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Wednesday, August 12, 2020 | History
3 edition of Historical researches into some aspects of the culture and civilization of North-East India found in the catalog.
Historical researches into some aspects of the culture and civilization of North-East India
by G. P. Singh
Published 2009 by Gyan Pub. House in New Delhi .
Tribes -- India, Northeastern,
India, Northeastern -- Civilization
Statement G.P. Singh.
LC Classifications DS483.4+
As the book begins, the author works exceptionally hard to describe the difference between Culture and Civilization, and he does this fairly successfully, showing that while the tradition of Civilization progresses east to west across the world (i.e. Babylonian Civilization spread to Egypt and Greece, which progressed to Rome and the rest of 5/5(2). Use this free India map in your classroom or school research project. british in india history timeline - Yahoo Image Search resul ndia is the largest country in the Indian Subcontinent and shares borders with Pakistan to the west, China and Nepal to the north, Bhutan to the north-east, and Bangladesh and Myanmar to the east.
Throughout its history, India was intermittently disturbed by incursions from beyond its northern mountain wall. Especially important was the coming of Islam, brought from the northwest by Arab, Turkish, Persian, and other raiders beginning early in the 8th century ce. A very well written book with a good comprehensive (in terms on coverage but not so much in depth) information about the history of India and seen through the lens of geography of India. The book covers how the geography of India has influenced it's history and also to some extent vice versa (for example the location of cities and their /5.
This article tries to show how the study of the Indian state in the recent decades by focusing on the understanding of processes has led to a nuanced reading of the sources in their entirety, and coherent long-term view of early India, which at times disturbs some of Author: Bhairabi Prasad Sahu. India is a vast country, having variety of geographical features and climatic conditions. India is home to some of the most ancient civilizations, including four major world religions, Hinduism, Buddhism, Jainism and Sikhism. A combination of these factors has resulted into an exclusive culture- Indian culture.
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Historical researches into some aspects of the culture and civilization of North-East India by G. P. Singh Download PDF EPUB FB2
This book deals with various facets of cultures and civilization of north-east India. It contains valuable information about the pre-historic megalithic cultures of the hill tribes, the genesis and growth of tribal culture, their exposure to civilization Aryanisation and Sanskritization, especially in.
- Buy Historical Research Into Some Aspects of the Culture and Civilization of North-East India book online at best prices in india on Read Historical Research Into Some Aspects of the Culture and Civilization of North-East India book reviews & author details and more at Free delivery on qualified : G.P.
Singh. The history and civilization of north-east India has been one of the areas of his study and research. In this area too, he has contributed numerous papers to journals and books.
He has authored twelve scholarly books which have been widely acclaimed by the scholars (in the field) in India and abroad. Aspects of Indian History and Civilization.
Buddha Prakash. Shiva Lal Agarwala, - India - pages. 0 Reviews. From inside the book. What people are saying - Write a review. We haven't found any reviews in the usual places. Contents. The growth of contacts between India and SouthEast Asia. 5: CHAPTER PAGE 6 A NEW APPROACH TO. This book explores the complex linkages between state, society, and culture in medieval and modern India.
It discusses issues like secularism, composite culture, pluralism, historiography, and the use of n over the past decade, this collection of essays explores the complex linkages between state, society, and culture in medieval and modern India. The long span of Indian history covering more than years and enumerating several civilizations has been a constant reminder of the countrys rich multicultural extravaganza and world-renowned heritage.
The people and their lifestyles, their dance forms and musical styles, art & handicrafts, and such other elements go on to reflect the varied hues of Indian culture and heritage, which truly.
The book encompasses in its broad parameter all aspects of Indian Civilization and Culture for which we feel proud. The work supplies the readers with necessary, useful and up-to-date information on the subject.
Treatment on the topic like economy, trade and commerce, medicine, law, philosophy, education, art, architecture and science prevailing in ancient India have been knuckled down in.
The vernacular architecture of north-east India across the three bioclimatic zones is widely varied in its built forms. However no serious study has ever been made on the vernacular architecture of north-east India in relation to bioclimatism, socio-economic status, cultural setup and by: The Indian cultural history is very rich and has carved a niche of its own.
It continues to inspire other cultures of the world. Every state in India has a culture of its own and even then they all stand unified and form one single culture of India.
Read about the cultural history of India. The history and civilization of north-east India has been one of the areas of his study and research. In this area too, he has contributed numerous papers to journals and books. He has authored twelve scholarly books which have been widely acclaimed by the scholars (in the field) in India and : Dr.
G.P. Singh. The Kokrajhar district is an administrative district in the state of Assam in the North East lies between 89°46′ East to 90°38′ East and 26°19′ North to 26°54′ North Latitude covering an area of 3, km is bounded by Bhutan on the North, the Sonkosh River, Cooch Bihar, and Jalpaiguri district of the West Bengal on the West.
and the riverine tract of Brahmaputra Cited by: 6. The word Kirata is a derivation from Kirati or Kiranti to name the group of people in Eastern Nepal and Northeast India.
One school of thought says that it comes from the Sanskrit word Kirata found in the Yajurveda; they are described as the "handsome" mountain people and hunters in the forests.
They are described as "Kiratas" in the Mahabharata and Kirtarjuniya. Papers presented at the Seminar on "Ethnicity, Culture, and Nationalism: Problems in the Context of North-East India", held in Sept.
at the North Eastern Hill University. Preview this book» What people are saying - Write a review. Northeast India (officially North Eastern Region, NER) is the easternmost region of India representing both a geographic and political administrative division of the country.
It comprises eight states – Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and region shares an international border of 5, kilometres (3, mi) (about 99 percent of its total Country: India.
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NASA Images Solar System Collection Ames Research Center. Brooklyn Museum. Full text of "Researches into the early history of mankind and the development of civilization". Important Aspects of Indian Culture Indian Culture or Hinduism, as it is popularly known, is like a huge tree with its branches representing various systems of religious i Mantra, from which the Vedas are said to be originated is the foundation of this Divine Culture.
Vol. 4 I C O N Journal of Archaeology and Culture EDITORIAL BOARD Prof. K.K. Thaplyal: Ex. Head, Department of Ancient Indian History Culture and Archaeology, University of Lucknow Prof. The volumes of Project on the History of Science, Philosophy and Culture in Indian Civilization aim at discovering the main aspects of India's heritage and present them in an interrelated way.
In spite of their unitary look, these volumes recognize the difference between the areas of material civilization and those of ideational culture. The non-Brahman high priests, the Chantai, continued to minister their rites and rituals, but absorbed the important Hindu deities, resulting in their national pantheon transforming into the Chaturdasa Devata, with the deities being identified with a corresponding Brahmanical name.
The history and civilization of north-east India has been one of the areas of his study and research. In this area too, he has contributed numerous papers to journals and books. He has authored twelve scholarly books which have been widely acclaimed by the scholars (in the field) in India and Range: $ - $.
The book i'm translating, Topography and General History of Algiers, was translated in They forget the introduction, the publication license, the marginalias and took away a .Books shelved as culture-and-civilization: Lost in the Cosmos: The Last Self-Help Book by Walker Percy, Homo Ludens: A Study of the Play Element in Cultu.The Vedic period is named after the Indo-Aryan culture of north-west India, although other parts of India had a distinct cultural identity during this period.
The Vedic culture is described in the texts of Vedas, still sacred to Hindus, which were orally composed in Vedic Vedas are some of the oldest extant texts in India. The Vedic period, lasting from about to BCE.
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package com.twitter.finagle.protobuf.rpc.impl

import com.twitter.finagle.protobuf.rpc.RpcFactory
import com.twitter.finagle.protobuf.rpc.RpcControllerWithOnFailureCallback
import com.twitter.finagle.protobuf.rpc.RpcServer

import java.util.concurrent.ExecutorService

class RpcFactoryImpl extends RpcFactory {

  def createServer(sb: ServerBuilder[(String, Message), (String, Message), Any, Any, Any], port: Int, service: Service, executorService: ExecutorService): RpcServer = new RpcServerImpl(sb, port, service, executorService)

  def createStub[T <: Service](cb: ClientBuilder[(String, Message), (String, Message), Any, Any, Any], service: { def newStub(c: RpcChannel): T }, executorService: ExecutorService): T = {
    service.newStub(new RpcChannelImpl(cb, service.asInstanceOf[T], executorService))
  }

  def createController(): RpcController = { new RpcControllerWithOnFailureCallback() }

  def release(stub: { def getChannel(): RpcChannel }) {
	stub.getChannel().asInstanceOf[RpcChannelImpl].close()
  }
}

redpajama_set_name: RedPajamaGithub


text:
As matrics brace for 2021 results, task team releases final report into 2020 leaked exam papers scandal
Prega Govender
The maths paper 2 and physical science paper 2 were leaked to some pupils via WhatsApp before they could write the two exams in 2020. File image
Image: SAMORN TARAPAN/123rf.com
With days to go before the release of the 2021 matric results, an investigation launched into the leaked 2020 question papers has found the reluctance of implicated pupils to surrender their cellphones compromised the investigation.
This is what is contained in the final report by the national investigating task team (NITT), which the basic education department handed over for tabling in the National Assembly on Monday.
The chair of NITT, Hugh Amoore, said in the report a forensic investigation that analysed data obtained from 21 cellphones "provided detailed confirmatory evidence of the extent (limited to two sub-questions) and timing (at 10.34pm on the night before the exam) of the leaks to the TDP WhatsApp support group".
"It confirmed the identity of the pupil ("A") who distributed these two sub-questions to the TDP WhatsApp support group. This identity has been known since early December 2020. The forensic investigation has identified the make of cellphone used by 'A'," the report reads.
Media told not to publish 2021 matric results over POPI Act concerns
In a notice to media houses this week, the department cited the Protection of Personal Information Act as the reason for the decision.
According to the report, "A" surrendered a phone but not the phone he had used.
"We have made recommendations to the department of basic education in regard to 'A'," the report states.
The maths paper 2 and physical science paper 2 were leaked to some pupils via WhatsApp before they could write the two exams in 2020.
The report stated that in hearings in another province, two groups of pupils who were not part of the TDP group admitted having had prior access to the maths paper 2 and physical science paper 2.
"We understand they co-operated with the investigators and showed remorse. It is not the role of the NITT to consider the appropriateness of the sanctions recommended in these or other cases, but what is of importance to us is that one of these candidates identified 'B' as the source of the question papers."
Amoore said the information had been passed over to the Hawks.
"'B' appears to have been an intermediary, and it will be important for the Hawks to establish how and from who 'B' obtained the question paper and to who 'B' distributed it (and possibly other question papers). We have made recommendations in this regard."
The report stated that all the evidence showed the leaks were not widespread and the leaked questions or question papers reached candidates on the day or one day before the exams were to be written.
"It is a matter of concern to the NITT, as it is to the department and to Umalusi, that progress in following up the identified sources of the leaks by the Hawks seems to have been slow. There is evidence that a leak or leaks occurred at the government printer and at the printer contracted to print for the Gauteng department of education.
"We have made recommendations in this regard; these for the present must remain confidential so as not to prejudice the investigations by the Hawks and others that are essential if the culprits are to be brought to book."
Top matric achievers and parents have to give consent to attend results event
It is unclear whether all 27 top achievers in last year's matric exams will attend an event next week where the results will be released because the ...
Amoore said the source of the leaks of the 2016 NSC [National Senior Certificate] exam papers has not yet been identified or brought to book despite the arrest at the time of the head of the New Era school and the fact that the Hawks were at the time able to take possession of the leaked question papers printed by or for the Gauteng education department.
"In that case, as with the present case, the case was escalated to the Hawks."
He said the follow-up actions regarding implicated 2020 candidates require attention, adding: "An important reason for this is that the regulations for dealing with NSC irregularities do not help and are in urgent need of a revamp. We have made recommendations in this regard."
The report stated that the department "has what we believe to be incontrovertible evidence that one or more 2020 NSC examination question papers leaked from the Government Printing Works (GPW)".
"The evidence obtained from two sources — a pupil in the Eastern Cape who reported having received a leaked paper and a pupil in Gauteng who reported having received a leaked question paper from someone in Mpumalanga — in both cases in the form of screen shots of question papers in the print office origination state, can only have come from the GPW."
The report stated a second source of 2020 NSC examination question paper leaks has been identified as a person connected to the printer contracted by the Gauteng education department.
Themba Daniel Shikwambana, 31, who was employed at a Johannesburg-based company contracted by the department of basic education to print the 2020 matric exam papers, was arrested on November 30 2020. He was granted bail of R1,000.
A nurse, Thobile Sweetbetter Duze, was arrested in Port Shepstone in KwaZulu-Natal on June 10 2021, and charged with theft. She was released on bail of R3,000 and will make another appearance in the Johannesburg regional court on January 17.
Recommendations in the report made to the department include:
requiring the GPW to satisfy the department that any future exam paper printing will be secure;
the department, in collaboration with the provincial education departments that have exam papers printed by the GPW, must ensure full oversight of all printing assignments; and
where a third-party printer is contracted to print question papers by a provincial education department, it must ensure adherence to the Standard Operating Procedure for printing, packaging, distribution and storage.
TimesLIVE

redpajama_set_name: RedPajamaCommonCrawl


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Home The Platinum Jubilee of Her Majesty The Queen 2022 UK £5 BU Coin
The Platinum Jubilee of Her Majesty The Queen 2022 UK £5 BU Coin
Fulfilled by The Royal Mint - Unpackaged
Whilst the long-reigning Queen Elizabeth II has celebrated various impressive milestones during her reign, no royal event has inherited as much importance as that of the Platinum Jubilee Celebrations marking 70 years of the Queen's unprecedented reign. Having spent more time on the throne than any other British monarch, Queen Elizabeth is one of the most beloved figures in Great Britain, and is adored by millions of people across the globe.
Queen Elizabeth II's reign remains unmatched, and to mark the once-in-a-lifetime occasion that is the Queen's Platinum Jubilee, we are extremely proud to introduce a new Brilliant Uncirculated UK £5 Coin, featuring an all-new reverse design specifically devoted to this iconic royal event.
Depicted quite beautifully on the reverse of this coin is the inclusion of a strong graphic image including the number 70. A particularly significant feature of this side of the coin, is the inscription of the dates 1952 and 2022 highlighting the Queen's unforgettable reign.
Featured on the obverse of this Special Coin is John Bergdahl's unique illustration of a portrait of Her Majesty The Queen on Horseback – this design is reminiscent of previous jubilee designs.
Be sure not to miss out on this incredible release!
Height: 27.3000
Edition limit: Unlimited
Base metal: Cupro-nickel
Plating: None
Country of issue: United Kingdom
Face value: Five Pounds


The Town of Summerville's citizens have an opportunity to volunteer their time and expertise on a number of citizen committees, boards, and commissions.
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text:
Q: Azure SSO (sample code) is redirecting to wrong location after signing in I am trying out the sample code of the Microsoft identity platform to add authentication and authorization to web applications.
The sample from the link (https://github.com/Azure-Samples/ms-identity-aspnet-webapp-openidconnect) is working well in IIS Express.
When hosting the same application on a custom website in IIS, it is not working as expected. The steps which I have followed, and problems occurred are mentioned below.
HOSTING IN IIS EXPRESS

*

*The following is the landing page of this application.

*In IIS Express, when clicking Sign in with Microsoft, it prompts for the username and password.

*After entering the credentials, it is being redirected successfully to the index page which is configured in the Web.config file.

HOSTING IN CUSTOM IIS WEBSITE

*

*The same application is hosted in a custom website in IIS.

*Here also while clicking the Sign in with Microsoft, it prompts for username and password

*After entering the username and password, it is not redirecting to the configured page (https://localhost/AppTest/) and instead, it is landing in the URL https://localhost/.

*Also, the network log of the browser shows that the location header has the value as "/" in the response header of the redirect uri request.

*For debugging, I have added a custom middleware.

*It is used before the UseOpenIdConnectAuthentication as mentioned below.

*While debugging with Visual Studio, it is observed that after authentication the UseOpenIdConnectAuthentication is making a new unknown redirect request instead of the redirect uri configured in the Web.config and the request and its response have the location path value as "/".

I cannot understand how this new redirect request arises meanwhile the same app is working well in IIS Express. Please help me to resolve this issue. Thank you.

A: It is found that while hosting this application in IIS, after authentication, instead of landing in the URL configured in UseOpenIdConnectAuthentication, a new redirect occurs to the URL which is given to the instance of the AuthenticationProperties in the SignIn Action.
Modifying this redirect value in SignIn action solved the problem.

A: For your error,I think the most likely reason is that directory browsing is not enabled,so the program is led to the unknown redirect uri.
So you can past this code snippet inside your web.config file to solve the error.
<system.webServer>
    <directoryBrowse enabled="true" />
</system.webServer>

or you can
1.Open IIS Manager

2.Click the Application

3.Click Directory Browsing

4.Click Open Feature (On the right you will see under Actions)

5.Click Enable

For more information, please refer to this.And you can follow this.
UPDATE
This is where I made the changes to the sample code you provided and some configurations.It works.

redpajama_set_name: RedPajamaStackExchange


She wants a law practice with a team she can trust to keep clients happy when she's on vacation.
She wants to be able to take some time off with her spouse.
She'd like to tour Europe, and enjoy two months a year in a vacation home.
But she's a control freak.
That's how she describes herself. And she's right.
Being a control freak has served her well. She built a thriving practice with a bunch of employees that generates a healthy revenue because she's firmly in control. It's works for her.
But her control-freak ways mean she has to show up. She's the one employees turn to when a decision needs to be made. She's the leader they've been trained to follow. Very little happens in her law firm without her input and seal of approval.
Uh oh. This control freak built a business with herself at the center. She's the star her team orbits. When she takes a day off, the team defers making decisions or calls her on her mobile phone. They can't move forward without her input.
You're already seeing the problem, right? The control freak wants time away, but her team needs her presence to get things done. This doesn't work out well.
If you're a control freak, you've taught your team to depend on you for decisions and guidance. You can't get away because you've tied yourself to the business like the cement block you wanted to tie to the body of that client you dreamed of dumping into the river.
If your vision for your law firm involves spending more time away, you need to toss your control freak approach into that river as well.
What's the solution? How does a control freak let go?
If you're a control freak, letting go is hard. It's fraught with upset and disappointment.
The work product will deviate from your approach.
You will be upset, angry, and ready to give up. You'll believe things would have been done right if they'd been done your way.
But you have to let go if you want opportunities to get away from your business, disconnect, and feel comfortable knowing your team is getting the job done.
Your team needs to make decisions without you. If they can't decide, you can't leave. You can be present and make the decisions, or you can be absent and accept that they will sometimes fail to meet your standards.
The list above may have made you even more resistant to letting go. I feel your pain. Those concerns reinforce your desire to hold on tightly, stay in the center, and remain in control. After all, it's your name and business on the line.
Most of my control freak friends live in a fantasy world where they only remember the things that went well. When they're in control, they tend to rate their team's work highly. They see happy clients, an excellent work product, and deadlines being met.
Control freaks are great at seeing their outcomes through rose-colored glasses.
Control freaks do their best to only see the things that go well. They ignore the mistakes, blown deadlines, upset clients, uncollected fees, high turnover, and arguments with their employees.
They blame mistakes on their employees, even though they tightly control the environment in which the employee operates.
Take off the rose-colored glasses. Welcome to the real world.
Things don't go nearly as well for control freaks as their imagined, well-controlled fantasies lead them to believe.
I'm not saying control freaks screw up more often than other law firm owners, but they certainly don't screw up less. Things can go badly in any law firm. Being a control freak doesn't magically prevent breakdowns.
As much as we might deny it to ourselves, we screw up. We make mistakes.
Even when we run every piece of a process, we still end up with errors because we're human.
Even with firm control, we can't eliminate all mistakes. We simply screw up sometimes.
Yes, when we learn to let go, screw-ups will happen. But they would have happened anyway. When you let go, someone else on the team will do the screwing up.
We may see our team's mistakes differently than we would see our own, but it's all the same to a client. A mistake is a mistake. It doesn't matter who made it.
If you're a control freak, you need to delegate some of your decision making. You need to let go and coach others to step forward.
Let associates make case decisions.
Let the office manager make hiring decisions.
Let the bookkeeper handle the money.
And stop keeping the firm's metrics secret!
You need to let others do the work and make whatever decisions are necessary along the way.
You might hesitate because you know your team will make mistakes. You're right.
You need to find a way to delegate in a comfortable and reassuring manner. You need to let go without pushing your own control-freak buttons because that will tempt you to jump right back in when things get messy and/or you get nervous.
Letting go is easier than you imagine. I say that because you probably have a history of successfully delegating, even though you don't see it that way.
As much as you might believe you're a diehard control freak, the truth is that you delegate constantly.
Someone dry cleans your suit, fixes your car, cleans your house, and provides you with clean water and sewer services. Someone probably mows your law. You even let someone clean your teeth and examine your wrinkly body parts.
You believe you can do it better.
Unfortunately, if those three triggers happen often enough, they'll keep you from your vision of spending time away from the business (or working on the business instead of handling the day-to-day operations).
You've got to break the pattern by disrupting one of those three control-freak triggers.
Unfortunately, the first trigger ("you think you know more than others") is the hardest to change.
Personally, I often think I know more than others. I'm often wrong, but that doesn't slow me down.
The same is true of my belief that I can do it better than others (the third trigger). Again, I'm often wrong, but I rarely let reality get in the way.
We're pretty good at letting go when the distance increases. Many of us believe we know more than our accountant, but we're willing to stay out of the mix because the accountant is across town.
I suspect that if we were allowed to sit and watch as our accountant prepared our tax return, we'd offer all sorts of suggestions and ideas. Proximity makes a big difference.
So if you're a control freak, attack your tendencies with an assault on "close proximity." If you distance yourself from the work, you'll slowly condition yourself to become comfortable letting go.
Outsourcing is nearly the same as delegation, but it doesn't push your control freak triggers quite as hard. It's a gateway drug that encourages you to let go. If you start outsourcing, you'll experience a high when you don't have to do things yourself.
You'll persuade yourself to step back and put even more distance between yourself and your team. You'll let people do the work you've trained them to do. You'll let them make their own decisions, which fosters more learning and growth so they do an even better job for your clients and your firm.
Suddenly your vision of more time away is within sight.
Here's your 5 step plan for making delegation via outsourcing work in your law firm.
Put some distance between you and the work. Outsource it to someone far away. That'll reduce the likelihood of tripping your control-freak triggers.
Think of it like you think of your tax return. Ship the work off and let someone else do it.
Find someone in Ukraine to build your website.
Find someone in Delhi to do your billing.
Find someone in the Philippines to do filing.
Find someone in Cape Town to handle document review.
Use our outsourcing course to help you organize and outsource the work. Use websites like Indeed and Freelancer to find qualified professionals.
Put some distance (and time zones!) between you and your work so you can't easily retake control.
Expect some pushback from your team.
Outsourcing work (especially the work they handle daily) feels like a threat. Change is hard, even if they complain about you or the work. They'll reinforce your hesitation to outsource by helping you find a rationale to turn back.
Over time, their resistance will wane. Outsourcing feels different than delegating the same work internally. Sell it as an opportunity to lighten the load. You need your team onboard, or it'll all go poorly.
Get them to buy in by having them help you locate outsourcers, vendors and firms to handle the work. Have them develop specifications for the work and systems for managing it all.
As with all approaches to getting work done, there will be mistakes.
When mistakes happen, you want your team to react by helping you solve the problem that caused the mistake. You don't want them to push you to bring the work back in-house.
You'll only become better at delegating if you shift the culture toward your team solving their own problems.
Change is hard. When you start letting go and outsourcing more work, you'll find resistance in yourself and your team.
There will be a long list of reasons to turn back to your control freak ways. But your vision requires letting go and this approach of putting physical distance between yourself and the work has worked before, so it'll work again.
Take small steps at first. Outsource one thing at a time. Get it to work. Like anything new it'll take some tweaking and optimizing.
Instead of fully outsourcing your calendar management, get some help from a virtual assistant to set up certain events. Take baby steps as you build comfort. Don't push yourself too hard.
Your resistance will diminish as you move forward. Control freaks aren't good at letting go, so you have to move slowly.
Keep your eye on the vision you've created for your role in the law firm. Delegation is essential to that vision. Outsourcing is the path of least resistance in your quest to comfortably delegate your day-to-day activities. You want your team to work without turning to you for answers.
Outsourcing forces you to develop systems.
These systems look different than the ones you created for internal work. They're more about preparing the necessary information to hand off and monitor the status of the work, and less about the step-by-step process of doing the work.
Systems for outsourcing are easier to create. For example, it's easier to develop a system for sending the right information to the accountant to prepare the tax return than it is to develop a system for actually preparing the tax return.
Documenting systems for outsourced work is critical to the success of the outsourcing, but since the systems are simpler, you're more likely to do it. Developing a culture which creates systems is critical to achieving your vision of being less involved in the moment-by-moment decision making.
Use the outsourcing process as an opportunity to shift the culture of your team toward documenting systems. The team will learn that systems are valuable, since they won't be able to turn to you to solve every problem, make every decision, and fix every issue.
Take note of your progress as you delegate through outsourcing.
Keep in mind that you're using outsourcing as a gateway to better delegate to your internal team. You're putting physical distance between you and the work to make this easier for you.
You're taking baby steps, but it only helps if you learn from the experience and help your team learn along the way.
Give yourself some time to grow into letting go. Give yourself a chance to absorb and internalize the benefits of letting go.
You didn't become a control freak overnight and you won't reverse course quickly either. You'll be tempted to backslide when you discover errors and discrepancies or suffer disappointments.
It's hard to let go. It's easy to take it all back under your control, but you must resist.
Keep a close eye on your progress as you let go. Celebrate the victories so you can start to see yourself less as a control freak and more as a master delegator. It'll happen if you follow the 5-step plan.
Outsourcing will encourage better delegation behavior within your team. You'll get more comfortable letting go and they'll get more comfortable making decisions without your input.
Trust will develop on both sides. The team will also see the benefits of outsourcing.
As you grow, push yourself further. You'll find you can delegate even more: phone answering to a receptionist service; human resources to a professional employer organization; client surveys to a professional; bookkeeping to a service; brief drafting, legal research, and more to lawyer contractors.
You might become an outsourcing addict, outsource it all, and find yourself managing a small team of project managers who supervise the outside vendors. That's not a common approach among law firms, but it has an appeal.
Keep letting go and you'll move closer to your vision of having time away from the business, maybe for vacation or family time–or maybe for putting more of your mental energy to work on the business instead of in the business.
If you've created a culture and process that can't function without your presence, change is more difficult than making a simple decision. You didn't arrive at this point by accident.
You need to change your perspective and give yourself time to get comfortable doing things differently. You need to grow into a new role, in which you communicate your vision to your team and support them to get it done.
Abandoning your control-freak approach is a big change. The plan above will get you moving in the right direction.
Transitioning from control freak to delegator isn't easy, but it's worth it for the chance to go away on vacation and trust others to make decisions without your input.
Transitioning to a delegator may be the only way to achieve your vision.
Take something off your plate today with our in-depth video course on outsourcing everything from one-off design tasks to entire business systems.


text:
NIFT-UG-->View question
Read the passage given below and answer the questions that follows by choosing the correct option. (NIFT-UG)
Soapy is a wanderer who spends his time on doing nothing. Madison Square Park is his permanent shelter. With
the advent of winter he finds it difficult to keep himself warm. So he wants to go to the island to save himself
from the severity of the season. He makes many attempts to get himself arrested. He plans to dine at a posh
restaurant and declares his insolvency. But he is not even allowed to set foot inside the restaurant. Then be
breaks the plate glass of a shop window with a stone. The policeman refuses to arrest him. He becomes
desperate but never gives up making attempts.
He enters a moderate restaurant and consumes many items. He, then, declares that he has no money.
Instead of calling the policemen, two waiters beat him hard. After that, he misbehaves with a woman but of no
use. He steals an umbrella, the umbrella-man disappears from the scene.
Soapy feels much disgusted. He stands beside an old Church. The calm and serene atmosphere and the
anthem played by an organist bring solace to his soul. There comes sudden change in him. He thinks of
reforming himself and finding some useful work the next day. The next minute he is arrested by the police who
sees him on doing nothing. He is given punishment for three months on the Island.
46. In the passage, the meaning of the word 'Soapy' means:
(a) slippery (b) full of soap (c) name of the person (d) none of these
47. Which of the following is not associated with the events in the passage?
(I) Soapy dines in a posh restaurant (II) Soapy steals the umbrella
(III) Soapy breaks the glass (IV) Soapy eats in a moderate restaurant
(a) All except III (b) All except IV (c) All except II (d) All except I
48. The change in Soapy is due to:
(a) beating he takes from the waiters (b) the events in the church
(c) the events in the posh hotel (d) none of these
49. The meaning of 'island' in the passage is:
(a) a cottage (b) a resort (c) a prison (d) a comparatively hotter place
50. How many attempts were made by Soapy to go to island?
(a) four (b) three (c) five (d) none of these
Asked On2019-04-11 15:21:05 by:naikaumprakash
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The answer to the question 46 is option c. The answer to the question 47 is option d. The answer to the question 48 is option c. The answer to the question 49 is option a. The answer to the question 50 is option b. By closely understanding and analysing the question, we arrive to this solution. This is a common but simple questions which can be easily solved. Variants of this questions are also present which can be solved easily as this one.
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Read the passage given below and answer the questions that follow by choosing the correct option. PASSAGE Books are by far, the most lasting product of human effort. Temples crumble into ruin, pictures and statues decay, but books survive. Time does not destroy the great thoughts, which are as fresh today as when they first passed through their author's mind. These thoughts speak to us through the printed page. The only effect of time has been to throw out of currency the bad products. Nothing in literature, which is not good, can live for long. Good books have always helped man in various spheres of life. No wonder that the World keeps its books with great care.
Read the passage given below carefully and answer the following questions : (Political Science CBSE 2018)
Read the passage given below and answer the questions that follows by choosing the correct option. PASSAGE Somerset Maugham is a prominent short-story writer. His stories present a curious incident. Humour is the dominant feature in his stories. "The Luncheon' is a typical example. It is the tale of a society lady who says one thing and does another thing as regards food matters. The author lived in Paris 20 years ago. He was a writer by profession. His revenue was very low so he could just keep his body and soul together. One day, he received a letter from his lady fan. In it, she said that she was passing through Paris and would like to talk to him over a luncheon at Foyot's restaurant. The writer was thrilled and so he went to the restaurant to meet her. She was a woman of forty. In appearance, she was imposing rather than attractive. She was a talkative woman. At the hotel, the writer was started to see the high prices. But the lady assured him that she would never eat more than one thing for luncheon. Saying so, she placed order for a salmon. Then, she wanted to have caviare, white wine, asparagus, ice, coffee ad peaches. The writer became a dumb man. At last, she said that she won't eat more than one thing for luncheon. She advised the writer to follow her example. The writer had to spend the whole month without a penny in his pocket. After 20 years, he saw her now at the play. She had grown extremely fat. He felt that he had his revenge at last.
Read the passage given below and answer the questions that follow by choosing the correct option. PASSAGE Voltaire was a great French writer of the eighteenth century. Indeed, he was one of the greatest men whom european civilization produced. He was truthful, tolerant and benevolent. He sneered at everything. He was a man of critical genius and humanity. He was a man of perfect character and had a dynamic personality. He was anti-Nazi by nature. Above all, he was a man of contradictions. He was mischievous but a free-man. Frederick was a German dictator who believed in force, fraud and cruelty. He was a great organizer and was over-ambitious. He was a cultivated but sensitive gentleman. He believed in freedom of speech but he was a tyrant. Voltaire loved the human race while Frederick despised the human race. This is the dividing line between Voltaire and Frederick. Voltaire believed in humanity whereas Frederick did not. Frederick was a cynic whereas Voltaire was an optimist. On being invited, Voltaire visited Frederick and stayed in Berlin. In the begining, they were cordial and praised each other. But in the later years, Voltaire found the hidden cruelty in Frederick. Frederick also saw the 5 tiresome little monkey in Voltaire. Voltaire involved in a shady financial transaction. He quarrelled with another French man, drank too much chocolate and thus he gave more trouble to the King. At last, he left Berlin after a stay of two years. But Frederick, who was cynical worked through his Gestapo and got Voltaire and his niece arrested. Voltaire was imprisoned and tormented. With the king's intervention, Voltaire was released. Voltaire, for all his faults, was a free-man but Frederick was a tyrant.
Read the passage given below and answer the questions that follow by choosing the correct option. PASSAGE A customer went to a photo studio to have his photo taken. The photographer was a man of strange behavior. He was a drooping man in a grey suit looking like a natural scientist with a dim eye. His odd and unusual appearance itself would provoke laughter. It was a pity that the photographer did not receive the customer properly. He made him wait for an hour. After that, the photographer made the customer to sit in a 33 beam of sunlight in the inner room. The photographer rolled a machine into the middle of the room and crawled into it twice. The second time he drew a little black cloth over himself. But the photograph was not taken. Moreover, he showed signs of disapproval. The customer had to wait patiently for a snap. To the customer's astonishment the photographer commented on his face, head and ears. He simply said that he would not like them. The customer could not bear these unpleasant words. As he started to rise from his seat the photographer pulled a string. The photograph was taken at last. The photographer asked the customer to come on Saturday to have a look at his proof. The author went to the studio on Saturday. On seeing the proof he felt stunned. It was, in no way, a resemblance to his actual features. Though he spoiled the photograph, the photographer posed himself as a talented photographer. He also said that he had to do more adjustments in the print. The customer, unable to do anything burst into tears. Though the photographer had made a fuss the helpless customer saw the horror and experienced the pity.
Read the given extract and answer the following questions by choosing the most appropriate option. CBSE-english-2016
Read the passage given above carefully and answer the following questions: - CBSE Class 12 Humanities - Political Science 2016
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Read the following information to answer these questions.
Complete the passage by choosing the correct option from those given in brackets: CBSE-english-2016
Choose the most appropriate option from the ones given below to complete the following passage.CBSE English
Read the passage given below and answer the questions that follow by choosing the correct option. PASSAGE The government of India has decided to stop all imports of food by 1951 by increasing the internal production and by EQUITABLE distribution of all available internal supplies. Hyderabad State is deficit in food grains, especially in rice and wheat, and has to depend on imports. To achieve self sufficiency, it is imperative that food production must be increased and that all-available internal SURPLUS in the State must be procured for even distribution within the State. This Levy system is designed to PROCURE from the cultivator's only surplus grain, after making a LIBERAL allowance for their own requirements. This system is called the graded levy system, for the quantity levied is fixed according to the estimated gross production, which is based on the quality of the soil as reflected by average assessment, and is subject to decrease or increase according to the size of the holding. The important features of this new levy system are: government monopoly in major cereal food grains and a ban on the movement of food grains from villages; compulsory levy of grains; guarantee of prices for levy grains; rationing in all areas - statutory in urban areas and non-statutory in rural areas.
Read the following information carefully and answer the questions given below. (1) a - b means a is mother of b. (2) a x b means a is father of b. a + b means a is daughter of b. 73. If P-Q x R + S then which of the following is not true? (a) Q is wife of S (b) R is Qs daughter (c) S is wife of Q (d) P is mother-in-law of S. 74. Which of the following would definitely indicate that K is daughter of L? (a) K x L – O (b) K + L (c) L + M (d) O + K x L (NIFT-UG)
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text:
Q: Product of $N$ Pauli matrices I am trying to perform diagonalization on an Ising Hamiltonian, with $N$ spins, containing a term of the form
\begin{equation}
\sum_{i = 1}^{N} \sigma_i^{x} = \sum_ {i=1}^{N} 1_1 \otimes \cdots 1_{i-1} \otimes \sigma_i^{x} \otimes 1_{i+1} \otimes \cdots \otimes 1_{N},   \qquad (*)
\end{equation}
where
$
\sigma_i^{x} =
\left(
\begin{matrix}
 0 & 1 \\
 1 & 0
\end{matrix}
\right).
$
Apart from $(*)$, the Hamiltonian contains additional operators.
The size of this Hamiltonian matrix is $2^{N}$, so finding its eigenvalues is computationally expensive even for small $N$. This matrix is very sparse, so I was wondering if there is an efficient way of knowing exactly which values of $(*)$ are non-zero, without computing explicitly the tensor product.
Thank you.

A: Your expression seems to have a state on the LHS and an operator on the RHS, so it's a bit confusing.  I'll assume that what you want are the eigenvectors of the RHS operator. To simplify I will replace the $\sigma_x$ by $\sigma_z$, then the state
$$
\vert m_1\rangle\otimes \vert m_2\rangle \otimes \ldots \otimes  \vert m_N\rangle
$$
where $m_i =\pm 1/2$ and $\sigma_z\vert m\rangle=m\vert m\rangle$  is  an eigenstate of your RHS operator $(\sigma_z)_{\rm tot}$  with eigenvalue
$$
m_{\rm tot}= m_1+m_2+\ldots+m_N.
$$
How to  change back to $\sigma_x$ should  now be clear!


Posted on May 4, 2018 at 3:10 pm.
The Arlington School Board approved its FY 2019 Budget totaling $637,184,475 at last night's meeting.
$2,643,200 of one-time funding to postpone the increase in class size at all grades levels (by 1 at elementary, 0.75 at middle, 0.5 at high) for one year, restoring 28.0 FTE positions.
*Funds for these two items were provided by the reduction of $117,000 from the furniture and technology for relocatables line item.
More information about the FY19 budget is available online.
Before the start of the School Board meeting, members of the community joined the School Board in the new Board Room for a ribbon cutting ceremony to celebrate the new Syphax Education Center. The building was named after Evelyn Reid Syphax, a former Arlington elementary school teacher who served as a member of the Arlington School Board, as well as on the boards of many other community and civic organizations.
For more information on the Syphax Education Center and the relocation of APS offices, click here.
World Languages Update – Staff provided the Board with an update on the World Languages program that included proficiency of students, enrollment in offered-languages as well as recommendations. The full report is available online.
Construction Contract Award for Yorktown High School Internal Modifications – The Board awarded the contract for internal modifications to Yorktown High School in the amount of $1,628,804 to Bennett Group, Inc. The Board also approved the total budget of $3.75 million to be funded by the Capital Reserve.
Proposed FY 2019-2028 Capital Improvement Plan (CIP) – Staff presented the Superintendent's Proposed FY 2019-28 CIP. Click here to read more.
Discovery Elementary School Exemplary Project – Staff presented its proposal for the Discovery exemplary project, "Profile of a Discovery Graduate." The project is based on state's new Profile of Virginia Graduate. It includes five "Discovery Fundamentals" that guide program of authentic instruction: Sustainability; Personalized Learning; Computer Science; Design Thinking; and Social Responsibility. The purpose of the project is to ensure that, in addition to having proficiency in all Virginia Standards of Learning, students will also innovators, able to analyze, design, and create, consistent with the skills we believe will be necessary in an unpredictable future.
Construction Contract for Career Center Internal Modifications – Staff is recommending that the Board award a construction contract to Bennett Group, Inc. for $1,624,751.34 to complete the summer 2018 internal modifications as well as approve a projected budget of $4,542,111.34. Full details of the project are available on BoardDocs.
Change to Guaranteed Maximum Price and Budget for Fleet Elementary School – Staff is asking the Board to approve an increase to the Fleet Project Guaranteed Maximum Price contract with an individual change order of $190,000 for accessibility improvements to Jefferson Middle School. Full details are available online.
The next School Board meeting will be held on Thu, May 17 (2110 Washington Blvd.) at 6 p.m. The earlier start time is to accommodate the rescheduled Staff Service Awards reception. The agenda will be posted one week before the meeting on BoardDocs.
Citizens who want to comment on any of the items discussed at the School Board meeting should email the Board at school.board@apsva.us or call 703-228-6015. Citizens may call 703-228-2400 on the Monday after the School Board meetings to listen to a meeting summary. School Board meetings are also broadcast live on Comcast Cable Channel 70 and Verizon FiOS Channel 41; live-streamed on the APS website, and re-broadcast on Fridays at 9 p.m. and Mondays at 7:30 p.m. immediately following the meeting. All materials and minutes of the meeting will be posted to the website at www.apsva.us/schoolboard upon School Board approval at a subsequent meeting.


text:
The dark side of Vermont
I am wondering when Tom Bauerle, on WBEN-AM, will do his annual show on ghost stories. He invites people to call in and tell about times when they felt a brush with the supernatural. That is always a lot of fun and it will be a welcome change from discussing the economy and the election. Surely Bauerle's ghost show is coming up fast. Lastnight WNED-FM played "The Flying Dutchman," a ghostly opera. So the radio is up and running with October spookiness.
Wagner got the spookiness right in "The Flying Dutchman." The eerie call of the Dutchman's sailors always gets to me. The way it echoes. The last scene is a classic, when the Dutchman finally announces who he is, and the sailors burst into that cry. I found this clip of the scene on You Tube. Senta is kind of fat but that kind of comes with the territory. But the Dutchman is great, this great biker type. And the staging is beautiful. Watch it. It gave me shivers.
Leonard Pennario's "Midnight on the Cliffs" is considered spooky music by some people because it was used in the movie "Julie," in which Pennario's friend Louis Jourdan played a creepy concert pianist trying to murder his wife, played by Doris Day. I kind of regret that music was used in that movie because I always imagine it to be a romantic, passionate theme, not a creepy theme. Pennario got the idea for it when he was walking on the beach in Newport, Rhode Island.
Wagner got the idea for "The Flying Dutchman" while he was traveling and was involved in a shipwreck. I forget the details. But it reminds me once when my family was traveling and we ran across a spooky situation.
This was in Vermont. I was about 10 years old so the details are foggy now. Maybe I will quiz my mom about this and see if she remembers more. But we stopped at this motel, the Mountain Valley Inn. And strange truths began to surface.
One thing, as I remember, the proprietors of the inn were named Phillips and when my parents registered, the Phillipses commented that their best friends were named Kunz. This was very odd because it just so happens that my parents' best friends were named Phillips. My Uncle James Phillips gave me away when I got married. That is how close they are to our family.
Another thing, my parents' house key fit the lock. Isn't that strange?
There was a sadness in the place, I remember that. There had been a suicide. I don't know how my parents got that out of the owners but they did. My father probably did. He always shared my interest in the supernatural, which my mother still does not.
Unfortunately my dad is gone so I can't quiz him on what else happened. My mom didn't pay as much attention. What I do remember, though, is that a strange atmosphere hung over the car when we left that place and continued on our way the next day. My parents were whispering about things. I sometimes wonder about that place, the Mountain Valley Inn. I wonder if it is still there.
Posted by Mary Kunz Goldman on October 15, 2008
OKAY -- Mary, THAT is really weird. JUST THIS WEEK, I was JUST telling the kids the story of the big St. Bernard, Yuka, who cornered George in the hallway of this little inn in Vermont and was licking the hell out of his face while he cried hysterically. Was THAT the Mountain Valley Inn?
spooooky - Margie


text:
E-book – $17.95
Trade Paper – $17.95
Alan Grostephan (Author)
In Bogotá, a taut, moving novel set in present-day Colombia, Wilfredo decides to uproot his family from their small town, where his ferry service on the river subjects him to the gruesome errands demanded by the local paramilitary. Moving in with relatives in a slum in Bogotá, the family tries desperately to achieve the smallest measure of comfort and hope in a world of almost total ruin, wracked by deprivation, fear, and ceaseless violence.
Alan Grostephan depicts with startling immediacy an urban landscape of extreme harshness and oppressive instability. The tension between the desperate conditions surrounding his characters and their efforts to hold on to their humanity gives Bogotá a ferocious energy. As Wilfredo and his family fight to stay alive and stay together, their plight emerges as equally enraging and uplifting, constituting a portrait of a society always on the verge of disintegration.
Alan Grostephan has published his work in various journals, including Faultline and Orange Coast Review. He translated and edited Stories of Life and Death (2005), a collection of poetry and fiction by emerging Colombian writers.
"Bogatá is a miracle of a novel, with its compellingly elegant style in service of an anguished story: how to stay human in the face of the ravaging poverty and violence that is slum life in present-day Colombia. Alan Grostephan is a gifted, serious, compassionate writer, a most welcome new voice."—Christine Schutt, author of Prosperous Friends
"It is hard to believe this fine book is a first novel. There is so much story and so much confidence here. Bogotá is simply a great big rich story which starts as a kind of thriller in a village by a river and then takes on the weight of the ages, a family wronged by time and circumstance, and each episode rows us closer to catastrophe. This is a knowing and caring novel written with a steady hand by a significant new talent."—Ron Carlson, author of The Signal


This article is about the Hong Kong horse race. For other uses, see Elizabeth Cup (disambiguation).
The Queen Elizabeth II Cup is a Group One Thoroughbred horse race at Sha Tin Racecourse in the New Territories, Hong Kong. Established in 1975 by the Royal Hong Kong Jockey Club, it is run annually in April at a distance of 2,000 metres (ten furlongs) on turf. Prior to 1997 it was run at 2,200 metres (eleven furlongs). Sponsored by Swiss watchmaker Audemars Piguet since 1999, it currently offers a purse of HK$20 million (US$2.6 million)since 2014/15.
The Queen Elizabeth II Cup was first run at the Happy Valley Racecourse in Happy Valley, Hong Kong to commemorate a visit to Hong Kong by Queen Elizabeth II and the Duke of Edinburgh, Prince Philip. It evolved to an international event in 1995, with Hong Kong horses competing against those from the United Arab Emirates, the United Kingdom, France, Japan and Australia.
Since becoming a Group One race in 2001, every winner of the Audemars Piguet Queen Elizabeth II Cup has been a descendant of Northern Dancer. The 2006 race saw Irridescence upset the great filly and European Horse of the Year, Ouija Board.
The Queen Elizabeth II Cup has been sponsored by the Swiss haute horlogerie brand Audemars Piguet for around 17 years now, they continue to make special edition pieces (mostly based on the Royal Oak) to celebrate their sponsorship of the event. They join companies such as Omega SA, Rolex and Longines as one of the official sports event partners that provide both sponsorship and race timers.
This article about sports in Hong Kong is a stub. You can help Wikipedia by expanding it.
This horse racing-related article is a stub. You can help Wikipedia by expanding it.
This page was last edited on 7 April 2019, at 03:23 (UTC).


text:
Neural classification of Norwegian radiology reports: using NLP to detect findings in CT-scans of children
Fredrik A. Dahl ORCID: orcid.org/0000-0002-6835-09851,2,
Taraka Rama3,
Petter Hurlen4,
Pål H. Brekke5,
Haldor Husby2,
Tore Gundersen6,
Øystein Nytrø ORCID: orcid.org/0000-0002-8163-23627 &
Lilja Øvrelid8
With a motivation of quality assurance, machine learning techniques were trained to classify Norwegian radiology reports of paediatric CT examinations according to their description of abnormal findings.
13.506 reports from CT-scans of children, 1000 reports from CT scan of adults and 1000 reports from X-ray examination of adults were classified as positive or negative by a radiologist, according to the presence of abnormal findings. Inter-rater reliability was evaluated by comparison with a clinician's classifications of 500 reports. Test–retest reliability of the radiologist was performed on the same 500 reports. A convolutional neural network model (CNN), a bidirectional recurrent neural network model (bi-LSTM) and a support vector machine model (SVM) were trained on a random selection of the children's data set. Models were evaluated on the remaining CT-children reports and the adult data sets.
Test–retest reliability: Cohen's Kappa = 0.86 and F1 = 0.919. Inter-rater reliability: Kappa = 0.80 and F1 = 0.885. Model performances on the Children-CT data were as follows. CNN: (AUC = 0.981, F1 = 0.930), bi-LSTM: (AUC = 0.978, F1 = 0.927), SVM: (AUC = 0.975, F1 = 0.912). On the adult data sets, the models had AUC around 0.95 and F1 around 0.91.
The models performed close to perfectly on its defined domain, and also performed convincingly on reports pertaining to a different patient group and a different modality. The models were deemed suitable for classifying radiology reports for future quality assurance purposes, where the fraction of the examinations with abnormal findings for different sub-groups of patients is a parameter of interest.
Neural models have been trained to classify Norwegian radiology reports from CT-scans of children, with high accuracy.
The models performed well also on radiology reports from a different patient population (adults) and a different modality (X-ray images).
The developed models are robust with respect to different contexts, and may be used in quality assurance processes.
Quality assurance (QA) of hospital radiology is an important field, and QA of routines for CT-scanning is of particular interest because it accounts for nearly 70% of the radiation exposure from diagnostic radiological procedures over-all [1]. Several large epidemiological studies performed world-wide in the past decade have identified increased risks of central nervous system tumours and other types of cancer related to diagnostic radiation exposure in childhood and adolescence [2,3,4,5]. Therefore, criteria for applying CT scans should be monitored in order to limit potentially harmful exposure. A basic QA parameter is the fraction of the examinations that produces positive findings for a given sub-group of patients, which may be a tool to indicate areas for review. In particular, it is interesting to identify possible subsets of patients that may have a rate of findings that might be too low to warrant the radiation risk, but also sub-groups where the rate is so high that it might indicate that the criteria used are too restrictive [6, 7].
The present article describes the development of a tool that can be used to estimate the fraction of positive findings in a set of radiology examinations for a hospital in Norway. The classification task is more complex than one might imagine, since the hospital's written radiology reports are unstructured (or semi-structured), and lack a binary conclusion of positive findings. We examine the use of different machine learning (ML) techniques for the given natural language processing (NLP) task. According to the "AI Index" [8], which seeks to monitor the progress in artificial intelligence (AI) in a balanced way, ML with end-to-end training is currently the most successful approach for virtually all standardized NLP test problems. This refers to methodologies where the human experts only provide annotated or categorized text examples, as opposed to the inclusion of human knowledge engineering in the way of formalized syntax rules, ontologies or knowledge graphs. The present project follows this trend and uses end-to-end training with no human input except an expert's labelling of positive and negative examples.
Within the field of clinical NLP [9], radiology reports have provided data for a number of systems performing tasks such as coding of findings [10], suggestions for repeat examinations [11] and detection of nosocomial infections [12]. The system presented in [13] automated the coding of radiology reports using the SNOMED CT reference terminology. Chapman et al. [14] present a document-level classification of CT pulmonary angiography reports using a standard Naive Bayes classifier. Whereas deep neural networks have been extensively employed to process medical images, there are still only a few studies that employ neural network architectures to process textual radiology reports. A recent study [15] makes use of a convolutional neural network to extract pulmonary embolism findings from thoracic computed tomography (CT) reports. Another study [16] employs a recurrent neural network to distinguish between fractures and non-fractures in a set of orthopaedic surgeon-classified reports.
The studies referred above were performed on English clinical text, and smaller languages are to a large extent under-studied within the field of clinical NLP, although this is slowly changing [17]. The present work provides the first published results for radiological text written in the Norwegian language. In addition to large differences in vocabulary, Norwegian also differs from English in having three genders for nouns and more inflection forms in general, as well as arbitrarily long compound words, much like German. This tends to produce a higher number of low-frequency tokens. Although Norwegian is placed in the German language family, it also has grammatical similarities with English, however, such as the tendency to end sentences with a preposition (although sometimes frowned upon in both languages), which is never seen in German. Our study is unique also in the use of an unselected sample of all CT-examinations of children in a general hospital over a substantial period of time, with the task of identifying reports that document any kind of abnormal findings across different medical specialties.
The radiology reports under study are from Akershus University Hospital, which is located just outside Oslo. It serves a population of around 600.000 people and has close to 1000 beds. The primary data set consisted of all written reports from CT-scans of children (< 20 years) in the time period 2006–2017, for a total of 13.506. In addition, 1000 randomly chosen reports from CT-scans of adults and 1000 randomly chosen reports from chest x-ray of adults from the same hospital were included in order to test the models' external validity.
The radiologist in the project (PH) labelled each report as negative or positive, according to his judgement regarding the presence of any description of abnormal findings, and we define this as the reference standard (RS). For all three data sets, abnormality was defined as any deviation from normal physiology, regardless of the clinical implications or the purpose of the given examination. The reports were semi-structured in the sense that most of them contained the token "R:" followed by summary (Children-CT: 85%, Adult-CT: 87%, Adult-X-ray: 47%). However, the summary seldom gave a binary conclusion that could be used for classification and frequently omitted details of abnormal findings that would be significant enough to trigger a positive classification.
In order to evaluate the reliability of the RS, a hospital clinician (PB) labelled a random sample of 500 children-CT reports. Prior to this, he discussed a different random sample of 100 children-CT reports with the radiologist, in order to align their evaluations. A clinician was chosen because he represents the users of the reports, and the agreement between those who write the reports and those who read them was deemed particularly important. In addition, the radiologist also re-labelled the same 500 reports after a period of three months as an intra-rater reliability test. The reliability tests were performed with Cohen's kappa index. Statistical significance tests were performed with bootstrap, using 10.000 resampled data sets generated by random sampling with replacement [18].
Prior to any analysis, a test set consisting of 10% of the CT-Children reports was sampled at random, and stored together with the CT-Adults and X-ray Adults data sets. These data sets were not accessed until all model selection and training was finished, in order to guarantee unbiased performance estimates. The different ML algorithms had access to the remaining 90% of the CT-Children data, called the development data set. In order to eliminate overfitting, the development data set was split further into a training set (80%) and a validation set (20%), where the former was used to train a given model with a given setting of hyper-parameters, and the latter was used to evaluate the performance of the resulting model. This made it possible to search for hyper-parameters that give good performance on unseen data, thus avoiding overfitting. When suitable hyper-parameters had been determined, these were used to train the model on the full development data set. The models that were developed had different sets of hyper-parameters, and no formalized optimization method was used in the search. Rather, the researchers used their modelling experience and tested a broad selection of hyper-parameter configurations in an ad-hoc manner. The final models were evaluated on the held-out Test set, the CT-Adult set and the X-ray Adults set. Hence, all the performance estimates were created on data that were not a part of the model development or training. Figure 1 illustrates the various parts of the data set, where the development data set is the union of "Training" and "Validation". The 500 reports used for reliability testing were sampled uniformly from the entire children-CT data set.
The chosen performance measures were AUC (area under the receiver-operator-characteristic curve) and F1-score. The AUC can be defined for models that give a numerical output and classify cases by comparing the output to a threshold, so that one can trade sensitivity for specificity by adjusting the threshold. The AUC has a convenient graphical representation that illustrates how the model's sensitivity can be traded for its specificity. It can also be defined as the probability that a random positive case has a higher model score than a random negative one. The F1-score is commonly used in the ML and NLP communities. There are several alternatives to the F1 measure, including Cohen's Kappa, which can all be computed from the confusion matrix in terms of True or False Positives (TP/FP) and True or False Negatives (TN/FN), as shown in Table 1. For completeness we report the TP/FP/FN/TN figures as well.
Table 1 The confusion matrix
The F1 score is defined as the harmonic mean of the model's positive predictive value (precision) TP/(TP + FP) and specificity (recall) TP/(TP + FN), and is given by the expression 2*TP/(2*TP + FP + FN) [19].
Although the main purpose of the clinician's labelling (and the radiologist's re-labelling) of the 500 children-CT reports was reliability testing, these were also evaluated by the F1-score. These scores were interpreted as reference values for human performance, against which the ML models were compared.
The radiology reports were extracted from the hospital's EPR system DIPS and pre-processed using the UDPipe package, with the Norwegian model trained on the Norwegian UD treebank [20]. While this package supports more advanced pre-processing like syntax parsing, which could potentially be useful, the present application used only tokenization and sentence segmentation. Token in this context are essentially words (case insensitive) and punctuation marks that are not a part of standard abbreviations.
Although radiology reports to a varying degree mention body parts that were examined and found normal, the focus is on descriptions of abnormal findings. One would therefore expect to see an association between the report length and classification status. As a point of reference we therefore tested a trivial model that predicts the classification from the number of tokens in the report, only.
Our first nontrivial model was a support vector machine (SVM) over a bag-of-words representation of the texts. The model structure was linear, and term frequency–inverse document frequency (TFIDF) was used, which is a standard method for scaling the impact of words according to their frequency within and between documents [20]. No limit was set to the number of tokens in the selected configuration. SVM is arguably the most widely used non-neural ML method, which sometimes performs comparably to NN methods [21].
Subsequently, a bidirectional recurrent neural network (RNN) model of type long short-term model (LSTM) was developed [22]. LSTMs have a wide-spread use in NLP, mostly for sequence labeling tasks but also for text classification tasks such as the current task [23]. Simple feed-forward neural networks require that the input length has to be fixed, which often is not the case with sequential data. An LSTM model solves this by processing one input symbol at a time, and including the model's own output from the previous step as additional input. In this way, the model has the ability to maintain information about previous symbols that have been read, and can be trained to produce a final output that summarizes the entire document (positive or negative, in the present application). The present application uses a configuration that combines an LSTM model that reads forward with one that reads backward, hence the label "bidirectional". Vector representations of the input words were used, and these were randomly initialized and tuned during training. Different settings for hyper-parameters were explored, and the final model used 32 hidden units and word vectors of dimension 30. The token set was limited to the 10.000 most common ones. During training a 50% dropout rate was used.
A convolutional neural network (CNN) model was also developed, which solves the variable input length problem in a different way. It uses a kernel, in the form of a "sliding window" that is trained to identify features in short sub-strings of the input. The kernel output features are fed into a computational layer that filters out the maximum value for each feature (so-called "max pooling"). These are propagated through a hidden layer of neurons, which is connected to a single output node.
After hyper-parameter tuning, a model with a kernel width of 5, with 64 features and 10 hidden nodes was chosen, and the token set size was set to 5.000. Since Kim [24], CNNs have been widely used in NLP for text classification tasks, like sentiment analysis.
In order to illustrate the different models' data requirements, we evaluated their F1-performances on the children-CT test set after training on sub-samples of varying size from the training data set. We also evaluated the F1-performances as a function of the number of tokens included for the models.
We first give some descriptive statistics of the Children CT patients: 45% of the patients were female, while their age distribution is shown in Fig. 2. Rounded to full percentages, 26% were referred from the Emergency department, 24% from Paediatrics, 16% from Orthopaedics, 11% from Surgery, 6% from Internal medicine, 9% from external sources and 9% from other units, including Psychiatry. 68% were emergency cases.
Age distribution of the CT children scan
All reports were written in Norwegian and the number of tokens per report varied from 3 to 402. The Children-CT, Adult-CT and Adult-X-ray data sets had an average of 61, 87 and 31 tokens per report, respectively. The development part of the Children-CT data set had a vocabulary of 22.033 unique tokens. This vocabulary covered 98% of the tokens in the test set reports, 95% of the tokens in the Adult-CT reports and 95% of the tokens in Adult-X-ray reports. (The cover of the respective vocabularies is lower, but this is irrelevant since tokens that are not seen during training are indistinguishable for the model.)
In the CT-children data set 45% of the reports were classified as positive. In the CT-adults data set 71% were positive and in the X-ray adult data set 66% were positive. There was a substantial uncertain area of reports that to a varying degree suggest possible findings without drawing firm conclusions, and in these cases professional judgement can go either way. An example of this is the following short report, translated from Norwegian: "No enlarged intra-abdominal glands. The liver seems somewhat large, but the spleen is not enlarged. Unremarkable pancreas. No free intra-abdominal fluid." It is not immediately clear whether a liver that seems somewhat large represents an actual abnormality or not. Note also the typical abbreviated syntax with missing predicate verbs.
Table 2 gives the results of the human classifiers on the 500 random reports with Kappa-score, F1, precision (P = TP/(TP + FP)), recall (R = TP/(TP + FN) and the elements of the confusion matrix. Although the confidence intervals overlap substantially, the differences between the radiologist's and clinician's Kappa and F1-scores are statistically significant (p-values 0.019 and 0.014). This is because their misclassifications are strongly correlated.
Table 2 Reliability results
The performances of the different ML models on the children-CT test set are given in Table 3. The F1-scores of the Bi-LSTM and CNN models are statistically significantly higher than the SVM F1-score (p-values 0.0142 and 0.0188). For comparison, the trivial model based only on word counts had an F1-score of 0.77 and an AUC of 0.86.
Table 3 Model classification results on the Children-CT test set
Figure 3 shows the ROC-curve for the CNN classifier, where the area under the curve is 98.1% of the perfect performance, indicated by the rectangle. The ROC-curves for bi-LSTM and SVM are visually similar.
ROC-curve for the CNN classifier
Tables 4 and 5 give the ML models' performances on the adult-CT and adult-X-ray data sets, respectively.
Table 4 Model classification results on the adut-CT data set
Table 5 Model classification results on the adut X-ray data set
Figure 4 shows the analyses of the models' sensitivity to the size of the vocabulary, using logarithmic X-axes. With a vocabulary of size 10, the strongest predictor for a negative classification for the SVM model was the word "ingen" (meaning "no"), while the strongest predictor for a positive classification was the word "med" (meaning "with"), closely followed by two other prepositions: "på" (meaning "on") and "av" (meaning "of").
The F1-scores of the models for different vocabulary sizes
Figure 5 shows the analyses of the models' sensitivity to the size of the training set, using logarithmic X-axes.
The F1-scores of the models for training set sizes
Our main finding is that all three models perform very well on the test data set, with AUC scores around 0.98 and F1-scores around 0.92. The F1-scores are clustered around the radiologist's score, which may in practice represent an upper limit to the possible performance on this task. Although a report's word count is associated with its classifications, this correlation was not strong enough to give reliable classifications by itself.
The study relied on the labelling effort of a single radiologist, which is a potential weakness. However, the reliability tests showed convincing results, with an inter-rater Cohen's kappa level of 0.80 and a test–retest Cohen's kappa of 0.86. A total of 15.506 reports were labelled, which is a very high number, and it is unavoidable that a human will make a few random errors. More importantly, there is a substantial uncertain area of reports that to a varying degree suggest possible findings without drawing firm conclusions, and in these cases professional judgement can go either way.
The neural models performed slightly better than SVM with higher F1-scores, which was statistically significant. The difference was mainly in the models' specificity (recall). Over-all, the reports are written in a relatively simple (even over-simplified) syntax with few negations, which is probably beneficial for SVM relative to the neural models and may explain the small differences.
All three models showed robust performance as a function of vocabulary size, with reasonable performance for 10 tokens, adequate performance with 100 and close to maximum performance with 1000. It was unsurprising that the word for "no" was the strongest negative predictor in the smallest SVM model, but more of a surprise that the strongest positive ones were prepositions. The reason may be that descriptions of specific locations in the body were used mostly in the context of abnormal findings.
The analysis of training set sizes showed that the SVM model was relatively robust, while the performance of the neural models deteriorated quickly for training sets with fewer than 1000 reports. It is a well-known fact that neural models tend to require more data, but the present analysis is likely to overestimate this effect since the hyper-parameters were adjusted to the full data set.
The models were trained on a large and unselected material of CT-scan reports, spanning a wide range of medical specialities. Therefore, the models could not rely on simple disease-specific cues, and our goal was to create robust models that could generalize the concept of abnormal findings. This was confirmed, since the models performed well on the external validation data sets, with F1-score around 0.91 and AUC around 0.95. From a scientific point of view, it is very satisfying that these models can perform adequately outside of their original scope. For CT images of children and adults, the distribution of the underlying diseases will necessarily be different. One might therefore expect that a model trained from children reports could experience problems with reports that describe findings relating to diseases like heart attack or stroke that mostly affect the elderly. The favourable performance therefore indicates that the models do in fact capture a generic concept of abnormality. This conclusion is strengthened further by the fact that the models work well even on X-ray reports of adults, which are different in terms of modality as well as patient group. The goal of the study was to develop models that may be used for subsequent QA, and a robust performance is key for such applications.
The study of Chapman et al. [14] uses a syntax rule based text processing algorithm for identifying features in clinical report documents. Although not directly comparable to the present one, their most similar task appears to be disease classification, for which they reported an F1-score similar to ours (0.90). The present study has more similarities with that of Chen et al. [15], who also trained a CNN to classify radiology reports as positive or negative. They reported an AUC of 0.97 and an F1-score of 0.938, which are virtually identical to our results, so our study successfully replicates their results in a different setting. The languages are different, and an application of this kind to a Scandinavian language is novel. The other study used a larger set of radiology reports, but had fewer labelled training examples, so for the most relevant parameters the present study is larger. There are also methodological differences, as Chen et al. compared their CNN model to a pre-existing rule-based classifier, while we compared SVM, bi-LSTM and CNN models. The most important contribution of our study may be the successful validation of our models on radiology reports relating to a different population (adults versus children) and a different image modality (X-ray versus CT).
As there are major differences between the clinical importance of a positive or negative finding between different patient groups, it is not possible to define a specific level or range that is generally appropriate. However, knowledge of the current practice is essential in any QA work, and the rate of positive findings in a specific sub-group is a basic parameter of interest. We believe that our model may be applied in QA not only in our own hospital, but also others.
Our study shows that SVM, CNN and bi-LSTM models can be trained to classify Norwegian radiology reports from CT-scans of children. While the neural models CNN and bi-LSTM perform slightly better, even SVM was sufficient for close to perfect performance. All the three models performed well also on CT-reports for adults and X-ray reports for adults. This shows that the models are robust with respect to different contexts and may be used in quality assurance processes.
Until the present, neural network language modelling has been a cutting-edge technology reserved for specialists in the field, but this is changing rapidly as the technology is maturing. In the near future, we expect applications like the present one to be as commonplace as today's use of linear or logistic regression for quantitative analysis.
Our study also indicates that NLP tools can be used to chart our practice with a precision sufficient to initiate valuable discussions in our continuous effort to maintain good quality.
Under Norwegian privacy law, the radiology texts cannot be made available because re-identification cannot be ruled out.
Area under the receiver operator characteristic curve
bi-LSTM:
Bidirectional long short-term memory
bi-RNN:
Bidirectional recurrent neural network
CT:
HER:
Electronic health record
NLP:
RNN:
Recurrent neural network
Receiver operator characteristic
SVM:
Vector machine
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This work was funded by the Norwegian Research Council (NRC), specifically as part of the BigMed project (bigmed.no), Project Number 259055, an IKTPLUSS Lighthouse project. The NRC has no involvement in any research activity beyond providing funding, and has no role in the study design, data collection, analyses, or interpretation.
Health Services Research Unit, Akershus University Hospital, Lørenskog, Norway
Fredrik A. Dahl
Institute for Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
Fredrik A. Dahl & Haldor Husby
Department of Linguistics, University of North Texas, Denton, TX, USA
Taraka Rama
Division of Diagnostics and Technology, Akershus University Hospital, Lørenskog, Norway
Petter Hurlen
Department of Cardiology, Oslo University Hospital Rikshospitalet, Oslo, Norway
Pål H. Brekke
Data and Analytics, Akershus University Hospital, Lørenskog, Norway
Tore Gundersen
Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
Øystein Nytrø
Department of Informatics, University of Oslo, Oslo, Norway
Lilja Øvrelid
Haldor Husby
FD participated in programming the models, evaluating them, interpreting results and drafting the manuscript. TR participated in programming the models and reading, revising the manuscript. PH participated in defining the study, evaluating radiology reports, interpreting results and reading, revising the manuscript. PB participated in evaluating radiology reports, interpreting results and revising the manuscript. HH participated in extracting radiology reports and revising the manuscript. TG participated in extracting radiology reports and revising the manuscript. ØN participated in revising the manuscript. LØ participated in model development and revising the manuscript. All authors read and approved the manuscript.
Correspondence to Fredrik A. Dahl.
The study was approved as a quality assurance project by the hospital's Privacy Ombudsman (Approval number 2016_113). Under Norwegian law (Helsepersonelloven §26), this approval gives exemption from consent by the participants.
Dahl, F.A., Rama, T., Hurlen, P. et al. Neural classification of Norwegian radiology reports: using NLP to detect findings in CT-scans of children. BMC Med Inform Decis Mak 21, 84 (2021). https://doi.org/10.1186/s12911-021-01451-8
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