US expresses concern over China attempts to influence Taiwan vote
Self-ruled island that China considers renegade province due to hold elections for president, legislature on January 11
The United States is concerned about China's attempts to influence Taiwan's presidential election, the top US official in Taiwan said on Friday, as China stepped up pressure on the self-ruled island in the run-up to the vote in January.
The comment came days after Taiwan denounced China for trying to interfere in its democratic process in the run-up to the January 11 elections for the presidency and legislature, including sailing an aircraft carrier group through the Taiwan Strait on Sunday.
More:
-
Taiwan's president denounces China and defends island's sovereignty
-
China's history with Taiwan
-
How China uses access to its economy against its critics
"We are aware that China is attempting to apply pressure through various means on Taiwan. Certainly, these attempts to influence Taiwan's democratic process are of concern," said Brent Christensen, the de facto US ambassador to Taiwan.
"We believe that malign actors are using disinformation campaigns to make people lose faith in democratic institutions," he told reporters in Taipei.
Beijing has in recent months stepped up efforts to "reunify" what it considers a wayward province, flying regular bomber patrols around it and seeking to isolate it diplomatically.
The US, like most countries, has no diplomatic relations with Taiwan but is bound by law to help provide the island with the means to defend itself. It is democratic Taiwan's strongest backer on the international stage.
Christensen said any effort to determine the future of Taiwan by other than peaceful means, including boycotts or embargoes, was of "grave concern" to the US.
'Strong relationship'
Taiwan and the United States have very close ties, he added, noting this year's $10bn in US arms sales to the island.
"We have, I believe, a strong relationship in security matters that we anticipate will only grow stronger."
President Tsai Ing-wen of the pro-independence Democratic Progressive Party is leading in the polls, before her main rival, Han Kuo-yu of the China-friendly Kuomintang party.
Tsai's government has repeatedly warned of Beijing's campaign to influence the vote, including snatching more of Taiwan's few remaining diplomatic allies and a "warfare" of disinformation to mislead voters.
Chinese President Xi Jinping said in January that China reserved the right to use force to bring Taiwan under its control but would strive to achieve peaceful "reunification."
Beijing suspects Tsai of pushing for the island's formal independence, which Xi has warned would lead to a "grave disaster."
China described the carrier sailing as routine and not aimed at anyone in particular, dismissing Taiwan's complaints.
Taiwan has lost seven diplomatic allies to China since Tsai took office in 2016, leaving 15 nations that recognise the island.
China has sent about 2,000 bomber patrols a year to the narrow Taiwan Strait separating the two sides, the island's Defence Minister Yen Teh-fa told parliament last week.


The objective of this game is to score the most amount of points by matching the given density curve with its corresponding histograms.
This game requires 1 player, and is played against the enemy AI
A typical gameboard is shown below:
Board:Shows all of the possible histograms that can be
matched to the density curve
Current Graph:Shows the current graph whose matches need to be
found
Timer: Shows the amount of time remaining for the current round
Score: Shows scores as well as multipliers for both players
The first round begins when the current graph is revealed. At this point, it is the job of both players to find the correct histograms within the time limit. Once the time limit is reached, the corresponding histograms are flipped face down, and the next round begins with a different graph.
Players recieve 1 point per each correct histograms(there are two correct histograms per graph), and lose one point for each incorrect histogram. No points are awarded or deducted if nothing is selected.
At the end of the round, before the points earned are added to the total score, they are first multiplied by the score multiplier. Initially, the score multiplier is set to 1x. The multiplier is doubled by choosing both of the corresponding histograms in a round. Choosing an incorrect histogram resets the multiplier back to 1x.
The game ends when all graphs have been shown
The following options can be configured before pressing the start
button:
Number of Graphs: Determines the number of graphs, as well as
the number of rounds to be presented
Sample Size: Determines the sample size of the histograms
Game Type: Determines the pool that the graphs will be drawn
from.
Game speed: Determines the length of each round
Enemy Difficulty: Determines the difficulty level of the Enemy AI


Here at Student Planet we want to give you tips and tricks to help you live sustainably, efficiently, affordably and healthy so that you fulfill your potential. Check out our website and... Plan it with Student Planet! We feel like every student can benefit from living a more balanced life - how many of your five a day do you really get? How many hours of sleep? How many hours of excerice? And more importantly, how many hours of fun?! Life is what you make it. Blast off on a spaceship into Student planet to discover many tips!
From the Student Planet team!
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Processing Jennifer Kramer's Homework Sketches from the Dadageek Sept 22 - Nov 3, 2016 Processing Class.
Sketches created in Processing 3.2.1, and converted to P5.js
Processing Jennifer Kramer's Homework Sketches from the Dadageek Sept 22 - Nov 3, 2016 Processing Class.
Sketches created in Processing 3.2.1, and converted to P5.js


Legends and Pundits react to Bangladesh's historic win over Team India in Delhi
by SportsCafe DeskAfter eight consecutive losses, including several heartbreaking ones, at the hands of team India in T20 cricket, Bangladesh finally broke the duck in Delhi and did it in style. After the bowlers restricted India to a par score, Rahim guided the team home and Twitterati reacted on the historic win.
Superb performance from Bangladesh!
Well played!👏
Brilliant piece of batting!
Lucky man Rahim!👍
Astonishing win by Tigers!
Take a bow, Bangladesh!
Historic win!


« CSS « CSS Reference
The outline-width
CSS property is used to set the width of the outline of an element. An outline is a line that is drawn around elements, outside the border edge, to make the element stand out:
medium
visual
In browsers previous to Gecko 1.8 (Firefox 1.5) essentially the same effect can be achieved using Mozilla CSS Extension
-moz-outline-width
.
<length>
values.thin medium thick 2px 1ex 5em
outline
property
| Feature | Chrome | Firefox (Gecko) | Internet Explorer | Opera | Safari (WebKit) |
|---|---|---|---|---|---|
| Basic support | 1.0 | 1.5 (1.8) | 8.0 | 7.0 | 1.2 (125) |
| Feature | Android | Firefox Mobile (Gecko) | IE Phone | Opera Mobile | Safari Mobile |
|---|---|---|---|---|---|
| Basic support | ? | ? | ? | ? | ? |


Johnson
This page contains an index of all the individuals in the database with the surname of Johnson. Selecting the person’s name will take you to that person’s individual page.
| Given Name | Birth | Death | Partner | Parents |
|---|---|---|---|---|
| Albert Carl | 1911-01-25 | 1991-12-04 | Hastie, Phillis Miriam (nee Pearce) | Johnson, Richard Willoughby Bongers, Frances "Fanny" |
| Albert Edmond | 1888 | Johnson, Ellison Furber, Martha Ann Burcombe- | ||
| Alice | 1859-10-28 | 1860-04-00 | Johnson, William (Carpenter & Joiner) Lord, Ruth | |
| Amy | 1882 | Johnson, John James Kidd, Sarah Jane Matiilda | ||
| Brian F. | c1947 | 1948-08-11 | Johnson, Ralph Essedon Rutter, Mabel Agnes | |
| Cecil Robert | 1922-10-15 | 2001-10-07 | Hamnett, Grace Lilian | Johnson, Richard Willoughby Bongers, Frances "Fanny" |
| Charles Hotham | 1897 | Johnson, William Henry MacDonald, Alice Hannah | ||
| Clara | 1861-04-00 | 1887-08-00 | Salom, Henry Clarence | Johnson, William (Carpenter & Joiner) Lord, Ruth |
| Daniel | Wooding, Sarah Elizabeth | |||
| Denise, married John ?? | c1944 | Klower, Maxwell John | Johnson, Ivan St.Vincent Watts, Enid Myra Patricia | |
| Dorothy Frances | 1910-01-18 | 1910-01-24 | Johnson, Richard Willoughby Bongers, Frances "Fanny" | |
| Dorothy Frances | c1943 | Johnson, Ralph Essedon Rutter, Mabel Agnes | ||
| Edwin Thomas | 1880 | Johnson, John James Kidd, Sarah Jane Matiilda | ||
| Eileen May | 1915-07-22 | 1995-12-26 | Simmonds, Roy Durnam Stewart, Halliday, William Horace Edgar | Johnson, Richard Willoughby Bongers, Frances "Fanny" |
| Eliza | 1882 | Johnson, Ellison Furber, Martha Ann Burcombe- | ||
| Ellison | 1854-09-23 | 1940-09-00 | Furber, Martha Ann Burcombe- | Johnson, William (Carpenter & Joiner) Lord, Ruth |
| Ellison | 1884 | Johnson, Ellison Furber, Martha Ann Burcombe- | ||
| Ellison Stanley | 1894 | Johnson, William Henry MacDonald, Alice Hannah | ||
| Emma Jane | 1870-07-00 | Gray, Robert | Johnson, William (Carpenter & Joiner) Lord, Ruth | |
| Ernest Eric | 1898 | Johnson, Louis Henry "Harry" Cross, Marion Jessie | ||
| Eugene | 1907 | 1907 | Johnson, Eugene Virgil Seymour, Gertrude Agnes | |
| Eugene Virgil | 1883 | 1941 | Seymour, Gertrude Agnes | Johnson, Robert & Margaret Mary |
| George Wilkinson | 1866-12-00 | 1867-02-00 | Johnson, William (Carpenter & Joiner) Lord, Ruth | |
| Gysbert James | 1913-05-04 | 1934-04-17 | Johnson, Richard Willoughby Bongers, Frances "Fanny" | |
| Hamil William | 1883 | Johnson, John James Kidd, Sarah Jane Matiilda | ||
| Harold | 1891 | 1892 | Johnson, Ellison Furber, Martha Ann Burcombe- | |
| Harry Richard Willoughby | 1907-10-06 | 1932-02-25 | Smith, Ella B. | Johnson, Richard Willoughby Bongers, Frances "Fanny" |
| Hedley Victor | 1893 | Johnson, Ellison Furber, Martha Ann Burcombe- | ||
| Henry | Goddard, Ellen “Nellie” | |||
| Ida Florence | 1900 | Johnson, Louis Henry "Harry" Cross, Marion Jessie | ||
| Isabella Ruth | 1887 | 1888 | Johnson, John James Kidd, Sarah Jane Matiilda | |
| Ivan St.Vincent | 1913 | 1975-12-17 | Watts, Enid Myra Patricia | Johnson, Eugene Virgil Seymour, Gertrude Agnes |
| Jack Alex | 1899 | Johnson, William Henry MacDonald, Alice Hannah | ||
| Jean Narella | 1915 | 2008-07-29 | Brown, Maurice Edward | Johnson, Eugene Virgil Seymour, Gertrude Agnes |
| John | Turner, Mary | |||
| John James | 1852-05-31 | Kidd, Sarah Jane Matiilda | Johnson, William (Carpenter & Joiner) Lord, Ruth | |
| Kevin Leo | 1970 | Hart, Valda Esma | ||
| Leila Margaret | c1911 | 1996-08-10 | Keen, Frederick | Johnson, Eugene Virgil Seymour, Gertrude Agnes |
| Lilyan | 1886 | 1887 | Johnson, Ellison Furber, Martha Ann Burcombe- | |
| Louis Henry “Harry” | 1873-06-00 | Cross, Marion Jessie | Johnson, William (Carpenter & Joiner) Lord, Ruth | |
| Malcolm | c1945 | Johnson, Ralph Essedon Rutter, Mabel Agnes | ||
| Marshall Roy | 1895 | Johnson, Ellison Furber, Martha Ann Burcombe- | ||
| Martha | 1865 | 1937-07-10 | Procter, Ralph OliverThomas | |
| Martha | 1868-01-00 | Karlson, Gustav | Johnson, William (Carpenter & Joiner) Lord, Ruth | |
| Mary | 1863-04-00 | Shiells, Francis Alfred | Johnson, William (Carpenter & Joiner) Lord, Ruth | |
| Mary Josephine | c1920 | Skerritt, Noel Wesley | Johnson, Eugene Virgil Seymour, Gertrude Agnes | |
| Nellie | Boyer, Jean Paul | |||
| Oliver Preston | 1901 | Johnson, Ellison Furber, Martha Ann Burcombe- | ||
| Patricia Joan Gertrude | c1921 | 2009-06-17 | Neal, Maxwell George | Johnson, Eugene Virgil Seymour, Gertrude Agnes |
| Ralph Essedon | 1919-09-06 | 2010-08-23 | Rutter, Mabel Agnes | Johnson, Richard Willoughby Bongers, Frances "Fanny" |
| Richard Willoughby | 1881-11-24 | 1961-03-04 | Bongers, Frances “Fanny” | Johnson, Daniel Wooding, Sarah Elizabeth |
| Robert & Margaret Mary | ||||
| Ruth | 1865-05-31 | 1866-06-05 | Johnson, William (Carpenter & Joiner) Lord, Ruth | |
| Ruth Ellen | 1877-07-00 | Shiells, Horace Edward | Johnson, William (Carpenter & Joiner) Lord, Ruth | |
| Ruth Ellen | 1880 | 1892 | Johnson, Ellison Furber, Martha Ann Burcombe- | |
| Sarah Alice | 1885 | Johnson, John James Kidd, Sarah Jane Matiilda | ||
| Stanislaus Robert (Stan) | 1909 | 1992-07-28 | Jones, Eileen Anges | Johnson, Eugene Virgil Seymour, Gertrude Agnes |
| Stanley Keith | 1918-02-03 | 1992-10-20 | Fitzroy, Kathleen Esmae Selby | Johnson, Richard Willoughby Bongers, Frances "Fanny" |
| Thelma Maude | 1905-12-19 | 1991-05-01 | McKenna, Edward John | Johnson, Richard Willoughby Bongers, Frances "Fanny" |
| Warwick & Evelyn | Johnson, Stanley Keith Fitzroy, Kathleen Esmae Selby | |||
| William, (Carpenter & Joiner) | 1829-02-17 | 1908-01-31 | Lord, Ruth | Johnson, John Turner, Mary |
| William Henry | 1857-05-00 | MacDonald, Alice Hannah | Johnson, William (Carpenter & Joiner) Lord, Ruth | |
| William Henry | 1896 | Johnson, William Henry MacDonald, Alice Hannah | ||
| William Henry Burcombe | 1878-08-00 | Johnson, Ellison Furber, Martha Ann Burcombe- | ||
| [Living] | Johnson, Kevin Leo Hart, Valda Esma | |||
| [Living] | Johnson, Kevin Leo Hart, Valda Esma | |||
| [Living] | Lewis, Leonard Stanley | |||
| [Living] | Cruwys, [Living] | |||
| [Living] | Orams, [Living] | |||
| [Living] | Ivimey, Merle Edna | Johnson, Eugene Virgil Seymour, Gertrude Agnes | ||
| [Living] | Johnson, Stanley Keith Fitzroy, Kathleen Esmae Selby | |||
| [Living] | Hoad, [Living] | Johnson, Albert Carl Hastie, Phillis Miriam (nee Pearce) | ||
| [Living] | Johnson, Albert Carl Hastie, Phillis Miriam (nee Pearce) |


Getting Started
One easy way to get started with apiman is to simply download WildFly 8 and the apiman overlay. Unpack them both into the same location and you're off and running. You'll obviously also need to have Java installed - we currently recommend at least version 7.
Download
- WildFly 8.2.0.Final
- apiman 1.1.9.Final overlay for WildFly 8.2
Install
- Unpack the WildFly 8.2 zip
- Unpack the apiman 1.1.9.Final WildFly 8 overlay zip inside the wildfly directory
Run
- Start WildFly 8 using the "standalone-apiman.xml" configuration
-
Point your browser at the apiman UI and login with admin/admin123!
We strongly recommend you change the admin password(s) by logging in to keycloak!
Configure
The apiman distribution comes pre-configured with everything you should need to get started. Please see the User Guide for information about how to change apiman configuration options if you require more customization.
Or simply try this...
mkdir ~/apiman-1.1.9.Final cd ~/apiman-1.1.9.Final curl https://downloads.jboss.org/wildfly/8.2.0.Final/wildfly-8.2.0.Final.zip -o wildfly-8.2.0.Final.zip curl https://downloads.jboss.org/apiman/1.1.9.Final/apiman-distro-wildfly8-1.1.9.Final-overlay.zip -o apiman-distro-wildfly8-1.1.9.Final-overlay.zip unzip wildfly-8.2.0.Final.zip unzip -o apiman-distro-wildfly8-1.1.9.Final-overlay.zip -d wildfly-8.2.0.Final cd wildfly-8.2.0.Final ./bin/standalone.sh -c standalone-apiman.xml
Getting Started (Docker)
If you prefer to get started by using the apiman docker images, that's cool too! Skip all the steps above and follow the instructions in this section instead.
Download
- Use docker to pull the "apiman WildFly 8" docker image: jboss/apiman-wildfly
Run
- Use docker to run the "apiman WildFly 8" docker image
-
Point your browser at the apiman UI and login with admin/admin123!
We strongly recommend you change the admin password(s) by logging in to keycloak!
Configure
The apiman distribution comes pre-configured with everything you should need to get started. Please see the User Guide for information about how to change apiman configuration options if you require more customization.
More Info
For more information on apiman docker support, including how to extend the image for your own uses, click here!
Or simply try this...
docker pull jboss/apiman-wildfly docker run -it -p 8080:8080 -p 8443:8443 jboss/apiman-wildfly:1.1.9.Final


Biomedical Engineering Student.
Experiece in the area of teaching, codding, eletronic of small signals, processing of signals, embedded systems and development of desktop applications.
In general we studied signal processing of EEG with MATLAB.
Due to its importance I put the codes related to the book


--- layout: page title: Aziz Faozi description: Halo halo hai background: '/img/bg-about.jpg' ---
Once upon a time, in Tegalpingen Village, there live a legend called Aziz Faozi. Aziz Faozi has many interests like writing, coding, soldering, designing and etc.
Now I am CEO of ELoku. I am Electrical Engineer start from 2019. I have some hobbies and make some movement to make sure that we can build society using technology, also we can make our live meaningfull with our activity.


Create beautiful top responsive navigation menu in your pages
Download View project on GithubTop responsive menus are very used in web development. With this component you can create one of them.
When you resize your browser, the navigation options will be hide and a link will be showed instead. Clicking that link the menu will slide down.

 Brand

 ...

 ...

Stashy.Toggle(selector, options)
| Parameter | Type | Default | Description |
|---|---|---|---|
| selector | string | .st-toggle | CSS selector for your toggle menu |
| options | object | See options | You can pass the options to the offcanvas instance |
| Option | Type | Default | Description |
|---|---|---|---|
| closeOnClickOutside | bool | true | Close Toggle on click outside the element area |
| closeOnClick | bool | true | Close Toggle when click on any link inside the element area |
| fixed | bool | false | You can make the toggle fixed top by setting this option to true. Remember to add some paddding (>=50px) to your main container if you don´t want the toggle to hide your upmost content. Toggle component is going to stick fixed at top only on desktop measures (>= 768px) |
| Name | Description |
|---|---|
| layout() | Start the toggle menu layout |
var toggle = new Stashy.Toggle().layout();
less
For using just this component, create a .less file similar to Stashy.less, include in order the following imports and compile:
Stashy.Variables.less Stashy.Mixins.less Stashy.Toggle.lessJS
jQuery.js Stashy.Toggle.js


Net
Net
Load Analytics Zoo Model
Use Net.load
(in Scala) or Net.load
(in Python) to load an existing model defined using the Analytics Zoo Keras-style API. Net
(Scala) or Net
(Python) is a utility class provided in Analytics Zoo. We just need to specify the model path and optionally weight path if exists where we previously saved the model to load it to memory for resume training or prediction purpose.
Scala example
val model = Net.load("/tmp/model.def", "/tmp/model.weights") //load from local fs
val model = Net.load("hdfs://...") //load from hdfs
val model = Net.load("s3://...") //load from s3
Python example
model = Net.load("/tmp/model.def", "/tmp/model.weights") //load from local fs
model = Net.load("hdfs://...") //load from hdfs
model = Net.load("s3://...") //load from s3
Load BigDL Model
Scala example
val model = Net.loadBigDL("/tmp/model.def", "/tmp/model.weights") //load from local fs
val model = Net.loadBigDL("hdfs://...") //load from hdfs
val model = Net.loadBigDL("s3://...") //load from s3
Python example
model = Net.loadBigDL("/tmp/model.def", "/tmp/model.weights") //load from local fs
model = Net.loadBigDL("hdfs://...") //load from hdfs
model = Net.loadBigDL("s3://...") //load from s3
Load Torch Model
Scala example
val model = Net.loadTorch("/tmp/torch_model") //load from local fs
val model = Net.loadTorch("hdfs://...") //load from hdfs
val model = Net.loadTorch("s3://...") //load from s3
Python example
model = Net.loadTorch("/tmp/torch_model") //load from local fs
model = Net.loadTorch("hdfs://...") //load from hdfs
model = Net.loadTorch("s3://...") //load from s3
Load Caffe Model
Scala example
val model = Net.loadCaffe("/tmp/def/path", "/tmp/model/path") //load from local fs
val model = Net.loadCaffe("hdfs://def/path", "hdfs://model/path") //load from hdfs
val model = Net.loadCaffe("s3://def/path", "s3://model/path") //load from s3
Python example
model = Net.loadCaffe("/tmp/def/path", "/tmp/model/path") //load from local fs
model = Net.loadCaffe("hdfs://def/path", "hdfs://model/path") //load from hdfs
model = Net.loadCaffe("s3://def/path", "s3://model/path") //load from s3
TFNet
TFNet is a analytics-zoo layer that wraps a tensorflow frozen graph and can easily run in parallel.
TFNet cannot be trained, so it can only be used for inference or as a feature extractor for fine tuning a model. When used as feature extractor, there should not be any trainable layers before TFNet, as all the gradient from TFNet is set to zero.
Remarks:
- You need to install tensorflow==1.15.0 on your driver node.
- Your operating system (OS) is required to be one of the following 64-bit systems: Ubuntu 16.04 or later and macOS 10.12.6 or later.
- To run on other systems, you need to manually compile the TensorFlow source code. Instructions can be found here.
Export TensorFlow model to frozen inference graph
Analytics-zoo provides a useful utility function, export_tf
, to export a TensorFlow model
to frozen inference graph.
For example:
Python:
import tensorflow as tf
from nets import inception
slim = tf.contrib.slim
images = tf.placeholder(dtype=tf.float32, shape=(None, 224, 224, 3))
with slim.arg_scope(inception.inception_v1_arg_scope()):
logits, end_points = inception.inception_v1(images, num_classes=1001, is_training=False)
sess = tf.Session()
saver = tf.train.Saver()
saver.restore(sess, "/tmp/models/inception_v1.ckpt")
from zoo.util.tf import export_tf
export_tf(sess, "/tmp/models/tfnet", inputs=[images], outputs=[logits])
In the above code, the export_tf
utility function will frozen the TensorFlow graph, strip unused operation according to the inputs and outputs and save it to the specified directory along with the input/output tensor names.
Creating a TFNet
After we have export the TensorFlow model, we can easily create a TFNet.
Scala:
val m = TFNet("/tmp/models/tfnet")
Python:
m = TFNet.from_export_folder("/tmp/models/tfnet")
Please refer to TFNet Object Detection Example (Scala) or TFNet Object Detection Example (Python) and the Image Classification Using TFNet Notebook for more information.


---
layout: default
title: Jet Fighters International
theme: twitter-narrow
---
{% include JB/setup %}
Jet Fighters International, Inc.
For once you have tasted flight you will walk the earth with your eyes turned skywards, for there you have been, and there you will long to return.
Sign up today
General Info
Buying, Selling and Brokering Aero Vodochody L-39 Aircraft
Providing Ground & Flight Training for Type Ratings in the L-39 (Authorized Experimental Aircraft: AV-L39)
Discovery Flights so that you may experience what it’s like to fly a Jet Fighter;
Perform in Airshows
Acquire Sponsorships and
Participate in Flight Operations for US Government Special Missions.
General Info
Buying, Selling and Brokering Aero Vodochody L-39 Aircraft
Providing Ground & Flight Training for Type Ratings in the L-39 (Authorized Experimental Aircraft: AV-L39)
Discovery Flights so that you may experience what it’s like to fly a Jet Fighter;
Perform in Airshows
Acquire Sponsorships and
Participate in Flight Operations for US Government Special Missions.
General Info
Buying, Selling and Brokering Aero Vodochody L-39 Aircraft
Providing Ground & Flight Training for Type Ratings in the L-39 (Authorized Experimental Aircraft: AV-L39)
Discovery Flights so that you may experience what it’s like to fly a Jet Fighter;
Perform in Airshows
Acquire Sponsorships and
Participate in Flight Operations for US Government Special Missions.
General Info
Buying, Selling and Brokering Aero Vodochody L-39 Aircraft
Providing Ground & Flight Training for Type Ratings in the L-39 (Authorized Experimental Aircraft: AV-L39)
Discovery Flights so that you may experience what it’s like to fly a Jet Fighter;
Perform in Airshows
Acquire Sponsorships and
Participate in Flight Operations for US Government Special Missions.
General Info
Buying, Selling and Brokering Aero Vodochody L-39 Aircraft
Providing Ground & Flight Training for Type Ratings in the L-39 (Authorized Experimental Aircraft: AV-L39)
Discovery Flights so that you may experience what it’s like to fly a Jet Fighter;
Perform in Airshows
Acquire Sponsorships and
Participate in Flight Operations for US Government Special Missions.
General Info
Buying, Selling and Brokering Aero Vodochody L-39 Aircraft
Providing Ground & Flight Training for Type Ratings in the L-39 (Authorized Experimental Aircraft: AV-L39)
Discovery Flights so that you may experience what it’s like to fly a Jet Fighter;
Perform in Airshows
Acquire Sponsorships and
Participate in Flight Operations for US Government Special Missions.


@cryptochief "You speculating on potential attacks which probably wont happen" This is the sentiment every coin that's been 51% attacked has said prior, every banker said prior to 2008, every stock trader in the 20s, I can go on... You are ignoring the larger downside ri...


"""Mean shift clustering algorithm.
Mean shift clustering aims to discover *blobs* in a smooth density of
samples. It is a centroid based algorithm, which works by updating candidates
for centroids to be the mean of the points within a given region. These
candidates are then filtered in a post-processing stage to eliminate
near-duplicates to form the final set of centroids.
Seeding is performed using a binning technique for scalability.
"""
# Authors: Conrad Lee<EMAIL_ADDRESS># Alexandre Gramfort<EMAIL_ADDRESS># Gael Varoquaux<EMAIL_ADDRESS># Martino Sorbaro<EMAIL_ADDRESS>import numpy as np
import warnings
from collections import defaultdict
from ..externals import six
from ..utils.validation import check_is_fitted
from ..utils import check_random_state, gen_batches, check_array
from ..base import BaseEstimator, ClusterMixin
from ..neighbors import NearestNeighbors
from ..metrics.pairwise import pairwise_distances_argmin
from ..externals.joblib import Parallel
from ..externals.joblib import delayed
def estimate_bandwidth(X, quantile=0.3, n_samples=None, random_state=0,
n_jobs=1):
"""Estimate the bandwidth to use with the mean-shift algorithm.
That this function takes time at least quadratic in n_samples. For large
datasets, it's wise to set that parameter to a small value.
Parameters
----------
X : array-like, shape=[n_samples, n_features]
Input points.
quantile : float, default 0.3
should be between [0, 1]
0.5 means that the median of all pairwise distances is used.
n_samples : int, optional
The number of samples to use. If not given, all samples are used.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
n_jobs : int, optional (default = 1)
The number of parallel jobs to run for neighbors search.
If ``-1``, then the number of jobs is set to the number of CPU cores.
Returns
-------
bandwidth : float
The bandwidth parameter.
"""
X = check_array(X)
random_state = check_random_state(random_state)
if n_samples is not None:
idx = random_state.permutation(X.shape[0])[:n_samples]
X = X[idx]
nbrs = NearestNeighbors(n_neighbors=int(X.shape[0] * quantile),
n_jobs=n_jobs)
nbrs.fit(X)
bandwidth = 0.
for batch in gen_batches(len(X), 500):
d, _ = nbrs.kneighbors(X[batch, :], return_distance=True)
bandwidth += np.max(d, axis=1).sum()
return bandwidth / X.shape[0]
# separate function for each seed's iterative loop
def _mean_shift_single_seed(my_mean, X, nbrs, max_iter):
# For each seed, climb gradient until convergence or max_iter
bandwidth = nbrs.get_params()['radius']
stop_thresh = 1e-3 * bandwidth # when mean has converged
completed_iterations = 0
while True:
# Find mean of points within bandwidth
i_nbrs = nbrs.radius_neighbors([my_mean], bandwidth,
return_distance=False)[0]
points_within = X[i_nbrs]
if len(points_within) == 0:
break # Depending on seeding strategy this condition may occur
my_old_mean = my_mean # save the old mean
my_mean = np.mean(points_within, axis=0)
# If converged or at max_iter, adds the cluster
if (np.linalg.norm(my_mean - my_old_mean) < stop_thresh or
completed_iterations == max_iter):
return tuple(my_mean), len(points_within)
completed_iterations += 1
def mean_shift(X, bandwidth=None, seeds=None, bin_seeding=False,
min_bin_freq=1, cluster_all=True, max_iter=300,
n_jobs=1):
"""Perform mean shift clustering of data using a flat kernel.
Read more in the :ref:`User Guide <mean_shift>`.
Parameters
----------
X : array-like, shape=[n_samples, n_features]
Input data.
bandwidth : float, optional
Kernel bandwidth.
If bandwidth is not given, it is determined using a heuristic based on
the median of all pairwise distances. This will take quadratic time in
the number of samples. The sklearn.cluster.estimate_bandwidth function
can be used to do this more efficiently.
seeds : array-like, shape=[n_seeds, n_features] or None
Point used as initial kernel locations. If None and bin_seeding=False,
each data point is used as a seed. If None and bin_seeding=True,
see bin_seeding.
bin_seeding : boolean, default=False
If true, initial kernel locations are not locations of all
points, but rather the location of the discretized version of
points, where points are binned onto a grid whose coarseness
corresponds to the bandwidth. Setting this option to True will speed
up the algorithm because fewer seeds will be initialized.
Ignored if seeds argument is not None.
min_bin_freq : int, default=1
To speed up the algorithm, accept only those bins with at least
min_bin_freq points as seeds.
cluster_all : boolean, default True
If true, then all points are clustered, even those orphans that are
not within any kernel. Orphans are assigned to the nearest kernel.
If false, then orphans are given cluster label -1.
max_iter : int, default 300
Maximum number of iterations, per seed point before the clustering
operation terminates (for that seed point), if has not converged yet.
n_jobs : int
The number of jobs to use for the computation. This works by computing
each of the n_init runs in parallel.
If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
.. versionadded:: 0.17
Parallel Execution using *n_jobs*.
Returns
-------
cluster_centers : array, shape=[n_clusters, n_features]
Coordinates of cluster centers.
labels : array, shape=[n_samples]
Cluster labels for each point.
Notes
-----
For an example, see :ref:`examples/cluster/plot_mean_shift.py
<sphx_glr_auto_examples_cluster_plot_mean_shift.py>`.
"""
if bandwidth is None:
bandwidth = estimate_bandwidth(X, n_jobs=n_jobs)
elif bandwidth <= 0:
raise ValueError("bandwidth needs to be greater than zero or None,\
got %f" % bandwidth)
if seeds is None:
if bin_seeding:
seeds = get_bin_seeds(X, bandwidth, min_bin_freq)
else:
seeds = X
n_samples, n_features = X.shape
center_intensity_dict = {}
nbrs = NearestNeighbors(radius=bandwidth, n_jobs=n_jobs).fit(X)
# execute iterations on all seeds in parallel
all_res = Parallel(n_jobs=n_jobs)(
delayed(_mean_shift_single_seed)
(seed, X, nbrs, max_iter) for seed in seeds)
# copy results in a dictionary
for i in range(len(seeds)):
if all_res[i] is not None:
center_intensity_dict[all_res[i][0]] = all_res[i][1]
if not center_intensity_dict:
# nothing near seeds
raise ValueError("No point was within bandwidth=%f of any seed."
" Try a different seeding strategy \
or increase the bandwidth."
% bandwidth)
# POST PROCESSING: remove near duplicate points
# If the distance between two kernels is less than the bandwidth,
# then we have to remove one because it is a duplicate. Remove the
# one with fewer points.
sorted_by_intensity = sorted(center_intensity_dict.items(),
key=lambda tup: tup[1], reverse=True)
sorted_centers = np.array([tup[0] for tup in sorted_by_intensity])
unique = np.ones(len(sorted_centers), dtype=np.bool)
nbrs = NearestNeighbors(radius=bandwidth,
n_jobs=n_jobs).fit(sorted_centers)
for i, center in enumerate(sorted_centers):
if unique[i]:
neighbor_idxs = nbrs.radius_neighbors([center],
return_distance=False)[0]
unique[neighbor_idxs] = 0
unique[i] = 1 # leave the current point as unique
cluster_centers = sorted_centers[unique]
# ASSIGN LABELS: a point belongs to the cluster that it is closest to
nbrs = NearestNeighbors(n_neighbors=1, n_jobs=n_jobs).fit(cluster_centers)
labels = np.zeros(n_samples, dtype=np.int)
distances, idxs = nbrs.kneighbors(X)
if cluster_all:
labels = idxs.flatten()
else:
labels.fill(-1)
bool_selector = distances.flatten() <= bandwidth
labels[bool_selector] = idxs.flatten()[bool_selector]
return cluster_centers, labels
def get_bin_seeds(X, bin_size, min_bin_freq=1):
"""Finds seeds for mean_shift.
Finds seeds by first binning data onto a grid whose lines are
spaced bin_size apart, and then choosing those bins with at least
min_bin_freq points.
Parameters
----------
X : array-like, shape=[n_samples, n_features]
Input points, the same points that will be used in mean_shift.
bin_size : float
Controls the coarseness of the binning. Smaller values lead
to more seeding (which is computationally more expensive). If you're
not sure how to set this, set it to the value of the bandwidth used
in clustering.mean_shift.
min_bin_freq : integer, optional
Only bins with at least min_bin_freq will be selected as seeds.
Raising this value decreases the number of seeds found, which
makes mean_shift computationally cheaper.
Returns
-------
bin_seeds : array-like, shape=[n_samples, n_features]
Points used as initial kernel positions in clustering.mean_shift.
"""
# Bin points
bin_sizes = defaultdict(int)
for point in X:
binned_point = np.round(point / bin_size)
bin_sizes[tuple(binned_point)] += 1
# Select only those bins as seeds which have enough members
bin_seeds = np.array([point for point, freq in six.iteritems(bin_sizes) if
freq >= min_bin_freq], dtype=np.float32)
if len(bin_seeds) == len(X):
warnings.warn("Binning data failed with provided bin_size=%f,"
" using data points as seeds." % bin_size)
return X
bin_seeds = bin_seeds * bin_size
return bin_seeds
class MeanShift(BaseEstimator, ClusterMixin):
"""Mean shift clustering using a flat kernel.
Mean shift clustering aims to discover "blobs" in a smooth density of
samples. It is a centroid-based algorithm, which works by updating
candidates for centroids to be the mean of the points within a given
region. These candidates are then filtered in a post-processing stage to
eliminate near-duplicates to form the final set of centroids.
Seeding is performed using a binning technique for scalability.
Read more in the :ref:`User Guide <mean_shift>`.
Parameters
----------
bandwidth : float, optional
Bandwidth used in the RBF kernel.
If not given, the bandwidth is estimated using
sklearn.cluster.estimate_bandwidth; see the documentation for that
function for hints on scalability (see also the Notes, below).
seeds : array, shape=[n_samples, n_features], optional
Seeds used to initialize kernels. If not set,
the seeds are calculated by clustering.get_bin_seeds
with bandwidth as the grid size and default values for
other parameters.
bin_seeding : boolean, optional
If true, initial kernel locations are not locations of all
points, but rather the location of the discretized version of
points, where points are binned onto a grid whose coarseness
corresponds to the bandwidth. Setting this option to True will speed
up the algorithm because fewer seeds will be initialized.
default value: False
Ignored if seeds argument is not None.
min_bin_freq : int, optional
To speed up the algorithm, accept only those bins with at least
min_bin_freq points as seeds. If not defined, set to 1.
cluster_all : boolean, default True
If true, then all points are clustered, even those orphans that are
not within any kernel. Orphans are assigned to the nearest kernel.
If false, then orphans are given cluster label -1.
n_jobs : int
The number of jobs to use for the computation. This works by computing
each of the n_init runs in parallel.
If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
Attributes
----------
cluster_centers_ : array, [n_clusters, n_features]
Coordinates of cluster centers.
labels_ :
Labels of each point.
Notes
-----
Scalability:
Because this implementation uses a flat kernel and
a Ball Tree to look up members of each kernel, the complexity will tend
towards O(T*n*log(n)) in lower dimensions, with n the number of samples
and T the number of points. In higher dimensions the complexity will
tend towards O(T*n^2).
Scalability can be boosted by using fewer seeds, for example by using
a higher value of min_bin_freq in the get_bin_seeds function.
Note that the estimate_bandwidth function is much less scalable than the
mean shift algorithm and will be the bottleneck if it is used.
References
----------
Dorin Comaniciu and Peter Meer, "Mean Shift: A robust approach toward
feature space analysis". IEEE Transactions on Pattern Analysis and
Machine Intelligence. 2002. pp. 603-619.
"""
def __init__(self, bandwidth=None, seeds=None, bin_seeding=False,
min_bin_freq=1, cluster_all=True, n_jobs=1):
self.bandwidth = bandwidth
self.seeds = seeds
self.bin_seeding = bin_seeding
self.cluster_all = cluster_all
self.min_bin_freq = min_bin_freq
self.n_jobs = n_jobs
[docs] def fit(self, X, y=None):
"""Perform clustering.
Parameters
-----------
X : array-like, shape=[n_samples, n_features]
Samples to cluster.
"""
X = check_array(X)
self.cluster_centers_, self.labels_ = \
mean_shift(X, bandwidth=self.bandwidth, seeds=self.seeds,
min_bin_freq=self.min_bin_freq,
bin_seeding=self.bin_seeding,
cluster_all=self.cluster_all, n_jobs=self.n_jobs)
return self
[docs] def predict(self, X):
"""Predict the closest cluster each sample in X belongs to.
Parameters
----------
X : {array-like, sparse matrix}, shape=[n_samples, n_features]
New data to predict.
Returns
-------
labels : array, shape [n_samples,]
Index of the cluster each sample belongs to.
"""
check_is_fitted(self, "cluster_centers_")
return pairwise_distances_argmin(X, self.cluster_centers_)


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4-5
Dietary
Melt 25g/1oz of the butter in a large pan over a medium heat. Add the shallots, leeks, celeriac and carrots and a pinch of salt and gently fry for 4-5 minutes, until softened, but not coloured.
Add the white wine and bring to the boil. Cook to reduce the liquid completely, then add the water and chicken stock and bring back to the boil.
Simmer for 10-12 minutes, then add the courgettes, French beans, peas and cabbage and simmer for a further five minutes.
Add the cream and whisk in the remaining butter.
Add the tomatoes and the herbs. Stir well, and season, to taste, with salt and freshly ground black pepper and the caster sugar.
To serve, pour into warmed bowls.


Haskell Workshop
Introduction for everyone
Introduction for everyone
( λx.x + 1 ) 2
[ x := 2 ] # applying 2
( λ[ x := 2 ].x + 1 )
( 2 + 1 )
3 # reduced to identity function
# Applications are left associative
'1' ( λx.x )( λy.y ) z
'2' ( ( λx.x )( λy.y ) ) z # rewrite as
'3' [ x := ( λy.y ) ] # apply from left to right
'4' ( λy.y ) z
'5' [ y := z ]
'6' z # reduced to identity
'7' # "z" is a free variable
# It is just a shorthand for nested Lambda
'1' λxy.x + y
'2' λx.λy.x + y
'3' λx.( λy.x + y )
# apply with x = 1 and y = 2
'1' ( λxy.x + y ) 1 2
'2' λx.( λy.x + y ) 1 2
'3' λ[ x := 1 ].( λy.x + y ) 2 # apply x = 1
'4' ( λy.1 + y ) 2
'5' ( λ[ y := 2 ].1 + y ) # apply y = 2
'6' 1 + 2
'7' 3
# Reached beta normal form (cannot reduce further)


DfE enquiries
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Press release
All children to be given a phonics-based progress check in year 1 so teachers can identify those in need of extra support.
All children will be given a phonics-based progress check in year 1 so teachers can identify those not at the expected level in reading and in need of extra support, Education Secretary Michael Gove announced today.
Mr Gove said too many primary school children were failing to reach the expected standard and that the short, light-touch test would be designed to assess how well year 1 pupils could read simple, decodable words. He added that the screening check would be based on systematic synthetic phonics as it was internationally proven to drive up basic reading skills from a young age.
Mr Gove said it would be administered by pupils’ teachers and would be designed to
Today the government is launching a public consultation to ask teachers, parents, professionals and the public to submit views on how the check might work. It will be piloted in summer 2011 and will take place nationally from summer 2012. To help schools select an effective synthetic phonics programme, the government has published the core criteria that define the key features of such programmes.
Michael Gove said:
A solid foundation in reading is crucial to a child’s success as they progress through primary school, into secondary school and then in later life.
But, in spite of the hard work of teachers and pupils, too many children are currently not reaching the expected reading levels at age 7 and age 11.
We are determined to raise literacy standards in our schools, especially of those not achieving the expected level - a light-touch phonics-based check will provide reassurance that children in year 1 have learned this important skill, will enable us to pinpoint those who are struggling at an early age and will give them the help they need before it is too late.
It will be impossible to drill for and will be a true gauge of a child’s reading skills.
Parents want to know how their children are reading and this will tell them.
On phonics, Schools Minister Nick Gibb, speaking on a visit to Elmhurst Primary School in Newham, London, said:
There is more to reading than phonics - but there is also a weight of evidence that systematic synthetic phonics, taught in the first years of a child’s education, gives children key building blocks they need to understand words, underpins children’s attainment of a good standard of reading and can inspire a lifetime love of reading.
The government is determined to raise the standard of reading in the first years of primary school so that children can master the basic decoding skills of reading early and then spend the rest of primary school reading to learn.
The fact is that alternative methods have left too many young people with poor literacy levels, especially among children of more disadvantaged families, and we are determined that every child can read to their full potential.
Provisional figures released earlier this year showed that in 2010:
England has also slipped down the international table for reading in primary schools. The Progress in International Reading Literacy Study (PIRLS) of 10-year-olds saw England fall from third out of 35 countries in 2001 to 15th out of 40 countries in 2006.
Shahed Ahmed, the Headteacher of Elmhurst Primary - one of the schools where the check will be pre-trialled, said:
At Elmhurst Primary School we firmly believe that the best way to teach how to read in the early stages is through a rigorous, systematic, engaging phonics approach. I believe that all schools would benefit from taking this approach. It’s important that schools know for young children how good their phonics knowledge is.
An end-of-year-1 phonics check would encourage all schools to teach early reading properly through phonics, and they would then know then the strengths and weaknesses of their pupils.
Ruth Miskin, a leading authority on teaching children to read, said:
Despite numerous well-meaning initiatives over recent years, we still have 20% of children who are unable to access a secondary school curriculum. However, there are many determined heads who ensure that every child learns to read by 6- or 7-years-old. There is no reason why this success cannot be replicated across the whole country.
This reading check will help all headteachers focus their efforts upon the children who are most likely to slip through the net. If we catch these children early, they will have an equal opportunity to make the most of their education and lives.
Ofsted will inspect the teaching of reading and phonics in schools and the impact on pupils’ results, and on 14 November 2010 it published a report showing best practice in the teaching of phonics. The information provided from this test will allow Ofsted and schools to have a better conversation about each school’s teaching of phonics.
The government has also revised the core criteria that define the key features of an effective systematic synthetic phonics programme, to help schools in selecting a suitable programme. Publishers of products have been invited to submit new self-assessment forms for their products, assessing them against the new criteria.
Jan Tyson, headteacher at Turnfurlong Infants School in Aylesbury, Buckinghamshire, said:
Systematic synthetic phonics is key to teaching children reading and writing. It provides them with strategies to decode words, which is especially important because English is such a difficult language to learn with the many different ways to make the same sounds from different letters or combinations of letters.
Phonics focuses on sounds rather than, for example, having children try to recognise whole words.
In analytic phonics, words are broken down into their beginning and end parts, such as ‘str-‘ and ‘eet’, with an emphasis on ‘seeing’ the words and analogy with other words.
In synthetic phonics, children start by sequencing the individual sounds in words - for example, ‘s-t-r-ee-t’, with an emphasis on blending them together.
Once they have learned all these, they progress to reading books.
The ‘synthetic’ part comes from the word ‘synthesise’, meaning to assemble or blend together.
Children who learn using synthetic phonics are able to have a go at new words working from sound alone, whereas those using analytic phonics are more dependent on having prior knowledge of families of words.
It is a statutory requirement that all schools and early years settings in England teach ‘phonemic awareness’ and ‘phonic knowledge’.
In Clackmannanshire, Scotland, a 7-year study of the teaching of synthetic phonics to 300 children found they were 3 years ahead of their contemporaries in reading by the time they moved on to secondary school.
A 2005 Australian report, ‘Teaching Reading’, found: The incontrovertible finding from the extensive body of local and international evidence-based literacy research is that for children during the early years of schooling (and subsequently if needed) to be able to link their knowledge of spoken language to their knowledge of written language, they must first master the alphabetic code - the system of grapheme-phoneme correspondences that link written words to their pronunciations. Because these are both foundational and essential skills for the development of competence in reading, writing and spelling, they must be taught explicitly, systematically, early and well.
The US National Reading Panel report of 2006 said:Systematic synthetic phonics instruction had a positive and significant effect on disabled readers’ reading skills. These children improved substantially in their ability to read words and showed significant, albeit small, gains in their ability to process text as a result of systematic synthetic phonics instruction. This type of phonics instruction benefits both students with learning disabilities and low-achieving students who are not disabled. Moreover, systematic synthetic phonics instruction was significantly more effective in improving low socio-economic status (SES) children’s alphabetic knowledge and word reading skills than instructional approaches that were less focused on these initial reading skills… Across all grade levels, systematic phonics instruction improved the ability of good readers to spell.
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Published: 22 November 2010


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{% extends PATH+"/inc/layout_citizen.html" %} {% block case_content %}
Dates
From: 30th Apr 2016
To: 29th Apr 2019
Element details
Taxi to and from work 4 days a week
£5.00 per journey
8 journeys per week
Deductions
Employer £10.00 per week
Customer Nil
Support
Approved for £6,240 for 3 years
Used to date £3,120
Journeys approved for 2080
Journeys used 624
Remaining award
£3,120
1,456 units
| Date | Invoice reference |
Payment reference |
Units claimed |
Amount claimed |
Status | |
|---|---|---|---|---|---|---|
| 30th October 2017 | Freds 0005 | ATWIS00005 | 128 | £640 | Paid | View payment |
| 17th July 2017 | Freds 0004 | ATWIS00004 | 208 | £1,040 | Paid | View payment |
| 21st January 2017 | Freds 0003 | ATWIS00003 | - | - | Rejected | View payment |
| 13th October 2106 | Freds 0002 | ATWIS00002 | 168 | £840 | Paid | View payment |
| 18th May 2016 | Freds 0001 | ATWIS00001 | 16 | £80 | Paid | View payment |
| 30th April 2016 | initial award | initial award | initial award | initial award | initial award |


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- Detecting where safe encodings were not applied to emitted
user-inputs
- Detecting where Unicode character transformations might bypass
security filters
- Detecting where non-shortest UTF-8 encodings might bypass security
filters
map of 8 mile road michigan
intruder21
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WCF Binary-Encoded Message Inspector
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Chad Sowald's Request-To-Code
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Eyal's Extensions
- the day clinton sold america has written two extensions:
Thomas Deml's Export WCAT Script extension
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Per-Response Latency Extension
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©2013 Telerik
|


Look: Young M.A Puts On For Her City In Time For Thanksgiving
by Team SOHHNew York rapper Young M.A is paying it forward. The hip-hop artist has announced plans to get involved in a Brooklyn turkey giveaway just in time for Thanksgiving. Look and comment below!


--- layout: default title: Sitespeed.io - Release notes 2.4 description: Sitespeed.io 2.4 release focus on making the default setup even cleaner and works on Windows. author: Peter Hedenskog keywords: sitespeed.io, release, release-notes, 2.4 nav: image: https://www.sitespeed.io/img/sitespeed-2.0-twitter.png twitterdescription: Sitespeed.io 2.4 focus on adding extra value on the defaut setup and add support for Windows (again). ---
Here are the most important changes:
With the new release sitespeed.io has been tested (and fixed) to work on Windows 8.1. You can run analyse and fetch metrics, create the JUnit results and test multiple sites at once. Check out the timing metrics fetched using Internet Explorer 11:
Browser Time has been updated to a new version that works in Windows (and actually also fetch resource timing data, but that isn't used in sitespeed right now).
If you use Chrome or Internet Explorer that reports the first paint time, it will automatically be showed as metric on the summary page. All browser will also show the new fields as default: requestsMissingGzip (how many requests that are missing GZip), jsWeightPerPage (the Javascript weight per page in kb) and cssWeightPerPage (the CSS weight in kb per page).
Now you can hover on the each metric on the summary page to see a description of what it measured.
See all the changes done in this release.


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INTRO: I am an 18 year old male and have recently been experimenting with different types of mind altering substances. I have smoked MJ almost daily over this summer and occasionally during the school year before. I would not consider myself an experienced drug user. I have tried an average dose of mushrooms, LSA and 2CI as far as psycedelic drugs go. I have also taken many different types of pharmaceutical drugs, oh and ether as well.
Ok, so after having an interesting mushroom trip for my first time I was eager to try another substance that would make me hallucinate. I had heard from friends and read online reports about Salvia and how you could get visuals from this. Me and a few friends had accquired some rather expensive 20X salvia extract from a local headshop. Right after we got it we went to one of my friends house to smoke it. Me, Matt, Zack, and Henry (obviously not real names) went out onto Matts back porch and sat down to smoke.
It was a very warm muggy humid day outside and the sun was shining. I placed a small ammount of the salvia extract into my glass pipe and discussed with my friends who should take the first hit. They decided it should be me, I was somewhat nervous at this point and realized I didn't exactly know what to expect, and what happened next I never could have expected.
I took one hit and held it in as long as I could. Immediately I felt my body temperature rise and a strange feeling in my arms, I felt horribly sticky from the hummidity but it was abnormally uncomfortable and I began tugging on my shirt.  I saw no visuals and didn't feel all to different.
So I packed up the bowl again with alot more than the first time because this time my friend Zack would take a hit after me. I lit the bowl and breathed in deep. I held in the smoke and passed the bowl to my friend who did the same. We both exhaled and started to stare at each other. Right away I started getting hot again but this time it was alot more intense. My friend started laughing histarically and so did I.
This is when things got scarry. I felt as if I was being pulled into the chair I was sitting in and I could not stop laughing, but I wanted to so bad. My friend seemd to be experiencing similar effects. As we looked at each other and laughed I felt as if we were each a spoke in a wheel and were spinning. The laughing was the way the spinning got going but I felt as if it would never stop and this scared the shit out of me.  This loop had to be broken and my friend Zack luckily felt the same way and told me to go inside all while still laughing uncontrollably.
I immediately stood up which felt very wrong and difficult to do, and asked him why he told me to go inside, hoping he would say 'to break the loop' or something to that effect. He didn't say anything he just kept laughing. I was told later that I was talking very loudly at this point. I opened the sliding glass door and walked into the house. The AC felt good but I was still very altered. I just wanted to lay down. It was like something was pulling me to the ground.  Nothing was right, the texture of the carpet was completely foreign and rough. The whole time all of this was happening I remember just thinking how could this be real, this cant be happening, how can I prove its not real?!
I layed on the floor for a while still stuck in my state of dillusion and heard my friend Henry say he would make a sandwich. I figured this salvia should be wearing off by now so I will try to make one too. I was extremely hungry at this point. Making a sandwich was so hard, I could not for the life of me get a single slice of ham, I couldnt pick it up. I had to rip at it to get any out of the package and it was the most frustrating thing I had ever experienced, still very much in an altered state of mind but trying to perform normal activities.
Suddenly everyone traveled downstairs to Matts step brothers room who was gone at the time, I layed on his bed and my friends started going through his stuff looking for something. I wanted the feeling I was having to end, but it wouldnt stop, I kept thinking, oh ok im back to normal, then I would see that I was not at all. I remember getting angry and yelling at my friend Matt to get a kinfe.
I remember this part so clear. I yelled at Matt to go and get a butter knife so that we could get his step brothers safe open, he seemd to not listen to me so I yelled at him over and over again until he finaly left and got the knife. Later I apologized to him for yelling and he told me that none of this yelling had happened.
When I finally came down completely I felt somewhat stoned and more comfortable with my surroundings. I couldnt belive what had just happend to me. It was the most intense and copmletely un-explainable feeling in the world. I felt as if there was nothing that will ever make me feel the way I felt at that time. I sat down trying to comprehend what had happend and make something from it but found it difficult to do so. I only had bits and pieces left in my memory from the experience.
I went to work shortly after coming down from the salvia and felt kind of depressed. I felt there was no need for me and that it was pointless to work. I felt this way for the rest of the day and slept good at night. The next day I woke up back to normal, no depression.
CONCLUSION: After experimenting with this insane substance a few times after my initial experience, everything about it seems wrong to me. The way it makes me feel, the sound it makes when it burns, the harsh smoke when I breath it in, the places it takes you to. I do not like it all but yet it intrigues me so much that I would reluctantly try it again. To be honest, it scares me to death. I would not recomend it to anyone unless you are prepared to have you ego and everything else you know completely ripped away from you.
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.


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Boost C++ Libraries
Since 2005, Boost has participated in the Google Summer of Code™, initiative, a program in which student developers are sponsored for their contributions within open source organizations that are willing to mentor the participants.
Currently the application process begins around March/April by the student reviewing past GSoC project ideas and approaching the Boost developers mailing list to find a mentor willing to mentor them and codevelop a project proposal to be submitted to Google for funding the upcoming summer. Students are expected to be flexible with regard to what topics and proposals mentors are willing to mentor.
If you arrive at the process too late to prearrange a mentor, some potential mentors supply prewritten project proposals with C++ programming competency tests on this year's GSoC Project Ideas page. If you have in the past made a substantial contribution to a well known open source project or participated in previous editions of the GSoC, it may be possible to skip submission of the C++ programming competency test solution (decided on a case by case basis).
Submit the competency test solution as part of the project proposal you will submit to the Google Summer of Code program. You can copy and paste relevant parts of your code or you can include links to, for example, GitHub repository with your solution project.
Google Summer of Code 2020 overview
This year, in 2020, 10 students will work with us on improving and adding new features to many of our libraries. Once again, the competition has been very hard. A few numbers: - we received 93 proposals in total. 85 of them were complete, then we kept 47 proposals only - finally we selected 10 of them, covering 7 different Boost libraries - and countless hours of work from the most incredible team of mentors! Here is the list of projects for 2020:Boost.Real
- Kishan Shukla will be working on making the Real library ready for revision. His main goal is to reimplement the division algorithm so it can be used with any number base representation and the Karatsuba multiplication algorithm. Also, it will be working on implementing some operators and common irrational numbers as Pi. Mentors: Laouen Belloli and Damian Vicino.
Boost.Astronomy
- Syed Ali Hasan will be working on the astronomical coordinate system, he will implement different coordinate conversion using SOFA library Mentor: Pranam Lashkari
- Gopi Krishna Menon will be completing the parser for FITS file and also optimise the already developed components. Mentor: Sarthak Singhal
Boost.GIL
- Debabrata Mandal will be implementing histogram computation as a built-in feature, algorithms for histogram analysis,
image processing algorithms based on histogram and possibly integration with Boost.Histogram library.
Mentors: Mateusz Loskot, Pranam Lashkari - Olzhas Zhumabek will be extending GIL's portfolio of image processing algorithms, implementing for image scaling and sampling, Perona–Malik diffusion, Non-Maximum Suppression for object detection, Hysteresis threshold and some more. Mentor: Mateusz Loskot
Boost.Multiprecision
- Dimitris Los will work on extending and optimizing parts of Boost.Multiprecision to higher precision of many thousands of bits or more. Mentor: Christopher Kormanyos
Boost.uBlas
- Ashar Khan will stabilize and improve Boost.uBlas using the C++20-Standard. The main focus of his project will be enhancing code-coverage and documentation, integrating static code analysis and generating tensor concepts. Mentor: Cem Bassoy
- Tom Kwok will improve and finalize the implementation of data frames in uBlas, with the aim of delivering production-ready by the end of the summer. Data frames in uBlas are similar in functionalities to other implementations like in R, Python.Pandas or Julia. Mentor: David Bellot
Boost.Geometry
- Tinko Bartels will work on robust yet efficient geometric predicates for Boost Geometry. Mentor: Vissarion Fisikopoulos
Boost.Real
- Vikram Singh Chundawat will work in taking Real to revision ready state. His focus will be in improving the internal representation datatypes and providing functions required to conveniently support Taylor series. Mentors: Damian Vicino and Laouen Belloli
Github's for standalone GSoCs past and present
Since 2013 with Boost's transition to git we have kept a single umbrella org on github for those GSoCs which are fairly self standing. Incremental extensions to existing libraries usually enter that library's main git repo as an experimental branch. Here are those orgs:
- GSoC 2020: https://github.com/BoostGSoC20
- GSoC 2019: https://github.com/BoostGSoC19
- GSoC 2018: https://github.com/BoostGSoC18
- GSoC 2017: https://github.com/BoostGSoC17
- GSoC 2015: https://github.com/BoostGSoC15
- GSoC 2014: https://github.com/BoostGSoC14
- GSoC 2013: https://github.com/BoostGSoC13
Students may find examining past GSoC source code and commit histories of use.
Historical GSoC Ideas pages for years 2006 to now
- 2020 Project Ideas
- 2019 Project Ideas
- 2018 Project Ideas
- 2017 Project Ideas
- 2016 Project Ideas
- 2015 Project Ideas
- 2014 Project Ideas
- 2013 Project Ideas
- 2012 Project Ideas
- 2011 Project Ideas
- 2010 Project Ideas
- 2009 Project Ideas
- 2008 Project Ideas
- 2007 Project Ideas
- 2006 Project Ideas. An overview of Boost participation in Google Summer of Code™ 2006.


Kanata Masters Hockey Association - News Items
Welcome to the Kanata Masters Home Page
2016-2017 League Rules
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New Rules Regarding The Use of Spares
New Crease rule
Replacement and Disciplinary Committees
Congrats to Eagleson Flooring for winning the Championship.
Golf Tournament
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| View source on GitHub |
Generates parsing spec for tf.parse_example to be used with classifiers.
tf.compat.v1.estimator.classifier_parse_example_spec( feature_columns, label_key, label_dtype=tf.dtypes.int64, label_default=None, weight_column=None )
If users keep data in tf.Example format, they need to call tf.parse_example with a proper feature spec. There are two main things that this utility helps:
DNNClassifier
to corresponding tf.parse_example spec. This utility encodes
it by getting related information from users (key, dtype).Example output of parsing spec:
# Define features and transformations feature_b = tf.feature_column.numeric_column(...) feature_c_bucketized = tf.feature_column.bucketized_column( tf.feature_column.numeric_column("feature_c"), ...) feature_a_x_feature_c = tf.feature_column.crossed_column( columns=["feature_a", feature_c_bucketized], ...) feature_columns = [feature_b, feature_c_bucketized, feature_a_x_feature_c] parsing_spec = tf.estimator.classifier_parse_example_spec( feature_columns, label_key='my-label', label_dtype=tf.string) # For the above example, classifier_parse_example_spec would return the dict: assert parsing_spec == { "feature_a": parsing_ops.VarLenFeature(tf.string), "feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32), "feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32) "my-label" : parsing_ops.FixedLenFeature([1], dtype=tf.string) }
Example usage with a classifier:
feature_columns = # define features via tf.feature_column estimator = DNNClassifier( n_classes=1000, feature_columns=feature_columns, weight_column='example-weight', label_vocabulary=['photos', 'keep', ...], hidden_units=[256, 64, 16]) # This label configuration tells the classifier the following: # * weights are retrieved with key 'example-weight' # * label is string and can be one of the following ['photos', 'keep', ...] # * integer id for label 'photos' is 0, 'keep' is 1, ... # Input builders def input_fn_train(): # Returns a tuple of features and labels. features = tf.contrib.learn.read_keyed_batch_features( file_pattern=train_files, batch_size=batch_size, # creates parsing configuration for tf.parse_example features=tf.estimator.classifier_parse_example_spec( feature_columns, label_key='my-label', label_dtype=tf.string, weight_column='example-weight'), reader=tf.RecordIOReader) labels = features.pop('my-label') return features, labels estimator.train(input_fn=input_fn_train)
feature_columns
: An iterable containing all feature columns. All items
should be instances of classes derived from FeatureColumn
.label_key
: A string identifying the label. It means tf.Example stores labels
with this key.label_dtype
: A tf.dtype
identifies the type of labels. By default it is
tf.int64
. If user defines a label_vocabulary
, this should be set as
tf.string
. tf.float32
labels are only supported for binary
classification.label_default
: used as label if label_key does not exist in given
tf.Example. An example usage: let's say label_key
is 'clicked' and
tf.Example contains clicked data only for positive examples in following
format key:clicked, value:1
. This means that if there is no data with
key 'clicked' it should count as negative example by setting
label_deafault=0
. Type of this value should be compatible with
label_dtype
.weight_column
: A string or a NumericColumn
created by
tf.feature_column.numeric_column
defining feature column representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example. If it is a string, it is
used as a key to fetch weight tensor from the features
. If it is a
NumericColumn
, raw tensor is fetched by key weight_column.key
,
then weight_column.normalizer_fn is applied on it to get weight tensor.A dict mapping each feature key to a FixedLenFeature
or VarLenFeature
value.
ValueError
: If label is used in feature_columns
.ValueError
: If weight_column is used in feature_columns
.ValueError
: If any of the given feature_columns
is not a _FeatureColumn
instance.ValueError
: If weight_column
is not a NumericColumn
instance.ValueError
: if label_key is None.


SAVING GRACE (director: Nigel Cole; screenwriter: Craig Ferguson/Mark Crowdy/based on a story by Crowdy; cinematographer: John de Borman; editor: Alan Strachan; cast: Brenda Blethyn (Grace Trevethyn), Craig Ferguson (Matthew), Martin Clunes (Dr. Bamford), Tcheky Karyo (Jacques), Jamie Foreman (China), Bill Bailey (Vince, low-level drug dealer), Valerie Edmond (Nicky), Tristan Sturrock (Harvey), Clive Merrison (Quentin), Leslie Phillips (Vicar), Diana Quick (Honey), Phyllida Law (Margaret), Linda Kerr Scott (Diana), Ken Campbell (Sgt. Alfred); Runtime: 93; 2000-UK)
Reviewed by Dennis Schwartz
Another formula 'feel good' quirky comedy from the British Isles, much like "Waking Ned Devine," and a host of other popular low budget movies that turned a nifty profit off a thin story line. It is filled with likable establishment types living in a quaint town, such as tolerant vicars and policemen, plenty of eccentrics, and of locals who stick together through thick and thin. The strength of this film lies in its rational theme, that smoking pot should be legalized since it is even less dangerous than drinking alcohol. The film is tolerable until after its first hour, it then falls completely apart, becoming just too absurd to consider as anything but sitcom fluff gone completely bananas.
But its long overdue message of decriminalization for certain drugs is on target and allows the film to, at least, be viewed as containing a pertinent message. Its message is especially appropriate nowadays considering the 'War on Drugs' is about to be escalated into Colombia. The drug war has been a total failure so far and with no hope of it being won by law enforcement methods in the future, it has so far just ended up causing America's jails to be overcrowded with non-violent drug prisoners and offers zero hope of curtailing America's drug usage and drug supply. Billions of dollars have just been allocated by Congress to get officially involved in the Colombian Civil War, by backing a corrupt government (Remember Vietnam!) with the idea that military might could stop the cocaine and marijuana harvests in that country. Everything about this proposed action smacks of Vietnam, with this scenario closer to home, so what probably awaits such an effort is a more futile result than the other war.
The film's mild theme of showing squares getting into the weed business and finding it acceptable by the public, something that should have been accepted some 40 years ago, is a theme long overdue for public acceptance, but one that is better received late than never. There has to be some common sense in distinguishing what drugs should be legalized and how to educate the public on this, rather than making it a police matter altogether.
The film opens in a quaint Cornish seaside village at the funeral of the husband of a middle-aged woman, Grace (Brenda Blethyn), who fell out of an airplane, evidently committing suicide.  He left her with a stack of enormous bills and put up, without her knowledge, their comfortable 300 year old house as collateral for a failed business venture by taking out a second mortgage. The widow finds herself bankrupt and with no marketable skills to pay back her husband's enormous debts and prevent her from losing the house and becoming homeless. She also knows that he was having an affair with a sophisticated woman from London, Honey (Quick). What makes the affair doubly unsatisfying, is that when conferring with Honey, she finds out that he was a tiger with her in bed but refrained from having sex with her.
Even though the locals try to help the bankrupt widow out as best they can, the creditors swoop down on her like vultures. Her gardener and handyman, Matthew (Ferguson), decides to stay on with her, despite his last check bouncing. Since she is an expert amateur horticulturist, he convinces her to help him revive his hemp plants, which he grows for private use. The two soon see this as a possibly big business opportunity, as Grace works miracles with the plants which turn out to be high-quality stuff. For Grace, this is a way to save her house and get her out of debt; for the Scotsman, Matthew, it offers a chance for him to continue to live in this beautiful village and earn enough money to marry his fisherman girlfriend Nicky (Valerie).
The locals all let the illegal activities go on undisturbed: the police sergeant (Campbell) pretends not to notice what the bright lights in Grace's greenhouse is for, the friendly vicar (Phillips) counsels about the wisdom of not taking action over something that you can't control, and the doctor (Clunes) enjoys smoking the weed for recreation.
The story becomes incredulous when Grace decides to go to London and score with a big-time drug dealer instead of having Matthew go, because she finds out Nicky is pregnant and doesn't want Matthew to end up in jail. It is interesting to note, that no one thinks smoking grass is bad for you, only that one can go to jail.
The scene with Grace on Portobello Road in the Notting Hill section of London, trying to find a drug dealer, while dressed out of place, in a white dress and hat one would wear to a tea party in Cornwall but not on the streets of Notting Hill while looking to make a drug deal, was completely hokey and the film descended into a huge black hole, whereby it never recovered its dignity.
The film moves into the formula mode, whereas Grace beats the odds against her failing to survive, as she meets an international drug dealer, a Frenchman (Tcheky Karyo), who is first seen threatening to cut off her fingers but later becomes her business partner and husband as she becomes a millionaire and writes a best selling fiction book about her experiences, thereby becoming a celebrity. Things work out in a ridiculous way, where there is a total breakdown in the character development of Grace, where this matronly figure suddenly becomes merely a cartoon figure instead of live flesh and blood. There are a lot of silly giggles that come forth and the film has just enough of an acting performance by Brenda Blethyn to breathe air into it before it comes to its belated finale, as it drones on with its transparently gratuitous upbeat tone, until it makes the audience feel good that the sweet old lady succeeded. This 'unhip' film leaves one with the impression that smoking and growing pot is silly, but should be tolerated as something naughty middle-aged widows screwed by their terrible former husbands must do in order not to remain destitute. Ummm!
REVIEWED ON 9/13/2000 GRADE: C-
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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{{ offices.registeredOffice.deliveryAddress.addressCity }}
{{ offices.registeredOffice.deliveryAddress.addressRegion }}
{{ offices.registeredOffice.deliveryAddress.postalCode }}
{% if offices.registeredOffice.deliveryAddress.addressCountryDescription %}
{{ offices.registeredOffice.deliveryAddress.addressCountryDescription }}
{% else %}
{{ offices.registeredOffice.deliveryAddress.addressCountry }}
{% endif %}
{{ offices.registeredOffice.deliveryAddress.deliveryInstructions }}
{% else %}
Not Available
{% endif %}
|
|
Mailing Address
{% if offices.recordsOffice is defined %}
{{ offices.recordsOffice.mailingAddress.streetAddress }}
{{ offices.recordsOffice.mailingAddress.streetAddressAdditional }}
{{ offices.recordsOffice.mailingAddress.addressCity }}
{{ offices.recordsOffice.mailingAddress.addressRegion }}
{{ offices.recordsOffice.mailingAddress.postalCode }}
{% if offices.recordsOffice.mailingAddress.addressCountryDescription %}
{{ offices.recordsOffice.mailingAddress.addressCountryDescription }}
{% else %}
{{ offices.recordsOffice.mailingAddress.addressCountry }}
{% endif %}
{{ offices.recordsOffice.mailingAddress.deliveryInstructions }}
{% else %}
Not Available
{% endif %}
|
Delivery Address
{% if offices.recordsOffice is defined %}
{{ offices.recordsOffice.deliveryAddress.streetAddress }}
{{ offices.recordsOffice.deliveryAddress.streetAddressAdditional }}
{{ offices.recordsOffice.deliveryAddress.addressCity }}
{{ offices.recordsOffice.deliveryAddress.addressRegion }}
{{ offices.recordsOffice.deliveryAddress.postalCode }}
{% if offices.recordsOffice.deliveryAddress.addressCountryDescription %}
{{ offices.recordsOffice.deliveryAddress.addressCountryDescription }}
{% else %}
{{ offices.recordsOffice.deliveryAddress.addressCountry }}
{% endif %}
{{ offices.recordsOffice.deliveryAddress.deliveryInstructions }}
{% else %}
Not Available
{% endif %}
|
|
Mailing Address
{% if offices.businessOffice is defined %}
{{ offices.businessOffice.mailingAddress.streetAddress }}
{{ offices.businessOffice.mailingAddress.streetAddressAdditional }}
{{ offices.businessOffice.mailingAddress.addressCity }}
{{ offices.businessOffice.mailingAddress.addressRegion }}
{{ offices.businessOffice.mailingAddress.postalCode }}
{% if offices.businessOffice.mailingAddress.addressCountryDescription %}
{{ offices.businessOffice.mailingAddress.addressCountryDescription }}
{% else %}
{{ offices.businessOffice.mailingAddress.addressCountry }}
{% endif %}
{% else %}
Not Available
{% endif %}
|
Delivery Address
{% if offices.businessOffice is defined %}
{{ offices.businessOffice.deliveryAddress.streetAddress }}
{{ offices.businessOffice.deliveryAddress.streetAddressAdditional }}
{{ offices.businessOffice.deliveryAddress.addressCity }}
{{ offices.businessOffice.deliveryAddress.addressRegion }}
{{ offices.businessOffice.deliveryAddress.postalCode }}
{% if offices.businessOffice.deliveryAddress.addressCountryDescription %}
{{ offices.businessOffice.deliveryAddress.addressCountryDescription }}
{% else %}
{{ offices.businessOffice.deliveryAddress.addressCountry }}
{% endif %}
{% else %}
Not Available
{% endif %}
|


This is billie.
This is where I will be adding links to sprints and other relevant places.
Video will only show in Chrome
chuck your history here
https://stackoverflow.com/questions/22776636/set-jquery-cookie-for-each-checkbox
https://stackoverflow.com/questions/42661249/making-a-checkbox-persistent-on-page-reload
https://codepen.io/oculusriff/pen/RpoJWQ?editors=1010#0
https://www.youtube.com/watch?v=KQeT3nbHqsg&index=145&list=UU6hBefyLMtG7FXhZ55da3Vw
https://www.youtube.com/watch?v=HFsMKz_l7tg&feature=youtu.be&a=
https://code.visualstudio.com/
https://github.com/dev-academy-programme/curriculum/tree/master/resources/nt-time-ARTICLE
https://babun.github.io/
https://waffle.io/billiebambina
https://www.youtube.com/watch?v=UkIEygBpOvU
https://www.youtube.com/results?search_query=study+radio
https://enspiral-dev-academy.slack.com/z-app-2908811352-447041342551-0aacdae199623163e780770c8bb4535dfc55c90e748b261dfec64aa81324b7e
https://gitforwindows.org/
https://www.youtube.com/user/ArtzieMusic
https://www.youtube.com/watch?v=dgCnYsDTiXU
https://learnpythonthehardway.org/
https://www.youtube.com/watch?v=NjP4i2cKDRE
https://git-scm.com/book/en/v2/Git-Basics-Working-with-Remotes
https://www.git-tower.com/learn/git/ebook/en/command-line/remote-repositories/connecting-remote-repositories#start
https://classroom.udacity.com/courses/ud123/lessons/
https://twitter.com/slackhq/status/813799142833192960?lang=en
https://devacademy.co.nz/
https://www.youtube.com/watch?v=NWYGh9luFuk
https://stackoverflow.com/questions/3580013/should-i-use-past-or-present-tense-in-git-commit-messages?
https://www.youtube.com/watch?v=0fKg7e37bQE
https://www.youtube.com/watch?v=YDQuwWt2j_Y
https://www.youtube.com/watch?v=ENXvZ9YRjbo
https://www.youtube.com/watch?v=huOPVqztPdc
https://www.fastcompany.com/40464734/the-surprising-upsides-to-getting-angry-at-work
https://www.scruminc.com/what-is-timeboxing/
https://blog.bufferapp.com/make-it-big-by-starting-small
https://www.themuse.com/advice/the-best-tricks-for-getting-in-the-zone-at-work
https://francescocirillo.com/pages/pomodoro-technique
http://paidtoexist.com/counterintuitive-productivity/
https://www.howtogeek.com/163127/how-powershell-differs-from-the-windows-command-prompt/
http://www.foggmethod.com/
https://www.youtube.com/watch?v=C8XG02das-Ahttps://www.codeavengers.com/
https://www.trainingzone.co.uk/develop/talent/the-importance-of-reflection
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2414478
https://hbswk.hbs.edu/item/learning-by-thinking-how-reflection-improves-performance
https://www.git-tower.com/blog/git-cheat-sheet
https://www.git-tower.com/blog/command-line-cheat-sheet/
https://www.codecademy.com/courses/learn-html/lessons/intro-to-html/exercises
https://codecombat.com/community
https://www.warehousestationery.co.nz/office-products/post-it-sticky-notes-and-labels/post-it-notes-flags-tabs
https://www.codecademy.com/courses/learn-the-command-line/lessons/navigation/exercises/
https://www.typingtest.com/
https://www.youtube.com/watch?v=-15xE5U9BCQ
https://www.google.com/search?q=namennayo+cats
https://www.sitepoint.com/quick-tip-persist-checkbox-checked-state-after-page-reload/
http://www.phpforkids.com/html/html-form-check-boxes-radio-buttons.php
https://stackoverflow.com/questions/9709209/html-select-only-one-checkbox-in-a-group
https://www.textfixer.com/tools/remove-white-spaces.php
https://watchandcode.com/courses/60264/lectures/928384
https://plnkr.co/
https://www.w3schools.com/tags/tryit.asp?filename=tryhtml5_input_type_checkbox
https://learn.freecodecamp.org/
https://stackoverflow.com/questions/18422974/html-not-linking-css
http://kemanime.com/watch/shoujo-shuumatsu-ryokou/episode-12.html
https://wellytech2018.lilregie.com/booking/attendees/new
https://www.gosquared.com/resources/html-cheat-sheet/
https://www.hostingreviewbox.com/html5-cheat-sheet/
https://dansup.github.io/bulma-templates/
http://www.pulpandpixels.com/
https://www.google.com/search?q=tofukken&ie=utf-8&oe=utf-8&client=firefox-b-ab
https://blog.zipboard.co/a-beginners-guide-to-css-front-end-frameworks-8045a499456b
https://www.youtube.com/watch?v=RMPxx71ICxk
https://www.youtube.com/watch?v=8CrOL-ydFMI
https://css-tricks.com/little-css-stuff-newcomers-get-confused-about/
https://css-tricks.com/the-difference-between-id-and-class/
https://www.st-andrews.ac.uk/students/study-abroad/academic/agreement/
http://ec.europa.eu/dgs/education_culture/repository/education/opportunities/higher-education/doc/learning-studies-guidelines_en.pdf
https://www.careers.govt.nz/jobs-database/it-and-telecommunications/information-technology/software-developer/
https://www.thoughtco.com/how-to-write-a-learning-contract-31423
https://www.dummies.com/web-design-development/html5-and-css3/how-to-use-an-external-style-sheet-for-html5-and-css3-programming/
https://www.wingfinder.com
https://www.41q.com/
https://www.youtube.com/watch?v=6fJKlCUyhOs
https://www.udacity.com/
https://www.youtube.com/watch?v=PNhQakLdI9o
https://www.mindcoolness.com/blog/clarify-your-values/
https://www.heacademy.ac.uk/knowledge-hub/flipped-learning-0