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Project Name
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Want these icons in this portfolio item sample?You can download 60 of them for free, courtesy of
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Date: July 2014
Client: Round Icons
Category: Graphic Design
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Startup Framework
is a website builder for professionals. Startup Framework
contains components and complex blocks (PSD+HTML Bootstrap
themes and templates) which can easily be integrated into
almost any design. All of these components are made in the
same style, and can easily be integrated into projects,
allowing you to create hundreds of solutions for your future
projects.
Treehouse is a free PSD web template built by
Mathavan Jaya. This is bright and spacious design perfect for people or
startup companies looking to showcase their apps or other
projects.
You can download the PSD template in this portfolio sample
item at
FreebiesXpress.com.
Project Name
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Start Bootstrap's Agency theme is based on Golden, a free
PSD website template built by
Mathavan Jaya. Golden is a modern and clean one page web template that
was made exclusively for Best PSD Freebies. This template
has a great portfolio, timeline, and meet your team sections
that can be easily modified to fit your needs.
You can download the PSD template in this portfolio sample
item at
FreebiesXpress.com.
Project Name
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Escape is a free PSD web template built by
Mathavan Jaya. Escape is a one page web template that was designed with
agencies in mind. This template is ideal for those looking
for a simple one page solution to describe your business and
offer your services.
You can download the PSD template in this portfolio sample
item at
FreebiesXpress.com.
Project Name
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Dreams is a free PSD web template built by
Mathavan Jaya. Dreams is a modern one page web template designed for
almost any purpose. It’s a beautiful template that’s
designed with the Bootstrap framework in mind.
You can download the PSD template in this portfolio sample
item at
FreebiesXpress.com.


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|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface RequestInfoOperations
Request Information, accessible to Interceptors.
Each interception point is given an object through which the
Interceptor can access request information. Client-side and server-side
interception points are concerned with different information, so there
are two information objects: ClientRequestInfo
is passed
to the client-side interception points and ServerRequestInfo
is passed to the server-side interception points. But there is
information that is common to both, so they both inherit from a common
interface: RequestInfo
.
ClientRequestInfo
,
ServerRequestInfo
| Method Summary | |
|---|---|
Parameter[] |
arguments()
Returns an array of Parameter objects, containing the
arguments on the operation being invoked. |
String[] |
contexts()
Returns an array of String objects describing the
contexts that may be passed on this operation invocation. |
TypeCode[] |
exceptions()
Returns an array of TypeCode objects describing the
TypeCode s of the user exceptions that this operation
invocation may throw. |
Object |
forward_reference()
Contains the object to which the request will be forwarded, if the reply_status attribute is LOCATION_FORWARD . |
ServiceContext |
get_reply_service_context(int id)
Returns a copy of the service context with the given ID that is associated with the reply. |
ServiceContext |
get_request_service_context(int id)
Returns a copy of the service context with the given ID that is associated with the request. |
Any |
get_slot(int id)
Returns the data from the given slot of the PortableInterceptor.Current that is in the scope of
the request. |
String[] |
operation_context()
Returns an array of String objects containing the
contexts being sent on the request. |
String |
operation()
Returns the name of the operation being invoked. |
short |
reply_status()
Describes the state of the result of the operation invocation. |
int |
request_id()
Returns an id that uniquely identifies an active request/reply sequence. |
boolean |
response_expected()
Indicates whether a response is expected. |
Any |
result()
Returns an any containing the result of the operation invocation. |
short |
sync_scope()
Defines how far the request shall progress before control is returned to the client. |
| Method Detail |
|---|
int request_id()
request_id
. If GIOP is the transport mechanism used,
then these IDs may very well be the same, but this is not guaranteed
nor required.
String operation()
Parameter[] arguments()
Parameter
objects, containing the
arguments on the operation being invoked. If there are no arguments,
this attribute will be a zero length array.
Not all environments provide access to the arguments. With the Java
portable bindings, for example, the arguments are not available.
In these environments, when this attribute is accessed,
NO_RESOURCES
will be thrown with a standard minor code
of 1.
Note: Arguments are available for DSI/DII calls.
NO_RESOURCES
- thrown if arguments are not available.PortableInterceptor
package comments for
limitations / unimplemented featuresTypeCode[] exceptions()
TypeCode
objects describing the
TypeCode
s of the user exceptions that this operation
invocation may throw. If there are no user exceptions, this
will return a zero length array.
Not all environments provide access to the exception list. With
the Java portable bindings, for example, the exception list is
not available. In these environments, when this attribute is
accessed, NO_RESOURCES
will be thrown with a
standard minor code of 1.
Note: Exceptions are available for DSI/DII calls.
NO_RESOURCES
- thrown if exceptions are not available.PortableInterceptor
package comments for
limitations / unimplemented featuresString[] contexts()
String
objects describing the
contexts that may be passed on this operation invocation. If there
are no contexts, this will return a zero length array.
Not all environments provide access to the context list. With the
Java portable bindings, for example, the context list is not
available. In these environments, when this attribute is accessed,
NO_RESOURCES
will be thrown with a standard minor code
of 1.
Note: Contexts are available for DSI/DII calls.
NO_RESOURCES
- thrown if contexts are not available.PortableInterceptor
package comments for
limitations / unimplemented featuresString[] operation_context()
String
objects containing the
contexts being sent on the request.
Not all environments provide access to the context. With the Java portable bindings, for example, the context is not available. In these environments, when this attribute is accessed, NO_RESOURCES will be thrown with standard minor code of 1.
Note: operation_context
is available for
DSI/DII calls.
NO_RESOURCES
- thrown if operation context is not available.PortableInterceptor
package comments for
limitations / unimplemented featuresAny result()
TCKind
value of
tk_void
and no value.
Not all environments provide access to the result. With the Java
portable bindings, for example, the result is not available. In
these environments, when this attribute is accessed,
NO_RESOURCES
will be thrown with a standard minor code of
1.
Note: Result is available for DSI/DII calls.
NO_RESOURCES
- thrown if result is not available.PortableInterceptor
package comments for
limitations / unimplemented featuresboolean response_expected()
On the client, a reply is not returned when
response_expected
is false, so receive_reply
cannot be called. receive_other
is called unless an
exception occurs, in which case receive_exception
is
called.
On the client, within send_poll
, this attribute is true.
short sync_scope()
response_expected
is false. If
response_expected
is true, the value of
sync_scope
is undefined. This attribute may have one of
the following values:
Messaging.SYNC_NONE
Messaging.SYNC_WITH_TRANSPORT
Messaging.SYNC_WITH_SERVER
Messaging.SYNC_WITH_TARGET
receive_request_service_contexts
,
receive_request
, send_reply
or
send_exception
).
For SYNC_WITH_SERVER
and SYNC_WITH_TARGET
,
the server does send an empty reply back to the client before the
target is invoked. This reply is not intercepted by server-side
Interceptors.
PortableInterceptor
package comments for
limitations / unimplemented featuresshort reply_status()
PortableInterceptor.SUCCESSFUL
PortableInterceptor.SYSTEM_EXCEPTION
PortableInterceptor.USER_EXCEPTION
PortableInterceptor.LOCATION_FORWARD
PortableInterceptor.TRANSPORT_RETRY
receive_reply
interception point, this
will only return SUCCESSFUL
receive_exception
interception point,
this will be either SYSTEM_EXCEPTION
or
USER_EXCEPTION
.receive_other
interception point, this
will be any of: SUCCESSFUL
,
LOCATION_FORWARD
, or TRANSPORT_RETRY
.
SUCCESSFUL
means an asynchronous request returned
successfully. LOCATION_FORWARD
means that a reply
came back with LOCATION_FORWARD
as its status.
TRANSPORT_RETRY
means that the transport
mechanism indicated a retry - a GIOP reply with a status of
NEEDS_ADDRESSING_MODE
, for instance. send_reply
interception point, this
will only be SUCCESSFUL
.send_exception
interception point,
this will be either SYSTEM_EXCEPTION
or
USER_EXCEPTION
.send_other
interception point, this
attribute will be any of: SUCCESSFUL
, or
LOCATION_FORWARD
. SUCCESSFUL
means
an asynchronous request returned successfully.
LOCATION_FORWARD
means that a reply came back
with LOCATION_FORWARD
as its status.
SUCCESSFUL
,
SYSTEM_EXCEPTION
,
USER_EXCEPTION
,
LOCATION_FORWARD
,
TRANSPORT_RETRY
Object forward_reference()
reply_status
attribute is LOCATION_FORWARD
.
It is indeterminate whether a forwarded request will actually occur.
Any get_slot(int id) throws InvalidSlot
PortableInterceptor.Current
that is in the scope of
the request.
If the given slot has not been set, then an any containing a
type code with a TCKind
value of tk_null
is
returned.
id
- The SlotId
of the slot which is to be
returned.
InvalidSlot
- thrown if the ID does not define an
allocated slot.Current
ServiceContext get_request_service_context(int id)
id
- The IOP.ServiceId
of the service context
which is to be returned.
IOP.ServiceContext
obtained with the
given identifier.
BAD_PARAM
- thrown with a standard minor code of 26, if the
request's service context does not contain an entry for that ID.ServiceContext get_reply_service_context(int id)
id
- The IOP.ServiceId
of the service context
which is to be returned.
IOP.ServiceContext
obtained with the given
identifier.
BAD_PARAM
- thrown with a standard minor code of 26 if the
request's service context does not contain an entry for that ID.
|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


This demo page has been used from http://jasonm23.github.io/markdown-css-themes/.
This demo page has been used from http://jasonm23.github.io/markdown-css-themes/.
Any of the following color values can be used as the value of color in the front matter of the blog post. grey orange
You’ll find this post in your _posts directory. Go ahead and edit it and re-build the site to see your changes. You can rebuild the site in many different ways, but the most common way is to run jekyll serve, which launches a web server and auto-regenerates your site when a file is updated.


{% extends "base.html" %} {% block content %}
Gluu Replication Manager lets an admin to create, test and maintain a server cluster using OpenLDAP replication.
Setup Cluster


--- layout: page show_meta: false title: "Photos from our events" subheadline: "We are having fun!" sidebar: right header: no --- {% for image in page.images %}


Lists
Friends
Request
{{(userDetails | async)?.firstName}} {{(userDetails | async)?.lastName}}
{{(userDetails | async)?.email}}
Can't Find Your Friend? Invite Them To Join!


| End to End Report |
Data of visitors, orders and payments |
Yesterday /
This Week /
Last Week /
Month /
Custom Date Range
|
| Vistors Report |
Vistors related data |
Yesterday /
This Week /
Last Week /
Month /
Custom Date Range
|
| Product Report |
Products based on sales and quantity |
Yesterday /
This Week /
Last Week /
Month /
Custom Date Range
|
| Category Report |
Categories based on sales and quantity |
Yesterday /
This Week /
Last Week /
Month /
Custom Date Range
|
| Repeat Customers |
Repeated orders and customers |
Yesterday /
This Week /
Last Week /
Month /
Custom Date Range
|
| Payment Report |
Payment gateway metrics |
Yesterday /
This Week /
Last Week /
Month /
Custom Date Range
|
| Campaign Report |
Campaign and keyword report |
Yesterday /
This Week /
Last Week /
Month /
Custom Date Range
|
| Drop-Off Report |
Drop-Off data |
Yesterday /
This Week /
Last Week /
Month /
Custom Date Range
|
| Customer Segments |
Customer details based on segments |
Yesterday /
This Week /
Last Week /
Month /
Custom Date Range
|


If by any chance you get NullReferenceException trying to access RouteValues in ASP.NET MVC make sure you have Visual Studio 2008 SP1 installed.
I’d recommend to download the ISO because I had some problems using the web installer.
Have a look at this thread for more details.


Inverclyde Marathon
Greenock SCO
Race Winners
(15 runnings)
13
Sep
1998
14
Sep
1997
15
Sep
1996
10
Sep 1995
11
Sep
1994
20
Jun
1993
14 Jun
1992
11 Aug 1991
12 Aug
1990
20
Aug
1989
1986-1988
25
Aug
1985
26
Aug
1984
28
Aug
1983
29
Aug
1982
30
Aug
1981
Notes: 1981 was the inaugural year. This race was known as the
IBM Inverclyde Marathon for 1989.
2:29:32
Brian Scally (SCO) (nc)
3:04:05
Patricia Affleck (SCO) (nc)
2:38:44
John
Duffy-4
3:05:14
Margaret Creber (SCO)
2:28:25
Fraser Clyne-2
(nc)
2:56:47
Eleanor
Robinson-2 (nc)
2:42:20
Leo Sho-Silva (SCO)
3:06:23
Helen Slimon (SCO)
2:30:32
Jim Bennett (SCO)
3:49:24
Victoria Young (SCO)
2:26:40
Fraser Clyne (SCO) (nc)
2:55:42
Eleanor Robinson
(ENG) (nc)
2:35:03
John Duffy-3
3:15:54
Eleanor O'Brien (SCO)
2:24:50
Terry Mitchell (SCO) (nc)
2:50:12
Eileen Masson (SCO) (nc)
2:25:28
John Duffy-2
2:54:15
Rosemary Kay (SCO)
2:25:22
Gareth Spring (ENG)
3:06:23
Julie Harvey (SCO)
---
not held
---
not held
2:23:44
John Duffy (SCO)
3:07:29
Jane Baker (ENG)
---
n/a
---
n/a
2:22:21
John Stephens (SCO)
---
n/a
2:26:29
Tom Wiseman (SCO)
2:51:38
Leslie Watson-2
2:27:53
Bill Stoddart (SCO)
2:54:32
Leslie Watson (SCO)
(nc) => national championship
1996 Carol Lisle was the national champion (place
unknown, 3:27:17)
1993 Kate Todd was the national champion (place unknown, 3:10:08)
Most Wins (1981/1998)
Men
4
John Duffy (SCO)
1985,1990,1992,1997
Women
2
Leslie Watson
(SCO)
1981-82
Eleanor Robinson
(ENG)
1993,1996
Prize Money
(ADR qualified runners)
Prize money amounting to 3000 British pounds was reported to have been awarded
but the prize money structure is not known.
Race Time Bias
(sec/km)
Insufficient data to calculate the race time bias.
Competitive Level (1990-date)
Insufficient data to calculate the competitive level.
Quality Performances
Men No men under 2:20 Women No wpmen under 2:50
Last Updated on
16 Sep 2010
Contributor: Alex Wilson (43)


The Ultimate Bootstrap Skin Pack
How to use
Select a theme and download the CSS file for use in your own projects.


Murray scores 39, Nuggets cruise by Grizzlies
MEMPHIS, Nov 18 — Jamal Murray contributed a dunk and two assists to a 15-0, second-quarter flurry last night that erased a deficit and propelled the visiting Denver Nuggets to a 131-114 romp over the Memphis Grizzlies.
Murray finished with 39 points and eight assists for the Nuggets, who won for the sixth time in their last seven games.
Jaren Jackson Jr paced Memphis with 22 points.
Coming off an emotional one-point win in Mike Conley's homecoming on Friday, the Grizzlies bolted out to a 23-12 lead just past the midpoint of the first quarter.
But Denver dominated the final 17 1/2 minutes of the first half, rallying within 32-28 by the end of the first period before taking command after Memphis had managed to cling to a 40-38 advantage in the fifth minute of the second period.
Juancho Hernangomez got the game-swinging run going with a three-point play and a 3-point hoop off a Murray assist.
Murray then recorded his dunk and another assist on a Nikola Jokic layup, before Paul Millsap and Will Barton capped the burst with hoops that made it 53-40 with 4:34 remaining in the half.
Denver extended its lead to as many as 25 in the second period and never allowed the Grizzlies closer than 17 in the second half. The Nuggets' lead topped out at 33.
Murray's 39 points came on 14-for-24 shooting and 7-for-12 accuracy on 3-pointers. Millsap added 23 points and five steals for Denver, while Hernangomez had 15 points and Jerami Grant 12.
The Nuggets shot 55.1 percent for the game, and even better (18-for-32, 56.3 percent) on 3-pointers.
Jackson bombed in five 3-pointers to account for a majority of his team-high points for Memphis, which was shooting for a fourth straight win.
Jonas Valanciunas had 16 points and 10 rebounds for the Grizzlies, while Brandon Clarke chipped in with 19 points, Ja Morant 13 and De'Anthony Melton 10. — Reuters


Twitter trend algorithm analysis tool
Search
Track trends
Analyze data
Remaining queries
About
asfas


- Kotlin Generics Tutorial: Getting Started [Article]
- Android and Kotlin for Beginners [Video Course]
- Your First Kotlin Android App [Video Course]
- Programming in Kotlin [Video Course]
- Your Second Kotlin Android App [Video Course]
- Begginning Android Layouts [Video Course]
- Beginning Recycler View [Video Course]
- Android Animations [Video Course]
- Saving Data on Android [Video Course]
- Android Networking [Video Course]
- Android Background Processing [Video Course]
- Vector Graphics on Android [Article]
- Testing with MockK [Video Course]
- Android Unit Testing with Mockito [Article]
- Gradle Tutorial for Android: Getting Started [Article]
- Gradle Tips and Tricks for Android [Article]
- Beggining Git [Video Course]
- Mastering Git [Video Course]
- Android Basics: User Interface [Video Course]
- Lesson 1: Building Layouts: Part 1
- Lesson 2: Building Layouts: Part 2
- Lesson 3: Practice Set: Building Layouts
- Lesson 4: Project
- Lesson 5: Get Your Project Reviewed!
- User Input [Video Course]
- Lesson 1: Making an App Interactive: Part 1
- Lesson 2: Making an App Interactive: Part 2
- Lesson 3: Practice Set: Making an App Interactive
- Project: Score Keeper App
- Lesson 5: Object-Oriented Programming: Part 1
- Lesson 6: Object-Oriented Programming: Part 2
- Multi-screen Apps [Video Course]
- Lesson 1: Intents and Activities
- Lesson 2: Arrays, Lists, Loops & Custom Classes
- Lesson 3: Design a Report Card Class
- Lesson 4: Images and Visual Polish
- Lesson 5: Activity Lifecycle and Audio Playback
- Lesson 6: Fragments
- Project: (v1) Tour Guide App
- Networking [Video Course]
- Lesson 1: JSON Parsing
- Lesson 2: HTTP Networking
- Lesson 3: Threads & Parallelism
- Lesson 4: Book Listing App
- Lesson 5: Preferences
- Project: (v1) News App
- Data Storage [Video Course]
- Lesson 1: Database Basics
- Lesson 2: Using a Database in an Android App
- Lesson 3: Introduction to Content Providers
- Lesson 4: Loading Data Using CursorLoader
- Project: (v1) Inventory App
- Version Control with Git [Video Course]
- Lesson 1: What is Version Control?
- Lesson 2: Create a Git Repo
- Lesson 3: Review a Repo's History
- Lesson 4: Add Commits to a Repo
- Lesson 5: Tagging, Branching and Merging
- Lesson 6: Undoing Changes


Trump Entertainment Resorts files for bankruptcy after missing a $53.1 million bond interest payment in December 2008. It is Trump's sixth bankruptcy filing.


MusicCloud
Loading...
JavaScript OK
JavaScript Error
JavaScript is required to run this application.


-
3rd January 2019
7:00 AM
Planted
Block id # : 7812a
Location : Nalo Farms
-
31st January 2019
3:00 PM
Harvested
Block id # : 8912a
-
2nd February 2019
5:00 AM
Distributed by : Noh Foods Hawaii
Block id # : 9129a
-
2nd February 2019
7:00 AM
Delivered : Whole Foods Market
Block id # : 10929a


Projects
technologies
product goal
learning goal
Python, OpenCV
Use ML and CV techniques to detect cars in a video
selected project from Udacity Self-Driving Car Nanodegree
technologies
product goal
learning goal
Python, OpenCV
Use ML and CV techniques to detect lane lines in a video
selected project from Udacity Self-Driving Car Nanodegree
technologies
product goal
Python, Pandas, Scikit-Learn
Use ML and NLP on Supreme Court Oral Arguments to predict Justice votes
technologies
product goal
learning goal
Python, Scikit-Learn, Pandas, Flask
use information like first name and occupation to guess preferred presidential candidate
provide a front-end to a random forest machine-learning estimator via Flask API
technologies
product goal
response
Ruby (Sinatra), JavaScript, D3.JS, MySQL, Python
make SEC data such as executive compensation transparent with data visualization
opening presentation at Columbia's Millstein Governance Forum, see demo video here.
technologies
product goal
learning goals
JavaScript, Rails, Phaser, JQuery, AJAX, Quizlet OAuth and API, Postgres
a game for studying flashcards, more in the Readme
object-oriented JavaScript, project management
technologies
product goal
learning goals
original logic in Ruby, re-written in JavaScript
fast, front-end Sudoku solver made with our own recursive algorithm
algorithms, recursion, logic, JavaScript


{% extends "layout.html" %} {% block content %} Return to playlist {% if recommendations|length > 0 %}
Could not find any songs to recommend for this playlist.


| IRT Method |
Assumes Threads |
Emscripten |
Web API |
Limitations |
| close |
|
builtin |
Not relevant to Wasm, implementation detail. |
|
| dup |
|
builtin |
Not relevant to Wasm, implementation detail. |
|
| dup2 |
|
builtin |
Not relevant to Wasm, implementation detail. |
|
| read |
|
builtin |
Not relevant to Wasm, implementation detail. |
|
| write |
|
builtin |
Not relevant to Wasm, implementation detail. |
|
| seek |
|
builtin |
Not relevant to Wasm, implementation detail. |
|
| fstat |
|
builtin |
Not relevant to Wasm, implementation detail. |
|
| getdents |
|
builtin |
Not relevant to Wasm, implementation detail. |
|
| IRT Method |
Assumes Threads |
Emscripten |
Web API |
Limitations |
| mutex_create |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| mutex_destroy |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| mutex_lock |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| mutex_unlock |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| mutex_trylock |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| IRT Method |
Assumes Threads |
Emscripten |
Web API |
Limitations |
| cond_create |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| cond_destroy |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| cond_signal |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| cond_broadcast |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| cond_wait |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| cond_timed_wait_abs |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| IRT Method |
Assumes Threads |
Emscripten |
Web API |
Limitations |
| sem_create |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| sem_destroy |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| sem_post |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |
| sem_wait |
x |
PTHREADS=1 |
Implement on top of Atomics + futex. |
Forthcoming WebAssembly threads support will likely expose this directly. |


Welcome to Web Server administration with additional features
First... let's answer some questions
First... let's answer some questions
"Web server" can refer to hardware or software, or both of them working together.
On the hardware side, a web server is a computer that stores web server software and a website's
component files (e.g. HTML documents, images, CSS stylesheets, and JavaScript files).It is connected to
the Internet or local network and supports physical data interchange with other devices connected.
On the software side, a web server includes several parts that control how web users access hosted
files, at minimum an HTTP server. An HTTP server is a piece of software that understands URLs (web
addresses) and HTTP (the protocol that browsers uses to view webpages). It can be accessed through the
domain names (like www.google.com.ar or, it's knows, IP address) of websites it stores, and delivers
their content to the end-user's
device.
An embedded system is a combination of computer hardware and software, either fixed in capability or
programmable, designed for a specific function or functions within a larger system. Industrial machines,
agricultural and process industry devices, automobiles, medical equipment, cameras, household
appliances, airplanes, vending machines and toys, as well as mobile devices, are possible locations for
an embedded system.
In relationship, a Raspberry Pi is a low cost, credit-card sized computer that plugs into a computer
monitor or TV, and uses a standard keyboard and mouse. It is a capable little device that enables people
of all ages to explore computing, and to learn how to program in languages like Scratch and Python. It’s
capable of doing everything you’d expect a desktop computer to do, from browsing the internet and
playing high-definition video, to making spreadsheets, word-processing, and playing games.
This server runs on a Raspberry Pi 3+ with Broadcom BCM2837B0, Cortex-A53 (ARMv8) 64-bit SoC @ 1.4GHz
1GB LPDDR2 SDRAM 64-bit quad-core processor, dual-band wireless LAN, Bluetooth 4.2/BLE, faster Ethernet,
and Power-over-Ethernet support.
The operating system is Arch Linux
The CGI module works with perl language.


| % | File | Lines | Relevant | Covered | Missed |
|---|---|---|---|---|---|
| 50.00 | Branches.m | 45 | 20 | 10 | 10 |
| 100.00 | Empty.m | 0 | 0 | 0 | 0 |
| 50.00 | fixtures.m | 23 | 4 | 2 | 2 |
| 100.00 | fixtures_cpp.cpp | 0 | 0 | 0 | 0 |
| 100.00 | fixtures_m.m | 0 | 0 | 0 | 0 |
| 100.00 | fixtures_mm.mm | 0 | 0 | 0 | 0 |
| 0.00 | peekaview.m | 31 | 6 | 0 | 6 |
| 100.00 | BranchesTests.m | 38 | 10 | 10 | 0 |
| 100.00 | fixturesTests.m | 36 | 7 | 7 | 0 |
| 100.00 | peekaviewTests.m | 34 | 6 | 6 | 0 |


List of students as an unordered list
Apple
List of students as a table
Student ID
Student Name
CGPA
42011
John Doe
3.54


A Guide to Standardized Collection, Processing and Visualization of Cadre Harmonisé Food Security Indicators
2021-09-22
Chapter 1 Purpose & How to use this Guide
This guide presents standardized tools for collection, processing, analysis and visualization of food security direct evidence indicators and technical variables used in the Cadre Harmonisé process.
1.0.1 A brief overview of the chapters in this guide:
Chapter 2 Setting up R & Rstudio how to install R & Rstudio software, how to add required packages and an overview on Rmarkdown (used to generate reports).
Chapter 3 Gecodes explains how/why to use geo-codes in data collection (xlsforms) to speed up mapping and data processing.
Chapter 4 Household Hunger Scale paper and electronic versions of standardized questionnaire and SPSS/R syntax for calculating the Household Hunger Scale indicator.
Chapter 5 reduced Coping Strategy Index paper and electronic versions of standardized questionnaire and SPSS/R syntax for calculating the reduced Coping Strategy Index indicator.
Chapter 6 Food Consumption Score & Household Dietary Diversity Score paper and electronic versions of standardized questionnaire and SPSS/R syntax for calculating the Food Consumption Score/Groups and Household Dietary Diversity Score indicators.
Chapter 7 Livelihood Coping Strategies paper and electronic versions of standardized questionnaire and SPSS/R syntax for calculating the maximum coping strategy used for CARI light version and ENA version of the Livelihood Coping Strategies module.
Chapter 8 Combined Questionnaire & Syntaxes for all 5 indicators combines the questionnaires and syntaxes from chapter 4 - 7.
Chapter 9 Data Quality Monitoring Report provides instructions and R syntax for a data quality monitoring report, using raw survey data.
Chapter 10 Calculating Matrice Intermediare provides instructions and a generic R syntax to produce from a processed spss file the direct food security indicators and geocodes which can be copied into the matrice intermediare.
Chapter 11 Visualizing CH Direct Food security Indicators provides instructions and generic R syntax for visualizing maps and tables of the phasing of CH direct evidence indicators from the matrice intermediare or a processed data set.
1.0.2 About software
While use of the software R & Rstudio is necessary to produce the outputs in chapters 9, 10 and 11, users who aren’t ready to fully replace SPPS with R for their survey processes do not need to disrupt their normal data processing steps (cleaning, calculating, indicators, adding weights, etc) - this can remain in SPSS. Syntax for calculating indicators in SPSS is provided in chapters 4 - 8 .
The R syntax used for the data quality monitoring report is compatible with the “raw data” that is downloaded from data collection servers (Kobo/MoDA etc.) and the R syntax used to produce the matrice intermediare is compatible with the “processed data” in spss format.
1.0.3 About the example datasets
For this guide, two “semi-fictitious” data sets were created by randomly selecting and de-identifying several surveys. The results are “safe” data sets that serve as examples for the syntax and processes illustrated in this guide:
exampledataEnglish_raw - is what a “raw” data set typically should look like when collecting with standardized modules and downloaded from a ODK server in spss format.
exampledataEnglish_processed - is what a “processed data” typically should look like when applying standardized syntax to calculate indicators and adding survey weights.
1.0.4 About the questionnaires and variable names
The standardized questionnaires were developed from official guidelines/references listed in the chapters and were adapted reflecting after consultation with several food security analysts. While many questions in this guide were taken verbatim from the official references, the wording of some question modules follow the opinion that instructing enumerators to repeat the content of a question such as recall period (i.e. in the last 7 days…) and qualifier (…because lack of food or lack of money to buy food) explicitly within the question text is better practice than having enumerators read introductory text (i.e. In the last 7 days, because of lack of food did you have to use the following strategies…) and then read off a list of food items/strategies.
The variable names in this guide were largely copied from a draft master codebook which is forthcoming.
1.0.5 About coding style
Most of the code in this guide uses the tidyverse, but it might not be elegant or efficient. It gets the job done but future work will be to try to make it comply with the tidyverse style code: https://style.tidyverse.org/.
For now, one key thing to note. Most of the time, the code refers to the label (i.e. “Yes”) rather than the value (i.e. “1”). There are trade-offs with this approach. Using a label makes it easier to read what the code is trying to do and troubleshoot a problem. The disadvantage is it makes the code more long winded and it makes it more fussy because the labels (and sometimes they can be long) must exactly match the code.
1.0.6 One last about - About proposing fixes and suggestions
Finally, this guide is just a start. If you spot an error, find something confusing, have a suggestion or a better way of doing something - don’t be a stranger. You can create an issue https://docs.github.com/en/github/managing-your-work-on-github/creating-an-issue on the github repository of this guide https://github.com/WFP-VAM/RBD_FS_CH_guide_EN


Thank you, , for using this assistant to find which Epicodus track is right for you. Remeber , these are merely suggestions based upon what you answered. According to what you have chosen, these would be the options that most fit your personality: , , .


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--- title: Interpretation of Predictions keywords: fastai sidebar: home_sidebar summary: "Classes to build objects to better interpret predictions of a model" description: "Classes to build objects to better interpret predictions of a model" nb_path: "nbs/20_interpret.ipynb" ---
Interpretation
is a helper base class for exploring predictions from trained models. It can be inherited for task specific interpretation classes, such as ClassificationInterpretation
. Interpretation
is memory efficient and should be able to process any sized dataset, provided the hardware could train the same model.
{% include note.html content='Interpretation
is memory efficient due to generating inputs, predictions, targets, decoded outputs, and losses for each item on the fly, using batch processing where possible.' %}
With the default of k=None
, top_losses
will return the entire dataset's losses. top_losses
can optionally include the input items for each loss, which is usually a file path or Pandas DataFrame
.
To plot the first 9 top losses:
interp = Interpretation.from_learner(learn)
interp.plot_top_losses(9)
Then to plot the 7th through 16th top losses:
interp.plot_top_losses(range(7,16))
Like Learner.show_results
, except can pass desired index or indicies for item(s) to show results from.


| Modifier and Type | Method and Description |
|---|---|
MultiLayerSpace.Builder |
MultiLayerSpace.Builder.layer(Layer layer) |
ComputationGraphSpace.Builder |
ComputationGraphSpace.Builder.layer(String layerName,
Layer layer,
String... layerInputs) |
| Modifier and Type | Method and Description |
|---|---|
MultiLayerSpace.Builder |
MultiLayerSpace.Builder.addLayer(LayerSpace<? extends Layer> layerSpace,
ParameterSpace<Integer> numLayersDistribution) |
MultiLayerSpace.Builder |
MultiLayerSpace.Builder.addLayer(LayerSpace<? extends Layer> layerSpace,
ParameterSpace<Integer> numLayersDistribution,
boolean duplicateConfig)
Deprecated.
|
ComputationGraphSpace.Builder |
ComputationGraphSpace.Builder.addLayer(String layerName,
LayerSpace<? extends Layer> layerSpace,
InputPreProcessor preProcessor,
String... layerInputs) |
ComputationGraphSpace.Builder |
ComputationGraphSpace.Builder.addLayer(String layerName,
LayerSpace<? extends Layer> layerSpace,
String... layerInputs) |
MultiLayerSpace.Builder |
MultiLayerSpace.Builder.layer(LayerSpace<? extends Layer> layerSpace,
ParameterSpace<Integer> numLayersDistribution) |
ComputationGraphSpace.Builder |
ComputationGraphSpace.Builder.layer(String layerName,
LayerSpace<? extends Layer> layerSpace,
InputPreProcessor preProcessor,
String... layerInputs) |
ComputationGraphSpace.Builder |
ComputationGraphSpace.Builder.layer(String layerName,
LayerSpace<? extends Layer> layerSpace,
String... layerInputs) |
| Modifier and Type | Class and Description |
|---|---|
class |
LayerSpace<L extends Layer>
LayerSpace contains common Layer hyperparameters; should match
Layer in terms of features |
| Modifier and Type | Method and Description |
|---|---|
Layer |
Bidirectional.getValue(double[] parameterValues) |
| Modifier and Type | Class and Description |
|---|---|
class |
FixedLayerSpace<T extends Layer>
A layer space that wraps a DL4J layer, without any optimizable hyperparameters
|
| Modifier and Type | Field and Description |
|---|---|
protected T |
FixedLayerSpace.layer |
| Modifier and Type | Method and Description |
|---|---|
List<String> |
ParamInitializer.biasKeys(Layer layer)
Bias parameter keys given the layer configuration
|
boolean |
ParamInitializer.isBiasParam(Layer layer,
String key)
Is the specified parameter a bias?
|
boolean |
ParamInitializer.isWeightParam(Layer layer,
String key)
Is the specified parameter a weight?
|
long |
ParamInitializer.numParams(Layer layer) |
List<String> |
ParamInitializer.paramKeys(Layer layer)
Get a list of all parameter keys given the layer configuration
|
List<String> |
ParamInitializer.weightKeys(Layer layer)
Weight parameter keys given the layer configuration
|
| Modifier and Type | Field and Description |
|---|---|
protected Layer |
NeuralNetConfiguration.layer |
protected Layer |
NeuralNetConfiguration.Builder.layer |
| Modifier and Type | Method and Description |
|---|---|
ComputationGraphConfiguration.GraphBuilder |
ComputationGraphConfiguration.GraphBuilder.addLayer(String layerName,
Layer layer,
InputPreProcessor preProcessor,
String... layerInputs)
Add a layer and an
InputPreProcessor , with the specified name and specified inputs. |
ComputationGraphConfiguration.GraphBuilder |
ComputationGraphConfiguration.GraphBuilder.addLayer(String layerName,
Layer layer,
String... layerInputs)
Add a layer, with no
InputPreProcessor , with the specified name and specified inputs. |
ComputationGraphConfiguration.GraphBuilder |
ComputationGraphConfiguration.GraphBuilder.appendLayer(String layerName,
Layer layer)
Add a layer, with no
InputPreProcessor , with the specified name
and input from the last added layer/vertex. |
ComputationGraphConfiguration.GraphBuilder |
ComputationGraphConfiguration.GraphBuilder.appendLayer(String layerName,
Layer layer,
InputPreProcessor preProcessor)
Add a layer and an
InputPreProcessor , with the specified name
and input from the last added layer/vertex. |
NeuralNetConfiguration.ListBuilder |
NeuralNetConfiguration.ListBuilder.layer(int ind,
@NonNull Layer layer) |
ComputationGraphConfiguration.GraphBuilder |
ComputationGraphConfiguration.GraphBuilder.layer(int layerName,
Layer layer,
String... layerInputs)
Add a layer, with no
InputPreProcessor , with the specified name and specified inputs. |
NeuralNetConfiguration.ListBuilder |
NeuralNetConfiguration.ListBuilder.layer(Layer layer) |
NeuralNetConfiguration.Builder |
NeuralNetConfiguration.Builder.layer(Layer layer)
Layer class.
|
ComputationGraphConfiguration.GraphBuilder |
ComputationGraphConfiguration.GraphBuilder.layer(String layerName,
Layer layer,
InputPreProcessor preProcessor,
String... layerInputs)
Add a layer and an
InputPreProcessor , with the specified name and specified inputs. |
ComputationGraphConfiguration.GraphBuilder |
ComputationGraphConfiguration.GraphBuilder.layer(String layerName,
Layer layer,
String... layerInputs)
Add a layer, with no
InputPreProcessor , with the specified name and specified inputs. |
NeuralNetConfiguration.ListBuilder |
NeuralNetConfiguration.Builder.list(Layer... layers)
Create a ListBuilder (for creating a MultiLayerConfiguration) with the specified layers
Usage: .list(
new DenseLayer.Builder()...build(),
...,
new OutputLayer.Builder()...build())
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractLSTM
LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks
http://www.cs.toronto.edu/~graves/phd.pdf
|
class |
ActivationLayer
Activation layer is a simple layer that applies the specified activation function to the input activations
|
class |
AutoEncoder
Autoencoder layer.
|
class |
BaseLayer
A neural network layer.
|
class |
BaseOutputLayer |
class |
BasePretrainNetwork |
class |
BaseRecurrentLayer |
class |
BaseUpsamplingLayer
Upsampling base layer
|
class |
BatchNormalization
Batch normalization layer
See: Ioffe and Szegedy, 2014, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift https://arxiv.org/abs/1502.03167 |
class |
CapsuleLayer
An implementation of the DigiCaps layer from Dynamic Routing Between Capsules
Input should come from a PrimaryCapsules layer and be of shape [mb, inputCaps, inputCapDims].
|
class |
CapsuleStrengthLayer
An layer to get the "strength" of each capsule, that is, the probability of it being in the input.
|
class |
CenterLossOutputLayer
Center loss is similar to triplet loss except that it enforces intraclass consistency and doesn't require feed
forward of multiple examples.
|
class |
Cnn3DLossLayer
3D Convolutional Neural Network Loss Layer.
|
class |
CnnLossLayer
Convolutional Neural Network Loss Layer.
|
class |
Convolution1D
1D convolution layer.
|
class |
Convolution1DLayer
1D (temporal) convolutional layer.
|
class |
Convolution2D
2D convolution layer
|
class |
Convolution3D
3D convolution layer configuration
|
class |
ConvolutionLayer
2D Convolution layer (for example, spatial convolution over images).
|
class |
Deconvolution2D
2D deconvolution layer configuration
Deconvolutions are also known as transpose convolutions or fractionally strided convolutions. |
class |
Deconvolution3D
3D deconvolution layer configuration
Deconvolutions are also known as transpose convolutions or fractionally strided convolutions. |
class |
DenseLayer
Dense layer: a standard fully connected feed forward layer
|
class |
DepthwiseConvolution2D
2D depth-wise convolution layer configuration.
|
class |
DropoutLayer
Dropout layer.
|
class |
EmbeddingLayer
Embedding layer: feed-forward layer that expects single integers per example as input (class numbers, in range 0 to
numClass-1) as input.
|
class |
EmbeddingSequenceLayer
Embedding layer for sequences: feed-forward layer that expects fixed-length number (inputLength) of integers/indices
per example as input, ranged from 0 to numClasses - 1.
|
class |
FeedForwardLayer
Created by jeffreytang on 7/21/15.
|
class |
GlobalPoolingLayer
Global pooling layer - used to do pooling over time for RNNs, and 2d pooling for CNNs.
|
class |
GravesBidirectionalLSTM
Deprecated.
use
Bidirectional instead. With the Bidirectional
layer wrapper you can make any recurrent layer bidirectional, in particular GravesLSTM. Note that this layer adds the
output of both directions, which translates into "ADD" mode in Bidirectional.
Usage: .layer(new Bidirectional(Bidirectional.Mode.ADD, new GravesLSTM.Builder()....build())) |
class |
GravesLSTM
Deprecated.
Will be eventually removed. Use
LSTM instead, which has similar prediction accuracy, but supports
CuDNN for faster network training on CUDA (Nvidia) GPUs |
class |
LearnedSelfAttentionLayer
Implements Dot Product Self Attention with learned queries
Takes in RNN style input in the shape of [batchSize, features, timesteps]
and applies dot product attention using learned queries.
|
class |
LocallyConnected1D
SameDiff version of a 1D locally connected layer.
|
class |
LocallyConnected2D
SameDiff version of a 2D locally connected layer.
|
class |
LocalResponseNormalization
Local response normalization layer
See section 3.3 of http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf |
class |
LossLayer
LossLayer is a flexible output layer that performs a loss function on an input without MLP logic.
|
class |
LSTM
LSTM recurrent neural network layer without peephole connections.
|
class |
NoParamLayer |
class |
OutputLayer
Output layer used for training via backpropagation based on labels and a specified loss function.
|
class |
Pooling1D
1D Pooling (subsampling) layer.
|
class |
Pooling2D
2D Pooling (subsampling) layer.
|
class |
PReLULayer
Parametrized Rectified Linear Unit (PReLU)
f(x) = alpha * x for x < 0, f(x) = x for x >= 0
alpha has the same shape as x and is a learned parameter. |
class |
PrimaryCapsules
An implementation of the PrimaryCaps layer from Dynamic Routing Between Capsules
Is a reshaped 2D convolution, and the input should be 2D convolutional ([mb, c, h, w]).
|
class |
RecurrentAttentionLayer
Implements Recurrent Dot Product Attention
Takes in RNN style input in the shape of [batchSize, features, timesteps]
and applies dot product attention using the hidden state as the query and
all time steps as keys/values.
|
class |
RnnLossLayer
Recurrent Neural Network Loss Layer.
|
class |
RnnOutputLayer
A version of
OutputLayer for recurrent neural networks. |
class |
SelfAttentionLayer
Implements Dot Product Self Attention
Takes in RNN style input in the shape of [batchSize, features, timesteps]
and applies dot product attention using each timestep as the query.
|
class |
SeparableConvolution2D
2D Separable convolution layer configuration.
|
class |
SpaceToBatchLayer
Space to batch utility layer configuration for convolutional input types.
|
class |
SpaceToDepthLayer
Space to channels utility layer configuration for convolutional input types.
|
class |
Subsampling1DLayer
1D (temporal) subsampling layer - also known as pooling layer.
|
class |
Subsampling3DLayer
3D subsampling / pooling layer for convolutional neural networks
Supports max and average pooling modes
|
class |
SubsamplingLayer
Subsampling layer also referred to as pooling in convolution neural nets
Supports the following pooling types: MAX, AVG, SUM, PNORM
|
class |
Upsampling1D
Upsampling 1D layer
Repeats each step size times along the temporal/sequence axis (dimension 2)For input shape [minibatch, channels, sequenceLength] output has shape [minibatch, channels, size *
sequenceLength] Example: If input (for a single example, with channels down page, and sequence from left to right) is: [ A1, A2, A3] [ B1, B2, B3] Then output with size = 2 is: [ A1, A1, A2, A2, A3, A3] [ B1, B1, B2, B2, B3, B2] |
class |
Upsampling2D
Upsampling 2D layer
Repeats each value (or rather, set of depth values) in the height and width dimensions by size[0] and size[1] times respectively. |
class |
Upsampling3D
Upsampling 3D layer
Repeats each value (all channel values for each x/y/z location) by size[0], size[1] and size[2] If input has shape [minibatch, channels, depth, height, width] then output has shape [minibatch, channels, size[0] * depth, size[1] * height, size[2] * width] |
class |
ZeroPadding1DLayer
Zero padding 1D layer for convolutional neural networks.
|
class |
ZeroPadding3DLayer
Zero padding 3D layer for convolutional neural networks.
|
class |
ZeroPaddingLayer
Zero padding layer for convolutional neural networks (2D CNNs).
|
| Modifier and Type | Method and Description |
|---|---|
<E extends Layer> |
CapsuleLayer.Builder.build() |
<E extends Layer> |
CapsuleStrengthLayer.Builder.build() |
abstract <E extends Layer> |
Layer.Builder.build() |
<E extends Layer> |
PrimaryCapsules.Builder.build() |
| Modifier and Type | Method and Description |
|---|---|
Layer |
Layer.clone() |
| Modifier and Type | Method and Description |
|---|---|
static void |
LayerValidation.generalValidation(String layerName,
Layer layer,
IDropout iDropout,
List<Regularization> regularization,
List<Regularization> regularizationBias,
List<LayerConstraint> allParamConstraints,
List<LayerConstraint> weightConstraints,
List<LayerConstraint> biasConstraints) |
| Modifier and Type | Class and Description |
|---|---|
class |
Cropping1D
Cropping layer for convolutional (1d) neural networks.
|
class |
Cropping2D
Cropping layer for convolutional (2d) neural networks.
|
class |
Cropping3D
Cropping layer for convolutional (3d) neural networks.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ElementWiseMultiplicationLayer
Elementwise multiplication layer with weights: implements
out = activationFn(input .* w + b) where:- w is a learnable weight vector of length nOut - ".*" is element-wise multiplication - b is a bias vector Note that the input and output sizes of the element-wise layer are the same for this layer created by jingshu |
class |
FrozenLayer
FrozenLayer is used for the purposes of transfer learning.
|
class |
FrozenLayerWithBackprop
Frozen layer freezes parameters of the layer it wraps, but allows the backpropagation to continue.
|
class |
RepeatVector
RepeatVector layer configuration.
|
| Modifier and Type | Field and Description |
|---|---|
protected Layer |
FrozenLayer.layer |
| Modifier and Type | Method and Description |
|---|---|
Layer |
FrozenLayer.clone() |
Layer |
FrozenLayerWithBackprop.clone() |
| Modifier and Type | Method and Description |
|---|---|
FrozenLayer.Builder |
FrozenLayer.Builder.layer(Layer layer) |
| Constructor and Description |
|---|
FrozenLayer(Layer layer) |
FrozenLayerWithBackprop(Layer layer) |
| Modifier and Type | Class and Description |
|---|---|
class |
Yolo2OutputLayer
Output (loss) layer for YOLOv2 object detection model, based on the papers: YOLO9000: Better, Faster, Stronger -
Redmon & Farhadi (2016) - https://arxiv.org/abs/1612.08242
and You Only Look Once: Unified, Real-Time Object Detection - Redmon et al. (2016) - http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Redmon_You_Only_Look_CVPR_2016_paper.pdf This loss function implementation is based on the YOLOv2 version of the paper. |
| Modifier and Type | Class and Description |
|---|---|
class |
Bidirectional
Bidirectional is a "wrapper" layer: it wraps any uni-directional RNN layer to make it bidirectional.
|
class |
LastTimeStep
LastTimeStep is a "wrapper" layer: it wraps any RNN (or CNN1D) layer, and extracts out the last time step during forward pass,
and returns it as a row vector (per example).
|
class |
SimpleRnn
Simple RNN - aka "vanilla" RNN is the simplest type of recurrent neural network layer.
|
class |
TimeDistributed
TimeDistributed wrapper layer.
|
| Modifier and Type | Method and Description |
|---|---|
Layer |
LastTimeStep.getUnderlying() |
| Modifier and Type | Method and Description |
|---|---|
Bidirectional.Builder |
Bidirectional.Builder.rnnLayer(Layer layer) |
void |
Bidirectional.Builder.setLayer(Layer layer) |
| Constructor and Description |
|---|
Bidirectional(@NonNull Bidirectional.Mode mode,
@NonNull Layer layer)
Create a Bidirectional wrapper for the specified layer
|
Bidirectional(@NonNull Layer layer)
Create a Bidirectional wrapper, with the default Mode (CONCAT) for the specified layer
|
LastTimeStep(Layer underlying) |
TimeDistributed(Layer underlying) |
TimeDistributed(@NonNull Layer underlying,
RNNFormat rnnDataFormat) |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractSameDiffLayer |
class |
SameDiffLambdaLayer
SameDiffLambdaLayer is defined to be used as the base class for implementing lambda layers using SameDiff
Lambda layers are layers without parameters - and as a result, have a much simpler API - users need only extend SameDiffLambdaLayer and implement a single method |
class |
SameDiffLayer
A base layer used for implementing Deeplearning4j layers using SameDiff.
|
class |
SameDiffOutputLayer
A base layer used for implementing Deeplearning4j Output layers using SameDiff.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MaskLayer
MaskLayer applies the mask array to the forward pass activations, and backward pass gradients, passing through
this layer.
|
class |
MaskZeroLayer
Wrapper which masks timesteps with activation equal to the specified masking value (0.0 default).
|
| Modifier and Type | Method and Description |
|---|---|
MaskZeroLayer.Builder |
MaskZeroLayer.Builder.setUnderlying(Layer underlying) |
MaskZeroLayer.Builder |
MaskZeroLayer.Builder.underlying(Layer underlying) |
| Constructor and Description |
|---|
MaskZeroLayer(Layer underlying,
double maskingValue) |
| Modifier and Type | Class and Description |
|---|---|
class |
VariationalAutoencoder
Variational Autoencoder layer
See: Kingma & Welling, 2013: Auto-Encoding Variational Bayes - https://arxiv.org/abs/1312.6114
This implementation allows multiple encoder and decoder layers, the number and sizes of which can be set
independently.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BaseWrapperLayer
Base wrapper layer: the idea is to pass through all methods to the underlying layer, and selectively override
them as required.
|
| Modifier and Type | Field and Description |
|---|---|
protected Layer |
BaseWrapperLayer.underlying |
| Constructor and Description |
|---|
BaseWrapperLayer(Layer underlying) |
| Modifier and Type | Class and Description |
|---|---|
class |
OCNNOutputLayer
An implementation of one class neural networks from:
https://arxiv.org/pdf/1802.06360.pdf
The one class neural network approach is an extension of the standard output layer with a single set of weights, an
activation function, and a bias to: 2 sets of weights, a learnable "r" parameter that is held static 1 traditional
set of weights. 1 additional weight matrix
|
| Modifier and Type | Method and Description |
|---|---|
protected boolean |
BaseNetConfigDeserializer.requiresActivationFromLegacy(Layer[] layers) |
protected boolean |
BaseNetConfigDeserializer.requiresDropoutFromLegacy(Layer[] layers) |
protected boolean |
BaseNetConfigDeserializer.requiresIUpdaterFromLegacy(Layer[] layers) |
protected boolean |
BaseNetConfigDeserializer.requiresLegacyLossHandling(Layer[] layers) |
protected boolean |
BaseNetConfigDeserializer.requiresRegularizationFromLegacy(Layer[] layers) |
protected boolean |
BaseNetConfigDeserializer.requiresWeightInitFromLegacy(Layer[] layers) |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractLayer<LayerConfT extends Layer>
A layer with input and output, no parameters or gradients
|
| Modifier and Type | Method and Description |
|---|---|
List<String> |
OCNNParamInitializer.biasKeys(Layer layer) |
boolean |
OCNNParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
OCNNParamInitializer.isWeightParam(Layer layer,
String key) |
long |
OCNNParamInitializer.numParams(Layer layer) |
List<String> |
OCNNParamInitializer.paramKeys(Layer layer) |
List<String> |
OCNNParamInitializer.weightKeys(Layer layer) |
| Modifier and Type | Class and Description |
|---|---|
class |
IdentityLayer
Identity layer, passes data through unaltered.
|
| Modifier and Type | Field and Description |
|---|---|
protected Layer |
KerasLayer.layer |
| Modifier and Type | Method and Description |
|---|---|
Layer |
KerasLayer.getLayer()
Gets corresponding DL4J Layer, if any.
|
| Modifier and Type | Method and Description |
|---|---|
void |
KerasLayer.setLayer(Layer layer) |
| Modifier and Type | Class and Description |
|---|---|
class |
TFOpLayer |
| Modifier and Type | Method and Description |
|---|---|
Layer |
KerasLSTM.getLSTMLayer()
Get DL4J Layer.
|
Layer |
KerasSimpleRnn.getSimpleRnnLayer()
Get DL4J SimpleRnn layer.
|
| Modifier and Type | Method and Description |
|---|---|
Layer |
KerasBidirectional.getUnderlyingRecurrentLayer()
Return the underlying recurrent layer of this bidirectional layer
|
| Modifier and Type | Method and Description |
|---|---|
List<String> |
BatchNormalizationParamInitializer.biasKeys(Layer layer) |
List<String> |
BidirectionalParamInitializer.biasKeys(Layer layer) |
List<String> |
ConvolutionParamInitializer.biasKeys(Layer layer) |
List<String> |
DefaultParamInitializer.biasKeys(Layer layer) |
List<String> |
DepthwiseConvolutionParamInitializer.biasKeys(Layer layer) |
List<String> |
EmptyParamInitializer.biasKeys(Layer layer) |
List<String> |
FrozenLayerParamInitializer.biasKeys(Layer layer) |
List<String> |
FrozenLayerWithBackpropParamInitializer.biasKeys(Layer layer) |
List<String> |
GravesBidirectionalLSTMParamInitializer.biasKeys(Layer layer) |
List<String> |
GravesLSTMParamInitializer.biasKeys(Layer layer) |
List<String> |
LSTMParamInitializer.biasKeys(Layer layer) |
List<String> |
PReLUParamInitializer.biasKeys(Layer layer) |
List<String> |
SameDiffParamInitializer.biasKeys(Layer layer) |
List<String> |
SeparableConvolutionParamInitializer.biasKeys(Layer layer) |
List<String> |
SimpleRnnParamInitializer.biasKeys(Layer layer) |
List<String> |
VariationalAutoencoderParamInitializer.biasKeys(Layer layer) |
List<String> |
WrapperLayerParamInitializer.biasKeys(Layer layer) |
protected boolean |
DefaultParamInitializer.hasBias(Layer layer) |
protected boolean |
DefaultParamInitializer.hasLayerNorm(Layer layer) |
protected boolean |
SimpleRnnParamInitializer.hasLayerNorm(Layer layer) |
boolean |
BatchNormalizationParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
BidirectionalParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
ConvolutionParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
DefaultParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
DepthwiseConvolutionParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
EmptyParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
FrozenLayerParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
FrozenLayerWithBackpropParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
GravesBidirectionalLSTMParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
GravesLSTMParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
LSTMParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
PReLUParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
SameDiffParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
SeparableConvolutionParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
SimpleRnnParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
VariationalAutoencoderParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
WrapperLayerParamInitializer.isBiasParam(Layer layer,
String key) |
boolean |
BatchNormalizationParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
BidirectionalParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
ConvolutionParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
DefaultParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
DepthwiseConvolutionParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
EmptyParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
FrozenLayerParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
FrozenLayerWithBackpropParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
GravesBidirectionalLSTMParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
GravesLSTMParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
LSTMParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
PReLUParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
SameDiffParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
SeparableConvolutionParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
SimpleRnnParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
VariationalAutoencoderParamInitializer.isWeightParam(Layer layer,
String key) |
boolean |
WrapperLayerParamInitializer.isWeightParam(Layer layer,
String key) |
long |
BatchNormalizationParamInitializer.numParams(Layer l) |
long |
BidirectionalParamInitializer.numParams(Layer layer) |
long |
Convolution3DParamInitializer.numParams(Layer l) |
long |
ConvolutionParamInitializer.numParams(Layer l) |
long |
Deconvolution3DParamInitializer.numParams(Layer l) |
long |
DefaultParamInitializer.numParams(Layer l) |
long |
DepthwiseConvolutionParamInitializer.numParams(Layer l) |
long |
ElementWiseParamInitializer.numParams(Layer layer) |
long |
EmptyParamInitializer.numParams(Layer layer) |
long |
FrozenLayerParamInitializer.numParams(Layer layer) |
long |
FrozenLayerWithBackpropParamInitializer.numParams(Layer layer) |
long |
GravesBidirectionalLSTMParamInitializer.numParams(Layer l) |
long |
GravesLSTMParamInitializer.numParams(Layer l) |
long |
LSTMParamInitializer.numParams(Layer l) |
long |
PReLUParamInitializer.numParams(Layer l) |
long |
SameDiffParamInitializer.numParams(Layer layer) |
long |
SeparableConvolutionParamInitializer.numParams(Layer l) |
long |
SimpleRnnParamInitializer.numParams(Layer layer) |
long |
WrapperLayerParamInitializer.numParams(Layer layer) |
List<String> |
BatchNormalizationParamInitializer.paramKeys(Layer layer) |
List<String> |
BidirectionalParamInitializer.paramKeys(Layer layer) |
List<String> |
ConvolutionParamInitializer.paramKeys(Layer layer) |
List<String> |
DefaultParamInitializer.paramKeys(Layer layer) |
List<String> |
DepthwiseConvolutionParamInitializer.paramKeys(Layer layer) |
List<String> |
EmptyParamInitializer.paramKeys(Layer layer) |
List<String> |
FrozenLayerParamInitializer.paramKeys(Layer layer) |
List<String> |
FrozenLayerWithBackpropParamInitializer.paramKeys(Layer layer) |
List<String> |
GravesBidirectionalLSTMParamInitializer.paramKeys(Layer layer) |
List<String> |
GravesLSTMParamInitializer.paramKeys(Layer layer) |
List<String> |
LSTMParamInitializer.paramKeys(Layer layer) |
List<String> |
PReLUParamInitializer.paramKeys(Layer layer) |
List<String> |
SameDiffParamInitializer.paramKeys(Layer layer) |
List<String> |
SeparableConvolutionParamInitializer.paramKeys(Layer layer) |
List<String> |
SimpleRnnParamInitializer.paramKeys(Layer layer) |
List<String> |
VariationalAutoencoderParamInitializer.paramKeys(Layer l) |
List<String> |
WrapperLayerParamInitializer.paramKeys(Layer layer) |
List<String> |
BatchNormalizationParamInitializer.weightKeys(Layer layer) |
List<String> |
BidirectionalParamInitializer.weightKeys(Layer layer) |
List<String> |
ConvolutionParamInitializer.weightKeys(Layer layer) |
List<String> |
DefaultParamInitializer.weightKeys(Layer layer) |
List<String> |
DepthwiseConvolutionParamInitializer.weightKeys(Layer layer) |
List<String> |
EmptyParamInitializer.weightKeys(Layer layer) |
List<String> |
FrozenLayerParamInitializer.weightKeys(Layer layer) |
List<String> |
FrozenLayerWithBackpropParamInitializer.weightKeys(Layer layer) |
List<String> |
GravesBidirectionalLSTMParamInitializer.weightKeys(Layer layer) |
List<String> |
GravesLSTMParamInitializer.weightKeys(Layer layer) |
List<String> |
LSTMParamInitializer.weightKeys(Layer layer) |
List<String> |
PReLUParamInitializer.weightKeys(Layer layer) |
List<String> |
SameDiffParamInitializer.weightKeys(Layer layer) |
List<String> |
SeparableConvolutionParamInitializer.weightKeys(Layer layer) |
List<String> |
SimpleRnnParamInitializer.weightKeys(Layer layer) |
List<String> |
VariationalAutoencoderParamInitializer.weightKeys(Layer layer) |
List<String> |
WrapperLayerParamInitializer.weightKeys(Layer layer) |
| Modifier and Type | Method and Description |
|---|---|
TransferLearning.Builder |
TransferLearning.Builder.addLayer(Layer layer)
Add layers to the net
Required if layers are removed.
|
TransferLearning.GraphBuilder |
TransferLearning.GraphBuilder.addLayer(String layerName,
Layer layer,
InputPreProcessor preProcessor,
String... layerInputs)
Add a layer with a specified preprocessor
|
TransferLearning.GraphBuilder |
TransferLearning.GraphBuilder.addLayer(String layerName,
Layer layer,
String... layerInputs)
Add a layer of the specified configuration to the computation graph
|
| Modifier and Type | Method and Description |
|---|---|
static RNNFormat |
TimeSeriesUtils.getFormatFromRnnLayer(Layer layer)
Get the
RNNFormat from the RNN layer, accounting for the presence of wrapper layers like Bidirectional,
LastTimeStep, etc |
static void |
OutputLayerUtil.validateOutputLayer(String layerName,
Layer layer)
Validate the output layer (or loss layer) configuration, to detect invalid consfiugrations.
|
static void |
OutputLayerUtil.validateOutputLayerForClassifierEvaluation(Layer outputLayer,
Class<? extends IEvaluation> classifierEval)
Validates if the output layer configuration is valid for classifier evaluation.
|
Copyright © 2020. All rights reserved.


I write this with fellow indiviudals like myself in mind. I just graduated from college with a good degree and have enjoyed some recreational drug use: to which I will elaborate. I had an opportunity to get a great job, which coincidentally would be in the same town as my girlfriend, so it really would mean the world to me to get this job. I was worried one pill I took might of did me in, I shall relate my experience to you my friends.
To preface: I am 165 pounds, I play basketball for a few hours at least 3 times a week, and I am of a skinny build and have always had a fast metabolism and that certainly works in my favor. I interviewed with a company Monday of the week of this test. Things went alot better than I had expected. Generally the interview process involves multiple interviews but I guess that they really liked me. I talk to a recruiter for 30 mins, then a manager of the department, then his manager. Then the VP of the company comes and interviews me. I am stoked of course, but upon leaving they gave me a form to hook up with an outsourced background checking company.
I call them up and they ask me questions about my information on the application etc, no big deal I don't have anything to worry about in that regard. They mention a drug screening, and they'd like it to be completed in 2 days.  Well I went for the second day of course. I had partied some the past week, which I will explain. Did not expect to be tested so soon. Boy, here goes nothing. Oh yeah, I had possibly broken my ankle so I was prescribed proproxyphene 100/650 and Naproxen. The proproxyphene at no time showed up as an opiate on my home-kit test.
I stopped smoking weed regularly at least a month and a half ago, if not 2 months. After that I would say I smoked various random occassions no more than 10 times. My tolerance dropped like a rock, so when I did smoke I would take just 2-3 hits and be really blown--which was cool I must add.  I was not very worried about THC showing up on my drug test.
I started occassionally taking hydrocodone, several times a week I would take 30mg or so, in the form of lortab. I liked the fact that it would get out of your system pretty quickly (2-3) days, and I just enjoy it. I took them last 4 days before the test.
I had taken on various occassions 700 mgs or 2 somas. The last time I took them was 4 days before the test.
This is what I was worried about screwing me!!! I was drunk and bowling with some friends and I kind of had it in my mind to pop a vicodin (it is not healthy to do so when drinking...keep that in mind) but I had none and a friend offered me valium. I knew, maybe I shouldn't take this but being drunk my wisdom certainly did not kick in and I popped it. 15 mg, it was 1 and a half pills. I had a great time. This was 5 days before my test.
Alright. So I wanted to just make sure that I was clean because I am the type of guy that likes assurances. I bought a 12-panel drug test kit, which is far more expensive, but hey whatever I wanted to cover all the bases. I passed it for everything except BENZOS! This was the day before my test.
Oh boy, let me tell you, that is a heart sinking feeling. A test in 2 days for a job that will mean the world to me and my girlfriend and I was not testing clean for BENZOS!
 Warning: drinking toooo much water can cause your electrolytes to drop and seriously harm you. I do not know the limit but I understand it is hard to reach. Electrolyte replenishing substances such as gatorade and the like certainly can help. Be careful.
Anywho, the next day I woke up 5 hours before the test and started the day off with a large energy drink. Then I drank a bottle of gatorade. I drank 2 more cups of water after that. I had bought another 12-panel drug test. I took it about an hour and 15 minutes after all of this drinking. Okay. It appeared I might of passed for benzos and again everything else was okay. Essentially if a line forms nearby the indicator you pass. The line is supposed to range from pink to red. It was very faint next to Benzos, but it was a line. I interpret this to believe I still have benzos in system, but they were diluted to below or very near the cutoff point. I also purchased B-12 viatmins and took 2 hoping for a nice yellow piss. I drank a small powerade with some creatine in it. I hear they check creatine levels for dilution, figured it couldn't hurt.
Continue drinking water and I decide to buy a detoxx drink. I purchased Ready Clean, Grape Flavor. Tasted not that great, but easy enough to get down. Chase that with water as per the directions. Few extra gulps of water too. This is 1.5 hours before the test. I have been pissin like a drunken racehorse this whole time, must of been nearly 10 pisses for the day leading up to this test.
I say alright, here goes, and I walk into the place. It is a corporate office setting and not a lab. I sign some paperwork and then go to pee. I pee and its a nice solid yellow color, not appearing to be diluted. The cup itself has a strip on it that measures the pH and also the temperature. My temp was 94 degrees. The pH was somewhere above 4. I am sure it was slightly acidified urine due to the cranberry extract I had been taking. I capped the cup and returned it to the lady.
Then I sign the form and LOW AND BEHOLD! IT WAS ONLY A 5-PANEL DRUG TEST! I had no worries for THC, Opiates, Cocaine, Methamphetamine, or PCP. NONE! I did all of this for nothing!!!! I was so relieved though. I spent nearly 200 dollars on 3 drug tests, a detoxx, and tons of drinks, and for nothing. It is worth it though, you never know and you should be prepared. On the paperwork was a box for the lady to check says is the sample between 90-100 degrees? Mine was. So keep that in mind if you care to alter or substitute etc. Also, they selected the 50ng/ml cutoff for THC which is the standard for most home test kits. There was an option for 20ng/ml or 100ng/ml also, so keep in mind they might try and go for the 20ng/ml so a clear on the home test doesn't assure you will pass the drug test at the facility.
My advice: don't give in to temptation if its really important to you. Abstain for a good month, if not 2.  Maybe most companies only go with the 5 panel because it is the cheapest, and those are the drugs that are most publicized and that they feel will damage their company. Now my only worry is that my sample was too diluted. Even if it was, fine re-test me. Hopefully it would just be a 5-panel again and I have no worries. I hope it just goes through fine and not too diluted and I am good to go!
Anyways, I am quite relieved as that will not show up and if they do re-test me I hope it would just be a 5-panel and if not well I have at least more time for that stupid valium to clear out.
There ya go buddies.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Number of threadsedit
The Apache Http Async Client starts by default one dispatcher thread, and a
number of worker threads used by the connection manager, as many as the number
of locally detected processors (depending on what
Runtime.getRuntime().availableProcessors()
returns). The number of threads
can be modified as follows:
RestClient restClient = RestClient.builder(new HttpHost("localhost", 9200)) .setHttpClientConfigCallback(new RestClientBuilder.HttpClientConfigCallback() { @Override public HttpAsyncClientBuilder customizeHttpClient(HttpAsyncClientBuilder httpClientBuilder) { return httpClientBuilder.setDefaultIOReactorConfig( IOReactorConfig.custom().setIoThreadCount(1).build()); } }) .build();


We are committed to respecting your data privacy and providing transparency about our data practices. For My Fertility collects, stores, and uses Personal Data from our users (“you”) in connection with the ForMyFertility mobile application and related services (collectively, the “App”).
Infertility means not being able to get pregnant after one year of trying (or six months if a woman is 35 or older). Women who can get pregnant but are unable to stay pregnant may also be infertile. About 10 percent of women (6.1 million) in the United States ages 15-44 have difficulty getting pregnant or staying pregnant, according to the Centers for Disease Control and Prevention (CDC). Pregnancy is the result of a process that has many steps.
With so much bad information on the web, how can couples find reliable fertility information online? At For My Fertility evaluate sites carefully for bias and conflict of interest. Checking to see if the information is current (less than three years old). We look for the credentials of the author (i.e. doctor, nurse, psychologist) to make sure the information is written by a health professional.


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Scheme, like ML, is a programming language with strict evaluation
while others, like Haskell, use lazy evaluation. Scheme, however,
possesses the primitives delay
and force
that make it possible to express lazy algorithms.
Lazy evaluation does not go well in conjunction with imperative,
non-functional, side-effecting code. It should, however, be
applicable in a purely functional setting. This is the case for the
delayed evaluation model as described in the R7RS as long as no
dynamically bound variables, also known as parameter objects, are
present. It is the purpose of this SRFI to rework the specification
in the R7RS so that lazy evaluation works with purely functional code
that makes use of dynamic environments or, more generally, the dynamic
extent. This is done by remembering the dynamic extent in effect when
the delay
expression is evaluated.
Another perceived misfeature of the R7RS model of delayed evaluation
is the apparent need of the delay-force
special form to
express iterative lazy algorithms. It is shown that
the delay-force
special form is unneeded and that the
implementation can (and should) handle iterative lazy algorithms
without space leaks.


{% extends "_post.html" %}
{% hyde
title: "Threesome"
snip: "A Vim plugin for resolving three-way merge conflicts."
created: 2011-10-10 00:00:02
exclude: False
%}
{% block article %}
[Threesome](http://sjl.bitbucket.org/threesome.vim/) is a Vim plugin that helps
you resolve three-way merge conflicts. Check out the site for more information.
{% endblock %}


{% extends "tutorial.html" %}
{% block headauthor %}Pete LePage Many new computers, mobile phones and other devices are now shipping standard with accelerometers, gyroscopes, compasses and other hardware designed to determine capture motion and orientation data. At the same time, web browsers are providing increasingly more access to that new hardware. One example of that are the new HTML5 DeviceOrientation and DeviceMotion events. These events provide developers with information about the orientation, motion and acceleration of the device. Today, Chrome, and Safari Mobile, support these events in their stable releases, Opera has a test implementation; and Firefox has an implementation using a slightly different event model, though they have indicated they will likely implement the HTML5 DeviceOrientation and DeviceMotion events in the future. These events have a lot of fun uses, such as using orientation to control the direction of character in a game, or motion to determine how high a character should jump. Additionally, you can use these events beyond gaming. When used with GeoLocation, you can create a more accurate turn-by-turn navigation system. In this article, we’ll take a look at three different events, and use CSS to rotate an image based on the orientation of the device. There are a couple of things that we need to make sure we understand before we can start using these events, which end is up and what are the axes we’re going to use? The easiest way to answer the first question is to take your device, and put it on flat, level surface in it’s “resting” position, the position it will be most stable in. For a mobile phone, that’s probably on the table, with the screen facing up, and the the bottom of the phone is closest to you. Your laptop is likely positioned with it’s screen open facing you, and the keyboard in-line with the surface. Acceleration data is returned as a coordinate frame with three axes, x, y and z. The x-axis runs side-to-side across the mobile phone screen, or the laptop keyboard and is positive towards the right side. The y-axis runs front-to-back across the mobile phone screen or the laptop keyboard and is positive towards as it moves away from you. The z-axis comes straight up out of the mobile phone screen or the laptop keyboard and is positive as it moves up. The rotation data uses Euler angles to represent the difference between the device in it’s normal position and it’s current position. With the HTML5 device orientation events, the data is returned as the number of degrees different from normal. An easier way to think about it is how much the device is tilted side-to-side, usually referred to as beta. How much its tilted front-to-back, usually known as gamma, and finally how much has it been rotated around the z-axis, known as alpha. One thing to keep in mind is that most people don’t use their phones when they’re in this flat position, and instead have rotated them around the x-axis so the screen is facing them, this will affect the acceleration data that is returned from the device motion events. The device orientation event returns only the rotation data, which includes how much the device is leaning side-to-side (beta), front-to-back (gamma) and if the phone or laptop has a compass, the direction the device is facing (alpha). Let’s see a couple examples: Firefox 3.6+ has a slightly different implementation of the device orientation event, and instead uses the MozOrientationEvent. The MozOrientationEvent provides information about the side-to-side tilt (using the parameter x), the front-to-back tilt (using the parameter y), and instead of providing the compass direction, it provides the vertical acceleration (using the parameter z). The Introduction
Which end is up?
The events
Device orientation
MozOrientation
MozOrientation
event returns three values:
X
- the amount of side-to-side tilt. When the laptop or phone is level, the value is zero, and approaches +1 when tilted to the left, and approaches -1 when tilted to the right.Y
- the amount of front-to-back tilt. When the laptop is level, the value is zero and approaches +1 as it’s titled away from you and approaches -1 as it’s tilted towards you.Z
- the amount of vertical acceleration. When undergoing normal earth gravity the value is -1, and decreases if the device is moved up. The value is 0 if the device is thust up.
The device motion event is a superset of the device orientation event; it returns data about the rotation information and also acceleration information about the device. The acceleration data is returned in three axes: x, y and z. They are measured in meters per second squared (m/s^2). Because some devices might not have the hardware to exclude the effect of gravity, the event returns two properties, accelerationIncludingGravity
and acceleration
, which excludes the effects of gravity, (when this is the case, the acceleration data will be null).
A laptop in its normal position, with the screen facing up, the data returned would be:
| Not accelerating | Accelerating up | Accelerating forward | Accelerating left | Accelerating up & to the right | |
|---|---|---|---|---|---|
| acceleration | {0, 0, 0} | {0, 0, 5} | {0, 2, 0} | {3, 0, 0} | {5, 0, 9} |
| accelerationIncludingGravity | {0, 0, 9.81} | {0, 0, 15} | {0, 2, 9.81} | {3, 0, 9.81} | {5, 0, 11} |
A mobile phone rotated along the x-axis so the screen is perpendicular to it’s normal position would return:
| Not accelerating | Accelerating up | Accelerating forward | Accelerating left | Accelerating up & to the right | |
|---|---|---|---|---|---|
| acceleration | {0, 0, 0} | {0, 5, 0} | {0, 0, 2} | {3, 0, 0} | {5, 5, 0} |
| accelerationIncludingGravity | {0, 9.81, 0} | {0, 15, 0} | {0, 9.81, 2} | {3, 9.81, 0} | {5, 11, 0} |
Since DeviceOrientation and MozOrientation are similar, we’ll look at them together in this sample.
Check to see if either the DeviceOrientationEvent or the MozOrientationEvent is supported.
if (window.DeviceOrientationEvent) { console.log("DeviceOrientation is supported"); } else if (window.OrientationEvent) { console.log("MozOrientation is supported"); }
One thing to note, some devices (for example, the original iPad) say that they support the event when in fact they don’t. In these cases, the event is fired only once, and all of the properties are null.
For this example, we’re going to display the orientation data and then apply that information as a CSS3 transform to an image.

Device Orientation

 Event Supported

 Tilt Left/Right [tiltLR]

 Tilt Front/Back [tiltFB]

 Direction [direction]

 Motion Up/Down

Now that we know what events are supported, add the appropriate event listeners:
if (window.DeviceOrientationEvent) { // Listen for the deviceorientation event and handle DeviceOrientationEvent object window.addEventListener('deviceorientation', devOrientHandler, false); } else if (window.OrientationEvent) { // Listen for the MozOrientation event and handle OrientationData object window.addEventListener('MozOrientation', mozDevOrientHandler, false); }
The HTML5 DeviceOrientation event returns three pieces of data:
alpha
the direction the device is facing according to the compassbeta
the angle in degrees the device is tilted front-to-backgamma
the angle in degrees the device is tilted left-to-right.Since Firefox uses the MozOrientationEvent
which returns similar data but using different parameters and a different measurement system, we want to normalize that before we pass it to our deviceOrientationHandler
function.
if (window.DeviceOrientationEvent) { document.getElementById("doEvent").innerHTML = "DeviceOrientation"; // Listen for the deviceorientation event and handle the raw data window.addEventListener('deviceorientation', function(eventData) { // gamma is the left-to-right tilt in degrees, where right is positive var tiltLR = eventData.gamma; // beta is the front-to-back tilt in degrees, where front is positive var tiltFB = eventData.beta; // alpha is the compass direction the device is facing in degrees var dir = eventData.alpha // deviceorientation does not provide this data var motUD = null; // call our orientation event handler deviceOrientationHandler(tiltLR, tiltFB, dir, motUD); }, false); } else if (window.OrientationEvent) { document.getElementById("doEvent").innerHTML = "MozOrientation"; window.addEventListener('MozOrientation', function(eventData) { // x is the left-to-right tilt from -1 to +1, so we need to convert to degrees var tiltLR = eventData.x * 90; // y is the front-to-back tilt from -1 to +1, so we need to convert to degrees // We also need to invert the value so tilting the device towards us (forward) // results in a positive value. var tiltFB = eventData.y * -90; // MozOrientation does not provide this data var dir = null; // z is the vertical acceleration of the device var motUD = eventData.z; // call our orientation event handler deviceOrientationHandler(tiltLR, tiltFB, dir, motUD); }, false); } else { document.getElementById("doEvent").innerHTML = "Not supported on your device or browser." }
We’ve already normalized our data, we’ll display it in the table we created in step 2, and also add rotate the image using a CSS3 transform.
document.getElementById("doTiltLR").innerHTML = Math.round(tiltLR); document.getElementById("doTiltFB").innerHTML = Math.round(tiltFB); document.getElementById("doDirection").innerHTML = Math.round(dir); document.getElementById("doMotionUD").innerHTML = motionUD; // Apply the transform to the image document.getElementById("imgLogo").style.webkitTransform = "rotate(" + tiltLR + "deg) rotate3d(1,0,0, " + (tiltFB * -1) + "deg)"; document.getElementById("imgLogo").style.MozTransform = "rotate(" + tiltLR + "deg)"; document.getElementById("imgLogo").style.transform = "rotate(" + tiltLR + "deg) rotate3d(1,0,0, " + (tiltFB * -1) + "deg)";
Try it out, and view the source to see it in action.
The DeviceMotionEvent
provides the acceleration and rotation data of the device. In our example, we’re going to use accelerationIncludingGravity
to determine the orientation of the device and get a similar result to the sample we built with DeviceOrientation
.
First thing we want to do is determine if the DeviceMotionEvent is supported using feature detection.
if (window.DeviceMotionEvent) { console.log("DeviceMotionEvent supported"); }
Like DeviceOrientationEvent
, if a browser cannot provide motion information, the event will be fired once and all properties will be null.
Now that we know that the browser and device supports the DeviceMotionEvent, we can add an event listener.
if ((window.DeviceMotionEvent) { window.addEventListener('devicemotion', deviceMotionHandler, false); } else { document.getElementById("dmEvent").innerHTML = "Not supported on your device." }
For this example, we’re just going to display data that we receive.

Device Motion

 Event Supported

 accelerationIncludingGravity

 Calculated Left-Right Tilt

 Calculated Front-Back Tilt

We want to display the raw data that we get back, but also convert that into something that we can use to rotate our image, so we need to do a little math to convert from m/s^2 to an angle.
function deviceMotionHandler(eventData) { // Grab the acceleration including gravity from the results var acceleration = eventData.accelerationIncludingGravity; // Display the raw acceleration data var rawAcceleration = "[" + Math.round(acceleration.x) + ", " + Math.round(acceleration.y) + ", " + Math.round(acceleration.z) + "]"; // Z is the acceleration in the Z axis, and if the device is facing up or down var facingUp = -1; if (acceleration.z > 0) { facingUp = +1; } // Convert the value from acceleration to degrees acceleration.x|y is the // acceleration according to gravity, we'll assume we're on Earth and divide // by 9.81 (earth gravity) to get a percentage value, and then multiply that // by 90 to convert to degrees. var tiltLR = Math.round(((acceleration.x) / 9.81) * -90); var tiltFB = Math.round(((acceleration.y + 9.81) / 9.81) * 90 * facingUp); // Display the acceleration and calculated values document.getElementById("moAccel").innerHTML = rawAcceleration; document.getElementById("moCalcTiltLR").innerHTML = tiltLR; document.getElementById("moCalcTiltFB").innerHTML = tiltFB; // Apply the 2D rotation and 3D rotation to the image var rotation = "rotate(" + tiltLR + "deg) rotate3d(1,0,0, " + (tiltFB) + "deg)"; document.getElementById("imgLogo").style.webkitTransform = rotation; }
When you load the page and move the device around the bottom of the HTML5 logo should always point down, towards earth.
Try it out, and view the source to see it in action.
Here are a few other resources and demos you can check out that use the HTML5 device orientation or device motion events.


| Package | com.whirled.contrib.persist |
| Class | public class PersistenceManager |
| Inheritance | PersistenceManager flash.events.EventDispatcher |
| Property | Defined by | ||
|---|---|---|---|
| loaded : Boolean [read-only]
| PersistenceManager |
| Property | Defined by | ||
|---|---|---|---|
| _cookieFactory : CookieFactory | PersistenceManager | ||
| _cookieManagers : Map | PersistenceManager | ||
| _debugLogging : Boolean | PersistenceManager | ||
| _eventMgr : EventHandlerManager | PersistenceManager | ||
| _gameCtrl : GameControl | PersistenceManager | ||
| _loaded : Boolean = false | PersistenceManager | ||
| _trophyProperties : Map | PersistenceManager |
| Method | Defined by | ||
|---|---|---|---|
|
PersistenceManager(gameCtrl:GameControl, cookieFactory:CookieFactory, debugLogging:Boolean = false)
| PersistenceManager | ||
|
getProperty(name:String, playerId:int = 0):PersistentProperty
Server-side warning! Any CookieProperties are essentially being read once at the game
start, and are currently not updated afterwards.
| PersistenceManager | ||
|
whenLoaded(callback:Function):void
Call the given function when this manager is loaded.
| PersistenceManager |
| Method | Defined by | ||
|---|---|---|---|
|
allPlayersPresent():Boolean
| PersistenceManager | ||
|
createCookieManager(cookieProperties:Map, playerId:int):CookieManager
| PersistenceManager | ||
|
getPropertyKey(name:String, playerId:int):String
| PersistenceManager | ||
|
getPrototypes():Array
| PersistenceManager | ||
|
init():void
| PersistenceManager | ||
|
loadingComplete():void
| PersistenceManager | ||
|
occupantEntered(event:OccupantChangedEvent):void
| PersistenceManager |
| _cookieFactory | property |
protected var _cookieFactory:CookieFactory
| _cookieManagers | property |
protected var _cookieManagers:Map
| _debugLogging | property |
protected var _debugLogging:Boolean
| _eventMgr | property |
protected var _eventMgr:EventHandlerManager
| _gameCtrl | property |
protected var _gameCtrl:GameControl
| _loaded | property |
protected var _loaded:Boolean = false
| loaded | property |
loaded:Boolean
[read-only]Implementation
public function get loaded():Boolean
| _trophyProperties | property |
protected var _trophyProperties:Map
| PersistenceManager | () | constructor |
public function PersistenceManager(gameCtrl:GameControl, cookieFactory:CookieFactory, debugLogging:Boolean = false)
Parameters
gameCtrl:GameControl |
|
cookieFactory:CookieFactory |
|
debugLogging:Boolean (default = false )
|
| allPlayersPresent | () | method |
protected function allPlayersPresent():Boolean
Returns
Boolean |
| createCookieManager | () | method |
protected function createCookieManager(cookieProperties:Map, playerId:int):CookieManager
Parameters
cookieProperties:Map |
|
playerId:int |
CookieManager |
| getProperty | () | method |
public function getProperty(name:String, playerId:int = 0):PersistentProperty
Server-side warning! Any CookieProperties are essentially being read once at the game start, and are currently not updated afterwards. We'd need to add "refresh" functionality to CookieManagers to deal with this - and we still wouldn't know when we have dirty data.
Parametersname:String |
|
playerId:int (default = 0 )
|
PersistentProperty |
| getPropertyKey | () | method |
protected function getPropertyKey(name:String, playerId:int):String
Parameters
name:String |
|
playerId:int |
String |
| getPrototypes | () | method |
protected function getPrototypes():Array
Returns
Array |
| init | () | method |
protected function init():void
| loadingComplete | () | method |
protected function loadingComplete():void
| occupantEntered | () | method |
protected function occupantEntered(event:OccupantChangedEvent):void
Parameters
event:OccupantChangedEvent |
| whenLoaded | () | method |
public function whenLoaded(callback:Function):void
Call the given function when this manager is loaded. If this manager is already loaded, the given function will be called immediately.
Parameterscallback:Function |


{% extends 'base.html' %} {% block content %}
Our innovative solution supports telemonitoring of CHF patients with a wearable device that noninvasively takes passive measurements of objective CHF indicators (bioimpedance, cardiac output, daily weights)


Applying a Template
How to apply a form or list template to a Form and List module which has not yet been configured. This topic assumes one or more form or list templates have been created. See "Saving a Template"
- Add a Form and List module - OR - Go to a Form and List module that hasn't been configured.
- View the available list of forms/lists. Note: If no templates are listed, select Scan For New Module Templates from the module actions menu. This will refresh the templates list to show any newly created templates.
- Click the Apply link beside the required template.
Applying a Template


Annotations for presentation model classes and fields that may be used to define static presentation model properties that don't change in application execution time.


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threefry.h
threefry2x32
threefry2x32(): threefry.h
threefry2x32(threefry2x32_ctr_t in, threefry2x32_key_t k): threefry.h
r123::Threefry2x32()
threefry2x32_ctr_t
threefry.h
threefry2x32_key_t
threefry.h
Threefry2x32_R
r123
threefry2x32_R
threefry.h
threefry2x32_rounds
threefry.h
threefry2x32_ukey_t
threefry.h
threefry2x32keyinit
threefry.h
Threefry2x64
r123::Threefry2x64()
threefry2x64(): threefry.h
threefry2x64(threefry2x64_ctr_t in, threefry2x64_key_t k): threefry.h
threefry2x64_ctr_t
threefry.h
threefry2x64_key_t
threefry.h
threefry2x64_R
threefry.h
Threefry2x64_R
r123
threefry2x64_rounds
threefry.h
threefry2x64_ukey_t
threefry.h
threefry2x64keyinit
threefry.h
threefry4x32
threefry4x32(): threefry.h
threefry4x32(threefry4x32_ctr_t in, threefry4x32_key_t k): threefry.h
r123::Threefry4x32()
threefry4x32_ctr_t
threefry.h
threefry4x32_key_t
threefry.h
threefry4x32_R
threefry.h
Threefry4x32_R
r123
threefry4x32_rounds
threefry.h
threefry4x32_ukey_t
threefry.h
threefry4x32keyinit
threefry.h
Threefry4x64
r123::Threefry4x64()
threefry4x64(): threefry.h
threefry4x64(threefry4x64_ctr_t in, threefry4x64_key_t k): threefry.h
threefry4x64_ctr_t
threefry.h
threefry4x64_key_t
threefry.h
Threefry4x64_R
r123
threefry4x64_R
threefry.h
threefry4x64_rounds
threefry.h
threefry4x64_ukey_t
threefry.h
threefry4x64keyinit
threefry.h
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All Classes
Animation
Boss
Boss2
Boss_Template
Boss_Template.Direction
BossSegment
Canvas
Enemy
Enemy_Shooter
Enemy_Template
Enemy_Template.Direction
EnemySegment
Food
Framework
Framework.GameState
Game
Genocider
Gun
Invincibility
Item
Laser
LaserBolt
MovingBackground
Projectile
Projectile.Direction
Shrinker
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Snake.Direction
SnakeSegment
SuperFood
SuperGun
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Window


{% extends "admin/change_form.html" %} {% block object-tools-items %} {% include "django_object_actions/includes/change.html" %} {{ block.super }} {% endblock %}


| HYTOOL Function Reference |
Barker (1988) general radial flow model
s = grf_dim(p,d) p(1) = a in [m] = equivalent of the slope of Jacob model p(2) = t0 in [s] = equivalent of the time of Jacob model p(3) = n [-] = the fractional flow dimension
This function computes drawdown for a constant rate intereference test with the General Radial Flow model of Barker (1988). This solution is a generalisation of flow equation in 1D, 2D, 3D and non integer flow dimension. Reference: Barker, J.A. 1988. A Generalized radial flow model fro hydraulic tests in fractured rock. Water Resources Research 24, no. 10: 1796-1804. WARNING: The function grf_pre must be used to initialize the pumping rate history before making any call to grf_dim or grf_rpt.
s=grf_dls(p,d);
grf_dmo, grf_pre, grf_rpt, grf_gss Published output in the Help browser showdemo grf_dim
| Hytool |
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