| Package | Description |
|---|---|
| org.jmisb.api.common |
Common interfaces and utilities.
|
| org.jmisb.api.klv |
Key-Length-Value processing, including framing and parsing.
|
| org.jmisb.api.klv.eg0104 |
EG 0104.5 "Predator UAV Basic Universal Metadata Set".
|
| org.jmisb.api.klv.st0102.localset |
ST 0102 Local Set-specific types.
|
| org.jmisb.api.klv.st0102.universalset |
ST 0102 Universal Set-specific types.
|
| org.jmisb.api.klv.st0601 |
ST 0601: UAS Datalink Local Set.
|
| org.jmisb.api.klv.st0806 |
ST 0806: Remote Video Terminal Metadata.
|
| org.jmisb.api.klv.st0806.poiaoi |
ST 0806 Point Of Interest (POI) and Area of Interest (AOI) Local Sets.
|
| org.jmisb.api.klv.st0806.userdefined |
ST 0806 User Defined Local Set.
|
| org.jmisb.api.klv.st0808 |
ST 0808: Ancillary Text Metadata Sets.
|
| org.jmisb.api.klv.st0903 |
ST 0903: Video Moving Target Indicator and Track Metadata.
|
| org.jmisb.api.klv.st0903.algorithm |
ST 0903 Algorithm Local Set.
|
| org.jmisb.api.klv.st0903.ontology |
ST 0903 Ontology Local Set.
|
| org.jmisb.api.klv.st0903.vchip |
ST 0903 VChip Local Set.
|
| org.jmisb.api.klv.st0903.vfeature |
ST 0903 VFeature Local Set.
|
| org.jmisb.api.klv.st0903.vmask |
ST 0903 VMask Local Set.
|
| org.jmisb.api.klv.st0903.vobject |
ST 0903 VObject (Ontology) Local Set.
|
| org.jmisb.api.klv.st0903.vtarget |
ST 0903 VTarget Local Set.
|
| org.jmisb.api.klv.st0903.vtrack |
ST 0903: VTrack Local Set.
|
| org.jmisb.api.klv.st0903.vtracker |
ST 0903 VTracker Local Set.
|
| org.jmisb.api.klv.st1206 |
SAR Motion Imagery Metadata.
|
| org.jmisb.api.klv.st1303 |
ST 1303: Multi-Dimensional Array Pack.
|
| org.jmisb.api.video |
Video processing, including muxing/demuxing and encoding/decoding.
|
| Class and Description |
|---|
| IInvalidDataHandlerStrategy
Strategy to handle invalid data.
|
| InvalidDataHandler
Handler for invalid data cases.
|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| KlvParseException
Indicates an error occurred during metadata parsing.
|
| Class and Description |
|---|
| Beta
Specifies that a class or method may undergo breaking changes in a future release, even a minor
or patch revision.
|
Copyright © 2021 West Ridge Systems. All rights reserved.


Only gave away half my beer! Won't be dispensing at the venue tomorrow, but hit me up if you missed out. Ruby Red and Baby Porter! #roa4
3/6/2014, 6:59:00 PM
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Only gave away half my beer! Won't be dispensing at the venue tomorrow, but hit me up if you missed out. Ruby Red and Baby Porter! #roa4
3/6/2014, 6:59:00 PM
Favs: 0
Retweets: 0
link


add search params
delete search params
get search params
add node params
delete node params
current search params:


Project Teams Gathering
Currently at the PTG
List of Etherpads
Useful links
We use etherpads to track the agenda of topics that should be covered in each track.


Class ImportRequestModel
This class contains properties and information to request the system to start an import process.
Inheritance
System.Object
ImportRequestModel
Inherited Members
System.Object.ToString()
System.Object.Equals(System.Object)
System.Object.Equals(System.Object, System.Object)
System.Object.ReferenceEquals(System.Object, System.Object)
System.Object.GetHashCode()
System.Object.GetType()
System.Object.MemberwiseClone()
Namespace: Vasont.Inspire.Models.Transfers
Assembly: Vasont.Inspire.Models.dll
Syntax
public class ImportRequestModel
Properties
| Improve this Doc View SourceFiles
Gets or sets a list of form file objects.
Declaration
public List<IFormFile> Files { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<Microsoft.AspNetCore.Http.IFormFile> |
FolderId
Gets or sets the identity of the folder where new components created by the import process will be stored.
Declaration
public long FolderId { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Int64 |
ImportFiles
Gets or sets a list of ImportRequestFileModel that defines files to be imported.
Declaration
public List<ImportRequestFileModel> ImportFiles { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<ImportRequestFileModel> |
ImportId
Gets or sets the import identity used for the importing of the components specified.
Declaration
public long? ImportId { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Nullable<System.Int64> |
ProjectFolderId
Gets or sets the identity of the project folder where new components created by the import process will be stored.
Declaration
public long ProjectFolderId { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Int64 |
SendNotification
Gets or sets a value indicating whether notifications are sent.
Declaration
public bool SendNotification { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
WaitForCompletion
Gets or sets a value indicating whether import will process in the background or return when completed.
Declaration
public bool WaitForCompletion { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |


For thousands of years, climate was the principal force shaping the Platte River. The harsh extremes of frigid winters, stormy summers, drought, flood, and unrelenting winds formed the braided channels and meanders of its broad valley. On the ground, millions of bison, the rugged Great Plains megafauna, grazed the prairie, trampling the earth and fertilizing the soil. Beaver built dams and engineered elaborate wetland complexes. Prairie fires raced across the landscape, disturbing grass and burning down trees. Ice formed in blocky sheets, flooding the lowlands and bulldozing trees and gravel down sandy straits. Each spring, snowmelt swept down from the mountains out to the broad, sinuous channels, reshaping sandbars, scouring vegetation and creating waterways anew.
The historic prairie river was one to two miles wide and shallow, with small, sparsely wooded islands and braided channels supporting a wide variety of habitat and diverse ecological systems. For thousands of years Native American groups roamed through the Platte Valley. By the late 1600s, the Pawnee settled along the lower Platte Valley (near the Missouri confluence) and the Loup River, a tributary of the Platte, and built large earthlodge villages where they farmed corn, beans and squash. They frequented the central Platte on hunting trips, sometimes building seasonal tipi camps from which they hunted bison, elk and smaller game, and gathered wild plants.
As Euro-American explorers started arriving to colonize this new land, the Platte River Valley began to change. Beginning in the late seventeenth century, the Spanish, French, and other colonial settlers introduced native groups to horses, guns, and other trade goods as well as deadly Old World diseases. These changes affected native settlement, social and subsistence patterns and in some cases caused further friction between warring groups.
In the early nineteenth century, starting with explorers like Lewis and Clark, westward expansion brought increasing numbers of Euro-American settlers to the region. The human population, and impact on the river, grew.
For the first half of the 1800s, explorers, fur traders and trappers, Mormons, Oregon Trail and Gold Rush hopefuls headed west. Lured by fortune and adventure in the frontier, thousands carted their wagons across the country, carving trails along the river and giving it the name, “The Great Platte River Road.” Later, waves of eager homesteaders followed, settling land formerly claimed by native people. White hunters decimated the buffalo to sell hides back east. New settlers clashed with resident Native American groups, resulting in decades of unrest and conflict as the American military conquered the west under the concept of manifest destiny.
Those who stayed tried to make a living off the land, plowing up prairie and chopping down riparian forests for timber. The mid-century westward expansion of the railroad created scores of small towns in its wake and spurred the building of bridges to cross the broad river. Irrigation canals took root at points all along its length, pulling water out of the river for crops, livestock, and power generation. In the early twentieth century, the U.S. Bureau of Reclamation began building major dams and diversions in the headwaters, significantly changing the seasonal flows of water.
The Platte evolved as a river shaped by natural disturbance. But with these new man-made changes, the Platte’s ever-changing nature began to stagnate. Beaver trapping, mining, farming and timber harvest all left their mark on the river system. With water held back by dams or diverted for irrigation, river flows and channel width declined by as much as 80 percent. Channels no longer flooded in spring. Sandbars grew dense with woody vegetation. Wetlands were drained or plowed under. Bridges constrained the banks. Birds struggled to find the habitat they had used on the Platte for centuries, instead finding monoculture fields of corn and beans.
By the early twentieth century, human activity, rather than climate, became the dominant factor shaping the Platte River.


Speech
Michael Gove, Education Secretary, on why there has never been a better time to be a teacher.
Above my desk at home there’s a simple slogan - ‘If you can read this, thank a teacher’.
And I do. Every day. I give thanks for the wonderful opportunities I’ve been given by a succession of great teachers - from Mr Gillanders to Mike Duncan, Mrs Christie to Cath Richmond.
Every day I also give thanks for the amazing work being done by the teachers who are starting the new school year this week.
I am fortunate as Education Secretary because we have the best generation of teachers ever in our classrooms - including the very best generation ever of young teachers - those who have entered our classrooms over the last few years.
Whenever I can, I give thanks for their work - not just privately, but on any public platform I’m given. Including this one.
This government is determined to do all it can to support the teaching profession.
Because there can never have been a more important time to be a teacher.
Teachers hold in their hands the success of our country and the wellbeing of its citizens; they are the key to helping every child in this country to realise their full potential.
Teachers are the most important fighters in the battle to make opportunity more equal.
Teachers are the critical guardians of the intellectual life of the nation.
Teachers give children the tools by which they can become authors of their own life story and builders of a better world.
It is teachers, not poets, who are the unacknowledged legislators of mankind.
And it is because the teaching profession is so crucial that our programme of education reform has been designed to empower teachers; to give them more freedom, more power and more prestige.
I know that - sometimes - the speed with which I want to improve our schools, and - occasionally - the style with which I have made my case, have led some to argue that I am - implicitly or explicitly - seeking to criticise teachers.
But nothing could be further from the truth.
I want to defend teachers - and teaching - from the critics and cynics.
Because there are attacks directed at teaching - and I want to fight them.
In too many ways and by too many people - the importance of teaching is being denied.
There are 4 principal attacks on the work teachers do, which I want to anatomise today:
The first attack holds that teaching is a depressing and demotivating activity - and that the profession is suffering reputational decline.
The second attack is a denial that teaching can make any real difference.
The third attack is the sidelining of the teacher from the activity of learning.
And the fourth attack comes from those who believe teachers can’t be trusted - that they need outsiders at every turn to monitor, police and approve their activities.
I want to deal with each of those attacks in turn.
The first attack on teaching comes - perhaps surprisingly, perhaps unsurprisingly - from the leadership of the 2 biggest classroom unions.
Chris Keates, the General Secretary of the NASUWT , has described teaching as a profession ‘in crisis’ - in which morale is at ‘rock bottom’, or ‘an all-time low’.
She claims that teachers are ‘angry, frustrated and demoralised’; that three-quarters of teachers feel professionally disempowered, that recruitment into initial teacher training is plummeting, and she has cited what she has claimed is research showing that over half of respondents have considered leaving the teaching profession.
Why is morale so low? Well, according to Chris Keates’s closest colleague, it’s because this government’s education reforms are a breach of international humanitarian law.
According to Dr Patrick Roach, the Deputy General Secretary of the NASUWT , this government’s Education Act was ‘a crime against humanity.’
It was ‘a smash and grab raid that will tear apart our schools and our communities’.
Dr Roach is not, however, alone in suggesting that government reforms constitute cruel and unusual punishments inflicted on suffering innocents.
Martin Powell-Davies of the National Union of Teachers National Executive has claimed that our education reform plans will make teaching ‘a totally unbearable profession’.
It has come to something when the General-Secretary of the NUT ranks almost as a voice of moderation. But even though Christine Blower doesn’t indulge in the hyperbole of others she still presents teaching as a profession in the grip of some terrible malaise.
She has argued that ‘it is hardly surprising that some teachers are voting with their feet and leaving the profession. A combination of pension cuts, pay freezes, an ever-increasing workload and continual inspection and criticism from government at every turn will make retention of teachers increasingly difficult’. And she has argued that ‘those who remain’ face ‘plummeting morale’.
The picture these union leaders paint is of a profession which no one rational would wish to join - a profession which is unattractive, unrewarding and unfulfilling.
The truth, however, is very different.
Teaching - as a profession - has never been more attractive, more popular or more rewarding.
Take the figures cited by the NASUWT about teacher opinion - they come from a self-selecting sample of their members, unrepresentative of the profession as a whole.
And despite their survey’s annual claims, it’s clearly not the case that more than half of NASUWT ’s members are actually leaving the profession every year - in fact, year after year, more people in England join the teaching profession than leave it.
Indeed, teaching has a far better retention rate than many other careers for highly-qualified people. Overall, teachers are only half as likely to leave their chosen profession as graduates in popular non-teaching roles (44% of graduates in non-teaching roles switched career within their first 3.5 years, compared to just 21% of teachers).
The numbers who would recommend the profession are up.
Ninety-eight per cent of school leaders surveyed by the National College for Teaching and Leadership this year say that overall ‘it’s a great job’; and 91% say they would ‘recommend their job to other staff’.
The numbers who think that the profession is rewarding are up.
The Teaching Agency’s annual survey of final year undergraduates at leading universities found that 86% thought that teaching is a rewarding career; 66% think teaching involves ‘demanding work that has real status and kudos’; 61% think teaching is a ‘great career option for the long term’. When asked to choose an adjective that best described a teaching career, the largest number of respondents said ‘rewarding’.
Last year’s survey found that 71% of undergraduates thought the image of teaching was improving, while 72% thought their friends and family would react positively to them becoming a teacher - up 6% since 2010.
The numbers of highly-qualified people entering teaching are up.
More than 7 out of 10 new teachers now have a first or upper-second class degree, the highest proportion ever recorded and an increase of 9 percentage points since 2010 to 2011. Teach First has moved up to third place in the rankings of the Times ‘Top 100 Employers 2013’, its highest ever position; first place in High Fliers research of 100 major graduate recruiters.
According to the OECD , teachers in England are comparatively well-paid - with annual salaries in England higher than the OECD average, and higher than those in progressive Scandinavian nations such as Finland, Norway or Sweden.
Teachers in England already progress up the pay scales twice as quickly as the OECD average, and our reforms to pay progression will mean that, from this year, the best teachers will have the opportunity to access greater rewards even earlier in their careers.
And school leaders are now free to reward their best teachers more than ever before - with more autonomy to attract, retain and reward those teachers who have the greatest impact on their pupils’ performance.
And research from March this year found that 69% of career changers thought that teaching was a career for them to consider, a rise of 5% in just 4 months; 71% of students felt that teaching was a career for them to consider, a rise of 10% in just 4 months.
I think that growing enthusiasm for teaching reflects the fact that opportunities for teachers are now greater than ever.
As national, local, and specialist leaders of education, leaders of academy chains and teaching schools …
There are opportunities to change lives as subject leaders, like Simon Mazumder at Altrincham Girls Grammar – Head of Maths, a specialist leader of education, who is currently doing brilliant work on primary maths in collaboration with Manchester University.
And there are opportunities to become educational pioneers by opening free schools, which can reshape how we teach and how students learn.
Educational innovation has a new generation of heroes and heroines in Peter Hyman and Oli de Botton at School 21, in the team behind the Greenwich Free School, or in the inspirational classroom practitioner Mark Lehain who has opened an academically ambitious new school in one of the most disadvantaged parts of Bedford.
There are also new opportunities to shape the whole educational debate.
In the past, the education debate has been dominated by education academics - which is why so much of the research and evidence on how children actually learn has been so poor.
Now, thankfully, teachers are taking control of their profession’s intellectual life, taking the lead in pioneering educational research and creating a living evidence base.
Later this week the brilliant Tom Bennett - teacher, blogger, behaviour guru and free thinker - is bringing together other teachers at the ResearchED 2013 conference to debate how to use the most rigorous evidence to improve teaching itself. Some of the most impressive names in the profession - such as Tom Sherrington, Joe Kirby and Daisy Christodoulou will be speaking.
Their initiative follows on from the groundbreaking work we commissioned from Dr Ben Goldacre - the author of ‘Bad Science’ - to help us understand how better use of evidence could improve teaching practice. His paper on ‘Building Evidence Into Education’ generated a vast amount of enthusiastic debate and discussion among teachers when it was published in March this year, both on and offline.
And his paper sits alongside fascinating research carried out by teaching schools, exciting schemes like the National College for Teaching and Leadership’s Test and Learn programme, and the projects run by the Education Endowment Foundation, a new charity working to ensure that children from all backgrounds can make the most of their talents.
I am particularly encouraged by the work being done by teachers to shape new curricula, using the freedoms enjoyed by academies. Caroline Nash, the inspirational sponsor of Pimlico Academy, has set up a wonderful new organisation - the Curriculum Centre - to help teachers develop challenging and aspirational knowledge-rich programmes of study. In Ark’s academies new and more ambitious maths curricula have been developed by brilliant young teachers. And David Benson, the newly-appointed principal designate of the Kensington Aldridge Academy, is also developing aspirational new approaches to the curriculum for students from disadvantaged communities.
As well as leading the education debate, teachers are also our most valued public service workers.
More classroom teachers than ever before are being honoured for their work - in the 2013 honours alone, Ann Hambly of Queen Elizabeth’s Grammar School, Ashbourne; Paul Hughes of Queensbury Upper School, Bedfordshire; Peter Latham, a former PE teacher from Otley, West Yorkshire; Maggie Morgan from St Paul’s Nursery and Primary School, Brighton and Hove; Linda Wainwright from Slade Green Infants School.
In fact, around 10% of all 2013 honours were awarded to people from the world of education.
So - far from the picture drawn in such unremittingly bleak colours by the teaching unions, the reality of teaching in England today is that there’s never been a better time to be a teacher.
In fact, I could not put it any better than Gerard Kelly, the outgoing editor of the bible of teaching, the Times Educational Supplement - who wrote in his valedictory editorial that:
Contrary to most reports, teaching in Britain has never been in better health…The quality of recruits is phenomenally high, the pay isn’t bad, the profession’s status is rising, schools have never been better equipped and teachers’ pensions remain generous compared with most. Students have never been more motivated and parents rarely so supportive. Most encouraging of all are the widespread acceptance that a ‘satisfactory’ education isn’t really good enough and the determination of schools and teachers to take ownership of their profession, sharing ideas and best practice in ways unknown only a few years ago…
The fact is that teaching, for all its bureaucratic indignities, petty frustrations and ceaseless initiatives, is a more respected profession and a more attractive graduate destination than it has been for many years. The stresses are endlessly cited, less so the equally stratospheric satisfaction levels. It really is the ‘noblest of professions’.
The second attack on teaching has also - perhaps surprisingly, perhaps unsurprisingly - come from within the teaching unions. But it’s very far from restricted to them.
The essence of this attack is a belief that teaching cannot actually make that much of a difference to the life chances of children.
The people who make this argument exist on both Left and Right.
From the Left - and from the perspective of a teaching union general secretary - Dr Mary Bousted makes the argument more eloquently than anyone. I say this in a spirit of genuinely respectful disagreement because I believe Dr Bousted is one of the most impressive people in the education debate.
Mary has argued that the socio-economic circumstances of children determine their fate far more than the level of academic expectations at school or the quality of teaching.
She maintains that the most important factor in educational success abroad is not great teaching, high expectations of students or the valuing of knowledge but ‘less wealth inequality’ and ‘far more balanced school intakes’.
In essence - for Mary - it is overwhelmingly the case that deprivation is destiny.
From the Right - and from the perspective of a distinguished academic - a parallel case is made by Professor Charles Murray. Again, I have enormous respect for Charles Murray and his work - but on this occasion, I disagree with him.
He and others like him have argued that, because children inherit different cognitive abilities, the quality of teaching cannot significantly alter that and therefore large numbers of children are not equipped to succeed academically.
In essence - for Charles - it is overwhelmingly the case that genetics are destiny.
But if a child’s background and genetic makeup were all that mattered, then we would expect the same sorts of pupils in the same sorts of schools to get the same sorts of results. Whereas even the most cursory glance at schools in England and America reveals huge variations in performance, even in those schools with the most similar pupil populations.
There are schools with relatively gifted - or wealthy - intakes which perform poorly, coasting along without generating real progress.
And there are - thankfully - many state schools where children from poor backgrounds, who may have been dismissed as unacademic, perform brilliantly.
Indeed there are some schools where the children - irrespective of background - all perform well.
Schools like the Ark academies in England – like ULT ’s Paddington Academy and Thomas Jones Primary in West London.
In all of these schools children from the poorest, most deprived backgrounds achieve just the same (impressively high) marks as their richer, luckier peers.
And in other schools children who have been labelled as likely to perform at below the average academic level defy that categorisation.
In schools like Cuckoo Hall Primary or Durand Academy far more children than the national average are registered as having special educational needs. But the vast majority of children - regardless of the challenges they face - achieved at or above the expected level in numeracy and literacy.
Why do these schools succeed, transforming poor children’s lives and life chances, for good? Why do their children manage to achieve far over the odds, giving the lie to those pessimists and fatalists from Left and Right and defeating both the poverty of their backgrounds and their so-called innate ‘genetic limitations’?
Because they share a single common denominator - a single-minded focus on teaching. On recruiting the best candidates, giving them the best training and development; maximising the time children spend being instructed by passionate experts in the disciplines of rigorous thought.
An overwhelming body of academic literature shows that teacher quality and pupil performance are inextricably linked.
McKinsey’s report, ‘How the world’s best-performing school systems come out on top’, showed that quality of teaching is the most important driver of quality in any education system - more important than any other factor.
Studies in Tennessee have shown that an individual pupil taught for 3 consecutive years can make as much as 2 years more progress when taught by a top-performing teacher than by a poorly-performing teacher - teachers working in the same building, teaching pupils in the same grade, from the same backgrounds. Analysis in England has identified a quantifiable, measurable improvement in pupils’ exam grades when taught by a high-quality teacher.
Other studies have shown that the benefits of high-quality teaching last a lifetime - with pupils taught by top-performing teachers ‘more likely to attend college, earn higher salaries, live in better neighbourhoods, and save more for retirement’.
All over the world, in individual schools and wide-ranging academic literature, the evidence is crystal clear.
Great teaching can and does make a huge difference to children’s performance.
Great teaching involves empathy and energy, authority and resilience; detailed planning; constant self-improvement. A great teacher has the ability to ‘read’ a classroom and understand its dynamics, instantly; shows inspirational leadership, exciting and motivating pupils to help them achieve their full potential.
But common to all the great teachers I know is a love of children and a love of knowledge.
And that shouldn’t be surprising - because the very best academic research also proves the vital importance of an education which is knowledge rich.
This concept is, however, undermined by the third attack on teaching, an old one - as old as Rousseau, in fact. It’s the belief that education should not be an activity in which the teacher imparts knowledge to the child but a pursuit - by the child - of what it finds interesting.
In Émile, Rousseau wrote: ‘Let [a child] know nothing because you have told him, but because he has learnt it for himself. If ever you substitute authority for reason he will cease to reason, he will be a mere plaything of other people’s thoughts’.
Various so-called progressive thinkers subsequently took up the same position.
As David Green of Civitas has pointed out, these included influential writers like Ivan Illich, whose book ‘Deschooling Society’ (1971) complained that ‘real learning is not the result of instruction … most learning requires no teacher’; to Carl Rogers, author of ‘Freedom to Learn’ (1969), who claimed that teaching was “based upon a distrust of the student. The attitude of teachers was: ‘Don’t trust him to follow his own leads; guide him; tell him what to do; tell him what he should think; tell him what he should learn”.
These arguments have been particularly pernicious in crucial areas like the teaching of reading, for example - where research has consistently and comprehensively shown, both in this country and around the world, that systematic, phonic instruction by a teacher is the most effective and successful way of teaching children to read.
Ideologues, however, have long argued against phonics and direct instruction, claiming instead that children should be allowed to discover letters and words for themselves. This mindset - which holds that direct instruction (what you and I would call teaching) is harmful to children’s creativity and curiosity - is not new.
From the Hadow report of 1933, which stated that ‘the child should begin to learn the 3 Rs when he wants to do so’; to the Plowden report of 1967, which declared that the ‘skills of reading and writing…can best be taught when the need for them is evident to children’ and the Bullock report of 1975, stating that ‘we do not suggest that children of any age should be subjected to a rigorous and systematic training programme’…the educational establishment has conspired against teachers.
Again and again, in this country and abroad, educational thinkers who call themselves progressive, but who are anything but, have converged on the belief that the importance of teaching should be downgraded.
These theorists have consistently argued for ways of organising classrooms and classroom activity which reduce the teacher’s central role in education.
All too often, we’ve seen an over-emphasis on group work - in practice, children chatting to each other - in the belief that is a more productive way to acquire knowledge than attending to an expert.
Although, as the great Texan President Lyndon B Johnson said, ‘you aren’t learning anything when you’re talking.’
Some schools have been pressured to fit in with prevailing doctrines, even against their own instincts. Some nurseries and schools in Kent, for example, reported to us that they were told to remove tables and chairs from their classrooms; were told that they were not allowed to keep children sitting still for longer than 1 minute for every year of their lives - not even during registration, or when listening to a story; were told that children were not allowed to tidy up, or be asked to put their coats on, in case it interrupted ‘child-initiated play’.
And it’s not just group work - almost any activity which is not direct instruction has been lauded by the so-called progressives while direct instruction has been held up to criticism and ridicule.
In her fantastic book ‘Seven Myths About Education’, Daisy Christodoulou recalls her own teacher training - when she was told that she talked too much in lesson practice - and in a bizarre inversion of LBJ’s wisdom she was told that when she was talking, the pupils weren’t learning.
So what happens in classrooms when teaching is marginalised?
The teacher Matthew Hunter records on his blog a series of lessons aimed at history students between the ages of 11 to 16 that he had encountered.
They included studying the battle of Hastings by re-enacting it on a field with softballs, spending 3 lessons making castles out of cardboard boxes, making plasticine models to represent Hitler’s main aims as Fuhrer and recreating life on a slave ship by making pupils gather under their desks.
Another teacher records a lesson for A level English students in which they were asked to depict literary characters on a paper plate - drawing a face on the plate - and then asked to use stickers to define the character’s principal traits - pinning the stickers on their clothes and mingling with other students, while they introduce themselves ‘in character’.
Allied to these teaching methods which have nothing to do with passing on knowledge, there has also been an emphasis on teachers having to put their own learning aside so that work is ‘relevant’ to the students. This has resulted in the dumbing of educational material down to the level of the child - with GCSE English papers that ask students about Tinie Tempah, or Simon Cowell - rather than encouraging the child to thirst after the knowledge of the teacher.
I believe that we need to move away from these approaches to education - I would call them pedagogies but they don’t leave much place for the pedagogue - towards an education system which believes, right from the early years, in the importance of teaching.
Because schools are - above all - academic institutions. We need teachers to actively pass on knowledge, organised in academic disciplines such as physics and history - to introduce children to precisely those areas of human thought and achievement which they are most unlikely to discover or understand on their own.
Children naturally learn to talk; they do not naturally learn to read, or to play the violin, or to carry out long division.
The most impressive scientific evidence on how children learn - from experts like Paul Kirschner, Richard E Clark and John Sweller - all points towards the importance of direct instruction. Their work on ‘why minimally guided teaching techniques do not work’ is hugely powerful.
Their thinking is reinforced by contemporary advocacy from the very best teachers at the sharp end - like Daisy Christodoulou. In ‘Seven Myths about Education’, again, she points out that learning depends on teachers passing on key ‘building blocks’ of knowledge to students so that they become lodged firmly in the memory. Using an instant recall of times tables, for example, to tackle long multiplication.
Although the work may initially be hard, it brings its own special rewards. Only after building fluency in scales can musicians play a great sonata or concerto; only after learning how letters on the page correspond to sounds and words can children discover the magic and mystery of English literature.
Daniel Willingham’s research in cognitive science has provided compelling evidence that a traditional knowledge-rich curriculum is the key to educational success.
As he has written, ‘knowledge does much more than just help students hone their thinking skills: it actually makes learning easier. Knowledge is not only cumulative, it grows exponentially. Those with a rich base of factual knowledge find it easier to learn more - the rich get richer. In addition, factual knowledge enhances cognitive processes like problem solving and reasoning. The richer the knowledge base, the more smoothly and effectively these cognitive processes - the very ones that teachers target - operate. So, the more knowledge students accumulate, the smarter they become.’
Willingham’s comment that ‘the rich get richer’ is, sadly, not just a metaphor. All too often, children from more affluent backgrounds effortlessly acquire this broad knowledge base at home, equipping them with the tools needed at school and beyond; those from less privileged backgrounds miss out.
The educationalist E D Hirsch has proved this phenomenon beyond any doubt - with research demonstrating that students with a higher level of ‘background’ knowledge were able to understand and analyse complex texts much better than their peers without that knowledge, who tended to come from poorer, less privileged backgrounds. As he wrote:
African-American students at a … community college could read just as well as university…students when the topic was roommates or car traffic, but they could not read passages about Lee’s surrender to Grant [a pivotal battle in the American Civil War]. They had not been taught the various things they needed to know in order to understand ordinary texts addressed to a general audience.
This, then, is the perverse result of so-called ‘progressive’ denigration of knowledge. Gramsci put it best: ‘the most paradoxical aspect of it all is that this new type of [education] is advocated as being democratic, while in fact it is destined not merely to perpetuate social differences, but crystallize them in Chinese complexity.’
In short, for too long - whether driven by a romantic, Rousseau-ian reluctance to crush a child’s delicate spirit, or a glib, Google-era insistence that knowledge is irrelevant in a world where ‘you can just look it up’ - the role of the teacher has been eroded.
Which is why it is so encouraging that a growing number of teachers - indeed the most popular teachers on the web, like Andrew Old, whose blog has received more than 600,000 hits; Tom Bennett, with almost eight and a half thousand followers on Twitter, and Joe Kirby, with almost 2,000 - are arguing for a restoration of knowledge and direct instruction; in short, standing up for the importance of teaching.
The fourth attack on teaching is one in which governments - including Conservative governments - have at times been complicit.
It’s the belief that teachers need others to validate the work they do - whether those others are university academics, or inspectors, or examiners - who have never been teachers.
Take, for example, the whole practice of teacher training.
The evidence shows the best teacher training is led by teachers; that the skills which define great teaching - managing behaviour, constructing compelling narratives, asking the right questions, setting appropriate tasks - are best learnt from great teachers; that the classroom is the best place for teachers to learn as well as to teach.
The work of Doug Lemov in the United States - teacher, founder of a charter school, author of ‘Teach Like a Champion’, which has transformed the debate around teacher training and won followers all over the world - has found support right across the American political spectrum.
In this country, schools play a central role in all of the ITT providers judged to be outstanding under Ofsted ’s tough new regime.
We have already taken a number of steps to put teachers and schools in charge of recruitment and training.
Brand new teaching schools have become centres of excellence in training and development - and we will be expanding the number of teaching school alliances beyond the planned 500. The first group of teaching schools have now been operating for 3 years and I can now confirm that their funding will continue beyond the planned 4 years into a fifth year.
While our new teacher training scheme, School Direct, gives aspiring teachers the opportunity to work in a great school from day one, just like student medics in hospitals - learning from more experienced colleagues and immediately putting their new skills into practice.
We’ve also done a lot to deal with the systematic shortages of specialist maths and physics teachers that we inherited. We’ve collaborated with the Institute of Physics and the Institute of Mathematics to introduce prestigious new scholarships worth £20,000 and brought in new training bursaries of up to £20,000 to attract the brightest graduates into these core subjects, more if trainees go to schools with a high proportion of pupils on FSM .
But we’ve got to go even further. So we will soon be announcing even greater incentives in shortage subjects, where recruitment has historically been most difficult, and we will do even more to encourage would-be teachers to study maths and physics at A level and beyond. And we’ve ensured that - at least in maths and physics - there will no longer be any cap on the number of teachers recruited each year, no published target for ITT places; on the contrary, we want to recruit as many new teachers in these subjects as we can.
Schools can now also use their new powers to attract and reward great teachers in specialist subjects, in particular - giving them the power to pay great physics and maths teachers more, right from day one.
As schools take more control over training the next generation of teachers, many of the best academy chains and teaching school alliances are now playing an even greater role in training the next generation of teachers as accredited SCITTs , school-centred initial teacher training providers.
We want to see their numbers increase, enabling more aspiring teachers than ever before to benefit from the expertise and experience of some of the best in the business - so we will be bringing forward proposals to support this later in the year.
The best higher education institutions welcome our changes because they know that discriminating schools will increasingly choose partners in HE who deliver the best quality training and development.
Many have in fact been working hand in glove with schools for many years, and School Direct is just an extension of what they already do. Oxford University, for example, has collaborated with local secondary schools on an internship programme called Oxford City Learning for many years now, and School Direct places have simply been incorporated within that successful scheme.
But sadly, there are some vested interests within some universities that oppose the shift towards school-centred teacher training by SCITTs or through School Direct; those, perhaps, which have long relied on an effective monopoly of teacher training to sustain their finances.
So it’s vital for the future of the profession that we defend teachers from self-interested attacks - and stand up for the principle of teachers teaching teachers.
We also need to defend teaching from the wrong sort of inspection.
I am a passionate believer in the power of good inspection to improve education.
And to those who question whether schools need to be inspected by any outside body at all - suggesting, perhaps, that schools should be the only state-funded institutions not accountable to any form of external authority - I merely point out that without Ofsted , exam results would be the only arbiter of a school’s performance - making a system more pressurised, more crude and more ‘high stakes’ than the one we have now.
Inspection can be a catalyst for rapid and effective school improvement.
We know that schools judged inadequate by Ofsted have generally made more rapid and sustained improvement than those marked ‘satisfactory’, the next rung up - which have tended to coast along at the same level.
This, incidentally, is part of the reason why Sir Michael Wilshaw has, quite rightly, changed the old grade of ‘satisfactory’ into ‘requires improvement’ - sending the message that every school should, at least, reach ‘good’, and should be aiming even higher.
Ofsted also provides an essential service in highlighting brilliant practice, the schools which make a difference and the teachers we should celebrate.
And it has now changed the way it reports its findings so that every inspection report for an outstanding school clearly states on the very first page why that school is outstanding - making it much easier to understand why the best schools are doing so well.
But there have been occasions - in the past - when inspection has not achieved what it should.
Too few inspectors had recent - or current - experience of teaching.
The framework, prior to 2010, required schools to be judged against more than 27 different criteria - putting ‘quality of teaching’ on a par with ‘whether pupils adopted healthy lifestyles’ and ‘the extent to which pupils contribute to the school and wider community’.
And Ofsted ’s guidance provided too little clarity about what constituted good teaching; or allowed inspectors’ personal prejudices and preferences to be interpreted as ‘the Ofsted way’.
As a result, and as teacher bloggers like Andrew Old have chronicled, time and again too much emphasis was given to particular practices like group work and discovery learning; while Ofsted inspectors marked teachers down for such heinous crimes as ‘talking too much’, ‘telling pupils things’ or ‘dominating the discussion’.
The good news is that Ofsted - under its inspirational new leadership - is moving to address all these weaknesses and give us a system of inspection of which we can be proud.
The numbers of inspectors with the right experience and credentials is rising. In 2010 to 2011, just 15% of inspections included a serving headteacher or senior leader - today, it’s 52% - and 88 national leaders of education have already trained as inspectors, with 45 more training this term and applications already oversubscribed for the next round.
Outdated, misleading guidance has been replaced with a clear directive to reward great teaching - whether it matches the inspector’s personal preferences or not.
And the Ofsted framework has been transformed so that, rather than peripherals, teaching now matters above all - in particular, the sort of teaching which generates excellence. That means less focus on processes, pedagogies, lesson plans and structures, and more focus on how well pupils learn. And a school cannot now be awarded an overall ‘outstanding’ grade unless its teaching is judged to be ‘outstanding’.
It is thanks to Sir Michael Wilshaw - himself a great teacher - that these changes have been made - and they all point in one direction - the affirmation of the importance of teaching.
That phrase - the ‘Importance of teaching’ - was the title of this government’s first and only education white paper; our most important priority then, and our most important priority now.
It is the silver thread running through every single one of our policies, every part of our reform agenda.
It’s because we know teaching can make such a difference that we have instituted policies that help teachers make that difference.
Clearing away the distractions and slashing the unnecessary bureaucracy and central prescription which sapped so much of teachers’ time and energy; in numbers alone, we’ve removed or simplified over 50 unnecessary duties and regulations; and cut the volume of guidance issued to schools by 75%, over 21,000 pages.
And giving teachers as much freedom, autonomy and independence as possible, to get on with they do best - teach.
Every teacher in the classroom knows - as Gerard Kelly so rightly said - that teaching is the noblest of professions.
Which is why everything the Department for Education does, has done and will do is designed to reinforce the importance of teaching.
Published: 5 September 2013
Updated: 13 September 2013


1 The elder unto the wellbeloved Gaius, whom I love in the truth.
2 Beloved, I wish above all things that thou mayest prosper and be in health, even as thy soul prospereth.
3 For I rejoiced greatly, when the brethren came and testified of the truth that is in thee, even as thou walkest in the truth.
4 I have no greater joy than to hear that my children walk in truth.
5 Beloved, thou doest faithfully whatsoever thou doest to the brethren, and to strangers;
6 Which have borne witness of thy charity before the church: whom if thou bring forward on their journey after a godly sort, thou shalt do well:
7 Because that for his name's sake they went forth, taking nothing of the Gentiles.
8 We therefore ought to receive such, that we might be fellowhelpers to the truth.
9 I wrote unto the church: but Diotrephes, who loveth to have the preeminence among them, receiveth us not.
10 Wherefore, if I come, I will remember his deeds which he doeth, prating against us with malicious words: and not content therewith, neither doth he himself receive the brethren, and forbiddeth them that would, and casteth them out of the church.
11 Beloved, follow not that which is evil, but that which is good. He that doeth good is of God: but he that doeth evil hath not seen God.
12 Demetrius hath good report of all men, and of the truth itself: yea, and we also bear record; and ye know that our record is true.
13 I had many things to write, but I will not with ink and pen write unto thee:
14 But I trust I shall shortly see thee, and we shall speak face to face. Peace be to thee. Our friends salute thee. Greet the friends by name.
| << 2 John | Contents | Jude >> |


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Get locations and sizes of radial basis functions for use in rbf network. Radial basis function are a simple, fast method for universal function approximation / regression. The idea is to find a mapping X->y where X and y are continuous real variables. The mapping is linear over features of X: y=rbfWeight*features(X). The features are of the form: f_j(x) = exp(-||x-mu_j||_2^2 / 2 sig_j^2 ). The number of basis functions controls the complexity of the network. Too many basis functions can lead to overfitting, too few to bad interpolation. Rbf networks are trained in two phases. First, the radial basis functions are found in an unsupervised manner (using only Xtrain), for example by clustering Xtrain. Next, given the basis functions, Xtrain and ytrain, the basis weights are found by solving the system: rbfWeight * features(Xtrain) = ytrain At this point, to interpolate any new points, Xtest, use: ytest = rbfWeight * features(Xtest) The code below achieves all three steps: rbfBasis = rbfComputeBasis( Xtrain, k, cluster, scale, show ); rbfWeight = rbfComputeFtrs(Xtrain,rbfBasis) \ ytrain; ytest = rbfComputeFtrs(Xtest,rbfBasis) * rbfWeight; Note, in the returned rbfBasis struct there are a number of flags that control how the rbf features are computed. These can be altered to achieve the desired effect. For an in depth discussion of rbf networks see: Christopher M. Bishop. "Neural Networks for Pattern Recognition" USAGE rbfBasis = rbfComputeBasis( X, k, [cluster], [scale], [show] ) INPUTS X - [N x d] N points of d dimensions each k - number of basis functions to use cluster - [1]: Computes cluster centers for use as rbf functions. - 0: Evenly centered basis functions (ok for small d) scale - [5] Alter computed value of sigma by given factor set larger for smoother results, too small -> bad interp show - [0] will display results in figure(show) if show<0, assumes X is array Nxs^2 of N sxs patches OUTPUTS rfbBasis .d - feature vector size .k - number of basis functions actually used .mu - [d x k] rbf centers .vars - [1 x k] rbf widths .var - rbf average width .globalVar - [1] if true use single average var for rbfs .constant - [0] if true include extra basis with constant activation .normalize - [0] if true normalize overall rbf response to sum to 1 EXAMPLE See also RBFDEMO, RBFCOMPUTEFTRS Piotr's Computer Vision Matlab Toolbox Version 2.50 Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] Licensed under the Simplified BSD License [see external/bsd.txt]


205_yaqizhang.py
class Solution:
def findMaxValueOfEquation(self, points: List[List[int]], k: int) -> int:
# maximum value of the equation yi + yj + |xi - xj| where |xi - xj| <= k
# window ...
# yi + yj + xi - xj
# stand at x
# maximum of yj - xj
n = len(points)
# xs = [x for x, _ in points]
# xs.sort()
evicted = [False] * n
lo = 0
pq = []
ans = -math.inf
for i, (x, y) in enumerate(points):
while x - points[lo][0] > k:
evicted[lo] = True
lo += 1
while pq and evicted[pq[0][1]]:
heapq.heappop(pq)
if pq:
cur = x + y - pq[0][0]
ans = max(ans, cur)
heapq.heappush(pq, (-(y - x), i))
return ans


You can insert descriptive comment lines and blank lines anywhere in your file list to clarify its layout. If the file list is more than a screen or so long, it may be worth subdividing it into sections to make it easier for your participants to find files.
You can move lines, or marked blocks of lines, up and down your file list using the Move up and Move down buttons.
You can sort file entries in the file list by highlighting the block (or blocks) of files you want to sort and selecting Sort flist. Moderate will sort each contiguous block of marked lines independantly. Indented continuations of file descriptions are kept with the relevant entry, embedded comment lines are kept with the following file entry (any comment lines at the top or bottom of the marked block are just left where they are).


We use Github issues to keep track of new component submissions, bugs, design feedback, and any other suggestions related to Rivet. If you have created a component you think would be useful to others or would like to make a suggestion, let us know.
See how you can help


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[vulners] Search everything | https://vulners.com/search
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https://cxsecurity.com/
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http://secunia.com/
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[0day.today](http://0day.today/) | Easy to navigate database of exploits
[Snyk Vulnerability DB](https://snyk.io/vuln/) | detailed info and remediation guidance for known vulns, also allows you to test your code
[iedb](http://iedb.ir/) | Iranian Exploit DataBase
[rapid7](https://rapid7.com/db) | Vulnerability & Exploit Database - Rapid7
https://code.google.com/p/google-sec...ry&cells=tiles
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http://farlight.org/index.html


The smash hit game! Control your cell and eat other players to grow larger! Play with millions of players around the world and try to become the biggest cell of all!


An analogy to describe the differences between HTML and CSS
Let’s say you’ve got an electric guitar and you pop it into the amp.
You can play solos, chords and pluck away, this is the HTML component.
Now CSS you add a pedal board, with this you’re adding effects like an echo, distortion, reverb and a whole lot of feeling.
CSS adds flavour, enhancing the HTML.
Explain control flow and loops using an example process from everyday life, for example 'waking up' or 'brushing your teeth'. (But not those ones).
It’s lunch time Friday and you are on a student budget.
Firstly did you bring a packed lunch today? Yes, you don’t need to go get something, easy.
Didn’t bring anything? Ok, how much money do I have left.
$30? A nice lunch and a beer is in order!
$20? Burger burger it is
$10? Sub of the day
$5? Alright, two minute noodles will do
Describe what the DOM is and an example of how you might interact with it.
This is the Document Object Model. This is a language neutral interface which you can interact with using Javascript
For example, you can have a button that will trigger the page background colour to change to blue.
Explain the difference between accessing data from arrays and objects.
Accessing data in an array is a lot simpler.
Data is a list-like object where you can select elements using their index (where they are in that list) e.g. myArray[3] will give you the 4th element in the list.
Objects are similar, but the elements are pairs.
These pairs are broken down into key and value.
For example myFriend[name] or myFriend[age].
Explain what functions are and why they are useful.

Particularly useful when you have a complicated task that needs to be performed repeatedly.
They can also take parameters, for example a function for addition will take two parameters and return the sum


Studied Computer Science at Rice University in Houston, TX. Since then, I've worked 3 years in Mathematics education as a teacher and private tutor teaching Math, Statistics and Programming to high school and college students in Puerto Rico. I also have 2 years working in Python and web development. I have worked in projects building internal automation tools, databases, cryptocurrency analysis and IOT devices.
Freelance IT and web developer.
Mathematics, Statistics and Programming tutor Monday-Saturday.
Math teacher at Manuel Cuevas Middle School. Math tutor after-school at iQRX PR.
Developed and taught 2 beginner courses introducing Python and Arduino for beginners.
Developer of several full-stack document processing apps and database systems to automate data processing of big data files and thousands of physical forms for private companies and government agencies.
Developed database using Microsoft Access and SQL to store and process Change Request and Variance forms for the Safety and Mission Assurance dept. Studied database design and integration with Linux system management.
Completed ML courses from Andrew Ng and SuperDataScience. Intent to continue formal studies in this field.
Global Financial Markets and Instruments
Probability for Actuaries
B.A. Mathematics (unfinished)


In this page, we introduce the Person Re-identification in the Wild (PRW) dataset as well as the baseline evaluation codes.
If you use this dataset in your research, please kindly cite our work as,
PRW (Person Re-identification in the Wild) Dataset
The PRW dataset is annotated from the same video as the Market-1501 dataset, i.e., captured with 6 cameras in the summer of 2014 in Tsinghua University. We have annotated 11,816 video frames, 932 identities belonging to 34,304 bounding boxes. Same with Market-1501, we perform cross-camera retrieval, but readers should keep in mind that the identities in PRW are not corresponed to those in Market-1501. The query images are again hand-drawn bounding boxes. For each ID, multiple queries and multiple ground truths should exist, so that one can develop sophisticated multiple-query or re-ranking methods.
In a nutshell, the PRW provides as input: 1) query boxes, 2) video frames. One should output a rank list of the detected bounding boxes and calculate mAP (or CMC) according to the ground truth labels.
| Dataset | # frame | # ID | # annotated box | # box/ID | # gallery box | # camera |
| PRW | 11,816 | 932 | 34,304 | 36.8 | 100-500k | 6 |
| CAMPUS | 214 | 74 | 1,519 | 20.5 | 1,519 | 3 |
| EPFL | 80 | 30 | 294 | 9.8 | 294 | 4 |
| Market-1501 | 0 | 1,501 | 25,259 | 19.9 | 19,732 | 6 |
| RAiD | 0 | 43 | 6,920 | 160.9 | 6,920 | 4 |
| VIPeR | 0 | 632 | 1,264 | 2 | 632 | 2 |
| i-LIDS | 0 | 119 | 476 | 2 | 238 | 2 |
| CUHK03 | 0 | 1,360 | 13,164 | 9.7 | ~1,316 | 2 |
Baseline Codes
The baseline evaluation code is provided on Github. Click here.
For the current version, we provide the detection results using DPM detector pre-trained on the INRIA pedestrian detection dataset. Then, we extract the BoW descriptor and use XQDA as in metric learning.
We are actively adding baseline detectors to the project. Please stay tuned.
Dataset Download
The dataset package is provided on Baidu Cloud below. All versions are shown. One will want to download the latest version.
v2016.04.20 (2.67GB) on Baidu, v2016.04.20 (2.67GB) on GoogleDrive. ID_train/test are added. The "generate_query.m" is modified.
The package contains three folders.
1) "frames". There are 11,816 frames in this folder.
2) "annotations". For each frame, we provide its annotated data. All annotated boxes are pedestrians. Each MAT file records the bounding box position within the frame and its ID. The coordinates of each box are formatted in [x, y, w, h]. The ID of each box takes the value of [1, 932] as well as -2. "-2" means that we do not know for sure the ID of the person, and is not used in the testing of person re-id, but is used in train/test of pedestrian detection (potentially used in the training of person re-identification).
3) "query_box". It contains the query boxes of the PRW dataset. All togther there are 2057 queries. For naming rule, for example, in "479_c1s3_016471.jpg", "479" refers to the ID of the query, and "c1s3_016471" refers to the video frame where the query is cropped. Note that 1) the query IDs are not included in the training set, 2) the query images are not normalized (we typically use 128*64 for BoW extraction, and 224*224 for CNN feature extraction), 3)all queries are hand-drawn boxes, 4) we select one query image for each testing ID under each camera, so the maximum number of queries per ID is 6. In addition, we provide the bounding box information of each query in "query_info.txt", so one can generate the queries from the video frames through function "generate_query.m".
In addition, we provide the train/test split of the PRW dataset. One do not have to perform 10-fold cross validation. In detail, "frame_test.mat" and "frame_train.mat" specify the train/test frames, and "ID_test.mat" and "ID_train.mat" specify the train/test IDs. Note that a small portion of IDs used in training may appear in the testing frames, but will not appear in the testing IDs.


The Challenge
I designed the web and mobile UX/UI for a product that allows government-based animal shelters to track animal-related law enforcement work they did in the community. The key design challenge was to make things mobile-friendly—the product is intended to help officers work more efficiently while doing fieldwork, so it was extremely important that the mobile product help them achieve their goals easily and efficiently. Tackling this design challenge started with developing a deep empathy with the people I was designing for. Based on the insights I gained, I prototyped ideas and eventually arrived at final solutions that catered to their needs.
User Needs
Research has revealed that no existing software solution truly fulfilled target users’ needs. Among other things, users were frustrated that products were often inaccessible in the field and that it was difficult to view their own assigned items and track them. To create a truly innovative product, I developed a full understanding of users’ goals and pain points. Key insights were:
1) Users need real-time and easy access for viewing, editing, and creating new activities/cases.
2) Certain information (such as priority level) is critical and needs to be easily accessible.
Final Designs
Once users log in, they have immediate access to their own to-do list arranged in order of priority. By making the to-do list front and center, I’ve enabled the product to become a true part of and enhance user workflow. In addition, based on the insight that an item’s priority and status information are crucial pieces of information that determine whether/when users work on that item, I’ve made those pieces of information visually prominent (i.e., clear, large labels for priority information and a custom mini-progress bar for status information) so that users can easily scan the list for the information.
Users also have real-time access to newly assigned items, which are made prominent visually using colors and an icon. This allows users to easily take note of and take action on those items. Moreover, the product makes it very easy to take action on items. There’s a clear section for action buttons at the bottom, and only key action buttons (i.e., Edit and Change Status buttons) are exposed, with the rest hidden under the More icon. Furthermore, the buttons are contextual—for example, when an item’s status is “New,” the button reads Open Activity (or Case) and when it’s “Open,” the button reads Close Activity (or Case). This contextuality lightens users’ cognitive load by making it very clear to them what actions they’re about to take.
Users can very easily create new items. In addition to breaking down a long process into multiple small steps, the form guides users along by being conversational in its headings. It also utilizes tools like auto-fills and pickers wherever possible.
In the desktop version, less emphasis is put on making an individual officer’s to-do list prominent and users can easily check out other officers’ assigned items. This makes sense because desktop users are often dispatch teams that assign items to officers.
Here’re more screens from the project:


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{% extends "emaillayout.html" %} {% block title %} Reset Password {% endblock %} {% block body %}
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4 Diferencijalna ekspresija
import pysam
from scipy import stats
# import matplotlib.pyplot as plt
import glob
import os
import pandas as pd
import numpy as np
def read_count(gene, bamfile):
"""
Compute the number of reads contained in a bamfile that overlap
a given interval
"""
bam_iter = bamfile.fetch(gene[0], gene[1], gene[2])
pysam_gene_count = 0
for x in bam_iter:
pysam_gene_count += 1
return pysam_gene_count
## ['/opt/aligned/sample_07_accepted_hits.bam', '/opt/aligned/sample_10_accepted_hits.bam', '/opt/aligned/sample_03_accepted_hits.bam', '/opt/aligned/sample_09_accepted_hits.bam', '/opt/aligned/sample_02_accepted_hits.bam', '/opt/aligned/sample_05_accepted_hits.bam', '/opt/aligned/sample_01_accepted_hits.bam', '/opt/aligned/sample_06_accepted_hits.bam', '/opt/aligned/sample_04_accepted_hits.bam', '/opt/aligned/sample_08_accepted_hits.bam']
counts = []
samples = []
for path in paths:
sname = os.path.basename(path).replace('_accepted_hits.bam', '')
bamfile = pysam.AlignmentFile(path)
gene_counts={}
with open('/opt/gencode.v27.chr20.bed', 'r') as f:
for line in f:
tokens = line.split('\t')
current_gene = (tokens[0], int(tokens[1]), int(tokens[2]))
count = read_count(current_gene, bamfile)
gene_counts.update({tokens[3].rstrip() : count})
counts.append(gene_counts)
samples.append(sname)
## sample_01 sample_02 sample_03 ... sample_08 sample_09 sample_10
## DEFB125 1016 465 1146 ... 1260 285 426
## DEFB126 559 223 555 ... 749 132 240
## DEFB127 419 145 527 ... 720 114 218
## DEFB128 368 143 458 ... 457 87 138
## DEFB129 395 188 573 ... 730 144 186
## DEFB132 3381 1522 4193 ... 2012 516 711
## AL034548.1 1218 572 1554 ... 3932 889 1037
## C20orf96 1035 385 1384 ... 1815 295 403
## ZCCHC3 2748 1150 2804 ... 3683 814 1023
## NRSN2-AS1 4344 1921 5404 ... 5986 1337 1737
##
## [10 rows x 10 columns]
## (1357, 10)
KiR = np.exp((np.log(df)).mean(axis=1)) #geometric mean
df = df[(KiR != 0).values]
KiR = KiR[(KiR != 0).values]
df.shape
## (1347, 10)
4.1 Diferencijalna Eskpresija
- Gen ili transkript nazivamo diferencijalno ekspresovanim ako je uocena statisticki znacajna promena u ekspresiji izmedju neke dve grupe uzoraka koje posmatramo.
- Otkrivanje diferencijalno ekspresovanih gena je cesto cilj statisticke analize RNK sekvenciranih podataka.
4.2 Between-sample normalization
- The general point of between-sample normalization (BSN) is to be able to compare expression features (genes, transcripts) ACROSS experiments. This is not to be confused with within-sample normalization methods (comparing different features within a single sample).
- Most BSN methods address two issues :
- Variable sequencing depth (the total number of reads sequenced) between experiments.
- Finding a “control set” of expression features which should have relatively similar expression patterns across experiments (e.g. genes that are not differentially expressed) to serve as a baseline.
Harold Pimentel: In RNA-Seq, 2 != 2: Between-sample normalization
- One way to do the total count normalization would be to divide by the total counts :
| Gene | Control Counts | Treatment Counts | Control Normalized | Treatment Normalized |
|---|---|---|---|---|
| G1 | 2.00 | 6.00 | 0.20 | 0.06 |
| G2 | 2.00 | 6.00 | 0.20 | 0.06 |
| G3 | 2.00 | 6.00 | 0.20 | 0.06 |
| G4 | 2.00 | 6.00 | 0.20 | 0.06 |
| FG | 2.00 | 76.00 | 0.20 | 0.76 |
If one compares the control and treatment proportions, it seems that every gene is differentially expressed. We are typically under the assumption that the majority of the genes do not change in expression. Under that assumption, it is much more plausible that genes one through four remain equally expressed, whereas “funky gene” (FG) is the only differentially expressed gene.
We might consider normalizing by the sum of the total counts while omitting the last gene since its highly overexpressed and is throwing off the normalization :
| Gene | Control Counts | Treatment Counts | Control Normalized | Treatment Normalized |
|---|---|---|---|---|
| G1 | 2.00 | 6.00 | 0.25 | 0.25 |
| G2 | 2.00 | 6.00 | 0.25 | 0.25 |
| G3 | 2.00 | 6.00 | 0.25 | 0.25 |
| G4 | 2.00 | 6.00 | 0.25 | 0.25 |
| FG | 2.00 | 76.00 | 0.25 | 3.17 |
Between sample normalization procedures estimate sample-specific normalization factors that are used to rescale the observed counts.
Using these normalization methods, the sum of the normalized counts across all genes are therefore not necessarily equal between samples (as it would be if only the library sizes were used for normalization), but the goal is instead to make the normalized counts for non-differentially expressed genes similar between the samples.
Popular examples of these normalization procedures are TMM (trimmed mean of M-values) and DESeq. These two methods perform similarly and are both based on an assumption that most genes are equivalently expressed in the samples, and that the differentially expressed genes are divided more or less equally between up- and downregulation.
With DESeq sample-specific normalization constants are estimated with the median-of-ratios method:
- Let’s try estimating sample specific normalization factor for the two dummy samples from the example. We first calculate the geometric means for each gene:
| Gene | Control Counts | Treatment Counts | KiR |
|---|---|---|---|
| G1 | 2.00 | 6.00 | 3.464 |
| G2 | 2.00 | 6.00 | 3.464 |
| G3 | 2.00 | 6.00 | 3.464 |
| G4 | 2.00 | 6.00 | 3.464 |
| FG | 2.00 | 76.00 | 12.329 |
- Next we divide each count with the geometric mean expression for that gene:
| Gene | Control Counts/KiR | Treatment Counts/KiR |
|---|---|---|
| G1 | 0.57735 | 1.73205 |
| G2 | 0.57735 | 1.73205 |
| G3 | 0.57735 | 1.73205 |
| G4 | 0.57735 | 1.73205 |
| FG | 0.16222 | 6.16441 |
- It is obvious that sample specific normalization factors are 0.57735 and 1.73205 (sample medians).
- Finally we divide each sample by its normalization factor:
| Gene | Control Normalized | Treatment Normalized |
|---|---|---|
| G1 | 3.4641 | 3.4641 |
| G2 | 3.4641 | 3.4641 |
| G3 | 3.4641 | 3.4641 |
| G4 | 3.4641 | 3.4641 |
| FG | 3.4641 | 43.87864 |
## sample_01 sample_02 sample_03 ... sample_08 sample_09 sample_10
## DEFB125 1.351510 0.618555 1.524440 ... 1.676086 0.379115 0.566677
## DEFB126 1.411339 0.563021 1.401240 ... 1.891043 0.333268 0.605942
## DEFB127 1.268995 0.439151 1.596087 ... 2.180612 0.345264 0.660241
## DEFB128 1.450690 0.563719 1.805478 ... 1.801536 0.342962 0.544009
## DEFB129 1.049116 0.499326 1.521882 ... 1.938873 0.382463 0.494014
## DEFB132 1.840957 0.828730 2.283092 ... 1.095536 0.280962 0.387140
## AL034548.1 0.854659 0.401367 1.090427 ... 2.759048 0.623803 0.727653
## C20orf96 1.276008 0.474650 1.706276 ... 2.237638 0.363693 0.496842
## ZCCHC3 1.376184 0.575914 1.404228 ... 1.844427 0.407647 0.512313
## NRSN2-AS1 1.335328 0.590508 1.661168 ... 1.840072 0.410988 0.533947
##
## [10 rows x 10 columns]
Normalized, this is how our simulated dataset looks like:
## sample_01 sample_02 ... sample_09 sample_10
## DEFB125 799.307029 844.137999 ... 708.873105 816.735707
## DEFB126 439.776210 404.823169 ... 328.320175 460.132793
## DEFB127 329.635478 263.225828 ... 283.549242 417.953953
## DEFB128 289.512782 259.595127 ... 216.392842 264.576356
## DEFB129 310.754209 341.285901 ... 358.167463 356.602914
## DEFB132 2659.898687 2762.963514 ... 1283.433410 1363.143399
## AL034548.1 958.224372 1038.380506 ... 2211.186631 1988.157109
## C20orf96 814.254700 698.909956 ... 733.745845 772.639648
## ZCCHC3 2161.905233 2087.653115 ... 2024.641077 1961.316029
## NRSN2-AS1 3417.509582 3487.288378 ... 3325.485406 3330.211088
##
## [10 rows x 10 columns]
4.3 Exploratory analysis
Data quality assessment and quality control (i.e. the removal of insufficiently good data) are essential steps of any data analysis. These steps should typically be performed very early in the analysis of a new data set, preceding or in parallel to the differential expression testing.
The dataset we are investigating is simulated, but let us pretend that samples 6 through 10 are coming from the “treated” population and that samples 1 through 5 are “untreated” or “control” samples.
A useful initial step in an RNA-seq analysis is often to assess overall similarity between samples:
- Which samples are similar to each other, which are different?
- Does this fit to the expectation from the experiment’s design?
- What are the major sources of variation in the dataset?
To explore the similarity of our samples, we will be performing sample-level QC using Principal Component Analysis (PCA) and hierarchical clustering methods. Our sample-level QC allows us to see how well our replicates cluster together, as well as, observe whether our experimental condition represents the major source of variation in the data. Performing sample-level QC can also identify any sample outliers, which may need to be explored further to determine whether they need to be removed prior to DE analysis.
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(normdf.T)
principalDf = pd.DataFrame(data = principalComponents
, columns = ['principal component 1', 'principal component 2'])
categories = np.array(["untreated", "treated"])
finalDf = pd.concat([principalDf, pd.DataFrame(np.repeat(categories, [5, 5], axis=0), columns=['condition'])], axis = 1)
finalDf
## principal component 1 principal component 2 condition
## 0 7253.669019 -1133.085852 untreated
## 1 6777.834829 -351.825929 untreated
## 2 6777.584161 1550.295036 untreated
## 3 6891.705861 -334.630787 untreated
## 4 7100.443338 351.964060 untreated
## 5 -7020.208240 2833.669670 treated
## 6 -7027.034271 -2662.133319 treated
## 7 -7052.228614 957.489013 treated
## 8 -6578.866744 -1450.586265 treated
## 9 -7122.899341 238.844374 treated
## principal component 1 principal component 2 condition
## 1 7253.669 -1133.0859 untreated
## 2 6777.835 -351.8259 untreated
## 3 6777.584 1550.2950 untreated
## 4 6891.706 -334.6308 untreated
## 5 7100.443 351.9641 untreated
## 6 -7020.208 2833.6697 treated
## 7 -7027.034 -2662.1333 treated
## 8 -7052.229 957.4890 treated
## 9 -6578.867 -1450.5863 treated
## 10 -7122.899 238.8444 treated
4.4 Intro to statistical inference
For each gene, we are interested in differences in mean expression between the two sample groups.
import seaborn as sns
aux = {"norm" : normdf.loc['AL031055.1'], "group" : np.repeat(categories, [5, 5], axis=0)}
df = pd.DataFrame(data=aux)
sns.stripplot(x="group", y="norm", data=df, jitter=True).set_title('AL031055.1')
aux = {"norm" : normdf.loc['AL023803.2'], "group" : np.repeat(categories, [5, 5], axis=0)}
df = pd.DataFrame(data=aux)
sns.stripplot(x="group", y="norm", data=df, jitter=True).set_title('AL023803.2')
cMean = normdf.loc['AL023803.2'][0:5].mean()
tMean = normdf.loc['AL023803.2'][5:10].mean()
print(cMean)
##<PHONE_NUMBER>056803
##<PHONE_NUMBER>8364734
## 47.335209477967055
So the expression level for AL023803.2 in treated samples is about 10% higher. But these averages are random variables. They can take many values.
If we repeat the experiment, by sequencing samples from a new batch of patients (or even resequencing the same ones) we get different means. Every time we repeat this experiment, we get a different value of mean difference.
When comparing measured values we need to be skeptical. How do we know that this difference is due to the treatment? What happens if compare two sets of untreated samples? Will we see a difference this big? Statisticians refer to this scenario as the null hypothesis. The name “null” is used to remind us that we are acting as skeptics: we give credence to the possibility that there is no difference.
So what do we do?
Because we do not have access to entire populations of untreated and treated samples, we cannot confidently say just by comparing the mean expression values that a gene is differentialy expressed.
We can perform a simple permutation test. Permutation tests take advantage of the fact that if we randomly shuffle the treated and untreated labels for the 10 measurements that we have - then the null is true. So we shuffle the labels, calculate the mean difference and assume that the ensuing distribution approximates the null distribution. Here is how we generate a null distribution by shuffling the data 1,000 times:
normexp = normdf.loc['AL023803.2']
np.random.seed(6)
means =[]
for i in range(1000) :
aux = np.random.permutation(normexp)
cmean = aux[0:5].mean()
tmean = aux[5:10].mean()
means.append(tmean-cmean)
betaHat <- reticulate::py$diff
b1 <- unlist(reticulate::py$means)
hist(b1, breaks = 50)
abline(v=c(betaHat, -betaHat), col = "red")
## 0.135
- Null hypothesis: there is no significant difference between specified populations, any observed difference being due to sampling or experimental error.
- The p-value is defined as the probability of obtaining a result equal to or “more extreme” than what was actually observed, when the null hypothesis is true.
- The alternative hypothesis is considered true if the statistic observed would be an unlikely realization of the null hypothesis according to the p-value.
4.5 Central Limit Theorem
CLT: When the sample size is large, the average \(\bar{Y}\) of a random sample follows a normal distribution centered at the population average \(\mu_{Y}\) and with standard deviation equal to the population standard deviation \(\sigma_{Y}\), divided by the square root of the sample size \(N\).
We refer to the standard deviation of the distribution of a random variable as the random variable’s standard error.
https://rajeshrinet.github.io/blog/2014/central-limit-theorem/
Population mean and variance (deviation squred) are parameters defined as:
\[\mu_{Y} = \frac{1}{n}\sum_{i=1}^{n}y_{i}\] \[\sigma_{Y}^2 = \frac{1}{n}\sum_{i=1}^{n}(y_{i}-\mu_{Y})^{2}\]
where \(n\) is the number of elements in population.
Normal distribution \(N(\mu, \sigma)\) is defined with the density function:
\[f(x)=\frac{1}{\sqrt{2\pi\sigma^{2}}}e^{-\frac{\left (x-\mu_{X} \right )^{2}}{2\sigma^{2}}}\]
If we subtract a constant from a random variable, the mean of the new random variable shifts by that constant. Mathematically, if \(X\) is a random variable with mean \(\mu\) and \(a\) is a constant, the mean of \(X − a\) is \(\mu − a\). A similarly intuitive result holds for multiplication and the standard deviation (SD). If \(X\) is a random variable with mean \(\mu\) and SD \(\sigma\), and \(a\) is a constant, then the mean and SD of \(aX\) are \(a\mu\) and \(| a | \sigma\) respectively.
Knowing this, the CLT implies that if we take many samples of size \(N\), then the quantity:
\[\frac{\bar{Y}-\mu_{Y}}{\sigma_{Y}/\sqrt{N}}\]
is approximated with a normal distribution centered at 0 and with standard deviation 1.
If \(X\) and \(Y\) are two random variables independent of each other, then the variance of \(X + Y\) is the sum of the variances \(\sigma_{X}^{2} + \sigma_{Y}^{2}\). The sum (or the difference) of normal variables is again normal. Under the null hypothesis (when the mean difference is 0), the ratio:
\[\frac{\bar{Y}-\bar{X}}{\sqrt{\frac{\sigma_{Y}^{2}}{N}+\frac{\sigma_{X}^{2}}{M}}}\]
is approximated by a normal distribution centered at 0 and standard deviation 1. Using this approximation makes computing p-values simple because we know the proportion of the distribution under any value. For example, only 5% of these values are larger than 2 (in absolute value).
However, we can’t claim victory just yet because we don’t know the population standard deviations: \(\sigma_{X}\) and \(\sigma_{Y}\). These are unknown population parameters, but we can get around this by using the sample standard deviations, call them \(s_{X}\) and \(s_{Y}\). These are defined as:
\[s_{X}^2 = \frac{1}{M-1}\sum_{i=1}^{M}(X_{i}-\bar{X})^{2}\] \[s_{Y}^2 = \frac{1}{N-1}\sum_{i=1}^{N}(Y_{i}-\bar{Y})^{2}\]
\(M − 1\), \(N − 1\): the degrees of freedom.
So we can redefine our ratio as:
\[\frac{\bar{Y}-\bar{X}}{\sqrt{\frac{s_{Y}^{2}}{N}+\frac{s_{X}^{2}}{M}}}\]
The CLT tells us that when \(M\) and \(N\) are large, this random variable is normally distributed with mean 0 and SD 1.
4.6 The t-distribution
The CLT relies on large samples, what we refer to as asymptotic results. When the CLT does not apply, there is another option that does not rely on asymptotic results. When the original population from which a random variable, say \(Y\), is sampled is normally distributed with mean 0, then we can calculate the distribution of:
\[\sqrt{N}\frac{\bar{Y}-0}{\sigma_{Y}}\]
This is the ratio of two random variables so it is not necessarily normal. The fact that the denominator can be small by chance increases the probability of observing large values. In 1908. William Sealy Gosset, an employee of the Guinness brewing company, deciphered the distribution of this random variable and published a paper under the pseudonym “Student”. The distribution is therefore called Student’s t-distribution.
\[f(t)=\frac{\Gamma(\frac{\nu+1}{2})}{\sqrt{\nu\pi}\Gamma(\frac{\nu}{2})}\left (1+\frac{t^2}{\nu} \right )^{-\frac{\nu+1}{2}}\]
where \(\nu\) is the degrees of freedom.
So for small sample sizes we shall be computing that same ratio:
\[\frac{\bar{Y}-\bar{X}}{\sqrt{\frac{s_{Y}^{2}}{N}+\frac{s_{X}^{2}}{M}}}\]
only this time we shall be comparing it to the t-distribution instead of normal. All such tests are usually called Student’s t-tests, though strictly speaking that name should only be used if the variances of the two populations are also assumed to be equal; the form of the test used when this assumption is dropped is sometimes called Welch’s t-test and that’s the one that we’ll be implementing.
def welch_test(a, b):
# pandas.Series.var() returns an unbiased variance estimate, normalized by N-1 by default
var_a = a.var()
var_b = b.var()
Na = len(a)
Nb = len(b)
# std deviation estimate
s = np.sqrt(var_a/Na + var_b/Nb)
# calculate the t-statistics
t = (a.mean() - b.mean())/s
# degrees of freedom - Welch–Satterthwaite equation, yeah it's a bit more complicated than Na + Nb - 2
deg = (((var_a)/Na + (var_b)/Nb)**2)/ ((var_a**2)/(Na**2*(Na-1)) + (var_b**2)/(Nb**2*(Nb-1)))
# p-value after comparison with the t
p = 2*stats.t.sf(np.abs(t), deg)
return (p)
p = [(index, welch_test(row.values[0:5], row.values[5:10])) for index, row in normdf.iterrows()]
## (1347, 1)
## pval
## gene
## DEFB125 6.449128e-01
## DEFB126 3.094837e-01
## DEFB127 5.164307e-01
## DEFB128 3.106288e-03
## DEFB129 5.639828e-01
## DEFB132 6.687543e-07
4.7 Multiple testing
- In genomic studies you don’t usually fit just one regression model or calculate just one p-value. You calculate many p-values.
- In our simulated dataset, just from chromosome 20 (which is one of the shortest in human genome) we have 1347 genes.
- If you use the standard cutoff of 0.05 when calling p-value significant, you’ll know that since p-values are uniformly distributed when the null is true, about 1 out of every 20 times you’ll still call that result statistically significant, even when there is no true biological effect.
np.random.seed(6)
pvals =[]
for i in range(20000) :
firstg = np.random.normal(32, 5, 20)
secondg = np.random.normal(32, 5, 20)
pval = welch_test(firstg, secondg)
pvals.append(pval)
| Called significant | Not called significant | Total | |
|---|---|---|---|
| Null true | \(V\) | \(n_{0} - V\) | \(n_{0}\) |
| Alternative true | \(S\) | \(n_{1} - S\) | \(n_{1}\) |
| Total | \(R\) | \(n - R\) | \(n\) |
- \(V\) represents the number of type I errors (false positives);
- \(n_{1} - S\) represents the number of type II errors (false negatives);
- \(S\) is the number of true positives and \(n_{0} - V\) the number of true negatives.
A multiple testing correction procedure is needed to adjust the statistical confidence measures based on the number of tests performed. Different error rates are defined for this purpose:
- Family wise error rate:
\[FWER = P_{r}\left (V \geq 1 \right ) = 1 - P_{r}\left (V = 0 \right )\]
is the probability of making one or more false discoveries, or type I errors when performing multiple hypotheses tests.
- False discovery rate:
\[FDR=E\left \lfloor \frac{V}{V+S} \right \rfloor\]
is the expected proportion of “discoveries” (rejected null hypotheses) that are false (incorrect rejections).
Suppose 1200 out of 20,000 genes are found significant at 0.05 level (threshold \(\alpha = 0.05\)).
- No correction: expect 0.05 * 20,000 = 1000 false positives
- False Discovery Rate correction: expect 0.05 * 1200 = 60 false positives
- Family Wise Error Rate correction: expect no false positives (probability of having at least one false positive is ≤ 0.05)
Benjamini-Hochberg correction: the p-values are sorted in increasing order and the i-th p-value is considered significant if it’s less or equal to \(\frac{i\alpha}{n}\).
Bonferroni correction: p-values less or equal to α/n are considered significant.
res = res.sort_values(by=['pval'])
m = res.shape[0]
qval = np.zeros(m)
for i in range(0, m):
qval[i] = (res['pval'][i]*m/(i+1))
res['qval'] = qval
alpha = 0.05
res['de'] = res['qval'] < alpha
After performing Benjamini-Hochberg (FDR) correction:
## pval qval de
## gene
## FLRT3 9.418961e-10 0.000001 True
## PLCG1-AS1 3.296634e-09 0.000002 True
## RBM39 3.591833e-09 0.000002 True
## TOP1 5.383997e-09 0.000002 True
## RALGAPB 5.399923e-09 0.000001 True
## RTFDC1 7.302393e-08 0.000016 True


willkommen
Droidcon DE 2015
Getting Started in VR for Android
The Dream of the 90s is Alive
The Program
- Introductions
- Software
- Headsets
- Building a World
- Making the World "Real"
- Interaction
- Challenges
- Moving On
Turn on Developer Mode for Android
Install the Cardboard App
Exercise: Assemble Google Cardboard
All Game Engines Have Similar Pattern
Camera's See Into Your Scene
Run it again ... on Android!
Headset SDKs Provide Data
Fullscreen Stereoscopic View
Rotating the Camera to Match Headset Movement
We could this all ourselves ...
Headset Game Engine Plugins Do It ALL For You
Import the Carbdboard Unity Package
Not Just a Cardboard Problem
One Interaction to Rule Them All
Carbon Five Nights at Freddy's
Exercise: Making It Interactive
The Ground is Constantly Shifting


Roger Daltrey vocal surgery - Entertainment News
by WENNRoger Daltrey is to have an operation to scrape his throat to check and remove pre-cancerous cells from his vocal cords.
The Who legend, 75, first noticed a problem with his voice in 2009 and was diagnosed with dysplasia - abnormal, precancerous cells in his throat.
He told British newspaper The Sun he is having an operation on Friday to ensure his voice is OK for a 2020 tour.
"I can't do much talking - I'm off for a voice op on Friday," he said.
The Who previously had to postpone several U.S. concerts in September after losing his voice.
Roger's doctor Rajiv Pandit says the operation, and regular checks and work, including laser treatment, helps stop the star getting cancer, as well as enabling him to perform.
"In Roger's case, his dysplasia left untreated would have not only ruined his singing career but could have turned into cancer," he explains.
The Who's U.K. tour gets under way in Manchester in March.


House of Representatives launches probe into $396.33 million spent in fixing refineries between 2015 to 2019
by Amos ABBAAdvertisement
THE House of Representative has begun investigation into the financial allocations set aside for ‘Turn-Around Maintenance’ of the petroleum refineries in Port Harcourt, Warri and Kaduna, estimated to have cost $396.33 million in four years, according to a report.
The inquiry was initiated following the motion titled “Call for investigation of the $396.33 million allegedly spent in four years on turn around maintenance of the nation’s three refineries.”
At the plenary session, Ifeanyi Momah, representing Ihiala federal constituency of Anambra State called for the inquiry, alleging that the amount spent on maintenance of the facilities had not yielded the desired results.
The House also called on the Federal Government to consider “divesting a certain percentage of its shareholding in the Port Harcourt, Warri and Kaduna refineries to competent investors under a transparent and fair bidding process.”
Also, the House mandated the Committee on Petroleum Resources (Downstream) to conduct an investigative hearing into the maintenance expenses made from 2015 to date while the committee was to submit its findings within eight weeks.
Momah stated that Nigeria had been living with the “derogatory appellation” of being a major oil-producing nation that is heavily reliant on importation of refined petroleum products for its domestic consumption as a result of its low local refining capacity.
In a Twitter post, he stated the Nigerian people need an explanation of how the money was spent in refurbishing the refineries.
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He also said this was in spite of the fact that the country has three major refineries with an installed capacity to refine 445,000 barrels per day, enough for domestic consumption and export.
“This objective has not been realised owing to a combination of factors, including corruption and inefficiency in the running of the refineries.
“The House observes the assertion by the Nigeria National Resource Charter in the report that the NNPC spent a whopping $396.33m between 2013 and 2017 to carry out repair works.
“We also observed the claim that the Nigerian National Petroleum Corporation, NNPC, spent N276.872 billion on operating expenses of the refineries between 2015 and 2018, as well as $36 billion on the importation of petroleum products between 2013 and 2017,” he said.
According to the lawmaker, the goal of establishing local refining facilities as a socio-economic game-changer has continued to elude the country’s oil and gas industry.


This visualization was developed in March 2014 by: Alan Leung, David Wang, Eric Wu
The libraries used in this project were D3 with TopoJSON, Select2, and
The original import/export data for each state came from the US census page on foreign trade.
The data for the hscodes came from the Foreign trade website.
The data for this project was obtained from CS 424 Project 1.
The application consists of a visualization of the import/export data for the US, there is a map showing the countries that traded with a specific state as well as a graph with a breakdown of the data
Alan: creation of the graph for the visualization, hosting the project on his page
David: creation of the map of the states and the world map, Implementing the map using TopoJSON
Eric: creating a parser to get the data, modifying the data into a csv for David and Alan, creating the multi-select autocomplete select list for the commodities, creating the about page.


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What do Mark Zuckerberg, Art Garfunkel, Wolf Blitzer, Gene Wilder and Myself have in common?
We’re brothers.


“Live Unafraid, Die Strong.” A simple phrase, synonymous with other phrases but this one rings true. I’ve been involved in plenty of harrowing situations where it was “me or the bad guy” as a police officer. Very quickly I realized peak physical wellness was vital, not only in life or death situations but in everyday life as well. Never have I been afraid of danger, in that I personally study “Odinism” or Norse Mythology. Vikings knew their destiny so they lived and died unafraid and strong. This lifestyle is what I wanted to instill in others. Steps by me were taken to promote combat preparedness and fitness in the county I patrolled by opening a free weight lifting class.
This sparked my interest in powerlifting and assisting others to achieve greatness. With some study I rapidly grew in size and strength, learning what I could from fitness pros like Jay Cutler, Kai Greene, Jim Stoppani and the like. In time I gained a handful of endorsements to competitively lift. While I never placed in my competitions I learned so much from men and women above me. I took that experience and molded it into what you see today… The Iron Viking brand is here and I plan to outfit everyone in the best gym gear around!


* IGCSE prepares students for further academic work including progression to AS Level and A Level study.
* The International General Certificate of Secondary Education (IGCSE) is an English dialect educational modules offered to understudies to set them up for International Baccalaureate, A Level and BTEC Level 3 (which is prescribed for higher-level understudies).
* We have a team of well experienced and highly qualified Cambridge Certified teachers who are dedicated to their profession.
* Board pattern sample papers.
* Complete school curriculum will be introduced and practiced, refining the children for the school exams.
* Worksheets and notes will be provided relevant to the topics taught.
We make you practice large amounts of material from your board in a short amount of time which impact on your grades quickly and efficiently.
Smart and well-designed courses designed according to your individual needs. Whether you need the extra boost for top grades or are really struggling , we can help you reach your goals.
We have a team of well experienced and highly qualified teachers who are dedicated to their profession.
Score more with us complete coverage of excellent Test Papers of designed according to your syllabus.
To promote questioning and doubt free environment we prefer a batch of Five students.
Counseling offers an opportunity to consider issues that concern you, we provide you free counseling sessions to boost your confidence just before exams.
Why Direction Classes?
All the teachers are well qualified, experienced and subject experts who are dedicated in overall development of
a child. after the explanation lots of worksheet practice is given to the child which are chapter wise and
sample paper solving is encouraged.
What are our Previous Results?
With the efforts of our students and dedicated teacher's we have provided highly satisfying results.
What are our Methodology?
Unique Techniques for fundamental clearing, focus on basics for the overall development of student,
Teaching through Digital aids, Live Examples sharing, Exhaustive test series and practice sessions
for better results.
How to Overcome Hesitation?
We here in Direction classes provide individual attention to every student and encourage
them to ask questions and learn themselves.
* Direction Classes SHOP NO-40,
MAYURESH SHRISHTI,OPPOSITE ASIAN PAINTS,
LBS ROAD,BHANDUP (WEST) Mumbai (M.H)
* Direction Classes SHOP NO. UG-49, Dreams Mall Bhandup (W), Mumbai (M.H).
* Direction Classes C-wing Shop No. 18 , Golden Willows, Veena Nagar Mulund, Mumbai (M.H)
<EMAIL_ADDRESS>http://directionclasses.com/
Phone: +919987758132


Zoraida Cabrera-Mieles
www.zoracabrera-writing.com
Comments, questions, business? Anything you need to say this is the place.