More than quarter of Britons hate clothes shopping in stores – poll
Some 29% do not feel confident when buying clothes, and 26% say they hate in-store shopping as a result, the survey suggests.
More than a quarter of Britons say they hate shopping for clothes in stores because it knocks their body confidence, according to a survey.
More than half of Britons (57%) never reach the point where they feel confident about their body image, the poll for the Fashion Retail Academy school suggests.

Living in the digital age, in which the airbrush is used a little too liberally, it is easy to see why people feel self conscious about their body image.Fashion Retail Academy
Of those surveyed, 33% of 18 to 24-year-olds said they were body confident, falling to 32% for 25 to 34-year-olds.
The figure rises again to 33% for 35 to 44-year-olds, falls to 30% for 45 to 54-year-olds and then peaks at 43% for those aged 55 and over.
The Fashion Retail Academy suggested that the lack of body confidence in the UK could be due to the rise of social media fashion adverts “which are filling our screens with non representative body types”.
The school’s principal and chief executive, Lee Lucas, said: “Living in the digital age, in which the airbrush is used a little too liberally, it is easy to see why people feel self conscious about their body image. Consumers are being constantly bombarded with unrealistic images of the human form.
“However, people should not shy away from shopping in-store, where there are stylists and personal shoppers in the majority of shops now who can help you find styles to suit your body shape.
“Fashion brands and shops have already started to adapt to body consciousness, with adverts starring models of all shapes, styles and sizes.
“Shopping for clothes should be an exercise in boosting body confidence, not knocking it.”
– OnePoll surveyed 2,000 people online between August 23-28.


--- layout: page title: About header-img: "img/about-bg.png" ---
https://medium.com/@kristinelpetrosyan/going-after-my-dreams-1a02543de4ed


More Jataka Tales
by Ellen C. Babbitt
“The Hawks and Their Friends”
Additional Information
- Year Published: 1922
- Language: English
- Country of Origin: India
- Source: Babbitt, E.C. (Ed.). (1922). More Jataka Tales. New York, NY: D. Appleton-Century Company.
-
Readability:
- Flesch–Kincaid Level: 3.4
- Word Count: 1,056
- Genre: Fairy Tale/Folk Tale
- Keywords: friendship, hero
- ✎ Cite This
Downloads
A family of Hawks lived on an island in a lake not far from the great forest. On the northern shore of this lake lived a Lion, King of Beasts. On the eastern shore lived a Kingfisher. On the southern shore of the lake lived a Turtle.
“Have you many friends near here?” the Mother Hawk asked the Father Hawk.
“No, not one in this part of the forest,” he said. “You must find some friends. We must have some one who can help us if ever we are in danger, or in trouble,” said the Mother Hawk. “With whom shall I make friends?” asked the Father Hawk. “With the Kingfisher, who lives on the eastern shore, and with the Lion on the north,” said the Mother Hawk, “and with the Turtle who lives on the southern shore of this lake.” The Father Hawk did so.
One day men hunted in the great forest from morning until night, but found nothing. Not wishing to go home empty-handed, they went to the island to see what they could find there. “Let us stay here to-night,” they said, “and see what we can find in the morning.”
So they made beds of leaves for themselves and lay down to sleep. They had made their beds under the tree in which the Hawks had their nest. But the hunters could not go to sleep because they were bothered by the flies and mosquitoes. At last the hunters got up and made a fire on the shore of the lake, so that the smoke would drive away the flies and mosquitoes. The smoke awoke the birds, and the young ones cried out.
“Did you hear that?” said one of the hunters. “That was the cry of birds! They will do very well for our breakfast. There are young ones in that nest.” And the hunters put more wood on the fire, and made it blaze up.
Then the Mother bird said to the Father: “These men are planning to eat our young ones. We must ask our friends to save us. Go to the Kingfisher and tell him what danger we are in.” The Father Hawk flew with all speed to the Kingfisher’s nest and woke him with his cry.
“Why have you come?” asked the Kingfisher. Then the Father Hawk told the Kingfisher what the hunters planned to do.
“Fear not,” said the Kingfisher. “I will help you. Go back quickly and comfort my friend your mate, and say that I am coming.” So the Father Hawk flew back to his nest, and the Kingfisher flew to the island and went into the lake near the place where the fire was burning.
While the Father Hawk was away, one of the hunters had climbed up into the tree. Just as he neared the nest, the Kingfisher, beating the water with his wings, sprinkled water on the fire and put it out. Down came the hunter to make another fire. When it was burning well he climbed the tree again. Once more the Kingfisher put it out. As often as a fire was made, the Kingfisher put it out. Midnight came and the Kingfisher was now very tired.
The Mother Hawk noticed this and said to her mate: “The Kingfisher is tired out. Go and ask the Turtle to help us so that the Kingfisher may have a rest.”
The Father Hawk flew down and said, “Rest awhile, Friend Kingfisher; I will go and get the Turtle.”
So the Father Hawk flew to the southern shore and wakened the Turtle. “What is your errand, Friend?” asked the Turtle. “Danger has come to us,” said the Father Hawk, and he told the Turtle about the hunters. “The Kingfisher has been working for hours, and now he is very tired. That is why I have come to you.” The Turtle said, “I will help you at once.” Then the Turtle went to the island where the Hawks lived. He dived into the water, collected some mud, and put out the fire with it. Then he lay still.
The hunters cried: “Why should we bother to get the young Hawks?  He will make a fine breakfast for all of us. We must be careful or he will bite us. Let us throw a net over him and turn him over.”
They had no nets with them, so they took some vines, and tore their clothes into strings and made a net.
But when they had put the net all over the Turtle, they could not roll him over. Instead, the Turtle suddenly dived down into the deep water. The men were so eager to get him that they did not let go of the net, so down they went into the water. As they came out they said: “Half the night a Kingfisher kept putting out our fires. Now we have torn our clothes and got all wet trying to get this Turtle. We will build another fire, and at sunrise we will eat those young Hawks.” And they began to build another fire.
The Mother Hawk heard them, and said to her mate: “Sooner or later these men will get our young. Do go and tell our friend the Lion.” At once the Father Hawk flew to the Lion. “Why do you come at this hour of the night?” asked the Lion. The Hawk told him the whole story.
The Lion said: “I will come at once. You go back and comfort your mate and the young ones.” Soon the Lion came roaring. When the hunters heard the Lion’s roar they cried, “Now we shall all be killed.” And away they ran as fast as they could go. When the Lion came to the foot of the tree, not one of the hunters was to be seen. Then the Kingfisher and the Turtle came up, and the Hawks said: “You have saved us. Friends in need are friends indeed.”


#include <BotA.h>
Public Member Functions | |
| virtual const char * | SOLVER_NAME ()=0 |
| BotA (Base *base, Level *level, int maxNodeNumber, int maxRamSize, int openTableSize, int closeTableSize, int costLimit, int deadlockedBoxesSearch=2, bool onlyPushNumber=false, bool quickSearch=false) | |
| int | getMinReject (void) const |
| int | getCostLimit (void) const |
| int | getQuickSearch (void) const |
| virtual int | getSize (void) |
| int | getInitialCost () |
Protected Member Functions | |
| virtual void | process () |
| void | saveMostUsedPositions (int numberOfPos) |
| bool | isDeadTreeNode (TreeNode *treeNode) |
| void | deleteDeadTreeNode (TreeNode *treeNode) |
| bool | workOnAlreadySearched (TreeNode *treeNode, int *counter) |
| TreeNode * | firstTreeNode (void) |
| Child ** | findPonderedChildren (TreeNode *treeNode) |
| virtual void | addTreeNodeToCloseList (TreeNode *treeNode) |
| virtual void | initHashTable (void) |
| virtual void | initCloseNodeList (void) |
| virtual TreeNode * | createTreeNode (Node *node, TreeNode *parentTreeNode, int pushCost) |
| void | moveTreeNode (TreeNode *treeNode, TreeNode *newParent) |
| virtual int | f (TreeNode *treeNode, int pushCost) const =0 |
| virtual int | g (TreeNode *treeNode, int pushCost) const =0 |
| virtual int | h (TreeNode *treeNode) const =0 |
| virtual bool | testSolution (TreeNode *treeNode) |
| virtual bool | solutionNode (const TreeNode *treeNode) const |
| int * | mostUsedPositions (int number, bool withGoals) |
| int | findMaxCellAndReplaceValue (int *tab, int tabLength, bool withGoals) |
| virtual void | printInfos (TreeNode *treeNode) |
Protected Attributes | |
| int | costLimit |
| int | minReject |
| bool | quickSearch |
| BotA::BotA | ( | Base * | base, | |
| Level * | level, | |||
| int | maxNodeNumber, | |||
| int | maxRamSize, | |||
| int | openTableSize, | |||
| int | closeTableSize, | |||
| int | costLimit, | |||
| int | deadlockedBoxesSearch = 2 , |
|||
| bool | onlyPushNumber = false , |
|||
| bool | quickSearch = false | |||
| ) |
Constructor for a solver of a level
| base | main class of the game | |
| level | Level to be used | |
| maxNodeNumber | Limit number of nodes to explore | |
| maxRamSize | Limit max ram size to allocate | |
| openTableSize | size of Open Table to allocate (hashtable) | |
| closeTableSize | size of Close Table to allocate (hashtable) | |
| limit | of accepted f(x). if costLimit = -1, costLimit = +infinity | |
| deadlockedBoxesSearch | number of boxes to use when testing every possible positions of deadlock | |
| onlyPushNumber | only keep number of pushes in Stats object. don't generate solution Path (CPU saving for deadlocks and penalties) | |
| quickSearch | don't test penalties of every nodes before adding it on the tree |
| virtual const char* BotA::SOLVER_NAME | ( | ) | [pure virtual] |
Name of this solver
Implemented in BotBestMovesS.
| int BotA::getMinReject | ( | void | ) | const [inline] |
minReject
| int BotA::getCostLimit | ( | void | ) | const [inline] |
limit of accepted f(x)
| int BotA::getQuickSearch | ( | void | ) | const [inline] |
quick search
| int BotA::getSize | ( | void | ) | [virtual] |
Return size in octets of the actual state of this solver
| int BotA::getInitialCost | ( | ) |
Get cost of first treeNode
| void BotA::process | ( | ) | [protected, virtual] |
Try to resolve this level with a fixed max number of nodes or a fixed max number of ram size to allocate. (first to be reached)
| void BotA::saveMostUsedPositions | ( | int | numberOfPos | ) | [protected] |
If ram limit or node limit are reached, we save a list of sub-states of boxes where cells are most used by boxes in the tree. This list of sub-states will be tested before next solving of same level
| number | of "top" cell positions we want to keep. If we take 15, we will have list of every sub-states of 1,2,3,4,5 and 6 boxes for 15 most used positions |
| bool BotA::isDeadTreeNode | ( | TreeNode * | treeNode | ) | [protected] |
Test if a treenode is a dead branch (treenode and all children are in openTable) or not
| treeNode | node to be tested |
| void BotA::deleteDeadTreeNode | ( | TreeNode * | treeNode | ) | [protected] |
delete a dead treenode from the tree and all its children. Also delete this node from openTable.
| treeNode | node to be deleted |
| bool BotA::workOnAlreadySearched | ( | TreeNode * | treeNode, | |
| int * | counter | |||
| ) | [protected] |
If this node is already searched, we look at the cost of stocked treeNode and move what it need to be moved to keep the lowest cost
| treeNode | treenode we want to look if it's new or beter than anything existing | |
| counter | counter of children of actual treeNode |
| TreeNode * BotA::firstTreeNode | ( | void | ) | [protected] |
Get first treeNode from closeNodeList and delete it from the list
| Child ** BotA::findPonderedChildren | ( | TreeNode * | treeNode | ) | [protected] |
Get list of children from a treeNode and move this treenode from close list to open list. This function take macro children too. (children with many pushes to put a box on a goal in one step)
| treeNode | treeNode we want to find children |
| void BotA::addTreeNodeToCloseList | ( | TreeNode * | treeNode | ) | [protected, virtual] |
Add a new TreeNode to the waiting list at the right position. New Treenode is added TO THE CORRECT PLACE DEPENDING OF ITS COST (best-first search)
| void BotA::initHashTable | ( | void | ) | [protected, virtual] |
Initialize empty hashtables
| void BotA::initCloseNodeList | ( | void | ) | [protected, virtual] |
Initialize empty waiting node list
| TreeNode * BotA::createTreeNode | ( | Node * | node, | |
| TreeNode * | parentTreeNode, | |||
| int | pushCost | |||
| ) | [protected, virtual] |
Create a TreeNode from a node and its parent treeNode in the search tree (if he got one, else NULL) and assign a cost at it
| node | Node we want to put in the new TreeNode | |
| parentTreeNode | Parent TreeNode we want to attach to new TreeNode | |
| pushCost | number of pushes from its parent (usefull for macro-pushes) |
Move a treenode in the tree by split it from its parent and attach it to a new parent
| treeNode | treenode we want to move in the tree | |
| newParent | new parent for this treenode |
| virtual int BotA::f | ( | TreeNode * | treeNode, | |
| int | pushCost | |||
| ) | const [protected, pure virtual] |
Cost function to evaluate the cost of a treenode with its position in the tree (parent of treenode must be assigned before calling this function)
| treeNode | to be computed | |
| pushCost | number of pushes from its parent (usefull for macro-pushes) |
| virtual int BotA::g | ( | TreeNode * | treeNode, | |
| int | pushCost | |||
| ) | const [protected, pure virtual] |
Cost function to evaluate the cost of getting to this treeNode from the initial treenode (parent of treenode must be assigned before calling this function)
| treeNode | to be computed | |
| pushCost | number of pushes from its parent (usefull for macro-pushes) |
| virtual int BotA::h | ( | TreeNode * | treeNode | ) | const [protected, pure virtual] |
Cost function to evaluate the cost of admissibly estimated distance to a goal treenode from this treenode (parent of treenode must be assigned before calling this function)
Implemented in BotBestMovesS.
| bool BotA::testSolution | ( | TreeNode * | treeNode | ) | [protected, virtual] |
Test if a treeNode is a solution node and create Stats object.
| treeNode | treenode we want to test |
| bool BotA::solutionNode | ( | const TreeNode * | treeNode | ) | const [protected, virtual] |
Test if a treenode is a solution treenode (based on h(x)=0 value)
| treeNode | TreeNode to tested |
| int * BotA::mostUsedPositions | ( | int | number, | |
| bool | withGoals | |||
| ) | [protected] |
Find most used positions (boxes on them). Positions with goals or without goals.
| number | number of most used positions we want to find | |
| withGoals | true if we want to take care of goals, false if not |
| int BotA::findMaxCellAndReplaceValue | ( | int * | tab, | |
| int | tabLength, | |||
| bool | withGoals | |||
| ) | [protected] |
find max cell of a tab, keep it, and replace it with -1
| tab | tab of values | |
| tabLength | length of tab | |
| withGoals | true if we want to take care of goals, false if not |
| void BotA::printInfos | ( | TreeNode * | treeNode | ) | [protected, virtual] |
During solving, print usefull infos in the console
| treeNode | treenode to draw |
int BotA::costLimit [protected] |
limit of accepted f(x)
int BotA::minReject [protected] |
minimum value of rejected f(x) for next iteration of IDA*. Maybe costLimit+1 but somethimes it's costLimit+2 or more
bool BotA::quickSearch [protected] |
Test penalties of every nodes


Implementation of Chan-Vese like segmentation algorithm for 3D medical images
Authors:
- Ahsanul Karim<EMAIL_ADDRESS>- Sourabh Daptardar<EMAIL_ADDRESS>License:
BSD
About the code:
This software implements Chan-Vese like segmentation algorithm based upon level sets. Major differences are : 1. It uses 'gaussian' curvature, instead of mean curvature 2. Targeted for 3D objects rather than 2D images.
This code was created for course project in medical imaging course by Prof. Allen Tanenbaum at Stony Brook University. https://www.cs.stonybrook.edu/people/faculty/AllenTannenbaum
Requires:
MATLAB
Steps to run
On Matlab prompt: cd src/ main
main.m contains demos to run the Slicer3D sample data files as well as synthetic 3D dataset like 3D blobs, 3D solid sphere.
Sample data
Slicer3D sample data (MR images) is placed in the data/ directory This data was obtained from Slicer 3D app : http://www.slicer.org/ Refer to the website for terms of usage.
Demos
- Blob: https://www.youtube.com/watch?v=lxOBL_5ZKCA
- MR Head: https://www.youtube.com/watch?v=tsz1UCLJ4qw
Also shipped along with the following third party codes which the software uses:
nrrdread.m : Read NRRD images into matlab http://www.mathworks.com/matlabcentral/fileexchange/34653-nrrd-format-file-reader/content/nrrdread.m BSD license
vol3d.m : Render 3D graphics in matlab http://www.mathworks.com/matlabcentral/fileexchange/22940-vol3d-v2 BSD license
References:
Active Contours Without Edges, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 10, NO. 2, FEBRUARY 2001 Tony F. Chan, and Luminita A. Vese http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=902291
Summer Research School on Medical Imaging, 2012, The Fields Institute for Research in Mathematical Sciences. Todd Wittman http://www.math.ucla.edu/~wittman/Fields/
For general overview on level set methods http://www.museth.org/Ken/Publications_files/Breen-etal_SIG04.pdf


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22 January 2015 | | categories: projects, rstats, school | blog archive
-[ introduction ]-
I’m currently (2016) taking the courses in the Johns Hopkins University/Coursera Data Science Specialization. This is a collection of things related to those courses and will be updated as I move through the specialization. Please don’t steal my stuff and please credit me if you use any of this.
-[ developing data products (january - february 2016) ]-
-[ practical machine learning (december 2015 - january 2016) ]-
-[ regression models (november 2015) ]-
-[ statistical inference (july 2015) ]-
-[ reproducible research (may 2015) ]-
-[ exploratory data analysis (april 2015) ]-
-[ getting and cleaning data (february 2015) ]-
-[ r programming (january 2015) ]-
-[ the data scientist’s toolbox (december 2014) ]-


Rebel Archives Calculator: for cubic and linear feet
· This tool estimates space occupied based on the exterior of each container (including the lid).
· It uses the width entered for each box to convert cubic feet to linear feet.
· Check out the calcuator
tips for more information.


If you were to analyze Dr. Naccarato's professional DNA, you would find the following: He is here to teach doctors to succeed and prosper so they can get on with the other important things in life, such as God, family, and friends. Examine how he lives his life and you will see that he is unconditionally committed to achieving this objective. His purpose is more than just a series of goals, it is a part of who he is and everything he does. It is that important to him. This work is not only what he does, it is who he is. It is what he feels he was born to do.
In Dr. Jim's personal coaching program, he will help you:
Throughout Dr. Naccarato's career, he has learned to love challenges. Although it initially seems easier to achieve goals with little opposition, he has found that he grows so much more while overcoming seemingly insurmountable challenges. It is during these times that he finds himself on his knees looking up to God to draw strength and clarify his resolve, intent, and commitment.
Dr. Jim has helped others go through this process so that they no longer fear opposition. His goal is to help doctors see these inevitable challenges as defining moments and opportunities they need to become the best that they can be.


Speech
Education Secretary Michael Gove speaks at Policy Exchange about the purpose of the government’s education reforms so far.
[Political references have been removed from this speech]
I’d like to begin by thanking Policy Exchange for this platform today. And I’d like also to thank Policy Exchange for the intellectual leadership it’s given to education reform.
Policy Exchange has been consistent in arguing for better schools, especially for the most disadvantaged children, in order to make opportunity more equal.
It has developed detailed policies to extend parental choice to all, get more great new schools in disadvantaged areas and redistribute resources to help the poorest students. Those ideas have influenced the coalition’s programme in government.
The expansion of the academy programme has ensured that communities denied a choice of good schools have at last been given the schools they deserve.
The introduction of free schools has set a new - and higher - bar for quality and innovation in state education. Already almost twice as likely as other schools to have been judged outstanding under the new, tougher Ofsted framework, they are proving an enormous success.
And the pupil premium has ensured £6.25 billion of additional money has been directed towards the poorest students in the country.
A relentless focus on closing the gap between the poorest students and their peers has been at the heart of everything we’ve done in government.
Alongside the pupil premium, I have established the Education Endowment Foundation, an independent research body to champion the best ways of helping poorer students, changed the nature of league tables and inspection to place more emphasis on the achievement of the most disadvantaged, and reformed children’s services to ensure the most vulnerable children are rescued from neglect and supported to succeed more effectively than ever before.
All of these reforms have been part of a long-term plan for our schools - shaped and supported by Policy Exchange’s work - and driven by a clear sense of moral purpose.
I want every child to be able to go to a state school which excels, which nurtures their talents, which introduces them to the best that has been thought and written, which prepares them for the world of work and adult responsibility, which imbues them with the strength of character to withstand life’s adversities and treat other humans with courtesy and dignity, which gives them the chance to appreciate art and culture, to enjoy music and drama, to participate in sport and games, which nurtures intellectual curiosity and which provides a secure grounding in the practical skills the modern world requires.
The reason I want that for every child is that I want that for my own. And I don’t see why as an education secretary I should settle for children going to a school I wouldn’t send my own children to. That would be morally indefensible.
Which is why I find it hard to understand why anyone should wish to defend the state of the education system we inherited.
How can it be right that more than a fifth of children left primary school without having reached a basic level of literacy and numeracy?
We wouldn’t accept a fifth of hospital operations going wrong or a fifth of flights ending badly. So why should we accept a system in which school standards were still too low?
Is it right that two-fifths of students should have left school without a grade C in English and maths GCSEs ? These are the basic minimum level qualifications most employers or universities demand. But almost 40% of children failed to secure them. And among the poorest children - those eligible for free school meals - a majority left school without these qualifications.
I don’t know anyone in this room - anyone in Parliament - anyone leading a school or leading a teachers union who would accept their own child leaving school without this bare minimum. But we accepted this fate for hundreds of thousands of children every year.
Changing that - rescuing the next generation - giving them the foundation they need to succeed - that has been the driving moral purpose of our education reforms. And I challenge anyone to explain to me why that is wrong, indeed why we shouldn’t be more driven and more determined to end this waste of human potential.
And yet people do. People do still say we’re being too demanding and driving too hard. We have university academics - indeed the chairs of organisations like the National Association for the Teaching of English - saying that we should not introduce 15- and 16-year-old children to Charles Dickens because his work will put them off literature for life.
We have historians who will defend teaching World War One to secondary school children through the medium of Blackadder, and providers of historical teaching materials who argue GCSE students should learn about Hitler through the medium of Mr Men books.
We have political opponents who argue that expecting 16-year-olds to get GCSEs in English, maths, science, a language and one of the humanities is creating a barrier to success and setting up children to fail.
Believe me, I know what real barriers to success look like. I spent the first 4 months of my life in care. Both my parents had to leave school at 15. My sister spent all her school career set apart from other children who were just as bright as her in a school for children with special needs. And I know what setting children up to fail looks like.
It’s sending working-class children to school without daring to think they might be intellectually curious and capable of greatness, denying them access to anything stretching or ambitious, setting expectations so low you can never be surprised by someone’s potential, giving children flimsy photocopied worksheets instead of proper rigorous textbooks, feeding them a diet of dumbed-down courses and easy-to-acquire qualifications, lowering pass marks and inflating grades to give the illusion of progress, shying away from anything which might require grit, application, hard work and perseverance, and then sending these poor children into the adult world without the knowledge, skills, character and accomplishments they need, and deserve, to flourish.
That is setting children up to fail. And that is what I will not tolerate.
Now some might allow that while the driving moral purpose of our reforms is right, the guiding principles have been wrong.
But which principles to follow?
I have been conscious throughout my time in opposition and government that there has been no single unchallenged consensus on how to improve our schools.
Listen to conversations between teachers, as I have done. Study the experience of different nations at different times. Consider the theories of philosophers. Reflect on the debate in the twittersphere and between bloggers, and you can see there is a wide spectrum of opinion.
There is no such thing as “the view of teachers” any more than there is “the view of politicians” as an unchallenged and unified Weltanschauung.
So instead of setting out to follow a consensus that doesn’t exist - and I suspect never will - I have set out to follow the evidence.
In government we have applied a simple set of tests to help frame education policy. For us, what’s right is what works.
And we’ve been lucky that our time in office has coincided with an increasing - and increasingly robust - body of evidence of what works in education.
Two men, more than any others, have studied what works across nations and cultures. Andreas Schleicher of the OECD and Sir Michael Barber, formerly of Tony Blair’s Delivery Unit and McKinsey, and now with Pearson, have studied the common characteristics of high-performing education systems.
The first is school autonomy and parental choice. The more autonomy enjoyed at the level of the school principal, the better. If headteachers, rather than bureaucrats, can spend the money allocated to education, if they can hire and fire the professionals they want, if they can vary the curriculum and the hours of study, if they can be captains of their ship, then standards rise. And that is what we have seen with our academy and free school programme.
The second driver of excellence - which must accompany autonomy - is proper accountability. Parents - and governments - must have accurate, fair and timely information about performance. So choice can be informed and state intervention proportionate. The strongest form of accountability comes from the data generated by externally set and marked tests and the judgements made by expert inspectors.
We have altered the tests and league tables by which schools are judged, to make them both more ambitious for all children and fairer to all schools, giving credit primarily for the progress students make, whatever their starting point. Ofsted have also improved their inspection regime, making it more proportionate and focused. We are now in a better position than ever before to identify what works, and seek to spread it, more quickly than ever before; and to identify failure, and deal with it, more quickly than ever before.
The third essential element in school success is the quality of teaching. The difference between the progress made by children in a class with an excellent teacher and those in a class with an underperforming teacher can be as much as a year’s worth of learning, particularly for children from disadvantaged backgrounds. We are lucky to have the best generation of teachers ever in our schools. But we must continue to aim higher. That is why we have introduced a series of reforms to improve the quality of teaching. We have dramatically increased the number of Teach First trainees, put high-performing schools at the centre of initial teacher training, made it easier for teachers from independent schools and experts from the outside world to enter state education, and our new teachers’ standards require a higher level of professionalism for all.
All of these principles - autonomy, accountability, teacher quality - are common across high-performing systems. And I think there’s growing acceptance of their centrality among policymakers worldwide. But there are 2 other factors which I’d like to stress today as critical.
One is behaviour. The other is the curriculum.
Unless classrooms are ordered and purposeful places, teachers can’t teach and children can’t learn. One of the factors which deters otherwise gifted teachers from staying in the profession is the poor behaviour they observe in class, the backchat and disruption which impede study, and the lack of support they as teachers experience from school leadership teams which are insufficiently rigorous in policing bad behaviour.
Policy Exchange’s own work showed that the biggest deterrent stopping talented graduates entering teaching was their fear of being unsafe in the classroom. And poor behaviour is the second biggest cause of teachers leaving the profession.
We have made a series of changes to help teachers ensure behaviour is better - in class and outside:
But we need to do more.
Critically, we need to tackle the root causes of truancy and misbehaviour.
Children only have one chance at education - we can’t let them miss out on its transformative effect. We need to ensure every child is in school, benefiting from great teaching in every classroom, every school day. That is why we’ve tightened the rules on attendance, and absence figures are down.
But there’s more to do. We need to ensure that those parents who don’t play their part in ensuring their children attend school, ready to learn and showing respect for their teacher, face up to their responsibilities. We will, later this year, be outlining detailed proposals to ensure parents play their full part in guaranteeing good behaviour and outlining stronger sanctions for those who don’t.
And just as we need all parents to discharge their responsibilities, so we need all schools to play their part. Critically, we need to ensure that all children leave primary school fully literate and numerate.

That is one critical reason why I have said we need - as a nation - to commit to eliminating illiteracy and innumeracy - to save lives which are currently wasted.
The number of children who genuinely cannot ever read - whose learning difficulties are so severe they cannot decipher prose - is tiny. But the number of children who currently leave primary school unable to read is indefensibly high.
We’ve taken action to deal with this scandal. Not least by introducing a phonics screening check at the end of year 1 to make sure every child is decoding fluently and identify those children who need extra help. But we need to do more.
A determined national commitment to ensuring children are properly literate and numerate is not in any way a narrowing of the curriculum, it is a precondition of enjoyment of a fully rounded curriculum. And this commitment is not one that can be delivered by schools or government alone; it will require the backing of all those who want children in this country to reach their full potential.
Fluency in reading and writing and mastery of mathematics are the keys which secure access to a broad and enriching academic curriculum. There is growing evidence - both from this country’s best schools and from other nations - that access to a stretching academic curriculum to the age of 16 helps improve performance for all children, all round. The work of Dr Cristina Iannelli at Edinburgh University demonstrates that the type of curriculum you study - specifically enjoyment of core academic subjects - is more important than the type of school you attend, whether grammar, independent or comprehensive, in determining future success.
The more children who enjoy a stretching academic curriculum - for longer - the better, for all children. And the experience of Poland - the fastest-improving nation in Europe educationally - reinforces that. As does the example of Germany, which has also dramatically improved its ranking in international league tables with a stronger emphasis on an academic core for all. Following an academic curriculum to the age of 16 is not, in any way, a downplaying of the importance of vocational education and training. Academic study to 16 is a prelude to vocational training, not an alternative to it.
It shouldn’t surprise us that the emphasis on a knowledge-based curriculum improves standards for all - the very best cognitive science, analysed and shared by thinkers like Dan Willingham and Daisy Christodoulou reinforces the fact that a knowledge-based academic curriculum can stimulate critical thinking and creativity.
And our curriculum and qualifications changes provide more scope than ever before for higher-order thinking skills and genuine creativity. They demand the exercise of advanced problem-solving skills in maths and science. They require extended essay writing and the mounting of complex arguments. And, of course, our new national curriculum is one of the first among developed nations to include computing and coding. It amazes me that some try to caricature our curriculum as backward-looking when it reflects the insights of cutting-edge cognitive science and has been singled out for praise as world-leading by Eric Schmidt of Google.
It is encouraging that the changes we’ve made to the national curriculum and accountability have been so widely welcomed. But it’s important that we maintain the highest possible level of ambition, and I want to say more about that today as well.
I’ve outlined these 5 key characteristics of successful school systems:
These qualities - of course - don’t just define high-performing education jurisdictions - they also characterise high-performing schools in this country.
Take one of my favourite schools - Burlington Danes Academy. If you want to see a model of autonomy and strong leadership in operation, observe the headteacher Dame Sally Coates in action.
Every half term, children are assessed across subject areas and also graded for their level of application, social contribution and sporting performance. They are told how well they’ve done. And they know that their performance at the end of the term will be reassessed and published for every student and every parent to see. This rank order system is hugely popular with parents - and also with students. Both are given objective measures of performance - and clear goals to aim for. Parents who were in the past assured in vague airy and amiable terms that their child was a nice lad and doing perfectly well now have hard data to help them support their child’s performance. They know if their child is underperforming expectations, and in what way. Students also know which teachers are most likely to help them climb the rank order system and clamour to be taught by the most gifted professionals.
What Sally Coates has done is replace the harmful competitiveness of street culture - the contest over who is coolest, whose trainers are smartest, whose attitude is hardest, whose backchat is the most fly, with the competitiveness of academic culture. The competitiveness which will help these children win out in later life - who is hardest working, who is the most community-minded, who is most eager to learn, who is most determined to improve.
What Sally has done in Burlington Danes is not unique and incapable of replication - indeed she’s written a brilliant guide which outlines how to match her performance - and she has protégés across the school system like David Benson at Kensington Aldridge Academy who will emulate her great work - but it is still striking that her school is significantly more successful than most, even as it has a much tougher and more challenging intake.
The same holds with other schools I hugely admire - from Barry Day’s Nottingham Academy to Liam Nolan’s Perry Beeches in Birmingham - all have challenging intakes - all dramatically outperform other schools.
And at primary level - Durand Primary in Stockwell, Thomas Jones in North Kensington and Wyndham Primary Academy in Derby. All again have challenging intakes which dramatically outperform most other schools.
The fact that I single out these schools sometimes generates criticism. Not of those schools which are underperforming, but of me for pointing out that these schools are excelling and asking why more don’t match them.
But I’m not in the least apologetic for asking why more schools aren’t as good as these. It was the question that Barack Obama’s education secretary Arne Duncan put to me after we both visited Mossbourne Community Academy. Always ask why every school isn’t as good as this, he told me. And don’t worry if the adults complain and say the question’s unfair. What’s fair is giving every child the same chance.
Arne was right - what matters in our school system is not what adults want, but what children need.
This determined focus on helping children, on refusing to excuse underperformance, on demanding better for the next generation, has generated opposition.
I certainly don’t seek out that opposition. But nor am I going to be deflected by it. Because when I ask for the specifics behind criticism of our policies I don’t see, or hear, evidence that stands up to scrutiny.
It’s been argued that schools are underfunded. But school spending overall has been protected in real terms at a time when significant savings have had to be found elsewhere in the public sector. It’s been argued that teachers are undervalued. Well, I don’t think we can ever value teachers highly enough. Which is why I always take every opportunity I can - at every party conference, in speeches public and private, through the honours system - to emphasise how fortunate we are to have the best generation of heads and teachers ever.
It’s why I’ve appointed teachers to run Ofsted and the National College for Teaching and Leadership. It’s why I’ve asked teachers to review teaching standards and initial teacher training. It’s also why I’ve ensured good teachers can be paid more and ensured that teachers have been invited to take up residencies in the Department of Education, advising on the development and implementation of policy.
It’s been argued that our drive towards autonomy means more schools can go wrong more quickly, without adequate action being taken. That is the opposite of the truth. Academies and free schools are more accountable than local authority maintained schools. The system of independent audit and publication of academies’ and free schools’ accounts, backed up by regulation by the Education Funding Agency, is more stringent than the rules for charities, limited companies, and local authority maintained schools. Indeed, when the Audit Commission took a look at local authority schools last year, they encountered at least 191 cases of fraud - a figure they acknowledged was almost certainly an underestimate.
All free schools are scrutinised by Ofsted prior to opening, supported by independent education advisers in the months after they’re set up, and inspected by Ofsted in the second year of opening. When performance is too poor - as it was with 2 schools - Al Madinah and the Discovery Free School - radical action is taken, and taken quickly. To put this in context: 2 state schools go into special measures every school day - not every year, month or week, but every school day – and 73 local authority maintained schools have gone into special measures so far this year alone.
And the speed of improvement in local authority schools is often far, far too slow. There are 35 local authority schools which have been in special measures for 18 months or more. Where action has been taken it has often been because the Department for Education has been more determined to ensure schools improve than local authorities. We have already taken more than 900 schools that were struggling under council control and given them the support of an academy sponsor - and many are already seeing significant improvements.
It’s been argued that our reforms place insufficient emphasis on culture and creativity. Again, the facts don’t even begin to bear this out.
On the contrary - we’ve set up new programmes from the BFI Film Academy to help train the next generation of talented filmmakers to the new National Youth Dance Company, which works with Arts Council England, and the Sadler’s Wells Trust to bring together talented young dancers.
We’ve placed a particular emphasis on encouraging drama. We are funding the Shakespeare School Festival to give thousands of children the chance to stage an abridged version of a Shakespeare play in a local theatre - with over 1,000 schools and 62,000 students benefiting so far. We’re also funding the Royal Shakespeare Company to help actors get into the classroom, and all state schools now receive a free copy of the RSC Shakespeare Toolkit for Teachers, offering over 60 hours of teaching resources on ‘Macbeth’, ‘Romeo and Juliet’ and ‘A Midsummer Night’s Dream’.
And building on Darren Henley’s independent review into music education, 123 new music education hubs have been set up across the country, ensuring that every child aged 5 to 18 has the opportunity to learn a musical instrument and to sing, as well as to perform as part of an ensemble or choir.
It’s also been argued that we’ve neglected vocational and technical education. Again, that’s the opposite of the truth. We’ve introduced the biggest reforms to vocational education since 1944.
We’ve stripped out poor-value, meaningless qualifications and are replacing them with new qualifications giving 14- to 16-year-olds real-life skills in practical subjects. After 16, we’ve introduced the new, rigorous Tech Levels - every single one endorsed by employers, trade or professional bodies as leading to a skilled occupation.
Because maths and English are essential in every job, we’re reforming GCSEs so that all students master the basics necessary for employment and ensuring that all young people who don’t secure a good grade by 16 carry on studying these vital subjects afterwards. And we’ve put employers in the driving seat of new apprenticeships - more than 400 employers across 37 sectors are now helping to design and deliver new apprenticeships. As a result there is - at last - the prospect of a genuine equality of worth and parity of esteem between all qualifications.
It’s been argued by some that our reforms lead to an atomised system which works against collaboration. Again, nothing could be further from the truth. Academy chains, teaching school alliances, professional development partnerships, all show how open collaboration can improve standards.
Right across the country, 548 new teaching schools have formed 450 alliances with schools and partners - meaning that one-fifth of schools around England are now working in collaboration with a teaching school, with numbers rising all the time.
And this collaboration achieves real results. Look at Harrison Primary in Hampshire, for example. They work with over 30 schools, using experienced teachers, specialist leaders of education or SLEs , to improve standards. And over the past 2 years, 70% of schools supported by SLEs in their alliance have improved their Ofsted ratings from ‘requires improvement’ to ‘good’.
Of course, I accept there have been mistakes and missteps along the way.
Trying to reform GCSE content and structure and the GCSE market at the same time was a bridge too far. But then who can doubt, given the tendency of some exam boards to try to compete on the ease of their course rather than the quality of their service, that further reform may be necessary?
Some academy chains expanded too fast, and some schools did not get the support they needed as a result.
But then - in many more local authority areas, poor schools have been left in the wrong hands for far too long.
Take Knowsley. If you want a case study in a policy that has really scarred the lives of thousands and deserves to be recorded as a scandal, consider what happened with this local authority. Back in 2010, it proudly proclaimed that it was the first local authority to have all its secondary schools rebuilt under the Building Schools for the Future programme. One of them - a national flagship - cost the taxpayer £24 million.
But that wasn’t Knowsley’s only educational claim to fame. It was also one of the worst performing councils in England - and had been for almost 10 years.
As the TES said:
flush with Building Schools for the Future money, [the local authority] frequently opted for unconventional methods in a bid to boost performance…schools were rebranded ‘centres for learning’, teachers renamed ‘progress leaders’ and Knowsley itself became an ‘innovation zone’ …[after] an audit into students’ ‘learning styles’, it concluded the majority were ‘kinaesthetic learners’ who learned best through physical activity. Teaching methods and school buildings were changed accordingly.
But improvements came there none. That flagship £24 million school closed after just 2 years. And last year, again, the local authority was the worst performing in the country - with the smallest percentage of pupils attaining 5 A* to C grades, including English and maths, in England.
GCSE results for 2013 showed that 43.7% of pupils in Knowsley gained 5 A* to C, including English and mathematics, compared to 59.6% in the North West region and 60.6% nationally.
Just 10% of the pupils in Knowsley took the EBacc , our roster of the rigorous academic subjects valued by universities and employers - 10% compared to 23% nationally.
And if you think Knowsley is atypical, consider the entrenched failures of so many other local authorities where standards have continued to be far too low for far too long.
Take Nottingham - last year, the poorest-performing local authority in the East Midlands. Just half of young people achieved 5 A* to C grades at GCSE including English and maths.
After Ofsted carried out urgent inspections in the area, Nottingham local authority set up a dedicated challenge board aiming to improve standards. But 5 months on, the area is no further forward, and Nottingham schools are still underperforming.
Or take Derby - another local authority vehemently opposed to academies - where our efforts to tackle chronic underperformance have met resistance at every turn.
Yet it’s one of the worst performing local authorities in the country - at the end of primary, at GCSE , throughout their school system - and has been for many years. Last year, Ofsted even took the step of carrying out a focused week of inspections in the area - and warned the local authority that:
there is still much work to do in establishing and embedding a clear, strategic vision for school improvement that will lead to sustained and demonstrable impact across the city’s schools.
I would rather we sought to intervene quickly in the case of failure, and sought also to learn just as quickly from our mistakes, than assume the defensive and defeatist posture of the past.
Indeed it’s important in reviewing what’s been achieved so far to bear in mind the facts on the ground. We have fewer 16- to 18-year-olds who are NEET - not in employment, education or training than at any time since consistent records began. Their number is down by more than a third under this government.
Fewer children are in failing schools than ever before - even as the bar on what counts as failure has been raised. There are 250,000 fewer children in underperforming secondary schools now than in 2010.
More children are studying the subjects that secure good jobs and great college places than ever before. At A level the numbers studying maths, further maths, physics and chemistry are all up by between 15% and 19% since 2009 to 2010.
Teachers are better qualified than ever before.
More schools are good or outstanding than ever before.
Ofsted record that last year saw the biggest single improvement in school performance since records began.
More students are graduating into higher education and more disadvantaged students are making it into higher education.
At primary school the gap between the poorest and the rest has narrowed.
And the impact of many of our reforms has still to be felt - the oldest free schools have barely been in operation for 3 years, most have been open for less than 18 months. Many of our weakest schools have only been in the hands of strong sponsors for a year or two, after years of poor leadership. Our new national curriculum only takes effect this September.
So I expect there to be significant further improvements in our school system in the years to come - provided we do not put at risk the gains we have made by retreating on reform.
Indeed, far from retreating, we have to ask how we can accelerate improvement in our schools. As Adrian Wooldridge and John Micklethwait make clear in their fascinating new book, ‘The Fourth Revolution’, other nations - especially but not exclusively in Asia - are:
So we have to ask ourselves - do we want to go backwards, opt out of the future, ask our children to accept this nation’s inevitable decline?
Or will we take advantage of the opportunities the future brings, ensure our public institutions serve every citizen fairly and equip our children for success?
I believe we have to embrace reform, lean in to the future, set standards higher than ever before. We need to ensure that more schools enjoy greater autonomy than ever before and more parents have a wider choice than ever before.
We need to ensure that accountability is sharper, more nuanced and effective than ever before. That is why I welcome Sir Michael Wilshaw’s leadership in saying more inspections have to be conducted by serving school leaders and inspection has to be more focused on underperforming schools and lighter touch for high-performing schools.
We also need to ensure all relevant bodies, including Ofsted, are in a position to do everything necessary to deal with those schools where student or adult behaviour is unacceptable and where children are not being kept safe. Some of those changes will be difficult for both the DfE and Ofsted - but we must not shy away from doing what’s right.
We need to do even more to secure the very best people in teaching. Andrew Carter’s review into initial teacher training will help us shape a better landscape for trainee teachers. But there are clearly areas on which we can already build.
The maths and physics chair programme provides additional funding for postgraduates in these subjects to teach in our schools and help mentor other teachers.
We must explore how to introduce more, and more powerful, incentives for mathematicians and scientists to stay in education and commit to the classroom. We need to bridge the gap between high-performing secondaries and underperforming primaries by getting specialist mathematicians and scientists teaching students from the end of key stage 1. We also need to bridge the gap between our best universities and schools by getting more higher education institutions like King’s College London and Exeter University to set up specialist schools like their maths free schools and, of course, university technical colleges.
As I said, right at the beginning of my remarks, there is a moral purpose to our schools policy. We want to ensure disadvantage is not destiny. We want liberate children from any accidents of birth or background to determine their own fate.
And that means making sure every child gets to attend a school which is a place of order, calm, learning and purpose.
Great schools are where children, in Willy Russell’s words, learn to sing better songs, they’re the places where children can acquire the skills to become authors of their own life story. Ensuring every child has that chance is my mission. And there is so much more to do.
Published: 7 June 2014


This is a simple tic tac toe game developed in python.
This is a sanke game developed in javascript.
This is a simple bouncing ball game developed in python.


Configuration Screen
Player
Starter Settings
No. of 'A' patients
No. of 'B' patients
No. of doctors
No. of surgeons
No. of nurses
Total time in seconds
Agent
Starter Settings
Co-operation Type
High
Low
Mode
Normal
Practice
Type
Early Slow
Early Fast
NH No. of 'A' patients
NH No. of 'B' patients
NH No. of doctors
NH No. of surgeons
NH No. of nurses
Patient help time in seconds
Submit


This example shows how to deploy Twilio Verify and Twilio functions for serverless user verification. Follow this tutorial on the Twilio Blog to deploy your own.
Enter your phone number:
Enter your email:
Select your preferred language:


site in progress
I'm a noobie software developer! I made a couple of small games in a block based programming language, before moving onto other languages like Python!
I'm now learning web design stuff, primarily JS, including node.js, while also learning game dev tools like Unity. This is one of my very pretty websites.


{% extends "base.html" %} {% block stuff %}
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Modify existing user in Genomics-Status.
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Buddha Jumps Over Wall
Materials
Winter Melon, Chicken Wings, Spring Onion, Ginger, Soy Sauce, Oyster Sauce, Salt, Sugar
Process
Slice the winter melon and chicken wings into pieces
Stir fry the ginger slice with oil and then add the wings in
Pour some soy sauce and oyster sauce then stir several time
Pour hot water that barely cover the materials (less than half) in and add potatoes to stew with cover on
After well-mixed, put the soup in without bones and boiled for some time
Open the cover and saucing with same salt, sugar, and spring onion before turning off the stove
Story
It is a dish with rich taste and mixing texture, soft but also chewey. I missed it so much when I am abroad because it is hard to find all the materials and cook. I can easily finish a pot when I am at home with rice. It is the dish I usually have when I come back home from school.


module Gen: sig
.. end
Generate Random Values
type 'a
t = Random.State.t -> 'a
A random generator for values of type 'a
type 'a
sized = int -> Random.State.t -> 'a
Random generator with a size bound
val return : 'a -> 'a t
Create a constant generator
val (>>=) : 'a t -> ('a -> 'b t) -> 'b t
Monadic bind for writing dependent generators. First generates an 'a
and then
passes it to the given function, to generate a 'b
.
val (<*>) : ('a -> 'b) t -> 'a t -> 'b t
Infix operator for composing a function generator and an argument generator
into a result generator
val map : ('a -> 'b) -> 'a t -> 'b t
map f g
transforms a generator g
by applying f
to each generated element
val map2 : ('a -> 'b -> 'c) -> 'a t -> 'b t -> 'c t
map f g1 g2
transforms two generators g1
and g2
by applying f
to each
pair of generated elements
val map3 : ('a -> 'b -> 'c -> 'd) ->
'a t -> 'b t -> 'c t -> 'd t
map f g1 g2 g3
transforms two generators g1
, g2
, and g3
by applying f
to each triple of generated elements
val map_keep_input : ('a -> 'b) -> 'a t -> ('a * 'b) t
map f g
transforms a generator g
by applying f
to each generated element.
Returns both the generated elememt from g
and the output from f
.
val (>|=) : 'a t -> ('a -> 'b) -> 'b t
val oneof : 'a t list -> 'a t
Constructs a generator that selects among a given list of generators
val oneofl : 'a list -> 'a t
Constructs a generator that selects among a given list of values
val oneofa : 'a array -> 'a t
Constructs a generator that selects among a given array of values
val frequency : (int * 'a t) list -> 'a t
Constructs a generator that selects among a given list of generators.
Each of the given generators are chosen based on a positive integer weight.
val frequencyl : (int * 'a) list -> 'a t
Constructs a generator that selects among a given list of values.
Each of the given values are chosen based on a positive integer weight.
val frequencya : (int * 'a) array -> 'a t
Constructs a generator that selects among a given array of values.
Each of the array entries are chosen based on a positive integer weight.
val shuffle_a : 'a array -> unit t
Shuffle the array in place
val shuffle_l : 'a list -> 'a list t
Creates a generator of shuffled lists
val unit : unit t
The unit generator
val bool : bool t
The Boolean generator
val float : float t
Generates floating point numbers
val pfloat : float t
Generates positive floating point numbers
val nfloat : float t
Generates negative floating point numbers
val nat : int t
Generates small natural numbers
val neg_int : int t
Generates negative integers
val pint : int t
Generates positive integers uniformly
val int : int t
Generates integers uniformly
val small_nat : int t
val small_int : int t
val int_bound : int -> int t
Uniform integer generator producing integers within 0... bound
.
Raises Invalid_argument
if the bound is too high (typically 2^30)
val int_range : int -> int -> int t
Uniform integer generator producing integers within low,high
Raises Invalid_argument
if the range is too large (typically 2^30)
val (--) : int -> int -> int t
val ui32 : int32 t
Generates (unsigned) int32
values
val ui64 : int64 t
Generates (unsigned) int64
values
val list : 'a t -> 'a list t
Builds a list generator from an element generator. List size is generated by
QCheck.Gen.nat
val list_size : int t -> 'a t -> 'a list t
Builds a list generator from a (non-negative) size generator and an element generator
val list_repeat : int -> 'a t -> 'a list t
list_repeat i g
builds a list generator from exactly i
elements generated by g
val array : 'a t -> 'a array t
Builds an array generator from an element generator. Array size is generated by
QCheck.Gen.nat
val array_size : int t -> 'a t -> 'a array t
Builds an array generator from a (non-negative) size generator and an element generator
val array_repeat : int -> 'a t -> 'a array t
array_repeat i g
builds an array generator from exactly i
elements generated by g
val opt : 'a t -> 'a option t
An option generator
val pair : 'a t -> 'b t -> ('a * 'b) t
Generates pairs
val triple : 'a t ->
'b t -> 'c t -> ('a * 'b * 'c) t
Generates triples
val quad : 'a t ->
'b t ->
'c t -> 'd t -> ('a * 'b * 'c * 'd) t
Generates quadruples
Since 0.5.1
val char : char t
Generates characters upto character code 255
val printable : char t
Generates printable characters
val numeral : char t
Generates numeral characters
val string_size : ?gen:char t -> int t -> string t
Builds a string generator from a (non-negative) size generator.
Accepts an optional character generator (the default is
QCheck.Gen.char
)
val string : ?gen:char t -> string t
val small_string : ?gen:char t -> string t
Builds a string generator. String size is in the range
0-10
.
Accepts an optional character generator (the default is
QCheck.Gen.char
)
val join : 'a t t -> 'a t
Collapses a generator of generators to simply a generator.
Since 0.5
val sized : 'a sized -> 'a t
Create a generator from a size-bounded generator by first
generating a size using
QCheck.Gen.nat
and passing the result to the size-bounded generator
val sized_size : int t -> 'a sized -> 'a t
Create a generator from a size-bounded generator by first
generating a size using the integer generator and passing the result
to the size-bounded generator
Since 0.5
val fix : ('a sized -> 'a sized) -> 'a sized
Fixpoint combinator for generating recursive, size-bounded data types.
The passed size-parameter should decrease to ensure termination.
Example:
type tree = Leaf of int | Node of tree * tree
let leaf x = Leaf x
let node x y = Node (x,y)
let g = QCheck.Gen.(sized @@ fix
(fun self n -> match n with
| 0 -> map leaf nat
| n ->
frequency
[1, map leaf nat;
2, map2 node (self (n/2)) (self (n/2))]
))
val generate : ?rand:Random.State.t -> n:int -> 'a t -> 'a list
generate ~n g
generates n
instances of g
val generate1 : ?rand:Random.State.t -> 'a t -> 'a
generate1 g
generates one instance of g


What channel is KSI vs Logan Paul on? TV, YouTube and live stream information
The rivalry between KSI and Logan Paul has been brewing for the past year and they finally step into the ring again this weekend
by Aaron Flanagan, Martin Domin, https://www.mirror.co.uk/authors/aaron-flanagan/, https://www.facebook.com/mirrorfightingKSI and Logan Paul return to the ring tonight for their eagerly-awaited rematch as the social media rivals look to finally settle their beef.
What began as a call out from KSI - real name Olajide William Olatunji - last year led to a sold-out first fight in Manchester which ended in a controversial draw.
The pair had planned to rematch early in 2019 but negotiations dragged on until promoter Eddie Hearn stepped in.
His involvement means both men have turned professional after being granted licences by the California State Athletic Commission,
And tonight at the Staples Center in Los Angeles, they will dispense with the head guards and pull on 10oz gloves.
They will also headline a show featuring world champions Billy Joe Saunders and Devin Haney.
Here is everything you need to know about the fights...
When is the fight?
The highly-anticipated rematch between KSI and Logan Paul takes place on Saturday November 9.
However, the main event won't be until the early hours of Sunday November 10.
What UK time is the fight likely to start?
If you are watching from the UK, the fight is likely to start between 4am and 5am.
That is about 9pm to 10pm local time in Los Angeles, where the fight is taking place.
What TV channel is the fight being shown on?
If you are in the UK, the fight is being shown on Sky Sports Box Office.
For those in the US, it is being shown on the DAZN streaming service.
Here is the full TV channel listing around the world:
- UK and Ireland: Sky Sports Box Office
- USA, Canada, Germany, Austria, Switzerland, Italy, Spain and Brazil: DAZN
- Poland: TVP
- Taiwan: Eleven Sport
- Malaysia/Brunei: Astro
- China: CCTV
- New Zealand: Sky Network
- Indonesia: Reddentes
- Phillippines: Tap DMV
- Latin America: Space
- Rest of the World: FITE.tv
Can you live stream the fight?
In the UK, you can stream the Sky Sports Box Office coverage on their website if you have purchased it.
DAZN, for those in the USA, is a live streaming service for subscribers.
Will the fight be shown on YouTube?
It will not be shown on YouTube, unlike the first time that the fight took place.
Only on the channels listed above can the fight be watched.
Is there a way to watch the fight for free?
On this occasion, the fight is only being shown on a pay-per-view basis.
But you can follow the action as it happens on the Mirror Fighting live blog.
Betting odds
- KSI: 7/4
- Draw: 10/1
- Logan Paul: 8/15
Odds courtesy of Ladbrokes.


Using the quantitative research methodology HNC applies techniques applicable in this area to investigate products or services using different data collection processes such as structured customer surveys and questionnaires to generate solid facts through reliable statistics for informed decision and insights into market situation peculiar to the clients. This is done by means of CATI, CAPI, OUTCOME and SMS.
We offer advisory services for startups or already existing organizations, helping to formulate strategies that will grow and improve the business in a generally competitive market with other existing players. The process trails from client engagement to kick off meetings, proposals and project commissioning.
Our setup for the quantitative research involves the planning phase where questionnaire designs are usually specific to the objectives, questions, insights and business outcomes that the clients want answered or have in view as well as the sampling procedures and the timelines for deliverables.
In designing the survey for the set study, we at HNC ensure that there is a deep understanding of the project by teaming with the clients to ensure that the output of the study is reliable. This is reflected in the sharing and review of questionnaires to capture the objectives in addition to the team selection process, testing of the scripts, a pilot survey to ensure that it’s representative and feasible. This stage covers all required applications, software and the mobile devices employed for the projects.
Project execution is all encompassing involving, fieldwork, Data capturing, GPS monitoring for the interviewers, quality control to ensure that the data collected is appropriate and rightly done to detect frauds and any unmet quota. Also real time updates, data cleaning, live feed back and results. This is execution is based on experience of the team, the quality of work being delivered, training of the working team, certification of the participants.
Data from the field are managed and analysed using standard procedures. Modelling and formatting are done ensuring that results are presented on the basis of objectives and outcomes indicated by the client while making certain only important and relevant data are presented in tables and charts discussed.
Presentations of the findings and outcomes with relevant insights and other information that address the objectives alongside feasible recommendations are made to the client after draft submissions and then final reports following reviews.


Windsor
The University of Windsor is located in Windsor Ontario, a short drive to the US city of Detroit and a three and a half hour drive to Toronto.
The University of Windsor was created in 1963, it was created from four colleges Essex College, Canterbury College, Iona College and Holy
Redeemer College and incorporated into the University of Windsor. This university offers over 200 undergraduate programs with specializations in acting, media and film.
- Top Programs
- Acting
- Media and Film
- Libreal Arts
- Location
- Map
- Windsor Ontario
- Located on the border of Canada/US
- Number of Students:
- 16,491
- Average Tuition:
- Domestic 6,032 CAD
- International 19,121 CAD
- Entry Requirements:
- Ontario Secondary School Diploma
- Average of 70% or Higher
- Six Grade 12 U or M courses
- ENG4U
- International rating:
- 952
- Canadian rating:
- 38
- Average class size:
- 86 Students per class
- Student/faculty ratio: 26:1
University of Windsor
401 Sunset Ave
Windsor ON N9B 3P4
Tel:<PHONE_NUMBER>
Admissions:<PHONE_NUMBER> ext 3315
Fax:<PHONE_NUMBER>
http://www.uwindsor.ca


Aaron Sorkin
American screenwriter, director, producer, and playwright. His works include the Broadway plays A Few Good Men, The Farnsworth Invention and To Kill a Mockingbird; the television series Sports Night, The West Wing, Studio 60 on the Sunset Strip.


|
UART001App
|
Data Structures | |
| struct | DebugLog_CommandFromHostType |
| Format of Command sent by the Host. More... | |
| struct | DebugLog_GUIConfigurableType |
| GUI configurable elements for every channel. More... | |
Defines | |
| #define | NO_OF_CHANNELS 1 |
| #define | COMPILE_TIME_FILTER DEBUGLOG_LEVEL_NOTSET |
| #define | PBC_SEVERITY DEBUGLOG_LEVEL_WARNING |
| #define | INVARIENT_ASSERTION NO_ASSERT |
| #define | ASSERT_OPTIONS SENDTO_DEBUGLOG |
Typedefs | |
|
typedef struct DebugLog_CommandFromHostType | DebugLog_CommandFromHostType |
| Format of Command sent by the Host. | |
| typedef status_t(* | PhyInterfaceInitFuncPtr )(void *) |
| Function pointer for the Physical Interface Init Function. | |
| typedef status_t(* | PhyInterfaceWriteFuncPtr )(void *, uint32_t, uint8_t *) |
| Function pointer for the Physical Interface Write Function. | |
|
typedef struct DebugLog_GUIConfigurableType | DebugLog_GUIConfigurableType |
| GUI configurable elements for every channel. |
| #define ASSERT_OPTIONS SENDTO_DEBUGLOG |
| #define INVARIENT_ASSERTION NO_ASSERT |
| #define NO_OF_CHANNELS 1 |
| #define PBC_SEVERITY DEBUGLOG_LEVEL_WARNING |


DfE enquiries
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Press release
More children will have the opportunity of a great education as record numbers of weak schools are turned into academies this year.
More children will have the opportunity of a great education as record numbers of weak schools are turned into academies this year.
Forty-five sponsored academies will open in September, with a further forty-nine due to open during this academic year. This is the highest ever number of new sponsored academies to open in one year.
In addition, 185 good schools will become academies this month - on top of the 796 who have already converted. These schools can now take charge of their own affairs and enjoy the professional freedom and control that academy status brings.
Since September 2010, 1,097 schools have become academies (116 sponsored academies, 981 converter academies), meaning that 1300 academies are now open, compared to 203 opened before the Academies Act of July 2011. This is more than a six-fold increase.
This means that more than 40 per cent of all secondary schools are now open or in the process of opening as academies.
As of this September:
Education Secretary, Michael Gove, said today:
Teachers, not politicians or bureaucrats, should run schools. They should be free to innovate in the classroom. That is why thousands of schools are becoming academies.
Every child should be able to attend a good school. But we have inherited one of the most stratified and segregated education systems in the developed world.
Thankfully, record numbers of weak schools are becoming academies this year, so we are giving more and more children opportunities that have historically been the preserve of those from wealthy backgrounds.
Academies benefit from greater freedoms to innovate and raise standards. These include:
A full list of academies can be found in the academies section of the Department for Education’s website .
The 29 LAs where the majority of secondary schools are academies are:
Rutland
North East Lincolnshire
Bromley
Solihull
Sutton
Bexley
Swindon
Reading
Medway
Harrow
Thurrock
Plymouth
Southwark
Gloucestershire
Buckinghamshire
Havering
Cambridgeshire
Milton Keynes
Southend-on-Sea
Somerset
Darlington
Hillingdon
Peterborough
Kent
Barnet
Bournemouth
Hackney
North Somerset
Westminster
3 opened in 2002
9 opened in 2003
5 opened in 2004
10 opened in 2005
19 opened in 2006
37 opened in 2007
47 opened in 2008
70 opened in 2009
3 opened in January 2010
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Published: 5 September 2011


You early bird! You want to register for Code Camp already? Alas, registration isn't opened yet. (ಥ﹏ಥ)
Check back soon when registration opens.
I promise to check back soon (͡° ͜ʖ ͡°)


ReduceLROnPlateau
Reduce learning rate when a metric has stopped improving.
Inherits From: Callback
tf.compat.v1.keras.callbacks.ReduceLROnPlateau
tf.compat.v2.keras.callbacks.ReduceLROnPlateau
Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001) model.fit(X_train, Y_train, callbacks=[reduce_lr])
monitor
: quantity to be monitored.factor
: factor by which the learning rate will be reduced. new_lr = lr *
factorpatience
: number of epochs with no improvement after which learning rate
will be reduced.verbose
: int. 0: quiet, 1: update messages.mode
: one of {auto, min, max}. In min
mode, lr will be reduced when the
quantity monitored has stopped decreasing; in max
mode it will be
reduced when the quantity monitored has stopped increasing; in auto
mode, the direction is automatically inferred from the name of the
monitored quantity.min_delta
: threshold for measuring the new optimum, to only focus on
significant changes.cooldown
: number of epochs to wait before resuming normal operation after
lr has been reduced.min_lr
: lower bound on the learning rate.__init__
__init__( monitor='val_loss', factor=0.1, patience=10, verbose=0, mode='auto', min_delta=0.0001, cooldown=0, min_lr=0, **kwargs )
Initialize self. See help(type(self)) for accurate signature.
in_cooldown
in_cooldown()
on_batch_begin
on_batch_begin( batch, logs=None )
A backwards compatibility alias for on_train_batch_begin
.
on_batch_end
on_batch_end( batch, logs=None )
A backwards compatibility alias for on_train_batch_end
.
on_epoch_begin
on_epoch_begin( epoch, logs=None )
Called at the start of an epoch.
Subclasses should override for any actions to run. This function should only be called during TRAIN mode.
epoch
: integer, index of epoch.logs
: dict. Currently no data is passed to this argument for this method
but that may change in the future.on_epoch_end
on_epoch_end( epoch, logs=None )
Called at the end of an epoch.
Subclasses should override for any actions to run. This function should only be called during TRAIN mode.
epoch
: integer, index of epoch.logs
: dict, metric results for this training epoch, and for the
validation epoch if validation is performed. Validation result keys
are prefixed with val_
.on_predict_batch_begin
on_predict_batch_begin( batch, logs=None )
Called at the beginning of a batch in predict
methods.
Subclasses should override for any actions to run.
batch
: integer, index of batch within the current epoch.logs
: dict. Has keys batch
and size
representing the current batch
number and the size of the batch.on_predict_batch_end
on_predict_batch_end( batch, logs=None )
Called at the end of a batch in predict
methods.
Subclasses should override for any actions to run.
batch
: integer, index of batch within the current epoch.logs
: dict. Metric results for this batch.on_predict_begin
on_predict_begin(logs=None)
Called at the beginning of prediction.
Subclasses should override for any actions to run.
logs
: dict. Currently no data is passed to this argument for this method
but that may change in the future.on_predict_end
on_predict_end(logs=None)
Called at the end of prediction.
Subclasses should override for any actions to run.
logs
: dict. Currently no data is passed to this argument for this method
but that may change in the future.on_test_batch_begin
on_test_batch_begin( batch, logs=None )
Called at the beginning of a batch in evaluate
methods.
Also called at the beginning of a validation batch in the fit
methods, if validation data is provided.
Subclasses should override for any actions to run.
batch
: integer, index of batch within the current epoch.logs
: dict. Has keys batch
and size
representing the current batch
number and the size of the batch.on_test_batch_end
on_test_batch_end( batch, logs=None )
Called at the end of a batch in evaluate
methods.
Also called at the end of a validation batch in the fit
methods, if validation data is provided.
Subclasses should override for any actions to run.
batch
: integer, index of batch within the current epoch.logs
: dict. Metric results for this batch.on_test_begin
on_test_begin(logs=None)
Called at the beginning of evaluation or validation.
Subclasses should override for any actions to run.
logs
: dict. Currently no data is passed to this argument for this method
but that may change in the future.on_test_end
on_test_end(logs=None)
Called at the end of evaluation or validation.
Subclasses should override for any actions to run.
logs
: dict. Currently no data is passed to this argument for this method
but that may change in the future.on_train_batch_begin
on_train_batch_begin( batch, logs=None )
Called at the beginning of a training batch in fit
methods.
Subclasses should override for any actions to run.
batch
: integer, index of batch within the current epoch.logs
: dict. Has keys batch
and size
representing the current batch
number and the size of the batch.on_train_batch_end
on_train_batch_end( batch, logs=None )
Called at the end of a training batch in fit
methods.
Subclasses should override for any actions to run.
batch
: integer, index of batch within the current epoch.logs
: dict. Metric results for this batch.on_train_begin
on_train_begin(logs=None)
Called at the beginning of training.
Subclasses should override for any actions to run.
logs
: dict. Currently no data is passed to this argument for this method
but that may change in the future.on_train_end
on_train_end(logs=None)
Called at the end of training.
Subclasses should override for any actions to run.
logs
: dict. Currently no data is passed to this argument for this method
but that may change in the future.set_model
set_model(model)
set_params
set_params(params)


Languages:
PHP, JavaScript, TypeScript, Go, SQL, CSS, HTML
Tools & Services:
Git, Docker, Swagger, NPM, Webpack, Xdebug, Chrome Developer Tools, AWS
Frameworks:
Laravel, Vue.js, Labstack/Echo
Libraries:
PHPUnit, Ava, Axios, Bootstrap


"NUTS "were among the presents Jacob sent into Egypt for the purpose of conciliating Joseph (Genesis 43:11). This was the fruit of the pistachio tree, which resembles the sumac. It is of the size of an olive. In Cant. 6:11 a different Hebrew word (‘egoz), which means “walnuts,” is used.
"NUTS "were among the presents Jacob sent into Egypt for the purpose of conciliating Joseph (Genesis 43:11). This was the fruit of the pistachio tree, which resembles the sumac. It is of the size of an olive. In Cant. 6:11 a different Hebrew word (‘egoz), which means “walnuts,” is used.


Stereotype Threat
There is a lot of information out there about a phenomenon known as stereotype threat. I became aware of this phenomenon during the last few days. My first impressions are: my personal observations line up with the data, but I am not sure if I have ever experienced this myself. I may have though: As a native New Yorker, I have a heavy New York accent. Now that I think about it, people outside of New York generally do not seem to respond to me in the same way as those in New York, almost like I am stereotyped by them as foolish. Has this impacted my performance? I don't think so, but maybe it has.
But I see this phenomenon at work frequently in communities that I live near and work in. There is clearly a different expectation regarding education for the various ethnic and racial groups in my area. I have heard stories from classrooms of certain kids being labeled "white" in a negative way by other students, simply because the kid in question could read well or spell well. It seems like some view doing well in school as a white thing, and actually diss their friends who do well in school, like they shouldn't be doing well in school.
Also, I have heard stories of some families having a sort of ceiling placed on education by fathers.  If dad got a high school diploma and no more, then none of his children should even think about going to college. Doing so would be a slap in dad's face.
Obviously when large parts of people groups view education as a negative thing, nothing good can come from that. I am thankful that the home I grew up in valued education, with no restrictions or unspoken limits placed upon me. This type of stereotyping needs to be intentionally pushed against. I am a big proponent of mentoring youth to change their mindsets, which is key to overcoming this specific phenomenon.


{% extends "account/email/base.html" %} {% load account i18n %} {% block subject %} {% trans "Change Password" %} {% endblock %} {% block content %} {% user_display user as user_display %}
{% blocktrans %} You're receiving this e-mail because you or someone else has requested a password for your user account. It can be safely ignored if you did not request a password reset. Click the link below to reset your password. {% endblocktrans %}
{{ password_reset_url }}
{% if username %}{% blocktrans %} In case you forgot, your username is {{ username }}.{ {% endblocktrans %}
{% endif %} {% endblock %}


This app was created to provide a resource for winter adventurers going into the backcountry.
This is not meant to replace any avalanche reporting or education, but to be used when there is no or limited reporting for the area being accessed.
The questions and ratings used in the Danger Rator are taken from Avalanche Canada’s Danger Rator.
The Danger Rator is not meant to be used in spring conditions, and it is not a flawless system.
It is only meant to help backcountry travelers make better decisions.
Always refer first to professional avalanche forecasting and reporting if possible.


A soft button wrapper object that is capable of storing and switching between states
The name of this button
@property (nonatomic, copy, readonly) NSString *_Nonnull name;
var name: String { get }
All states available to this button
@property (nonatomic, strong, readonly) NSArray<SDLSoftButtonState *> *_Nonnull states;
var states: [SDLSoftButtonState] { get }
The name of the current state of this soft button
@property (nonatomic, copy, readonly) SDLSoftButtonState *_Nonnull currentState;
@NSCopying var currentState: SDLSoftButtonState { get }
Describes an on-screen button which may be presented in various contexts, e.g. templates or alerts
@property (nonatomic, strong, readonly) SDLSoftButton *_Nonnull currentStateSoftButton;
var currentStateSoftButton: SDLSoftButton { get }
The handler to be called when the button is in the current state and is pressed
@property (nonatomic, strong, readonly) SDLRPCButtonNotificationHandler _Nonnull eventHandler;
var eventHandler: SDLRPCButtonNotificationHandler { get }
Create a multi-state (or single-state, but you should use initWithName:state: instead for that case) soft button. For example, a button that changes its image or text, such as a repeat or shuffle button.
- (nonnull instancetype)
initWithName:(nonnull NSString *)name
states:(nonnull NSArray<SDLSoftButtonState *> *)states
initialStateName:(nonnull NSString *)initialStateName
handler:(nullable SDLRPCButtonNotificationHandler)eventHandler;
init(name: String, states: [SDLSoftButtonState], initialStateName: String, handler eventHandler: SDLRPCButtonNotificationHandler? = nil)
The name of the button
The states available to the button
The handler to be called when the button is in the current state and is pressed
The first state to use
Create a single-state soft button. For example, a button that brings up a Perform Interaction menu.
- (nonnull instancetype)initWithName:(nonnull NSString *)name
state:(nonnull SDLSoftButtonState *)state
handler:(nullable SDLRPCButtonNotificationHandler)
eventHandler;
init(name: String, state: SDLSoftButtonState, handler eventHandler: SDLRPCButtonNotificationHandler? = nil)
The name of the button
The single state of the button
The handler to be called when the button is pressed
The button object
Create a single-state soft button. For example, a button that brings up a Perform Interaction menu.
- (nonnull instancetype)initWithName:(nonnull NSString *)name
text:(nullable NSString *)text
artwork:(nullable SDLArtwork *)artwork
handler:(nullable SDLRPCButtonNotificationHandler)
eventHandler;
init(name: String, text: String?, artwork: SDLArtwork?, handler eventHandler: SDLRPCButtonNotificationHandler? = nil)
The name of the button
The text to be displayed on the button
The artwork to be displayed on the button
The handler to be called when the button is pressed
The button object
Transition the soft button to another state in the states
property. The wrapper considers all transitions valid (assuming a state with that name exists).
Warning
This method will throw an exception and crash your app (on purpose) if you attempt an invalid transition. So…don’t do that.
- (BOOL)transitionToStateNamed:(nonnull NSString *)stateName;
func transition(toState stateName: String) -> Bool
The next state.
YES if a state was found with that name, NO otherwise.
Transition the soft button to the next state of the array set when in the states
property
- (void)transitionToNextState;
func transitionToNextState()
Return a state from the state array with a specific name.
- (nullable SDLSoftButtonState *)stateWithName:(nonnull NSString *)stateName;
func state(withName stateName: String) -> SDLSoftButtonState?
The name of the state to return
The state, or nil if no state with that name exists


|
Primary Contacting Parent/Guardian
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Parent/Guardian
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English Name
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||
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Chinese Name
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||
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Occupation/Specialty
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Primary Phone
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||
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Secondary Phone
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||
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E-Mail
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||
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Mandarin Proficiency
|
Non-speaker
Listen/Speak Only
Native Educated
|
Non-speaker
Listen/Speak Only
Native Educated
|
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Street Address
|
|||
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City
|
State
|
||
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Zip Code
|
Phone
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Doctor Name
|
Phone
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Emergency Contact Name
|
Phone
|


This test that setting HTMLSelectElement.length is capped to 10,000, but that you can add additional Option elements by calling add.


ID
NSE
BSE
Last Qtr
Name
% Score
Price
PE
PEG
Div Yield
Valuation Color Band
Sector
Owned
Save To Watchlist
Remove from Watchlist
Compare Watchlist
Compare Any 2


Sometimes some fetaures of your app must behave differently for different flavors o build type. Think about when you need to enable logging for a specific flavor only, or you have to disable crash reporting for your “dev build”. All this behaviours can be configured at compile time, insted of using many if/else blocks that are evaluated at runtime.
In the following example I’ll use Gradle and Dagger.
First step: create an interface CrashReporter
and two implementations: CrashReporterRealImpl
and CrashReporterNullImpl
.
Second step: configure the build.gradle
file for our app.
android {
// constants
def BOOLEAN = "boolean"
def TRUE = "true"
def FALSE = "false"
// app features
def FEATURE_CRASH_REPORTING_ENABLED = "FEATURE_CRASH_REPORTING_ENABLED"
...
defaultConfig {
...
// default features
buildConfigField BOOLEAN, FEATURE_CRASH_REPORTING_ENABLED, FALSE
}
productFlavors {
developer {
...
}
qateam {
...
}
playstore {
...
buildConfigField BOOLEAN, FEATURE_CRASH_REPORTING_ENABLED, TRUE
}
}
}
Third step: use dagger to provide the right instance of CrashReporter
.
Final step: inject and use!
It could seem a lot of code for just enabling/disabling a feature, but with this approach the code is modular (for example you could easily replace CrashReporterRealImpl with another one that uses another service, or you can temporarly enable crash reporting for another flavor for testing purposes) and there’s no logic that needs to be tested.
With this approach you could also develop new functions directly in the master branch without using a “feature branch”.
Suppose that we have to work on our new feature called “Experimental Feature”. We just need to create an interface and two implementations as done before:
Then create the definitions in build.gradle and add provide method to the dagger module:
Now we can continue to develop in the master branch and commit even when our ExperimentalFeatureControllerRealImpl
does not work correctly: FEATURE_EXPERIMENTAL_ENABLED will be TRUE only for our “dev build”, and in FALSE for production build.
When ExperimentalFeatureControllerRealImpl
will be ready for production, we will just have to set FEATURE_EXPERIMENTAL_ENABLED to TRUE also for production build!


◆ MixedRealityTransform() [1/2]
| Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.MixedRealityTransform |
( |
Transform |
transform | ) |
|
◆ MixedRealityTransform() [2/2]
| Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.MixedRealityTransform |
( |
Vector3 |
position, |
|
|
Quaternion |
rotation, |
|
|
Vector3 |
scale |
|
) |
| |
◆ Equals() [1/3]
| bool Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.Equals |
( |
MixedRealityTransform |
other | ) |
|
◆ Equals() [2/3]
| bool IEqualityComparer. Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.Equals |
( |
object |
left, |
|
|
object |
right |
|
) |
| |
◆ Equals() [3/3]
| override bool Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.Equals |
( |
object |
obj | ) |
|
◆ GetHashCode() [1/2]
| override int Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.GetHashCode |
( |
| ) |
|
◆ GetHashCode() [2/2]
| int IEqualityComparer. Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.GetHashCode |
( |
object |
obj | ) |
|
◆ NewRotate()
| static MixedRealityTransform Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.NewRotate |
( |
Quaternion |
rotation | ) |
|
|
static |
Create a transform with only given rotation
◆ NewScale()
| static MixedRealityTransform Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.NewScale |
( |
Vector3 |
scale | ) |
|
|
static |
Create a transform with only given scale
◆ NewTranslate()
| static MixedRealityTransform Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.NewTranslate |
( |
Vector3 |
position | ) |
|
|
static |
Create a transform with only given position
◆ operator!=()
◆ operator+()
◆ operator==()
◆ ToString()
| override string Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.ToString |
( |
| ) |
|
◆ Forward
| Vector3 Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.Forward |
|
get |
The Z axis of the pose in world space.
◆ Identity
The default value for a Six Dof Transform.
◆ Position
| Vector3 Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.Position |
|
getset |
The position of the transform.
◆ Right
| Vector3 Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.Right |
|
get |
The X axis of the pose in world space.
◆ Rotation
| Quaternion Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.Rotation |
|
getset |
The rotation of the transform.
◆ Scale
| Vector3 Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.Scale |
|
getset |
The scale of the transform.
◆ Up
| Vector3 Microsoft.MixedReality.Toolkit.Utilities.MixedRealityTransform.Up |
|
get |
The Y axis of the pose in world space.
The documentation for this struct was generated from the following file:


Gateway
BOT
29-Sep-18 06:34 PM
[dscrdvrsc] <stretcher83> Same thing aswell, always add exemptions in your antivirus for the new miner you have downloaded, even de activate the antivirus before you download the miner first, then when its run ok add your exemptions and then re activate your antivirus
[dscrdvrsc] <Cragorn> @cryptoprofit.zone Are you sure the cpu meet the requirements for mining the verushash?
Try to add "pause" after the standard command in the .bat file, then run. It should stop an show you an error instead of just closing the window.
[dscrdvrsc] <cryptoprofit.zone> @Cragorn yes its a i7 8700 CPU
[dscrdvrsc] <cryptoprofit.zone> ./nheqminer -v -l eu-veruscoin.miningpools.cloud:2082 -u RDERJmfEGNkyeEo4ALoWPzpjczt4hzpKj9.worker -p x -t 12 pause
[dscrdvrsc] <cryptoprofit.zone> lke so ?
[dscrdvrsc] <cryptoprofit.zone> ther miner on its own runs but when i try to start with the batch file it crashes
[dscrdvrsc] <Cragorn> Yes. Not sure if pause needs to be on next line, but should work. The window closes before you can read any errors?  Command on Windows is without that.
[dscrdvrsc] <stretcher83> So it needs $(nproc)
[dscrdvrsc] <stretcher83> At the end i think
[dscrdvrsc] <cryptoprofit.zone> no i am on windows
[dscrdvrsc] <Cragorn> Not if he knows how many threads he want to or can use.
[dscrdvrsc] <cryptoprofit.zone> sorted working now
[dscrdvrsc] <stretcher83>
[dscrdvrsc] <Cragorn> Perfect
[dscrdvrsc] <cryptoprofit.zone> needsed the backslash removing thanks
[dscrdvrsc] <stretcher83>
[dscrdvrsc] <stretcher83> At least you got it
[dscrdvrsc] <cryptoprofit.zone> well miners runiing but it is refusing conections ill sort it
[dscrdvrsc] <cryptoprofit.zone> SORTED !!!
[dscrdvrsc] <stretcher83>
[dscrdvrsc] <stretcher83> Just noticed a post in the #pools about the pool you are connecting to was not working, i know they sometimes do maintenance on the pool but not for long periods of time
[dscrdvrsc] <cryptoprofit.zone> yeh i swapped pools
[dscrdvrsc] <stretcher83>


Sentiment Lexicon¶
Sentiment lexicon wrapper and generator.
Installation¶
pip install sentiment-lexicon
Usage¶
The module provides a single class, Lexicon
, that can be used as a simple wrapper around sentiment lexicon data.
The sentiment value of a given word can be accessed via the value
instance method.
from sentiment_lexicon import Lexicon
lexicon = Lexicon(words, values)
lexicon.value('good') # => 1
The class can also generate a sentiment lexicon based on positive and negative input documents.
lexicon = Lexicon.from_labelled_text(positive_documents, negative_documents)
More information is available in the documentation.
-
class
sentiment_lexicon.
Lexicon
(words, values, default=None, normalize=False)¶ A class wrapping a sentiment lexicon.
Provides a
value()
method for finding the sentiment value of a word.- Parameters
words (
List
[str
]) – List of words.values (
List
[float
]) – List of values.default (
Optional
[float
]) – The value given to words that are not in the lexicon.normalize (
Optional
[bool
]) – Determines if the values are normalized to [-1, 1].
-
default
¶ The default value.
-
data
¶ The lexicon data.
-
range
¶ The range that the sentiment values are spanning.
- Raises
ValueError – If
words
andvalues
do not have the same length.
Examples
Creating a lexicon requires one sentiment value per word
>>> Lexicon(['good'], [1]) <sentiment_lexicon.lexicon.Lexicon object at ...>
otherwise an exception is raised.
>>> Lexicon(['good'], []) Traceback (most recent call last): ... ValueError: words and values must have the same length
Normalizing the sentiment values is done by passing
True
as thenormalize
parameter.>>> lexicon = Lexicon(['good', 'bad', 'maybe'], [10, -8, 2], normalize=True) >>> lexicon.data good 1.0 bad -0.8 maybe 0.2 dtype: float64
-
value
(word, default=None)¶ Returns the sentiment value of a given word.
- Parameters
- Raises
KeyError – If
word
is not found in the lexicon and adefault
is not provided. –
- Return type
float
- Returns
The sentiment value for the word.
Examples
>>> lexicon = Lexicon(['good'], [1]) >>> lexicon.value('good') 1.0
>>> lexicon.value('bad') Traceback (most recent call last): ... KeyError: 'bad not present in the lexicon'
>>> lexicon = Lexicon(['good'], [1], default=0) >>> lexicon.value('bad') 0
>>> lexicon.value('bad', default=-1) -1
-
static
from_labelled_text
(positive, negative, min_df=0.0, alpha=0.5, ignore_case=True, **kwargs)¶ Generate a
Lexicon
based on positive and negative documents using pointwise mututal information.- Parameters
positive (
List
[str
]) – List of positive documents.negative (
List
[str
]) – List of negative documents.min_df (
Optional
[float
]) – The number of documents that a word must occur in before it is added to the lexicon.alpha (
Optional
[float
]) – Determines how much the PMI of the two classes affect each other when computing the sentiment values.ignore_case (
Optional
[bool
]) – Determines if the case of the words in the documents are ignored. IfTrue
, the words good and Good will be treated as the same.kwargs – Parameters passed to the
Lexicon
constructor.
Examples
>>> Lexicon.from_labelled_text(['This is good'], ['This is bad']) <sentiment_lexicon.lexicon.Lexicon object at ...>
>>> Lexicon.from_labelled_text(['This is good'], []) Traceback (most recent call last): ... ValueError: there must be at least one positive and one negative document
- Return type
Lexicon


This is a crawler and visualisation tool for IoT catalogues in the application/vnd.tsbiot.catalogue.json format. Given a starting URL, it will crawl any linked catalogues then render a directed graph of discovered relationships. The nodes and edges shown in the graph can be filtered.
Select an example catalogue from the box below, or enter the URL of any public catalogue and click "crawl".
You may also browse the example catalogue files. For a description of what each catalogue shows, see examples/README.txt.
The filters act independently.
The graph is in the form: subject --predicate--> object
The urn:X-tsbiot:rels:hasResource is automatically added to connect each catalogue with its items
Drag graph nodes around for a clearer view