${value_of('project_name', 'TurboGears')} Admin
This is a fully-configurable administrative tool to help you administer your website. Below is links to all of your models.
They will bring you to a listing of the objects in your database.
${model}s


Full-Stuck developer with background in Network Engineering & entrepreneurship Passionate about using code to maximize efficiency while developing creative, customer-focused solutions.
TECHNICAL SKILLS Languages & Frameworks:
HTML, CSS, JavaScript, Asp.netTools:
Node.js, Express.js, APIs, EJS, Bootstrap, DOM, React.js, SQL, MongoDB, Mongoose
- PROJECTS
Opinion web Site, July 2015 - Opinion web using from MVC model in asp.net. - EXPERIENCE
UTIT, Jun 2015 - Jan 2020 | Network Engineer
- Manage FTTH project
- Manage Customer Transactions.
- Manage Customers Complements
- Manage OSP Networks.
- Summarized Project Progress Reports.
- Responsible for costing, estimating and planning projects.
- EDUCATION
- Code Fellows, Jun 2021
Certificate Advanced Software Development in Full-Stack JavaScript. - LTUC-ASAC, Jun 2021
Certificate - Advanced Software Development in Full-Stack JavaScript. - Yarmouk University JUN 2015
Certificate – BA (Computer Engineer).
- Code Fellows, Jun 2021
- MY Home :)
- Irbid
- OM Qais
- Petra
- Dead Sea
- Aqaba beach
- Jarash
- Wadi Rum Protected Area
- Roman Theater Amman
- Ajloun forest


Show Wheel
Smooth Color
Random Color
Ball Size
Ball Trail
Gravity
on Wikipedia
Roll, pitch or yaw your phone.
Change the settings in the menu.
Gravity
on Wikipedia
?
I


Home
Order
Contacts
First Class Pizza Corner
Need value for your money?
Your search for the best pizza has come to an end!


Address:
Password:
|
Execute
Ctrl+c
|
MicroPython WebPad
See
https://github.com/nickzoic/mpy-webpad/
Use serial terminal to set up device on network, record IP address, import webrepl_setup and set password
Enter device IP address and WebREPL password at top
Click 'Connect'
MicroPython version info should be displayed.
Type some Python in the box (sorry no syntax highlighting or autotabs yet!)
Click 'Execute' to run MicroPython code
Expressions are evaluated but not printed -- print(expr) to display a value
It's asynchronous, so loops and sleeps are fine.


NFC East Small Multiples
Dallas Cowboys
Dallas Cowboys
Dallas Cowboys
Dallas Cowboys
Toggle
Using Small Multiples to survey the landscape of the NFC east over the last 10 years.


Guidance
Materials for administering the Modified Large Print (MLP) and Unified English Braille (UEB) versions of the 2016 key stage 1 English grammar, punctuation and spelling test
Ref: ISBN 978-1-78644-072-3 , STA/16/7488/BRp PDF , 56.1KB , 4 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
Ref: ISBN 978-1-78644-235-2 , STA/16/7409/e PDF , 232KB , 4 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
Ref: ISBN 978-1-78644-067-9 , STA/16/7483/MLp PDF , 94.7KB , 8 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
Ref: ISBN 978-1-78644-068-6 , STA/16/7484/MLp PDF , 192KB , 16 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
PDF , 163KB , 2 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
Key stage 1 English grammar, punctuation and spelling tests were administered in schools in May 2016. Modified test materials and Mark Scheme Amendments were also provided. There is no braille version of Paper 1: spelling.
Please refer to the copyright ownership report for details of how schools, educational establishments and third parties can use these materials.
Published: 17 January 2017


?The
New
York
Botanical
Garden
Ethnobotany
of
the
Dominican
Republic
BOTANICAL
^cARDe^
NEW_VpfiK
BOTANICAL
GARDPN
01529922
No.
523
Poaceae
Bambusa
vulgaris
Schrad.
ex
J.
C.
Wendl.
det.
Peguero,
B.,
06
Jun
2001
Dominican
Republic.
San
Cristóbal:
Cambita
Garabitas,
4km
up
main
road
to
La
Colonia,
Don
Florencio’s
house.
18°28’37.7"N,
70°11’56"W.
ca.
472
m.
Home
garden.
Tree
erect
to
4m,
growing
in
clumps.
Photo.
Medicinal
plant.
N.V.:
bambu
[Spanish]
A.
Ososki
&
J.
Saborio
28
Jul
2001
Supported
by
the
Fulbright
Foundation
01529922
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Indivisible Brooklyn is part of the nationwide Indivisible grassroots movement and is open to all people dedicated to inclusion, tolerance and fairness. We are organizing to take political action in three areas: federal, state, and city governments. Our aim is to promote progressive policy through civic engagement, build strength in our community, and block the the regressive policies of the Trump administration.
Sign up to get updates from Indivisible Brooklyn and get involved in local activism.
{% include mailchimp.html %}


WELCOME
My Info
Info Page
My Hobby
Hobby Page
School Details
School Page
Contact with me
Contact Page


This page is still in the works.
Place your mouse over the blue bar on the side to se the navigation menu. An example of where to use this is to have the default stylesheet show the sidebar and then with the stylesheet changer create an auto-hide sidebar.
The only browser known to work with this page is Mozilla 1.3b or later. Something is buggy in Mozilla 1.4a. When the sidebar is fixed positioned it fails; but when it is absolute positioned it works fine. This can be checked out by switching to the alternate stylesheet title "Absolute."


---
layout: post
title: It is the voicings of God
date: '2009-12-31T17:09:00.002-06:00'
author: Jabell
tags:
- Quotes
- Story
modified_time: '2009-12-31T17:12:45.802-06:00'
blogger_id: tag:blogger.com,1999:blog-19739956.post-8055197858555098785
blogger_orig_url: http://theabellsix.blogspot.com/2009/12/it-is-voicings-of-god.html
---
I see craft in the world. I cannot watch dust swirl on the sidewalk without seeing God drag His finger, or listen to spring rain running in the streets without hearing Him roll his Rs. For those who believe in an ex nihilo creation, the world is inevitably art, and it is inevitably art from top to bottom, in every time and in every place. The world cannot exist apart from the voice of God. It is the voicings of God.
—N. D. Wilson, Notes from the Tilt-A-Whirl, p. 98


These are some of the many commands that we have enlisted! For more try b!help
An implemented Music Command that plays music derived from youtube with Spotify Support!
A image command that searches images up and sends the found image
A currency command that gives you a reason to show off!
With Discord Together you can watch youtube videos with your friends or even play chess!
Based of a local server it makes it easy for any physical changes to the server
Need another prefix? Well that's easy! With a literal command you can change the prefix of the bot!


Abierto ahora Abierto de 8.00am a 6:00pm
You do know, you do know that they don’t want you to have lunch. I’m keeping it real with you, so what you going do is have lunch. I’m giving you cloth talk, cloth. Special cloth alert, cut from a special cloth. You should never complain, complaining is a weak emotion, you got life, we breathing, we blessed. The key to more success is to get a massage once a week, very important, major key, cloth talk. Egg whites, turkey sausage, wheat toast, water.  - Coco - Chef


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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
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java.lang.Object javassist.expr.ExprEditor
public class ExprEditor
A translator of method bodies.
The users can define a subclass of this class to customize how to modify a method body. The overall architecture is similar to the strategy pattern.
If instrument()
is called in
CtMethod
, the method body is scanned from the beginning
to the end.
Whenever an expression, such as a method call and a new
expression (object creation),
is found, edit()
is called in ExprEdit
.
edit()
can inspect and modify the given expression.
The modification is reflected on the original method body. If
edit()
does nothing, the original method body is not
changed.
The following code is an example:
CtMethod cm = ...; cm.instrument(new ExprEditor() { public void edit(MethodCall m) throws CannotCompileException { if (m.getClassName().equals("Point")) { System.out.println(m.getMethodName() + " line: " + m.getLineNumber()); } });
This code inspects all method calls appearing in the method represented
by cm
and it prints the names and the line numbers of the
methods declared in class Point
. This code does not modify
the body of the method represented by cm
. If the method
body must be modified, call replace()
in MethodCall
.
CtClass.instrument(ExprEditor)
,
CtBehavior.instrument(ExprEditor)
,
CtBehavior.instrument(ExprEditor)
,
MethodCall
,
NewExpr
,
FieldAccess
,
CodeConverter
| Constructor Summary | |
|---|---|
ExprEditor()
Default constructor. |
| Method Summary | |
|---|---|
boolean |
doit(CtClass clazz,
MethodInfo minfo)
Undocumented method. |
void |
edit(Cast c)
Edits an expression for explicit type casting (overridable). |
void |
edit(ConstructorCall c)
Edits a constructor call (overridable). |
void |
edit(FieldAccess f)
Edits a field-access expression (overridable). |
void |
edit(Handler h)
Edits a catch clause (overridable). |
void |
edit(Instanceof i)
Edits an instanceof expression (overridable). |
void |
edit(MethodCall m)
Edits a method call (overridable). |
void |
edit(NewArray a)
Edits an expression for array creation (overridable). |
void |
edit(NewExpr e)
Edits a new expression (overridable). |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public ExprEditor()
| Method Detail |
|---|
public boolean doit(CtClass clazz, MethodInfo minfo) throws CannotCompileException
CannotCompileException
public void edit(NewExpr e) throws CannotCompileException
e
- the new expression creating an object.
CannotCompileException
public void edit(NewArray a) throws CannotCompileException
a
- the new expression for creating an array.
CannotCompileException
public void edit(MethodCall m) throws CannotCompileException
CannotCompileException
public void edit(ConstructorCall c) throws CannotCompileException
super()
or this()
included in a constructor body.
The default implementation performs nothing.
CannotCompileException
edit(NewExpr)
public void edit(FieldAccess f) throws CannotCompileException
CannotCompileException
public void edit(Instanceof i) throws CannotCompileException
CannotCompileException
public void edit(Cast c) throws CannotCompileException
CannotCompileException
public void edit(Handler h) throws CannotCompileException
CannotCompileException
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
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- General
- This page manages which addons (also known as plugins) are installed.
- Manage Repos
- If your webserver has the necessary write permissions, you will see a button labeled Manage Repos,
which opens a control panel for managing what plugin repositories are installed. These repos are
stored in extend/addon/[repo name]/. The official Hubzilla
addon repo can be added by entering the repo URL
https://framagit.org/hubzilla/addons.git
and choosing a name for the repo such as official. You should see this repo in the list similar
to the following:


no breakdown of business functionality into re-usable modules
business service code highly duplicated
no unit testing of the services
## Technology Stack at BrokerCompare
* React front end
* Clojure REST service - based on Catacumba
* MongoDB - business rule definitions
* MySQL - storage of the transactions
note:
Here is what we built after the introduction of Clojure.
For practical reasons we kept React, a REST service backend was rewritten in Clojure,
a MongoDB for business rule definitions and MySQL database for storage of the transactions.
(time 0 : total time 0 mins)
Technology Stack at BrokerCompare
Business domain DSL with Spec
org.clojure/test.check Free testing with spec generators and properties
midje - tabular facts
funcool/catacumba
com.novemberain/monger - (Mongo DB)
MySQL
funcool/catacumba-prone
hikari-cp - for fast JDBC connection pool
Business domain DSL definition with Spec
```clojure
(def Fidelity-fees
{ :name "Fidelity",
:account {:holding {:funds {:rates [[0 7499.99 0.0035 "cancel-if-no-regular-dealing"] [7500 249999.99 0.0035] [250000 Infinity 0.002]],
:max 2000.0,
:type "percentage-all"}
:shares {:rates [[0 7499.99 0.0] [7500 Infinity 0.0035]]
:type "percentage-all"
:max 45}},
:periodic {:shares {:rates [[0 7500 45.0] [7500 Infinity 0.0]], :type "fixed"},
:funds {:rates [[0 7500 45.0] [7500 Infinity 0.0]], :type "fixed" :cancel-if-regular-dealing true}}},
:dealing {:shares {:type "percentage",
:percent-commission true,
:rates 0.001}},
:regular-dealing {:shares {:type "percentage",
:percent-commission true,
:rates 0.001}
}})
```
note:
I'm going to show you a few code excerpts to give you a feel for how some of the feature were useful to us
.
Here, you can see an example of DSL that was created using Spec to define the semantic of
a stock and share broker platform.
One of them is Fidelity. just draw your attention to how the fee structure of the :funds and :shares are
expressed using a rich semantic.
```clojure
(def Fidelity-fees
{:name "Fidelity",
:account {:holding {:funds {:rates [[0 7499.99 0.0035 "cancel-if-no-regular-dealing"]
[7500 249999.99 0.0035] [250000 Infinity 0.002]],
:max 2000.0,
:type "percentage-all"}
:shares {:rates [[0 7499.99 0.0] [7500 Infinity 0.0035]]
:type "percentage-all"
:max 45}},
...})
```
```clojure
(s/def ::segment (s/and (s/cat :low ::positive-number
:high ::positive-number
:value ::rate
:option (s/? ::segment-option))
#(<= (-> % :low) (-> % :high))))
(s/def ::chart (s/coll-of ::segment :min-count 1))
(s/def ::rates (s/or :n (s/and ::rate #(> % 0)) :chart ::chart))
...
(s/def ::basic-fees (s/and (s/keys :req-un [::tiered/rates ::tiered/type]
:opt-un [::max ::min ::period-in-months
::min-trades-to-cancel ::cancel-if-funds
::funds-ratio ::percent-commission ::loyalty-discount
::free-trades
::cancel-if-regular-dealing])
(s/or :no-min #(-> % :min nil?)
:no-max #(-> % :max nil?)
:min-max #(<= (-> % :min) (-> % :max)))
seq))
(s/def ::shares ::basic-fees)
(s/def ::funds ::basic-fees)
(s/def ::shares-and-funds-fees (s/and (s/keys :opt-un [::shares-and-funds ::funds-and-shares ::shares ::funds])
shares-and-funds-valid-combination?))
...
(s/def ::type #{"percentage" "fixed" "percentage-all"})
(s/def ::holding ::common/shares-and-funds-fees)
(s/def ::opening-and-periodic-exclusive #(< (count (select-keys % [:opening :periodic])) 2))
(s/def ::single-account (s/and (s/keys :opt-un [::holding ::opening ::periodic])
::opening-and-periodic-exclusive))
(s/def ::account ::single-account)
```
note:
The grammar of this semantic is defined with a tree of Specs. You can see on this example,
how an :account is composed of :holding -> :shares -> :rates :max :type. See how the :rate is defined
with a :segment. the segment is composed of for values :low :high :value and an :option.
Spec provides validation out of the box
```clojure
(s/valid? ::fees broker-fees)
```
note:
Not only do you get the grammar so you can validate your data throughout your program [next page].
It also provides a business interface for the business owner to validate their changes in fee structure.
here s/valid? is the spec function that validate the broker fee.
But it also provides very high level semantic for testing properties [next page].
Spec and test.check expresse business invariants
```clojure
(defspec test-form-data->portfolio-investments-split-properly-trading
(for-all [portfolio-form-data (s/gen ::portfolio/form-data)]
(let [portfolio (form-data->portfolio portfolio-form-data)]
(investments-split-properly-trading? portfolio-form-data portfolio))))
```
note:
Here we can the specification of invariant propertes which exist between the
submitted form-data and the portfolio object.
Spec also provides explanations [next page].
Spec provides explanation out of the box
```clojure
(facts "form data -> portfolio is valid"
(tabular (fact "basic"
(s/explain-str ::portfolio/portfolio (second (form-data->portfolio ?form-data))) => success)
?form-data
myform
...))
```
note:
Here the s/explain-str function explains to the developer what was wrong in the form-data in case the test fails
Midje - tabular facts
```clojure
(facts "First-Year Cost - Scalar"
(tabular (fact "ISA only with no account charge - Hargreaves Lansdown"
(first-year-cost-scalar
{:duration ?duration
:deals {:funds {:trades ?funds-trades} :shares {:trades ?shares-trades}}
:isa {:investing-shares {:amount ?shares-amount}
:investing-funds {:amount ?funds-amount}}}
Hargreaves-Lansdown-fees) => (roughly ?cost))
?funds-amount ?funds-trades ?shares-amount ?shares-trades ?duration ?cost
50000 2 0 0 10 225
300000 6 0 0 10 1250
0 0 8000 4 10 83.8
50000 4 25000 6 10 342))
```
note:
Midje provides a nice tabular source of facts that can be tested.
Here we are testing the function first-year-cost-scala with parameterized by this table.
Benefits to BrokerCompare


A directed Graph with weights.
More...
#include <Graph.hpp>
|
| std::vector< std::vector< int > > | edges |
| | The edges of the graph. edge[i] is a vector s.t. we have an edge i->edge[i][j] for every j. More...
|
| |
| std::vector< std::vector< int > > | path |
| | The shortest path from a source to any other point. path[i] consists of the procession of nodes from i to source. More...
|
| |
| std::vector< int > | distance |
| | The distance from a selected source to all points. - 1 if disconnected. More...
|
| |
| std::vector< int > | disconnected |
| | The disconnected points from each source. More...
|
| |
| int | source |
| | A source of the graph. More...
|
| |
A directed Graph with weights.
◆ WeightedGraph() [1/4]
| Mackey::WeightedGraph::WeightedGraph |
( |
| ) |
|
|
default |
◆ WeightedGraph() [2/4]
template<typename T >
| Mackey::WeightedGraph::WeightedGraph |
( |
const Graph< T > & |
G, |
|
|
const std::vector< std::vector< int >> & |
weights |
|
) |
| |
Constructor from given graph and weights.
◆ WeightedGraph() [3/4]
| Mackey::WeightedGraph::WeightedGraph |
( |
const std::vector< std::vector< int >> & |
edges, |
|
|
const std::vector< std::vector< int >> & |
weights |
|
) |
| |
Constructor from given edges and weights.
◆ WeightedGraph() [4/4]
template<typename T >
| Mackey::WeightedGraph::WeightedGraph |
( |
Graph< T > & |
G | ) |
|
Constructor from given graph and zero weights.
◆ Graph< WeightedGraph >
◆ SFINAE
The documentation for this class was generated from the following file:


Technical blog from Craig Russell.
This article outlines a solution for allowing Presenters to survive a View’s configuration change in an MVP architecture
onRetainCustomNonConfigurationInstance()
getLastCustomNonConfigurationInstance()
Use these methods to store, and later retrieve, any object you want to survive an Activity’s configuration change; perfect for a Presenter in an app using an MVP architecture.
The source code for a sample project showing this in action is available on GitHub: https://github.com/CDRussell/SurvivingPresenters
You’ll find a full explanation of the source below.
The Model View Presenter (MVP) design pattern dictates that you separate your presentation (the View) from the business logic and the data sources (the Presenter and the Model). Translated into Android terminology, this means having no logic in your Activity and making these as lightweight as possible. The Activity remains passive and is instructed on what to do and when to do it by the Presenter; also known as a Passive View design.
Android views, however, are complicated and have lifecycles that need carefully managed. We know that in Android if your Views are Activities then these will be destroyed and rebuilt on configuration changes (like when the user rotates the device).
If you have a long running operation which is a concern of your Presenter and Model, like posting data to a server or saving data into a DB, how do you keep that executing whilst your View is being destroyed and rebuilt?
If you’ve been reading up on MVP, you might have noticed that there are many opinions on how it should be implemented. There is no absolute right or wrong here, and whilst that provides enough flexibility to find something right for your project, it can be daunting not to have a best practice implementation to use as a reference.
I therefore present to you, if I may be so bold, a best practice for allowing the Presenter to outlive the View. We don’t just want the Presenter to live forever though. We want the Presenter to survive configuration changes whilst the view is being destroyed and rebuilt. But we also want the Presenter itself to be destroyed when the user has no more need of that View.
In other words, if the user rotates their device, we want the Presenter to stick around. But if the user presses the back button and the Activity is destroyed for good, we want the Presenter to be destroyed here too.
Libraries and frameworks have been created to solve this solution. If you are interested in using one, you can check out Nucleus or Mosby. However, my reservation with libraries/frameworks like these is that you must place a lot of faith in them to use them. Not only do you have to trust they play nice with a typical Android project setup today, you must trust that those developers will keep the project compatible tomorrow, and the next day. In other words, it is 3rd party magic that is crucial to the project. You might be ok with that.
When the use of a library will be pervasive throughout my project (every Activity will depend on it) I don’t want there to be magic involved. I want to know how to solve the problem myself in case the magician were ever to… disappear. At some point along the journey to understanding 3rd party libraries, you have enough knowledge to solve the problem yourself and keep it simple in doing so.
The solution lies in the combination of two simple methods:
public Object onRetainCustomNonConfigurationInstance()
public Object getLastCustomNonConfigurationInstance ()
onRetainCustomNonConfigurationInstance(), defined in FragmentActivity, is provided as part of Android Support Library. New apps created in recent versions of Android Studio default to using AppCompatActivity as a subclass for all your defined Activities. AppCompatActivity in turn inherits from FragmentActivity.
In other words, chances are high that you already have access to this method.
The purpose of onRetainCustomNonConfigurationInstance() is to allow your Activity the ability to return a single object which can be re-used across configuration changes. This object will not serialised and deserialised, nor will it be destroyed and recreated; this object will survive the configuration change altogether. The exact same instance of the object that was used by the old View can be re-attached to the new View.
This is the perfect mechanism for storing our Presenter in MVP; when a View (Activity) is being destroyed due to a configuration change, we can return the Presenter instance from the onRetainCustomNonConfigurationInstance() method and when the new View has been recreated, we can re-attach the Presenter.
To retrieve the object stored by onRetainCustomNonConfigurationInstance() we use its opposite
getLastCustomNonConfigurationInstance();
getLastCustomNonConfigurationInstance() returns the value stored using onRetainCustomNonConfigurationInstance(), or null if nothing was stored.
As such, to meaningfully use the value returned from this method, we need to check to ensure it isn’t null and cast it to the right type of object; you’ll know the type you need to use as it has to match the object saved from onRetainCustomNonConfigurationInstance().
The sample project is available on GitHub: https://github.com/CDRussell/SurvivingPresenters
In this sample, we have a simple Activity which contains two buttons, one to increment a counter and one to decrement the counter. We have a TextView which is used to display the current count.
We use the MVP pattern to separate concerns. The Presenter is in charge of holding the current count and changing its value and is able to instruct the view to show the user a new value. The View is in charge of responding to a user clicking on either the increment or decrement button and informing the Presenter about it.
We use Paul Blundell’s excellent suggested structure of the required MVP classes (https://www.novoda.com/blog/better-class-naming/) to define an interface as follows:
interface IntroMvp {
interface View {
void updateCount(int count);
}
interface Presenter {
void attachView(View view);
void detachView();
void incrementValue();
void decrementValue();
}
}
Note, the Model is omitted here for brevity.
Our Activity must implement our IntroMvp.View interface and extend from AppCompatActivity
public class IntroActivity extends AppCompatActivity implements IntroMvp.View
The Activity holds a reference to an instance of the IntroMvp.Presenter which it attaches to in the onCreate() method.
private IntroMvp.Presenter presenter;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout._activity\_intro_);
attachPresenter();
}
private void attachPresenter() {
presenter = (IntroMvp.Presenter) getLastCustomNonConfigurationInstance();
if (presenter == null) {
presenter = new IntroPresenter();
}
presenter.attachView(this);
}
We retrieve an instance from the getLastCustomNonConfigurationInstance() method if it exists. If it doesn’t exist, we need to create an instance of the Presenter.
Finally we call presenter.attachView passing this which allows the Presenter to gain access to the newly created View.
@Override
protected void onDestroy() {
presenter.detachView();
super.onDestroy();
}
@Override
public Object onRetainCustomNonConfigurationInstance() {
return presenter;
}
The system will automatically invoke onRetainCustomNonConfigurationInstance()
at the correct time and ask you which object you want to survive the current configuration change. You simply need to return the reference to the Presenter.
When the View is being destroyed, we inform the Presenter so that it doesn’t try to update an invalid View.
The rest of the Activity is simply handling the user pressing buttons and updating the view when told to by the Presenter.
public void incrementButtonPressed(View view) {
presenter.incrementValue();
}
public void decrementButtonPressed(View view) {
presenter.decrementValue();
}
@Override
public void updateCount(final int count) {
runOnUiThread(new Runnable() {
@Override
public void run() {
output.setText(getString(R.string._updated\_count_, count));
}
});
}
Running the sample app, you can see that the Presenter continues to exist and hold the current value of the counter, even after the device is rotated. Additionally, if you hit the back button to properly destroy the Activity and relaunch it, the Presenter’s count will be reset to 0 as we’re dealing with a brand new instance of it in this scenario.
There are a large number of words in this article, but they all lead to one simple point. Use
onRetainCustomNonConfigurationInstance()
getLastCustomNonConfigurationInstance()
to store, and later retrieve, any object you want to survive an Activity’s configuration change; perfect for a Presenter in an app using an MVP architecture.
Home


(beta) Quantized Transfer Learning for Computer Vision Tutorial¶
Tip
To get the most of this tutorial, we suggest using this Colab Version. This will allow you to experiment with the information presented below.
Author: Zafar Takhirov
Reviewed by: Raghuraman Krishnamoorthi
Edited by: Jessica Lin
This tutorial builds on the original PyTorch Transfer Learning tutorial, written by Sasank Chilamkurthy.
Transfer learning refers to techniques that make use of a pretrained model for application on a different data-set. There are two main ways the transfer learning is used:
ConvNet as a fixed feature extractor: Here, you “freeze” the weights of all the parameters in the network except that of the final several layers (aka “the head”, usually fully connected layers). These last layers are replaced with new ones initialized with random weights and only these layers are trained.
Finetuning the ConvNet: Instead of random initializaion, the model is initialized using a pretrained network, after which the training proceeds as usual but with a different dataset. Usually the head (or part of it) is also replaced in the network in case there is a different number of outputs. It is common in this method to set the learning rate to a smaller number. This is done because the network is already trained, and only minor changes are required to “finetune” it to a new dataset.
You can also combine the above two methods: First you can freeze the feature extractor, and train the head. After that, you can unfreeze the feature extractor (or part of it), set the learning rate to something smaller, and continue training.
In this part you will use the first method – extracting the features using a quantized model.
Part 0. Prerequisites¶
Before diving into the transfer learning, let us review the “prerequisites”, such as installations and data loading/visualizations.
# Imports
import copy
import matplotlib.pyplot as plt
import numpy as np
import os
import time
plt.ion()
Installing the Nightly Build¶
Because you will be using the beta parts of the PyTorch, it is
recommended to install the latest version of torch
and
torchvision
. You can find the most recent instructions on local
installation here.
For example, to install without GPU support:
pip install numpy
pip install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
# For CUDA support use https://download.pytorch.org/whl/nightly/cu101/torch_nightly.html
Load Data¶
Note
This section is identical to the original transfer learning tutorial.
We will use torchvision
and torch.utils.data
packages to load
the data.
The problem you are going to solve today is classifying ants and bees from images. The dataset contains about 120 training images each for ants and bees. There are 75 validation images for each class. This is considered a very small dataset to generalize on. However, since we are using transfer learning, we should be able to generalize reasonably well.
This dataset is a very small subset of imagenet.
Note
Download the data from here
and extract it to the data
directory.
import torch
from torchvision import transforms, datasets
# Data augmentation and normalization for training
# Just normalization for validation
data_transforms = {
'train': transforms.Compose([
transforms.Resize(224),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
data_dir = 'data/hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=16,
shuffle=True, num_workers=8)
for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
Visualize a few images¶
Let’s visualize a few training images so as to understand the data augmentations.
import torchvision
def imshow(inp, title=None, ax=None, figsize=(5, 5)):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
if ax is None:
fig, ax = plt.subplots(1, figsize=figsize)
ax.imshow(inp)
ax.set_xticks([])
ax.set_yticks([])
if title is not None:
ax.set_title(title)
# Get a batch of training data
inputs, classes = next(iter(dataloaders['train']))
# Make a grid from batch
out = torchvision.utils.make_grid(inputs, nrow=4)
fig, ax = plt.subplots(1, figsize=(10, 10))
imshow(out, title=[class_names[x] for x in classes], ax=ax)
Support Function for Model Training¶
Below is a generic function for model training. This function also
Schedules the learning rate
Saves the best model
def train_model(model, criterion, optimizer, scheduler, num_epochs=25, device='cpu'):
"""
Support function for model training.
Args:
model: Model to be trained
criterion: Optimization criterion (loss)
optimizer: Optimizer to use for training
scheduler: Instance of ``torch.optim.lr_scheduler``
num_epochs: Number of epochs
device: Device to run the training on. Must be 'cpu' or 'cuda'
"""
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
if phase == 'train':
scheduler.step()
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(best_model_wts)
return model
Support Function for Visualizing the Model Predictions¶
Generic function to display predictions for a few images
def visualize_model(model, rows=3, cols=3):
was_training = model.training
model.eval()
current_row = current_col = 0
fig, ax = plt.subplots(rows, cols, figsize=(cols*2, rows*2))
with torch.no_grad():
for idx, (imgs, lbls) in enumerate(dataloaders['val']):
imgs = imgs.cpu()
lbls = lbls.cpu()
outputs = model(imgs)
_, preds = torch.max(outputs, 1)
for jdx in range(imgs.size()[0]):
imshow(imgs.data[jdx], ax=ax[current_row, current_col])
ax[current_row, current_col].axis('off')
ax[current_row, current_col].set_title('predicted: {}'.format(class_names[preds[jdx]]))
current_col += 1
if current_col >= cols:
current_row += 1
current_col = 0
if current_row >= rows:
model.train(mode=was_training)
return
model.train(mode=was_training)
Part 1. Training a Custom Classifier based on a Quantized Feature Extractor¶
In this section you will use a “frozen” quantized feature extractor, and train a custom classifier head on top of it. Unlike floating point models, you don’t need to set requires_grad=False for the quantized model, as it has no trainable parameters. Please, refer to the documentation for more details.
Load a pretrained model: for this exercise you will be using ResNet-18.
import torchvision.models.quantization as models
# You will need the number of filters in the `fc` for future use.
# Here the size of each output sample is set to 2.
# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
model_fe = models.resnet18(pretrained=True, progress=True, quantize=True)
num_ftrs = model_fe.fc.in_features
At this point you need to modify the pretrained model. The model
has the quantize/dequantize blocks in the beginning and the end. However,
because you will only use the feature extractor, the dequantizatioin layer has
to move right before the linear layer (the head). The easiest way to do that
is to wrap the model in the nn.Sequential
module.
The first step is to isolate the feature extractor in the ResNet
model. Although in this example you are tasked to use all layers except
fc
as the feature extractor, in reality, you can take as many parts
as you need. This would be useful in case you would like to replace some
of the convolutional layers as well.
Note
When separating the feature extractor from the rest of a quantized model, you have to manually place the quantizer/dequantized in the beginning and the end of the parts you want to keep quantized.
The function below creates a model with a custom head.
from torch import nn
def create_combined_model(model_fe):
# Step 1. Isolate the feature extractor.
model_fe_features = nn.Sequential(
model_fe.quant, # Quantize the input
model_fe.conv1,
model_fe.bn1,
model_fe.relu,
model_fe.maxpool,
model_fe.layer1,
model_fe.layer2,
model_fe.layer3,
model_fe.layer4,
model_fe.avgpool,
model_fe.dequant, # Dequantize the output
)
# Step 2. Create a new "head"
new_head = nn.Sequential(
nn.Dropout(p=0.5),
nn.Linear(num_ftrs, 2),
)
# Step 3. Combine, and don't forget the quant stubs.
new_model = nn.Sequential(
model_fe_features,
nn.Flatten(1),
new_head,
)
return new_model
Warning
Currently the quantized models can only be run on CPU. However, it is possible to send the non-quantized parts of the model to a GPU.
import torch.optim as optim
new_model = create_combined_model(model_fe)
new_model = new_model.to('cpu')
criterion = nn.CrossEntropyLoss()
# Note that we are only training the head.
optimizer_ft = optim.SGD(new_model.parameters(), lr=0.01, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
Train and evaluate¶
This step takes around 15-25 min on CPU. Because the quantized model can only run on the CPU, you cannot run the training on GPU.
new_model = train_model(new_model, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25, device='cpu')
visualize_model(new_model)
plt.tight_layout()
Part 2. Finetuning the Quantizable Model¶
In this part, we fine tune the feature extractor used for transfer learning, and quantize the feature extractor. Note that in both part 1 and 2, the feature extractor is quantized. The difference is that in part 1, we use a pretrained quantized model. In this part, we create a quantized feature extractor after fine tuning on the data-set of interest, so this is a way to get better accuracy with transfer learning while having the benefits of quantization. Note that in our specific example, the training set is really small (120 images) so the benefits of fine tuning the entire model is not apparent. However, the procedure shown here will improve accuracy for transfer learning with larger datasets.
The pretrained feature extractor must be quantizable. To make sure it is quantizable, perform the following steps:
Fuse
(Conv, BN, ReLU)
,(Conv, BN)
, and(Conv, ReLU)
usingtorch.quantization.fuse_modules
.Connect the feature extractor with a custom head. This requires dequantizing the output of the feature extractor.
Insert fake-quantization modules at appropriate locations in the feature extractor to mimic quantization during training.
For step (1), we use models from torchvision/models/quantization
, which
have a member method fuse_model
. This function fuses all the conv
,
bn
, and relu
modules. For custom models, this would require calling
the torch.quantization.fuse_modules
API with the list of modules to fuse
manually.
Step (2) is performed by the create_combined_model
function
used in the previous section.
Step (3) is achieved by using torch.quantization.prepare_qat
, which
inserts fake-quantization modules.
As step (4), you can start “finetuning” the model, and after that convert it to a fully quantized version (Step 5).
To convert the fine tuned model into a quantized model you can call the
torch.quantization.convert
function (in our case only
the feature extractor is quantized).
Note
Because of the random initialization your results might differ from the results shown in this tutorial.
# notice quantize=False model = models.resnet18(pretrained=True, progress=True, quantize=False) num_ftrs = model.fc.in_features
# Step 1 model.train() model.fuse_model() # Step 2 model_ft = create_combined_model(model) model_ft[0].qconfig = torch.quantization.default_qat_qconfig # Use default QAT configuration # Step 3 model_ft = torch.quantization.prepare_qat(model_ft, inplace=True)
Finetuning the model¶
In the current tutorial the whole model is fine tuned. In general, this will lead to higher accuracy. However, due to the small training set used here, we end up overfitting to the training set.
Step 4. Fine tune the model
for param in model_ft.parameters():
param.requires_grad = True
model_ft.to(device) # We can fine-tune on GPU if available
criterion = nn.CrossEntropyLoss()
# Note that we are training everything, so the learning rate is lower
# Notice the smaller learning rate
optimizer_ft = optim.SGD(model_ft.parameters(), lr=1e-3, momentum=0.9, weight_decay=0.1)
# Decay LR by a factor of 0.3 every several epochs
exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=5, gamma=0.3)
model_ft_tuned = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25, device=device)
Step 5. Convert to quantized model
from torch.quantization import convert
model_ft_tuned.cpu()
model_quantized_and_trained = convert(model_ft_tuned, inplace=False)
Lets see how the quantized model performs on a few images
visualize_model(model_quantized_and_trained)
plt.ioff()
plt.tight_layout()
plt.show()


--- sort_order: 5 tei_id: rdx_b6zxc.p.idp59344 annotation_count: 3 images: small-thumbnail: https://readux.ecds.emory.edu/books/emory:b6zxc/pages/emory:h6sjh/mini-thumbnail/ json: https://readux.ecds.emory.edu/books/emory:b6zxc/pages/emory:h6sjh/info/ full: https://readux.ecds.emory.edu/books/emory:b6zxc/pages/emory:h6sjh/fs/ page: https://readux.ecds.emory.edu/books/emory:b6zxc/pages/emory:h6sjh/single-page/ thumbnail: https://readux.ecds.emory.edu/books/emory:b6zxc/pages/emory:h6sjh/thumbnail/ title: Page 1 number: 1 permalink: "/pages/1/" ---


Strawberry Oatmeal!!
These are the ingredients
Strawberries
Oatmeal
Love...
Directions
Add the ingredients to a bowl
Stir the ingredients
Let's put everything in a microwave and heat it for 30 seconds


This is a sample of the jQuery.FilterTable plugin which shows the usage of the quickList
optional parameter.
Note that the “neighbor island” quick list item works because within some cells exists code that is hidden with CSS: neighbor island
.
| Campus | Island | Fall 2011 Enrollment |
|---|---|---|
| University of Hawaiʻi at Hilo | Hawaiʻi neighbor island | 4,100 |
| University of Hawaiʻi at Mānoa | Oʻahu | 20,400 |
| University of Hawaiʻi—West Oʻahu | Oʻahu | 1,600 |
| Hawaiʻi Community College | Hawaiʻi neighbor island | 3,900 |
| Honolulu Community College | Oʻahu | 4,600 |
| Kapiʻolani Community College | Oʻahu | 9,000 |
| Kauaʻi Community College | Kauaʻi neighbor island | 1,400 |
| Leeward Community College | Oʻahu | 7,900 |
| Maui College | Maui neighbor island | 4,500 |
| Windward Community College | Oʻahu | 2,700 |
Note that while the filter is case-insensitive, it is UTF-8 aware so searching for oahu
will not find Oʻahu
.
$('table').filterTable({ quickList: quickList: [ 'Oʻahu', 'Hawaiʻi', 'university', 'college', 'neighbor island' ] });


Comments
-
Greg Hurrell
Thanks for reporting this, John. I haven't had a two-screen setup for a very long time so I haven't noticed this issue but I don't doubt that what you're describing is the case. I am not looking at the code right at this minute, but I am fairly sure there is a constraint in there that is preventing the floater from moving above the Menu Bar.
Thanks for bringing this to my attention. I'll look into it and see if I can remove that constraint.
-
john
Figured I'd update the ticket to show the problem exists in 2.8. Still not a major problem, as the temporary solution I first posted still works to get the floater to the top screen.
-
Greg Hurrell
Thanks for the confirmation, John.
-
Greg Hurrell
Ok, I've now fixed this for version 3.0. Adding the "fixed-in-prerelease" keyword. To be notified when the new version is released you can add yourself to the announcements mailing list:
-
Greg Hurrell
Changing assignment to reflect my new email address.
http://wincent.com/a/news/archives/2006/05/change_of_email.php
-
Greg Hurrell
Synergy 3.0 is now out. Release announcement:
http://wincent.com/a/news/archives/2006/09/synergy_30_now.php
Marking as FIXED/CLOSED.
Add a comment
Comments are now closed for this issue.


Spoj FCTRL2 Explanation and Solution
Spoj · Competitive CodingProblem:
In this Problem , we are required to find Factorials of numbers ranging from 1 to 100, but Since an unsigned 64-bit Integer can store upto 19 decimal digits, where as 100! has 150+ digits, so we can’t use Int Data type.
The simplest data structure which can be used to store such results is an Array. In the simplest form we can store 1-digit at every index of array.
How to store a two (or more) digit number in an Array (one digit per index) ?
We need to store the input ‘n’ (1-100) into an array to find it’s Factorial To store a Number in an array, we can grab the digits from least significant position (For Example 1456) one by one and put it into the array. For example: To put 123 into an array (let say a[50]), we will grab the last digits one by one, final array would be given as a[] = {3,2,1} i.e. a[0]=3, a[1]=2, a[2]=1 & so on…
LOGIC used for this: Let n = 123 then 1). let rem = n%10 (this means remainder when n is divided by 10) 2. a[index] = rem 3. n = n/10 (this removes the last digit from n, Now we are ready to grab the second last digit & so on) Repeating this step iteratively, we can save n into an array.
How To Find the Factorial of the Number stored in array?
After we have put the input number (1-100) into an array, we have to now find its Factorial. To do that we have to multiply the number ‘n’ with (n-1), (n-2) … & so on …3.2.1 . Now we need to find an Algorithm for multiplying an Integer with a number stored into an array & we can use that algorithm for all multiplications.
Let us take an Example: Let the n be 45, so we have to find it’s factorial
45! = 45 x 44 x 43 x … x 37 x … 4 x 3 x 2 x 1. Now put the number 45 into a sufficiently large array (such as a[200]). Now lets us multiply the array a[] = {5,4} with a number, let say 37
The array will be: a[0] = 5 a[1] = 4 and the value of m will be 2 specifying that there are 2 digits in the array currently.
Codechef Tutorial
Now, to multiply this array with the value 37. We start off from the index 0 of the array to index 1. At every iteration, we calculate 37 * a[index]. We also maintain a temporary variable called temp which is initialized to 0. Now, at every step, we calculate x = a[index] * 37 + temp. The new value of a[index] will be x % 10 and the new value of temp will be temp / 10. We are simply carrying out multiplication the way it is carried out usually (The Process is shown in Figure below). So, for the current situation, the iterations will be something like this.
Initialize temp = 0 Iteration 1 : array = (5, 4) temp = 0 index = 0, a[index] = 5 x = a[index] * 37 + temp = 5 * 37 + 0 = 185. the new value of a[index] = 185 % 10 which is 5 and the new value of temp is 185 / 10 which is 18
Iteration 2 : array : (5, 4) temp = 18 index = 1, a[index] = 4 x = a[index] * 37 + temp = 4 * 37 + 18 = 166. the new value of a[index] = 166 % 10 which is 6 and the new value of temp is 166 / 10 which is 16
We have finished 2 iterations and this is the value of ‘m‘, the array size at the moment. The required number of iterations is now over, but the value oftemp is still greater than 0. This means that the current value of temp is to be incorporated into the array. For that, we keep appending the last digit oftemp to the array and divide temp by 10 till temp becomes 0. So, we will get something like Iteration 1 : temp = 16 , array = (5, 6) So, we add 16 % 10 to the array so that the array becomes (5, 6, 6) and we divide temp by 10 so that temp becomes 1. We update the value of ‘m’ to m + 1 that is m = 3 Iteration 2 : temp = 1, array = (5, 6, 6) Now, we add 1 % 10 to the array so the array becomes (5, 6, 6, 1) and we divide temp by 10 so that temp becomes 0. We update the value of ‘m’ to m + 1 that is m = 4
The value of temp is now 0 and our multiplication is now over. The final array we get is (5, 6, 6, 1)
Voila, we have the answer to 45 * 37 in our array with the Least significant digit in the 0th position. :)
For finding the factorial, we need to carry out this exact multiplication operation at every step as we loop from 1 to N. At the end of the Nth iteration, our array will contain the answer and the value of m will be the number of digits in the answer. We can then just print the array from the Most significant digit to the least for the answer.
Code in C++
// Source Code Of Above Algorithm:
##include<iostream>
using namespace std;
int main() {
int t;
cin>>t;
while(t--) {
int a[200],rem,i=0,n,m=0,flag;
cin>>n;
flag = n;
while(flag!=0) {
rem = flag%10;
a[i] = rem;
flag = flag/10;
i++;
m++;
}
int temp,x=0,index=0;
for(i=2;i<n;i++) {
temp = 0;
for(index=0;index<m;index++) {
x = a[index]*i + temp;
a[index] = x%10;
temp = x/10;
}
while(temp!=0) {
a[index] = temp % 10;
temp = temp/10;
index++;
m++;
}
}
for(i=m-1;i>=0;i--) {cout<<a[i];}
cout<<"\n";
}
return 0;
}
Thanks to this Tutorial at Codechef
Code in Python
For Python Freaks, it’s a cakewalk. 10 lines of code!
def fact(a):
factl = 1
for i in range(1,a+1,1): factl = factl*i
return factl
def main():
t = int(input())
for i in range(t):
n = int(input())
print(fact(n))
if __name__ == "__main__":main()


public interface NamingConvention
The main goal of the naming convention is to reduce the amount of configuration required in the mapping (especially when mapping between column and property names).
Note that if you do not define a NamingConvention the default one will be used and you can configure it's behaviour via properties.
| Modifier and Type | Method and Description |
|---|---|
String |
getColumnFromProperty(Class<?> beanClass,
String propertyName)
Return the column name given the property name.
|
String |
getForeignKey(String prefix,
String fkProperty)
Return the foreign key column given the local and foreign properties.
|
TableName |
getM2MJoinTableName(TableName lhsTable,
TableName rhsTable)
Returns the ManyToMany join table name (aka the intersection table).
|
String |
getPropertyFromColumn(Class<?> beanClass,
String dbColumnName)
Return the property name from the column name.
|
String |
getSequenceName(String tableName,
String pkColumn)
Return the sequence name given the table name (for DB's that use sequences).
|
TableName |
getTableName(Class<?> beanClass)
Returns the table name for a given Class.
|
boolean |
isUseForeignKeyPrefix()
Return true if a prefix should be used building a foreign key name.
|
void |
loadFromProperties(PropertiesWrapper properties)
Load setting from properties.
|
void |
setDatabasePlatform(DatabasePlatform databasePlatform)
Set the associated DatabasePlaform.
|
void setDatabasePlatform(DatabasePlatform databasePlatform)
This is set after the DatabasePlatform has been associated.
The purpose of this is to enable NamingConvention to be able to support database platform specific configuration.
databasePlatform
- the database platformTableName getTableName(Class<?> beanClass)
This method is always called and should take into account @Table annotations etc. This means you can choose to override the settings defined by @Table if you wish.
beanClass
- the bean classTableName getM2MJoinTableName(TableName lhsTable, TableName rhsTable)
lhsTable
- the left hand side bean tablerhsTable
- the right hand side bean tableString getColumnFromProperty(Class<?> beanClass, String propertyName)
String getPropertyFromColumn(Class<?> beanClass, String dbColumnName)
This is used to help mapping of raw SQL queries onto bean properties.
beanClass
- the bean classdbColumnName
- the db column nameString getSequenceName(String tableName, String pkColumn)
Typically you might append "_seq" to the table name as an example.
tableName
- the table nameboolean isUseForeignKeyPrefix()
This by default is true and this works well when the primary key column names are simply "id". In this case a prefix (such as "order" and "customer" etc) is added to the foreign key column producing "order_id" and "customer_id".
This should return false when your primary key columns are the same as the foreign key columns. For example, when the primary key columns are "order_id", "cust_id" etc ... and they are the same as the foreign key column names.
String getForeignKey(String prefix, String fkProperty)
prefix
- the local column used to prefix the fk columnfkProperty
- the property name of the foreign keyvoid loadFromProperties(PropertiesWrapper properties)
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Sep 27, 2009 . This TV antenna is not only omnidirectional, but also compatible with . are a super-easy way to get answers from the Instructables community.
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Nov 20, 2011 . Homemade HDTV Antenna that Looks Great! . New and Improvedby TheUnusualTech3,798 views · The BEST DIY Antenna for HDTV !!!... 4: .
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Homemade HDTV Antenna | eHow.com
Homemade HDTV antenna design can surprisingly be done with simple wire hangers, aluminum foil, and a 2-by-4 about four feet in length. Alternatively, you .
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Digital television broadcasts demand sound antenna designs, ensuring that even people at the fringes of reception areas are able to receive reliable signals.
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Oct 31, 2009 . Build a Massive HDTV Antenna for Long Distance Gains One of the great things about digital television is also one of the not-so-great things.
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Over the Air HDTV - CNET Home audio & video Forums
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NBC’s Bell, producer for Fallon and Olympics, departs
NEW YORK (AP) - NBC executive Jim Bell, who has produced the last four Olympics for the network and been in charge backstage at Jimmy Fallon’s “Tonight” show for the past year, says he’s leaving NBC.
Bell said in a statement that the timing was perfect to forge a new path that would build on his experience in news, sports and entertainment.
He was a longtime producer of the “Today” show before moving to the Olympics, where he was in charge of NBC’s prime-time telecasts. Since 2017, Bell had been in charge of all Olympics content across all platforms.
At “Tonight,” he steered Fallon into more live shows. But the show hasn’t gained ground against ratings leader Stephen Colbert at CBS.
NBC says a former “Tonight” producer, Gavin Purcell, will fill in at the show.
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Mr. Harrell has been the CEO and General Partner of a very successful Atlanta based automotive group for the past 12 years. He has been responsible for the direct supervision and oversight of 14 automotive dealerships at any given time, with over 600 employees, approximately $300 million in gross annual sales and over $1 billion in assets.
Mr. Harrell has participated in a broad range of financial holdings and real estate transactions primarily related to the automotive industry. As Managing Member and CEO of South Abaco Land Development, Ltd., he oversaw the purchase of the 300 acre Conch Sound Point parcel and the 400 acre High Bank Bay parcel, both on the island of Great Abaco, Bahamas. Once purchased, he played an integral part in the planning for The Resorts of South Abaco Project.
His success stems from his true attention to detail and ability to listen and grasp complex concepts.
EDUCATION
Northwood University
• Bachelors Degree Marketing 1990
• Bachelors Degree Management 1990
• Assoc. Degree Automotive Management 1989
PROFESSIONAL AFFILIATIONS
• Porsche Cars North America, Inc.
• Lexus | North America
• Volvo | North America
• Daimler Chrysler | North America
• Honda Motor Company | North America
• Ford Motor Company
• Toyota Motor Company | North America
• Nissan | North America
• Kia | North America
COMMUNITY AFFILIATIONS
• 14-year member of Liberty Church
• Served as an Elder, Assistant Pastor, and headed Men's Ministry and Servant Outreach
• Year board member of Begin Again Ministries – feeding Atlanta's homeless
CURRENT & PREVIOUS REAL ESTATE HOLDINGS
• SALD One and SALD Two
• East Tennessee Real Estate Investments
• Northwest Vehicle Investments
• Stone Mountain Real Estate Investments
• Harrell Investments
• HSH Properties
• Running Ben Properties
• Stone Mountain Real Estate Texas
• Greer Real Estate Investments
• South Real Estate Investments
• South Atlanta Real Estate Investments
• Harrell Companies Incorporated – Atlanta, GA
• HSH Properties – Atlanta, GA
• Mall of Georgia Auto Mall – Atlanta, GA
• Union City Auto Mall – Atlanta, GA
• Arbor Place Auto Mall – Atlanta, GA
• Sweetwater Ridge at Magnolia Pointe – Clearmont, FL
• Lake Equity Holdings – Clearmont, FL
• North American Real Estate – Atlanta, GA
Thomas Boynton’s contributions to the companies he has been associated with, apply the vital, broad strokes necessary to transform visions into reality. He provides the leadership and skills necessary to design, plan, and execute concurrent projects across the spectrum, encompassing luxury resort and renewable energy project development.
For 25 years he has been in the forefront of project development. As an engineer, nuclear power background and training, owning his own infrastructure design, construction and development company for 17 years, to taking the position as President of an Atlanta based private equity firm to mange their offshore real assets and domestic business interests, his diverse knowledge coupled with technical expertise has afforded him the intrinsic ability to recognize opportunity and shape it into reality.
Thomas Boynton Group, a medium sized infrastructure company, grew into a recognized leader in landfill construction through the 1990’s and into 2006. During this period, his group was involved with the construction, closure, repair or retrofit of over 40 domestic US landfills from Florida to Alaska. The utility division within Thomas Boynton Group, was responsible for much of the utility construction at Hartsfield-Jackson International Airport from 1994 through 2003 to include preparation for the Olympics, followed by the South Cargo Ramp, the South Cargo Ramp infrastructure, the North South Deicing facility, and the most notable of the airport projects being the complete infrastructure construction and utility relocation for the new 5th runway | International runway as managed by 5R, a Joint Venture between CW Matthews, and APAC.
Mr. Boynton has worked on and has been a part of many Luxury Destination Resort Projects. In 2007 he was named President of an Atlanta based private equity group, and assembled a world-class team of professionals with experience in all the necessary disciplines for the design, development and operation of a luxury destination property. He identified and secured the branding partners that clearly defined the experience, the two most notable of these partners being GE and Apple. The project in Abaco was the first EcoMagination Luxury Resort Destination in Latin America and the Caribbean. The association with (Apple) created the iExperience™ for the Resort. ROSA represented a $638 million investment into the Bahamas. His financial acumen allowed the firm to secure a $360 million bond commitment for the Bahamas projects in an economic climate not conducive for any type of development success.
With over 25 years experience in the industry, his focus directs innumerable lines of communication allowing him to build a successful plastics fusion | utility company to running a private equity company and orchestrating their $10 million Private Placement for their resort development in the Bahamas. Mr. Boynton clearly possesses a unique ability to structure, diversify and create opportunity. His experience in the areas mentioned, as well as his ability to adapt and overcome, set him apart in this well-defined business arena.
Education
• US Navy Nuclear Power School
• US Naval Academy - Attended
• University of Georgia - BSAE
Kevin Case possesses over 25 years of retail, multi-family, office and hospitality real estate development and has directed over US$3.0 Billion in commercial real estate development projects. His depth of knowledge and expertise represents a 360 - degree view of property development ranging from inception and land acquisition through all phases of construction, occupancy, and disposition. Mr. Case has significant financial acumen and fiscal awareness and these skill sets have made him particularly skillful in asset optimization, yield management, and value engineering. He served more than 25 years with Thomas Land and Development, one of the largest privately held commercial real estate development firms in the U.S., where he directed and coordinated a team of architects, engineers, consultants and contractors globally. Perhaps one of his greatest strengths is as a team leader and his proven ability to quickly and adroitly solve complex problems.
Mr. Case has played an instrumental role in transforming undeveloped real estate into viable, income-producing, highly successful properties. The hallmark of his project oversight has been to create desirable destinations and communities that are self-sustaining. His vast knowledge and expertise of working diligently with municipalities and understanding the nuances associated with the master planning, zoning and entitlement process together with his ability to comfortably navigate a variety of complex legal matters has proven to be of tremendous value-add to Thomas Land. In tandem with his unique ability to facilitate both public and private financial structures, without compromise to design directives, Mr. Case is driven to derive the maximum financial benefit for his company and the Team.
Mr. Case obtained his undergraduate degree in Business Management from Auburn University. He has been married for 33 years to his wife Tammy, and they have two sons.
Education
Auburn University – BS Management
Hal Thannisch is a strategic thinker and has tremendous experience in the conceptualization, creation and realization of destination luxury resort properties, mixed-use, leisure and business hotels, conference and convention facilities, recreational facilities and master-planned communities. He served as development executive for both Rosewood Hotels and Ritz-Carlton Hotels for more than a decade. As a leader in his field for 25 years, he possesses the global vision, broad experience and proven technical expertise required to redefine the international standards for “destination-making” large scale, master-planned developments.
Mr. Thannisch has brought to fruition some of the world’s best hotels, convention / conference centers and resorts and his international knowledge enable him to position his properties as the foremost luxury hotels, resorts and spas in their market areas. Recognized as a consummate professional by leaders in his field, Thannisch’s skills and talents are in demand all over the world. An architect by training, Thannisch brings an unsurpassed element of design to his projects, ensuring that owners realize dynamic leisure and lifestyle properties of enduring value.
Thannisch’s highly successful development work regularly brings him into contact with the world’s elite – business leaders, politicians, economists and other experts in his field. With the goal of creating an unrivaled and remarkable experience for visitors, travelers and guests, Thannisch is setting the stage for investors and owners to have their projects recognized by global industry leaders founded on a dedication to quality, design, service, and technology.
Mr. Thannisch has orchestrated the creation of several hospitality projects including those that have been named the Best in the World by several notable travel and hospitality industry publications and organizations. He has been directly involved with and managed the development of over $2.75 billion in Hospitality and Commercial Mixed Use developments. He continues to make extraordinary contributions to the development of sought-after destination resorts by crafting environments that provide an exquisite ambiance and a total guest experience. A few of his most Notable Developments include:
• 10 Trinity Square, London, UK** - £350,000,000
• Las Ventanas al Paraiso, Cabo San Lucas, México* - $102,000,000
• Pelican Hill Resort & Golf Club, Newport Beach, California* - $850,000,000
• St. Andrews Hotel on the Old Course, St. Andrews, Fife, Scotland* - $84,000,000 (First Renovation 1987)


View Source or open this file in a text editor to see how jQuery is installed from a CDN, with a local fallback.


Port:
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Data bits:
7
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Stop bits:
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Connection Status:
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LED 1
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Supercharge Doctrine ODM and Mongo DB. Document behaviors to make development fast and simple.
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MIRACLE MILE A film review by Shane R. Burridge Copyright 1995 Shane R. Burridge
(1989) 87 min
Right from the pre-credits opening you'll assume from Anthony Edwards' retrospective narration that you are about to see a quirky, affectionate romance. But by the time MIRACLE MILE has ended you'll have realized that this is a cunning maneuver by writer/director Steve de Jarnatt to plant a subconscious sense of reassurance in the minds of the audience, one that will prevent most people watching from believing that the worst can happen, right up until the very end.
Ten minutes into the story, Harry Washello (Edwards) is late for his second date with new romance Mare Winningham. While waiting on a street corner in the early hours of the morning he answers a phone ringing in a public booth. To describe the events that happen afterward would undermine the surprising developments that Jarnatt has in store, but suffice to say that Harry is informed that inadvertently launched nuclear warheads are winging their way towards his sleeping city. Throughout the rest of the film we watch in frustration as Harry gets swept up in the circumstances he sets snowballing into motion. As in our worst nightmares, he never gains control; he can't seem to get beyond the same limited geographical area; he (like the audience) is deprived of essential knowledge - we are never sure whether the missile scare is real, a hoax, or literally his nightmare.
Jarnatt juggles humor and action with ease, but it is at the moment when the film's nightmare logic spills over into the daylight, where it shouldn't belong, that MIRACLE MILE becomes just plain chilling. We watch his portrayal of suppressed panic, which breaks at different levels for different people, and wonder if we would fare any better in a similar situation. Jarnatt and cinematographer Theo Van de Sande create images that will stick with you, including an unforgettable final shot viewed from within a helicopter cockpit. MIRACLE MILE was Jarnatt's pet project after CHERRY 2000, but was held over for release and undeservedly overlooked. A film that looks good on a big screen, with a characteristically persistent score by Tangerine Dream. For the sharp-eyed: Jarnatt plants a clue pointing toward the conclusion of the film by inserting a shot of study notes for Thomas Pynchon's book GRAVITY'S RAINBOW.
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rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
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#include <old_sm.h>
Data Fields | |
| L_Oper * | op |
| OLD_SM_Cb * | sm_cb |
| unsigned int | flags |
| unsigned int | sched_cycle |
| unsigned int | min_cycle |
| unsigned int | max_cycle |
| unsigned short | sched_slot |
| unsigned short | min_slot |
| unsigned short | max_slot |
| unsigned short | num_unsched_in |
| unsigned short | num_unsched_out |
| OLD_SM_Dep * | input_dep |
| OLD_SM_Dep * | output_dep |
| OLD_SM_Oper * | next_smop |
| OLD_SM_Oper * | prev_smop |
Definition at line 84 of file old_sm.h.
| unsigned int OLD_SM_Oper::flags |
| unsigned int OLD_SM_Oper::max_cycle |
| unsigned short OLD_SM_Oper::max_slot |
| unsigned int OLD_SM_Oper::min_cycle |
| unsigned short OLD_SM_Oper::min_slot |
| struct OLD_SM_Oper* OLD_SM_Oper::next_smop |
| unsigned short OLD_SM_Oper::num_unsched_in |
| unsigned short OLD_SM_Oper::num_unsched_out |
| L_Oper* OLD_SM_Oper::op |
| struct OLD_SM_Oper* OLD_SM_Oper::prev_smop |
| unsigned int OLD_SM_Oper::sched_cycle |
| unsigned short OLD_SM_Oper::sched_slot |
| struct OLD_SM_Cb* OLD_SM_Oper::sm_cb |


Restrict playback to a region within your wavesurfer.js instance.
container
- required - the element in which to place the timeline, or a CSS selector to find itheight
- the height (in pixels) of the timeline. The default is 20.notchPercentHeight
- the height (in percent) of the minor notch lines in the timeline. The default is 90.primaryColor
- the color of the modulo-ten notch lines (e.g. 10sec, 20sec). The default is '#000'.secondaryColor
- the color of the non-modulo-ten notch lines. The default is '#c0c0c0'.primaryFontColor
- the colour of the labels next to the main notches (e.g. 10sec, 20sec). The default is '#000'.secondaryFontColor
- the colour of the labels next to the secondary notches (e.g. 5sec, 15sec). The default is '#c0c0c0'.timeInterval
- number of intervals that records consists of. Usually it is equal to the duration in minutes. (Integer or function which receives pxPerSec value and reurns value)primaryLabelInterval
- number of primary time labels. (Integer or function which receives pxPerSec value and reurns value)secondaryLabelInterval
- number of secondary time labels (Time labels between primary labels, integer or function which receives pxPerSec value and reurns value).formatTimeCallback
- custom time format callback. (Function which receives number of seconds and returns formatted string)offset
- offset (in seconds) for the start of the timeline. The value may also be negative and the default is 0.


KILLALL(1) User Commands KILLALL(1) NAME killall - kill processes by name SYNOPSIS killall [-Z,--context pattern] [-e,--exact] [-g,--process-group] [-i,--interactive] [-o,--older-than TIME] [-q,--quiet] [-r,--regexp] [-s,--signal signal] [-u,--user user] [-v,--verbose] [-w,--wait] [-y,--younger-than TIME] [-I,--ignore-case] [-V,--version] [--] name ... killall -l killall -V,--version DESCRIPTION killall sends a signal to all processes running any of the specified com‐ mands. If no signal name is specified, SIGTERM is sent. Signals can be specified either by name (e.g. -HUP or -SIGHUP ) or by number (e.g. -1) or by option -s. If the command name is not regular expression (option -r) and contains a slash (/), processes executing that particular file will be selected for killing, independent of their name. killall returns a zero return code if at least one process has been killed for each listed command, or no commands were listed and at least one process matched the -u and -Z search criteria. killall returns non- zero otherwise. A killall process never kills itself (but may kill other killall pro‐ cesses). OPTIONS -e, --exact Require an exact match for very long names. If a command name is longer than 15 characters, the full name may be unavailable (i.e. it is swapped out). In this case, killall will kill everything that matches within the first 15 characters. With -e, such entries are skipped. killall prints a message for each skipped entry if -v is specified in addition to -e, -I, --ignore-case Do case insensitive process name match. -g, --process-group Kill the process group to which the process belongs. The kill sig‐ nal is only sent once per group, even if multiple processes belonging to the same process group were found. -i, --interactive Interactively ask for confirmation before killing. -l, --list List all known signal names. -o, --older-than Match only processes that are older (started before) the time specified. The time is specified as a float then a unit. The units are s,m,h,d,w,M,y for seconds, minutes, hours, days, weeks, Months and years respectively. -q, --quiet Do not complain if no processes were killed. -r, --regexp Interpret process name pattern as an extended regular expression. -s, --signal Send this signal instead of SIGTERM. -u, --user Kill only processes the specified user owns. Command names are optional. -v, --verbose Report if the signal was successfully sent. -V, --version Display version information. -w, --wait Wait for all killed processes to die. killall checks once per sec‐ ond if any of the killed processes still exist and only returns if none are left. Note that killall may wait forever if the signal was ignored, had no effect, or if the process stays in zombie state. -y, --younger-than Match only processes that are younger (started after) the time specified. The time is specified as a float then a unit. The units are s,m,h,d,w,M,y for seconds, minutes, hours, days, weeks, Months and years respectively. -Z, --context (SELinux Only) Specify security context: kill only processes hav‐ ing security context that match with given expended regular expression pattern. Must precede other arguments on the command line. Command names are optional. FILES /proc location of the proc file system KNOWN BUGS Killing by file only works for executables that are kept open during exe‐ cution, i.e. impure executables can't be killed this way. Be warned that typing killall name may not have the desired effect on non-Linux systems, especially when done by a privileged user. killall -w doesn't detect if a process disappears and is replaced by a new process with the same PID between scans. If processes change their name, killall may not be able to match them correctly. killall has a limit of names that can be specified on the command line. This figure is the size of an unsigned long multiplied by 8. For most 32 bit systems the limit is 32 and similarly for a 64 bit system the limit is usually 64. AUTHORS Werner Almesberger<EMAIL_ADDRESS>wrote the original version of psmisc. Since version 20 Craig Small<EMAIL_ADDRESS>can be blamed. SEE ALSO kill(1), fuser(1), pgrep(1), pidof(1), pkill(1), ps(1), kill(2). Linux 2012-06-16 KILLALL(1)


AgoraARKit
public class AgoraARKit
The AgoraARKit
object provides static properties to be used when initializing the Agora engine
-
The
agoraAppId
is a static value that is used to connect to the Agora.io service. Get your Agora App Id from https://console.agora.ioAgoraARKit.agoraAppId = "<Your Agora APP ID>"
Note
theagoraAppId
can be set within theAppDelegate
or within anyViewController
, the only requirement is that theagoraAppId
is set before joining a channel. This takes place within theviewWillAppear
fucntion for bothARBroadcaster
orARAudience
ViewControllersWarning
This value defaults to nil, and will throw a runtime error if not set.Declaration
Swift
public static var agoraAppId: String!
-
The
agoraToken
is a static value that is used to as the user’s channel token. You can set either a dynamic token or a temp token. Generate a temp token usic https://console.agora.io. Default isnil
AgoraARKit.agoraToken = "<Your Agora Token>"
Note
TheagoraToken
can be set within theAppDelegate
or within anyViewController
, the only requirement is that theagoraToken
is set before joining a channel. This takes place within theviewWillAppear
fucntion for bothARBroadcaster
orARAudience
ViewControllersWarning
You must set a token if you have enabled the certificate on the channel.- If you have not enabled certificate security on your AppiId, then you do not need to set this value.
Declaration
Swift
public static var agoraToken: String?


PERIL FROM THE PLANET MONGO A film review by Rick Pali Copyright 1997 Rick Pali
Released in 1966 for TV, this film is a re-editing of a 12 part serial called Flash Gordon Conquers the Universe. I'm in a bit of confusion over the serial's original release date. The film itself claims 1936 while the Internet Movie Database says 1940. Regardless, you get the idea.
This is one in a series of Flash Gordon serials starring Larry 'Buster' Crabbe, also of Buck Rogers and Tarzan fame. This time, Ming the Merciless is back and he still has his sights on conquering the Earth. He's got a new secret weapon in the form of a metal that burns with the heat of the sun when exposed to oxygen. He's had his scientists make 'projectiles' out of them so he can burn the Earth to ashes at the first sign of resistance. Ming's decided to test them on Arboria to teach Prince Barin a lesson. Once the metal starts burning, there's no way to stop it. The projectiles have been fired and they're on their way, with only Flash to stop them...
Let me tell you, I was very much looking forward to seeing this film and I was not disappointed. I've seen clips of the Flash Gordon serials and still clearly remember the spaceships with the sparks and smoke flying out the back. And the sound! I don't know where they got it, but it's unmistakable and I don't think I'll ever forget it.
It's not that this film has a brilliant plot, characterisation, and terrific acting. That's not it at all. I bet that it wasn't even so hot, in those areas, for its own time, but looking at this film is looking at history. Much like Star Trek today, the Flash Gordon of sixty years ago is what some people expected the future to be like. Watch it with your 1990s eyes, and of course it's funny. But look at it as an entertainment slanted vision of the future of the 1930s, compare it to our visions, and it's fascinating. Both the differences and the similarities are interesting. This was done before World War Two and it really shows. That conflict completely redefined the art of war, and those changes are not reflected here. You don't expect mounted warriors, or astronauts fighting with swords. Some of the uniforms look straight from World War One or even the Crimean War. But you really can't expect them to have known what was coming. Heck, at least they've got ray-guns. And the shooting effect was done by scratching the beam into the film itself!
And look at Dale Arden (Carol Hughes). Is there any doubt that she's along for the ride to keep the men and boys watching? She doesn't do much more than get taken prisoner by Ming, but her short dress leaves no doubt that she's got wonderful legs. I'll sheepishly admit that I was mad at Ming for making her change into less revealing clothes when he kidnapped her. Some things never change, I guess.
As you might expect, the physics are all over the place. Most people in the spaceships aren't even sitting, nevermind belted in. Given inertia, that's not really reasonable. Mongo is another planet, but the spaceships are never shown in space. Ming's scientists are working in a vacuum, with these really funky helmets...but with bare hands. At least there's an airlock.
But picking this film apart for mistakes in the science and other errors, completely misses the point. This film is sixty years old and is a prime example of not only very early science fiction, but a type of moviemaking that just doesn't exist any more: the serial. It's been edited into a single film, but the cliff-hanger breaks are still recognisable.
If you're the type that can't even watch a black and white film simply because it's not colour, steer very clear. But if you're a more seasoned, wide-ranging viewer, see one of the Flash Gordon serials if you get the chance. It's the grandfather of your beloved Star Wars and Star Trek.
*** (out of five)
--- http://www3.sympatico.ca/rpali/
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
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The formatting of the review is likely to differ from the original due
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