols at the
beginning of the value in the variable:
$file = ">>/tmp/foo";
open FOO, $file or die "can't append to $file: $!";
The reason this isn't suggested is because the symbols help you know exactly
what you're doing, and if you have 100 lines of code between the line where
you set the variable, and the line that you use it in the open()
function, you might not remember how the file is being opened. In addition,
it's best to leave a variable holding a filename to hold JUST a filename, or
you'd have to make adjustments every time you need to read the filename out of
the variable. Therefore, it is good practice to keep the symbols and the
filename separate:
open FOO, $foo; # you can tell instantly
open FOO, ">$foo"; # how the filename in $foo
open FOO, ">>$foo"; # is being used here
"Open this file or die!"
Error handling is very important when using files. You can never be too sure;
did you open the file successfully? If not, why? Perl's die()
function, and the $! variable can answer your questions:
$file = "/tmp/resutls.txt"; # NOTE the misspelling
open RESULTS, $file;
$result = ;
# you aren't sure if the file opened correctly
# unless you're using the -w switch to Perl:
# it will whine about "reading on unopened filehandle"
open RESULTS, $file or
die "can't open $file: $!";
$result = ;
# now you know, because Perl will complain
# with "can't open /tmp/resutls.txt: file not found..."
This remark on Perl's behalf will let you know that something is wrong with
your request to open a file. The general rule is that you should always
check the return value of system calls; open() is a system call. The
$! variable holds the value of the latest system error, and comes in
handy when die()ing.
Closing a File
To close a file, you simply use the close() function on the filehandle
that you used to open the file.
close FH;
close FH or die "can't close file: $!";
Calling close() is a system call as well, and it can't hurt to ensure
that a file was closed properly.
Reading
The <FH> Operator
It is usually not smart to slurp the contents of a file into an array; for
large files, this can use a large amount of memory. It is much more sound to
iterate on the contents of the file, using a while loop:
open FH, "file" or die $!;
@contents = ;
close FH;
foreach $element (@contents) {
# line is held in $element
chomp $element; # remove ending newline
}
# ...much better if written as...
open FH, "file" or die $!;
while () { # until end of file...
# line is held in $_
chomp; # if you don't want the ending newline
}
close FH;
Perl does not automatically remove the ending newline from a line when you get
it; more specifically, it does not remove the ending $/ at the end of
a line -- look below to learn about this variable and its usefulness. Use the
chomp() function to safely get rid of this ending sequence; while you
had to use chop() in Perl 4, Perl 5 has added this safer function. A
common mistake when using a while loop is skipping lines in the file,
like so:
while () { # this stores the line in $_
$line = ; # this put the NEXT line in $line
}
If you only use the $line variable there, you'll end up missing every
other line. What was meant here was one of the following:
while (defined($line = )) { ... }
while () { $line = $_; }
while (!eof(FH)) { $line = ; }
The first example there shows how while (<FH>) actually works: it
is the same as while (defined($_ = <FH>)). This is only
true when this is the ONLY statement in the while loop's condition.
The reason defined() is required here is to ensure a line consisting
of a 0 and nothing else (a rare case, but hey...) is still considered
a line. The third example uses the eof() function; this function is
three-fold in nature, but we will only discuss the eof(FH) usage here
(the rest will be explained in a future column, and you can read it on your own
in the perlfunc documentation (see the Resources
section at the end of the article)).
The <FH> notation returns either a single line of the file, if
used in scalar context, or the remaining lines in the file, if used in list
context:
$first = ;
$second = ;
@rest = ;
($first,$second,@rest) = ;
That final line does the same as the first three; because there's a list on the
left hand side, <FH> is called in list context. <FH>
returns false (specifically, undef if called in scalar context, and an
empty list in list context) upon reaching the end of the file, and the next
call will start from the beginning of the file. Because @rest =
<FH> is in list context, @rest does not have a final element
of undef.
The "End of Line" Variable, $/
When you read from a file using <FH>, you get the content from
your current position to the end of the "line"... but what denotes the end of
a line? The $/ variable, which defaults to \n, is what Perl
uses to determine if it's reached the end of a line. If you change the value,
Perl changes its definition of a line. Here's an example:
{
local $/ = "\n%%\n"; # why use local?
chomp($line = );
}
The "end of line" string \n%%\n is a common one used for signature
file quotes, as well as for the fortunes for the popular fortune
program found on many Unix boxes. Why do we use local() here, instead
of my()? Short answer is, we have to, because $/ is a
special Perl variable. Enclosing the code in a pair of braces as shown is a
way of ensuring $/ gets its original value back. Also, we can use
chomp() to remove the value of $/ from the end of a string.
There are two special values $/ can be set to: undef, and
"". They are not the same value, mind you. Setting it to
undef means that is no "end of line" marker, so using the
<FH> operator will return the entire file as one long string.
This is not as inefficient as you may think it would be; it is a fast, and
effective way to get the entire contents of a file into a string. The other
value, "", turns on "paragraph mode", meaning a "line" will be any
series of characters ended by two or more newline characters. In this special
case, chomp() removes all newlines at the end of the string. Please
note, however, that $/ is a string, and not a regular
expression. Setting it to "\n+" will make a line a string of
characters ending in a newline followed by a plus sign.
Writing
print() and select()
The print() function is rather simple one to use; the syntax is (says
the perlfunc manpage):
print LIST
print FILEHANDLE LIST
print
print FILEHANDLE
FILEHANDLE can either be a filehandle (FOO), or a variable
containing a reference to a filehandle, or a string containing the name of a
filehandle (that will be discussed in the second article on files and
filehandles). LIST is a regular list. If the LIST is
omitted, print() uses $_. If the FILEHANDLE is
omitted, print() defaults to STDOUT, or the filehandle
currently select()ed.
The one argument version of the select(FH) function makes the given
filehandle the default one; Perl programs start out as though you had said
select(STDOUT). This function returns the filehandle that is
currently select()ed:
print "This goes to STDOUT\n";
$oldfh = select(NEWFH);
print "This goes to NEWFH\n";
select($oldfh);
print "This goes to STDOUT\n";
Note: this example shows the use of a scalar in place of a filehandle. This
"magic" is explained in the next article on this topic, which will describe
more advanced file and filehandle operations.
Here-docs
As a programmer who's looked over other peoples' code, I must say one of the
ugliest things I've seen is the overuse of print statements. I see
gunk like:
print "Click Here!\n";
print " \n";
print "Other Links\n";
# etc...
There are a couple things I find unfavorable: the need to backslash "
everywhere, the multiple statements when ONE will do, and sometimes, the
programmer doesn't put any \n's in at all, and the output is very
messy to the eye. Since we know that print() can take a list, we
could say:
print "Come, listen to a story\n",
"About a man named Jed.\n",
"etc.\n";
But if we want to include quotes in there, as well as variables, single quotes
around the lines won't help: the \n's won't be interpolated, and
neither will the variables. We could use the qq() operator, which
allows for a different symbol than " to be used to delimit quoted
text:
print qq!You can't use a \! in here without
putting a backslash in front of it\n!;
But just like regular quotes, you need to backslash the quote character. To
get around this, we could use paired delimiters, like {}:
print qq{You can nest { these things } safely\n};
And as the example shows, pairs can be nested; the number of left and
right units of the pair must match. To make a hanging } or {
you'd need to backslash it. The final workaround is one I highly suggest, the
here-doc. Borrowed from sh, they have a rather simple syntax:
print <
Note the semicolon after the label on the print() statement!
You can also use backticks around the label, but that is seldom done. A very
important rule is that if you do not use quotes around the label, it must
immediately follow the <<. Another one is that the
closing label must be reproduced exactly as shown in the print
statement, on its own line, and that there must then be a newline after the
closing label:
print FH << " two leading spaces";
la dee da
two leading spaces
that line above was NOT a valid close to
this here-doc
two leading spaces
If you get an error like "Can't find string terminator "END TEXT" anywhere
before EOF at filename line nnn." then be sure you typed the
label the same way in the beginning and the ending. If they are the same, and
your ending label is on the last line of your file, be sure there is actually
a newline after that last line.
You can have multiple here-docs in one statement:
print HTML << "end header", << "end body";
"I can use quotes!"
end header
This is now in the 'end body' section.
end body
As an aside, here-docs can be used when passing arguments to functions, etc.:
makeHTML(<< "end of body", $title);
blah blah blah
end of body
$text = << 'EOF';
this is a multi-line string
placed into $text. and since
pressing enter makes a real newline,
I can make newlines while using
single quotes!
EOF
Resources
To read more on opening files, read perlopentut, available at http://language.perl.com/newdocs/pod/perlopentut.html.
The documentation on the functions mentioned here is all available in the
perlfunc section of the docs, or by typing perlfunc -f NAME at
your command prompt. $/ is documented in perlvar. Here-docs
are discussed in perldata. All this documentation is also found online
at http://language.perl.com/.
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Custom sampling currently tweaked for best low/mid frequency resolution.
Sampling:
Height Offset
Sampling Normalization


Registration
Name :-
Father's Name :-
Mother's Name :-
Gender :-
Select Gender
Male
Female
Others
Community :-
Select
GEN
OBC
SC
ST
Candidate of Persons with Benchmark Disability :-
Select
YES
NO
Date Of Birth :-
Place Of Birth :-
Name of the State where You were born :-
Select State
Gujarat
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Delhi
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Nationality :-
Select Nationality
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Confirm Mobile Number :-
E-mail Address :-
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Desired Password :-
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Please Don't Enter Any Invalid Or Misinformation.


The Locator component can be used to find your whereabouts on the planet using either your device's GPS or information from your network.
loc = app.CreateLocator(
type,
options ) →
app object: Locator
The provider specifies where the locator should retreive its information from . “Network” means get the location from the cell network and wifi, “GPS” tells the locator to use the device's GPS
To Start the locator use the Start method. You can also stop it with Stop.
To get regulat updates about your current positino use the SetOnChange function.
Example - Get Location Updates
function OnStart()
{
loc = app.CreateLocator("GPS,Network");
loc.SetOnChange(loc_OnChange);
loc.Start();
app.ShowPopup("Locating");
}
function loc_OnChange(pos)
{
var msg = pos.latitude + ", " + pos.longitude;
app.ShowPopup( msg );
}
The following methods are available on the Locator object:
string: comma “,” separated: “GPS”, “Network”
string: comma “,” separated
number
Returns the approximate initial bearing in degrees East of true North when traveling along the shortest path between this location and the given location.
Returns the approximate distance in meters between this location and the given location.
Returns the control class name.
string
string: comma “,” separated: “boolean”, “char”, “byte”, “short”, “int”, “long”, “float”, “double”
Allows access to other functions defined on the object in Java via reflection.
Define a function called when some of the values changed.
number: seconds
Set Locator update frequency in seconds
Start the locator.
Stop the locator.


By default, all HTML elements have a static position,cannot be moved. To manipulate the position, remember to first set the CSS position property of the elements to relative, fixed, or absolute!


Click the column headers to sort the table by Year, Institute/PI, Category or, by Conference/Journal.
Currently: Unsorted [reset] |
Total Papers: |
| [Year] | [Research Title] | [Institute/PI] | [Category] | [Conference/Journal] |
|---|---|---|---|---|
| 2022 | Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs | EPFL, Mahsa Shoaran | neuro-tech | Artificial Intelligence Circuits and Systems (AICAS) IEEE |
| 2021 | The hardware lottery | Google Brain, Sara Hooker | comp-arch | Communications of the ACM |
| 2021 | Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration | Berkeley, Yakun Sophia Shao | comp-arch | Design Automation Conference (DAC) ACM/IEEE |


legend position: inner?
x-align?
left
right
y-align?
top
bottom
opacity?
Legend
A functional legend can be added to all 2D plot.


Technical blog from Craig Russell.
This post describes code confessions; openly confessing your coding sins to your team.
I want to tell you about something I do regularly which I call a Code Confession
. It’s how I unburden myself from the guilt of having written bad code, bring it to the attention of the team to improve it and seek absolution for my coding crimes.
Let’s say I’ve written some code but there is something I just don’t like about it. There is a smell. The code solves the core problem though, and I know that if I had to, I could live with it. But yet I know that there is still something bad about it and, despite my own best efforts, it’s the best I’ve come up with.
The smell could be anything; it could be as simple as a method I can’t quite name well, or it could be more of an architectural decision, an added dependency on a library I didn’t want to add, a hacky workaround etc…
We all write bad code sometimes; there is no shame in it. Sometimes it’s because we don’t know any better. Sometimes it’s because we have to take shortcuts to get something shipped. Sometimes it’s because we’re tired. Sometimes it’s because we’re trying to juggle too many tasks at the same time. Sometimes it’s because we’re just too close to the problem we’re trying to solve.
No matter the particular reason for the bad code, the thing which often helps is to borrow a second pair of eyes on the problem. And so I grab a teammate on a call…
“I have a confession to make…“ is my opening gambit. My teammates know what’s coming next; they’ve heard my confessions many times before.
And I get it all out in the open. You see, the worst thing you can do with this is to bury it. You can try to hide it in the codebase and hope no one notices in the code review. You can leave it festering in there and hope you’re never held accountable for it. But you are accountable. Everyone on the team is accountable for it. And this is why Code Confessions
work.
At a Code Confession, I’m telling the team “this code I’ve just written sucks”, and highlighting that it’s a problem for me. But I’m also warning them that this sucky solution, which only exists in my local branch currently, will become their problem soon. Once it is in the main codebase, it’s everyone’s responsibility to look after it.
The Code Confession
is the time for the team to either come up with a better solution or to admit defeat as a group, and humbly accept that it’s the best we can do.
Sometimes during a Code Confession
I’ll hear that actually the solution isn’t all that bad, and that can be nice to hear. But more often than not, we’ll work together to find a better solution. One that smells less and one that the team is going to be happier with long term. By highlighting code I don’t like but can’t improve on my own, and confessing it upfront to the team, we come together to improve it or we agree to live with it.
The team feels satisfied as they got to help solve a problem, I feel better that I’m not carrying this problem alone, and the codebase gets better because everyone feels shared responsibility.
Give Code Confessions
a try the next time you’re left a little unsatisfied with some code you’ve written, and see how good it feels. Involve your team and absolve yourself.
Forgive me teammates, for I have sinned…
Image Attribution: Photo by Shalone Cason on Unsplash Shalone Cason
Home


This application is designed to help the user determine their Akan name based
on their birthday and gender.
Akan names are derived from Ghanian culture. Frequently in Ghana,
children are given their first name as a 'day name' which corresponds to the day in the week they were born
So, The application will compute the exact day of your birthday and the give you your respective "day name"!


Last week in ManageIQ - Azure provider is moved to gem
Hi. I, ZitaNemeckova will tell you more about this weeks news in our Last Week in ManageIQ.
Featured
We’ve merged 110 pull requests this week that contained 310 commits and were done by 44 contributors.
Anyone can be a ManageIQ contributor. Just have a look at our open issues and solve one or report an issue if ManageIQ is not working as it should be. Any help is appreciated.
Improved
It is hard to work on internationalization (it’s also hard to pronounce). You can see here how much work mzazrivec did last week. 11 PRs to improve i18n. You should always make sure that you don’t leave any string that is displayed in the UI untranslated.
Tests got some nice enhancements this week. NickLaMuro made them go faster with his PR that speeds up travis builds by caching bower assets. And himdel’s PR makes tests fail early when bower install fails. It will no longer bug us.
From now on use ::Settings
instead of get_vmdb_config
in app/views/*
as yrudman shows in his PR.
Fixed
There were too many bugs (exactly 80) fixed this week to choose from. So if you want to see which bugs we got rid of, have a look here.
There were two gem fixes that may interest you, kbrock fixed fix_auth
after the pending gem was extracted and chrisarcand removed gems/pending
paths from code climate config in his PR.
New
OpenStack devel petrblaho added connection options to OpenStack Cloud VM in this PR.
14 daily log rotations used to be saved. But now you can specify the number of rotated logs to save when an external disk volume is configured for vmdb/log thanks to jvlcek’s PR. Isn’t it neat?
The news of this week is here. The Azure provider was moved to a gem by durandom in this PR. Another big step towards pluggable providers was done. More to come.
Deleted
It is always nice to see someone delete duplicate code like in this PR Refactor changes - Removed duplicate code in discover method by Jaganathancse.
Wrapping up
That’s all from me. Stay tuned for the next Last Week in ManageIQ to have the latest news from our development.


A window into my work
A part of my life
Projects
Here I have gathered some of the projects I have worked on, most of them independently and some of them as part of a team.
A part of my life
Here I have gathered some of the projects I have worked on, most of them independently and some of them as part of a team.
Let's be social
There are 10 kinds of people, those who understand binary and those who don’t.


AngularCache
Inspect line 138 in "demo.js" loaded by this page to see how the caches were instantiated.
| method |
{{info.id}}
{{info.id}}
|
|---|---|
| info().id | "{{info.id}}" |
| info().capacity | {{info.capacity}} |
| info().size | {{info.size}} |
| info().maxAge | {{info.maxAge | milliseconds}} |
| info().aggressiveDelete | {{info.aggressiveDelete}} |
| info().storageMode | "default" "{{info.storageMode}}" |
| info().cacheFlushInterval | {{info.cacheFlushInterval | milliseconds}} |
| info().onExpire Check out your browser's console to see these in action. |
|
| get("") | {{selectedValues[index]}} |
| keySet() |
|
| keys() |
|
$angularCacheFactory
| method | output |
|---|---|
| info() |
|
| keySet() |
|


Used Car Price Prediction
BRAND
Ambassador
Audi
Bentley
BMW
Chevrolet
Datsun
Fiat
Force
Ford
Honda
Hyundai
Isuzu
Jaguar
Jeep
Lamborghini
Land Rover
Mahindra
Maruti Suzuki
Mercedes-Benz
Mini
Mitsubishi
Nissan
Porsche
Renault
Skoda
Tata
Toyota
Volkswagen
Volvo
CITY
Delhi
Kolkata
Mumbai
Chennai
Pune
Coimbatore
Hyderabad
Jaipur
Kochi
Bangalore
Ahmedabad
PURCHASE YEAR
KILOMETRES DRIVEN
FUEL TYPE
CNG
Diesel
LPG
Petrol
TRANSMISSION
Automatic
Manual
MILEAGE
ENGINE
POWER
SEATS
2
4
5
6
7
8
9
10
OWNER TYPE
First
Second
Third
Fourth or Above
{{ prediction_text }}


Gateway
BOT
11-Jun-19 05:34 PM
[dscrd-hush] <halo kitty rules> I've taken advantage of recent Agama breach for study of BTCH realistic supply and major ownership. Due to security breach huge movement of BTCH happened. On BTCH explorer I've took a look at every single june block so far.
In my timezone first june block was 182097,while last block analyized was 182554. Also I reviewed a destiny of BTCH balances, which directly originated from the richest HUSH balances. I'll post results in Word and pdf shape and also in plain text. But please note that Discord can slightly rearenge form and drop off some of characters.
At fork time there were 38 addresses of 10000 or more HUSH (BTCH). They were looked under microscope. Owners of the biggest balances probably should take the most care of their funds. They should be a good indicator of happening at the rest of the ownership. It includes feb. 1 2018 balances with t-HUSH addresses, original BTCH addresses and status.
Some findings are interesting, some pointless. The research has been primarly made for my own understanding of overall picture, but I can share the results. My intention was not to bash anyone or anything.
(edited)
[dscrd-hush] <halo kitty rules> As I'm missing data about transfer of DEX and SUPERNET balances to BTCH chain, some conclusions are part of speculation. At some points I might be just interpreting things wrong.
From block 182097 up to block 182160 value transfer was basically 0. Block 182161 happened 1.5 hour prior to ca333's announcement of Agama security breach (KMD official channel on Discord). In that block team moved 265k BTCH of SUPERNET holdings and movement has begun.
From up of block 182161 until now, 1.5 million of BTCH were moved. Since blocks 182404 and 182405 were mined, there was only 1 transaction of 10000+ BTCH. That is 1 transaction in last 5 days. Right now movement is normal again, namely it is going back to basically 0. It is good time to draw the line.
[dscrd-hush] <halo kitty rules> Including block 182161, there were 5 moves which included balances of 100000 or more coins. 4 of them are sure in control of KMD team, while for 5th transfer I can say it is just likely under KMD control.
As said, block 182161 contains SUPERNET holdings, namely 265000 of coins.
Block 182175 represent old MSHARK funds, it carried 100000 of coins. Block was recorded 2 hours after ca333's announcement.
[dscrd-hush] <halo kitty rules> Block 182184 had 153000 BTCH inside. These coins originated from pre-airdrop (18th january 2018). We are talking about original address. It can only be premine, now called dev funds. Block was recorded 2.5 hours after ca333's announcement.
Block 182190 happened 3.5 hours after announcement. It included 361000 BTCH, which originated from SUPERNET and DEX. Add that with block time and my conclusion is: these funds are also in control of KMD team.
So far there are already 879000 of BTCH within KMD command.
At blocks 182251 and 182252 additional 28000 BTCH were transferred to 361000 bulk. KMD sum is already at 900000+ BTCH.
[dscrd-hush] <halo kitty rules> Block 182383 was recorded much later (06.06.2019). There were 192000 BTCH within. According to atomicexplorer, this BTCH is coinsiquence of 180000 DEX and 12000 SUPERNET. In chaos funds were likely forgotten and claimed day later. It is very likely these funds are property of KMD people.
So that leaves me with 0.9 M or 1.1M of BTCH whale addresses within KMD control. The second of the two is more likely. Of course, some other (smaller) balances are also of KMD people, but I haven't dug deeper.
[dscrd-hush] <halo kitty rules> Interesingly, balances of 10000 or more BTCH and without proof or clear indication of KMD ownership were very scarce. There were only 13 of them so far:
block 182185 had 23300 coins (which happened just a single block after transfer of 153000 coins)
182195 had 10600 of them,
182245 had 20000,
182261 had 13200,
182299 had 22500,
182331 had 11500,
182340 had 15400,
182365 had 12700,
182391 had 28500,
182399 had 15800,
182404 had 19900,
182405 had 15000 and
182535 had 21300.
[dscrd-hush] <halo kitty rules> All 13 balances combined for 229700 BTCH. That means that balances of less than 10000 BTCH leave us with only roughly 150000 BTCH.
Conclusion 1:
I've underestimated the amount of BTCH under KMD ownership and the amount of claimed BTCH. KMD controls more than 1M of coins, while overall I can describe 1.5M of coins as active. However, there are probably another 0.5M of coins which were claimed earlier, especially in early 2018. So with term circulating supply I would describe 1.5M or 2M BTCH. Coins are heavily loaded on only a few addresses, addresses with middle-to-low amounts represent the minority of overall balance.
[dscrd-hush] <halo kitty rules> Part2: 10000.00000000 t1JV2RD4yhJjJCBJbHEmG8DYnwgLrKwm1n8 R9tcVPY8KokGfYctoywG5vn5THcND6WCf6 active in 2018(#)
10000.8639524 t1QTk23ywPuvpKHFY8HsFpdTqeFVvUFwX8t RFsL6ET62QwnneZqf33FnLh89rmSMNN3Jv active in 2018(##)
10012.15571232 t1WVckkHrKasj23WFp9NTuid4HXegfGdLim RMuCpvkzx5thVQpZLtYTsRrLo8vCZpi5cW never claimed
10052.9775897 t1TF334MdzNDswg9X5mrPcpMzSdjtCKJooA RJed7EpncsErR3TpcX2PaXbGxF1Q3PH6nK never claimed
10285.888 t1bGBNu1iwwnzGaDy6h43dsLkmFYBedrE7L RSfmT5UsaSoxjwYGdSE3baa3GrohUwSJRH never claimed
(edited)
[dscrd-hush] <halo kitty rules> 10701.54208747 t1X7HmcrDsxwfatwBAFczLQz5kKnYT5GGw8 RNWsqoKNWTxe4GFUgznzJ8DNFw44JMzrx9 never claimed
10839.95649812 t1TTRAJ5sDYyRKMcaSGQ9mCTa7y2NDRhMTb RJs1EUZ1r3zPnivsy1a9iugrdaHt9pcaSS never claimed
11537.6616096 t1LW71CYLtfQwDuaJNUasT6FyNVRubeBJCH RBuh5P1VXARuhGtbuDksQoVFt6hRSA69gw active in 2018(##)
11699.24759346 t1e7zfvqksDYT93KR6Bsyx48jWAU6NVwq4m RVXak7JuViZRcQdicw3yumN21mjcFege64 never claimed
11722.2914045 t1gvWMvfumW1hBXzw1joC72xcYdjFBWS3sU RYL6S794Q12feuKEYUyC4kBu4Ezm9pAA3q active in 2018(###)
[dscrd-hush] <halo kitty rules> 11849.80827171 t1g6Y5PPiJPEke1G69CvcDnKju3gpeitBxF RXW89ZrrvtFj7NaPfx6cBVZ2QexLZNybdf never claimed
12480.67637599 t1h4HEah8CopvNPUdXBkuHJ9poTKz2XTKWZ RYTsJmAGqJqtqknw3vvuF1P7K4bVvxSH6z never claimed
12746.87102245 t1QtzMgj3VYjRmZWsAXq6HH8fSXEeouodp7 RGJaRsCC83kQEvqAhH16EzMwx8WAhTeTLY active in june 2019
12850.9794 t1c98qEKiSqdPo2AMxps8eg2CNSd9HnngZs RTYiuQns5LeNGPUfVa38cPFUt3tfAWD2Q6 active in 2018(#)
13135.41774078 t1cWAu4NfDP8EdwCYEHo1UWd1rhzdbyagmJ RTukyEqoqt9D7JWqm2y1SDrJNKG9UuDkNa never claimed
[dscrd-hush] <halo kitty rules> 13160.7744 t1YR7DTnABpggHCNpdRzuYiUu197hStk5SD RPphHeFJpKhekZh8ABAuWRiBWkPDEixSnG active in 2018(#)
15385.87673672 t1ao6zFVRDYW6cetJd5XNtGSzUVTSevf8Wp RSCh4RxZr3X562Cc9phNqygGz6ixbWZraW active in june 2019
15736.91 t1dYusvgP7WmxX8fwFvxmYUMxtUVL1c3ruU RUxVx79Xk1nvzVzEng8mWBbFQ5kqrMBwo6 never claimed
15999.24620381 t1gGnQY4PTyx1FiENpdehgavvimWTL6Au9Y RXgNUiXY6Uxyj5YgMNpheJADENmyFdGqoD never claimed
16244.57823327 t1TPEa7pxKx42bpAhV7SaaJn5eRfEMot6Na RJnpeJJ6xT515BV11rcaY21NA2vkGrWMzd active in 2018(####)
16742.9898 t1QFvzbRQBq5Fft3iNEss7QdmB6r4VxpMX9 RFfX4mtYpL6E9FN1tz3s57s3gi7aPoets5 active in 2018(#)
[dscrd-hush] <halo kitty rules> 17009.98 t1YGUngtc5XVLFCCfmqEAAcR2fXuJaDvx5v RPg4rsMki2WJiZWyJaQA8KeKB9ApQDF7yp never claimed
20000.55004048 t1VJeeYBr3GV1LXRWgrgzPtp6EFZX8MpCTT RLiEiiezfmVyotjpDbrzMc3Njrq2x5HZoW never claimed
21000.06763181 t1Kgt1zEricRr47tjmLJceEm7A922ZgmKdy RB6U6Ai1M7SpXVD3J5UcbwzPfkHYP6JKMs active in june 2019
21178.1587534 t1K8JmZiqpxHK5GsSjAAn725Bk9EjQ3zmvH RAXtqkBzTTJHYeBkFuLn4jJUFkWFEG1Xhu never claimed
[dscrd-hush] <halo kitty rules> 22604.0704622 t1S5S6gbiBT88iXujoZy6xeR62XEMVzQpK5 RHV2As4rox97BuE3LK96vMeNY8VsGRTmBj active in june(#####)
24600 t1aRSSDDedA9qHdDd19Tm7ucdgKZ3oBEPb1 RRq2WPgoFfAokzXvXtdm5cqvBvpZhmNmMe active in 2018
30348.89117747 t1e6HPF65FWxxpdgmnRr2o7iBkdL4C8zjYy RVVsTRZDt1ywJ115KB22kpwUGEba21aJfj active in june 2019
34122.80443966 t1Z6HWkR6fvegWkXGskVtf34RAkEGw44oZv RQVsavtFJRfez7qaQVftckHhgMVnpGmiYd active in may 2019
38715.98 t1M4BhLvMCBWDnAmcB5sz1UqZB4QCCguteo RCTmmXPVpgXCFY5uhq3yyC4qgffi67uyXf never claimed
[dscrd-hush] <halo kitty rules> 42803.65788361 t1fRRFpcFynKSekizQwJkLEGjF2Y1d3cKa2 RWq1L15QcHLR883HwgUkHwW1kdoXWGsnGb never claimed
43520.55608576 t1J9DGXbPb2MCRtUdVxBz4ERY7vjQQLXnD7 R9YoLi4YDXNAuFnw2hMz1wepdXzvHrX7GK never claimed
46940.08959832 t1Q7g5PuobMtRTLwp5pXEvowGdhJbpx9GA7 RFXG9aNxDruPviG7cZhEtXAZ9Ja7e6Wj4x never claimed
66444.76248529 t1X8TYzSXrnFNEXB667xyxbBeZdZu3FYHhG RNY3dAugVHGLhtVPcs8yvJQw5EqQw2VvXQ never claimed
79011.15554515 t1QAwax9sidmTLoe1oDHvRAgJ3nLTJzi9No RFaXf8d2M8nRpAxKKxTvNsuaZPbyA9kYCQ never claimed
[dscrd-hush] <halo kitty rules> 81288.9399 t1g6d3YYhHU2DB5PQvqQX8m6ymmHj7DLmaL RXWD7j1quy3BeShiTaaX6ULGHNZF2KoKCs active in 2018
117885.41 t1bK5SrcY1qDLgCxP4Yrb7oQs1B99ryqr1U RSifX35geLEK9aGgbJ2b5We9WnQfnBoqb9 never claimed
149997.15000001 t1MRFp9u6jUbGi5ZcHfiC45SAseKxrdSPa2 RCpqtLNFMycFBSsupQtC1nfTPFbUihJ2ir never claimed
745550 t1bEBr1LdBQtHun7B5L82R65FgpWyyWFx8L RSdmvBomouuGP9RUc5J2NoJYCRnVqT3j5V never claimed(######)
[dscrd-hush] <halo kitty rules> (#)… addresses shared by same entity. Transfered to same addresses, alltogether combined for 56000 and 31500 BTCH (sum 87500 BTCH).
(##)… addresses shared by same entity. Transfered to same address. With other minor balances combined for 50000 BTCH. Active in august 2018. No. 1 candidate for massive Coinexchange selloff, which happened the same day as transaction occured (aug. 10 2018). It was biggest daily selloff/volume in BTCH history, both fiat or BTCH measured.
(###)… send to Coinexchange and sold the same day. Now »owned« by Coinexchange or their users.
[dscrd-hush] <halo kitty rules> (####)… when BTCH market opened on Coinexchange, claimed funds ASAP. Very likely sent to Coinexchange and sold everything.
(#####)… linked with KMD. Funds ended on R9JCEd6xnCxNUSpLrHEWvzPSh7CNXm7z75 . That account is BTCH whale, controlled by KMD.
(######)… Cryptopia's balance.
Interesingly:
- almost none of the biggest HUSH whales has claimed his coins
- only a very few of HUSH whales have sold their coins
- not many of these balances could be described as active
[dscrd-hush] <halo kitty rules> Conclusion:
I've expected much higher % of these funds would be claimed. I can argue that the majority of BTCH out there is mainly of DEX and SUPERNET origin. I can barely name BTCH as HUSH's fork or airdrop.
That could explain why HUSH community doesn't care about BTCH. It has never cared and I can't blame them for that. HUSH has been seen as a coin for private messanging, while BTCH is without a vision. Maybe HUSH and BTCH development/community should split apart.


The questions no one cares about, but everyone's wondering
The question was:
Raw data available here.
This question occurred to me one morning while making my bed. I was already late and had to run out, but I just could not bring myself to leave my bed in such a chaotic state. I imagined myself coming home to sheets strewn about and finding death & decay abound. As I reflected about how my sense of self worth was intimately tied to the tidyness of my sheets, I began to wonder if I was alone in this experience.
I realized that I did not know a single person who makes their sheets every morning. All the people that I knew intimately enough to know this fact about them just happened to not make their bed. With everyone else, this wasn't really a question that ever came up. Was I just a rare enigma in this world (certainly one of my aspirations) or were there others who felt strongly about this too?
In fact, almost 50% of Ole's make their beds every morning!
This was very surprising to me, and statistically should be surprising to you too. Almost no one expected that other people regularly make their bed.
I almost always try to make a prediction about the results before I send out the survey. I started doing this after finding that some of the results looked obvious and that the survey didn't seem very insightful. But of course it will always seem obvious in retrospect. I try to come up with a hypothesis and think through whether the data will confirm or deny it. If I find that my belief in the hypothesis doesn't change regardless of the outcome, then something is broken with the design of the survey.
What I expected to find was that people who make their bed would predict that others probably do so too, and that those who don't would predict that they don't. This was what I called the Common Person hypothesis, that people believe that their actions are normal and that others are most likely to behave the way they do. Another hypothesis was that everyone who made their bed would predict others won't and vice versa. I called this the Extraordinary Masses - where everyone believes they are special.
It seems that neither is true. You can see in the table below that very few people believe others make their bed, regardless of whether or not they make their own bed.
| My Bed is Made | My Bed is Messy | |
|---|---|---|
| I Predict Made | 16 | 13 |
| I Predict Messy | 60 | 79 |
Which to me means that most people who don't make their bed feel like it's normal, while those who do make their bed are silent about it.
There's another reason I wanted to ask people to predict others' answers. This one comes out of the popular game EyeWire currently being developed at Princeton. It's a game where hundreds of thousands of players are entrusted with reconstructing brain data from raw scans, with the goal of doing for the brain what the human genome project did for DNA.
In a setting like that, you want to ensure that you're getting correct answers from a large group of people even if there aren't a lot of experts. In a paper called Finding Truth Even if the Crowd is Wrong, they talk about not just asking people what they think the answer is, but also what they think other people think.
Here's how it works. Suppose the question is
Is Philadelphia the capital of Pennsylvania?
If there aren't a lot of experts in the crowd, most would answer "Yes". Philadelphia sounds like an important city and most people know it's in Pennsylvania. These people would also likely predict that others would say "Yes".
Those who actually know that the capital is Harrisburg would answer "No". These experts would know that this is a common misconception and would thus predict that most answers would be "Yes". It is this discrepency between answer and prediction that gives us a way to extract the truth from the crowd.
This is a very different situation from the one in this survey, but I was absolutely fascinated by this idea that asking people to predict what others think the answer will be gives us this powerful extra insight, and it might be something to revisit in future surveys.
What I thought was most absolutely bizarre were the reasons why people weren't making their bed. I labelled each reason submitted with one of the following 4 categories. You can see the full data with the labels I applied here.
Moral obligation were those who, like me, felt that making the bed brought them some peace of mind. Hurry or lazy were mostly people who would do it if they had extra time or motivation. Lofted were people who found it too physically hard to make a lofted bed (a perfectly valid excuse).
Pay attention to the orange segment. That's ephemeral pleasure - this argument that "Why make the bed if it's just going to get messy again?"
I absolutely cannot understand this line of thinking.
Most things in life don't last, but they're still worth doing. We live in an eternal struggle against entropy. The world will always tend towards disorder. Our lives are short and our joy is transient. We do things not because they will last, but because there's value in creating a world we all want to live in.
I think it's worthwhile to bake a beautiful cake even if it will be gone over a meal. I think it's worthwile to get to know people intimately even if they'll be gone out of your life in less than a semester. I think it's worthwhile to build elaborate surveys even if it will all be buried one day.
I think it's worthwhile to make your bed even if it's going to be messed up again (if that makes you happy). I want you to know you're not alone.
– Omar


We create an open ecosystem to encourage the transparency and adoption growth: open REST APIs, whitelabel solutions, services for partners. The full solution set is open sourced under MIT license, which encourage the transparency and ecosystem growth.
Lykke coins are colored coins registered on the Bitcoin blockchain, representing shares of Lykke Corp. Each registered share of Lykke Corp corresponds to 100 Lykke coins.
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SolarCoin represents 1 MWh of solar electricity generation. As a verified solar electricity producer, you may get SolarCoins for free.
World’s premier platform for binary options trading. Each token provides you with two exclusive token holder rights - Receive dividend payments or Convert your tokens into shares.
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We welcome our partners to integrate applications with Lykke Exchange. We provide Lykke Wallet API used by our own Lykke Wallet application along with related services for supporting the partners’ users, such as email/ sms notifications as part of the functionality of Lykke Wallet API, finance operations and KYC/AML checks for the users.
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Risk warning: Trading leveraged products can result in losses that exceed your deposits. Ensure you understand the risks. Read full Risk Disclosure.


devito.finite_differences package¶
Submodules¶
devito.finite_differences.differentiable module¶
-
class
devito.finite_differences.differentiable.
Differentiable
[source]¶ Bases:
sympy.core.expr.Expr
This class represents Devito differentiable objects such as functions, sum of functions, product of function, or any FD approximation. Differentiable objects provide FD shortcuts to easily compute FD approximations.
-
default_assumptions
= {}¶
-
indices
¶
-
laplace
¶ Generates a symbolic expression for the Laplacian, the second derivative wrt. all spatial dimensions.
-
laplace2
(weight=1)[source]¶ Generates a symbolic expression for the double Laplacian wrt. all spatial dimensions.
-
space_order
¶
-
time_order
¶
-


Hello There!
I'm Joanna Santhosh.
An enthusiastic coder.
I'm a first year student that has a strong passion for creating exceptional
yet simple websites and application.
I'm a first year student that has a strong passion for creating exceptional
yet simple websites and application.
Hi! I'm an undergraduate student at the University of Waterloo currently pursuing an Electrical Engineering degree. I developed a small passion for programming after joining a mini web development bootcamp and decided to delve deep into it.
I'm passionate about bringing to life creativity through design. Apart from coding, my hobbies include arts, music (playing the piano or singing), and poetry.
Here are a few technologies I'm familiar with:


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Beef Wellington
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Ken's Cheese House 34 Chatley Avenue Seattle, WA 98765 October 23, 2017 Fred Flinston Sales Manager Cheese Specialists Inc. 456 Rubble Road Rockville, IL 78777


Queen launch new app which allows fans to sing just like Freddie Mercury
"Don't stop me now"
Queen have launched a new app which allows fans to sing like Freddie Mercury.
- Read more: Queen: 20 things you probably never knew about ‘Bohemian Rhapsody’
The app entitled FreddieMeter, shows users how closely their voice matches the late singer’s legendary range by analysing pitch, timbre, and melody to assign them a score of 0 to 100.
Fans can pick one of four songs on the app – ‘Bohemian Rhapsody’, ‘Don’t Stop Me Now’, ‘Somebody to Love’ or ‘We Are the Champions’ – and challenge friends.
The device created by Google Creative Lab and Google Research has been trained on Mercury’s isolated vocals as well as samples of people trying to sing like him.
It has been launched in support of Mercury Phoenix Trust, a charity founded by Brian May, Roger Taylor and Jim Beach to raise awareness and funds for the fight against HIV/AIDS, and in honour of the 44th anniversary of the band’s first-ever live performance of ‘Bohemian Rhapsody’ at the Empire Theatre in Liverpool in November 1975.
You can view footage below and download it here.
Meanwhile, May recently ruled out plans for the rock giants – who are touring with Adam Lambert – to play Glastonbury next year after clashing with Michael Eavis over the badger cull.
“We won’t [play Glastonbury] and there are a lot of reasons for that,” he said.
He continued: “One of them is that Michael Eavis has frequently insulted me, and I don’t particularly enjoy that. What bothers me more is that he’s in favour of the badger cull, which I regard as a tragedy and an unnecessary crime against wildlife.
“There’s a little bit of a schism there, I wouldn’t do Glastonbury. Unless things changed radically.”


discoverEUse discoverE for searching the library catalog, databases, and other digital collections.
EUCLIDEUCLID includes a catalog of more than 2 million items, Reserves Direct, Announcements and My Account that lets users view and renew items on loan, bills, and requests.
DatabasesResearch assistance, subject guides, and useful resources compiled by your friendly librarians. Know what we know - find it in LibGuides!
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eJournalsThe eJournals @ Emory University Libraries database includes online, full text journals to which Emory's libraries have paid subscriptions.


How to configure the methods by which the News Feeds (RSS) module retrieves information from news sources. The two options provided are using Ajax, and/or Background Loading. Ajax causes the module to render immediately without any content. The module then uses Ajax to load its contents directly once the complete page has loaded. This is useful if you find the module hampers page loading. Background loading uses the DNN Schedule to manage the frequency with which new data is retrieved. The Schedule runs independently of the News Feed (RSS) modules, enabling the data to be retrieved at the optimum time, rather than when the module is being viewed, which can cause delays.
Tip: By default, Ajax is disabled and Background Download is enabled. These are the recommended settings.
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[gecode-users] Cumulative constraint propagation
Mikael Zayenz Lagerkvist
zayenz at gmail.com
Wed Oct 4 16:26:45 CEST 2006
Hi,
On 10/4/06, Jérémie Vautard <jeremie.vautard at laposte.net> wrote:
> I saw that the cumulative constraint in Gecode only supports value
> consistency. But does it means that this constraint performs no
> propagation at all and simply checks a total assignment or is there
> still some propagation when only a few variables get instanciated ?
The simple answer is that the propagation done by the cumulatives
propagator is stronger than naive value consitency, but it is also
much weaker than what could be done. But it does reason on bounds.
The detailed answer is that the cumulatives propagator implements the
sweep-algorithm described by Beldiceanu and Carlsson in the paper that
introduced the constraint. It does not, however, implement the task
intervals propagation nor the constructive disjunction on
machine-assignments that was described in the paper.
So, in this particular case, value/bounds consitency is not really the
best classification-scheme.
Cheers,
Mikael
--
Mikael Zayenz Lagerkvist, http://www.ict.kth.se/~zayenz/
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strided
`Prev `__
S
`Next `__
--------------
**strided**: /stri:´d@d/, adj.
[scientific computing] Said of a sequence of memory reads and writes to
addresses, each of which is separated from the last by a constant
interval called the stride length. These can be a worst-case access
pattern for the standard memory-caching schemes when the stride length
is a multiple of the cache line size. Strided references are often
generated by loops through an array, and (if your data is large enough
that access-time is significant) it can be worthwhile to tune for better
locality by inverting double loops or by partially unrolling the outer
loop of a loop nest. This usage is borderline techspeak; the related
term memory stride is definitely techspeak.
--------------
+------------------------+----------------------------+---------------------------+
| `Prev `__ | `Up <../S.html>`__ | `Next `__ |
+------------------------+----------------------------+---------------------------+
| STR | `Home <../index.html>`__ | stroke |
+------------------------+----------------------------+---------------------------+


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yeah sorry, I know it's not your fault, I'm just a bit frustrated when people are not responsible for what they wrote
In Komodo, which is a system very similar to Delegated Proof of Stake, the Notary nodes automatically put their OP_RETURN proofs into the Bitcoin blockchain just by the virtue of existing. These nodes get their rewards by securing the DPoS system
And yet he claimed he understand how dPoW works ...


This application is governed by the MIT license.
You can use, modify and/or redistribute these codes under the terms of the MIT license.
This application was developped by Maxime Godin and Amina Bouchafaa, graduate students at Ecole Polytechnique under the supervision of François Roueff, professor at Télecom ParisTech.
A report which summarizes the methods implemented in this application can be found, together with the source code, on the Github repository tvar_forecasting.
Maxime Godin, Amina Bouchafaa
Ecole Polytechnique
March 29th, 2018


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The cost of a colonoscopy in the United States varies widely, from place to place, and even within a city. The map and graph shows the gap between the lowest and highest amount paid for a colonoscopy in metropolitan areas, based on an analysis by Healthcare Blue Book.
Source: NYT and JournalismCourses.org


This is a paragraph of text. In the text is a span element and also a link.
This is the second paragraph. It contains an emphasized element.


sea ice MJO circulation anomaly Antarctic extent Fig. convection phases response wave phase (Fig. anomalies {border-style Rossby px;} time #ffffff;} \documentclass[pt]{minimal} \setlength{\oddsidemargin}{-pt} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amsmath} \usepackage{amssymb} \usepackage{mathrsfs} \usepackage{upgreek} \usepackage{wasysym} meridional temperature total forcing waves Indian Southern atmospheric intraseasonal propagation variability western wind diabatic Atmos. Clim. Sci. melting region Hemisphere cyclonic impact oscillation tropical (). North Ocean Res. austral concentration eastward high index pattern variation (see Figure Geophys. days. enhanced filtered lines mechanism polar px; ray shows standard teleconnection variables winter zonal {font-family Arctic Maritime Oscillation Sea air anticyclonic composite data day days effect font-size geopotential heating high-latitude model regions scale study surface -hPa Lee Lett. National Seas Seo analysis anomalous black calculated change deviations double; equator height latitudes linear location near solid stationary (i.e., ./GL Amundsen-Bellingshausen Atmospheric Madden–Julian Pacific Supplementary Weather areas averaged background border-color border-width changes evolution exhibits forcing. indicate induces influence investigate lag northerly obtained physical propagate represent shown significant similar solid; studies suppressed tracing tropics used using wavenumber {font-style {font-weight (blue) (contour (e.g., ./JCLI-D--. ./s--- Africa Annular Antarctica Black Commons Creative Dyn. Korea MJO, MJO-induced Madden‐Julian Mode Mon. Nature Northern OLR Research Rev. S). Weddell Yoo \,\phi \lambda a). advection anomalies, arises arrows associated b). barotropic based basic bold courier; decrease denote difference dynamical em; experiment extent. flow formation horizontal ice. lead line local mean modes performed phases. plot prediction produces pt;} reach region. respect responsible sea-ice seen starting thermal train winter,


Chapter 5 Introduction to Inference
In a Bayesian analysis, the posterior distribution contains all relevant information about parameters after observing sample data. We often use certain summary characteristics of the posterior distribution to make inferences about parameters.
Example 5.1 Continuing the kissing study in Example 4.2 where \(\theta\) can only take values 0.1, 0.3, 0.5, 0.7, 0.9. Consider a prior distribution which places probability 1/9, 2/9, 3/9, 2/9, 1/9 on the values 0.1, 0.3, 0.5, 0.7, 0.9, respectively.
Find the mode of the prior distribution of \(\theta\), a.k.a., the “prior mode”.
Find the median of the prior distribution of \(\theta\), a.k.a., the “prior median”.
Find the expected value of the prior distribution of \(\theta\), a.k.a., the “prior mean”.
Find the variance of the prior distribution \(\theta\), a.k.a, the “prior variance”.
Find the standard deviation of the prior distribution of \(\theta\), a.k.a, the “prior standard deviation”.
Now suppose that \(y=8\) couples in a sample of size \(n=12\) lean right. Recall the Bayes table.
theta prior likelihood product posterior 0.1 0.1111 0.0000 0.0000 0.0000 0.3 0.2222 0.0078 0.0017 0.0181 0.5 0.3333 0.1208 0.0403 0.4207 0.7 0.2222 0.2311 0.0514 0.5365 0.9 0.1111 0.0213 0.0024 0.0247 Find the mode of the posterior distribution of \(\theta\), a.k.a., the “posterior mode”.
Find the median of the posterior distribution of \(\theta\), a.k.a., the “posterior median”.
Find the expected value of the posterior distribution of \(\theta\), a.k.a., the “posterior mean”.
Find the variance of the posterior distribution \(\theta\), a.k.a, the “posterior variance”.
Find the standard deviation of the posterior distribution of \(\theta\), a.k.a, the “posterior standard deviation”.
How have the posterior values changed from the respective prior values?
Show/hide solution
The prior mode is 0.5, the value of \(\theta\) with the greatest prior probability.
The prior median is 0.5. (Add up the prior probabilities until they go from below 0.5 to above 0.5. This happens when you add in the prior probability for \(\theta=0.5\).)
The prior mean is 0.5. Remember that an expected value is a probability-weighted average value \[ 0.1(1/9) + 0.3(2/9) + 0.5(3/9) + 0.7(2/9) + 0.9(1/9) = 0.5. \]
The prior variance is 0.0533. Remember that variance is the probability-weighted average squared deviation from the mean \[ (0.1-0.5)^2(1/9) + (0.3 - 0.5)^2(2/9) + (0.5-0.5)^2(3/9) + (0.7-0.5)^2(2/9) + (0.9-0.5)^2(1/9) = 0.0533 \]
The prior standard deviation is 0.231. Remember that standard deviation is the square root of the variance: \(\sqrt{0.0533} = 0.231\).
The posterior mode is 0.7, the value of \(\theta\) with the greatest posterior probability.
The posterior median is 0.7. (Add up the posterior probabilities until they go from below 0.5 to above 0.5. This happens when you add in the posterior probability for \(\theta=0.5\).)
The posterior mean is 0.614. Now the posterior probabilities are used in the probability-weighted average value \[ 0.1(0.000) + 0.3(0.018) + 0.5(0.421) + 0.7(0.536) + 0.9(0.025) = 0.614. \]
The posterior variance is 0.013. Now the posterior probabilities are used in the probability-weighted average squared deviation from the posterior mean \[ (0.1-0.614)^2(0.000) + (0.3 - 0.614)^2(0.018) + (0.5-0.614)^2(0.421) + (0.7-0.614)^2(0.536) + (0.9-0.614)^2(0.025) = 0.013 \]
The posterior standard deviation is \(\sqrt{0.013} = 0.115\).
The measures of center (mean, median, mode) shift from the prior value of 0.5 towards the observed sample proportion of 8/12. However, the posterior distribution is not symmetric, and the posterior mean is less than the posterior median. In particular, note that the posterior mean (0.614) lies between the prior mean (0.5) and the sample propotion (0.667).
The measures of variability (SD, variance) are smaller for the posterior than for the prior. After observing some data, there is less uncertainty about \(\theta\). The prior probability is “spread” over the five possible values of \(\theta\), while almost all of the posterior probability is concentrated at 0.5 and 0.7.
A point estimate of an unknown parameter is a single-number estimate of the parameter. Given a posterior distribution of a parameter \(\theta\), three possible point estimates of \(\theta\) are the posterior mean, the posterior median, and the posterior mode. In particular, the posterior mean is the expected value of \(\theta\) according to the posterior distribution.
Recall that the expected value, a.k.a., mean, of a discrete random variable \(U\) is its probability-weighted average value \[ \text{E}(U) = \sum_u u\, P(U = u) \] In the calculation of a posterior mean, \(\theta\) plays the role of the variable \(U\) and the posterior distribution provides the probability-weights.
Reducing the posterior distribution to a single-number point estimate loses a lot of the information the posterior distribution provides. In particular, the posterior distribution quantifies the uncertainty about \(\theta\) after observing sample data. The posterior standard deviation summarizes in a single number the degree of uncertainty about \(\theta\) after observing sample data.
Recall that the variance of a random variable \(U\) is its probability-weighted average squared distance from its expected value \[ \text{Var}(U) = \text{E}\left[\left(U - \text{E}(U)\right)^2\right] \] The following is an equivalent formula for variance: “expected value of the square minus the square of the expected value.” \[ \text{Var}(U) = \text{E}(U^2) - \left(\text{E}(U)\right)^2 \] The standard deviation of a random variable is the square root of its variance is \(\text{SD}(U)=\sqrt{\text{Var}(U)}\). Standard deviation is measured in the same measurement units as the variable itself.
In the calculation of a posterior standard deviation, \(\theta\) plays the role of the variable \(U\) and the posterior distribution provides the probability-weights.
Example 5.2 Continuing the kissing study in Example 4.3. Now assume a prior distribution which is proportional to \(1-2|\theta-0.5|\) for \(\theta = 0, 0.0001, 0.0002, \ldots, 0.9999, 1\). Use software to answer the following.
Find the mode of the prior distribution of \(\theta\), a.k.a., the “prior mode”.
Find the median of the prior distribution of \(\theta\), a.k.a., the “prior median”.
Find the expected value of the prior distribution of \(\theta\), a.k.a., the “prior mean”.
Find the variance of the prior distribution \(\theta\), a.k.a, the “prior variance”.
Find the standard deviation of the prior distribution of \(\theta\), a.k.a, the “prior standard deviation”.
For what range of values is the prior probability that \(\theta\) lies in that range equal to 95%?
Find the prior probability that \(\theta\) is greater than 0.5.
Now suppose that \(y=8\) couples in a sample of size \(n=12\) lean right. Recall the prior, likelihood, and posterior.
# prior theta = seq(0, 1, 0.0001) prior = 1 - 2 * abs(theta - 0.5) # shape of prior prior = prior / sum(prior) # scales so that prior sums to 1 # data n = 12 # sample size y = 8 # sample count of success # likelihood, using binomial likelihood = dbinom(y, n, theta) # function of theta # posterior product = likelihood * prior posterior = product / sum(product)
Find the mode of the posterior distribution of \(\theta\), a.k.a., the “posterior mode”.
Find the median of the posterior distribution of \(\theta\), a.k.a., the “posterior median”.
Find the expected value of the posterior distribution of \(\theta\), a.k.a., the “posterior mean”.
Find the variance of the posterior distribution \(\theta\), a.k.a, the “posterior variance”.
Find the standard deviation of the posterior distribution of \(\theta\), a.k.a, the “posterior standard deviation”.
For what range of values is the posterior probability that \(\theta\) lies in that range equal to 95%?
Find the posterior probability that \(\theta\) is greater than 0.5.
How have the posterior values changed from the respective prior values?
Show/hide solution
## [1] 0.5
## [1] 0.5
## [1] 0.5
## [1] 0.04166666
## [1] 0.2041241
# prior 95% credible interval
prior_cdf = cumsum(prior)
c(theta[max(which(prior_cdf <= 0.025))], theta[min(which(prior_cdf >= 0.975))])
## [1] 0.1117 0.8882
## [1] 0.4999
## [1] 0.6154
## [1] 0.6126
## [1] 0.6113453
## [1] 0.01302593
## [1] 0.1141312
# posterior 95% credible interval
posterior_cdf = cumsum(posterior)
c(theta[max(which(posterior_cdf <= 0.025))], theta[min(which(posterior_cdf >= 0.975))])
## [1] 0.3857 0.8253
## [1] 0.829704
In the previous problem, the center of the posterior distribution is closer to the sample proportion than the center of the prior distribution. There is less uncertainty about \(\theta\) after observing some data, so the posterior standard deviation is less than the prior standard deviation. The 95% posterior interval is narrower than the prior interval, and its centered is shifted towards the posterior mean. The posterior concentrates more probability above 0.5 than the prior does.
Bayesian inference for a parameter is based on its posterior distribution. Since a Bayesian analysis treats parameters as random variables, it is possible to make posterior probability statements about a parameter.
A Bayesian credible interval is an interval of values for the parameter that has at least the specified probability, e.g., 95%. Credible intervals can be computed based on both the prior and the posterior distribution, though we are primarily interested in intervals based on the posterior distribution. The endpoints of a 95% central posterior credible interval correspond to the 2.5th and the 97.5th percentiles of the posterior distribution.
Central credible intervals are easier to compute, but are not the only or most widely used credible intervals. A 95% highest posterior density interval is the interval of values that contains 95% of the posterior probability and is such that the posterior density within the interval is never lower than the posterior density outside the interval. If the posterior distribution is relatively symmetric and unimodal, central posterior credible intervals and highest posterior density intervals are similar.
Example 5.3 Continuing the kissing study in Example 4.4, we’ll now perform a Bayesian analysis on the actual study data in which 80 couples out of a sample of 124 leaned right. Assume a prior distribution which is proportional to \(1-2|\theta-0.5|\) for \(\theta = 0, 0.0001, 0.0002, \ldots, 0.9999, 1\). Use software to answer the following questions. Recall the prior, likelihood, and posterior.
# prior
theta = seq(0, 1, 0.0001)
prior = 1 - 2 * abs(theta - 0.5) # shape of prior
prior = prior / sum(prior) # scales so that prior sums to 1
# data
n = 124 # sample size
y = 80 # sample count of success
# likelihood, using binomial
likelihood = dbinom(y, n, theta) # function of theta
# posterior
product = likelihood * prior
posterior = product / sum(product)
- Find a 95% central posterior credible interval for \(\theta\). How does the credible interval compare to the one from the previous example (with \(n=12\))?
- Write a clearly worded sentence reporting the credible interval from the previous part in context.
- Given the shape of the posterior distribution, how could your approximate a posterior 95% central posterior credible interval?
- Find the posterior probability that \(\theta\) is greater than 0.5. How does this probability compare to the one from the previous example (with \(n=12\))?
- Write a clearly worded sentence reporting the probability from the previous part in context.
- Given the shape of the posterior distribution, how could your approximate the posterior probability that \(\theta\) is greater than 0.5?
- Now consider the other two prior distributions from Example 4.4. Would any of the conclusions from this problem change substantially if we had chosen one of the other priors?
Show/hide solution
## [1] 0.64
## [1] 0.6385
## [1] 0.6378017
## [1] 0.001803819
## [1] 0.04247139
# posterior 95% credible interval
posterior_cdf = cumsum(posterior)
c(theta[max(which(posterior_cdf <= 0.025))], theta[min(which(posterior_cdf >= 0.975))])
## [1] 0.5526 0.7188
## [1] 0.999182
Show/hide solution
- A 95% central posterior credible interval for \(\theta\) is [0.552, 0.719]. This interval is narrower (more precise) than the one for \(n=12\). With a larger sample size, the likelihood is more “peaked” and so the posterior probability is concentrated over a narrower range of values.
- There is a posterior probability of 95% that the population proportion of kissing couples who lean heads to the right is between 0.552 and 0.719.
- The posterior distribution is approximately Normal, with posterior mean 0.638 and posterior standard deviation 0.042. A Normal distribution places 95% of probability on values that fall within 2 standard deviations of the mean. So an approximate 95% posterior credible interval has endpoints \(0.638 \pm 2 \times 0.042\), yielding an interval of \([0.552, 0.723]\).
- The posterior probability that \(\theta\) is greater than 0.5 is 0.9992. This probability compare to the one from the previous example since with the larger sample size, the posterior standard deviation is smaller and the posterior distribution is concentrated even more near the observed sample proportion of 0.645.
- There is a posterior probability of 99.92% that the population proportion of kissing couples who lean heads to the right is greater than 0.5.
- Use standardization (\(z\)-scores) and the empirical rule. A value of 0.5 is 3.24 standard deviations below the posterior mean: \((0.5 - 0.638)/0.042 = -3.24\). By the empirical rule for Normal distributions, 99.7% of the probability corresponds to values within 3 standard deviations of the mean. Therefore the posterior probability that \(\theta\) is less than 0.5 is pretty small.
- We saw in Example 4.4 that with the sample size of \(n=124\), the posterior distribution was basically the same for each of the three priors. Sothe conclusions from this problem would not change substantially if we had chosen one of the other priors.
In many situations, the posterior distribution of a single parameter is approximately Normal, so an approximate 95% credible interval has endpoints \[ \text{posterior mean} \pm 2 \times \text{posterior SD} \] Also, posterior probabilities of hypotheses about a parameter can often be approximated with Normal distribution calculations — standardizing and using the empirical rule.
- Compute a 95% confidence interval for \(\theta\).
- Write a clearly worded sentence reporting the confidence interval in context.
- Explain what “95% confidence” means?
- Conduct a (null) hypothesis (significance) test of whether the sample data provide strong evidence that more than half of all kissing couples lean their heads to the right. Compute the corresponding p-value.
- Write a clearly worded sentence reporting the hypothesis test in context.
- Interpret the p-value.
- Compare the numerical results of the Bayesian and frequentist analysis. How does the interpretation of these results differ between the two approaches?
Show/hide solution
The observed sample proportion is \(\hat{p} = 80/124 = 0.645\) and its standard error is \(\sqrt{\hat{p}(1-\hat{p})}/n\). The usual formula for a 95% confidence interval for a population prportion is \[ \hat{p} \pm 2\sqrt{\frac{\hat{p}(1-\hat{p})}{n}} \] Plugging in \(n=124\) and \(\hat{p} = 80/124\) yields the interval \([0.559, 0.731]\).
We estimate with 95% confidence that the population proportion of kissing couples who lean heads to the right is between 0.559 and 0.731.
Confidence is in the estimation procedure. Over many samples, 95% of samples will yield confidence intervals, computed using the above formula, that contain the true parameter value (a fixed number) The intervals change from sample to sample; the parameter is fixed.
The null hypothesis is \(H_0:\theta = 0.5\). The alternative hypothesis is \(H_a:\theta>0.5\). The standard deviation of the null distribution is \(\sqrt{0.5(1-0.5)/124} = 0.045\). The standardized statistic is \((0.645 - 0.5) / 0.045 = 3.23\). Assuming the null distribution is approximately Normal, the p-value is approximately 0.0006.
With a p-value of 0.0006 we have strong evidence to reject the null hypothesis and conclude that the population proportion of kissing couples who lean heads to the right is greater than 0.5
Interpreting the p-value
- If the population proportion of kissing couples who lean heads to the right is equal to 0.5
- Then we would observe a sample proportion of 0.645 or more in about 0.06% of random samples of size 124
- Since we actually observed a sample proportion of 0.645, which would be unlikely if the population proportion were 0.5
- The data provide evidence that the population proportion is not 0.5
The numerical results are similar: the 95% posterior credible interval is similar to the 95% confidence interval, and the p-value (0.0006) is similar to the posterior probability that \(\theta\) is less than 0.5 (0.0008 = 1-0.9992). However, the interpretation of these results is very different between the two approaches. The Bayesian approach provides probability statements about the parameter; the frequentist approach develops procedures based on the probability of what might happen over many samples.
Since a Bayesian analysis treats parameters as random variables, it is possible to make probability statements about parameters. In contrast, a frequentist analysis treats unknown parameters as fixed — that is, not random — so probability statements do not apply. In a frequentist approach, probability statements (like “95% confidence”) are based on how the sample data would behave over many hypothetical samples.
In a Bayesian approach
- Parameters are random variables and have distributions
- Observed data are treated as fixed, not random
- All inference is based on the posterior distribution of parameters which quantifies our uncertainty about the parameters.
- The posterior distribution quantifies how our prior “beliefs” about the parameters have been updated to reflect the observed data.
In a frequentist approach
- Parameters are treated as fixed (not random), but unknown numbers
- Data are treated as random
- All inference is based on the sampling distribution of the data which quantifies how the data behaves over many hypothetical samples.
Example 5.5 Continuing Example 4.7. Assume body temperatures (degrees Fahrenheit) of healthy adults follow a Normal distribution with unknown mean \(\mu\) and known standard deviation \(\sigma=1\). Suppose we wish to estimate \(\mu\), the population mean healthy human body temperature. In a recent study15, the sample mean body temperature in a sample of 208 healthy adults was 97.7 degrees F.
We’ll again use a grid approximation and assume that any multiple of 0.0001 between 96.0 and 100.0 is a possible value of \(\mu\): \(96.0, 96.0001, 96.0002, \ldots, 99.9999, 100.0\). Assume a prior distribution which is proportional a Normal distribution with mean 98.6 and standard deviation 0.7 over \(\mu\) values in the grid. Recall the prior, likelihood, and posterior.
# prior
theta = seq(96, 100, 0.0001)
prior = dnorm(theta, 98.6, 0.7)
prior = prior / sum(prior)
# data
n = 208 # sample size
y = 97.7 # sample mean
sigma = 1
# likelihood
likelihood = dnorm(y, theta, sigma / sqrt(n)) # function of theta
# posterior
product = likelihood * prior
posterior = product / sum(product)
- What does the prior standard deviation of 0.7 represent?
- What does the population standard deviation of 1 represent?
- Compute the posterior standard deviation. What does it represent?
- Compute the posterior mean.
- Compute a 95% credible interval for \(\mu\).
- Write a clearly worded sentence reporting the credible interval in context.
- Compute the posterior probability that \(\mu\) is less than 98.6.
- Write a clearly worded sentence reporting the probability in the previous part in context.
Show/hide solution
- The prior standard deviation of 0.7 quantifies, in a single number, our degree of prior uncertainty about the population mean human body temperature \(\mu\). We have a prior probability of 68% that \(\mu\) is between 97.9 and 99.3, a prior probability of 95% that \(\mu\) is between 97.2 and 100, etc (assuming a Normal prior).
- The population standard deviation of 1 represents the person-to-person variability in body temperatures. If we were to measures body temperatures for many people, body temperatures would vary by about 1 degree F from person to person. About 68% of body temperatures would be within 1 degree of \(\mu\), about 95% would be within 2 degrees of \(\mu\), etc (assuming that individual body temperatures follows a Normal distributions.)
- The posterior standard deviation of 0.069 (see code below) quantifies, in a single number, our degree of posterior uncertainty about the population mean human body temperature \(\mu\) after observing the sample data.
- The posterior mean is 97.71, which is pretty close to the observed sample mean.
- Code gives [97.57, 97.85]. Since the posterior distribution is approximately Normal, we can approximate the endpoints of the confidence interval with \(97.71 \pm 2 \times 0.069\).
- There is a posterior probability of 95% that the population mean human body temperature is between 97.57 and 97.85 degrees Fahrenheit.
- The posterior probability that \(\mu\) is less than 98.6 is essentially 1. The value 98.6 is 12.9 standard deviations above the posterior mean: \((98.6 - 97.71)/0.069=12.9\).
- There is a posterior probability of close to 100% that the population mean human body temperature is less than 98.6 degrees Fahrenheit.
Source and a related article.↩


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from IPython.core.display import HTML
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Veni Vidi Vox - Experience Music in a whole new way
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INTERCEPT
Copyright (c) 1993 Henri Vergne
This game is played on a 13x13 square board with the following setup:
- GOAL CELL - the marked cells on the bottom and top row.
- PIECES - There are the following pieces:
- Passage stones (in the diagram, the black/white stacks of size 1).
- Rest stones (in the diagram, the black/white stacks of size 2).
- Passage and rest stones may: (i) move to an (orthogonal or diagonal) adjacent empty cell or (ii) jump over (orthogonal or diagonal) adjacent passage or rest stones (of either color) landing on the immediate next cell (which must be empty). Jumps can be multiple but are not mandatory.
- Double stones (in the diagram, the black/white stacks of size 3 or 4).
- Double stones move like passage or rest stones, except they can slide a line of one or more empty cells before they make a jump. Double stones can also jump over the enemy double stone.
- The player can change the size of his double stack from 3 to 4 stones or vice versa. With size 3 they can act as passage stones and with 4 like rest stones, regarding the ball (see below). This size change costs an entire turn.
- The ball (the red stack).
- The ball may move if it is (orthogonal or diagonal) adjacent to a friendly passage stone. Then, if the passage stone is also adjacent to a friendly rest stone, the player may transfer the ball to this rest stone. This sequence can be repeated if the player has more passage and rest stones adjacent to this new ball position.
- However, on each transfer, the ball cannot end in a cell adjacent to its previous position (i.e., each transfer must move the ball at a distance of two cells).
- The rest stone carrying the ball cannot move or jump and cannot be jumped.
- Goal cells are seen as rest stones regarding ball transfers.
- Any enemy piece adjacent to his goal cell can be stacked over with a friendly stack (by jump or move). Covered stacks cannot move or jump. A player can only cover one enemy stack at any given moment.
- TURN - On each turn, each player may do two actions:
- Moves one friendly stack or changes the size of his double stone.
- Optionally, transfer the ball.
- GOAL - The player that moves the ball to the adversary goal cell wins the game.
Some jump examples Piece b4 can jump over b5 landing on b6 and then jump over c5 landing on d4. But it cannot continue its jump over d3 because d3 is a double stone (rest and passage stones can only jump rest and passage stones).
The double stone at c2 can jump over c5 and then d6. After the second jump it must land at e6 (always land on the immediate next cell).
A transfer example The rest piece at d5 has the ball. White could transfer it to b4 using c5 and then transfer it to c2 (the double piece is acting as a rest piece) using b3.
White could not transfer d5 to c6 because the ball would be adjacent to its previous position.
A winning example White's turn. He moves d5 over the enemy stack at c5 (it is legal, since c5 is adjacent to his own goal cell).
Then, White makes the transfer from c2 to b4 (thru b3) and then from b4 to c6 (thru c5). White transferred the ball into the enemy goal cell, winning the game.


Dot Carmichael
I have an older sister and two younger brothers.
I have been playing clarinet since I was in 4th grade.
Notes
- October 4
- October 5
- October 6
- October 7
- October 10
- October 11
- October 12
- October 13
- October 14
- October 17
- October 18
- October 19
- October 20
- October 21
- In-Class Notes
Other
There are no hugs in computer science


| Package | Description |
|---|---|
| org.apache.jena.permissions |
JenaSecurity is a SecurityEvaluator interface and a set of dynamic proxies that apply that
interface to Jena Graphs, Models, and associated methods and classes.
|
| org.apache.jena.permissions.graph |
Secured implementation of the Graph interface and associated classes.
|
| org.apache.jena.permissions.graph.impl | |
| org.apache.jena.permissions.impl | |
| org.apache.jena.permissions.model | |
| org.apache.jena.permissions.model.impl | |
| org.apache.jena.permissions.utils |
| Modifier and Type | Method and Description |
|---|---|
boolean |
SecuredItem.isEquivalent(SecuredItem securedItem)
Return true if this secured item is equivalent to another secured item.
|
static boolean |
SecuredItem.Util.isEquivalent(SecuredItem si1,
SecuredItem si2)
Secured items are equivalent if their security evaluators and
modelIRIs are equal.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
SecuredGraph
The interface for secured Graph instances.
|
interface |
SecuredPrefixMapping
The interface for secured PrefixMapping instances.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SecuredGraphImpl
Implementation of SecuredGraph to be used by a SecuredItemInvoker proxy.
|
class |
SecuredPrefixMappingImpl
Implementation of SecuredPrefixMapping to be used by a SecuredItemInvoker
proxy.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ItemHolder<Base,Secured extends SecuredItem>
A class that holds the original item and the secured version of it.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SecuredItemImpl
An abstract implementation of SecuredItem that caches security checks.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
SecuredItemImpl.isEquivalent(SecuredItem securedItem) |
| Constructor and Description |
|---|
SecuredItemInvoker(Class<?> securedClass,
SecuredItem securedItem)
Constructor.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
SecuredAlt
The interface for secured Alt instances.
|
interface |
SecuredBag
The interface for secured Bag instances.
|
interface |
SecuredContainer
The interface for secured Container instances.
|
interface |
SecuredLiteral
The interface for secured Literal instances.
|
interface |
SecuredModel
The interface for secured Model instances.
|
interface |
SecuredProperty
The interface for secured Property instances.
|
interface |
SecuredRDFList |
interface |
SecuredRDFNode
The interface for secured RDFNode instances.
|
interface |
SecuredReifiedStatement
The interface for secured ReifiedStatement instances.
|
interface |
SecuredResource
The interface for secured Resource instances.
|
interface |
SecuredSeq
The interface for secured Seq instances.
|
interface |
SecuredStatement
The interface for secured Statement instances.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SecuredAltImpl
Implementation of SecuredAlt to be used by a SecuredItemInvoker proxy.
|
class |
SecuredBagImpl
Implementation of SecuredBag to be used by a SecuredItemInvoker proxy.
|
class |
SecuredContainerImpl
Implementation of SecuredContainer to be used by a SecuredItemInvoker proxy.
|
class |
SecuredLiteralImpl
Implementation of SecuredLiteral to be used by a SecuredItemInvoker proxy.
|
class |
SecuredModelImpl
Implementation of SecuredModel to be used by a SecuredItemInvoker proxy.
|
class |
SecuredPropertyImpl
Implementation of SecuredProperty to be used by a SecuredItemInvoker proxy.
|
class |
SecuredRDFListImpl |
class |
SecuredRDFNodeImpl
Implementation of SecuredRDFNode to be used by a SecuredItemInvoker proxy.
|
class |
SecuredReifiedStatementImpl
Implementation of SecuredReifiedStatement to be used by a SecuredItemInvoker
proxy.
|
class |
SecuredResourceImpl
Implementation of SecuredResource to be used by a SecuredItemInvoker proxy.
|
class |
SecuredSeqImpl
Implementation of SecuredSeq to be used by a SecuredItemInvoker proxy.
|
class |
SecuredStatementImpl
Implementation of SecuredStatement to be used by a SecuredItemInvoker proxy.
|
| Modifier and Type | Method and Description |
|---|---|
static SecuredModel |
SecuredModelImpl.getInstance(SecuredItem securedItem,
Model model)
Get an instance of SecuredModel
|
| Constructor and Description |
|---|
SecuredSelector(SecuredItem securedItem) |
SecuredSelector(SecuredItem securedItem,
Selector selector) |
| Constructor and Description |
|---|
PermStatementFilter(Collection<SecurityEvaluator.Action> actions,
SecuredItem securedItem)
Creates a filter that requires that the user have all the permissions
listed in the actions parameter
|
PermStatementFilter(Collection<SecurityEvaluator.Action> actions,
SecuredItem securedItem,
SecurityEvaluator evaluator)
Creates a filter that requires that the user have all the permissions
listed in the actions parameter
|
PermStatementFilter(SecurityEvaluator.Action[] actions,
SecuredItem securedItem)
Creates a filter that requires that the user have all the permissions
listed in the actions parameter
|
PermStatementFilter(SecurityEvaluator.Action[] actions,
SecuredItem securedItem,
SecurityEvaluator evaluator)
Creates a filter that requires that the user have all the permissions
listed in the actions parameter
|
PermStatementFilter(SecurityEvaluator.Action action,
SecuredItem securedItem)
Creates a filter that requires that the user have all the permissions
listed in the actions parameter
|
PermStatementFilter(SecurityEvaluator.Action action,
SecuredItem securedItem,
SecurityEvaluator evaluator)
Creates a filter that requires that the user have all the permissions
listed in the actions parameter
|
PermTripleFilter(Collection<SecurityEvaluator.Action> actions,
SecuredItem securedItem)
Creates a filter that requires that the user have all the permissions
listed in the actions parameter
|
PermTripleFilter(Collection<SecurityEvaluator.Action> actions,
SecuredItem securedItem,
SecurityEvaluator evaluator)
Creates a filter that requires that the user have all the permissions
listed in the actions parameter
|
PermTripleFilter(SecurityEvaluator.Action[] actions,
SecuredItem securedItem)
Creates a filter that requires that the user have all the permissions
listed in the actions parameter
|
PermTripleFilter(SecurityEvaluator.Action[] actions,
SecuredItem securedItem,
SecurityEvaluator evaluator)
Creates a filter that requires that the user have all the permissions
listed in the actions parameter
|
PermTripleFilter(SecurityEvaluator.Action action,
SecuredItem securedItem)
Creates a filter that requires that the user have all the permissions
listed in the actions parameter
|
PermTripleFilter(SecurityEvaluator.Action action,
SecuredItem securedItem,
SecurityEvaluator evaluator)
Creates a filter that requires that the user have all the permissions
listed in the actions parameter
|
RDFListSecFilter(SecuredItem securedItem,
SecurityEvaluator.Action perm) |
RDFListSecFilter(SecuredItem securedItem,
Set<SecurityEvaluator.Action> perms) |
Licensed under the Apache License, Version 2.0


Have you ever felt connected to someone because you faced a common struggle in life? If so, how were you able to help each other with that struggle?
Each of us starts out in life with a family of origin, the family that we are born into and raised in. We end up in life in a family of destination, the family we may marry into, give birth to, or end up living with as adults. There is another kind of family called a family choice. These are people who may have no blood relation to us but may become as close as or closer than family. This kind of friendship can bring us some of the greatest joy and comfort we will ever experience in life.
Read Proverbs 18:24. What does this verse imply about covenant friendship and family of choice?
Ruth made an alliance of friendship with Naomi that went beyond their bloodlines. She chose to reject ties with family members in order to support and protect a friend. Covenant friendship often runs deeper than ties with relatives.
Have you ever had to reject the beliefs or habits of your family? If so, were there friends whose lives and decisions you identified with more closely than your family ties?
Are there people in your life whom you have made your family of choice (those who are not related to you but whom you treat like family)? If so, how have those friendships sustained you in life?
One of God’s greatest gifts to us is the gift of friendship. Through our friends, we receive God’s love, kindness, laughter, and grace through a human delivery system. As you reflect on the friendships you’ve treasured in life, remember the source, the Giver of all good gifts.


--- layout: post title: "@Disclosedtech: Edsurge and the Holberton School" date: 2017-02-10 12:35:00 +0000 tags: image: https://s3.amazonaws.com/hackedu/2017-02-10-pigeon.jpg imagecredits: https://www.flickr.com/photos/57973238@N03/17170523540 ---
"The Holberton School Secures $2.3M to Increase and Diversify Accepted Students," Edsurge wrote on February 7, 2017. This article fails to disclose that Edsurge and the Holberton School share an investor: Reach Capital.
For more information about this disclosure alert, read this blog post.


Rome is where we ended our vacation, and we had to pack a lot into these last few days. We saw a lot but not nearly everything there is to see in Rome. It was by far the largest city I've ever been to in my life. It would take multiple trips to be able to see and do everything the city has to offer. We went to a lot of the main attractions like the Coleseum, Trevi Fountain, and Spanish Steps. We also went into the world's smallest country, the Vatican City. The art within the Vatican was the most beautiful that I have ever seen. I would have loved to see the Pope on the day of the week that he shows himself to whatever audiences amasses.