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This story is part of a series of specially designed stories that represents some of the best journalism The Post has to offer. Check out the rest of the special projects here.
05.31.18
Ohio University students will be returning to campus this August to both old and new residence halls. Every student will be able to use renovated spaces regardless of which residence hall they are living in, though.
On South and East Green, renovations of residence halls also mean new and improved study spaces.
Jefferson Hall, which opened in August after receiving about $39 million in renovations, has a new common area with study spaces and rooms which all connect to Jefferson Market.
Tanka, Luchs, Carr and Sowle halls all connect to the Living Learning Center. The Living Learning Center has two floors, as well as multi-purpose rooms, desktops and seating areas for students to study.
All four of those residence halls and the Living Learning Center opened to students in August 2015 after an almost $110 million renovation.
Georgia Hilliard, an incoming sophomore studying biochemistry, said she uses the Jefferson common area around two times a week. She said she likes all the study spaces and how she can grab a snack from Jefferson Market when she is working.
Hilliard said that students should know they can use the common area even if they do not live in Jefferson Hall.
“I would say at least give it a try once,” Hilliard said. “Even if you don’t want to come in here by yourself, which I understand, come with a friend and like sit and hang out.”
Kelly Hollis, an incoming sophomore studying marketing and fashion merchandising, said she uses the common area often because her residence hall study room is too small.
Lincoln Hall on East Green, Hollis’ residence hall, has its perks, she said. She likes how each room has a sink and the entire hall is renovated.
Meagan Hall | DIRECTOR OF PHOTOGRAPHY
Construction on South Green continues after the demolition of Fenzel, Martzolff and O’Bleness resident halls over the summer.
“Definitely just be grateful that you’re able to wash your face, brush your teeth, clean out your dishes in the sink in your own room,” Hollis said as advice to future residents. “Be appreciative of that.”
Morgan Villegas, an incoming sophomore studying pre-dentistry, does not live in one of the renovated dorms but does consistently use the Living Learning Center.
She said she likes how it's a nice study area and she feels motivated when she works in the center.
“I think finding just a good place to like study is really important,” Villegas said. “This is good lighting and it has the computers and printing services here so that’s pretty convenient.”
The next residence hall to receive major renovations will be Washington Hall on East Green.
Washington Hall will begin its $5.7 million construction on May 7 and the construction is scheduled to be finished by Feb. 13, 2019.
A previous Post report highlighted issues within Washington Hall that the renovations will address, including heating and cooling problems.
“Some of the air conditioners don’t go below 70 degrees, and some of the windows don’t open,” Jordan Hill, a then-freshman living in Washington Hall said in a previous Post report.
While Washington Hall is undergoing major renovations next year, it will not be open to students.
Other summer residence hall renovations include bathroom replacements in Bryan and Pickering halls and work on the exteriors of Tiffin, Perkins, Voigt and James halls.
Villegas, who lives in Ryors Hall, wishes some upgrades could be done to the building.
“I thought it was okay until I walked into the other dorm, like Adams and I was like ‘oh God,’ ” Villegas said.
She said a good start would be doors in the showers. She would also have liked a kitchen or some additional amenities in the hall.
Landing Page
This story is part of a series of specially designed stories that represents some of the best journalism The Post has to offer. Check out the rest of the special projects here.


Sacred Geometry Helper
Knowledge (Engineering) Ranks: {{inputs.engineeringRanks}}
2
20
Effective Spell level: {{inputs.spellLevel}}
1
9
Dice rolled:
Prime numbers to match: {{inputs.numbersToMatch.join(", ")}}
Expression:
{{inputs.solution}}
Random
Solve!


Web VR brings virtual reality to the web, making it easier for anyone to create, enjoy, and share VR experiences. With Web VR, you can open up a browser and get into VR just by clicking a link.
What is WebXR?
Platforms and Building Blocks
WebGL (Web Graphics Library) is a JavaScript API for rendering interactive 2D and 3D graphics within any compatible web browser without the use of plug-ins. WebGL is fully integrated with other web standards, allowing GPU-accelerated usage of physics and image processing and effects as part of the web page canvas. WebGL became a standard in modern bowsers in 2011
WebGL is great, but rather involved to program so we have helper libraries, such as ...
Three.js and Babylon started as 3d in the browser libraries and added VR features later. But we also have a VR first library built on top of Three.js...
Visual Inspector
https://github.com/aframevr/aframe-inspectorThe OBJ file format is a simple data-format that represents 3D geometry alone — namely, the position of each vertex, the UV position of each texture coordinate vertex, vertex normals, and the faces that make each polygon defined as a list of vertices, and texture vertices.
FBX (Filmbox) is a proprietary file format (.fbx) developed by Kaydara and owned by Autodesk since 2006. It is used to provide interoperability between digital content creation applications.
GLTF
Tools
VR Browsers


Welcome to the City of Trippville Zoning Map. This map is maintained by the City's Planning Department. We make every effort to ensure it is up to date.
Please contact our Planning Director, Nathaniel Keith at<EMAIL_ADDRESS>if you have any questions.


-
Today
✔ Doing Great! ★★★
You gained 334 points so far!
-
Sat
6/10 Workouts Done ★★✩
You gained 212 points this week!
-
Fri
✔ 10/10 Workouts Done ★★✩
Your gained 334 points this week!
-
Thu
6/10 Workouts Done ★★✩
You gained 212 points this week!
-
Wed
✔ 10/10 Workouts Done ★✩✩
Your gained 334 points this week!
-
Tue
6/10 Workouts Done ★✩✩
You gained 212 points this week!
-
Mon
6/10 Workouts Done ★✩✩
You gained 212 points this week!


TIP - 4 - TAT
Sub-total
Tax (not required)
Total
Tip %
Tip
Grand Total
Split
Total Per Person
TOTAL
0.00
TIP
0.00
GRAND TOTAL
0.00
PER PERSON
Rounding Options
None
Round Tip Up
Round Total Up
Effective Tip Rate:


|
UMI3D Unity SDK
2.4.r.220208
A UMI3D sdk implementation using Unity
|
Static Public Member Functions | |
| new static Coroutine | StartCoroutine (IEnumerator enumerator) |
| new static void | StopCoroutine (Coroutine coroutine) |
Protected Member Functions | |
| virtual void | Awake () |
| Unity function call when a MonoBehaviour is created. Make sure to always call base.Awake() when overriding. More... | |
| virtual void | OnDestroy () |
| Protected Member Functions inherited from umi3d.common.QuittingManager | |
| virtual void | OnApplicationQuit () |
Properties | |
| static bool | Exists [get] |
| State if an instance of T exist. More... | |
| static T | Instance [getset] |
| static rteference to the only instance of T More... | |
| Properties inherited from umi3d.common.QuittingManager | |
| static bool | ApplicationIsQuitting [getset] |
| Should be set to true when the application is quitting. More... | |
Additional Inherited Members | |
| Static Public Attributes inherited from umi3d.common.QuittingManager | |
| static bool | ShouldWaitForApplicationToQuit = false |
| Should be set to true in the client and the server when they deal with quitting the application. More... | |
| static UnityEvent | OnApplicationIsQuitting = new UnityEvent() |
| Raised when ApplicationIsQuitting is set to true. More... | |
|
protectedvirtual |
Unity function call when a MonoBehaviour is created. Make sure to always call base.Awake() when overriding.
Reimplemented in umi3d.cdk.UMI3DResourcesManager, umi3d.common.UMI3DLogger, and umi3d.edk.userCapture.UMI3DEmbodimentManager.
|
protectedvirtual |
|
static |
|
static |
|
staticget |
State if an instance of T exist.
|
staticgetset |
static rteference to the only instance of T


cosmic-lib demo page
(Source code)
Transaction
Sign & Send
Open
Open in side-frame
Open in new tab
Open in current window
Open with your SEP-0007 handler
Setup
Set this page as your SEP-0007 handler
Meta-Parameters
Page:
Network:
Horizon:
Callback:
Source:
Formats
URI:
Query:
JSON:
Lock:
XDR:
SEP-0007:
SEP-0007 extra:


The memory to reserve. Available memory per node can be obtained with a node inspect. The size is in bytes. Examples:
1073741274 for 1 GB<PHONE_NUMBER> for 4 GB


InputEx modules and dependencies are defined in the inputex-loader.js file. See the YUI loader page for more informations.


--- layout: default date: 2019-12-06 title: ptAsciiDoc header: AsciiDoc ---
Home/ Computing/ Formatting/ Asciidoc/
AsciiDoc(2002) by Stuart Rackham was initially designed as a markup language for DocBook, an XML format for writing technical books, articles and man pages. There was a need for a markup language because XML is better suited for computer input than human input.
AsciiDoc has many backends. It can produce pdfs via LaTeX or via ODT, overheads, and HTML.
Because of its technical origin, AsciiDoc has a full specification and does not permit of variations in how to markup text. AsciiDoc preceded MarkDown(2004).
We assume that the reader is coming here to learn how to use AsciiDoc, and not to view its output.
A *bold* word. + **Bold text.**. + An _italic_ word. + __Italic text.__ + A +monospace+ word + ++Monospace text.++ +
A Normal Asciidoc Paragraph. There is no markup. The words are wrapped to fit the space available. .... Literal Text .... ---- Listing Text ---- ==== Example Text ==== A list - one - two - three
Here is some more on AsciiDoc Paragraphs


See: Description
| Interface | Description |
|---|---|
| CryptoBinary |
XMLObject representing XML Digital Signature, version 20020212, CryptoBinary simple type.
|
| DEREncodedKeyValue |
XMLObject representing XML Digital Signature 1.1 DEREncodedKeyValue element.
|
| DigestMethod |
XMLObject representing XML Digital Signature, version 20020212, DigestMethod element.
|
| DSAKeyValue |
XMLObject representing XML Digital Signature, version 20020212, DSAKeyValue element.
|
| ECKeyValue |
XMLObject representing XML Digital Signature, version 20020212, ECKeyValue element.
|
| ECPointType |
XMLObject representing XML Digital Signature 1.1 ECPointType element.
|
| Exponent |
XMLObject representing XML Digital Signature, version 20020212, Exponent element.
|
| G |
XMLObject representing XML Digital Signature, version 20020212, G element.
|
| J |
XMLObject representing XML Digital Signature, version 20020212, J element.
|
| KeyInfo |
XMLObject representing XML Digital Signature, version 20020212, KeyInfo element.
|
| KeyInfoReference |
XMLObject representing XML Digital Signature 1.1 KeyInfoReference element.
|
| KeyName |
XMLObject representing XML Digital Signature, version 20020212, KeyName element.
|
| KeyValue |
XMLObject representing XML Digital Signature, version 20020212, KeyValue element.
|
| MgmtData |
XMLObject representing XML Digital Signature, version 20020212, MgmtData element.
|
| Modulus |
XMLObject representing XML Digital Signature, version 20020212, Modulus element.
|
| NamedCurve |
XMLObject representing XML Digital Signature 1.1 NamedCurve element.
|
| P |
XMLObject representing XML Digital Signature, version 20020212, P element.
|
| PgenCounter |
XMLObject representing XML Digital Signature, version 20020212, PgenCounter element.
|
| PGPData |
XMLObject representing XML Digital Signature, version 20020212, PGPData element.
|
| PGPKeyID |
XMLObject representing XML Digital Signature, version 20020212, PGPKeyID element.
|
| PGPKeyPacket |
XMLObject representing XML Digital Signature, version 20020212, PGPKeyPacket element.
|
| PublicKey |
XMLObject representing XML Digital Signature 1.1 PublicKey element.
|
| Q |
XMLObject representing XML Digital Signature, version 20020212, Q element.
|
| RetrievalMethod |
XMLObject representing XML Digital Signature, version 20020212, RetrievalMethod element.
|
| RSAKeyValue |
XMLObject representing XML Digital Signature, version 20020212, RSAKeyValue element.
|
| Seed |
XMLObject representing XML Digital Signature, version 20020212, Seed element.
|
| SignableXMLObject |
An XMLObject whose DOM representation can be digitally signed.
|
| Signature |
XMLObject representing an enveloped or detached XML Digital Signature, version 20020212, Signature element.
|
| SPKIData |
XMLObject representing XML Digital Signature, version 20020212, SPKIData element.
|
| SPKISexp |
XMLObject representing XML Digital Signature, version 20020212, SPKISexp element.
|
| Transform |
XMLObject representing XML Digital Signature, version 20020212, Transform element.
|
| Transforms |
XMLObject representing XML Digital Signature, version 20020212, Transforms element.
|
| X509Certificate |
XMLObject representing XML Digital Signature, version 20020212, X509Certificate element.
|
| X509CRL |
XMLObject representing XML Digital Signature, version 20020212, X509CRL element.
|
| X509Data |
XMLObject representing XML Digital Signature, version 20020212, X509Data element.
|
| X509Digest |
XMLObject representing XML Digital Signature 1.1 X509Digest element.
|
| X509IssuerName |
XMLObject representing XML Digital Signature, version 20020212, X509IssuerName element.
|
| X509IssuerSerial |
XMLObject representing XML Digital Signature, version 20020212, X509IssuerSerial element.
|
| X509SerialNumber |
XMLObject representing XML Digital Signature, version 20020212, X509SerialNumber element.
|
| X509SKI |
XMLObject representing XML Digital Signature, version 20020212, X509SKI element.
|
| X509SubjectName |
XMLObject representing XML Digital Signature, version 20020212, X509SubjectName element.
|
| XMLSignatureBuilder<XMLSignatureType extends org.opensaml.core.xml.XMLObject> |
Builder for XMLObjects from
org.opensaml.xmlsec.signature . |
| XPath |
XMLObject representing XML Digital Signature, version 20020212, XPath element.
|
| Y |
XMLObject representing XML Digital Signature, version 20020212, Y element.
|
| Class | Description |
|---|---|
| AbstractSignableXMLObject |
Base for signable XMLObjects.
|
Copyright © 1999–2016 Shibboleth Consortium. All rights reserved.


Next
Prev.
Question 2: Do you feel stronger around your Partner?
Question 3: Are you unsure of the future of your relationship?
Question 4: Does your friends think both of you are a committed couple?

Question 6 Are you comfortable seeing your partner with someone else?
Question 7: Does your partner believe you?.
Question 8: Do you believe in the future of your partner?
Question 9: My partner believes we re committed,
but I am not there yet.
Suestion 10: I am committed to our relationship,
but my partner isnt there yet.


Please make a small and self-contained program that exposes the bug, and then open an issue at the Github repo. Issue templates are available to help with the bug report process.
Please cite the following paper if you use mlpack in your work. Citations are useful for the continued development of the library.
R.R. Curtin, M. Edel, O. Shrit, S. Agrawal, S. Basak, J.J. Balamuta, R. Birmingham, K. Dutt, D. Eddelbuettel, R. Garg, S. Jaiswal, A. Kaushik, S.Kim, A. Mukherjee, N.G. Sai, N. Sharma, Y.S. Parihar, R. Swain, and C. Sanderson. "mlpack 4: a fast, header-only C++ machine learning library." Journal of Open Source Software 8:82, 2023. [bibtex]
mlpack is licensed under the permissive 3-clause BSD license. See also a Quick Summary of the license.
mlpack is a community-led effort. mlpack uses an open governance model and is fiscally sponsored by NumFOCUS. Consider making a tax-deductible donation to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs. See the community page for more details and a list of contributors.
See the vision document, which lays out a number of development goals and directions for mlpack in the short- to medium-term future.
mlpack development is done on GitHub and the source code is
available there. There are a few related repositories that might be worth checking out also:
• ensmallen: numerical optimization library
• examples: simple examples of mlpack usage
• models: additional implementations of machine learning models
Contributions are absolutely welcome for mlpack, and anyone is welcome to participate and contribute. See the community page for more details.
© 2007 - 2022 mlpack developers (BSD License).
Home • Quick Start • Documentation • Community • FAQ • GitHub


We’ve never been better connected than we are today. Whether we like it or not, in our tech-dependent world, we have become reliant on our ability to easily access information to efficiently reduce our costs.
The digital age has completely revolutionised the way we do business. It’s opened doors to new strategies only possible due to increased connectivity and access to information. But despite this technology in procurement hasn’t changed much so far.
The key challenges of procurement have remained the same for years and driving further savings from the same set of processes is increasingly difficult, with the steps taken to reach better prices often being time consuming and complicated.
Silverwing is here to help you harness the buying power of the whole marketplace – and all the advantages that come with it.
At Silverwing, we consider the decisions of buyers and suppliers to be fundamentally connected. Our platform brings together procurement professionals and business buyers in a way that allows them to think and act – not just at an individual level – but at a market level. We’re helping organisations make more informed decisions, with more transparency and better information.
We aim to provide organisations with information on market behaviour that can support them in coordinating and collaborating (anonymously) with each other. We believe this will be a key driver in the adoption of new technologies, and will help unlock greater value throughout the entire supply chain. Join us in harnessing new opportunities, and help your business solve everyday challenges – one purchase at a time.
*Registered office address is also the place of registration.


Page maintained by Ke-Sen Huang.
If you have additions or changes, send an e-mail.
Information here is provided with the permission of the ACM.
Note that when possible I link to the page containing the link to the actual PDF or PS of the preprint.
I prefer this as it gives some context to the paper and avoids possible copyright problems with direct linking.
Thus you may need to search on the page to find the actual document.
ACM Digital Library: Proceedings of the 2015 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Eurographics Digital Library: Proceedings of the 2015 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
the ACM Digital Library (DOI) Link for the paper
the Eurographics Digital Library Link for the paper
Paper Abstract
Author Preprint
Paper Video
Paper Presentation
Paper Images
Paper Data
Demo Program or Source Code
Related Links
Changelog
Deformation & Perception
- A new sharp-crease bending element for folding and wrinkling surfaces and volumes
-
Saket Patkar,
Ning Jin,
Ronald Fedkiw
- Real-Time Dynamic Wrinkling of Coarse Animated Cloth
-
Russel Gillette,
Craig Peters ,
Alla Sheffer,
Nicholas Vining,
Essex Edwards
- Multifarious Hierarchies of Mechanical Models for Artist Assigned Level-of-Detail
-
Richard Malgat,
Benjamin Gilles,
David Levin,
Matthieu Nesme,
Francois Faure
- Effect of Appearance on Perception of Deformation
-
Donghui Han,
John Keyser
Contacts
- Fully Momentum-Conserving Reduced Deformable Bodies with Collision, Contact, Articulation, and Skinning
-
Rahul Sheth,
Wenlong Lu,
Yue Yu,
Ronald Fedkiw
- Efficient Simulation of Knitted Cloth Using Persistent Contacts
-
Gabriel Cirio,
Jorge Lopez-Moreno,
Miguel Otaduy
- Hands On: Interactive Animation of Precision Manipulation and Contact
-
Benjamin Humberston,
Dinesh Pai
Characters & Control
- Hierarchical Planning and Control for Complex Motor Tasks
-
Daniel Zimmermann,
Stelian Coros,
Yuting Ye,
Robert Sumner,
Markus Gross
- Learning Reduced-Order Feedback Policies for Motion Skills
-
Kai Ding,
Libin Liu,
KangKang Yin,
Michiel van de Panne
- Computational Design of Walking Automata
-
Gaurav Bharaj,
Stelian Coros,
Bernhard Thomaszewski,
James Tompkin,
Bernd Bickel,
Hanspeter Pfister
- Animating Articulated Characters using Wiggly Splines
-
Christian Schulz,
Christoph von Tycowicz,
Hans-Peter Seidel,
Klaus Hildebrandt
Crowds
- BSwarm: Biologically-Plausible Dynamics Model of Insect Swarms
-
Xinjie Wang,
Jiaping Ren,
Xiaogang Jin,
Dinesh Manocha
- Energy-efficient mid-term strategies for collision avoidance in crowd simulation
-
Julien Bruneau,
Julien Pettre
Fluids
- Simulation of Fluid Mixing with Interface Control
-
Xiaowei He,
Huamin Wang,
Fengjun Zhang,
Hongan Wang,
Guoping Wang,
Kun Zhou,
Enhua Wu
- Functional Thin Films on Surfaces
-
Omri Azencot,
Orestis Vantzos,
Max Wardetzky,
Martin Rumpf,
Mirela Ben-Chen
- Divergence-Free Smoothed Particle Hydrodynamics
-
Jan Bender,
Dan Koschier
- A Material Point Method for Viscoelastic Fluids, Foams and Sponges
-
Daniel Ram,
Theodore Gast,
Chenfanfu Jiang,
Craig Schroeder,
Alexey Stomakhin,
Joseph Teran,
Pirouz Kavehpour
Rigs
- Learning an Inverse Rig Mapping for Character Animation
-
Daniel Holden,
Jun Saito,
Taku Komura
- Fully Automatic Generation of Anatomical Face Simulation Models
-
Matthew Cong,
Michael Bao,
Jane L. E,
Kiran Bhat,
Ronald Fedkiw
- Sketch Abstractions for Character Posing
-
Fabian Hahn,
Frederik Mutzel,
Stelian Coros,
Bernhard Thomaszewski,
Maurizio Nitti,
Markus Gross,
Robert Sumner
kesen.huang@gmail.com


Loader
{% include 'tiles/summary.html' %}
{% include 'tiles/bubble-graph.html' %}
{% include 'tiles/time-series.html' %}
{% include 'tiles/heat-map.html' %}
{% include 'tiles/world-map.html' %}
{% with graph_id='doughnutGraph' %}
{% include 'tiles/doughnut-graph.html' %}
{% endwith %}
{% include 'components/logo-download.html' %}


{% extends 'pizzas/base.html' %} {% block page_header %}
Add new topping
There are no toppings for this pizza yet.


November 8, 2018
Wait a minute. Wait a minute, Doc. Uhhh... Are you telling me that you built a time machine... out of a DeLorean?! Whoa. This is heavy.


( ? ) f s
Signature: f:('?8598 -> '?8599) -> s:'?8598 -> '?8599
Type parameters: '?8598, '?8599
|
Allows to use Tokens instead of strings
|
( ?<- ) f str action
Signature: f:('?8601 -> '?8602 -> '?8603) -> str:'?8601 -> action:'?8602 -> '?8603
Type parameters: '?8601, '?8602, '?8603
|
Allows to use Tokens instead of strings for TargetNames
|
( <=> ) x y
Signature: x:string -> y:string -> string
|
Defines that x and y are not dependent on each other but y is dependent on all dependencies of x.
|
( =?> ) x (y, condition)
Signature: x:string -> (y:string * condition:bool) -> string
|
Defines a conditional dependency - y is dependent on x if the condition is true
|
( ==> ) x y
Signature: x:string -> y:string -> string
|
Defines a dependency - y is dependent on x
|
addDependenciesOnSameLevel (...)
Signature: target:string -> dependency:string -> unit
|
Specifies that two targets have the same dependencies
|
And x y
Signature: x:'?8608 -> y:'?8608 list -> '?8608 list
Type parameters: '?8608
|
Appends the dependency to the list of dependencies
|
Dependency x
Signature: x:'?8606 -> '?8606 list
Type parameters: '?8606
|
Converts a dependency into a list
|
For x y
Signature: x:string -> y:string list -> unit
|
Allows to use For? syntax for Dependencies
|
Run targetName
Signature: targetName:string -> unit
|
Runs a Target and its dependencies
|
RunParameterTargetOrDefault (...)
Signature: parameterName:string -> defaultTarget:string -> unit
|
Runs the target given by the build script parameter or the given default target
|
RunTarget ()
Signature: unit -> unit
|
Runs the target given by the target parameter
|
RunTargetOrDefault defaultTarget
Signature: defaultTarget:string -> unit
|
Runs the target given by the target parameter or the given default target
|
targetsAreOnSameLevel x y
Signature: x:string -> y:string -> unit
|
Specifies that two targets are on the same level of execution
|


Moosehead.studio Template V1.2 (Public, Rabbid Rabbit)
Internal Version V1.3 (Internal, Beefy Badger
)
Moosehead Studio base template (22June 2018)
- Refactored V8.0.0 Normalize.scss to comply with scss-lint
Moosehead Studio base template (20 June 2018)
- Refactored Various SCSS files.
- Linted Documents.
- Updated Humans.txt.
- Remove Compass (Depreciated).
- Custom Vertical Rythem.
- Susy for grid math.
- Added Auto Prefixer.
- Custom default Fonts and Fallbacks.
- Added Breakpoint.
- Added Custom Breakpoints for @Media Queries.
- Default font varibles.
Moosehead Studio base template (8 June 2018)
- Cleaned up even more code/formatting/spelling.
- Added Placeholder text for index.html.
- Updated index.html with code descriptions.
- added some style to main.scss for index.html.
- Added descriptive text to top of each CSS document.
- Updated identification icons.
- Created scss.md in Docs.
- Updated docs (css, extend, faq, html, js, misc, toc, usage).
- Added Facebook OG to index.html.
- Added Tooltip support in index.html for when website is pinned.
- Hand created support for SCSS (normalize, material design icons, HTML5 Boilerplate)(Second Revision).
- Removed 404.html (didnt remove this from apache config as default error landing page).
- Added description of code to various CSS files for better 3rd party understanding.
- Added missing '&:' to .scss files that were removed due to human error.
- Updated meta tag with default explination text.
- Set html document language to “en".
- Added sitemap to robots.txt.
- Updated humans.txt.
Moosehead Studio base template (24 May 2018)
- Cleaned up template, removing more redundant files
- Hand created support for SCSS (normalize, material design icons, HTML5 Boilerplate)(First Revision)
- Added material design icons and custom classes with meaningful names 'md-icon', 'md-1x', 'primary-color'.
- Added support to material design icons to work like FontAwesome.
- Refined modernizer and converted to SCSS
- Converted main CSS stylesheet to SCSS
- Issues: & symbol's missing from SCSS main stylesheet.
Moosehead Studio base template (22 May 2018)
- Initial clone of repository (HTML5-Boilerplate).
Features
- HTML5 ready.
- Designed with progressive enhancement in mind.
- Includes:
- Placeholder CSS Media Queries.
- Useful CSS helper classes.
- Default print styles, performance optimized.
- An optimized version of the Google Universal Analytics snippet.
- Protection against any stray
console
statements causing JavaScript
errors in older browsers.
- Extensive inline and accompanying documentation.
Browser support
- Chrome (latest 2)
- Edge (latest 2)
- Firefox (latest 2)
- Internet Explorer 10+
- Opera (latest 2)
- Safari (latest 2)
This doesn't mean that This HTML5 tempplate cannot be used in older browsers,
just that we'll ensure compatibility with the ones mentioned above.
Documentation
Take a look at the documentation table of contents.
Contributing
Please take a moment to review these documentations and report any issues.
the guidelines:
License
The code is available under the MIT license.


Job Dependency Grapher
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2. Job Dependency (Back)


Monlthy Costs Calculator
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My Project
|
Extension to read executables based on an XML format. More...
Go to the source code of this file.
Classes | |
| class | XmlArchitectureCapability |
| Extension for building an XML format capable Architecture. More... | |
| class | XmlArchitecture |
| An Architecture that loads executables using an XML format. More... | |
Extension to read executables based on an XML format.


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Contains the core classes and interfaces for the
Quartz
job scheduler.
See the
Quartz
project for more information.


This is a camera parameter calculator. This application is driven by OpenCV.js web-assembly(ver.4.0.0-pre 2018/09/30). I recommend to run on Google Chrome.
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* If you don't need to know extrinsic parameters, you can set any non-zero number to edge length.


Hack
Cambridge
Ternary
Not twice, but three times.
Not 2D, but 3D.
Not binary, but ternary.
In January 2018, Cambridge University’s annual hackathon will yet again bring together 300 hackers, programmers, designers and more from universities all over the world.
For 24 hours they will build, break and innovate to produce projects that push the boundaries of technology.
Join us.


Class: Repository::Base::Internals::RecordDeleter
- Inherits:
-
Object
- Object
- Repository::Base::Internals::RecordDeleter
- Includes:
- Support
- Defined in:
- lib/repository/base/internals/record_deleter.rb
Overview
Stows away details of reporting update success/failure.
Instance Attribute Summary collapse
- #dao ⇒ Object readonly private
- #factory ⇒ Object readonly private
- #identifier ⇒ Object readonly private
Instance Method Summary collapse
-
#delete ⇒ Object
Command-pattern method returning indication of success or failure of attempt to delete identified record.
-
#initialize(identifier:, dao:, factory:) ⇒ RecordDeleter
constructor
Initializes a new instance of `SlugFinder`.
Constructor Details
#initialize(identifier:, dao:, factory:) ⇒ RecordDeleter
Initializes a new instance of `SlugFinder`.
23 24 25 26 27 |
# File 'lib/repository/base/internals/record_deleter.rb', line 23 def initialize(identifier:, dao:, factory:) @identifier = identifier @dao = dao @factory = factory end |
Instance Attribute Details
#dao ⇒ Object (readonly, private)
42 43 44 |
# File 'lib/repository/base/internals/record_deleter.rb', line 42 def dao @dao end |
#factory ⇒ Object (readonly, private)
42 43 44 |
# File 'lib/repository/base/internals/record_deleter.rb', line 42 def factory @factory end |
#identifier ⇒ Object (readonly, private)
42 43 44 |
# File 'lib/repository/base/internals/record_deleter.rb', line 42 def identifier @identifier end |
Instance Method Details
#delete ⇒ Object
Command-pattern method returning indication of success or failure of attempt to delete identified record.
32 33 34 35 36 37 38 |
# File 'lib/repository/base/internals/record_deleter.rb', line 32 def delete finder = SlugFinder.new slug: identifier, dao: dao, factory: factory result = finder.find return result unless result.success dao.delete identifier result end |


Hello, World!
The inevitable Hello World example.
<%= require('html-loader!./lambda/hello.js') %>
Hi there! This is a playground to test out Netlify’s Lambda Functions.
You can browse the code for this site on GitHub, or play around with the code yourself deploying a copy to Netlify.
The inevitable Hello World example.
<%= require('html-loader!./lambda/hello.js') %>
The Hello World example can get sweeter with some async syntactic sugar. With async, we can return the response instead of dealing with callbacks.
<%= require('html-loader!./lambda/hello_async.js') %>
Customize the greeting calling the Lambda endpoint with an optional
name
parameter.
<%= require('html-loader!./lambda/hello_name.js') %>
Let’s make sure we only process POST requests for our customized greeting.
<%= require('html-loader!./lambda/hello_name_post.js') %>
Storing secrets like API tokens can be tricky in web apps. Lambdas and environment variables to the rescue!
For this example, I’m using dotenv to define environment variables locally (in a real project, I wouldn’t commit that file to the repository!), and defining those same variables in the Netlify site dashboard under Settings > Build & deploy > Build environment variables, for production.
Note: environment variables get baked into your function at deploy time, so you need to trigger a new deploy after you change them.
<%= require('html-loader!./lambda/hello_env.js') %>
I see you like APIs!
You’ll need a Slack account to replicate this example on your own site (if you don’t already have a Slack account, you can create one for free).
Create a Slack incoming webhook at https://my.slack.com/services/new/incoming-webhook/
To test the function locally, add the Slack webhook URL to the .env file in the root folder of your repository.
SLACK_WEBHOOK_URL=https://hooks.slack.com/services/XXXXXXXXXX
To test the function in your deployed site, sign in to your Netlify dashboard, add the environment variable to your site’s Settings > Build & Deploy > Build environment variables, and trigger a new deploy.
<%= require('html-loader!./lambda/hello_slack.js') %>


| Modifier and Type | Class and Description |
|---|---|
class |
RakNetPacket
A generic RakNet packet that has the ability to get the ID of the packet
along with encoding and decoding.
|
| Modifier and Type | Method and Description |
|---|---|
Packet |
Packet.clear()
Clears the packets buffer.
|
Packet |
RakNetPacket.flip() |
Packet |
Packet.flip()
Flips the packet.
|
Packet |
Packet.pad(int length)
Writes the specified amount of null (0x00) bytes to the packet.
|
Packet |
Packet.read(byte[] dest)
Reads data into the specified byte array.
|
Packet |
Packet.setBuffer(byte[] buffer)
Sets the buffer of the packet
|
Packet |
Packet.write(byte[] data)
Writes the specified byte array to the packet.
|
Packet |
Packet.writeAddress(java.net.InetAddress address,
int port)
Writes an IPv4 address to the packet (IPv6 is not yet supported).
|
Packet |
Packet.writeAddress(java.net.InetSocketAddress address)
Writes an IPv4 address to the packet (Writing IPv6 is not yet supported).
|
Packet |
Packet.writeAddress(java.lang.String address,
int port)
Writes an IPv4 address to the packet (IPv6 is not yet supported).
|
Packet |
Packet.writeBoolean(boolean b)
Writes a boolean value to the packet.
|
Packet |
Packet.writeByte(int b)
Writes a byte to the packet.
|
private Packet |
Packet.writeCFU(byte[] data)
Writes a byte array of the specified flipped unsigned byte's casted back
to a byte to the packet.
|
private Packet |
Packet.writeCFUByte(byte b)
Writes a flipped unsigned byte casted back into a byte to the packet.
|
Packet |
Packet.writeConnectionType()
Writes the default connection type
JRAKNET to the packet. |
Packet |
Packet.writeConnectionType(ConnectionType connectionType)
Writes a connection type to the packet.
|
Packet |
Packet.writeDouble(double d)
Writes a double to the packet.
|
Packet |
Packet.writeFloat(double f)
Writes a float to the packet.
|
Packet |
Packet.writeInt(int i)
Writes an integer to the packet.
|
Packet |
Packet.writeIntLE(int i)
Writes a little-endian integer to the packet.
|
Packet |
Packet.writeLong(long l)
Writes a long to the packet.
|
Packet |
Packet.writeLongLE(long l)
Writes a little-endian long to the packet.
|
Packet |
Packet.writeMagic()
Writes the magic sequence to the packet.
|
Packet |
Packet.writeShort(int s)
Writes a short to the packet.
|
Packet |
Packet.writeShortLE(int s)
Writes a little-endian short to the packet.
|
Packet |
Packet.writeString(java.lang.String s)
Writes a UTF-8 String prefixed by an unsigned short to the packet.
|
Packet |
Packet.writeStringLE(java.lang.String s)
Writes a UTF-8 String prefixed by a little-endian unsigned short to the
packet.
|
Packet |
Packet.writeTriadLE(int t)
Writes a little-endian triad to the packet.
|
Packet |
Packet.writeUnsignedByte(int b)
Writes an unsigned by to the packet.
|
Packet |
Packet.writeUnsignedInt(long i)
Writes an unsigned integer to the packet.
|
Packet |
Packet.writeUnsignedIntLE(long i)
Writes an unsigned little-endian integer to the packet.
|
Packet |
Packet.writeUnsignedLong(java.math.BigInteger bi)
Writes an unsigned long to the packet.
|
Packet |
Packet.writeUnsignedLong(long l)
Writes an unsigned long to the packet.
|
Packet |
Packet.writeUnsignedLongLE(java.math.BigInteger bi)
Writes an unsigned little-endian long to the packet.
|
Packet |
Packet.writeUnsignedLongLE(long l)
Writes an unsigned little-endian long to the packet.
|
Packet |
Packet.writeUnsignedShort(int s)
Writes a unsigned short to the packet.
|
Packet |
Packet.writeUnsignedShortLE(int s)
Writes an unsigned little-endian short to the packet.
|
Packet |
Packet.writeUUID(java.util.UUID uuid)
Writes an universally unique identifier to the packet.
|
| Modifier and Type | Method and Description |
|---|---|
private static RakNetPacket |
RakNet.createBootstrapAndSend(java.net.InetSocketAddress address,
Packet packet,
long timeout,
int retries)
Sends a raw message to the specified address for the specified amount of
times in the specified interval until the packet is received or there is
a timeout.
|
| Constructor and Description |
|---|
Packet(Packet packet)
Constructs a
Packet that reads from and writes to the
specified Packet . |
RakNetPacket(Packet packet)
Constructs a
RakNetPacket that reads from and writes to the
specified Packet . |
| Modifier and Type | Method and Description |
|---|---|
EncapsulatedPacket |
RakNetClient.sendMessage(Reliability reliability,
int channel,
Packet packet) |
void |
RakNetClient.sendNettyMessage(Packet packet,
java.net.InetSocketAddress address)
Sends a raw message to the specified address.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AddChannel |
class |
ChatMessage |
class |
ChatPacket
Used to read and write data related to the chat protocol used for the
example.
|
class |
Kick |
class |
LoginAccepted |
class |
LoginFailure |
class |
LoginRequest |
class |
RemoveChannel |
class |
RenameChannel |
class |
UpdateUsername |
| Constructor and Description |
|---|
AddChannel(Packet packet) |
ChatMessage(Packet packet) |
ChatPacket(Packet packet)
Constructs a
ChatPacket that reads from and writes to the
specified Packet . |
Kick(Packet packet) |
LoginAccepted(Packet packet) |
LoginFailure(Packet packet) |
LoginRequest(Packet packet) |
RemoveChannel(Packet packet) |
RenameChannel(Packet packet) |
UpdateUsername(Packet packet) |
| Modifier and Type | Class and Description |
|---|---|
class |
ConnectionBanned |
class |
ConnectionRequest |
class |
ConnectionRequestAccepted |
class |
IncompatibleProtocol |
class |
NewIncomingConnection |
class |
OpenConnectionRequestOne |
class |
OpenConnectionRequestTwo |
class |
OpenConnectionResponseOne |
class |
OpenConnectionResponseTwo |
| Constructor and Description |
|---|
ConnectionBanned(Packet packet) |
ConnectionRequest(Packet packet) |
ConnectionRequestAccepted(Packet packet) |
IncompatibleProtocol(Packet packet) |
NewIncomingConnection(Packet packet) |
OpenConnectionRequestOne(Packet packet) |
OpenConnectionRequestTwo(Packet packet) |
OpenConnectionResponseOne(Packet packet) |
OpenConnectionResponseTwo(Packet packet) |
| Modifier and Type | Class and Description |
|---|---|
class |
CustomPacket |
| Modifier and Type | Field and Description |
|---|---|
protected Packet |
EncapsulatedPacket.buffer |
Packet |
EncapsulatedPacket.payload |
| Modifier and Type | Method and Description |
|---|---|
static int |
EncapsulatedPacket.calculateDummy(Reliability reliability,
boolean split,
Packet payload) |
| Constructor and Description |
|---|
CustomPacket(Packet packet) |
| Modifier and Type | Class and Description |
|---|---|
class |
Acknowledge |
| Constructor and Description |
|---|
Acknowledge(Packet packet) |
| Modifier and Type | Class and Description |
|---|---|
class |
ConnectedPing |
class |
ConnectedPong |
class |
UnconnectedPing |
class |
UnconnectedPingOpenConnections |
class |
UnconnectedPong |
| Constructor and Description |
|---|
ConnectedPing(Packet packet) |
ConnectedPong(Packet packet) |
UnconnectedPing(Packet packet) |
UnconnectedPingOpenConnections(Packet packet) |
UnconnectedPong(Packet packet) |
| Modifier and Type | Method and Description |
|---|---|
EncapsulatedPacket |
RakNetServer.sendMessage(long guid,
Reliability reliability,
int channel,
Packet packet) |
void |
RakNetServer.sendNettyMessage(Packet packet,
java.net.InetSocketAddress address)
Sends a raw message to the specified address.
|
| Modifier and Type | Field and Description |
|---|---|
private IntMap<Packet> |
SplitPacket.payloads |
| Modifier and Type | Method and Description |
|---|---|
Packet |
SplitPacket.update(EncapsulatedPacket encapsulated)
Updates the data for the split packet while also verifying that the
specified
EncapsulatedPacket belongs to this split packet. |
| Modifier and Type | Method and Description |
|---|---|
static boolean |
SplitPacket.needsSplit(Reliability reliability,
Packet packet,
int maximumTransferUnit) |
default EncapsulatedPacket[] |
GeminusRakNetPeer.sendMessage(long guid,
Reliability reliability,
int channel,
Packet... packets)
Sends the specified messages with the specified reliability on the
specified channel to the session with the specified globally unique ID
and returns copies of the generated encapsulated packets that will be
used when it is actually sent.
|
EncapsulatedPacket |
RakNetSession.sendMessage(long guid,
Reliability reliability,
int channel,
Packet packet) |
EncapsulatedPacket |
GeminusRakNetPeer.sendMessage(long guid,
Reliability reliability,
int channel,
Packet packet)
Sends a message with the specified reliability on the specified channel
to the session with the specified globally unique ID and returns a copy
of the generated encapsulated packet that will be used when it is
actually sent.
|
default EncapsulatedPacket[] |
GeminusRakNetPeer.sendMessage(long guid,
Reliability reliability,
Packet... packets)
Sends the specified messages with the specified reliability on the
default channel to the session with the specified globally unique ID and
returns copies of the generated encapsulated packets that will be used
when it is actually sent.
|
default EncapsulatedPacket |
GeminusRakNetPeer.sendMessage(long guid,
Reliability reliability,
Packet packet)
Sends a message with the specified reliability on the default channel to
the session with the specified globally unique ID and returns a copy of
the generated encapsulated packet that will be used when it is actually
sent.
|
default EncapsulatedPacket[] |
UnumRakNetPeer.sendMessage(Reliability reliability,
int channel,
Packet... packets)
Sends the specified messages with the specified reliability on the
specified channel and returns copies of the generated encapsulated
packets that will be used when it is actually sent.
|
EncapsulatedPacket |
UnumRakNetPeer.sendMessage(Reliability reliability,
int channel,
Packet packet)
Sends a message with the specified reliability on the specified channel
and returns a copy of the generated encapsulated packet that will be used
when it is actually sent.
|
EncapsulatedPacket |
RakNetSession.sendMessage(Reliability reliability,
int channel,
Packet packet) |
default EncapsulatedPacket[] |
UnumRakNetPeer.sendMessage(Reliability reliability,
Packet... packets)
Sends the specified messages with the specified reliability on the
default channel and returns copies of the generated encapsulated packets
that will be used when it is actually sent.
|
default EncapsulatedPacket |
UnumRakNetPeer.sendMessage(Reliability reliability,
Packet packet)
Sends a message with the specified reliability on the default channel and
returns a copy of the generated encapsulated packet that will be used
when it is actually sent.
|
void |
RakNetSession.sendRawMessage(Packet packet)
Sends a raw message.
|
| Modifier and Type | Field and Description |
|---|---|
private Packet |
PacketDataOutput.packet |
private Packet |
PacketDataInput.packet |
| Constructor and Description |
|---|
PacketDataInput(Packet packet)
Constructs a
PacketDataInput with the specified
Packet . |
PacketDataOutput(Packet packet)
Constructs a
PacketDataOutput with the specified
Packet . |


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public interface Injector
Builds the graphs of objects that make up your application. The injector tracks the dependencies for each type and uses bindings to inject them. This is the core of Guice, although you rarely interact with it directly. This "behind-the-scenes" operation is what distinguishes dependency injection from its cousin, the service locator pattern.
Contains several default bindings:
Injector
instance itself
Provider<T>
for each binding of type T
Logger
for the class being injected
Stage
in which the Injector was created
Guice
.
An injector can also inject the dependencies
of
already-constructed instances. This can be used to interoperate with objects created by other
frameworks or services.
Injectors can be hierarchical
. Child injectors inherit
the configuration of their parent injectors, but the converse does not hold.
The injector's internal bindings
are available for introspection. This
enables tools and extensions to operate on an injector reflectively.
| Method Summary | ||
|---|---|---|
Injector |
createChildInjector(Iterable<? extends Module> modules)
Returns a new injector that inherits all state from this injector. |
|
Injector |
createChildInjector(Module... modules)
Returns a new injector that inherits all state from this injector. |
|
|
findBindingsByType(TypeLiteral<T> type)
Returns all explicit bindings for type . |
|
|
getBinding(Class<T> type)
Returns the binding for the given type. |
|
|
getBinding(Key<T> key)
Returns the binding for the given injection key. |
|
Map<Key<?>,Binding<?>> |
getBindings()
Returns all explicit bindings. |
|
|
getInstance(Class<T> type)
Returns the appropriate instance for the given injection type; equivalent to getProvider(type).get() . |
|
|
getInstance(Key<T> key)
Returns the appropriate instance for the given injection key; equivalent to getProvider(key).get() . |
|
|
getMembersInjector(Class<T> type)
Returns the members injector used to inject dependencies into methods and fields on instances of the given type T . |
|
|
getMembersInjector(TypeLiteral<T> typeLiteral)
Returns the members injector used to inject dependencies into methods and fields on instances of the given type T . |
|
Injector |
getParent()
Returns this injector's parent, or null if this is a top-level injector. |
|
|
getProvider(Class<T> type)
Returns the provider used to obtain instances for the given type. |
|
|
getProvider(Key<T> key)
Returns the provider used to obtain instances for the given injection key. |
|
void |
injectMembers(Object instance)
Injects dependencies into the fields and methods of instance . |
| Method Detail |
|---|
void injectMembers(Object instance)
instance
. Ignores the presence or
absence of an injectable constructor.
Whenever Guice creates an instance, it performs this injection automatically (after first performing constructor injection), so if you're able to let Guice create all your objects for you, you'll never need to use this method.
instance
- to inject members onfor a preferred alternative that supports checks before
run time
<T> MembersInjector<T> getMembersInjector(TypeLiteral<T> typeLiteral)
T
.
typeLiteral
- type to get members injector forfor an alternative that offers up front error
detection
<T> MembersInjector<T> getMembersInjector(Class<T> type)
T
. When feasible, use Binder.getMembersInjector(TypeLiteral)
instead to get increased up front error detection.
type
- type to get members injector forfor an alternative that offers up front error
detection
Map<Key<?>,Binding<?>> getBindings()
The returned map does not include bindings inherited from a parent
injector
, should one exist. The returned map is guaranteed to iterate (for example, with
its Map.entrySet()
iterator) in the order of insertion. In other words,
the order in which bindings appear in user Modules.
This method is part of the Guice SPI and is intended for use by tools and extensions.
<T> Binding<T> getBinding(Key<T> key)
This method is part of the Guice SPI and is intended for use by tools and extensions.
ConfigurationException
- if this injector cannot find or create the binding.<T> Binding<T> getBinding(Class<T> type)
This method is part of the Guice SPI and is intended for use by tools and extensions.
ConfigurationException
- if this injector cannot find or create the binding.<T> List<Binding<T>> findBindingsByType(TypeLiteral<T> type)
type
.
This method is part of the Guice SPI and is intended for use by tools and extensions.
<T> Provider<T> getProvider(Key<T> key)
ConfigurationException
- if this injector cannot find or create the provider.for an alternative that offers up front error detection
<T> Provider<T> getProvider(Class<T> type)
ConfigurationException
- if this injector cannot find or create the provider.for an alternative that offers up front error detection
<T> T getInstance(Key<T> key)
getProvider(key).get()
. When feasible, avoid using this method, in favor of having Guice
inject your dependencies ahead of time.
ConfigurationException
- if this injector cannot find or create the provider.
ProvisionException
- if there was a runtime failure while providing an instance.<T> T getInstance(Class<T> type)
getProvider(type).get()
. When feasible, avoid using this method, in favor of having Guice
inject your dependencies ahead of time.
ConfigurationException
- if this injector cannot find or create the provider.
ProvisionException
- if there was a runtime failure while providing an instance.Injector getParent()
null
if this is a top-level injector.
Injector createChildInjector(Iterable<? extends Module> modules)
Just-in-time bindings created for child injectors will be created in an ancestor injector whenever possible. This allows for scoped instances to be shared between injectors. Use explicit bindings to prevent bindings from being shared with the parent injector.
No key may be bound by both an injector and one of its ancestors. This includes just-in-time
bindings. The lone exception is the key for Injector.class
, which is bound by each
injector to itself.
Injector createChildInjector(Module... modules)
Just-in-time bindings created for child injectors will be created in an ancestor injector whenever possible. This allows for scoped instances to be shared between injectors. Use explicit bindings to prevent bindings from being shared with the parent injector.
No key may be bound by both an injector and one of its ancestors. This includes just-in-time
bindings. The lone exception is the key for Injector.class
, which is bound by each
injector to itself.
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Note that sometimes in order to make "CTRL-1" or "CTRL-~" after you have started navigating through the Google slides, you need to click here at the bottom and then press "CTRL-1". Once you are scribbling, all commands work normally until you press "CTRL-~" and then Google slides seems to snag the keyboard from within the iframe. Kind of like shooting off-screen to reload in a video game. Note the black div just below the slide - that is a good way to have a subtle place to click outside the iframe.


Class: Aws::DeviceFarm::Types::ExecutionConfiguration
| 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-devicefarm/lib/aws-sdk-devicefarm/types.rb |
Overview
Represents configuration information about a test run, such as the execution timeout (in minutes).
Constant Summary
Instance Attribute Summary
-
#accounts_cleanup ⇒ Boolean
rw
True if account cleanup is enabled at the beginning of the test.
-
#app_packages_cleanup ⇒ Boolean
rw
True if app package cleanup is enabled at the beginning of the test.
-
#job_timeout_minutes ⇒ Integer
rw
The number of minutes a test run executes before it times out.
-
#skip_app_resign ⇒ Boolean
rw
When set to
true
, for private devices,Device
Farm does not sign your app again. -
#video_capture ⇒ Boolean
rw
Set to true to enable video capture.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#accounts_cleanup ⇒ Boolean
(rw)
True if account cleanup is enabled at the beginning of the test. Otherwise, false.
# File 'gems/aws-sdk-devicefarm/lib/aws-sdk-devicefarm/types.rb', line 1689
class ExecutionConfiguration < Struct.new( :job_timeout_minutes, :accounts_cleanup, :app_packages_cleanup, :video_capture, :skip_app_resign) SENSITIVE = [] include Aws::Structure end
#app_packages_cleanup ⇒ Boolean
(rw)
True if app package cleanup is enabled at the beginning of the test. Otherwise, false.
# File 'gems/aws-sdk-devicefarm/lib/aws-sdk-devicefarm/types.rb', line 1689
class ExecutionConfiguration < Struct.new( :job_timeout_minutes, :accounts_cleanup, :app_packages_cleanup, :video_capture, :skip_app_resign) SENSITIVE = [] include Aws::Structure end
#job_timeout_minutes ⇒ Integer
(rw)
The number of minutes a test run executes before it times out.
# File 'gems/aws-sdk-devicefarm/lib/aws-sdk-devicefarm/types.rb', line 1689
class ExecutionConfiguration < Struct.new( :job_timeout_minutes, :accounts_cleanup, :app_packages_cleanup, :video_capture, :skip_app_resign) SENSITIVE = [] include Aws::Structure end
#skip_app_resign ⇒ Boolean
(rw)
When set to true
, for private devices, Device
Farm does not sign
your app again. For public devices, Device
Farm always signs your
apps again.
For more information about how Device
Farm re-signs your apps, see
Do you modify my app? in the AWS Device
Farm FAQs.
# File 'gems/aws-sdk-devicefarm/lib/aws-sdk-devicefarm/types.rb', line 1689
class ExecutionConfiguration < Struct.new( :job_timeout_minutes, :accounts_cleanup, :app_packages_cleanup, :video_capture, :skip_app_resign) SENSITIVE = [] include Aws::Structure end
#video_capture ⇒ Boolean
(rw)
Set to true to enable video capture. Otherwise, set to false. The default is true.
# File 'gems/aws-sdk-devicefarm/lib/aws-sdk-devicefarm/types.rb', line 1689
class ExecutionConfiguration < Struct.new( :job_timeout_minutes, :accounts_cleanup, :app_packages_cleanup, :video_capture, :skip_app_resign) SENSITIVE = [] include Aws::Structure end


john-deere
Businessmodel of John Deere
Customer Segments
John Deere sells its products to a broad range of customers, spanning the following sectors:
- Industrial, comprising industrial operators such as construction, engineering, contracting, waste management, and oil and gas companies;
- Agricultural, comprising farming and crop producing businesses;
- Marine, including commercial marine companies and recreational marine customers;
- Retail and Distribution, comprising distributors, dealerships and independent sales agents; and
- Consumers, comprising individual members of the general public. John Deere includes among its customers Reinauer Transportation, Midland Machinery, Midwest Machinery, Monsanto, and HP Briggs.
John Deere’s principal markets are the US and Canada, which account for the largest portion of the Company’s sales. The Company, however, has a global customer base, including customers across Australia, Brazil, Finland, Ireland, New Zealand, Russia, Singapore, Argentina, China, Mexico, South Africa, Sweden and the UK.
Value Propositions
John Deere provides value to its customers in the following ways:
- Its brand recognition and standing, with the Company being one of the most recognisable brands of agricultural and industrial equipment in the world, counting among its customers a number of large, high-profile companies;
- Its track record for supplying quality and reliable products, with its products designed to be long-lasting, reliable, efficient, and safe for its customers;
- Its global sales and marketing reach, with the Company serving a broad spectrum of customers across the Americas, Europe, Asia, and Africa;
- Accessibility and ease of use, with the Company making sales through a broad network of dealerships, as well as online, and with its products being east for consumers to manage and operate. ### Channels
Deer and Company operates a website at www.deere.com, through which it provides information on its various products and services. The Company also makes select equipment and products available for sale online through its e-commerce portal www.e-marketing.deere.com.
John Deere’s products and services are marketed primarily through a network of independent retail dealerships and major retail outlets, which deal directly with the Company’s own in-house sales teams. In the US and Canada, equipment and products are distributed through agriculture and turf equipment sales and administration offices in Olathe, Kansas and Cary, North Carolina and one sales branch in Grimsby, Ontario, as well as one construction, earthmoving, material handling and forestry equipment sales and administration office in Moline, Illinois. These U facilities, John Deere market products to approximately 2,381 dealer locations, the majority of which are independently owned and operated.
Outside the US and Canada, John Deere’s equipment and products is sold to distributors and dealers for resale in more than 100 countries. The Company has sales and administrative offices in Argentina, Australia, Brazil, China, Finland, France, Germany, India, Ireland, Italy, Mexico, the Netherlands, New Zealand, Poland, Russia, Singapore, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine and the UK, and an administrative office is located in Kenya.
Customer Relationships
John Deere sells a selection of its products to customers on a self-service basis through its online store, which enables customers to purchase and organise delivery of products without interacting with members of the John Deere sales teams.
Much of John Deere’s sales are made to dealerships and distributors with which the Company maintains ongoing relationships and establishes multi-year sales agreements. The Company also cooperates with dealerships in providing financing solutions and support.
John Deere provides ongoing support services to customers, in the form of maintenance and protection plans, servicing and parts, and safety and training assistance, as well through a range of online resources such as manuals, FAQs, and recall information. Customers are able to contact the Company directly and receive personalised assistance over the phone or via email.
John Deere additionally operates several social media accounts – including with Facebook, Twitter, YouTube, LinkedIn, and Instagram – through which it can interact directly with customers.
Key Activities
John Deere is principally engaged in the manufacture and distribution of agricultural and industrial equipment, as well as ancillary financial services.
The Company operates through three core business segments: Agriculture and Turf, which primarily manufactures and distributes a full suite of agriculture and turf equipment, as well as related service parts; Construction and Forestry, which principally manufactures and distributes machinery and service parts used in construction, earthmoving, material handling and timber harvesting; and Financial Services, which provides credit services and commercial leasing in relation to new and used agriculture and turf equipment and construction and forestry equipment.
Key Partners
John Deere partners with a range of companies throughout the process of designing, manufacturing and distributing its products and parts. These partners can be broadly categorised as follows:
- Channel Partners, comprising distributors and independent sales agents that assist in extending the Company’s sales and marketing reach, particularly in operating jurisdictions outside of the US and Canada where it is not well-represented;
- Supplier Partners, comprising suppliers of tools, resources and services that assist in the processes of developing and manufacturing the Company’s various products and parts; and
- Strategic and Alliance Partners, comprising a range of companies, often industrial and technology companies, that collaborate share resources with the Company and collaborate on joint projects. Deer and Company has recently partnered with DN2K to develop a cloud software platform for agricultural retailers, and with Cree to develop more efficient agricultural machines.
Key Resources
John Deere’s key resources are its intellectual properties, its manufacturing facilities, its logistics and distribution infrastructure, its sales and marketing channels, its partners, and its personnel.
John Deere owns a significant number of patents, trade secrets, licenses and trademarks related to John Deere products and services that it considers to be material to its ongoing operations. Searches of records published by the US Patent and Trademark Office identified two patent applications filed in the names of John Deere Forestry and John Deere Fabriek Horst, including applications entitled ‘Trailed agricultural implement pump with hydraulic flow rate control’ and ‘Control of a boom construction and a tool articulated thereto’.
John Deere owns or leases nine facilities, comprising a centralised parts distribution centre and eight regional parts depots and distribution centres, throughout the US and Canada. Outside the US and Canada, the equipment operations also own or lease four centralized parts distribution centres in Brazil, Germany, India and Russia and regional parts depots and distribution centers in Argentina, Australia, China, Mexico, South Africa, Sweden and the UK.
Cost Structure
John Deere incurs costs in relation to the design and development of its products, the manufacture and distribution of its products, the operation of logistics and storage infrastructure, the management of its partnerships, the operation of its sales channels, and the retention of its personnel.
In 2015 the Company research and development costs amounted to $1.43 billion, while its selling, administrative and general expenses – including the payment of salaries and benefits to its international workforce of 57,200 full-time employees – totalled $2.87 billion.
Revenue Streams
John Deere derives its revenue through the sale of agricultural and industrial equipment and parts, as well as from various charges and fees associated with its provision of financial services.
In 2015 John Deere generated $28.86 billion in annual revenue, down around 20% on the $36.07 billion recorded by the Company in 2014. Sales by the Company’s Agriculture and Turf segment accounted for the majority of the Company’s revenue, with the segment recording $19.81 billion in sales for the year. The Construction and Forestry segment generated annual revenue of $5.96 billion, while the Company’s Financial Services segment generated $2.59 billion.


White to play 63
pips White=155 Black=161
(CorrectAnswer= 0/0, ErrorScore= 0)
1. Bar/22 8/2* eq: +0.259 50.56% (G:18.37% B:1.15%) 49.44% (G:13.38% B:0.68%) 2. Bar/16 eq: +0.190 (-0.069) 50.56% (G:14.42% B:0.67%) 49.44% (G:14.46% B:0.56%) 3. Bar/22 24/18 eq: +0.178 (-0.080) 49.96% (G:14.97% B:0.84%) 50.04% (G:14.96% B:0.52%) 4. Bar/22 13/7 eq: +0.148 (-0.111) 48.34% (G:15.55% B:0.74%) 51.66% (G:14.48% B:0.74%)


A | |
| AlphaPhiUCBParam [Obandit] | Use to instanciate a |
| AlphaUCBParam [Obandit] | Use to instanciate a |
B | |
| Bandit [Obandit] | A bandit algorithm. |
D | |
| DecayingEpsilonGreedyParam [Obandit] | Use to instanciate a |
E | |
| EpsilonGreedyParam [Obandit] | Use to instanciate a |
F | |
| FixedExp3Param [Obandit] | Use to instanciate a |
H | |
| HorizonExp3Param [Obandit] | Use to instanciate a |
K | |
| KBanditParam [Obandit] | Use to instanciate a |
R | |
| RangeParam [Obandit] | A Reward range. |
| RangedBandit [Obandit] | The type of a bandit with reward scaling. |
| RateBanditParam [Obandit] | Use to instanciate algorithms that need a parametrizable rate. |


| Matrix or Vector | Geometry | Shape Functions | Integration Points |
|---|---|---|---|
|
Stiffness and Stress Stiffness Matrices |
Quad |
2 x 2 | |
|
Triangle |
1 | ||
|
Mass Matrix |
Quad |
Same as stiffness matrix |
2 x 2 |
|
Triangle |
1 | ||
|
Damping matrices |
Same as stiffness matrix | ||
|
Pressure and Temperature Load Vector |
Same as stiffness matrix, specialized to edge |
2 | |
|
Flow Load Vector |
Linear across each edge |
2 |
| Load Type | Distribution |
|---|---|
|
Element Temperature |
Bilinear across element, constant thru thickness or around circumference |
|
Nodal Temperature |
Same as element temperature distribution |
|
Surface Pressure Load |
Linear along each edge |
|
Flow and heat flux |
Linear along each edge |
General Element Formulations gives the general element formulations used by this element.


NOTE: MySQL version of the database is temporarily not working, due to hosting problems. For detailed info click here.
|
Note: the present version
is under developement and
at the moment it's still
very preliminary. To switch to the "quickbase" version, clich here. |
Database compilation by
Marco Castellani
mkast@mporzio.astro.it


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## NULL
Typical morphological profiling datasets have millions of cells and hundreds of features per cells. When working with this data, you must
clean the data
normalize the features so that they are comparable across experiments
transform the features so that their distributions are well-behaved ( i.e., bring them in line with assumptions we want to make about their disributions)
select features based on their quality
aggregate the single-cell data, if needed
The cytominer package makes these steps fast and easy.
First, load the data, which is stored in a database backend created using https://github.com/cytomining/cytominer-database.
fixture <-
system.file("extdata", "fixture_intensities_shapes.sqlite",
package = "cytominer")
db <- DBI::dbConnect(RSQLite::SQLite(), fixture)
Then load associated metadata if it exists, and copy it to the backend so that we can use it later.
ext_metadata <-
readr::read_csv(system.file("extdata", "metadata.csv",
package = "cytominer")) %>%
dplyr::rename(g_well = Well)
## Parsed with column specification:
## cols(
## Well = col_character(),
## Type = col_character()
## )
ext_metadata <- dplyr::copy_to(db, ext_metadata)
Next, select a measurement table that you want to work. Here, we will pick intensities
but we can easily extend to using multiple or all measurement classes if needed by creating new views.
For this example, lets filter the data down to a few wells.
measurements <-
intensities %>%
dplyr::filter(g_well %in% c("A01", "A02", "A10", "A11"))
How many rows does this table have?
| n |
|---|
| 360 |
That actually 9 times the number of cells in this experiment (n = 40): each compartment and each channel gets its own row. Here, we have 3 compartments (cell, nucleus, cytoplasm) and 3 channels (CellMask, Hoechst, Alexa568). So that’s 3 x 3 x 40 = 360.
Next, do some setup stuff that we will need later
qualities <- c("q_debris")
groupings <-
c("g_plate",
"g_well",
"g_image",
"g_pattern",
"g_channel")
variables <-
colnames(measurements) %>%
stringr::str_subset("^m_")
measurements %<>%
dplyr::select(dplyr::one_of(c(groupings, qualities, variables)))
Let’s remove cells that come from images that were marked as having debris
debris_removed <-
measurements %>% dplyr::filter(q_debris == 0)
Then, remove cells where all the measurements are NA’s - this may happen if the identified cell mask was too small to measure any of the features.
na_rows_removed <-
cytominer::drop_na_rows(
population = debris_removed,
variables = variables
) %>%
dplyr::compute()
We need to normalize the data so that
features are on the same scale
plate-to-plate variation is reduced
The default for doing this is standardization
. Here, we take all the cells from control wells in the experiment (this is where the external metadata gets used) and compute normalizations parameters from that (in this case, just the mean and s.d.) and then apply it to the whole dataset (i.e. the population)
normalized <-
cytominer::normalize(
population = na_rows_removed %>%
dplyr::collect(),
variables = variables,
strata = c("g_plate", "g_pattern", "g_channel"),
sample =
na_rows_removed %>%
dplyr::inner_join(
ext_metadata %>%
dplyr::filter(Type == "ctrl") %>%
dplyr::select(g_well)
) %>% dplyr::collect()
)
## Joining, by = "g_well"
normalized %<>% dplyr::collect()
In some cases, we may have features that have no variance at all (e.g. Euler number). If these features have not already been removed by this stage, the standardization step will results in all values for that feature being NA ( because s.d. = 0). Lets remove them:
First, count how many cells have NA values per feature:
na_frequency <-
cytominer::count_na_rows(
population = normalized,
variables = variables)
na_frequency %>%
tidyr::gather(feature, na_count) %>%
knitr::kable()
| feature | na_count |
|---|---|
| m_intensities_first_quartile | 0 |
| m_intensities_integrated | 0 |
| m_intensities_maximum | 0 |
| m_intensities_mean | 0 |
| m_intensities_median | 0 |
| m_intensities_median_absolute_deviation | 0 |
| m_intensities_minimum | 0 |
| m_intensities_standard_deviation | 0 |
| m_intensities_third_quartile | 0 |
As it turns out, no feature has NA in this example. But lets run this cleaning operation anyway (no features will be dropped)
cleaned <-
cytominer::variable_select(
population = normalized,
variables = variables,
operation = "drop_na_columns"
)
Tranform the data so that assumptions we may later make about the data distribution are satisfied (e.g. Gaussianity). The default here is generalized_log
.
transformed <-
cytominer::transform(
population = cleaned,
variables = variables
)
Finally, we typically perform feature selection on the data. Feature selection is an expensive operation, so we usually want to train the feature selection model on a sample of the dataset. Here, we choose to aggregate the data instead of sampling it (i.e. collapse it to per-well aggregates)
aggregated <-
cytominer::aggregate(
population = transformed,
variables = variables,
strata = groupings
) %>%
dplyr::collect()
variables <-
colnames(aggregated) %>%
stringr::str_subset("^m_")
… and then apply feature selection on the per-cell data. Here correlation_threshold
- a method that reduces the redundancy of features - is used.
selected <-
cytominer::variable_select(
population = transformed,
variables = variables,
sample = aggregated,
operation = "correlation_threshold"
) %>%
dplyr::collect()
And now lets take a glimpse at the data!
selected %>%
dplyr::glimpse()
## Observations: 315
## Variables: 15
## $ g_plate <chr> "110000106828", "11000...
## $ g_well <chr> "A01", "A01", "A01", "...
## $ g_image <chr> "5a822385194aceb347a07...
## $ g_pattern <chr> "Cells", "Cells", "Cel...
## $ g_channel <chr> "Alexa568", "Alexa568"...
## $ q_debris <int> 0, 0, 0, 0, 0, 0, 0, 0...
## $ m_intensities_first_quartile <dbl> -1.401783492, -0.44869...
## $ m_intensities_integrated <dbl> -1.48831626, -0.067616...
## $ m_intensities_maximum <dbl> -1.25783096, -1.514831...
## $ m_intensities_mean <dbl> -1.4908295, -0.7858533...
## $ m_intensities_median <dbl> -1.5032257, -0.5397778...
## $ m_intensities_median_absolute_deviation <dbl> -1.4847843, -0.5372476...
## $ m_intensities_minimum <dbl> -2.3552231, -1.9025552...
## $ m_intensities_standard_deviation <dbl> -1.3409044908, -1.5861...
## $ m_intensities_third_quartile <dbl> -1.35890644, 0.0776661...
DBI::dbDisconnect(db)


#include <TunnelListener.h>
Inheritance diagram for WFAPI::TunnelListener:
Public Member Functions | |
| TunnelListener () | |
| virtual | ~TunnelListener () |
| virtual void | dataReceived (RequestID requestID, ReplyData replyData)=0 |
Implement a sub class of TunnelListener and add as a listener to the TunnelInterface to start getting tunnel callbacks.
The error function, declared in the super class Listener, will be called for all requests that returns with an error.
| WFAPI::TunnelListener::TunnelListener | ( | ) |
Constructor with no arguments.
| virtual WFAPI::TunnelListener::~TunnelListener | ( | ) | [virtual] |
Destructor.
| virtual void WFAPI::TunnelListener::dataReceived | ( | RequestID | requestID, | |
| ReplyData | replyData | |||
| ) | [pure virtual] |
Callback function for receiving the data requested through the tunnel interface. NOTE: Data will be deleted when this function has been excuted. Make a copy if you need the data later.
| requestID | The requestID for this request. | |
| replyData | Contains the requested data. |


Self awareness is made up three emotional competencies, and the first being emotion awareness. This is recognising one’s emotions and their effects. The second one being accurate self-assessment: knowing one’s strengths and limits. The third is self confidence: This is a strong sense of one’s self-worth and capabilities. Self awareness and mindfulness are closely related and both are trainable.
I decided to try the journalling self awareness practice because I have previously tried sometimes similar to the body scan practice. I used to keep journals when I was younger so I thought it would be an interesting experiment to try a journalling exercise as an adult.
like the expression ‘unfiltered brain dump’ used In the book, and I felt like it describes the exercise perfectly. My mind somewhat lighter and clearer after putting down my thoughts on paper about what is making me happy and what I am worried about. I was surprised at how such a short and simple exercise could help me put my thoughts/emotions into perspective.
That we are not are our emotions and that they should not define us. Mindfulness practice helps us shift our emotions from being existential (“I am”) to experiential (“I feel”). I really liked the traditional metaphor “Thoughts and emotions are like clouds—some beautiful, some dark—while our core being is like the sky. Clouds are not the sky; they are phenomena in the sky that come and go”. This is essentially saying that thoughts and emotions are a phenomena in the mind and body that come and go, but they are not what we are, and what defines us.


akka
.
japi
pf
package
pf
Visibility
Public
All
Type Members
class
DeciderBuilder
extends
AnyRef
final
class
FI
extends
AnyRef
class
FSMStateFunctionBuilder
[
S
,
D
]
extends
AnyRef
class
FSMStopBuilder
[
S
,
D
]
extends
AnyRef
class
FSMTransitionHandlerBuilder
[
S
]
extends
AnyRef
class
Match
[
I
,
R
]
extends
AbstractMatch
[
I
,
R
]
final
class
PFBuilder
[
I
,
R
]
extends
AbstractPFBuilder
[
I
,
R
]
class
ReceiveBuilder
extends
AnyRef
class
UnitMatch
[
I
]
extends
AbstractMatch
[
I
,
BoxedUnit
]
final
class
UnitPFBuilder
[
I
]
extends
AbstractPFBuilder
[
I
,
BoxedUnit
]
Ungrouped


Hello, I'm Jake. I'm a natural philosopher, adventure seeker, music afficianado, ameteaur theoretical
physicist, tinkerer and most importantly a nice guy. xD
I enjoy dodging jellyfish while snorkeling, bungy jumping, drum circles, music festivals and progressive
metal. Here's a joke:
I'm so metal I oxidize while you reduce.
I'm a closet weeb, and I play LoL. Silver 4 champion right here! I also enjoy travelling a good bit. I've been all over the world, set foot on five of seven continents. Still need to see pyramids and penguins though, but I'm working on that. I'm a big fan of snow, mountains, and the ocasional conspiracy theory as well. I have been in the tech industry for 2 years and studied code in college. I'm hoping to move into a development position after the completion of this program.
I am a very simple card. I am good at containing small bits of information. I am convenient because I require little markup to use effectively.
I am a very simple card. I am good at containing small bits of information. I am convenient because I require little markup to use effectively.
I am a very simple card. I am good at containing small bits of information. I am convenient because I require little markup to use effectively.


 Snom is a hero, a savior and a blessing to anyone who has ever set foot in this universe.
To officially join the Snom believers, join the discord server @ discord.gg/GjRXPGwWkd.
Check out the other links above, and if you want to make sure your Snom worshipping stays on point, visit the prayer room.
When all goes wrong, believe in Snom.
When you have to stay strong, believe in Snom.
When you want to make your dong long, believe in Snom.
Wherever you are, wherever you may be, whatever the situation is, believe in Snom.


| Action<A extends Action> |
Actions perform some work when matched to a Request and potentially contribute to the content of the Response.
|
| Api |
Contains the Collections, Endpoints and Actions that make up a REST API.
|
| Chain |
|
| Change |
|
| Collection |
Represents a REST Collection and maps JSON properties property names and logical cross Collection data relationships to underlying Db tables and column names.
|
| Db<T extends Db> |
An adapter to an underlying data source.
|
| Endpoint |
A single Endpoint, bundling one or more Path match relative Actions, is selected to service a Request.
|
| Engine |
Matches inbound Request Url paths to an Api Endpoint and executes associated Actions.
|
| EngineServlet |
|
| Index |
|
| Property |
|
| Relationship |
|
| Request |
|
| Request.Upload |
|
| Request.Validation |
Utility designed to make it easy to validate request properties or request body
json values while you are retrieving them.
|
| Response |
|
| Results<M extends java.util.Map<java.lang.String,java.lang.Object>> |
Results are returned by a Db and transformed by Actions into Response content.
|
| Rule<R extends Rule> |
Matches against an HTTP method and URL path to determine if the object
should be included when processing the associated Request.
|
| Rule.RuleMatcher |
|
| User |
|


Gecko does not prevent <embed tabindex="-1"> getting focus when using the Tab key. There is currently no sane way to prevent that other than visually hiding the element using visibility: hidden.
<embed tabindex="-1">
visibility: hidden


Tensor 2.0 is a Datathon aimed at getting participants more involved in the upcoming field of Data Science and Machine Learning It consists of 2 tracks, the Beginner's Track aimed at getting freshers interested and giving them an introduction to the field, and the Competitive Track which is open to all Undergraduate students and required skills such as finding the right data, analyzing and understanding it and applying those insights to formulate a conclusion.
 The workshop will go over the basics of preprocessing data, and pay special attention to important topics from numpy, pandas and matplotlib.
The entire Datathon is be split into two tracks- the Beginner Track and the Competitive Track. The Beginner Track is designed for novices| With a series of fun tasks, challenges and riddles all leading up to a Final puzzle!
A detailed interactive session on the hot topics of Machine Learning and Artificial Intelligence, as we go live with guest speaker and industry expert, Usha Rengaraju!


| Name | Buy | Sell | Source | Size | Type | Customizable | Material Required | Furniture Series |
|---|---|---|---|---|---|---|---|---|
| Acoustic Guitar | N/A | 3,210 Bells | DIY | 1 x 1 | Houseware | 5x Customization Kits | 8 Softwood, 3 Iron Nuggets | N/A |
| Amp | 5,100 Bells | 1,275 Bells | Store | 1 x 1 | Miscellaneous | N/A | N/A | N/A |
| Astronaut Suit | N/A | 6,250 Bells | DIY | 1 x 1 | Housewares | N/A | 5 Iron Nuggets, 5 Star Fragments | Space Set |


2015121
{% load static %} {% block resolvecomplaint %} {% comment %}The tab menu starts here!{% endcomment %}
| Student | Date | Type | Location | Specific Location | Details | Resolve | Status |
|---|---|---|---|---|---|---|---|
2015121
|
14 Oct'17 | Type #1 | Location #1 | Specific Location #1 | ... | ||
2015121
|
13 Oct'17 | Type #2 | Location #2 | Specific Location #2 | ... |


Constraining Cosmology using Galaxy Clusters¶
Reviews¶
Huterer et al. 2015 - Growth of cosmic structure: Probing dark energy beyond expansion
See Section 4.2: Cluster Abundance
The primary importance of clusters in the context of dark energy is their complementarity to geometric probes, i.e. their ability to distinguish between modified gravity and dark energy models with degenerate expansion histories. Galaxy clusters are statistically competitive with and often better than other probes. (Fig 5, right panel, the “sweet spot” is z=0.3 - 0.8.
The basic physics behind cluster abundances as a cosmological probe are conceptually simple; and A cluster abundance experiment is conceptually very simple.
The dependence of the number of galaxy clusters on the variance of the linear density field that allows us to utilize galaxy clusters to constrain the growth of structure.
Future surveys will almost certainly rely on weak lensing mass calibration to estimate cluster masses.
Other usages of clusters as a cosmological tool:
Galaxy clusters can probe dark energy in other ways as well, most notably by comparing cluster mass estimates from weak lensing and dynamical methods such as galaxy velocity dispersions
Because the growth of structure is also impacted by non-zero neutrino mass, galaxy cluster abundances can provide competitive constraints on the sum of neutrino masses.
Difficulties and limitations:
To achieve the goal of DES Stage III (IV) requirement, we must be able to measure cluster masses with 5% (2%) precision. See Applegate et al. (2014) (Currently can achieve 7%, but with 20% systematic differences among methods).
Systematic errors in shear measurements tend to be less critical for cluster abundance work than for cosmic shear work (only care about circular averaged tangential shear).
Key systematic is the calibration not only of the mean relation between cluster observables (optical, X-ray, or mm signals) and cluster mass, but also the scatter (shape and amplitude) about the mean.
Cluster centering remains an important systematic in optical and/or low resolution experiments (e.g. Planck). (See Oguri & Takada 2011 about self-calibration).
Optical observations benefit from a lower mass detection threshold than X-ray/mm over a large redshift range, which in turn result in improved statistical constraints.
The synergistic nature of multi-wavelength cluster cosmology will necessarily play a key role in future cluster abundance experiments. A balanced multi-wavelength approach will be critical to the success of cluster cosmology over the next 10–20 years.
Self-calibration: the cluster-clustering signal is itself an observable that one can use to calibrate cluster masses, and which is insensitive to all of the above systematic effects.
Huterer & Shafer 2018 - Dark energy two decades after: observables, probes, consistency tests
See Section 5.5 Galaxy Clusters
Clusters are versatile probes of cosmology and astrophysics and have had an important role in the development of modern cosmology
Recent cluster observations typically do not have enough signal-to-noise to determine the cluster masses directly; instead, forward-modeling can be applied to the mass function to recast the theory in the space of observable quantities (e.g. see Evrard et al. 2014).
The mass function’s near-exponential dependence on the power spectrum in the high-mass limit is at the root of the power of clusters to probe the growth of density fluctuations.
(The clusters’) two-point correlation function probes the matter power spectrum as well as the growth and geometry factors sensitive to dark energy.
Clusters can also be correlated with background galaxies to probe the growth (Cluster-Galaxy lensing, see Oguri & Takada 2011
The most important uncertainty is typically tied to parameters that describe the scaling relations between mass and observable properties of the cluster (e.g. Flux, temperature).
Weinberg 2013, PhR - Observational probes of cosmic acceleration
See Section 6: Clusters of galaxies
Key Papers¶
Theoretical¶
Predictions¶
-
Fisher information is extracted from the number density and spatial clustering of a photon-count-limited sample of clusters of galaxies up to z ˜ 2. We consider different scenarios for the availability of (i) X-ray follow-up observations, (ii) photometric and spectroscopic redshifts, and (iii) accurate knowledge of the observable - mass relation down to the scale of galaxy groups, but no additional observation-related systematics are taken into account.
Early stage¶
-
Important: “Our results indicate a formal statistical uncertainty of ~3% (68% confidence) on both Ωm and w when the SZE survey is combined with either the CMB or SN data; a large number of clusters in the X-ray survey further suppresses the degeneracy between w and both Ωm and h.”
Refregier et al. 2002 - Cosmology with galaxy clusters in the XMM large-scale structure survey
Systematic, (Self-)Calibration¶
Lima & Hu 2004 - Self-calibration of cluster dark energy studies: Counts in cells
Important Self-calibration (Using noise as signal!): The excess variance of counts due to the clustering of clusters provides such an opportunity and can be measured from the survey without additional observational cost.
Lima & Hu 2005 - Self-calibration of cluster dark energy studies: Observable-mass distribution
Given the shape of the actual mass function, the properties of the distribution may be internally monitored by the shape of the observable mass function.
Hu & Cohn 2006 - Likelihood methods for cluster dark energy surveys
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Self-calibration in combination with external mass inferences helps reduce photo-z requirements and provides important consistency checks for future cluster surveys.
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We show that for a particular X-ray survey (Sunyaev-Zeldovich effect [SZE] survey), the constraints on w degrade by roughly a factor of 3 (factor of 2) when one accounts for the possibility of nonstandard cluster evolution.
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The best constraints are obtained when one combines both the power spectrum constraints and the mass measurements with the cluster redshift distribution; when using the survey to extract the parameters and evolution of the mass-observable relations, we estimate uncertainties on w of ~4%-6%
Hu 2003 - Self-consistency and calibration of cluster number count surveys for dark energy
“we find that the ambiguity from the normalization of the mass-observable relationships, or an extrapolation of external mass-observable determinations from higher masses, can be largely eliminated with a sufficiently deep survey, even allowing for an arbitrary evolution”
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Halo assembly bias: the clustering amplitude of halos depends not only on the halo mass, but also on various secondary variables.
The impact of the secondary dependence is determined by (1) the scatter in the observable-mass relation and (2) the correlation between observable and secondary variables. Could be important to DES and LSST like survey
Cunha 2009 - Cross-calibration of cluster mass observables
We use a Fisher matrix analysis to study the improvements in the joint dark energy and cluster mass-observables constraints resulting from combining cluster counts and clustering abundances measured with different techniques.
The cross-calibrated constraints are less sensitive to variations in the mass threshold or maximum redshift range.
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We find that optimally combined optical and Sunyaev-Zeldovich effect cluster surveys should improve the Dark Energy Task Force figure of merit
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Considering clusters selected from optical imaging in the Dark Energy Survey, we find that approximately 200 low-redshift X-ray clusters or massive Sunyaev-Zel’dovich clusters can improve the dark energy figure of merit by 50%, provided that the follow-up mass measurements involve no systematic error.
The scatter in the optical richness–mass distribution, which needs to be made as tight as possible to improve the efficacy of follow-up observations
Evrard, Arnault, Huterer & Farahi 2014 - A model for multiproperty galaxy cluster statistics
We derive closed-form expressions for the space density of haloes as a function of multiple observables as well as forms for the low-order moments of properties of observable-selected samples.
Observational¶
Chandra Cluster Cosmology Project
Vilhlinin et al. 2009a - Chandra Cluster Cosmology Project. II. Samples and X-Ray Data Reduction
Vilhlinin et al. 2009b - Chandra Cluster Cosmology Project III: Cosmological Parameter Constraints
37 Chandra clusters at = 0.55 from ROSAT and 49 brightest z=0.05 clusters
The observed growth of massive galaxy clusters using ROSAT/Chandra
Mantz, Allen, Rapetti & Ebeling 2010a - I. Statistical methods and cosmological constraints
238 clusters from RASS; 94 Chandra follow-up.
Mantz, Allen, Ebeling, Rapetti & Drlica-Wagner 2010 - II. X-ray scaling relations
Rapetti, Allen, Mantz & Ebeling 2010 - III. Testing general relativity on cosmological scales
Mantz, Allen & Rapetti 2010 - IV. Robust constraints on neutrino properties
maxBCG clusters
Rozo et al. 2010 - Cosmological Constraints from the Sloan Digital Sky Survey maxBCG Cluster Catalog
SDSS-maxBCG: fully consistent with the WMAP five-year data, and in a joint analysis we find σ8 = 0.807 ± 0.020 and Ωm = 0.265 ± 0.016
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SDSS 2PCF + mass-to-galaxy number ratio within cluster
Cosmology and astrophysics from relaxed galaxy clusters in Chandra & ROSAT
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377 clusters at z>0.2 from 2500 square-degree South Pole Telescope SZ survey
Cluster mass calibration and scaling relations¶
Becker & Kravtsov 2011 - On the Accuracy of Weak-lensing Cluster Mass Reconstructions
Important: We find that correlated large-scale structure within several virial radii of clusters contributes a smaller, but nevertheless significant, amount to the scatter. The intrinsic scatter due to these physical sources is ≈20% for massive clusters and can be as high as ≈30% for group-sized systems.
We find that WL mass measurements can have a small, ≈5%-10%, but non-negligible amount of bias.
Rasia et al. 2012 - Lensing and x-ray mass estimates of clusters (simulations)
We confirm previous results showing that lensing masses obtained from the fit of the cluster tangential shear profiles with Navarro-Frenk-White functionals are biased low by ˜5-10% with a large scatter (˜10-25%)
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We find that scaling relations derived from optical and X-ray selected cluster samples are consistent with one another. These cluster scaling relations satisfy several non- trivial spatial abundance constraints and closure relations.
Using Velocity Distribution Function¶
The Velocity Distribution Function of Galaxy Clusters as a Cosmological Probe
Cluster Cosmology with the Velocity Distribution Function of the HeCS-SZ Sample
On the Cluster Physics of Sunyaev-Zeldovich and X-Ray Surveys
Penna-Lima et al. 2017 - Calibrating the Planck cluster mass scale with CLASH
1 - b_sz = 0.73 +/- 0.10
Lectures and Conferences¶
Ushering in DES Cluster Cosmology with redMaPPer by Eduardo Rozo
Galaxy clusters are the most massive gravitationally bound structures in the Universe.
Number of galaxy clusters as a function of halo mass measures the amount of structure in the Universe (sigma_8).
Optical selection allows detection of low mass systems; more abundant == better weak lensing halo mass == Better cosmology. Finding clusters in the optical maximizes the cosmological information that can be drawn from clusters.
Centering cluster is hard!
Cosmology with Clusters of Galaxies by Ben Maughan (Undergraduate Level)
KITP Conference: Astrophysics and Cosmology with Galaxy Clusters
Important References¶
Galaxy clusters have been recognized as powerful cosmological probes
Henry et al. 2009; Vikhlinin et al. 2009; Mantz et al. 2010; Rozo et al. 2010; Clerc et al. 2012; Benson et al. 2013; Hasselfield et al. 2013).
Early optical cluster finders can be divided into roughly two classes
Those based on photometric redshifts
Kepner et al. 1999; van Breukelen & Clewley 2009; Milkeraitis et al. 2010; Durret et al. 2011; Szabo et al. 2011; Soares-Santos et al. 2011; Wen et al. 2012
Those utilizing the cluster red sequence
Annis et al. 1999; Gladders & Yee 2000; Koester et al. 2007a; Gladders et al. 2007; Gal et al. 2009; Thanjavur et al. 2009; Hao et al. 2010; Murphy et al. 2012


Instructions for working with the software.
Physical Examination
Initial steps of skin scrappings and other medical procedures.
Image Capturing
Microscopic observation and image capturing of the specimens.
Image Processing
Uploading the captured image to the algorithm for detection.
View Statistics
Statistics of the diagnosis performed by the algorithm for the specimens.
Steps for medical procedures
- Scraping of diseased region using blunt tool (slide).
- Introducing 1 drop of 10% KOH to the slide.
- Introducing 2 drops of 1% CSB (Chicago Sky Blue) stain.
- Cover the slide using the cover slip and wait for 15 minutes.
Observing under the microscope
- Once the specimen is ready for observation, carefully place the slide onto the observation tray of microscope.
- Set/Install magnification to 40X.
- Locate the areas that shows Spores and Hyphae.
- Once located, capture the image using smartphone camera using camera mount.
Please Click the below browse button to insert the image from file browser
- Rename the image with patient's name without any special characters
- Correct naming : John.jpg
Wrong naming : Jo.hn.jpg
- Please select image files only having extension of .jpeg or .jpg
- The image should be renamed with patient's name
- The size of the image should not be more than 5 Mb
{% if show_example_modal %}
{% endif %}


I am a 21-year-old software developer currently studying computer science at the University of Siegen.


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- Integrate ES6 version with ELMS:LN (March '19)
- Improve mobile editing experience (March '19)
- Improve Grid plate / responsive design capabilities (April '19)
- Table editing capabilities (April '19)
- Improved media management after initial element creation (April '19)
- Improved Drag and Drop / reordering of elements (May '19)
- Continuous Accessibility improvements and authoring experience based on feedback


Active Model – model interfaces for Rails
Active Model provides a known set of interfaces for usage in model classes. They allow for Action Pack helpers to interact with non-ActiveRecord models, for example. Active Model also helps building custom ORMs for use outside of the Rails framework.
Prior to Rails 3.0, if a plugin or gem developer wanted to have an object interact with Action Pack helpers, it was required to either copy chunks of code from Rails, or monkey patch entire helpers to make them handle objects that did not exactly conform to the Active Record interface. This would result in code duplication and fragile applications that broke on upgrades.
Active Model solves this. You can include functionality from the following modules:
-
Add attribute magic to objects
class Person include ActiveModel::AttributeMethods attribute_method_prefix 'clear_' define_attribute_methods [:name, :age] attr_accessor :name, :age def clear_attribute(attr) send("#{attr}=", nil) end end person.clear_name person.clear_age
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Callbacks for certain operations
class Person extend ActiveModel::Callbacks define_model_callbacks :create def create run_callbacks :create do # Your create action methods here end end end
This generates
before_create
,around_create
andafter_create
class methods that wrap your create method. -
Tracking value changes
The ActiveModel::Dirty module allows for tracking attribute changes:
person = Person.new person.name # => nil person.changed? # => false person.name = 'bob' person.changed? # => true person.changed # => ['name'] person.changes # => { 'name' => [nil, 'bob'] } person.name = 'robert' person.save person.previous_changes # => {'name' => ['bob, 'robert']}
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Adding
errors
interface to objectsExposing error messages allows objects to interact with Action Pack helpers seamlessly.
class Person def initialize @errors = ActiveModel::Errors.new(self) end attr_accessor :name attr_reader :errors def validate! errors.add(:name, "can not be nil") if name.nil? end def ErrorsPerson.human_attribute_name(attr, options = {}) "Name" end end person.errors.full_messages # => ["Name can not be nil"] person.errors.full_messages # => ["Name can not be nil"]
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Model name introspection
class NamedPerson extend ActiveModel::Naming end NamedPerson.model_name # => "NamedPerson" NamedPerson.model_name.human # => "Named person"
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Observer support
ActiveModel::Observers allows your object to implement the Observer pattern in a Rails App and take advantage of all the standard observer functions.
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Making objects serializable
ActiveModel::Serialization provides a standard interface for your object to provide
to_json
orto_xml
serialization.s = SerialPerson.new s.serializable_hash # => {"name"=>nil} s.to_json # => "{\"name\":null}" s.to_xml # => "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<serial-person...
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Internationalization (i18n) support
class Person extend ActiveModel::Translation end Person.human_attribute_name('my_attribute') # => "My attribute"
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Validation support
class Person include ActiveModel::Validations attr_accessor :first_name, :last_name validates_each :first_name, :last_name do |record, attr, value| record.errors.add attr, 'starts with z.' if value.to_s[0] == z end end person = Person.new person.first_name = 'zoolander' person.valid? # => false
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Custom validators
class Person include ActiveModel::Validations validates_with HasNameValidator attr_accessor :name end class HasNameValidator < ActiveModel::Validator def validate(record) record.errors[:name] = "must exist" if record.name.blank? end end p = ValidatorPerson.new p.valid? # => false p.errors.full_messages # => ["Name must exist"] p.name = "Bob" p.valid? # => true
Download and installation
The latest version of Active Model can be installed with RubyGems:
% [sudo] gem install activemodel
Source code can be downloaded as part of the Rails project on GitHub
License
Active Model is released under the MIT license.
Support
API documentation is at
Bug reports and feature requests can be filed with the rest for the Ruby on Rails project here: