Welcome to the ManageIQ Community!
We are gearing up for an open source release in the coming weeks, and you’re probably curious what all the fuss is about. What you’ll find here is some introductory information and ways to sign up for news and, eventually, pre-release code as we get closer to the project launch. Read Red Hat’s press release – it will give you the background information you need.
Founding Community Members
As part of this announcement, we’re proud to announce that four Founding Community Members have joined the ManageIQ Community: AutoTrader Group, Booz Allen Hamilton, Chef, and CiRBA. Some of them have already produced high quality integrations that demonstrate the power of the ManageIQ platform. Look for more information from them over the coming weeks as they prepare their open source releases for the ManageIQ platform.
Where’s the Code, Dude?
You may be wondering, “Where’s the code?” The full project release will take place in a matter of weeks and will include full source code and builds. ManageIQ will be an open source cloud management platform, with lots of open source apps and add-on content that makes it usable for a number of users. Red Hat will remain a significant contributor to ManageIQ, but our goal is to create a community that has a wide variety of participating organizations from end users to ISVs to OEMs and more.
Why Cloud Management?
ManageIQ is about more than any single virtualization or cloud platform. It’s about managing resources in a hybrid cloud world. For example, OpenStack is a wonderful cloud platform, but many OpenStack deployments are side-by-side with services running in AWS, vSphere, RHEV/oVirt, Hyper-V, and a variety of other cloud and virtualization platforms. ManageIQ allows admins and operators to abstract away from the specific tooling and focus on managing applications and services, regardless of platform and tooling. For example, with ManageIQ, you have the capability of blocking or allowing services running on specific platforms based on a number of parameters: geographic location, pricing, performance, etc. Add logic and business processes that allow you to EOL specific tools and platforms, migrating existing services to updated providers, or providers that have patched specific security holes, eg. heartbleed.
The Gateway to the Open Cloud
We feel that the recent cloud wars are a replay of the previous Unix wars and the server application wars. In each case, proprietary vendors used whatever means necessary to lock in customers, essentially charging them a tax for migrating to another platform. The good news is that customers eventually resolved each of those in their favor with Linux and open source ecosystems for the former and web-based applications for the latter. Unfortunately, lots of money was wasted and efficiency lost along the way.
With cloud infrastructure and services, we’re fighting the same battle. It’s a burgeoning market and a host of vendors are lining up to keep customers in their place (on their platform). We believe that ManageIQ will be a way for cloud admins and operators to fully unleash the power of cloud computing, allowing them to abstract services away or towards particular platforms. This puts more power and choice into the hands of the people who need it most: you. You’re the one whose job depends on managing all these services efficiently. You’re the one charged with making sure applications running on any platform are fully patched, running efficiently, and not exposing your employer to more risk than anticipated.
Today is a great day to be in the cloud.


Please note that a more detailed installation procedure is described in the Ganeti installation tutorial. Refer to it if you are setting up Ganeti the first time. This quick installation guide is mainly meant as reference for experienced users. A glossary of terms can be found in the Glossary.
Before installing, please verify that you have the following programs:
These programs are supplied as part of most Linux distributions, so usually they can be installed via the standard package manager. Also many of them will already be installed on a standard machine. On Debian/Ubuntu, you can use this command line to install all required packages, except for RBD, DRBD and Xen:
$ apt-get install lvm2 ssh bridge-utils iproute iputils-arping make m4 \
ndisc6 python python-openssl openssl \
python-pyparsing python-simplejson python-bitarray \
python-pyinotify python-pycurl python-ipaddr socat fping
Note that the previous instructions don’t install optional packages. To install the optional package, run the following line.:
$ apt-get install python-paramiko python-psutil qemu-utils
If some of the python packages are not available in your system, you can try installing them using easy_install command. For example:
$ apt-get install python-setuptools python-dev
$ cd / && easy_install \
psutil \
bitarray \
ipaddr
On Fedora to install all required packages except RBD, DRBD and Xen:
$ yum install openssh openssh-clients bridge-utils iproute ndisc6 make \
pyOpenSSL pyparsing python-simplejson python-inotify \
python-lxm socat fping python-bitarray python-ipaddr
For optional packages use the command:
$ yum install python-paramiko python-psutil qemu-img
If you want to build from source, please see doc/devnotes.rst for more dependencies.
Note
Ganeti’s import/export functionality uses socat with OpenSSL for transferring data between nodes. By default, OpenSSL 0.9.8 and above employ transparent compression of all data using zlib if supported by both sides of a connection. In cases where a lot of data is transferred, this can lead to an increased CPU usage. Additionally, Ganeti already compresses all data using gzip where it makes sense (for inter-cluster instance moves).
To remedey this situation, patches implementing a new socat option for disabling OpenSSL compression have been contributed and will likely be included in the next feature release. Until then, users or distributions need to apply the patches on their own.
Ganeti will use the option if it’s detected by the configure script; auto-detection can be disabled by explicitly passing --enable-socat-compress (use the option to disable compression) or --disable-socat-compress (don’t use the option).
The patches and more information can be found on http://www.dest-unreach.org/socat/contrib/socat-opensslcompress.html.
Starting with Ganeti 2.7, the Haskell GHC compiler and a few base libraries are required in order to build Ganeti (but not to run and deploy Ganeti on production machines). More specifically:
Some of these are also available as package in Debian/Ubuntu:
$ apt-get install ghc libghc-json-dev libghc-network-dev \
libghc-parallel-dev \
libghc-utf8-string-dev libghc-curl-dev \
libghc-hslogger-dev \
libghc-crypto-dev libghc-text-dev \
libghc-hinotify-dev libghc-regex-pcre-dev \
libpcre3-dev \
libghc-attoparsec-dev libghc-vector-dev \
libghc-zlib-dev
Debian Jessie also includes recent enough versions of these libraries:
$ apt-get install libghc-base64-bytestring-dev \
libghc-lens-dev \
libghc-lifted-base-dev
In Fedora, some of them are available via packages as well:
$ yum install ghc ghc-json-devel ghc-network-devel \
ghc-parallel-devel ghc-deepseq-devel \
ghc-hslogger-devel ghc-text-devel \
ghc-regex-pcre-devel
The most recent Fedora doesn’t provide crypto, inotify. So these need to be installed using cabal.
If using a distribution which does not provide these libraries, first install the Haskell platform. You can also install cabal manually:
$ apt-get install cabal-install
$ cabal update
Then install the additional native libraries:
$ apt-get install libpcre3-dev libcurl4-openssl-dev
And finally the libraries required for building the packages (only the ones not available in your distribution packages) via cabal:
$ cabal install json network parallel utf8-string curl hslogger \
Crypto text hinotify==0.3.2 regex-pcre \
attoparsec vector base64-bytestring \
lifted-base==<IP_ADDRESS> lens==3.10
(The specified versions are suitable for Debian Wheezy, for other distributions different versions might be needed.)
Note
When installing additional libraries using cabal, be sure to first install all the required libraries available in your distribution and only then install the rest using cabal. Otherwise cabal might install different versions of libraries that are available in your distribution, causing conflicts during the compilation. This applies in particular when installing libraries for the optional features.
Optionally, more functionality can be enabled if your build machine has a few more Haskell libraries enabled: the ganeti-confd daemon (--enable-confd), the monitoring daemon (--enable-monitoring) and the meta-data daemon (--enable-metadata). The extra dependency for these is:
This library is available in Debian Wheezy or later, so you can use either apt:
$ apt-get install libghc-snap-server-dev
or cabal:
$ cabal install snap-server
to install it.
Note
If one of the cabal packages fails to install due to unfulfilled dependencies, you can try enabling symlinks in ~/.cabal/config.
Make sure that your ~/.cabal/bin directory (or whatever else is defined as bindir) is in your PATH.
To install, simply run the following command:
$ ./configure --localstatedir=/var --sysconfdir=/etc && \
make && \
make install
This will install the software under /usr/local. You then need to copy doc/examples/ganeti.initd to /etc/init.d/ganeti and integrate it into your boot sequence (chkconfig, update-rc.d, etc.). Also, Ganeti uses symbolic links in the sysconfdir to determine, which of potentially many installed versions currently is used. If these symbolic links should be added by the install as well, add the option --enable-symlinks to the configure call.
Before initialising the cluster, on each node you need to create the following directories:
After this, use gnt-cluster init.


Cardillac
Welcome to Car Price Predictor
This Web app predicts the price of a car you want to sell. Just fill the details below:
Select the company
{% for company in companies %}
{{ company }}
{% endfor %}
Select the model
Select Year of Purchase
{% for year in years %}
{{ year }}
{% endfor %}
Select the Fuel Type
{% for fuel in fuel_types %}
{{ fuel }}
{% endfor %}
Enter the Number of Kilometres that the car has travelled
Predict Price


Tethered is an experiment in movement-specific sound creation, making use of tethers, motion sensors, and an interactive sculptural object which I have named the Jellyfish.
Drone and Drums
Jason Treuting
Drone and Drums Translated is the 4th iteration of a solo I made for a collaboration I did with the French choreographers in Projet in Situ several years ago that explores an expanding and contracting 3:2 polyrhythm. It has had a life as a solo drum set piece, a song that So Percussion made with the guitarist/singer Grey Mcmurray, a quartet of flute/cello/guitar/drums made for a collaboration with Keila Perez-Vega and Duende for their 2017 Break my Echo show in Philadelphia and now, this electrified version for PLOrk. The orchestration was a collaboration between myself, Mike, Matt and Rajeev and the choices of synth sounds and processed trombone are as important to this arrangement as the notes on the page.
EVQ 1
Alex Dowling
This piece uses and misuses vocal effects such as autotune and harmonizers to create a type of ‘electronically-augmented’ choir.
Keylogger
PLOrk
What do a photocopier and keyboard have in common? Well, they are both sites of human intervention on otherwise mechanical devices. Keylogger, and the accompanying visual performance This is a Computer Science Department Independent Work project, are interested in working within the confines of their respective technologies and mediums, pushing them to the limits of what they can do, by using live coding and live photocopying.
Keylogger is simultaneously performed and written – it does not exist before the performance and will only exist after as a recording (if recorded). Its success as a live coding/photocopying piece depends on the fact that each performance is entirely improvisational, reacting in the moment, and human in nature.
Special thanks to Charlie Roberts, who created the programming language Gibberwocky, which we are using in this piece.
Opposite Earth
Jeff Snyder
This is an animated notation piece for an open instrumentation. It was written for PLOrk and is intended to be performed by any combination of acoustic and electronic instruments. The "conductor" changes the image components in real-time to guide the course of the piece. For instance, the conductor can add and remove rings (performers), planets (pitches), and ticks or open circles (percussive sounds) at will. The color of the lines that the elements cross conveys which of five pitch gamuts are used. Planet/moon collisions are sonically represented by robotically actuated percussion instruments. Thanks to Drew Wallace for much of the visual Javascript magic. Thanks to Steven Kemper for the percussion robots.


The following are some of the typing equations and formulas that are used in calculating your typing statistics when you take our free typing test.
Gross, or Raw WPM (Words Per Minute) is a calculation of exactly how fast you type with no error penalties. The gross typing speed is calculated by taking all words typed and dividing by the time it took to type the words in minutes.
When calculating typing speed, a "word" is any five characters. For instance, "I love keyboarding, don't you?" would be counted as 6 words (30 characters / 5) and not just 5. This makes sense because typing "deinstitutionalization" obviously should count more than typing "my". Spaces, numbers, letters, and punctuation are all included, but any function keys such as Shift or Backspace are not included.
This makes the number of words easy to calculate. Simply count all typed entries and divide by five to get the number of words typed. To give an example, if you typed 200 characters in 1 minute, your net wpm typing speed would be (200 characters / 5) / 1 min = 40 WPM. If you typed 200 characters in 30 seconds your net speed would be (200/5) / 0.5 = 80 WPM.
 However, Gross WPM is used in the calculation of the Net WPM calculation which merits its mention.
Net WPM is argueably the most useful tool in gauging typing abilities. Since errors play a part in its calculation, it is more a measure of typing productivity than of just typing speed. In other words, a fast but error-prone typist will receive a lower net typing speed than a slower but more accurate typist - relatively speaking of course.
This makes sense because proof-reading and correcting errors takes up more time than simply typing a passage correctly in the first place. Less mistakes also means less chance for errors being missed during proof-reading and making their way into the final product.
To calculate Net WPM, take your gross WPM result and subtract the amount of errors you left in per minute, also known as the error rate. To calculate error rate, simply divide the number of errors by the time you typed for in minutes. For example, if you typed for two minutes with a gross typing speed of 80 WPM and left in 8 mistakes, your error rate would be (8 errors / 2 mins) = 4 errors per minute. Your net typing speed would then be (80 - 4) = 76 WPM. Note that for every mistake you make per minute your typing speed goes down by 1 WPM.
This is a rather simple equation - the hard part is actually deciding which types of errors should be counted. There are two kinds of errors:
The first impulse is to count all errors regardless of correction, however there are two strong arguments as to why only the uncorrected errors should be penalized:
1) Fixing mistakes not only messes up the typist's rhythm and likely causes brief pauses, but function keys like the delete key take time to press and do not count as a keyed entry. This means that the time penalty is already built into the process of correcting a mistake. An additional time penalty on top of the inherent time penalty discourages the typist from correcting their mistakes in the first place. While typists should be encouraged to avoid mistakes at all costs, they should also be encouraged and not discouraged from fixing mistakes as they are typing. Errors left in uncorrected are much more devastating and undesirable than errors fixed immediately while typing.
2) Take the case of a large number of corrected errors. Assume a one minute test was taken with a typing speed of 20 WPM. 20 errors were made during the test but all of them were immediately corrected. If we then deduct the error penalty considering all errors, we calculate the net speed as 0 WPM! An error-free length of text was obviously typed (20 * 5 - 20 = 80 characters to be exact), yet a typing speed of 0 WPM suggests nothing was ever typed. If the mistakes were left uncorrected, obviously one-fifth of a passage typed incorrectly would be a blunder to proofread and 0 WPM would be acceptable since the resulting text would be useless. The typist is still encouraged to reduce errors because obviously they would have typed faster than 20 WPM if they hadn't wasted time correcting 20 errors!
These two points more than prove that only errors left uncorrected should be penalized.
Typing accuracy is defined as the percentage of correct entries out of the total entries typed. To calculate this mathematically, take the number of correct characters typed divided by the total number, multiplied by 100%. So if you typed 90 out of 100 characters correctly you typed with 90% accuracy.
It is interesting to note that all errors, whether corrected or not, should be counted in the accuracy calculation, unlike the net WPM calculation. This is because the calculation is more "live" than typing speed and literally describes the likelihood that the next character will be typed correctly, regardless of whether it will be corrected or not.


Licence agreement
This appendix includes the terms of the MNE software End-User
License Agreement (EULA).
THE GENERAL HOSPITAL CORPORATION
ACADEMIC RESEARCH USE
SOFTWARE LICENSE AGREEMENT FOR BINARY CODE
By downloading and/or using the MNE software which is the
subject of this Agreement (the “Software”), you
hereby accept and agree to all of the terms and conditions of this
Agreement. As used in this Agreement, “you” means
the individual who clicks the “I accept” button
required as a condition of downloading the Software and the not-for-profit
or governmental institution or entity which employs or is otherwise
affiliated with such individual at the time of such download (the “Institution”).
- License Grant. Subject
to all of the terms and conditions of this Agreement, [The General
Hospital Corporation, d/b/a Massachusetts General Hospital] [The
Brigham and Women’s Hospital, Inc.] (“Licensor”) hereby
grants you a non-exclusive, non-transferable, non-sublicensable license
under Licensor’s rights in the Software to copy and use the binary
code of the Software solely for research and educational purposes
under your direction at the Institution (“Research and
Educational Purposes,” which term shall include company
sponsored research conducted by you in accordance with Institution’s
policies).
- No Transfer. You may not sell, license,
distribute, rent, lease, offer on an ASP or service bureau basis,
grant a security interest in or otherwise transfer the Software
to any third party or use the Software for any commercial purpose.
- Installation and Maintenance. You are
solely responsible for installing and maintaining the Software and
for testing the Software for proper operation. Licensor shall have
no obligation to provide any support, maintenance, corrections,
debugging, improvements, modifications, upgrades or updates of the
Software or notice of any of the forgoing, or otherwise assist Licensee
in installing, configuring, integrating, understanding or using
the Software.
- Attributions and Acknowledgments. You
must preserve and maintain all applicable attributions, copyright
notices and licenses included in or applicable to the Software.
You agree to provide acknowledgement of Licensor and its designated
professional staff who participated in the creation of the Software
in publications or presentations in accordance with standard academic
practice, provided that you may not otherwise use Licensor’s name,
logos or trademarks or the name of any individual associated with
Licensor, or of any funding agency, in any advertising, promotional
or sales material or publicity or in any document employed to obtain
funds or financing, or to endorse or promote any research results
or products related to or arising from the Software, without the
prior written consent of a person authorized to make such consent.
- Third Party Software. This Agreement
does not grant any rights with respect to any third party software,
except those rights that Licensor has been authorized by a third
party to grant to you, and accordingly you are solely responsible
for obtaining any permissions from third parties which are necessary
to use and copy the Software.
- Compliance with Law. You must comply
with all applicable governmental laws, regulations and orders, including
without limitation those relating to export and import control,
in exercising your rights under this Agreement.
- Termination. You may terminate this
Agreement at any time by destroying all copies of the Software.
Licensor may terminate this Agreement at any time by providing
notice to you of such termination. Any use or copying of the Software
by you which is inconsistent with the terms and conditions of this
Agreement shall automatically render this Agreement null and void
and terminate the license granted hereunder. Upon any termination
of this Agreement you must stop using the Software and return or
destroy all copies of the Software, including any portion thereof.
- DISCLAIMERS.  The Software is provided “AS
IS”, is experimental, may contain errors and is subject to further
development and revision. Licensor does not guarantee the accuracy
of the Software, or of any results or data.  IN NO EVENT
SHALL LICENSOR OR ANY OF ITS TRUSTEES, DIRECTORS, OFFICERS, MEDICAL OR
PROFESSIONAL STAFF, EMPLOYEES, STUDENTS OR AGENTS (“LICENSOR’S
PERSONNEL”) BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT,
SPECIAL, INCIDENTAL, EXEMPLARY OR CONSEQUENTIAL DAMAGES HOWEVER CAUSED
AND ON ANY THEORY OF LIABILITY ARISING IN ANY WAY RELATED TO THE
SOFTWARE, EVEN IF LICENSOR OR ANY OF LICENSOR’S PERSONNEL HAS BEEN
ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.  For Software
supported by federal funding, the license granted under this Agreement
is subject to the rights, conditions and limitations imposed by
U.S. law including without limitation 35 U.S.C. § 202 et
seq. and regulations pertaining thereto.
- General. This Agreement constitutes
the entire understanding between you and Licensor with respect to
the subject matter hereof, and supercedes any prior or contemporaneous
oral or written agreements with respect thereto. C API Reference,
Browsing raw data with mne_browse_raw, The minimum-norm current estimates, Morphing and averaging, and Utilities
shall survive any termination of this Agreement. This
Agreement may be modified or amended only in a writing signed by
duly authorized representatives of both Parties hereto. The invalidity
or unenforceability of any provision of this Agreement shall not
affect any other provision hereof. This Agreement and the license
granted hereunder may not be assigned


| reverting_id | reverting_user_text | rev_user_text | reverting_comment | diff | |
|---|---|---|---|---|---|
| 110640 | 102554022 | Bluebot | NekoDaemon | Robot: Changing category U.S. philosophers | --- \n +++ \n @@ -4,5 +4,5 @@\n {{philosopher-stub}} +[[Category:American philosophers]] [[Category:American writers]] -[[Category:U.S. philosophers]] |
| 132523 | 61011849 | Cydebot | Kbdankbot | Robot - Speedily moving category 8th-century in religion to 8th century in religion per [[WP:CFD|CFD]]. | --- \n +++ \n @@ -51,7 +51,7 @@\n [[Category:Muslim dynasties]] [[Category:Tbilisi]] [[Category:736 establishments]] -[[Category:8th-century in religion]] +[[Category:8th century in religion]] [[Category:9th-century in religion]] [[Category:10th-century in religion]] [[Category:11th-century in religion]] |
| 132524 | 61011850 | Cydebot | Kbdankbot | Robot - Speedily moving category 9th-century in religion to 9th century in religion per [[WP:CFD|CFD]]. | --- \n +++ \n @@ -52,7 +52,7 @@\n [[Category:Tbilisi]] [[Category:736 establishments]] [[Category:8th century in religion]] -[[Category:9th-century in religion]] +[[Category:9th century in religion]] [[Category:10th-century in religion]] [[Category:11th-century in religion]] [[Category:1122 disestablishments]] |
| 132525 | 61011851 | Cydebot | Kbdankbot | Robot - Speedily moving category 10th-century in religion to 10th century in religion per [[WP:CFD|CFD]]. | --- \n +++ \n @@ -53,7 +53,7 @@\n [[Category:736 establishments]] [[Category:8th century in religion]] [[Category:9th century in religion]] -[[Category:10th-century in religion]] +[[Category:10th century in religion]] [[Category:11th-century in religion]] [[Category:1122 disestablishments]] [[ca:Emirat de Tblisi]] |
| 132526 | 61011853 | Cydebot | Kbdankbot | Robot - Speedily moving category 11th-century in religion to 11th century in religion per [[WP:CFD|CFD]]. | --- \n +++ \n @@ -54,7 +54,7 @@\n [[Category:8th century in religion]] [[Category:9th century in religion]] [[Category:10th century in religion]] -[[Category:11th-century in religion]] +[[Category:11th century in religion]] [[Category:1122 disestablishments]] [[ca:Emirat de Tblisi]] [[he:אמירות טביליסי]] |
| 138068 | 66465228 | Bluebot | NekoDaemon | Robot: Changing category United States musicians | --- \n +++ \n @@ -6,5 +6,5 @@\n Mr. Rome's signature style is marked by lush orchestrations, interweaving multidimensional horns and strings to accentuate the sensuality of the compositions. His various other projects include [[Jimmy Ruffin]]'s ''Fallin' in Love with You'' (1977), [[The Sweet Inspirations]]' ''Black Sunday'' (1977), [[Chi-Lites]]' ''My First Mistake'' (1977), Flower's ''Midnight Dancing'', ''Now'', and ''Our Never-Ending Love'' (1979), and, in collaboration with [[Phil Hurtt]], [[Stanley Turrentine]]'s ''Disco Dancing'' (1978) and Phil Hurtt's ''Giving It Back'' (1978). +[[Category:American musicians|Rome, Richie]] [[Category:Disco|Rome, Richie]] -[[Category:United States musicians|Rome, Richie]] |


{# 1. Extend the relevant layout #} {# --------------------------------------- #} {% extends 'sidebar-standard.html' %} {# --------------------------------------- #} {# 2. Assign Settings and variables #} {# --------------------------------------- #} {% set id = "standard" %} {% set section = "standard" %} {% set title = "" %} {% set hero_copy = "Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua." %} {# --------------------------------------- #} {# 3. Add models and macros #} {# --------------------------------------- #} {% import 'macros/button.html' as buttons %} {% import 'macros/icon.html' as icons %} {% import 'macros/alert.html' as alerts %} {# --------------------------------------- #} {# 4. Add page content into relevant areas Add/Delete blocks as necessary #} {# --------------------------------------- #} {% block hero %} {% include 'heroes/hero-standard.html' %} {% endblock %} {% block aside %}
The IATI standard consists of a number of schema, the main ones being Organisation and Activity.
Summary tables of the Activity Schema and of the Organisation Schema are also available.
The current suite IATI XML schemas consists of the following components:
In addition to the Organisation and Activity. core terms are described in the following:
The Registry Record provides a common format for the reporting of metadata to the IATI Registry.
Please note: Usage of this schema is not mandatory.
The IATI standard is a living entity that requires improvement over time. A CHANGES.txt - provides a comprehensive change log.
Previous versions of the IATI Schema are available:


Welcome to the *|COMPANY|* workspace on *|COMPANYNAME|* - the hub of all your marketing KPIs. With *|COMPANYNAME|*, you can easily answer questions on marketing data across multiple sources like advertising channels, analytics sources, and internal attribution/CRM data. Create your own reports in a few clicks, and easily share them with your team.
Please sign-in to Clarisights using your Google account linked with *|EMAIL|*.
Please sign-in to Clarisights using your exisiting account.
To get started, click on the Set Password button below to set your account password.
For any questions, please feel free to reach us at support@*|DOMAINNAME|*.
-Team *|COMPANYNAME|*


Test Area
text before widgets
text after widgets
The dotted border marks the confines of a widget. Background will be grey whilst focus remains on or inside widget.
Note that a mouse click on buttons, checkboxes and radios does not set focus in some browsers (e.g. Safari)!
Open developer console to view debug output.
text before widgets
text after widgets


Hogan returns to training a day after car crash
by Sam McClureInjury-riddled Fremantle star Jesse Hogan has returned to training despite being involved in a car accident only 24 hours earlier.
Hogan's car finished on its side after he clipped a parked car late on Sunday morning outside a teammate's house, according to the club.
The former Demon managed to climb out of his sedan before an ambulance and two fire trucks arrived at the scene in the suburb of Bicton.
A media spokesperson for St John's Ambulance WA said the male driver was "up and walking" when paramedics arrived and was treated for "a few small abrasions and lacerations".
Firefighters from the station in Fremantle attended the scene but weren't required for further action.
Hogan joined in with his teammates at training at the club's base of Cockburn on Monday, alongside new recruit and former Magpie James Aish.
When contacted by The Age, Fremantle stated there was no police involvement in the incident.
The club said a member of the public called for an ambulance.
Hogan was transported by ambulance to Fiona Stanley Hospital in Murdoch, where he was met by a club doctor.
He was released a short time later and despite original plans to skip the team's next training session, he presented on Monday and trained with the group.
The 24-year-old endured a difficult start to his Fremantle career, managing just 12 games.
His season ended in round 14 when he re-injured his foot against the Demons in round 14. He went under the knife, where surgeons inserted screws into his troublesome navicular bone. Hogan has played 83 games in total, booting 165 goals.
Meanwhile, four-time premiership Hawk Grant Birchall believes he can still play senior football having officially started training with the Brisbane Lions.
Birchall has played only eight matches in the past three years largely because of knee and calf problems, his final game for the Hawks curtailed by a hamstring strain against North Melbourne in round 21.
A veteran of 248 matches, he was restricted to jogging laps at Brisbane’s Yeronga training facility on Monday.
"It’s been a pretty tough few years with my body, so I thought a fresh start up here would do me the world of good," Birchall said.
"I’m sure the body will appreciate the warmer weather. I feel as though I can still play at the level. I think I proved that with the couple of games I played last year. I just need to get the work in and have full faith in the guys here."
He has reunited with coach Chris Fagan, the former Hawks football department boss, and Luke Hodge, his former Hawks captain, who is now a part-time mentoring coach with the Lions.
"Hodgey told me a few things and both him and Fages filled me in on what’s going on and I’m more than happy to join and get started," Birchall said.
- with Jon Pierik


{% extends 'sitewide/base.html' %} {% block title %}Finance{% endblock %} {% load markdownify %} {% load react %} {% block body %}
Access Missouri groups individuals, political organizations and companies together with their political spending habits to help you see how they influence state politics.
${{transaction.amount|floatformat:2}} Transaction
{{transaction.date}}
From {{transaction.t_from.name}} to {{transaction.t_to.name}}


Class: Aws::CloudTrail::Types::DescribeQueryResponse
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-cloudtrail/lib/aws-sdk-cloudtrail/types.rb |
Overview
Constant Summary
Instance Attribute Summary
-
#delivery_s3_uri ⇒ String
rw
The URI for the
::Aws::S3
bucket where::Aws::CloudTrail
delivered query results, if applicable. -
#delivery_status ⇒ String
rw
The delivery status.
-
#error_message ⇒ String
rw
The error message returned if a query failed.
-
#query_id ⇒ String
rw
The ID of the query.
-
#query_statistics ⇒ Types::QueryStatisticsForDescribeQuery
rw
Metadata about a query, including the number of events that were matched, the total number of events scanned, the query run time in milliseconds, and the query's creation time.
-
#query_status ⇒ String
rw
The status of a query.
-
#query_string ⇒ String
rw
The SQL code of a query.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#delivery_s3_uri ⇒ String
(rw)
The URI for the ::Aws::S3
bucket where ::Aws::CloudTrail
delivered query results,
if applicable.
# File 'gems/aws-sdk-cloudtrail/lib/aws-sdk-cloudtrail/types.rb', line 1480
class DescribeQueryResponse < Struct.new( :query_id, :query_string, :query_status, :query_statistics, :, :delivery_s3_uri, :delivery_status) SENSITIVE = [] include Aws::Structure end
#delivery_status ⇒ String
(rw)
The delivery status.
# File 'gems/aws-sdk-cloudtrail/lib/aws-sdk-cloudtrail/types.rb', line 1480
class DescribeQueryResponse < Struct.new( :query_id, :query_string, :query_status, :query_statistics, :, :delivery_s3_uri, :delivery_status) SENSITIVE = [] include Aws::Structure end
#error_message ⇒ String
(rw)
The error message returned if a query failed.
# File 'gems/aws-sdk-cloudtrail/lib/aws-sdk-cloudtrail/types.rb', line 1480
class DescribeQueryResponse < Struct.new( :query_id, :query_string, :query_status, :query_statistics, :, :delivery_s3_uri, :delivery_status) SENSITIVE = [] include Aws::Structure end
#query_id ⇒ String
(rw)
The ID of the query.
# File 'gems/aws-sdk-cloudtrail/lib/aws-sdk-cloudtrail/types.rb', line 1480
class DescribeQueryResponse < Struct.new( :query_id, :query_string, :query_status, :query_statistics, :, :delivery_s3_uri, :delivery_status) SENSITIVE = [] include Aws::Structure end
#query_statistics ⇒ Types::QueryStatisticsForDescribeQuery (rw)
Metadata about a query, including the number of events that were matched, the total number of events scanned, the query run time in milliseconds, and the query's creation time.
# File 'gems/aws-sdk-cloudtrail/lib/aws-sdk-cloudtrail/types.rb', line 1480
class DescribeQueryResponse < Struct.new( :query_id, :query_string, :query_status, :query_statistics, :, :delivery_s3_uri, :delivery_status) SENSITIVE = [] include Aws::Structure end
#query_status ⇒ String
(rw)
The status of a query. Values for QueryStatus
include QUEUED
,
RUNNING
, FINISHED
, FAILED
, TIMED_OUT
, or CANCELLED
# File 'gems/aws-sdk-cloudtrail/lib/aws-sdk-cloudtrail/types.rb', line 1480
class DescribeQueryResponse < Struct.new( :query_id, :query_string, :query_status, :query_statistics, :, :delivery_s3_uri, :delivery_status) SENSITIVE = [] include Aws::Structure end
#query_string ⇒ String
(rw)
The SQL code of a query.
# File 'gems/aws-sdk-cloudtrail/lib/aws-sdk-cloudtrail/types.rb', line 1480
class DescribeQueryResponse < Struct.new( :query_id, :query_string, :query_status, :query_statistics, :, :delivery_s3_uri, :delivery_status) SENSITIVE = [] include Aws::Structure end


nips nips2010 nips2010-246 nips2010-246-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Taehwan Kim, Gregory Shakhnarovich, Raquel Urtasun
Abstract: Sparse coding has recently become a popular approach in computer vision to learn dictionaries of natural images. In this paper we extend the sparse coding framework to learn interpretable spatio-temporal primitives. We formulated the problem as a tensor factorization problem with tensor group norm constraints over the primitives, diagonal constraints on the activations that provide interpretability as well as smoothness constraints that are inherent to human motion. We demonstrate the effectiveness of our approach to learn interpretable representations of human motion from motion capture data, and show that our approach outperforms recently developed matching pursuit and sparse coding algorithms. 1
[1] S. Bengio, F Pereira, Y. Singer, and D. Strelow. Group sparse coding. In NIPS, 2009.
[2] D. P. Bertsekas. Nonlinear Programming. Athena Scientiﬁc, Belmont, Massachusetts, 1999.
[3] A. diAvella and E. Bizzi. Shared and speciﬁc muscle synergies in natural motor behaviors. PNAS, 102(8):3076–3081, 2005.
[4] M. Elad and M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. on Image Processing, 15(12):3736–3745, 2006.
[5] Z. Ghahramani. Building blocks of movement. Nature, 407:682–683, 2000.
[6] R. Jenatton, G. Obozinski, and F. Bach. Structured sparse principal component analysis. In Proc. AISTATS10, 2010.
[7] H. Lee, Alexis Battle, Raina R, and A. Y. Ng. Efﬁcient sparse coding algorithms. In NIPS, 2007.
[8] J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online dictionary learning for sparse coding. In ICML, 2009.
[9] J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Non-local sparse models for image restoration. In ICCV, 2009.
[10] J. Mairal, G. Sapiro, and M. Elad. Learning multiscale sparse representations for image and video restoration. SIAM Multiscale Modelling and Simulation., 7(1):214–241, 2008b.
[11] S. G. Mallat and Z. Zhang. Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal. Proc. 41, pages 3397–3415, 1993.
[12] C. R. Mason, J. E. Gomez, and T. J. Ebner. Hand synergies during reach to grasp. J. of Neurophysiology, 86:2896–2910, 2001.
[13] F. A. Mussa-Ivaldi and E. Bizzi. Motor learning: the combination of primitives. Phil. Trans. Royal Society London, Series B, 355:1755–1769, 2000.
[14] F. A. Mussa-Ivaldi and S. Solla. Neural primitives for motion control. IEEE Journal on Ocean Engineering, 29(3):640–650, 2004.
[15] E. Todorov and Z. Ghahramani. Analysis of the synergies underlying complex hand manipulation. In Proceedings of Conf. of the IEEE Engineering in Medicine and Biology Society, pages 4637–4640, 2004.
[16] R. Urtasun, D. J. Fleet, A. Geiger, J. Popovic, T. Darrell, and N. D. Lawrence. Topologically-constrained latent variable models. In ICML, 2008. 9


Methods
(inner) editArticle()
Opens a page to edit this article
(inner) goBack()
Opens the search page of the mechanism browser
(inner) highlight(element, on)
Turns a highlight for an element on the page on or off
Parameters:
| Name | Type | Description |
|---|---|---|
element |
object | The element as a DOM object |
on |
boolean | A value whether the highlight should be turned on or off |
(inner) init3DModel()
Show the 3D model if there is one
(inner) initAxesIcons()
Shows the icons for the axes
(inner) initIncompleteRow()
Show a note that the mechanism is not complete if it is not
(inner) initTransmissionInfo()
Shows additional information about the transmission
(inner) updateRating()
Updates the rating of the article
(inner) vote(voteUp)
Votes this article up or down by sending a vote to the Django backend
Parameters:
| Name | Type | Description |
|---|---|---|
voteUp |
boolean | A value whether the vote is positive or negative |


|
eDSP
0.0.1
A cross-platform DSP library written in C++.
|
This class implements a resampler object to perform sample-rate conversion. More...
#include <resampler.hpp>
Public Types | |
| using | value_type = T |
| using | size_type = long |
| using | error_type = int |
Public Member Functions | |
| resampler (size_type channels, resample_quality quality, value_type factor) | |
| Creates a resampler with the given configuration. More... | |
| ~resampler ()=default | |
| Default destructor. More... | |
| template<typename InputIt , typename OutputIt > | |
| size_type | process (InputIt first, InputIt last, OutputIt d_first) |
| Resamples the input elements in the range [first, last) and stores the result in another range, beginning at d_first. More... | |
| resample_quality | quality () const |
| Returns the quality used in the resampling process. More... | |
| error_type | reset () |
| Resets the internal buffers. More... | |
| error_type | error () const |
| Returns the internal error code. More... | |
| const edsp::string_view | error_string () const |
| Returns a description of the internal error code. More... | |
Static Public Member Functions | |
| static bool | valid_ratio (value_type ratio) |
| Checks if a ratio is valid. More... | |
This class implements a resampler object to perform sample-rate conversion.
The class resamples an input signal in a factor proportional to the desired sample-rate..
| using edsp::io::resampler< T >::error_type = int |
| using edsp::io::resampler< T >::size_type = long |
| using edsp::io::resampler< T >::value_type = T |
|
inline |
Creates a resampler with the given configuration.
| channels | Number of channels. |
| quality | Quality of the resampling process. |
| factor | Resampling factor (output samplerate / input samplerate) |
|
default |
Default destructor.
|
inline |
Returns the internal error code.
|
inline |
Returns a description of the internal error code.
|
inline |
Resamples the input elements in the range [first, last) and stores the result in another range, beginning at d_first.
The output size is proportional to the input in a factor of \( \lambda \), where lambda is:
\[ \lambda = \frac{f_{si}}{f_{so}} \]
where \( f_{si} \) and \( f_{so} \) are the input size and the output size respectively.
| first | Input iterator defining the beginning of the input range. |
| last | Input iterator defining the ending of the input range. |
| d_first | Output iterator defining the beginning of the destination range. |
|
inline |
Returns the quality used in the resampling process.
|
inline |
Resets the internal buffers.
|
inlinestatic |
Checks if a ratio is valid.
| ratio | Ratio to be tested |


| 1 |
Worden_2019 |
Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019 |
Worden, Lee; Wannier, Rae; Hoff, Nicole A.; Musene, Kamy; Selo, Bernice; Mossoko, Mathias; Okitolonda-Wemakoy, Emile; Muyembe Tamfum, Jean Jacques; Rutherford, George W.; Lietman, Thomas M.; Rimoin, Anne W.; Porco, Travis C.; Kelly, J. Daniel |
2019 |
2019-08-05 |
COMM-USE |
N |
PMC6695208 |
31381606.0 |
10.1371/journal.pntd.0007512 |
1lg2203q |
0.990145 |
Li_C_2018, Liu_Z_2020, Mondor_2012, Chen_2018 |
Worden_2018, Kelly_2019, Kelly_2018 |
| 2 |
Worden_2018 |
Real-time projections of epidemic transmission and estimation of vaccination impact during an Ebola virus disease outbreak in Northeastern Democratic Republic of Congo |
Lee Worden; Rae Wannier; Nicole A. Hoff; Kamy Musene; Bernice Selo; Mathias Mossoko; Emile Okitolonda-Wemakoy; Jean Jacques Muyembe-Tamfum; George W. Rutherford; Thomas M. Lietman; Anne W. Rimoin; Travis C. Porco; J. Daniel Kelly |
2018 |
2018-11-05 |
BioRxiv |
N |
|
|
10.1101/461285 |
96arnumb |
0.989394 |
Mondor_2012, Li_C_2018, Chen_2018, Liu_Z_2020 |
Worden_2019, Kelly_2018, Kelly_2019 |
| 3 |
Kelly_2019 |
Projections of Ebola outbreak size and duration with and without vaccine use in Équateur, Democratic Republic of Congo, as of May 27, 2018 |
Kelly, J. Daniel; Worden, Lee; Wannier, S. Rae; Hoff, Nicole A.; Mukadi, Patrick; Sinai, Cyrus; Ackley, Sarah; Chen, Xianyun; Gao, Daozhou; Selo, Bernice; Mossoko, Mathais; Okitolonda-Wemakoy, Emile; Richardson, Eugene T.; Rutherford, George W.; Lietman, Thomas M.; Muyembe-Tamfum, Jean Jacques; Rimoin, Anne W.; Porco, Travis C. |
2019 |
2019-03-07 |
COMM-USE |
N |
PMC6405095 |
30845236.0 |
10.1371/journal.pone.0213190 |
22ylifqo |
0.884986 |
batista_2020, Griette_2020 |
Kelly_2018, Worden_2019, Worden_2018 |
| 4 |
Kelly_2018 |
Real-time projections of Ebola outbreak size and duration with and without vaccine use in Équateur, Democratic Republic of Congo, as of May 27, 2018 |
J. Daniel Kelly; Lee Worden; Rae Wannier; Nicole A. Hoff; Patrick Mukadi; Cyrus Sinai; Sarah Ackley; Xianyun Chen; Daozhou Gao; Bernice Selo; Mathais Mossoko; Emile Okitolonda-Wemakoy; Eugene T. Richardson; George W. Rutherford; Thomas M. Lietman; Jean Jacques Muyembe-Tamfum; Anne W. Rimoin; Travis C. Porco |
2018 |
2018-05-26 |
BioRxiv |
N |
|
|
10.1101/331447 |
p0nb85e6 |
0.875846 |
batista_2020 |
Kelly_2019, Worden_2018, Worden_2019 |
| 5 |
Xu_Q_2020 |
Bayesian Adaptive Clinical Trials for Anti-Infective Therapeutics during Epidemic Outbreaks |
Qingyang Xu; Shomesh Chaudhuri; Danying Xiao; Andrew W Lo |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>59634 |
20hk99h4 |
0.538766 |
Li_C_2018, Wu_Q_2014, Zheng_2020 |
|
| 6 |
Gibson_2007 |
The Value of Statistical Life and the Economics of Landmine Clearance in Developing Countries |
Gibson, John; Barns, Sandra; Cameron, Michael; Lim, Steven; Scrimgeour, Frank; Tressler, John |
2007 |
2007-03-31 |
PMC |
N |
PMC7117019 |
|
10.1016/j.worlddev.2006.05.003 |
xqfw6gmh |
0.530779 |
Lloyd_2009, Lloyd-Smith_2007, batista_2020 |
|
| 7 |
Chin_2019 |
Quantifying the success of measles vaccination campaigns in the Rohingya refugee camps |
Taylor Chin; Caroline O. Buckee; Ayesha S. Mahmud |
2019 |
2019-10-05 |
BioRxiv |
N |
|
|
10.1101/19008417 |
vok1dni9 |
0.481384 |
|
|
| 8 |
Guo_Z_2016 |
Predicting and Evaluating the Epidemic Trend of Ebola Virus Disease in the 2014-2015 Outbreak and the Effects of Intervention Measures |
Guo, Zuiyuan; Xiao, Dan; Li, Dongli; Wang, Xiuhong; Wang, Yayu; Yan, Tiecheng; Wang, Zhiqi |
2016 |
2016-04-06 |
COMM-USE |
N |
PMC4822846 |
27049322.0 |
10.1371/journal.pone.0152438 |
1amm2hh8 |
0.455179 |
Lu_J_2020 |
Chowell_2014 |
| 9 |
Krauer_2016 |
Heterogeneity in District-Level Transmission of Ebola Virus Disease during the 2013-2015 Epidemic in West Africa |
Krauer, Fabienne; Gsteiger, Sandro; Low, Nicola; Hansen, Christian H.; Althaus, Christian L. |
2016 |
2016-07-19 |
None |
N |
PMC4951043 |
27434164.0 |
10.1371/journal.pntd.0004867 |
9yzum3er |
0.418307 |
|
Hilton_2020 |
| 10 |
Matsuyama_2018 |
Uncertainty and sensitivity analysis of the basic reproduction number of diphtheria: a case study of a Rohingya refugee camp in Bangladesh, November–December 2017 |
Matsuyama, Ryota; Akhmetzhanov, Andrei R.; Endo, Akira; Lee, Hyojung; Yamaguchi, Takayuki; Tsuzuki, Shinya; Nishiura, Hiroshi |
2018 |
2018-04-02 |
COMM-USE |
N |
PMC5885970 |
29629244.0 |
10.7717/peerj.4583 |
7pfzgrvm |
0.382097 |
Li_C_2018, Lloyd_2009, Wu_Q_2014 |
Chin_2019, Nishiura_2006, Safi_2011 |
| 11 |
Zheng_2020 |
Comparative study of the lymphocyte change between COVID-19 and non-COVID-19 pneumonia cases suggesting uncontrolled inflammation might not be the main reason of tissue injury |
Yishan Zheng; Zhen Huang; Guoping Ying; Xia Zhang; Wei Ye; Zhiliang Hu; Chunmei Hu; Hongxia Wei; Yi Zeng; Yun Chi; Cong Cheng; Feishen Lin; Hu Lu; Lingyan Xiao; Yan Song; Chunming Wang; Yongxiang Yi; Lei Dong |
2020 |
2020-02-23 |
BioRxiv |
N |
|
|
10.1101/2<IP_ADDRESS>24885 |
5ge7ozpd |
0.378626 |
|
|
| 12 |
Chan_2013 |
Historical Epidemiology of the Second Cholera Pandemic: Relevance to Present Day Disease Dynamics |
Chan, Christina H.; Tuite, Ashleigh R.; Fisman, David N. |
2013 |
2013-08-22 |
COMM-USE |
N |
PMC3749991 |
23991117.0 |
10.1371/journal.pone.0072498 |
cnnosvx0 |
0.374640 |
Li_C_2018, Giardina_2017, Lemey_2009, Wang_2018, Chowell_2017 |
Safi_2011 |
| 13 |
Chapman_2020 |
Inferring transmission trees to guide targeting of interventions against visceral leishmaniasis and post-kala-azar dermal leishmaniasis |
Lloyd A. C. Chapman; Simon E. F. Spencer; Timothy M. Pollington; Chris P. Jewell; Dinesh Mondal; Jorge Alvar; T. Deirdre Hollingsworth; Mary M. Cameron; Caryn Bern; Graham F. Medley |
2020 |
2020-02-25 |
BioRxiv |
N |
|
|
10.1101/2<IP_ADDRESS>23325 |
nqn1qzcu |
0.357644 |
Li_C_2018, Chen_2018 |
|
| 14 |
Biswas_2020 |
Risk Assessment of nCOVID-19 Pandemic In India: A Mathematical Model And Simulation |
Swarnava Biswas; Moumita Mukherjee |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>60830 |
y3l6k0qu |
0.344473 |
Li_C_2018 |
Shi_P_2020, Li_S_2020 |
| 15 |
Roosa_2020 |
Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020 |
Roosa, Kimberlyn; Lee, Yiseul; Luo, Ruiyan; Kirpich, Alexander; Rothenberg, Richard; Hyman, James M.; Yan, Ping; Chowell, Gerardo |
2020 |
2020-02-22 |
COMM-USE |
Y |
PMC7073898 |
32098289.0 |
10.3390/jcm9020596 |
5ghslfgt |
0.331432 |
|
Roosa_2020, Distante_2020 |
| 16 |
Peak_2018 |
Prolonging herd immunity to cholera via vaccination: Accounting for human mobility and waning vaccine effects |
Peak, Corey M.; Reilly, Amanda L.; Azman, Andrew S.; Buckee, Caroline O. |
2018 |
2018-02-28 |
COMM-USE |
N |
PMC5847240 |
29489815.0 |
10.1371/journal.pntd.0006257 |
5yn52cuh |
0.306432 |
|
Chin_2019 |


Preparation time
30 mins to 1 hour
Cooking time
30 mins to 1 hour
Serves
Serves 8
These tasty burgers are perfect as an easy mid-week meal, or for a barbecue. In fact, the tomato relish goes well with any grilled meat, from steaks and chops to bangers.
For the burgers, put the breadcrumbs, garlic, coriander, mint sauce, onion and salt and black pepper in a food processor. Blend until it’s just combined with a fairly coarse texture. Tip into a bowl, add the mince and mix together. Using wet hands, shape into 8 burgers, then place on a baking tray, cover and chill in the fridge until needed.
For the tomato relish, heat the oil in a saucepan, add the onion and garlic, then cover with a lid and fry over a medium heat for 5–7 minutes, or until soft. Add all the remaining ingredients to the pan, stir, bring to the boil and cook for 2–3 minutes. Season with salt and pepper, then cover again with the lid, reduce the heat and leave to simmer for about 10 minutes.
For the mint mayonnaise, mix all the ingredients together in a small bowl.
To cook the burgers, heat the oil in a large frying pan, or alternatively light the barbecue and let the flames flare up and die down. Fry or barbecue the burgers for about 3–4 minutes on each side or until browned and cooked through.
To serve, slice the buns in half and toast the cut side. Spread one half of each bun with the tomato relish and add a lettuce leaf, a burger, a dollop of mint mayonnaise and a few slices of gherkin before popping on the top half of the bun.


cm-resize
demo
Example with the default handle. ...
Example with two handles, custom cssClass and resizableWidth/Height only. ...
Example with a custom handle element. This allows for handle UI outside of the editor area. ...


yimgnoscale Do Not Scale Images in HTML images
Don’t use a bigger image than you need just because you can set the width and height in HTML. If you need <img width="100" height="100" src="mycat.jpg" alt="My Cat" />
then your image (mycat.jpg) should be 100x100px rather than a scaled down 500x500px image.


55
1 "Come here! All you people who are in exile,
and listen to me!
If you are thirsty,
now is the time for you to come to me and get your water!
Now those who had no money,
you can come and buy wine and milk from me, the finest wine and the best milk!
You can get what you need from me,
and you can buy it now!
You can buy it even though you don't have any money,
and even though you could never afford it!
2 Why do you spend money to buy things that do not supply what you really need?
Why do you work hard to get money to buy things that do not truly satisfy you?
Pay attention to what I say
and acquire what is really good!
If you come to me, then in your heart you will truly be happy.
3 Listen to me and come to me;
pay attention to me, and if you do that, you will have new life within you.
I will make a covenant with you that will last forever
to faithfully love you like my love for King David.
4 By what he did, I showed my power to many people groups;
I caused him to be a leader and commander over the people of many nations.
5 And similarly, you will summon people of other nations to come to you,
nations that previously you have not heard about,
and they had not heard about you;
and they will come to you quickly
because they will have heard that I, Yahweh, your God, the Holy One of Israel, have honored you.
6 Seek to know Yahweh while it is still possible for you to do that;
call to Yahweh while he is near!
7 Wicked people should abandon their wicked behavior,
and evil people should stop thinking what is evil.
They should turn to Yahweh,
and if they do that, he will act mercifully toward them;
they should turn to Yahweh, their God,
because he will fully pardon them for all the wicked things that they have done.
8 Yahweh declares that what he thinks is not the same as what you think,
and what he does is very different from what you do.
9 Just as you people on the earth can never reach the sky,
in the same way, what Yahweh does you can never understand, although you may understand the things you do;
and what Yahweh thinks you can never understand, although you may understand what you think.
10 Yahweh sends the rain and snow down from the sky,
and they water the ground.
When the ground becomes moist, it causes plants to sprout and grow,
with the result that the soil produces seed for the farmer to plant and grain to produce flour to make bread for people to eat.
11 And similarly, the things that I promise to do, I will always cause to happen;
my promises will always come true.
They will accomplish the things for which I spoke them.
12 This is why you will leave Babylon joyfully;
you will have peace as Yahweh leads you out.
It will be as though the hills and mountains sang joyfully,
and the trees in the fields clapped their hands.
13 Instead of thornbushes and briers,
pine trees and myrtle trees will grow in your land.
As a result of that, people will honor Yahweh much more;
and what Yahweh does will cause everyone to remember what he has promised, and they will honor him."


This page tracks the new paper links made to my list of I3D 2012 papers.
Page maintained by Ke-Sen Huang.
If you have additions or changes, send an e-mail.
March 21, 2012
- Efficient Pixel-Accurate Rendering of Curved Surfaces
- Real-Time Multiple Scattering using Light Propagation Volumes
March 19, 2012
- A Framework for Rendering Complex Scattering Effects on Hair
February 5, 2012
- Delta Radiance Transfer
- Using Natural Vibrations to Guide Control for Locomotion
January 27, 2012
- Efficient Pixel-Accurate Rendering of Curved Surfaces
January 26, 2012
- Tabletop Ensemble: Touch-Enabled Virtual Percussion Instruments
January 11, 2012
- Ray Tracing Visualization Toolkit
- Interactive Simulation of Dynamic Crowd Behaviors using General Adaptation Syndrome Theory
January 10, 2012
- Rectilinear Texture Warping for Fast Adaptive Shadow Mapping
January 9, 2012
- 4D Parametric Motion Graphs for Interactive Animation
January 6, 2012
- Surface Based Anti-Aliasing
- Computing 2D Constrained Delaunay Triangulation Using the GPU
- Way Portals: Efficient Multi-Agent Navigation with Line-Segment Goals
January 1, 2012
- Fast, Effective BVH Updates for Animated Scenes
December 29, 2011
- Real-Time Bidirectional Path Tracing via Rasterization
- Lossless Compression of Variable-Precision Floating-Point Buffers on GPUs
December 28, 2011
December 27, 2011
- Way Portals: Efficient Multi-Agent Navigation with Line-Segment Goals
- An Example-Based Motion Synthesis Technique for Locomotion and Object Manipulation
- 4D Parametric Motion Graphs for Interactive Animation
December 21, 2011
- Dynamic Eye Convergence for Head-mounted Displays Improves User Performance in Virtual Environments
- Constant-Time All-Pairs Geodesic Distance Query On Triangle Meshes
- Multiresolution Attributes for Tessellated Meshes
- Tabletop Ensemble: Touch-Enabled Virtual Percussion Instruments
December 18, 2011
- Sleight of Hand: Perception of finger motion from reduced marker sets
December 17, 2011
- Interactive rendering of acquired materials on dynamic geometry using bandwidth prediction
December 16, 2011
- A Reconstruction Filter for Plausible Motion Blur
- Rectilinear Texture Warping for Fast Adaptive Shadow Mapping
- Multiresolution Attributes for Tessellated Meshes
- Page Created and Published
kesen.huang@gmail.com


Previous: Designators, Up: Notational Conventions
When a word having no pre-attached semantics is required (e.g., in an example), it is common in the Lisp community to use one of the words "foo," "bar," "baz," and "quux." For example, in
(defun foo (x) (+ x 1))
the use of the name foo is just a shorthand way of saying "please substitute your favorite name here."
These nonsense words have gained such prevalance of usage, that it is commonplace for newcomers to the community to begin to wonder if there is an attached semantics which they are overlooking—there is not.


|
{{::proposal.title}} {{::proposal.status}} {{::proposal.summary}} |
build |
I you want to increase yours chances of success, we encourage you to submit more than one submission. The number of submission is important to help us to build the best program for attendees.


{% extends "templates/base.html" %} {% block title %}Course Progress - Mon School{% endblock %} {% block head_include %} {% endblock %} {% block content %} Course Progress {% if not batch %} This page accessible only to the batch mentors. Back to Courses {% else %} {{ batch.title }} Mentors {% for m in mentors %} {{m.full_name}} {% endfor %} Lessons {% for lesson in report.lessons %} {% set p = report.get_progress_by_lesson(lesson.name) %} {{lesson.index_label}} {{ lesson.title }} {{p.value}} of {{p.count}} students ({{p.percent | round(1) }}%) completed all the exercises. {% endfor %} Students {% for p in report.get_progress_by_student() %} {{p.student.full_name}} Completed {{p.count}} of {{p.total}} exercises ({{p.percent | round(1)}}%). {% endfor %} {% endif %} {% endblock %}
This page accessible only to the batch mentors.
Back to Courses


This starter kit is greatly inspired and built upon the original starter app provided by angular/material-start
located at angular/material-start.
The main purpose of building this starter kit was to provide a base for building and producing quick, material design interfaces with AngularJS. While there are various starter kits available, this one tries to mimick the original material design specification by Google closely, and more and more features will be added to it in terms of transitions, animations, components and more.
Built using Angular Material, there are over 32 material design components that you can leverage right away. The Angular Material implementation of Google's Material Design provides a set of reusable, well-tested, and accessible UI components. To know more, visit the documentation.


Class Shift is a scheduled working time period, and can include breaks.
More...
|
| static int | SecondOfDay (LocalTime time) |
| | Calculate the second in the day for this time More...
|
| |
| static long | DeltaDays (LocalDate start, LocalDate end) |
| | Calculate the number of days between the start and end More...
|
| |
| | TimePeriod (string name, string description, LocalTime startTime, Duration duration) |
| | Constructor More...
|
| |
| | Named (string name, string description) |
| | Constructor More...
|
| |
Class Shift is a scheduled working time period, and can include breaks.
◆ Shift()
| Point85.ShiftSharp.Schedule.Shift.Shift |
( |
| ) |
|
|
inline |
◆ AddBreak()
| void Point85.ShiftSharp.Schedule.Shift.AddBreak |
( |
Break |
breakPeriod | ) |
|
|
inline |
Add a break period to this shift
- Parameters
-
◆ CalculateBreakTime()
| Duration Point85.ShiftSharp.Schedule.Shift.CalculateBreakTime |
( |
| ) |
|
|
inline |
Calculate the total break time for the shift
- Returns
- Sum of breaks
◆ CalculateWorkingTime() [1/2]
| Duration Point85.ShiftSharp.Schedule.Shift.CalculateWorkingTime |
( |
LocalTime |
from, |
|
|
LocalTime |
to |
|
) |
| |
|
inline |
Calculate the working time between the specified times of day. The shift must not span midnight.
- Parameters
-
| from | Starting local time |
| to | Ending local time |
- Returns
◆ CalculateWorkingTime() [2/2]
| Duration Point85.ShiftSharp.Schedule.Shift.CalculateWorkingTime |
( |
LocalTime |
from, |
|
|
LocalTime |
to, |
|
|
bool |
beforeMidnight |
|
) |
| |
|
inline |
Calculate the working time between the specified times of day
- Parameters
-
| from | Starting local time |
| to | Ending local time |
| beforeMidnight | If true, shifts ends before midnight |
- Returns
◆ CompareTo()
| int Point85.ShiftSharp.Schedule.Shift.CompareTo |
( |
Shift |
other | ) |
|
|
inline |
Compare one shift to another one
- Parameters
-
- Returns
- -1 if less than, 0 if equal and 1 if greater than
◆ CreateBreak()
| Break Point85.ShiftSharp.Schedule.Shift.CreateBreak |
( |
String |
name, |
|
|
String |
description, |
|
|
LocalTime |
startTime, |
|
|
Duration |
duration |
|
) |
| |
|
inline |
Create a break for this shift
- Parameters
-
| name | Name |
| description | Description |
| startTime | Staring time |
| duration | Duration |
- Returns
◆ IsInShift()
| bool Point85.ShiftSharp.Schedule.Shift.IsInShift |
( |
LocalTime |
time | ) |
|
|
inline |
Test if the specified time falls within the shift
- Parameters
-
- Returns
- True if this time is in the shift
◆ IsWorkingPeriod()
| override bool Point85.ShiftSharp.Schedule.Shift.IsWorkingPeriod |
( |
| ) |
|
|
inlinevirtual |
◆ RemoveBreak()
| void Point85.ShiftSharp.Schedule.Shift.RemoveBreak |
( |
Break |
breakPeriod | ) |
|
|
inline |
Remove a break from this shift
- Parameters
-
◆ SpansMidnight()
| bool Point85.ShiftSharp.Schedule.Shift.SpansMidnight |
( |
| ) |
|
|
inline |
Check to see if this shift crosses midnight
- Returns
- True if it does
◆ ToString()
| override string Point85.ShiftSharp.Schedule.Shift.ToString |
( |
| ) |
|
|
inline |
Build a string representation of this shift
- Returns
- String
◆ Breaks
| List<Break> Point85.ShiftSharp.Schedule.Shift.Breaks |
|
get |
◆ WorkSchedule
The documentation for this class was generated from the following file:


{% extends "base.html" %} {% import "bootstrap/wtf.html" as wtf %} {% block title %}Top Reddit{% endblock %} {% block head %} {{ super() }} {% endblock %} {% block page_content %}
Top Reddit is a little web-applet that queries data from Reddit and creates bar-graphs displaying information about the top sub-reddits from /r/all or /r/popular. The form below allows for query customization. Default parameters are the first 100 posts within the last 24hrs from /r/all. Larger requests require more time. Created by Samuel Fu and Robert Hou.
{{ wtf.quick_form(form) }} {% endblock %}


Popular Areas in apple student discount uk
Tech:Apple Student Discount - The Student Room
What Is Apple Student Discount? As a student or lecturer, you're entitled to save up to 15% off the price of a new mac and 3% off of an iPad with Apple On .
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What do I need for my apple student discount? iMac. . Im a Student at a UK University, and I dont get free AppleCare. Also, all you need is your .
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apple student discount uk - Studentbeans.com
Students: find apple student discount uk at studentbeans.com, the UK's biggest and best student money saving site. Sign up and start saving today.
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Student discounts: the best Apple and other technology deals ...
Aug 2, 2012 . We round up the best student discounts on technology products for university, including discounts on Apple and Microsoft products.
http://www.telegraph.co.uk/education/universityeducation/student-life/9443637/Student-discounts-the-best-Apple-and-other-technology-deals.html
Guide to UK education discounts - MacRumors Forums
The vast majority of UK HE students can receive this discount. Some Apple staff are unclear that the 3 year warranty is available. Update thanks .
http://forums.macrumors.com/showthread.php?t=1103022


|
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| PREV NEXT | FRAMES NO FRAMES |
| Packages that use OrthoMatcherRule | |
|---|---|
| gate.creole.orthomatcher |
| Uses of OrthoMatcherRule in gate.creole.orthomatcher |
|---|
| Classes in gate.creole.orthomatcher that implement OrthoMatcherRule | |
|---|---|
class |
MatchRule0
RULE #0: If the two names are listed in table of spurius matches then they do NOT match Condition(s): - Applied to: all name annotations |
class |
MatchRule1
RULE #1: If the two names are identical then they are the same no longer used, because I do the check for same string via the hash table of previous annotations Condition(s): depend on case Applied to: annotations other than names |
class |
MatchRule10
RULE #10: is one name the reverse of the other reversing around prepositions only? |
class |
MatchRule11
RULE #11: does one name consist of contractions of the first two tokens of the other name? |
class |
MatchRule12
RULE #12: do the first and last tokens of one name match the first and last tokens of the other? |
class |
MatchRule13
RULE #12: do the first and last tokens of one name match the first and last tokens of the other? |
class |
MatchRule14
RULE #13: do multi-word names match except for one token e.g. |
class |
MatchRule15
RULE #14: if the last token of one name matches the second name e.g. |
class |
MatchRule16
RULE #15: Does every token in the shorter name appear in the longer name? |
class |
MatchRule17
RULE #16: Conservative match rule Require every token in one name to match the other except for tokens that are on a stop word list |
class |
MatchRule2
RULE #2: if the two names are listed as equivalent in the lookup table (alias) then they match Condition(s): - Applied to: all name annotations |
class |
MatchRule3
RULE #3: adding a possessive at the end of one name causes a match e.g. |
class |
MatchRule4
RULE #4: Does the first non-punctuation token from the long string match the first token from the short string? |
class |
MatchRule5
RULE #4Name: Does all the non-punctuation tokens from the long string match the corresponding tokens in the short string? |
class |
MatchRule6
RULE #5: if the 1st token of one name matches the second name e.g. |
class |
MatchRule7
RULE #6: if one name is the acronym of the other e.g. |
class |
MatchRule8
RULE #7: if one of the tokens in one of the names is in the list of separators eg. "&" then check if the token before the separator matches the other name e.g. |
class |
MatchRule9
RULE #9: does one of the names match the token just before a trailing company designator in the other name? |
| Fields in gate.creole.orthomatcher with type parameters of type OrthoMatcherRule | |
|---|---|
private Map<Integer,OrthoMatcherRule> |
OrthoMatcher.rules
|
| Method parameters in gate.creole.orthomatcher with type arguments of type OrthoMatcherRule | |
|---|---|
static boolean |
OrthoMatcherHelper.executeDisjunction(Map<Integer,OrthoMatcherRule> allrules,
int[] executeRules,
String longName,
String shortName,
boolean[] mr)
|
protected void |
OrthoMatcher.modifyRules(Map<Integer,OrthoMatcherRule> rules)
Override this method to add, replace, remove rules |
|
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| PREV NEXT | FRAMES NO FRAMES |


Problem 8. Number as words
Write a script that converts a number in the range [0…999] to words, corresponding to its English pronunciation.
You could use Chrome console or Firebug to see the results.


{% extends "base.html" %} {% block announce %}
🔊 Liquigraph has reached EOL. Migrate to Liquibase now!


Assemblage¶
The Assemblage
represents all of the things that can potentially be discovered within an Area
. These are most commonly going to be artifacts represented as points, but can theoretically be other shapes as well. The Assemblage
must lie within the Area
, so an Area
object is a required parameter of the Assemblage
creation methods.
An Assemblage
is made from a list of Layer
objects, so most of the heavy lifting is done by creating each Layer
. We will walk through Layer
creation methods first, then we will put them together in an Assemblage
.
Creating a Layer
¶
A Layer
is intended to be a group of artifacts or other features that share time_penalty
and ideal_obs_rate
parameters. More practically, you can think of a Layer
standing in for a type of artifact. For example, you might expect those parameters to be the same for any Iron Age ceramics, so you can put all of the Iron Age ceramics into the same Layer
.
Each element of the Layer
(i.e., each individual artifact) is a Feature
object. Most of the time it will make more sense to use Layer
methods to create many Feature
objects at the same time, but it is possible to create the Feature
objects one-by-one and assembling them into a Layer
.
From a list of Feature
objects¶
To create a Feature
, we need a shapely
object, so let’s create a few simple points.
from shapely.geometry import Point
import prospect
from scipy.stats import beta
pt1 = Point(10, 10)
ft1 = prospect.Feature(
name="feature1",
layer_name="demo_layer",
shape=pt1,
time_penalty=prospect.utils.truncnorm(mean=10, sd=7, lower=0, upper=50),
ideal_obs_rate=beta(9, 1)
)
pt2 = Point(50, 50)
ft2 = prospect.Feature(
name="feature2",
layer_name="demo_layer",
shape=pt2,
time_penalty=prospect.utils.truncnorm(mean=10, sd=7, lower=0, upper=50),
ideal_obs_rate=beta(9, 1)
)
pt3 = Point(90, 90)
ft3 = prospect.Feature(
name="feature3",
layer_name="demo_layer",
shape=pt3,
time_penalty=prospect.utils.truncnorm(mean=10, sd=7, lower=0, upper=50),
ideal_obs_rate=beta(9, 1)
)
Note
Notice that we kept the time_penalty
and ideal_obs_rate
parameters constant. It is not required that all members of a Layer
have identical values for these parameters, but it is probably a good idea. If you need to use different values, it is probably best to use one Layer
per unique set of parameters.
Now let’s put our Feature
objects into a Layer
. The Layer
constructor will check and ensure that the Feature
objects are located within the Area
boundaries, so you must pass an Area
when creating a Layer
.
Note
Currently this spatial rule is only enforced if all of the elements in input_features
are Point
objects. It is my hope to include LineString
and Polygon
Feature
objects in this “clipping” operation in the future.
demo_area = prospect.Area.from_area_value(
name='demo_area',
value=10000
)
layer_from_list = prospect.Layer(
name='demo_layer',
area=demo_area,
assemblage_name='demo_assemblage',
input_features=[ft1, ft2, ft3]
)
type(layer_from_list)
prospect.layer.Layer
layer_from_list.df
| feature_name | layer_name | shape | time_penalty | ideal_obs_rate | |
|---|---|---|---|---|---|
| 0 | feature1 | demo_layer | POINT (10.00000 10.00000) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 1 | feature2 | demo_layer | POINT (50.00000 50.00000) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 2 | feature3 | demo_layer | POINT (90.00000 90.00000) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
type(layer_from_list.df)
geopandas.geodataframe.GeoDataFrame
We can use the plotting functionality from geopandas
to visualize the Layer
members within the Area
.
layer_from_list.df.plot(ax=demo_area.df.plot(), color="orange")
<AxesSubplot:>
From a shapefile¶
The from_shapefile()
method is useful for reading in existing datasets as Layer
objects. These could be data from a completed field survey or maybe data designed to test some custom question.
area_from_shp = prospect.Area.from_shapefile(
name="area_shp",
path="./data/demo_area.shp"
)
layer_from_shp = prospect.Layer.from_shapefile(
path="./data/demo_layer.shp",
name="demo_layer_from_shp",
area=area_from_shp,
assemblage_name="demo_assemblage",
time_penalty=prospect.utils.truncnorm(mean=10, sd=7, lower=0, upper=50),
ideal_obs_rate=beta(9, 1)
)
layer_from_shp.df
| feature_name | layer_name | shape | time_penalty | ideal_obs_rate | |
|---|---|---|---|---|---|
| 0 | demo_layer_from_shp_0 | demo_layer_from_shp | POINT (533579.123 4388848.757) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 1 | demo_layer_from_shp_1 | demo_layer_from_shp | POINT (533638.844 4388883.594) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 2 | demo_layer_from_shp_2 | demo_layer_from_shp | POINT (533621.702 4388842.121) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 3 | demo_layer_from_shp_3 | demo_layer_from_shp | POINT (533701.882 4388861.475) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 4 | demo_layer_from_shp_4 | demo_layer_from_shp | POINT (533669.810 4388763.047) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 5 | demo_layer_from_shp_5 | demo_layer_from_shp | POINT (533575.252 4388787.930) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 6 | demo_layer_from_shp_6 | demo_layer_from_shp | POINT (533771.556 4388874.194) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 7 | demo_layer_from_shp_7 | demo_layer_from_shp | POINT (533717.918 4388839.910) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
Let’s plot the resulting Layer
.
layer_from_shp.df.plot(ax=area_from_shp.df.plot(), color="orange")
<AxesSubplot:>
From pseudorandom points¶
To very quickly create a Layer
with a specific number of points, you can use the from_pseudorandom_points()
method. This method uses numpy
to draw \(n\) random values for coordinates for Point
objects.
area_from_shp = prospect.Area.from_shapefile(
name="area_shp",
path="./data/demo_area.shp"
)
layer_from_pseudo_rand = prospect.Layer.from_pseudorandom_points(
n=100,
name="demo_layer_from_pseu_rand",
area=area_from_shp,
assemblage_name="demo_assemblage",
time_penalty=prospect.utils.truncnorm(mean=10, sd=7, lower=0, upper=50),
ideal_obs_rate=beta(9, 1)
)
layer_from_pseudo_rand.df.shape
(100, 5)
layer_from_pseudo_rand.df.plot(ax=area_from_shp.df.plot(), color='orange')
<AxesSubplot:>
From point processes¶
prospect
offers methods for creating Layer
objects using existing point pattern types: Poisson, Thomas, and Matern.
Caution
For all of these point pattern types, the generated points are not guaranteed to fall within the given Area
, only within its bounding box. The generated GeoDataFrame
of points, df
, is clipped by the actual Area
bounds after they are generated, which can result in fewer points than expected. If you need to examine what has been clipped, all original points will remain in the input_features
attribute.
Poisson¶
A Poisson point process is usually said to be more “purely” random than most random number generators (like the one used in from_pseudorandom_points()
)
The rate (usually called “lambda”) of the Poisson point process represents the number of events per unit of area per unit of time across some theoretical space of which our Area
is some subset. In this case, we only have one unit of time, so the rate really represents a theoretical number of events per unit area. For example, if the specified rate is 5, in any 1x1 square, the number of points observed will be drawn randomly from a Poisson distribution with a shape parameter of 5. In practical terms, this means that over many 1x1 areas (or many observations of the same area), the mean number of points observed in that area will approximate 5.
area_from_shp = prospect.Area.from_shapefile(
name="area_shp",
path="./data/demo_area.shp"
)
layer_from_poisson = prospect.Layer.from_poisson_points(
rate=0.005,
name="demo_layer_from_poisson",
area=area_from_shp,
assemblage_name="demo_assemblage",
time_penalty=prospect.utils.truncnorm(mean=10, sd=7, lower=0, upper=50),
ideal_obs_rate=beta(9, 1)
)
layer_from_poisson.df.shape
(165, 5)
layer_from_poisson.df.plot(ax=area_from_shp.df.plot(), color='orange')
<AxesSubplot:>
Thomas¶
A Thomas point process is a two-stage Poisson process. It has a Poisson number of clusters, each with a Poisson number of points distributed with an isotropic Gaussian distribution of a given variance. The points that are used to define the parent clusters are not represented in the output.
Tip
This is an excellent way to generate artifact clusters.
area_from_shp = prospect.Area.from_shapefile(
name="area_shp",
path="./data/demo_area.shp"
)
layer_from_thomas = prospect.Layer.from_thomas_points(
parent_rate=0.001,
child_rate=10,
gauss_var=5,
name="demo_layer_from_thomas",
area=area_from_shp,
assemblage_name="demo_assemblage",
time_penalty=prospect.utils.truncnorm(mean=10, sd=7, lower=0, upper=50),
ideal_obs_rate=beta(9, 1)
)
layer_from_thomas.df.shape
(267, 5)
layer_from_thomas.df.plot(ax=area_from_shp.df.plot(), color='orange')
<AxesSubplot:>
Matern¶
The Matern process is similar to the Thomas point process. It has a Poisson number of parent clusters like the Thomas process, but in this case, each parent cluster has a Poisson number of points distributed uniformly across a disk of a given radius.
Tip
This is an excellent method for generating circular clusters of artifacts.
area_from_shp = prospect.Area.from_shapefile(
name="area_shp",
path="./data/demo_area.shp"
)
layer_from_matern = prospect.Layer.from_matern_points(
parent_rate=0.001,
child_rate=10,
radius=5,
name="demo_layer_from_matern",
area=area_from_shp,
assemblage_name="demo_assemblage",
time_penalty=prospect.utils.truncnorm(mean=10, sd=7, lower=0, upper=50),
ideal_obs_rate=beta(9, 1)
)
layer_from_matern.df.shape
(312, 5)
layer_from_matern.df.plot(ax=area_from_shp.df.plot(), color='orange')
<AxesSubplot:>
COMING SOON: from_rectangles()
In the future I plan to implement a convenience method for placing rectangles (or other polygon shapes) randomly within an Area
using a Poisson point process to determine the centerpoints of the polygons.
time_penalty
parameter¶
The time penalty is meant to reflect the amount of time added to the search time to record any particular Feature
object when it is found.
This parameter requires some knowledge or intuition about the recording methods that are (or could be) used in the field. For example, if special labeling or curation procedures are to be applied to some class of artifacts, that might justify a greater time penalty for that Layer
of artifacts. Recall though that this parameter is applied to all Features
that make up a Layer
, so take care to include only Feature
objects for which this time_penalty
value holds.
Let’s revisit the last example we saw. Here, we specify the time_penalty
parameter of the Layer
as a truncated normal distribution with a mean of 10, a standard deviation of 7, lower bound at 0, and upper bound at 50.
area_from_shp = prospect.Area.from_shapefile(
name="area_shp",
path="./data/demo_area.shp"
)
layer_from_matern = prospect.Layer.from_matern_points(
parent_rate=0.001,
child_rate=10,
radius=5,
name="demo_layer_from_matern",
area=area_from_shp,
assemblage_name="demo_assemblage",
time_penalty=prospect.utils.truncnorm(mean=10, sd=7, lower=0, upper=50),
ideal_obs_rate=beta(9, 1)
)
We can check that the time_penalty
column of the <Layer>.df
attribute is indeed a scipy
distribution.
layer_from_matern.df['time_penalty'].head()
0 <scipy.stats._distn_infrastructure.rv_frozen o...
1 <scipy.stats._distn_infrastructure.rv_frozen o...
2 <scipy.stats._distn_infrastructure.rv_frozen o...
3 <scipy.stats._distn_infrastructure.rv_frozen o...
4 <scipy.stats._distn_infrastructure.rv_frozen o...
Name: time_penalty, dtype: object
ideal_obs_rate
parameter¶
Of all the prospect
parameters, the ideal observation rate is perhaps the most difficult to define. It represents the frequency with which an artifact or feature will be recorded, assuming the following ideal conditions:
It lies inside or intersects the
Coverage
Surface visibility is 100%
The surveyor’s skill is 1.0
These assumptions are important to consider further. The ideal observation rate is specified here solely as a property of the materials (i.e., artifacts or features) themselves, unrelated to the distance from the observer, surface visibility, or surveyor skill. These other factors are all accounted for in other parts of the simulation, so users should avoid replicating that uncertainty here. For most Layer
objects, this value should probably be 1.0 or close to 1.0, but there are some scenarios where you might want to consider an alternate value. For instance:
If the
Layer
represents extremely small artifacts (e.g., beads, tiny stone flakes) that are hard to observe even in the best conditions.If the
Layer
represents artifacts or features that are difficult to differentiate from the surface “background” in a particular context. For example, in a gravelly area, ceramic sherds can be difficult to differentiate from rocks. A major caveat here is that this “background noise” is sometimes considered in surface visibility estimations, so the user should take care not to duplicate that uncertainty if it is already accounted for in theArea
building block.
Let’s look at the Layer
from above once again.
area_from_shp = prospect.Area.from_shapefile(
name="area_shp",
path="./data/demo_area.shp"
)
layer_from_matern = prospect.Layer.from_matern_points(
parent_rate=0.001,
child_rate=10,
radius=5,
name="demo_layer_from_matern",
area=area_from_shp,
assemblage_name="demo_assemblage",
time_penalty=prospect.utils.truncnorm(mean=10, sd=7, lower=0, upper=50),
ideal_obs_rate=beta(9, 1)
)
By setting the ideal_obs_rate
parameter to a Beta distribution (scipy.stats.beta(9, 1)
), we are saying, for example, that if there were 10 artifacts of this type in an area, even a highly-skilled surveyor in perfect visibility conditions would only discover 9 of them most of the time.
Creating an Assemblage
from Layer
objects¶
An Assemblage
is merely a collection of Layer
objects. You can pass your previously-created Layer
objects in a list to the Assemblage
constructor. We’ll pass it all of the Layer
objects we created above.
demo_assemblage = prospect.Assemblage(
name="demo_assemblage",
area_name="area_shp",
layer_list=[
layer_from_list,
layer_from_shp,
layer_from_pseudo_rand,
layer_from_poisson,
layer_from_thomas,
layer_from_matern
]
)
We can see that all of the Feature
objects from the various Layer
objects are part of one Assemblage
object.
demo_assemblage.df.head(10)
| feature_name | layer_name | shape | time_penalty | ideal_obs_rate | |
|---|---|---|---|---|---|
| 0 | feature1 | demo_layer | POINT (10.000 10.000) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 1 | feature2 | demo_layer | POINT (50.000 50.000) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 2 | feature3 | demo_layer | POINT (90.000 90.000) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 3 | demo_layer_from_shp_0 | demo_layer_from_shp | POINT (533579.123 4388848.757) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 4 | demo_layer_from_shp_1 | demo_layer_from_shp | POINT (533638.844 4388883.594) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 5 | demo_layer_from_shp_2 | demo_layer_from_shp | POINT (533621.702 4388842.121) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 6 | demo_layer_from_shp_3 | demo_layer_from_shp | POINT (533701.882 4388861.475) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 7 | demo_layer_from_shp_4 | demo_layer_from_shp | POINT (533669.810 4388763.047) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 8 | demo_layer_from_shp_5 | demo_layer_from_shp | POINT (533575.252 4388787.930) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |
| 9 | demo_layer_from_shp_6 | demo_layer_from_shp | POINT (533771.556 4388874.194) | <scipy.stats._distn_infrastructure.rv_frozen o... | <scipy.stats._distn_infrastructure.rv_frozen o... |


Distributed Analysis Scheme for High Energy Physics
Goal: A series of courses to transform a new student to a productive and collaborative researcher.
Three pillars: Motivation, Communication, and Distribution.
Contributing: welcome to fork and make pull request here!


This page should be running from a webserver, to allow fetching from the
json/
directory.
loading...


{% extends "cog/common/layout_left_center.html" %}
{% load cog_utils %}
{% block main %}
{% if request.method == 'GET' %}
{% if project|hasUser:request.user or project|hasUserPending:request.user %}
{% if project|hasUser:request.user %}
Are you sure you want to cancel your membership in project {{ project.short_name }}?
{% else %}
Are you sure you want to cancel your request for membership in project {{ project.short_name }}?
{% endif %}
Please remember that only project members can modify the content of the project workspace.
{% else %}
Sorry: You are not part of, nor have requested membership in, project {{ project.short_name }}.
{% endif %}
{% else %}
Your membership in project {{ project.short_name }} has been canceled.


AP Top Entertainment News at 3:05 a.m. EST
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Gronkowski will be in Miami for Super Bowl as party host
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Like her parents, Blue Ivy now an award-winning songwriter
Kylie Jenner sells stake in beauty empire to CoverGirl owner
Jon Voight, Alison Krauss honored with national medals
Free e-book aims to spark talk on ‘culture of health'
Copyright 2019 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.


turbo::CircleShape Class Reference
Simple cicle shape. More...
#include <CircleShape.hpp>
Inheritance diagram for turbo::CircleShape:
Collaboration diagram for turbo::CircleShape:
Public Member Functions | |
| CircleShape (unsigned short radius=10, const Color &color=Color()) | |
| void | draw () const override |
| Draw the drawable -_-. | |
Public Attributes | |
| unsigned short | radius = 10 |
| Radius of the circle in pixel. | |
| Color | color = Color() |
| Color of the circle. | |
| float | thickness = 1.0f |
Thickness of the border if is_filled is true | |
| bool | is_filled = true |
| Is filled or only show border. | |
Detailed Description
Simple cicle shape.
The documentation for this class was generated from the following files:
- include/turbo/graphics/CircleShape.hpp
- src/graphics/CircleShape.cpp


Credit and Contact¶
This work has been the result of collaboration between KTH Royal Institute of Technology and Vivid Economics and has been funded by the World Bank.
Contributions are described below:
- Original code: Konstantinos Pegios, Alexandros Korkovelos, Neeraj Baruah
- Conceptualization & Methodological review: Alexandros Korkovelos, Konstantinos Pegios, Mark Westcott
- Updates, Modifications: Alexandros Korkovelos, Youssef Almulla, Camilo Ramírez
- Funding: The World Bank (contract number: 7190531), KTH
For any questions, feedback or general inquiries please to not hesitate to contact the development team.
- KTH dES: Alexandros Korkovelos<EMAIL_ADDRESS>- Vivid Economics: Mark Westcott<EMAIL_ADDRESS>- The World Bank: Kabir Malik<EMAIL_ADDRESS>& Bonsuk Koo <bkoo@worldbank.org>


Individual back issues of Fireside Quarterly, delivered straight to your mailbox. Each issue features over 120 pagees of stories, along with three enormous illustrations in full color.
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Gravitational waves in relativity were first considered by Einstein in his 1916 article "Näherungsweise Integration der Feldgleichungen der Gravitation", where he used linearized theory to show that small perturbations of spacetime metric lead to wave equation and therefore gravitational waves propagation with speed of light. In 1918, Einstein published another article about gravitational waves, "Über Gravitationswelle", where he corrected and completed his 1916 work. This article also shows, that spherical system can not radiate energy in form of gravitational waves and that only transversal modes carry energy away from the system. In 1923 Eddington confirmed that longitudal waves are not physical (and do to carry energy) in an article "The Propagation of Gravitational Waves", where he managed to transform longitudal modes out. Einstein, however, considered existence of gravitational waves much earlier - he already lectured about gravitational waves at a conference in Wien in 1913. There is a record of a discussion between Einstein and Born from this conference, about propagation of such waves.
Born: I would like ask mister Einstein about the speed of propagation of gravitational waves according to his theory. It is not quite apparent to me that it propagates with the speed of light...
Einstein: It is rather easy to write down equations where perturbations are infinitesimal. In that case (metric components) g differs only infinitesimaly from the case where perturbations are absent. Perturbations, however, propagate with the same speed as light does.
Born: But for large perturbations it surely gets very complicated?
Einstein: Yes, it is a mathematically difficult problem. Particulary it is hard to find exact solutions, because the equations are nonlinear.
Einstein was aware that the speed of propagation should correspond to the speed of light even before he published the general theory in 1915. Despite Einsteins concern about difficulty of finding exact solutions, first exact solution describing gravitational wave was due to Brinkmann in 1925. In his study of conformal transformations of exact solutions, he found metric corresponding to pp-waves (plane fronted waves with parallel propagation). In todays terms, Brinkmann form of metric is in the form $$ \mathrm{d} s^2 = - \mathrm{d} u \mathrm{d} r - 2 H(\zeta, \bar{\zeta}, u) \mathrm{d} u^2 + 2 \mathrm{d} \zeta \mathrm{d} \bar{\zeta}, $$ where $H$ is a solution to the Laplace equation in the transverse space. Although Brinkmann derived the metric, however he was unaware of its physical importance as his study was focused on mathematical properties and not interpretation. Brinkmann form of metric was interpreted much later by Robinson (1957) and independently by Peres (1959).
In 1926, Jeffery and Baldwin derived another exact solution $$ \mathrm{d} s^2 = -2 \mathrm{d} u \mathrm{d} v + g_{ij} (u) \mathrm{d} x^i \mathrm{d} x^j.$$ This solution was rediscovered by Rosen in 1937 who interpreted it as a plane gravitational wave (with constant curvature along the wavefront). In the very same year, Rosen, this time together with Einstein, published another exact solution representing a gravitational wave, cylindrical in this case. These two publications, however, were accompanied by confusion about validity as the original conclusion, before prepublicaton review, pointed to the nonexistence of gravitational waves - Einstein and Rosen did some miscalcualtions.
The theoretical existence of gravitational waves was not sure thing up to late 1957, when several articles (Pirani, Bonor, Bondi) rediscovered and interpreted gravitational wave spacetimes. It should be noted, that Brdička computed exact gravitational wave coupled with EM in 1951. The peak of the first era of gravitational waves was in 1959, when Bondi, Pirani and Robinson published study of spacetimes with gravitational waves, from pyhsical view. It was shown, that Rosen planar solution is special case of Brinkmann solution and first important step towards gravitational wave detectors was made - local analysis in terms of geodesic deviation equation was performed in several articles, including the famous 1959 one. After 1959, there was no doubt of theoretical existence of gravitational waves. In 1960, Robinson and Trautman described a family of expanding (spherical) impulsive gravitational waves $$ \mathrm{d} s^2 = -2 \mathrm{d} u \mathrm{d} r - 2 H \mathrm{d} u^2 + 2 \frac{r^2}{P^2} \mathrm{d} \eta \mathrm{d} \bar{\eta} $$ (where $ 2H = \Delta \log P - 2r(\log P)_{,u} - \frac{2m}{r} - \frac{\Lambda}{3}r^2, \Delta = 2P \partial_{\eta} \partial_{\bar{\eta}} $) Only one year after that, in 1961, Kundt published study of a family of non-expanding gravitational waves, that generalized the Brinkmann solution, $$ \mathrm{d} s^2 = -2 \mathrm{d} u \left( \mathrm{d} r + H \mathrm{d} u + W \mathrm{d} \eta + \bar{W} \mathrm{d} \bar{\eta} \right) + 2P^{-2} \mathrm{d} \eta \mathrm{d} \bar{\eta},$$ where $P(\eta, \bar{\eta}, u)$ and $H(\eta, \bar{\eta}, u, r)$ are real functions and $W(\eta, \bar{\eta}, u, r)$ is a complex function, all determined by field equations.
Late 50s and early 60s could be called "the golden era of gravitational waves". Discontinuities on null characteristics were studied from mathematical point of view (Lichnerowicz 1955, Synge 1957) and new formalism in general relativity was formed - algebraic clasification and tetrad formalism helped in looking for radiative solutions. At this time, topological methods were first employed to study general relativity by Penrose. It took only few years for these methods to be used in construction in exact impulsive radiative spacetimes.
In 1972, Penrose published topological way to construct impulsive waves, the so called "cut and paste" method. This method, however, does not yield any form of a metric, continuous coordinate representation has to be found in order to construct exact metric in such a spacetime. This topological approach was not the only way of constructing impulsive waves. Aichelburg and Sexl performed a ultra-relativistic boost of Schwarzschild solution, where they increased velocity up to the speed of light while decreasing mass to 0, resulting in gravitational shock wave. Another way of construction was limit of sandwich waves, where the width of wave decreases while its intensity increases up to $\delta$-distribution like profile.