Uncertainty in evolutionary computation
Uncertainty ≠ Noise
JJ Merelo, @jjmerelo
GeNeura Team / @geneura
Just a few things are certain in life
...Fitness is not one of them.
There's no such thing as crisp fitness + noise
Fitness is a statistical variable
Fitness:
You wish
Fitness:
Usual approaches to uncertainty
Implicit averaging
Explicit averaging
Resampling...
That will not work with
non-gaussian
What's the shape and distribution of this variable
So far: Gamma
MERELO, Juan J., et al. Studying and Tackling Noisy Fitness in Evolutionary Design of Game Characters. En IJCCI (ECTA). 2014. p. 76-85.
Let's look at uncertain problems
Three problems
Planet wars
Ms. PacMan
Procedural Backstory generation
Skewness and kurtosis characterize a distribution
MADE
Ms. PacMan
Planet Wars
Conclusions
Gaussian is not the norm.
... But evolution approaches it.
Issues in uncertainty
Create a realistic benchmark
Deal with it statistically
New selection procedures needed for uncertain fitness


The goal of meedr is to provide quick and easy access to market expectations data to the main macroeconomic indicators in the Focus report, made available by the Central Bank of Brazil through the Expectations System data API. This data comes from several financial institutions, such as: banks, brokers, funds, consultancies, etc.
Warning: This package was definitively archived by CRAN on July 22, 2021 and is only available in this repository. Despite this, maintenance and bug fixes will continue to be done.
The meedr package offers an R interface to the API and other advantages:
memoise
to speed up repeated requests of data;Check the meedr pkgdown page for more general information ;)
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("schoulten/meedr")
These are some basic examples of using the package:
library(meedr)
# Monthly market expectations for IPCA indicator
ipca <- meedr::get_monthly(
indicator = "IPCA",
first_date = Sys.Date()-30,
reference_date = format(Sys.Date(), "%m/%Y"),
be_quiet = TRUE
)
head(ipca, 5)
#> # A tibble: 5 x 10
#> indicator date reference_date mean median sd coef_var min max
#> <chr> <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 IPCA 2021-09-10 09/2021 0.875 0.915 0.179 0.42 1.13 74
#> 2 IPCA 2021-09-10 09/2021 0.763 0.77 0.228 0.277 1.13 121
#> 3 IPCA 2021-09-09 09/2021 0.800 0.81 0.179 0.42 1.13 69
#> 4 IPCA 2021-09-09 09/2021 0.698 0.72 0.207 0.277 1.13 121
#> 5 IPCA 2021-09-08 09/2021 0.754 0.75 0.148 0.33 1.01 61
#> # ... with 1 more variable: n_respondents <int>
# Annual market expectations for SELIC and exchange rate (BRL) indicator
meedr::get_annual(
indicator = c("Selic", "Câmbio"),
reference_date = format(Sys.Date(), "%Y"),
be_quiet = TRUE
)
# Inflation over the next 12 months
# First, and a suggestion, run this for using parallel computing:
future::plan(future::multisession, workers = floor(future::availableCores()/2))
meedr::get_inflation_12m(
indicator = c("IGP-DI", "IGP-M", "INPC", "IPA-DI", "IPA-M", "IPCA", "IPCA-15", "IPC-Fipe"),
smoothed = "yes",
be_quiet = FALSE, # display messages
do_parallel = TRUE # turn on parallel computing
)
# Monthly market expectations for IGP-M indicator (Top 5 Focus)
meedr::get_monthly_top5(
indicator = "IGP-M",
first_date = NULL, # get all data to current date
calc_type = "long",
be_quiet = TRUE
)
# Annual market expectations for SELIC indicator (Top 5 Focus)
meedr::get_annual_top5(
indicator = "Selic",
detail = "Fim do ano", # argument deprecated by API BCB-Olinda
be_quiet = TRUE,
use_memoise = FALSE # disable caching system
)


Found 2 records.
Errata ID: 3814
Status: Verified
Type: Technical
Reported By: Gorry Fairhurst
Date Reported: 2013-11-29
Verifier Name: Martin Stiemerling
Date Verified: 2015-12-16
Section 10.3.1 says:
Service codes are assigned on a "first come, first served" basis according to Section 19.8 of the DCCP specification [RFC4340].
It should say:
Service Codes are generally assigned on a "first come, first served" basis, according to the rules specified in Section 19.8 of the DCCP specification [RFC4340]. This also defines exceptions to this policy. [RFC5595] updated the policy to require Service Codes assignments in a Standards-Track specification to be assigned from the Specifications-Required portion of the Service Code registry.
Notes:
RFC 6335 should have noted in Section 10.3.1: Exceptions to the FCFS
policy are documented in RFC 4340. RFC 5595 updated the usage of the SC
values described in RFC 4340.
Errata ID: 4999
Status: Reported
Type: Technical
Reported By: Mark Nottingham
Date Reported: 2017-04-19
Throughout the document, when it says:
Notes:
HOLD FOR UPDATE
Many port number assignments are to individuals, but the document does not
contemplate how they should be handled when the assignee is dead or
otherwise can't be contacted.
The most obvious procedure to follow is a transfer (8.5), but that requires
de-assignment (8.2), and that doesn't cover the case above.


BTC, ETH, XRP, BCH, LTC Nosedive – ‘Extreme Fear’ Indicator Suggests Bitcoin and Crypto Tremors Just Getting Started
by Daily Hodl StaffBitcoin’s parabolic move to the downside continues.
The leading cryptocurrency briefly dropped below $7,000 before bouncing to its current price of $7,194.
The drop once again pulled the overall crypto market into the deep red, with Ethereum, XRP, Bitcoin Cash and Litecoin dropping 3% or more in the last 24 hours.
Here’s the bird’s-eye view from COIN360 at time of publishing.
Amid the crypto market free fall, the needle on the Crypto Fear & Greed Index from Alternative.me has shifted into “extreme fear.”
The gauge analyzes market volatility, surveys and sentiment on Twitter and other social media platforms to judge the mood of the overall crypto market.
According to Alternative.me, extreme fear could be an indication that the market is beginning to be oversold, and a swing to the upside may be on the horizon.
“Extreme fear can be a sign that investors are too worried. That could be a buying opportunity. When Investors are getting too greedy, that means the market is due for a correction.”
Crypto analyst and economist Alex Krüger pinpoints Bitcoin’s fall from nearly $14,000 this past June on short-lived euphoria triggered by President Xi Jinping who declared China’s support for blockchain technology.
That pump has vaporized and China is reiterating its tough stance on crypto trading.
“Some asked ‘why did this happen?’ A rationalization:
#1 China news used to drive price +42% (front-running expected new demand).
#2 Price dripped lower on low volume => increased demand never materialized.
#3 Sellers sold every single rip. They just did.”
Fellow analyst DonAlt says today’s close is key and BTC needs to stay above $6,900 in order to trigger a relief rally and avoid another immediate big move to the downside.
“Hit my above mentioned $6.9k support, now it’s time to wait & see if we can close the daily above it.
If yes, temporary relief up to 7700$ or even $8250, if not I’ll bid the HTF support around $6000.”


The following table describes common VCS tasks.
| To perform this task |
Follow these steps |
| Check out sources. |
- In the Versioning window, right-click the VCS working directory's top
level node and choose CVS > Checkout.
- Specify the modules you wish to check out.
- To check out the entire repository select All.
- To check out a specific directory, click the Select button to choose
from a list of all of the modules in the repository.
- Click Finish to check out the files.
|
| Update local file versions. |
- Right-click the file's node that you wish to update.
- Choose CVS > Update.
|
| Diff files. |
- Right-click the appropriate revision node in the Versioning window.
- Choose Diff Graphical from the contextual menu.
|
| Commit local changes. |
- Ensure that your local copies of the files are up-to-date by right-clicking
and choosing CVS > Update
before committing your changes.
- Right-click the files or directories you wish to commit and choose CVS > Commit.
|
| Merge revisions. |
- In the Versioning window, expand the file's node to reveal the subnodes
for each revision and branch.
- Right-click the revision you wish to merge into your working copy and
choose Merge With Revision.
|
| Create a patch. |
- Right-click the locally modified file or folder and choose CVS > Diff
Textual.
- In the Diff dialog box, identify the revision numbers or dates of the
revisions you want to compare and click OK.
- In the Output window, ensure that the output shows the correct differences.
- Right-click in the output pane and choose Save To File from the contextual
menu.
|
| Apply a patch. |
- Right-click the file or folder on which you want to apply the patch and
choose Tools > Apply Patch.
- Type the path or navigate to the patch file you want to apply.
- Click Patch.
|
| Configure a VCS working directory. |
- Choose Versioning > Versioning
Manager.
- Select the appropriate VCS node and click Edit.
- Edit the necessary VCS settings in the working directory's Customizer.
|
| Configure global VCS options. |
- Choose Tools > Options,
expand the Source Creation and Management node, then select the Version
Control Settings node.
- Edit the desired properties in the Option window's right pane.
|


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PERLIVP(1) Perl Programmers Reference Guide PERLIVP(1) NAME perlivp - Perl Installation Verification Procedure SYNOPSIS perlivp [-a] [-p] [-v] [-h] DESCRIPTION The perlivp program is set up at Perl source code build time to test the Perl version it was built under. It can be used after running: make install (or your platform's equivalent procedure) to verify that perl and its libraries have been installed correctly. A correct installation is verified by output that looks like: ok 1 ok 2 etc. OPTIONS -h help Prints out a brief help message. -a run all tests Normally tests for optional features are skipped. With -a all tests are executed. -p print preface Gives a description of each test prior to performing it. -v verbose Gives more detailed information about each test, after it has been performed. Note that any failed tests ought to print out some extra information whether or not -v is thrown. DIAGNOSTICS · print "# Perl binary `$perlpath' does not appear executable.\n"; Likely to occur for a perl binary that was not properly installed. Correct by conducting a proper installation. · print "# Perl version `$]' installed, expected $ivp_VERSION.\n"; Likely to occur for a perl that was not properly installed. Correct by conducting a proper installation. · print "# Perl \@INC directory `$_' does not appear to exist.\n"; Likely to occur for a perl library tree that was not properly installed. Correct by conducting a proper installation. · print "# Needed module `$_' does not appear to be properly installed.\n"; One of the two modules that is used by perlivp was not present in the installation. This is a serious error since it adversely affects perlivp's ability to function. You may be able to correct this by performing a proper perl installation. · print "# Required module `$_' does not appear to be properly installed.\n"; An attempt to "eval "require $module"" failed, even though the list of extensions indicated that it should succeed. Correct by conducting a proper installation. · print "# Unnecessary module `bLuRfle' appears to be installed.\n"; This test not coming out ok could indicate that you have in fact installed a bLuRfle.pm module or that the "eval " require \"$module_name.pm\"; "" test may give misleading results with your installation of perl. If yours is the latter case then please let the author know. · print "# file",+($#missing == 0) ? '' : 's'," missing from installation:\n"; One or more files turned up missing according to a run of "ExtUtils::Installed -> validate()" over your installation. Correct by conducting a proper installation. · print "# Perl header `$_' does not appear to be properly installed.\n"; Correct by running h2ph over your system's C header files. If necessary, edit the resulting *.ph files to eliminate perl syntax errors. For further information on how to conduct a proper installation consult the INSTALL file that comes with the perl source and the README file for your platform. AUTHOR Peter Prymmer perl v5.14.2 2014-09-30 PERLIVP(1)


Congrats! Your Trello experience will never be the same!
1. Right click on the Pro4Trello icon in the top right corner.
2. Select "Options" (you can also click here):
3. Configure tweaks that will be applied across all boards.
4. Refresh page of your boards if they are currently opened.
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the date is:
First name
Last name
date
trace
*all variables are needed
check if the is any mistake before we create you account
HR


rotten
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Library
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Medicine
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Bodily Functions
> Vomiting
Vomiting
Roman vomitoria and
orgia
.
Max Hardcore
Gaping Maw:
Celebrate Youth
Pornopolis
|
Rotten
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Faces of Death
|
Famous Nudes


This map was created to visualize countries based on the number of times they are mentioned in Donald Trump's Tweets. The time frame can be adjusted by moving the slider then clicking the 'Update Map' button.
The map makes use of an algorithm to contruct contiuous area cartograms by James A. Dougenik, Nicholas R. Chrisman, and Duane R. Niemeyer, ©1985 by the Association of American Geographers. It also uses makes use of code to generate jenks to create the choropleth color scheme.
The map utilizes d3's equirectangular projection. The country topojson file is a 2018 version supplied by Natural Earth, so please note that country names and borders may have changed since. This map is intented for visualization purposes only.


Quality of service
1. Rate allocation
Proportional allocate maximum rateSet ratio: : :
2. Best serivce provision
Providing best quality service for
3. Service differentiation of maximum rate for a (group) user(s)
Service Port: Rate limit to:4. Scheduled Tasks
For above selected rules, set applying time


Huffman encoding#
This module implements functionalities relating to Huffman encoding and decoding.
AUTHOR:
Nathann Cohen (2010-05): initial version.
Classes and functions#
- class sage.coding.source_coding.huffman.Huffman(source)#
Bases:
SageObject
This class implements the basic functionalities of Huffman codes.
It can build a Huffman code from a given string, or from the information of a dictionary associating to each key (the elements of the alphabet) a weight (most of the time, a probability value or a number of occurrences).
INPUT:
source
– can be eitherA string from which the Huffman encoding should be created.
A dictionary that associates to each symbol of an alphabet a numeric value. If we consider the frequency of each alphabetic symbol, then
source
is considered as the frequency table of the alphabet with each numeric (non-negative integer) value being the number of occurrences of a symbol. The numeric values can also represent weights of the symbols. In that case, the numeric values are not necessarily integers, but can be real numbers.
In order to construct a Huffman code for an alphabet, we use exactly one of the following methods:
Let
source
be a string of symbols over an alphabet and feedsource
to the constructor of this class. Based on the input string, a frequency table is constructed that contains the frequency of each unique symbol insource
. The alphabet in question is then all the unique symbols insource
. A significant implication of this is that any subsequent string that we want to encode must contain only symbols that can be found insource
.Let
source
be the frequency table of an alphabet. We can feed this table to the constructor of this class. The tablesource
can be a table of frequencies or a table of weights.
In either case, the alphabet must consist of at least two symbols.
EXAMPLES:
sage: from sage.coding.source_coding.huffman import Huffman, frequency_table sage: h1 = Huffman("There once was a french fry") sage: for letter, code in sorted(h1.encoding_table().items()): ....: print("'{}' : {}".format(letter, code)) ' ' : 00 'T' : 11100 'a' : 0111 'c' : 1010 'e' : 100 'f' : 1011 'h' : 1100 'n' : 1101 'o' : 11101 'r' : 010 's' : 11110 'w' : 11111 'y' : 0110
We can obtain the same result by “training” the Huffman code with the following table of frequency:
sage: ft = frequency_table("There once was a french fry") sage: sorted(ft.items()) [(' ', 5), ('T', 1), ('a', 2), ('c', 2), ('e', 4), ('f', 2), ('h', 2), ('n', 2), ('o', 1), ('r', 3), ('s', 1), ('w', 1), ('y', 1)] sage: h2 = Huffman(ft)
Once
h1
has been trained, and hence possesses an encoding table, it is possible to obtain the Huffman encoding of any string (possibly the same) using this code:sage: encoded = h1.encode("There once was a french fry"); encoded '11100110010001010000111011101101010000111110111111100001110010110101001101101011000010110100110'
We can decode the above encoded string in the following way:
sage: h1.decode(encoded) 'There once was a french fry'
Obviously, if we try to decode a string using a Huffman instance which has been trained on a different sample (and hence has a different encoding table), we are likely to get some random-looking string:
sage: h3 = Huffman("There once were two french fries") sage: h3.decode(encoded) ' eierhffcoeft TfewrnwrTrsc'
This does not look like our original string.
Instead of using frequency, we can assign weights to each alphabetic symbol:
sage: from sage.coding.source_coding.huffman import Huffman sage: T = {"a":45, "b":13, "c":12, "d":16, "e":9, "f":5} sage: H = Huffman(T) sage: L = ["deaf", "bead", "fab", "bee"] sage: E = [] sage: for e in L: ....: E.append(H.encode(e)) ....: print(E[-1])<PHONE_NUMBER>00<PHONE_NUMBER>1 11000101<PHONE_NUMBER>1 sage: D = [] sage: for e in E: ....: D.append(H.decode(e)) ....: print(D[-1]) deaf bead fab bee sage: D == L True
- decode(string)#
Decode the given string using the current encoding table.
INPUT:
string
– a string of Huffman encodings.
OUTPUT:
The Huffman decoding of
string
.
EXAMPLES:
This is how a string is encoded and then decoded:
sage: from sage.coding.source_coding.huffman import Huffman sage: str = "Sage is my most favorite general purpose computer algebra system" sage: h = Huffman(str) sage: encoded = h.encode(str); encoded '11000011010001010101100001111101001110011101001101101111011110111001111010000101101110100000111010101000101000000010111011011000110100101001011100010011011110101011100100110001100101001001110101110101110110001000101011000111101101101111110011111101110100011' sage: h.decode(encoded) 'Sage is my most favorite general purpose computer algebra system'
- encode(string)#
Encode the given string based on the current encoding table.
INPUT:
string
– a string of symbols over an alphabet.
OUTPUT:
A Huffman encoding of
string
.
EXAMPLES:
This is how a string is encoded and then decoded:
sage: from sage.coding.source_coding.huffman import Huffman sage: str = "Sage is my most favorite general purpose computer algebra system" sage: h = Huffman(str) sage: encoded = h.encode(str); encoded '11000011010001010101100001111101001110011101001101101111011110111001111010000101101110100000111010101000101000000010111011011000110100101001011100010011011110101011100100110001100101001001110101110101110110001000101011000111101101101111110011111101110100011' sage: h.decode(encoded) 'Sage is my most favorite general purpose computer algebra system'
- encoding_table()#
Returns the current encoding table.
INPUT:
None.
OUTPUT:
A dictionary associating an alphabetic symbol to a Huffman encoding.
EXAMPLES:
sage: from sage.coding.source_coding.huffman import Huffman sage: str = "Sage is my most favorite general purpose computer algebra system" sage: h = Huffman(str) sage: T = sorted(h.encoding_table().items()) sage: for symbol, code in T: ....: print("{} {}".format(symbol, code)) 101 S 110000 a 1101 b 110001 c 110010 e 010 f 110011 g 0001 i 10000 l 10001 m 0011 n 00000 o 0110 p 0010 r 1110 s 1111 t 0111 u 10010 v 00001 y 10011
- tree()#
Returns the Huffman tree corresponding to the current encoding.
INPUT:
None.
OUTPUT:
The binary tree representing a Huffman code.
EXAMPLES:
sage: from sage.coding.source_coding.huffman import Huffman sage: str = "Sage is my most favorite general purpose computer algebra system" sage: h = Huffman(str) sage: T = h.tree(); T # optional - sage.graphs Digraph on 39 vertices sage: T.show(figsize=[20,20]) # optional - sage.graphs sage.plot
- sage.coding.source_coding.huffman.frequency_table(string)#
Return the frequency table corresponding to the given string.
INPUT:
string
– a string of symbols over some alphabet.
OUTPUT:
A table of frequency of each unique symbol in
string
. Ifstring
is an empty string, return an empty table.
EXAMPLES:
The frequency table of a non-empty string:
sage: from sage.coding.source_coding.huffman import frequency_table sage: str = "Stop counting my characters!"


Every input and output on every component in the circuit has a bit width associated with it. Often the bit width is 1, and there is no way of changing that, but many of Logisim's built-in components include attributes allowing you to customize the bit widths of their inputs and outputs.
The below screen shot illustrates a simple circuit for finding the bitwise AND of two three-bit inputs. Notice how the three-bit output is the bitwise AND of the two inputs. All components have been customized to deal with three-bit data via its Data Bits attribute; the screen shot shows the AND gate attributes, including the Data Bits attribute of 3.
All components in Logisim define a bit width for each of input and output. In contrast, a wire's bit width is undefined: Instead, the wire's width adapts to the components to which it is attached. If a wire connects two components demanding different bit widths, Logisim will complain of "Incompatible widths" and indicate the offending locations in orange. In the below, the output pin's Data Bits attribute has been changed to 1, and so Logisim complains that the wire cannot connect a three-bit value to a one-bit value.
Wires that connect incompatible locations (drawn in orange) do not carry values.
For single-bit wires, you can see at a glance what value the wire carries because Logisim colors the wire light or dark green depending the value. It does not display values for multi-bit wires: They are simply black. You can, though, probe a wire by clicking it using the poke tool ().
This probing feature is helpful for debugging circuits using wire bundles.
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--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/leanpkg.20does.20not.20work.html ---
after git clone Xena-UROP-2018 from GitHub, I want to do leanpkg upgrade
but it failed and post many error:
/usr/local/leanpkg/leanpkg/main.lean:1:0: error: file 'init' not found in the LEAN_PATH
/usr/local/leanpkg/leanpkg/resolve.lean:1:0: error: file 'init' not found in the LEAN_PATH
/usr/local/leanpkg/leanpkg/manifest.lean:1:0: error: file 'init' not found in the LEAN_PATH
/usr/local/leanpkg/leanpkg/toml.lean:1:0: error: file 'init' not found in the LEAN_PATH
I don't know how to do.
Which OS?
Are you using the command line?
If you type leanpkg
, what is the first line of the output? I have files with the same names as those which are giving you errors, in my unix install, but there is no mention of init
. It's certainly possible to get it all working on a mac.
@Blair Shi
Macos high Sierra 10.13.2
@Kevin Buzzard
dyn3159-100:xena-UROP-2018 shifengzheng$ leanpkg /usr/local/leanpkg/leanpkg/main.lean:1:0: error: file 'init' not found in the LEAN_PATH
@Sebastian Ullrich I just downloaded the package lean-3.4.1
and then in my vscode I set
"lean.executablePath": "/Users/shifengzheng/lean-3.4.1-darwin/bin/lean",
@Blair Shi Why do you have files in /usr/local/
then? Do you have an old Homebrew installation or something like that?
@Blair Shi @Blair Shi (wait -- there are two?) the error shows that the version of leanpkg
which is running is not the one in Users/shifengzheng/lean-3.4.1-darwin/bin/
, it is some other version installed in usr/local
. You might want to completely remove the /usr/local/leanpkg
directory and add Users/shifengzheng/lean-3.4.1-darwin/bin/
to the path on your command line
@Sebastian Ullrich before I did not have leanpkg
in my /usr/local/
but when I typed leanpkg
in terminal, it reported <unknown>:1:1: error: file '/usr/local/leanpkg/leanpkg/main.lean' not found
. So I put leanpkg into my /usr/local/
I don't think you can just move leanpkg
and hope for things to still work
I think you have to move the entire installation
What I'm saying is that you can move the directory /Users/shifengzheng/lean-3.4.1-darwin
to anywhere you like
but you should leave the contents intact.
When you've decided where to put it, point VS Code and your command line PATH to it, and then things should be OK.
I moved lean-3.4.1-darwin
to /usr/local/
and added path to LEAN_PATH and removed the previous leanpkg
in local
dyn3159-100:xena-UROP-2018 shifengzheng$ echo $LEAN_PATH /usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/lean-3.4.1-darwin/bin/
But stil not work.
dyn3159-100:xena-UROP-2018 shifengzheng$ leanpkg <unknown>:1:1: error: file '/usr/local/leanpkg/leanpkg/main.lean' not found
Did I set the wrong path or did I miss to do something?
Kevin said to modify PATH
, not LEAN_PATH
. Please unset LEAN_PATH
.
Could you post the output of which leanpkg
and bash -x leanpkg
?
@Sebastian Ullrich sorry for my misunderstanding. I unset LEAN_PATH
and did what you said
dyn3159-100:xena-UROP-2018 shifengzheng$ which leanpkg /usr/local/bin/leanpkg dyn3159-100:xena-UROP-2018 shifengzheng$ bash -x leanpkg ++ uname + unamestr=Darwin + [[ Darwin == \D\a\r\w\i\n ]] + command -v greadlink + READLINK=greadlink +++ greadlink -f leanpkg ++ dirname /Users/shifengzheng/xena-UROP-2018/leanpkg + leandir=/Users/shifengzheng/xena-UROP-2018/.. ++ greadlink -f /Users/shifengzheng/xena-UROP-2018/.. + leandir=/Users/shifengzheng + librarydir=/Users/shifengzheng/lib/lean + test -d /Users/shifengzheng/lib/lean + librarydir=/Users/shifengzheng + LEAN_PATH=/Users/shifengzheng/library:/Users/shifengzheng/leanpkg + PATH=/Users/shifengzheng/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin + exec lean --run /Users/shifengzheng/leanpkg/leanpkg/main.lean <unknown>:1:1: error: file '/Users/shifengzheng/leanpkg/leanpkg/main.lean' not found
Apparently you still have an old leanpkg
at /usr/local/bin
Blair -- I'll be at Imperial in about 10 minutes and will try and sort things out. Sorry it's taken so long -- I had other things to do this morning.
I find a solution to deal with this issue. I just put the Xena-UROP-2018
to my my_playground
which already set up mathlib
. Now it works
Last updated: Apr 24 2020 at 19:02 UTC


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Playlist Pretifier


Neuroplasticity’s facts:
Brain its an always changing elastic organ: By knowing that... you are aware that the more that you practice or do something... the easier and more comfortable it will be come for you on a long term. This is because the neurones paths get fired up every time that you think or do that task physically. If you are very aware of that you get an extra drive on the things wanna learn or do and in the long term you get to master those stuff.
I’ve begin meditating a few months ago, but I have been doing it around 3 times per week. It helped me quite a lot...
At the beginning of sprint 3 we got this challenge where we had to download this HeadSpace app and we got committed to meditate everyday.
By doing this... the neurones paths that get fired up when I do my meditation will strengthen on a daily basis... Since I started I’ve always wanted to get better at this to be able to get the most out of it. I feel that now that I’m gonna be doing this so often, it will become easier and easier on the long term and its’ gonna benefit me big times.
By thinking about the task or by doing it !
Growth mindset it’s knowing that you can learn new stuff even tho you might think that ‘You are not the type of person that eg: goes to the gym.. or meditates,’
Being aware that how you think about a situation will condition your results... If you think that you can’t and you are not gonna learn maths it will definitely make it harder for you to get good at it.
If you embark on a learning journey of something new with a Growth Mindset it will definitely give you best results.
We saw this already on the first sprint didn’t we..? So no. I knew this stuff already, always interesting tho.
When something challenging comes up I’ll definitely see it like that specific neurone path its just ‘strong’ enough yet lol By getting practice out of out and doing it over and over it will definitely get stronger and on the long term it will feel easier.


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# DONE: Implement Function
inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='inputs_real')
inputs_z = tf.placeholder(tf.float32, (None, z_dim), name="input_z")
learning_rate = tf.placeholder(tf.float32, name="learning_rate")
return inputs_real, inputs_z, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).
def discriminator(images, reuse=False, alpha=0.2):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# DONE: Implement Function
with tf.variable_scope('discriminator', reuse=reuse):
# Hidden layer 1
h1 = tf.layers.conv2d(images, 64, 5, strides=2, padding="SAME",
kernel_initializer = tf.random_normal_initializer(stddev=0.02))
lrelu1 = tf.maximum(alpha * h1, h1)
#print ('Layer 1:', lrelu1.shape)
# 14x14x64 now
# Hidden Layer 2
h2 = tf.layers.conv2d(lrelu1, 128, 5, strides=1, padding="SAME",
kernel_initializer = tf.random_normal_initializer(stddev=0.02))
bn2 = tf.layers.batch_normalization(h2, training=True)
lrelu2 = tf.maximum(alpha * bn2, bn2)
#print ('Layer 2:', lrelu2.shape)
# 14x14x128 now
# Hidden Layer 3
h3 = tf.layers.conv2d(lrelu2, 256, 5, strides=2, padding="SAME",
kernel_initializer = tf.random_normal_initializer(stddev=0.02))
bn3 = tf.layers.batch_normalization(h3, training=True)
lrelu3 = tf.maximum(alpha * bn3, bn3)
#print ('Layer 3:', lrelu3.shape)
#7x7x256 now
flat = tf.reshape(lrelu3, (-1, 7*7*256))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# DONE: Implement Function
with tf.variable_scope('generator', reuse=not is_train):
# First fully connected layer
h1 = tf.layers.dense(z, 7*7*256)
# Reshape it to start the convolutional stack
h1 = tf.reshape(h1, (-1, 7, 7, 256))
bn1 = tf.layers.batch_normalization(h1, training=is_train)
lrelu1 = tf.maximum(alpha * bn1, bn1)
#print ('Layer 1:', lrelu1.shape)
# 4x4x256 now
# Hidden Layer 2
h2 = tf.layers.conv2d_transpose(lrelu1, 128, 5, strides=1, padding="SAME")
bn2 = tf.layers.batch_normalization(h2, training=is_train)
lrelu2 = tf.maximum(alpha * bn2, bn2)
#print ('Layer 2:', lrelu2.shape)
# 8x8x128 now
# Hidden Layer 3
h3 = tf.layers.conv2d_transpose(lrelu2, 64, 5, strides=2, padding='SAME')
bn3 = tf.layers.batch_normalization(h3, training=is_train)
lrelu3 = tf.maximum(alpha * bn3, bn3)
#print ('Layer 3:', h3.shape)
# 16x16x64
# Output layer, 28x28x5
logits = tf.layers.conv2d_transpose(lrelu3, out_channel_dim, 5, strides=2, padding="SAME")
out = tf.tanh(logits)
#print ('Output:', logits.shape)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# DONE: Implement Function
smooth = 0.1
# Generator network
g_model = generator(input_z, out_channel_dim, is_train=True)
# Disriminator network
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
# Calculate losses
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
labels=tf.ones_like(d_logits_real) * (1 - smooth)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.zeros_like(d_logits_fake)))
d_loss = d_loss_real + d_loss_fake
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.ones_like(d_logits_fake)))
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# DONE: Implement Function
# Get the trainable_variables, split into G and D parts
t_vars = tf.trainable_variables()
g_vars = [var for var in t_vars if var.name.startswith('generator')]
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(d_loss, var_list=d_vars)
# https://blog.metaflow.fr/tensorflow-mutating-variables-and-control-flow-2181dd238e62
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
g_updates = [opt for opt in update_ops if opt.name.startswith('generator')]
with tf.control_dependencies(g_updates):
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# DONE: Build Model
# Set the image width, height, and image channels
_, image_width, image_height, image_channels = data_shape
# Impliment model_inputs and set the input_real, input_z, and learning rate
input_real, input_z, _learning_rate = model_inputs(image_width, image_height, image_channels, z_dim)
# Impliment model_loss and set the d_loss and g_loss
d_loss, g_loss = model_loss(input_real, input_z, image_channels)
# Impliment model_opt and set d_train_opt and g_train_opt
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
# Set batch counter
batch_count = 0
# Set number of images to print
n_images = 25
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for e in range(epoch_count):
for batch_images in get_batches(batch_size):
# Multiply batch_images by 2.0 since the dataset ranges from -0.5 to 0.5
batch_images = batch_images*2.0
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})
_ = sess.run(g_train_opt, feed_dict={input_z: batch_z})
# Increment the batch_count
batch_count += 1
# Print the d_train_loss and g_train_loss every 10 batches
if (batch_count % 10 == 0):
train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(e+1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Print the generator output every 100 batches
if (batch_count % 100 == 0):
show_generator_output(sess, n_images, input_z, image_channels, data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 100
learning_rate = 0.0001
beta1 = 0.4
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


Simba has launched its biggest ever Black Friday sale
by Joseph GreenTL;DR: Simba has launched its Black Friday sale with up to 40% off bundles, saving you up to £524 on list price.
This is such a sweet time for shoppers, because it seems like every day another Black Friday sale is launched. Simba is the latest retailer to drop its discounts, with up to 40% off bundles.
The great thing about bundle deals from brands like Simba is that they completely kit you out in one go. This means you don't have to keep going back to pick up more supplies, like pillows and mattresses protectors, because you've already got everything you need.
The Simba Black Friday sale is running until Nov. 29, so you have plenty of time to consider your options. With massive savings of up to 40% off Simba bundles, you could end up saving £524 on list price, so you shouldn't need to think things over for too long.
Save big on sleepy bundles in the Simba Black Friday sale.


--- layout: post title: The Horror of Blimps post_id: 279 categories: - odds & ends date: 2004-04-23 14:25:00.000000000 -06:00 author: Jamis permalink: 2004/4/23/the-horror-of-blimps.html ---
Okay. Now for something a little off the wall. I came across this while surfing the ‘Net and just had to share it.
Think, for a moment about how a largish toy blimp might terrorize a family. Ready? Okay. Now, go and read the story about the Horror of Blimps and tell me if that isn’t one of the funniest things you’ve read in a long time.
Warning: it has contains some mild language, and is lacking vital punctuation in several places. Read at your own risk…


public class MavenTask
extends java.lang.Object
| Modifier and Type | Class and Description |
|---|---|
static class |
MavenTask.Builder
A Builder to force proper creation of the
MavenTask object. |
| Modifier and Type | Field and Description |
|---|---|
private boolean |
cleanTarget |
private boolean |
copy |
private java.lang.String |
copyToDirectory |
private boolean |
doInstall |
private boolean |
enabled |
private java.util.UUID |
id |
private boolean |
rename |
private java.lang.String |
renameString |
private java.lang.String |
taskName |
private boolean |
unpackage |
private java.lang.String |
workingDirectory |
| Modifier | Constructor and Description |
|---|---|
private |
MavenTask(java.util.UUID id,
java.lang.String taskName,
java.lang.String workingDirectory,
java.lang.String copyToDirectory,
java.lang.String renameString,
boolean copy,
boolean rename,
boolean cleanTarget,
boolean unpackage,
boolean doInstall,
boolean enabled)
Constructs a MavenTask object to be used elsewhere.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
cleanTarget()
Retrieves whether to pre-pend the 'clean' command to the maven goal.
|
boolean |
doCopy()
Retrieves whether to copy the result to another location upon completion.
|
boolean |
doInstall()
Retrieves whether to run the maven task as a package goal or an install goal.
|
boolean |
doRename()
Retrieves whether to rename the result to another name upon completion.
|
boolean |
doUnpackage()
Retrieves whether to attempt to untar/unzip the result of the maven goal.
|
java.lang.String |
getCopyToDirectory()
Retrieves the copy-to directory of the object.
|
boolean |
getEnabled()
Retrieves whether the task should be ran or not.
|
java.util.UUID |
getId()
Retrieves the UUID of the object.
|
java.lang.String |
getRenameString()
Retrieves the string to rename the resulting file to for the object.
|
java.lang.String |
getTaskName()
Retrieves the task name.
|
java.lang.String |
getWorkingDirectory()
Retrieves the working directory of the object.
|
void |
setEnabled(boolean enabled)
Sets whether the task should be ran or not.
|
private java.util.UUID id
private java.lang.String taskName
private java.lang.String workingDirectory
private java.lang.String copyToDirectory
private java.lang.String renameString
private boolean copy
private boolean rename
private boolean cleanTarget
private boolean unpackage
private boolean doInstall
private boolean enabled
private MavenTask(java.util.UUID id, java.lang.String taskName, java.lang.String workingDirectory, java.lang.String copyToDirectory, java.lang.String renameString, boolean copy, boolean rename, boolean cleanTarget, boolean unpackage, boolean doInstall, boolean enabled)
id
- A UUID to match the object to components of the application.taskName
- The readable name to identify the task.workingDirectory
- The directory to begin the task inside.copyToDirectory
- The directory to copy the resulting task to.renameString
- The string to rename the resulting file/directory tocopy
- Whether to copy the result to another location.rename
- Whether to rename the result to another name.cleanTarget
- Whether to run the clean argument when building the process.unpackage
- Whether to unpackage the resulting package.doInstall
- Whether to run an install goal or a package goal.enabled
- Whether the task should be executed when processing.public java.util.UUID getId()
public java.lang.String getTaskName()
public java.lang.String getWorkingDirectory()
public java.lang.String getCopyToDirectory()
public java.lang.String getRenameString()
public boolean doCopy()
public boolean doRename()
public boolean cleanTarget()
public boolean doUnpackage()
public boolean doInstall()
public boolean getEnabled()
public void setEnabled(boolean enabled)
enabled
- Whether to execute the task or not.


Contact Information:
Email:
<EMAIL_ADDRESS>Phone:<PHONE_NUMBER>
Address: 234 Buckley Ln, Chicago, IN 56781


 I want a full screen every where I go.  I know I can't pull it off alone.
I'm hoping the future Galaxy Note/Tab 3.0 can be hugged by Backpocket 2.0's embrace.
(drawn with Papyrus on my Galaxy Note)So what do you think tech boys and girls? Leave your comments below.
[Update]
Aparently another company has already made a large pocket, but hasn't put it on the back yet. http://www.thinkofthe.com/product.php?name=large-pocket-shirt


{% extends "base.html" %} {% block content %}{% with app_path as path %} {% include "breadcrumbs.html" %} {% endwith %}
{{q.question}}


Like a golem or gargoyle, Earthshaker was one with the earth but now walks freely upon it. Unlike those other entities, he created himself through an act of will, and serves no other master. In restless slumbers, encased in a deep seam of stone, he became aware of the life drifting freely above him. He grew curious. During a season of tremors, the peaks of Nishai shook themselves loose of avalanches, shifting the course of rivers and turning shallow valleys into bottomless chasms. When the land finally ceased quaking, Earthshaker stepped from the settling dust, tossing aside massive boulders as if throwing off a light blanket. He had shaped himself in the image of a mortal beast, and named himself Raigor Stonehoof. He bleeds now, and breathes, and therefore he can die. But his spirit is still that of the earth; he carries its power in the magical totem that never leaves him. And on the day he returns to dust, the earth will greet him as a prodigal son.


-
اندیکاسیون هاindicated when trying to determine fluid responsiveness in patients
-
کنترااندیکاسیون هاThere are no contraindications to noninvasive monitoring of the heart, although most systems will need to be connected to an arterial line catheter, therefore contraindications to arterial line placement must be determined.
-
عوارض
• Some components of the values obtained are not reliable on spontaneously breathing patients and it is critical to check the ventilator waveforms for these breaths.
• Patients without adequate tidal volumes (at least 8 mL/ kg) will also have unreliable values.
• Stroke volume variation usually is more reliable when greater than 13 % and indicates fl uid responsiveness rather than when it is less than 13 % (similar to a low central venous pressure (CVP) being more informative than normal or high CVP levels). -
تکنیک1. Connect FloTrac to arterial line and distal FloTrac port to pressure bag. Infl ate bag to 300 mmHg.
2. Connect green FloTrac cord to green Vigileo cord and red FloTrac cord to arterial line port on heart monitor.
3. Turn the port on the FloTrac off to the patient and zero on your heart monitor as you would with an arterial line. At the same time, you should have pressed the “Enter” knob on the Vigileo system, scrolled it to “zero arterial pressure,” and pressed the knob again for that function.
4. You should now have an arterial waveform on your heart monitor and your cardiac values on the Vigileo system. -
تصاویر
-
فیلم


Open vSwitch on Linux uses autoconf and automake for generating Makefiles. It will be useful to maintain the same build system while compiling on Windows too. One approach is to compile Open vSwitch in a MinGW environment that contains autoconf and automake utilities and then use Visual C++ as a compiler and linker.
The following explains the steps in some detail.
This should install mingw at C:\Mingw and msys at C:\Mingw\msys. Add "C:\MinGW\bin" and "C:\Mingw\msys\1.0\bin" to PATH environment variable of Windows.
You can either use the MinGW installer or the command line utility 'mingw-get' to install both the base packages and additional packages like automake and autoconf(version 2.68).
Also make sure that /mingw mount point exists. If its not, please add/create the following entry in /etc/fstab - 'C:/MinGW /mingw'.
Install the latest Python 2.x from python.org and verify that its path is part of Windows' PATH environment variable.
You will need at least Visual Studio 2013 (update 4) to compile userspace binaries. In addition to that, if you want to compile the kernel module you will also need to install Windows Driver Kit (WDK) 8.1 Update.
It is important to get the Visual Studio related environment variables and to have the $PATH inside the bash to point to the proper compiler and linker. One easy way to achieve this for VS2013 is to get into the "VS2013 x86 Native Tools Command Prompt" (in a default installation of Visual Studio 2013 this can be found under the following location: C:\Program Files (x86)\Microsoft Visual Studio 12.0\Common7\Tools\Shortcuts) and through it enter into the bash shell available from msys by typing 'bash --login'.
There is support for generating 64 bit binaries too. To compile under x64, open the "VS2013 x64 Native Tools Command Prompt" (if your current running OS is 64 bit) or "VS2013 x64 Cross Tools Command Prompt" (if your current running OS is not 64 bit) instead of opening its x86 variant. This will point the compiler and the linker to their 64 bit equivalent.
If after the above step, a 'which link' inside MSYS's bash says, "/bin/link.exe", rename /bin/link.exe to something else so that the Visual studio's linker is used. You should also see a 'which sort' report "/bin/sort.exe".
For pthread support, install the library, dll and includes of pthreads-win32 project from ftp://sourceware.org/pub/pthreads-win32/prebuilt-dll-2-9-1-release to a directory (e.g.: C:/pthread). You should add the pthread-win32's dll path (e.g.: C:\pthread\dll\x86) to the Windows' PATH environment variable.
Get the Open vSwitch sources from either cloning the repo using git or from a distribution tar ball.
If you pulled the sources directly from an Open vSwitch Git tree, run boot.sh in the top source directory:
% ./boot.sh
In the top source directory, configure the package by running the configure script. You should provide some configure options to choose the right compiler, linker, libraries, Open vSwitch component installation directories, etc. For example,
% ./configure CC=./build-aux/cccl LD="which link
" \
LIBS="-lws2_32 -liphlpapi" --prefix="C:/openvswitch/usr" \
--localstatedir="C:/openvswitch/var" --sysconfdir="C:/openvswitch/etc" \
--with-pthread="C:/pthread"
By default, the above enables compiler optimization for fast code. For default compiler optimization, pass the "--with-debug" configure option.
Run make for the ported executables in the top source directory, e.g.:
% make
For faster compilation, you can pass the '-j' argument to make. For example, to run 4 jobs simultaneously, run 'make -j4'.
Note: MSYS 1.0.18 has a bug that causes parallel make to hang. You can overcome this by downgrading to MSYS 1.0.17. A simple way to downgrade is to exit all MinGW sessions and then run the command 'mingw-get upgrade msys-core-bin=1.0.17-1' from MSVC developers command prompt.
To run all the unit tests in Open vSwitch, one at a time:
% make check
To run all the unit tests in Open vSwitch, up to 8 in parallel:
% make check TESTSUITEFLAGS="-j8"
To install all the compiled executables on the local machine, run:
% make install
The above command will install the Open vSwitch executables in C:/openvswitch. You can add 'C:\openvswitch\usr\bin' and 'C:\openvswitch\usr\sbin' to Windows' PATH environment variable for easy access.
To get SSL support for Open vSwitch on Windows, do the following:
Note down the directory where OpenSSL is installed (e.g.: C:/OpenSSL-Win32).
While configuring the package, specify the OpenSSL directory path. For example,
% ./configure CC=./build-aux/cccl LD="which link
" \
LIBS="-lws2_32 -liphlpapi" --prefix="C:/openvswitch/usr" \
--localstatedir="C:/openvswitch/var" --sysconfdir="C:/openvswitch/etc" \
--with-pthread="C:/pthread" --enable-ssl --with-openssl="C:/OpenSSL-Win32"
Run make for the ported executables.
We directly use the Visual Studio 2013 IDE to compile the kernel datapath. You can open the extensions.sln file in the IDE and build the solution.
The kernel datapath can be compiled from command line as well. The top level 'make' will invoke building the kernel datapath, if the '--with-vstudiotarget' argument is specified while configuring the package. For example,
% ./configure CC=./build-aux/cccl LD="which link
" \
LIBS="-lws2_32 -liphlpapi" --prefix="C:/openvswitch/usr" \
--localstatedir="C:/openvswitch/var" --sysconfdir="C:/openvswitch/etc" \
--with-pthread="C:/pthread" --enable-ssl \
--with-openssl="C:/OpenSSL-Win32" --with-vstudiotarget="
Possible values for "
Once you have built the solution, you can copy the following files to the target Hyper-V machines.
./datapath-windows/x64/Win8.1Debug/package/ovsext.inf
./datapath-windows/x64/Win8.1Debug/package/OVSExt.sys
./datapath-windows/x64/Win8.1Debug/package/ovsext.cat
./datapath-windows/misc/install.cmd
./datapath-windows/misc/uninstall.cmd
The above path assumes that the kernel module has been built using Windows DDK 8.1 in Debug mode. Change the path appropriately, if a different WDK has been used.
01> Run ./uninstall.cmd to remove the old extension.
02> Run ./install.cmd to insert the new one. For this to work you will have to turn on TESTSIGNING boot option or 'Disable Driver Signature Enforcement' during boot. The following commands can be used: % bcdedit /set LOADOPTIONS DISABLEINTEGRITYCHECKS % bcdedit /set TESTSIGNING ON % bcdedit /set nointegritychecks ON
Note: you may have to restart the machine for the settings to take effect.
03> In the Virtual Switch Manager configuration you can enable the Open vSwitch Extension on an existing switch or create a new switch. If you are using an existing switch, make sure to enable the "Allow Management OS" option for VXLAN to work (covered later).
The command to create a new switch named 'OVS-Extended-Switch' using a physical NIC named 'Ethernet 1' is: % New-VMSwitch "OVS-Extended-Switch" -AllowManagementOS $true \ -NetAdapterName "Ethernet 1"
Note: you can obtain the list of physical NICs on the host using 'Get-NetAdapter' command.
04> In the properties of any switch, you should should now see "Open vSwitch Extension" under 'Extensions'. Click the check box to enable the extension. An alternative way to do the same is to run the following command: % Enable-VMSwitchExtension "Open vSwitch Extension" OVS-Extended-Switch
Note: If you enabled the extension using the command line, a delay of a few seconds has been observed for the change to be reflected in the UI. This is not a bug in Open vSwitch.
The following steps assume that you have installed the Open vSwitch utilities in the local machine via 'make install'.
01> Create the database. % ovsdb-tool create C:\openvswitch\etc\openvswitch\conf.db \ C:\openvswitch\usr\share\openvswitch\vswitch.ovsschema
02> Start the ovsdb-server and initialize the database. % ovsdb-server -vfile:info --remote=punix:db.sock --log-file --pidfile \ --detach % ovs-vsctl --no-wait init
If you would like to terminate the started ovsdb-server, run:
% ovs-appctl -t ovsdb-server exit
(Note that the logfile is created at C:/openvswitch/var/log/openvswitch/)
03> Start ovs-vswitchd. % ovs-vswitchd -vfile:info --log-file --pidfile --detach
If you would like to terminate the started ovs-vswitchd, run:
% ovs-appctl exit
(Note that the logfile is created at C:/openvswitch/var/log/openvswitch/)
04> Create integration bridge & pif bridge % ovs-vsctl add-br br-int % ovs-vsctl add-br br-pif
NOTE: There's a known bug that running the ovs-vsctl command does not terminate. This is generally solved by having ovs-vswitchd running. If you face the issue despite that, hit Ctrl-C to terminate ovs-vsctl and check the output to see if your command succeeded.
NOTE: There's a known bug that the ports added to OVSDB via ovs-vsctl don't get to the kernel datapath immediately, ie. they don't show up in the output of "ovs-dpctl show" even though they show up in output of "ovs-vsctl show". In order to workaround this issue, restart ovs-vswitchd. (You can terminate ovs-vswitchd by running 'ovs-appctl exit'.)
05> Dump the ports in the kernel datapath % ovs-dpctl show
Sample output is as follows:
% ovs-dpctl show system@ovs-system: lookups: hit:0 missed:0 lost:0 flows: 0 port 2: br-pif (internal) <<< internal port on 'br-pif' bridge port 1: br-int (internal) <<< internal port on 'br-int' bridge
06> Dump the ports in the OVSDB % ovs-vsctl show
07> Add the physical NIC and the internal port to br-pif.
In OVS for Hyper-V, we use the name of the adapter on top of which the Hyper-V virtual switch was created, as a special name to refer to the physical NICs connected to the Hyper-V switch. I.e. let us suppose we created the Hyper-V virtual switch on top of the adapter named 'Ethernet0'. In OVS for Hyper-V, we use that name('Ethernet0') as a special name to refer to that adapter.
Note: Currently, we assume that the Hyper-V switch on which OVS extension is enabled has a single physical NIC connected to it.
Internal port is the virtual adapter created on the Hyper-V switch using the
'AllowManagementOS' setting. This has already been setup while creating the
switch using the instructions above. In OVS for Hyper-V, we use a the name of
that specific adapter as a special name to refer to that adapter. By default it
is created under the following rule "vEthernet (
As a whole example, if we issue the following in a powershell console: PS C:\package\binaries> Get-NetAdapter | select Name,MacAddress,InterfaceDescription
Name MacAddress InterfaceDescription ---- ---------- -------------------- Ethernet1 00-0C-29-94-05-65 Intel(R) PRO/1000 MT Network Connection vEthernet (external) 00-0C-29-94-05-5B Hyper-V Virtual Ethernet Adapter #2 Ethernet0 00-0C-29-94-05-5B Intel(R) PRO/1000 MT Network Connection #2
PS C:\package\binaries> Get-VMSwitch
Name SwitchType NetAdapterInterfaceDescription ---- ---------- ------------------------------ external External Intel(R) PRO/1000 MT Network Connection #2
We can see that we have a switch(external) created upon adapter name 'Ethernet0' with an internal port under name 'vEthernet (external)'. Thus resulting into the following ovs-vsctl commands
% ovs-vsctl add-port br-pif Ethernet0
% ovs-vsctl add-port br-pif "vEthernet (external)"
Dumping the ports should show the additional ports that were just added. Sample output shows up as follows:
% ovs-dpctl show system@ovs-system: lookups: hit:0 missed:0 lost:0 flows: 0 port 4: vEthernet (external) (internal) <<< 'AllowManagementOS' adapter on Hyper-V switch port 2: br-pif (internal) port 1: br-int (internal) port 3: Ethernet0 <<< Physical NIC
% ovs-vsctl show a56ec7b5-5b1f-49ec-a795-79f6eb63228b Bridge br-pif Port "vEthernet (external)" Interface "vEthernet (external)" Port br-pif Interface br-pif type: internal Port "Ethernet0" Interface "Ethernet0" Bridge br-int Port br-int Interface br-int type: internal
08> Add the VIFs to br-int
Adding VIFs to openvswitch is a two step procedure. The first step is to assign a 'OVS port name' which is a unique name across all VIFs on this Hyper-V. The next step is to add the VIF to the ovsdb using its 'OVS port name' as key.
08a> Assign a unique 'OVS port name' to the VIF
Note that the VIF needs to have been disconnected from the Hyper-V switch before assigning a 'OVS port name' to it. In the example below, we assign a 'OVS port name' called 'ovs-port-a' to a VIF on a VM by name 'VM1'. By using index 0 for '$vnic', the first VIF of the VM is being addressed. After assigning the name 'ovs-port-a', the VIF is connected back to the Hyper-V switch with name 'OVS-HV-Switch', which is assumed to be the Hyper-V switch with OVS extension enabled.
Eg:
% import-module .\datapath-windows\misc\OVS.psm1
% $vnic = Get-VMNetworkAdapter <Name of the VM>
% Disconnect-VMNetworkAdapter -VMNetworkAdapter $vnic[0]
% $vnic[0] | Set-VMNetworkAdapterOVSPort -OVSPortName ovs-port-a
% Connect-VMNetworkAdapter -VMNetworkAdapter $vnic[0] \
-SwitchName OVS-Extended-Switch
08b> Add the VIFs to br-int in ovsdb
Eg:
% ovs-vsctl add-port br-int ovs-port-a
09> Verify the status % ovs-dpctl show system@ovs-system: lookups: hit:0 missed:0 lost:0 flows: 0 port 4: vEthernet (external) (internal) port 5: ovs-port-a port 2: br-pif (internal) port 1: br-int (internal port 3: Ethernet0
% ovs-vsctl show
4cd86499-74df-48bd-a64d-8d115b12a9f2
Bridge br-pif
Port "vEthernet (external)"
Interface "vEthernet (external)"
Port "Ethernet0"
Interface "Ethernet0"
Port br-pif
Interface br-pif
type: internal
Bridge br-int
Port br-int
Interface br-int
type: internal
Port "ovs-port-a"
Interface "ovs-port-a"
The Windows Open vSwitch implementation support VLAN tagging in the switch. Switch VLAN tagging along with patch ports between 'br-int' and 'br-pif' is used to configure VLAN tagging functionality between two VMs on different Hyper-Vs. The following examples demonstrate how it can be done:
01> Add a patch port from br-int to br-pif % ovs-vsctl add-port br-int patch-to-pif % ovs-vsctl set interface patch-to-pif type=patch \ options:peer=patch-to-int
02> Add a patch port from br-pif to br-int % ovs-vsctl add-port br-pif patch-to-int % ovs-vsctl set interface patch-to-int type=patch \ options:peer=patch-to-pif
03> Re-Add the VIF ports with the VLAN tag % ovs-vsctl add-port br-int ovs-port-a tag=900 % ovs-vsctl add-port br-int ovs-port-b tag=900
The Windows Open vSwitch implementation support VXLAN and STT tunnels. To add tunnels, the following steps serve as examples.
Note that, any patch ports created between br-int and br-pif MUST be beleted prior to adding tunnels.
01> Add the tunnel port between <IP_ADDRESS> <-> <IP_ADDRESS> % ovs-vsctl add-port br-int tun-1 % ovs-vsctl set Interface tun-1 type=port-type % ovs-vsctl set Interface tun-1 options:localip=<IP_ADDRESS> % ovs-vsctl set Interface tun-1 options:remoteip=<IP_ADDRESS> % ovs-vsctl set Interface tun-1 options:inkey=flow % ovs-vsctl set Interface tun-1 options:outkey=flow
02> Add the tunnel port between <IP_ADDRESS> <-> <IP_ADDRESS> % ovs-vsctl add-port br-int tun-2 % ovs-vsctl set Interface tun-2 type=port-type % ovs-vsctl set Interface tun-2 options:localip=<IP_ADDRESS> % ovs-vsctl set Interface tun-2 options:remoteip=<IP_ADDRESS> % ovs-vsctl set Interface tun-2 options:inkey=flow % ovs-vsctl set Interface tun-2 options:outkey=flow
Where port-type is the string stt or vxlan
We require that you don't disable the "Allow management operating system to share this network adapter" under 'Virtual Switch Properties' > 'Connection type: External network', in the HyperV virtual network switch configuration.
Checksum Offloads While there is some support for checksum/segmentation offloads in software, this is still a work in progress. Till the support is complete we recommend disabling TX/RX offloads for both the VM's as well as the HyperV.
Open vSwitch daemons come with support to run as a Windows service. The instructions here assume that you have installed the Open vSwitch utilities and daemons via 'make install'. The commands shown here can be run from MSYS bash or Windows command prompt.
Create the database.
% ovsdb-tool create C:/openvswitch/etc/openvswitch/conf.db \ "C:/openvswitch/usr/share/openvswitch/vswitch.ovsschema"
Create the ovsdb-server service and start it.
% sc create ovsdb-server binpath="C:/openvswitch/usr/sbin/ovsdb-server.exe C:/openvswitch/etc/openvswitch/conf.db -vfile:info --log-file --pidfile --remote=punix:db.sock --service --service-monitor"
One of the common issues with creating a Windows service is with mungled paths. You can make sure that the correct path has been registered with the Windows services manager by running:
% sc qc ovsdb-server
Start the service.
% sc start ovsdb-server
Check that the service is healthy by running:
% sc query ovsdb-server
Initialize the database.
% ovs-vsctl --no-wait init
Create the ovs-vswitchd service and start it.
% sc create ovs-vswitchd binpath="C:/openvswitch/usr/sbin/ovs-vswitchd.exe --pidfile -vfile:info --log-file --service --service-monitor"
% sc start ovs-vswitchd
Check that the service is healthy by running:
% sc query ovs-vswitchd
To stop and delete the services, run:
% sc stop ovs-vswitchd % sc stop ovsdb-server % sc delete ovs-vswitchd % sc delete ovsdb-server
AppVeyor (appveyor.com) provides a free Windows autobuild service for opensource projects. Open vSwitch has integration with AppVeyor for continuous build. A developer can build test his changes for Windows by logging into appveyor.com using a github account, creating a new project by linking it to his development repository in github and triggering a new build.
Investigate the working of sFlow on Windows and re-enable the unit tests.
Investigate and add the feature to provide QOS.
Sign the driver & create an MSI for installing the different OpenvSwitch components on windows.


Your Apartments
| # |
Address |
Location |
Status |
Actions |
{% for apartment in your_apartments %}
| {{ apartment.apartment_id }} |
{{ apartment.address }} |
{{ apartment.location }} |
{% if apartment.status == 'Sold' %}
{{ apartment.status }} |
{% elif apartment.status == 'Approved' %}
{{ apartment.status }} |
{% else %}
{{ apartment.status }} |
{% endif %}
|
{% endfor %}
Your Latest Searches
| # |
Age |
Query |
Actions |
{% for search in your_searches %}
| {{ search.search_id }} |
{{ search.history_age }} |
{{ search }} |
|
{% endfor %}
Apartments Pending Approval
| # |
Location |
Owner |
Status |
Actions |
{% for apartment in apartment_approvals %}
| {{ apartment.apartment_id }} |
{{ apartment.address }} - {{ apartment.location }} |
{{ apartment.owner }} |
{{ apartment.status }} |
|
{% endfor %}
Your Aprovals
| # |
Location |
Owner |
Status |
Actions |
{% for apartment in apartment_realtor %}
| {{ apartment.apartment_id }} |
{{ apartment.address }} - {{ apartment.location }} |
{{ apartment.owner }} |
{% if apartment.status == 'Sold' %}
{{ apartment.status }}
{% else %}
{{ apartment.status }}
{% endif %}
{% if apartment.featured %}
Featured
{% endif %}
|
{% if apartment.sold %}
{% endif %}
{% if apartment.featured %}
{% else %}
{% endif %}
|
{% endfor %}
For Sale pending aproval portfolio REAL ESTATE AGENT
| # |
Address |
Zip - City |
Status |
Actions |
| 1 |
Kalkofnsvegur 2 |
103 Reykjavík |
Pending |
|
| 1 |
Reynimelur 57 |
107 Reykjavik |
Pending |
|
| 1 |
Kalkofnsvegur 2 |
103 Reykjavík |
Pending |
|
| 1 |
Reynimelur 57 |
107 Reykjavik |
Pending |
|
For Sale Aproved portfolio REAL ESTATE AGENT
| # |
Address |
Zip - City |
Status |
Actions |
| 1 |
Krummahólar 3 |
111 Reykjavik |
Aproved |
|
| 1 |
Krummahólar 3 |
111 Reykjavik |
Aproved |
|
| 1 |
Krummahólar 3 |
111 Reykjavik |
Aproved |
|
Sold pending aproval portfolio REAL ESTATE AGENT
| # |
Address |
Zip - City |
Status |
Actions |
| 1 |
Geirsgata 23 |
109 Reykjavík |
Pending |
|
| 1 |
Grundargata 5 |
101 Reykjavik |
Pending |
|
Sold aproved portfolio REAL ESTATE AGENT
| # |
Address |
Zip - City |
Status |
Actions |
| 1 |
Framnesvegur 33 |
111 Reykjavik |
Aproved |
|
| 1 |
Framnesvegur 33 |
111 Reykjavik |
Aproved |
|


"
Thank you for participating in our marketing survey. We value your opinions and inputs. You will see three product images at a time and you need to select the product you would most likely purchaseo of the three shown by clicking on that image with your mouse. As soon as you make your selection three new product images will be shown. Continue to make your selection from each group of three until you have made a total of 25 selections for each round. You will be given the results of your selections after each round. Feel free to do as many rounds as you wish.


A New Dawn for
Corporate
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose
There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form by injected humour,or randomised words which dont look even slightly believable. If you are going to use a passage of Lorem Ipsum, you need to be sure there isnt anything embarrassing hidden in the middle of text.
"Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting"
Copyright ©2017 Consultis. All Rights Reserved
Comment(3)
Selana Gomez | Chief Executive Officer
octobar 19,2018There are many variations of passages of Lorem Ipsum available, but the majority have alteration in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum, you need to be sure there isn't anything barrassing hidden in the middle of text.
108 ReplaySelana Gomez | Chief Executive Officer
octobar 19,2018There are many variations of passages of Lorem Ipsum available, but the majority have alteration in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum, you need to be sure there isn't anything barrassing hidden in the middle of text.
108 ReplaySelana Gomez | Chief Executive Officer
octobar 19,2018There are many variations of passages of Lorem Ipsum available, but the majority have alteration in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum, you need to be sure there isn't anything barrassing hidden in the middle of text.
108 ReplayLeave a Replay


The asset plugin creates links to assets such as css, javascript, and images.
Each function in the asset takes two parameters:
| Parameter | Description |
|---|---|
| file | Required. The name of the image, javascript, or css file. |
| module | Required. The slug of the module where the image, javascript, or css file you are looking for is. |
Aside from these two parameters, each function will take any more parameters that are appropriate to the item being generated and use those. For example, you can pass an 'alt' parameter to an asset function dealing with images, and it will use it.
Each asset type has functions for the normal insert (an img tag for images, for instance), and the plain URLs and Paths. URLs are complete URLs, and paths are paths system paths to each of these assets.
Generates a css insert link tag.
Generates a full url to a css asset.
Generates a path to a css asset.
Generates an img tag.
Generates a full url to an image asset.
Generates a path to an image asset.
Generates a js script insert.
Generates a full url to a js asset.
Generates a path to a js asset.


Welcome to the Super Emote Tool!
What is this thing anyways?
I used to be like you hopping around to all the different pony subs trying to find the emote I wanted to use.
So I figured why not put them all in one place? After some serious nose to the grindstone copy pasta and things of that nature
the Super Emote Toool was born!
The page for (x) is missing some emotes!
First thing to note is I try to only include emotes unique to the sub.
I do this to reduce clutter and confusion and also because a lot of people use scripts to see emotes from other subs everywhere on reddit already!
Other than that if you feel that I have genuinely missed an emote please shoot me an
email!
Would you add my sub to the tool?
Shoot me an
email and we can discuss it! Keep in mind that it should have unique emotes and be pony related.
Who is the best pony?
Why that would be Cheerilee, thanks for asking!
Whats New in v0.04:
- - Removed the click-ability of the emotes.
- - When mousing over an emote it will show the code used to produce that emote.
Whats New in v0.03:


Please complete your speaker account by updating your profile and proposal(s).
Contact Brooke Meyer at<EMAIL_ADDRESS>or<PHONE_NUMBER> if you have any questions or issues.
PLEASE REVIEW: Speaker Details
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Add Co-Presenters
Select the presentation you’d like to add a co-presenter to.
If this list is empty, please complete a presentation proposal first.
Please select a co-presenter from the list below or select ‘Create New Co-Presenter’.


---
sort_order: 75
tei_id: rdx_b5h9g.p.idm185056928
annotation_count: 0
images:
small-thumbnail: https://readux.library.emory.edu/books/emory:b5h9g/pages/emory:pcksc/mini-thumbnail/
json: https://readux.library.emory.edu/books/emory:b5h9g/pages/emory:pcksc/info/
full: https://readux.library.emory.edu/books/emory:b5h9g/pages/emory:pcksc/fs/
page: https://readux.library.emory.edu/books/emory:b5h9g/pages/emory:pcksc/single-page/
thumbnail: https://readux.library.emory.edu/books/emory:b5h9g/pages/emory:pcksc/thumbnail/
title: Page 75
number: 75
---
{% raw %}JACOB HODGES.{% endraw %}
{% raw %}67{% endraw %}
{% raw %}prison discipline, and regarding Jacob as{% endraw %}
{% raw %}among the first and happiest results of{% endraw %}
{% raw %}that system which he had been chiefly{% endraw %}
{% raw %}instrumental in introducing to many of{% endraw %}
{% raw %}our penitentiaries, he became greatly in¬{% endraw %}
{% raw %}terested in his history. He had formed{% endraw %}
{% raw %}an exalted conception of his character as{% endraw %}
{% raw %}a Christian, and cherished profound ad¬{% endraw %}
{% raw %}miration for his superior order of mind.{% endraw %}
{% raw %}[n this he was not alone. The most ca¬{% endraw %}
{% raw %}sual observer was struck with the superior{% endraw %}
{% raw %}manliness and dignity of his deportment:{% endraw %}
{% raw %}and Judge Van Ness, when pronouncing{% endraw %}
{% raw %}his sentence of death, said to him, " You{% endraw %}
{% raw %}possess a more than ordinary strength of{% endraw %}
{% raw %}mind.  Whenever he visited Jacob, he{% endraw %}
{% raw %}was accustomed to request him to lead in{% endraw %}
{% raw %}prayer, and he remarked that he never feh{% endraw %}


§8-4 Buddha Day. April8 of each year shall be known and designated as "Buddha Day", provided that this day is not and shall not be construed to be a state holiday. [L 1963, c 162, §1; Supp, §1-43; HRS §8-4]


Some of the other codes that can be used are :
%a The abbreviated weekday name according to the cur rent locale. %A The full weekday name according to the current locale. %b The abbreviated month name according to the current locale. %B The full month name according to the current locale. %c The preferred date and time representation for the current locale. %C The century number (year/100) as a 2-digit integer. %d The day of the month as a decimal number (range 01 to 31). %e Like %d, the day of the month as a decimal number, but a leading zero is replaced by a space. %E Modifier: use alternative format, see below. %G The ISO 8601 year with century as a decimal number. The 4-digit year corresponding to the ISO week num ber (see %V). This has the same format and value as %y, except that if the ISO week number belongs to the previous or next year, that year is used instead. %g Like %G, but without century, i.e., with a 2-digit year (00-99). %h Equivalent to %b. %H The hour as a decimal number using a 24-hour clock (range 00 to 23). %I The hour as a decimal number using a 12-hour clock (range 01 to 12). %j The day of the year as a decimal number (range 001 to 366). %k The hour (24-hour clock) as a decimal number (range 0 to 23); single digits are preceded by a blank. (See also %H.) %l The hour (12-hour clock) as a decimal number (range 1 to 12); single digits are preceded by a blank. (See also %I.) %m The month as a decimal number (range 01 to 12). %M The minute as a decimal number (range 00 to 59). %n A newline character. %O Modifier: use alternative format, see below. %p Either `AM' or `PM' according to the given time value, or the corresponding strings for the current locale. Noon is treated as `pm' and midnight as `am'. %P Like %p but in lowercase: `am' or `pm' or a corre sponding string for the current locale. %r The time in a.m. or p.m. notation. In the POSIX locale this is equivalent to `%I:%M:%S %p'. %R The time in 24-hour notation (%H:%M). For a version including the seconds, see %T below. %s The number of seconds since the Epoch, i.e., since 1970-01-01 00:00:00 UTC. %S The second as a decimal number (range 00 to 61). %t A tab character. %T The time in 24-hour notation (%H:%M:%S). %u The day of the week as a decimal, range 1 to 7, Monday being 1. See also %w. %U The week number of the current year as a decimal number, range 00 to 53, starting with the first Sunday as the first day of week 01. See also %V and %W. %V The ISO 8601:1988 week number of the current year as a decimal number, range 01 to 53, where week 1 is the first week that has at least 4 days in the current year, and with Monday as the first day of the week. See also %U and %W. %w The day of the week as a decimal, range 0 to 6, Sunday being 0. See also %u. %W The week number of the current year as a decimal number, range 00 to 53, starting with the first Monday as the first day of week 01. %x The preferred date representation for the current locale without the time. %X The preferred time representation for the current locale without the date. %y The year as a decimal number without a century (range 00 to 99). %Y The year as a decimal number including the century. %z The time-zone as hour offset from GMT. Required to emit RFC822-conformant dates (using "%a, %d %b %Y %H:%M:%S %z"). %Z The time zone or name or abbreviation. %% A literal `%' character.


Quality
Quality-controlled variable bitrate. CRF is the default rate control method; it does not try to reach any particular bitrate target, instead it tries to achieve a given uniform quality and the size of the bitstream is determined by the complexity of the
source video. The higher the rate factor the higher the quantization and the lower the quality.
or
Setting this to anything other than 0 will disable CRF and enable VBR encoding.
{{preset}}
Trade Quality for Encoding Speed
Sets parameters to preselected values, trading off compression efficiency against encoding speed. These parameters are applied before all other input parameters are applied, and so you can override any parameters that these values control.
Use ffmpeg format. Example: bframes=6:pmode:psy-rd=3.2


If you need tickets of multiple types; just select one Pass Type for now, then Add it to your Order and come back for more!
You can also : Favourite this Order! Order your Favourite! Check Loyalty Points!
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Cost of the Current Order(s) : Rs.0
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Client send this snac as reply to SNAC(04,07).
Server transfer it to client without changes, but only if its format is valid.
This snac also used for auto-messages (away message/busy message/chat message/etc).
First dword in msg-id cookies is actually the computer uptime in milliseconds
(ie GetTickCount(), at least this is what the ICQ Lite and the other official
clients I have tested uses).
| 00 04 |
|
word |
|
SNAC family |
| 00 0B |
|
word |
|
SNAC subtype |
| 00 00 |
|
word |
|
SNAC flags |
| xx xx xx xx |
|
dword |
|
SNAC request-id |
|
xx xx xx xx xx xx xx xx |
|
qword |
|
msg-id cookie |
| |
| xx xx |
|
word |
|
message channel (see table below) |
| xx |
|
byte |
|
screenname string length |
| xx .. |
|
string |
|
screenname string |
| 00 03 |
|
word |
|
reason code (1 - unsupported channel, 2 - busted payload, 3 - channel specific) |
|
|
If channel is supported, here is channel-specific data
(see references below).
|
|
|


My name is Shimin Ho or you can call me Astrid. I'm an Autodidact in multiple languages and web development. Exposures to various industries and volunteering opportunities have enriched my learning experiences in life, which I find myself deeply-humbled by it. This website is a product of what I've learned!
- MS Office Applications
- Python
- Networking Foundations: LAN
- Communication
- Language: English, 中文, Deutsch, Norsk & 日本語
- Animal Husbandry
- Veterinary Nursing
- Front-end Web Development
- Ngee Ann Polytechnic | Diploma in Veterinary Bioscience | 2013 - 2016
Work
- Business Support Executive | PIAS | DEC 2020 - SEP 2021
- Process Executive | InfoSys BPM Limited | OCT 2018 - MAY 2019
- Veterinary Technician | Westcoast Vetcare | APR 2018 - SEP 2018
- Pet Shop Assistant | Pet Lovers Centre | MAY 2017 - OCT 2017
Volunteering
- Member & Assistant Treasurer | CCK Youth Executive Committee | NOV 2018 - PRESENT
- Member & Ad-hoc Volunteer | Singapore Red Cross | 2014 - 2017
- Member | Ngee Ann Polytechnic Rotaractors' Club | 2014 - 2016
- Member | Ngee Ann Polytechnic Leo Club | 2013 - 2016


Select a store from the drop-down and click Retrieve to display entries.
Changelog Entries:
Timestamps below are expressed in GMT. (Your offset is {{form.timezone}}.)
User:
{{change.user}}
Date:
{{change.datestamp}}
Summary:
{{change.summary}}
Commit ID:
{{change.revisionId.substring(0,7)}}


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You must collect your biometric residence permit (BRP) card within 10 days of arriving in the UK. You'll need your passport.

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