--- layout: stats title: Yale B Statistics permalink: "/stats/teams/yale-b.html" ---
| Year | Team | Status | Finish | W-L | PPG | PAPG | Margin | TU/G | N/G | BHrd | BPts | PPB |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2012 | Yale B | UG | 18 | 6-8 | 172.9 | 170.0 | 2.9 | 8.00 | 1.07 | 112 | 1375 | 12.28 |
| 2016 | Yale B | UG | 32 | 6-11 | 148.2 | 203.3 | -55.1 | 7.15 | 0.79 | 118 | 1330 | 11.27 |
| 2017 | Yale B | UG | 28 | 5-9 | 123.6 | 210.0 | -86.4 | 6.36 | 1.00 | 89 | 910 | 10.22 |
| 2019 | Yale B | UG | 34 | 5-9 | 153.2 | 189.3 | -36.1 | 7.57 | 1.79 | 106 | 1210 | 11.42 |
| Year | Round | Team | Opponent | Result | Score | 10 | -5 | PPB | Packet |
|---|---|---|---|---|---|---|---|---|---|
| 2012 | 1 (Prelims) | Yale B | Illinois A | Loss | 70 - 330 | 4 | 0 | 7.50 | No Packet Recorded |
| 2012 | 3 (Prelims) | Yale B | Minnesota A | Loss | 115 - 380 | 4 | 1 | 20.00 | No Packet Recorded |
| 2012 | 4 (Prelims) | Yale B | Alabama | Win | 390 - -5 | 17 | 0 | 12.94 | No Packet Recorded |
| 2012 | 5 (Prelims) | Yale B | Chicago C | Win | 190 - 35 | 10 | 2 | 10.00 | No Packet Recorded |
| 2012 | 6 (Prelims) | Yale B | Virginia A | Win | 215 - 175 | 9 | 1 | 14.44 | No Packet Recorded |
| 2012 | 7 (Prelims) | Yale B | South Carolina | Win | 255 - 105 | 12 | 1 | 11.67 | No Packet Recorded |
| 2012 | 8 (Tiebreaker) | Yale B | Minnesota A | Loss | 155 - 260 | 6 | 1 | 16.67 | No Packet Recorded |
| 2012 | 9 (Playoffs) | Yale B | Carleton | Loss | 175 - 185 | 9 | 3 | 11.11 | No Packet Recorded |
| 2012 | 10 (Playoffs) | Yale B | VCU A | Loss | 130 - 195 | 6 | 0 | 11.67 | No Packet Recorded |
| 2012 | 11 (Playoffs) | Yale B | George Mason | Win | 160 - 95 | 8 | 0 | 10.00 | No Packet Recorded |
| 2012 | 12 (Playoffs) | Yale B | South Carolina | Loss | 170 - 180 | 7 | 3 | 16.43 | No Packet Recorded |
| 2012 | 13 (Playoffs) | Yale B | Northwestern | Loss | 150 - 160 | 7 | 2 | 12.86 | No Packet Recorded |
| 2012 | 14 (Playoffs) | Yale B | Virginia B | Win | 195 - 5 | 10 | 1 | 10.00 | No Packet Recorded |
| 2012 | 15 (Playoffs) | Yale B | Rice | Loss | 50 - 280 | 3 | 0 | 6.67 | No Packet Recorded |
| Year | Team | Player | Games | Tossups | Negs | Pts | PPG |
|---|---|---|---|---|---|---|---|
| 2012 | Yale B | Spencer Weinreich | 14 | 49 | 2 | 480 | 34.29 |
| 2012 | Yale B | Sam Spaulding | 14 | 45 | 10 | 400 | 28.57 |
| 2012 | Yale B | Stephen Leh | 14 | 18 | 3 | 165 | 11.79 |
| Year | Round | Team | Opponent | Result | Score | 10 | -5 | PPB | Packet |
|---|---|---|---|---|---|---|---|---|---|
| 2016 | 1 (Prelims) | Yale B | Oxford A | Loss | 120 - 265 | 6 | 0 | 10.00 | Georgia Tech |
| 2016 | 3 (Prelims) | Yale B | Duke | Loss | 165 - 230 | 8 | 1 | 11.25 | MIT B + Stanford B |
| 2016 | 4 (Prelims) | Yale B | Dartmouth A | Loss | 70 - 330 | 4 | 0 | 7.50 | Stanford A + Louisville |
| 2016 | 5 (Prelims) | Yale B | Princeton | Loss | 185 - 225 | 9 | 1 | 11.11 | Maryland A + McGill |
| 2016 | 6 (Prelims) | Yale B | MIT A | Loss | 145 - 335 | 6 | 1 | 15.00 | WUSTL + Virginia |
| 2016 | 7 (Prelims) | Yale B | Chicago A | Loss | 25 - 565 | 2 | 1 | 5.00 | Dartmouth + Chicago B |
| 2016 | 8 (Prelims) | Yale B | McMaster A | Win | 145 - 80 | 8 | 3 | 10.00 | Columbia A + Columbia B |
| 2016 | 9 (Prelims) | Yale B | Stanford B | Win | 215 - 170 | 9 | 1 | 14.44 | MIT A |
| 2016 | 10 (Prelims) | Yale B | Berkeley B | Win | 235 - 125 | 10 | 1 | 14.00 | Michigan A |
| 2016 | 11 (Prelims) | Yale B | McGill | Loss | 120 - 145 | 6 | 0 | 10.00 | Berkeley A |
| 2016 | 12 (Tiebreaker) | Yale B | Princeton | Loss | 80 - 90 | 3 | 0 | 16.67 | Chicago A |
| 2016 | 13 (Playoffs) | Yale B | William and Mary | Loss | 95 - 215 | 5 | 1 | 10.00 | Editors 1 |
| 2016 | 14 (Playoffs) | Yale B | Michigan B | Loss | 100 - 155 | 6 | 0 | 6.67 | Editors 2 |
| 2016 | 15 (Playoffs) | Yale B | Michigan State | Loss | 135 - 145 | 7 | 1 | 10.00 | VCU + Virginia + Oxford |
| 2016 | 16 (Playoffs) | Yale B | Chicago D | Win | 260 - 100 | 12 | 0 | 11.67 | Editors 3 |
| 2016 | 17 (Playoffs) | Yale B | Penn A | Win | 150 - 75 | 9 | 2 | 7.78 | Editors 4 |
| 2016 | 18 (Playoffs) | Yale B | VCU A | Win | 200 - 105 | 8 | 0 | 15.00 | Editors 5 |
| Year | Team | Player | Games | Tossups | Negs | Pts | PPG |
|---|---|---|---|---|---|---|---|
| 2016 | Yale B | Adam Fine | 16.5 | 83 | 7 | 795 | 48.18 |
| 2016 | Yale B | Laurence Li | 16.5 | 35 | 6 | 320 | 19.39 |
| Year | Round | Team | Opponent | Result | Score | 10 | -5 | PPB | Packet |
|---|---|---|---|---|---|---|---|---|---|
| 2017 | 1 (Prelims) | Yale B | Penn A | Loss | 150 - 260 | 6 | 0 | 15.00 | Berkeley A + Tennessee |
| 2017 | 4 (Prelims) | Yale B | Maryland A | Loss | 100 - 425 | 5 | 0 | 10.00 | Ohio St + Cambridge + Oxford B |
| 2017 | 5 (Prelims) | Yale B | Carleton | Loss | 95 - 205 | 4 | 1 | 15.00 | Stanford + Harvard |
| 2017 | 6 (Prelims) | Yale B | Central Florida | Win | 170 - 120 | 8 | 0 | 11.25 | WUSTL + UVA + Toronto + Penn |
| 2017 | 7 (Prelims) | Yale B | Oxford B | Loss | 110 - 275 | 6 | 0 | 8.33 | Yale A + Duke |
| 2017 | 8 (Prelims) | Yale B | Chicago B | Win | 205 - 165 | 10 | 1 | 11.00 | Maryland |
| 2017 | 10 (Playoffs) | Yale B | MIT A | Loss | 80 - 260 | 4 | 2 | 12.50 | Editors 1 |
| 2017 | 11 (Playoffs) | Yale B | Cambridge | Loss | 70 - 295 | 4 | 2 | 10.00 | Editors 2 |
| 2017 | 12 (Playoffs) | Yale B | Harvard A | Loss | 75 - 220 | 5 | 3 | 8.00 | Editors 3 |
| 2017 | 13 (Playoffs) | Yale B | Virginia A | Win | 180 - 150 | 7 | 0 | 15.71 | Editors 4 |
| 2017 | 14 (Playoffs) | Yale B | NYU | Win | 175 - 95 | 9 | 1 | 10.00 | Editors 5 |
| 2017 | 15 (Playoffs) | Yale B | Tennessee | Loss | 95 - 230 | 7 | 3 | 5.71 | Editors 6 |
| 2017 | 16 (Playoffs) | Yale B | Johns Hopkins | Loss | 85 - 190 | 5 | 1 | 8.00 | Editors 7 |
| 2017 | 17 (Playoffs) | Yale B | Florida A | Win | 140 - 50 | 9 | 0 | 5.56 | Editors 8 |
| Year | Team | Player | Games | Tossups | Negs | Pts | PPG |
|---|---|---|---|---|---|---|---|
| 2017 | Yale B | Laurence Li | 14 | 50 | 7 | 465 | 33.21 |
| 2017 | Yale B | James Wedgwood | 14 | 23 | 5 | 205 | 14.64 |
| 2017 | Yale B | Anand Nanduri | 14 | 16 | 2 | 150 | 10.71 |
| Year | Round | Team | Opponent | Result | Score | 10 | -5 | PPB | Packet |
|---|---|---|---|---|---|---|---|---|---|
| 2019 | 1 (Prelims) | Yale B | Virginia A | Loss | 100 - 245 | 5 | 2 | 12.00 | Yale A + Michigan |
| 2019 | 2 (Prelims) | Yale B | Florida B | Win | 245 - 40 | 12 | 1 | 10.83 | UVA + Rutgers + Chicago B |
| 2019 | 3 (Prelims) | Yale B | NYU | Loss | 150 - 200 | 8 | 4 | 11.25 | McGill + UCLA + Oxford |
| 2019 | 5 (Prelims) | Yale B | Chicago C | Win | 195 - 150 | 10 | 1 | 10.00 | Chicago A + Penn St |
| 2019 | 6 (Prelims) | Yale B | Texas | Win | 225 - 160 | 11 | 1 | 10.91 | Minn A + Harvard A + B |
| 2019 | 7 (Prelims) | Yale B | Stanford A | Loss | 25 - 350 | 2 | 1 | 5.00 | Florida A + Michigan St |
| 2019 | 8 (Prelims) | Yale B | Ohio State | Loss | 30 - 365 | 2 | 4 | 15.00 | Illinois A + Wright St |
| 2019 | 10 (Playoffs) | Yale B | Michigan A | Loss | 140 - 145 | 7 | 0 | 10.00 | Editors 1 |
| 2019 | 11 (Playoffs) | Yale B | Maryland B | Loss | 130 - 190 | 7 | 0 | 8.57 | Editors 2 |
| 2019 | 12 (Playoffs) | Yale B | MIT A | Loss | 150 - 185 | 7 | 2 | 12.86 | Editors 3 |
| 2019 | 13 (Playoffs) | Yale B | UCLA | Win | 295 - 90 | 13 | 3 | 13.85 | Editors 4 |
| 2019 | 15 (Superplayoffs) | Yale B | Gettysburg | Loss | 160 - 195 | 7 | 0 | 12.86 | Editors 6 |
| 2019 | 16 (Superplayoffs) | Yale B | Penn State | Win | 170 - 80 | 8 | 4 | 13.75 | Editors 7 |
| 2019 | 17 (Superplayoffs) | Yale B | Rutgers | Loss | 130 - 255 | 7 | 2 | 10.00 | Editors 8 |
| Year | Team | Player | Games | Tossups | Negs | Pts | PPG |
|---|---|---|---|---|---|---|---|
| 2019 | Yale B | Hasna Karim | 14 | 37 | 10 | 320 | 22.86 |
| 2019 | Yale B | Sophie Lai | 14 | 29 | 3 | 275 | 19.64 |
| 2019 | Yale B | Stefan DSa | 14 | 24 | 10 | 190 | 13.57 |
| 2019 | Yale B | Varun Sikand | 14 | 16 | 2 | 150 | 10.71 |


Eugene Istrati has spent almost two decades as a technologist in a variety of industries, including digital media, cloud computing, and reputation marketing. He is a visionary and goal-oriented technology leader, with demonstrated experience in planning, developing and implementing cutting edge information solutions to address business opportunities.
Before co-founding Mitoc Group, Eugene worked as Director of Engineering at Hearst Corporation. He helped iconic publisher to redefine and transform their business in the digital world. Prior, Mr. Istrati worked as Program Manager at Amazon Web Services, helping businesses and developers accelerate their cloud computing journeys on AWS cloud.
Eugene blogs occasionally and speaks at industry conferences, technology meetups and other similar events. He has presented at ServerlessConf 2017 and AWS re:Invent 2017 on his favorite topic – serverless computing.
He graduated from University of Oradea, Romania with a dual Bachelor of Science degree in Mathematics and Computer Science and a Master of Science degree in Computer Science and Engineering. His masters degree was later assessed and validated by University of Columbia, New York.
Vladimir Ursu has started the entrepreneurial life early in high school working in industries like Finance, Digital Media, Cloud Computing. He is visionary goal oriented with 10+ years experience in entrepreneurship, planning, crisis management, quality assurance, finance, human resources.
Vladimir is passionate resolving project challenges and creating the process to meet the deadlines. Also, Vladimir is passioned in helping people/teams upgrade their professional level and keep them motivated using couching technics. In future trying himself in public speaking.
Before co-founding Mitoc Group, Vladimir founded several companies from which is PyCoding and 404 Moldova. Worked as Quality Assurance Team Lead, Operations Manager, Project Manager for respectable companies like Thomson Reuters and TD Ameritrade. He had helped to deliver several challenging projects in finance.
He graduated State University from Moldova with Bachelor of Science degree in Economics - Banks and Stock Exchange.
Igor Vignan is a Certified Project Manager (PMP) and Certified Scrum Master (PSM I) with strong technical background, advanced knowledge in Cloud Computing and Amazon Web Services. He is result oriented with over 8+ years of experience in IT, Project Management, Software Development, Quality Assurance, Risk & Resource Management.
Igor has passion for new technologies and technical aspects of software development. His main focus is providing value to the customer, creating a good teamwork environment and getting things done.
Before joining Mitoc Group, Igor worked as Senior Quality Assurance Engineer and Project Manager for a variety of respectable companies like Thomson Reuters and TD Ameritrade. He helped managing Quality Assurance efforts to deliver most innovative and complex financial software products to end users.
He graduated from Technical University of Moldova with a Bachelor of Science degree in Computer Systems Engineering and a Master of Science degree in Information Technology Engineering. His Bachelor and Masters degrees were later assessed and validated by World Education Services (WES) - Canada.


Diglin\Swisspost\StructType\Recipient::__construct
Constructor method for Recipient
Signature
public function __construct([string
$zIP = NULL,
[string
$postIdent = NULL,
[string
$title = NULL,
[bool
$personallyAddressed = NULL,
[string
$name1 = NULL,
[string
$firstName = NULL,
[string
$name2 = NULL,
[string
$name3 = NULL,
[string
$addressSuffix = NULL,
[string
$street = NULL,
[string
$houseNo = NULL,
[string
$pOBox = NULL,
[string
$floorNo = NULL,
[int
$mailboxNo = NULL,
[string
$city = NULL,
[string
$country = NULL,
[int
$hauskey = NULL,
[string
$phone = NULL,
[string
$mobile = NULL,
[string
$eMail = NULL,
[LabelAddress
$labelAddress = NULL]]]]]]]]]]]]]]]]]]]]] )
Parameters
$zIP
— string$postIdent
— string$title
— string$personallyAddressed
— object
$name1
— string$firstName
— string$name2
— string$name3
— string$addressSuffix
— string$street
— string$houseNo
— string$pOBox
— string$floorNo
— string$mailboxNo
— int$city
— string$country
— string$hauskey
— int$phone
— string$mobile
— string$eMail
— string$labelAddress
— \Diglin\Swisspost\StructType\LabelAddress
Violations
PHPMessDetector
| Line | Rule | Message |
|---|---|---|
| 260 - 284 | ExcessiveParameterList | The method __construct has 21 parameters. Consider reducing the number of parameters to less than 10. |


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves
Dietary
For the rösti, place the grated potato, free-range egg, salt and freshly ground black pepper, flour and fresh parsley into a bowl and mix together.
Heat the oil in a small frying pan and place a lightly greased chefs' ring on top.
Fill the chefs' ring with the potato mixture and flatten the top.
Cook the rösti mix for four minutes, turn over and slide the chefs' ring off the rösti. Cook for another four minutes or until golden-brown on both sides and cooked through.
Remove the rösti from the pan and drain on kitchen paper.
Heat a little more oil in the pan, add the mushrooms, garlic and chilli flakes and cook for three minutes. Season with salt and freshly ground black pepper and then set aside.
For the pesto, place the fresh watercress, extra virgin olive oil, garlic, salt and freshly ground black pepper into a clean food processor and blend until smooth.
To serve, place the rösti onto a clean plate, place the mushrooms on top of the rösti. Then pour the pesto round the side of the plate. Garnish with fresh chives.


Hello all,
I have a 2 or 3 week old emonTx v3.4, which was working fine with the default discrete sampling sketch.
I have uploaded the latest 3 phase sketch in git (4th May), it apparently runs well and sends out the data over the serial line, however, I can no longer receive anything via jeelink+emonhub (not a single byte).
I have checked all the configurations for RFM, and cannot find anything wrong.
I am using RFM69CW, 433Mhz, Group 210.
Any ideas what could be wrong, or what to look for?
Thanks
Re: emonTxV3_4_3Phase_Voltage does not communicate over rfm69cw
Experiment with changing the frequency. According to the RFM69CW data sheet, the frequency setting (Frf) should be 0xC64000, whereas if I remember correctly, I worked out that JeeLib set it to 0xC68000
So try changing line 619 to:
writeReg(0x08, 0x80); // RegFrfMid
I have only 868 MHz modules. I tested over a range of only about 100 mm, so it worked on 433 MHz at that range. It doesn't work over about 3 m with 0xC64000, whereas it does most of the time with 0xC68000.
Re: emonTxV3_4_3Phase_Voltage does not communicate over rfm69cw
Thanks, this was exactly the reason!
Re: emonTxV3_4_3Phase_Voltage does not communicate over rfm69cw
It appears to want to work at 433.75 MHz, not 433 MHz. I'll get Glyn to change it on GitHub.
Re: emonTxV3_4_3Phase_Voltage does not communicate over rfm69cw
I've checked line 619 to: writeReg(0x08, 0x80); // RegFrfLsb
Please confirm this is correct?
https://github.com/openenergymonitor/emonTxFirmware/blob/master/emonTxV3/RFM/emonTxV3.4/emonTxV3_4_3Phase_Voltage/emonTxV3_4_3Phase_Voltage.ino#L619
Robert: have you got a GitHub account? Can I give you push access so you can dive straight in to update the code. This can easily be done just by clicking edit just like a drupal page: https://help.github.com/articles/editing-files-in-your-repository/
Re: emonTxV3_4_3Phase_Voltage does not communicate over rfm69cw
No (to the GitHub account, and I haven't figured out how to use (i.e. make it useful to me, as distinct from operate) GitHub yet!) - and I need to check the other frequencies too. I shouldn't have believed the comments. I hope to get back to this in a day or two.


Pizza on Da-GO!
Enter the name for delivery:
Submit name and Begin selections
Toppings
Red Sauce
White Sauce
Cheese
Vegan Cheese
Sweet Red Onions
Black Olives
Locally Sourced Organic Chicken
Handmade Ground Black Bean and Walnut Paste
10in. Personal Pie
15in. Medium Pie
20in. Family Pie
Total
Your order total: $


Items in the cart:
{%if request.COOKIES%}
Product Name
Quantity
{%for key,value in request.COOKIES.items%}
{{key}}
{{value}}
{%endfor%}
{%else%}
Cart is empty
{%endif%}


--- layout: post title: "12 tips for PhD students from Philip Guo" date: 2016-09-01 comments: true archive: true notes: true tags: notes excerpt: Tweleve tips for PhD students ---
12 tips for PhD students from Philip Guo:
Tip 1. Start simple
Tip 2. Look at raw data
Tip 3. Be concrete
Tip 4. Incremental results
Tip 5. Find something interesting
Tip 6. Push hard on clean signals
Tips 7. Actionable
Tips 8. Comparative advantage is key
Tip 9. Find solid levers to pull
Tip 10. Document success and failures
Tip 11. Inspect outliers
Tip 12. Eliminate sources of bias


UPDATE DRUG STATUS
AVAILABLE
WITHDRAWN
Updated drug status with transaction Id {{transactionId}}
Failed to update data


"American Beauty" - More than Just Skin Deep by Homer Yen (c) 1999
`American Beauty' offers us a glimpse into what really fuels suburban families, like that of Lester Burnham (Kevin Spacey). In doing so, the film tears apart the suburban myth of well-trimmed rose-gardens and automatic garage door openers. Sure, Lester may have a nice house, a family, and a relatively secure future, but his life isn't much more exciting than that of a mannequin. And though he projects a constant smile, his life is just an endless spiral of boredom and the highlight of his day is his morning shower. What has made his life so bereft of passion? Perhaps it's his prissy wife, Carolyn (Annette Bening). She also smiles a lot and tries to excel as a real estate agent. But her life is equally vacuous and her soul is joyless.  Daughter Jane (Thora Birch) doesn't add any sanity to this family either. Even Lester admits that what neighbors see is `a commercial for how normal we are when we're anything but.'
 This film reveals some very surprising things that you would never think would exist. What gets Lester's juices flowing again is Angela (Mena Suvari), a friend of Jane's that he begins fantasizing about. Angela, who confidently boasts about her adult experiences, doesn't mind the attention and says that if Lester had more muscles, she would be interested. DING! Lester breaks out the weights and starts a running regimen. But this is just the beginning of the new Lester. He quits his job and blackmails his boss at the same time, he begins smoking powerful marijuana, and he buys a classic muscle car. But why should Lester have all the fun? His radical actions prompt Carolyn to begin an affair with her chief competitor while Jane begins to take an interest in a mysterious boy next door.
The wheels are set in motion and the film wonderfully strips any conventions that we have of suburbia and gives us some very strange yet sad characters with whom we can empathize. For Lester, it's time to let loose. He no longer cares about anything and while chaos can destroy the sanctity of his home, it is the only thing he appreciates now in his life. Consequently, the situations that he winds up in are often funny, poignant, and amusing. A counter-story to Lester's life features a militaristic, homophobic retired marine living next door. He preaches discipline to his son. But like the Burnham's nothing on the outside is as it really seems. The real interesting point that this film makes features another of Lester's neighbors, Jim and Jim (Scott Bakula and Sam Robards), who are gay. Despite their strange outward appearance, their lives were the most normal of all.
The beauty of `American Beauty' is it's harsh revelations and brutally frank situations. This is not a film that strives to entertain as a comedy. Rather it is a film that tries to scrape away at the true nature of people like Lester Burnham. As Lester, Kevin Spacey is comfortable and almost too real as the dispirited suburbanite whose last chance at living is fading fast. Annette Bening also shows might in her portrayal of the brittle housewife. However all of the wonderfully developed characters and their complexities will beg you to ask yourself what may really be going on in your own cul-de-sac world.
Grade: B
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Overview | Reports | Quarter | Contract
| Vendor Name: | WESA, A DIVISION OF BLUMETRIC ENVIRONMENTAL INC |
| Reference number: | K4A20-13-7224 |
| Contract Date: | 2014-03-27 |
| Description of work: | 0423 Engineering consultants-other (specify) |
| Contract period: | 2014-02-19 to 2014-03-31 |
| Delivery Date: | |
| Contract Value: | $22,120.00 |
| Comments: | Provide services to condust a compliance assessment for the Corporate Services Branch |


The aws s3 transfer commands, which include the cp, sync, mv, and rm commands, have additional configuration values you can use to control S3 transfers. This topic guide discusses these parameters as well as best practices and guidelines for setting these values.
Before discussing the specifics of these values, note that these values are entirely optional. You should be able to use the aws s3 transfer commands without having to configure any of these values. These configuration values are provided in the case where you need to modify one of these values, either for performance reasons or to account for the specific environment where these aws s3 commands are being run.
These are the configuration values you can set specifically for the aws s3 command set:
These are the configuration values that can be set for both aws s3 and aws s3api:
These values must be set under the top level s3 key in the AWS Config File, which has a default location of ~/.aws/config. Below is an example configuration:
[profile development]
aws_access_key_id=foo
aws_secret_access_key=bar
s3 =
max_concurrent_requests = 20
max_queue_size = 10000
multipart_threshold = 64MB
multipart_chunksize = 16MB
use_accelerate_endpoint = true
addressing_style = path
Note that all the S3 configuration values are indented and nested under the top level s3 key.
You can also set these values programatically using the aws configure set command. For example, to set the above values for the default profile, you could instead run these commands:
$ aws configure set default.s3.max_concurrent_requests 20
$ aws configure set default.s3.max_queue_size 10000
$ aws configure set default.s3.multipart_threshold 64MB
$ aws configure set default.s3.multipart_chunksize 16MB
$ aws configure set default.s3.use_accelerate_endpoint true
$ aws configure set default.s3.addressing_style path
Default - 10
The aws s3 transfer commands are multithreaded. At any given time, multiple requests to Amazon S3 are in flight. For example, if you are uploading a directory via aws s3 cp localdir s3://bucket/ --recursive, the AWS CLI could be uploading the local files localdir/file1, localdir/file2, and localdir/file3 in parallel. The max_concurrent_requests specifies the maximum number of transfer commands that are allowed at any given time.
You may need to change this value for a few reasons:
Default - 1000
The AWS CLI internally uses a producer consumer model, where we queue up S3 tasks that are then executed by consumers, which in this case utilize a bound thread pool, controlled by max_concurrent_requests. A task generally maps to a single S3 operation. For example, as task could be a PutObjectTask, or a GetObjectTask, or an UploadPartTask. The enqueuing rate can be much faster than the rate at which consumers are executing tasks. To avoid unbounded growth, the task queue size is capped to a specific size. This configuration value changes the value of that maximum number.
You generally will not need to change this value. This value also corresponds to the number of tasks we are aware of that need to be executed. This means that by default we can only see 1000 tasks ahead. Until the S3 command knows the total number of tasks executed, the progress line will show a total of .... Increasing this value means that we will be able to more quickly know the total number of tasks needed, assuming that the enqueuing rate is quicker than the rate of task consumption. The tradeoff is that a larger max queue size will require more memory.
Default - 8MB
When uploading, downloading, or copying a file, the S3 commands will switch to multipart operations if the file reaches a given size threshold. The multipart_threshold controls this value. You can specify this value in one of two ways:
Default - 8MB
Minimum For Uploads - 5MB
Once the S3 commands have decided to use multipart operations, the file is divided into chunks. This configuration option specifies what the chunk size (also referred to as the part size) should be. This value can specified using the same semantics as multipart_threshold, that is either as the number of bytes as an integer, or using a size suffix.
If set to true, will direct all Amazon S3 requests to the S3 Accelerate endpoint: s3-accelerate.amazonaws.com. To use this endpoint, your bucket must be enabled to use S3 Accelerate. All request will be sent using the virtual style of bucket addressing: my-bucket.s3-accelerate.amazonaws.com. Any ListBuckets, CreateBucket, and DeleteBucket requests will not be sent to the Accelerate endpoint as the endpoint does not support those operations. This behavior can also be set if --endpoint-url parameter is set to https://s3-accelerate.amazonaws.com or http://s3-accelerate.amazonaws.com for any s3 or s3api command.
There's two styles of constructing an S3 endpoint. The first is with the bucket included as part of the hostname. This corresponds to the addressing style of virtual. The second is with the bucket included as part of the path of the URI, corresponding to the addressing style of path. The default value in the CLI is to use auto, which will attempt to use virtual where possible, but will fall back to path style if necessary. For example, if your bucket name is not DNS compatible, the bucket name cannot be part of the hostname and must be in the path. With auto, the CLI will detect this condition and automatically switch to path style for you. If you set the addressing style to path, you must ensure that the AWS region you configured in the AWS CLI matches the same region of your bucket.


class: center, middle, inverse, title-slide # Prediction + Model validation --- class: center, middle # Model selection --- ## Data: Course evals ```r library(tidyverse) library(broom) library(modelr) # new! ``` ```r # Load data evals <- read_csv("../../data/evals-mod.csv") # Calculate bty_avg evals <- evals %>% rowwise() %>% mutate(bty_avg = mean(c(bty_f1lower, bty_f1upper, bty_f2upper, bty_m1lower, bty_m1upper, bty_m2upper))) %>% ungroup() ``` --- ## Full model .question[ What percent of the variability in evaluation scores is explained by the model? ] ```r full_model <- lm(score ~ rank + ethnicity + gender + language + age + cls_perc_eval + cls_did_eval + cls_students + cls_level + cls_profs + cls_credits + bty_avg, data = evals) glance(full_model)$r.squared ``` ``` ## [1] 0.1644867 ``` ```r glance(full_model)$adj.r.squared ``` ``` ## [1] 0.1402959 ``` --- ## Akaike Information Criterion $$ AIC = -2log(L) + 2k $$ - `\(L\)`: likelihood of the model - Likelihood of seeing these data given the estimated model parameters - Won't go into calculating it in this course - Used for model selection, lower the better - Value is not informative on its own - Applies a penalty for number of parameters in the model, `\(k\)` - Different penalty than adjusted `\(R^2\)` but similar idea - In R to get the AIC, use `AIC(model)` ```r glance(full_model)$AIC ``` ``` ## [1] 695.7457 ``` --- ## Model selection -- a little faster ```r selected_model <- step(full_model, direction = "backward") ``` ```r tidy(selected_model) %>% select(term, estimate) ``` ``` ## # A tibble: 8 x 2 ## term estimate ## <chr> <dbl> ## 1 (Intercept) 3.45 ## 2 ethnicitynot minority 0.205 ## 3 gendermale 0.185 ## 4 languagenon-english -0.161 ## 5 age -0.00501 ## 6 cls_perc_eval 0.00509 ## 7 cls_creditsone credit 0.515 ## 8 bty_avg 0.0650 ``` --- ## Selected variables | variable | selected | | ------------ | :----------:| | rank | | | ethnicity | x | | gender | x | | language | x | | age | x | | cls_perc_eval| x | | cls_did_eval | | | cls_students | | | cls_level | | | cls_profs | | | cls_credits | x | | bty_avg | x | --- ## Coefficient interpretation .question[ Interpret the slopes of `gender` and `bty_avg` in context of the data. ] ```r tidy(selected_model) %>% select(term, estimate) %>% mutate(estimate = round(estimate, 3)) ``` ``` ## # A tibble: 8 x 2 ## term estimate ## <chr> <dbl> ## 1 (Intercept) 3.45 ## 2 ethnicitynot minority 0.205 ## 3 gendermale 0.185 ## 4 languagenon-english -0.161 ## 5 age -0.005 ## 6 cls_perc_eval 0.005 ## 7 cls_creditsone credit 0.515 ## 8 bty_avg 0.065 ``` --- ## AIC ```r glance(full_model)$AIC ``` ``` ## [1] 695.7457 ``` ```r glance(selected_model)$AIC ``` ``` ## [1] 687.5712 ``` --- ## Parsimony
 Take a look at the variables in the full and the selected model. Can you guess why some of them may have been dropped? Remember: We like parsimonous models.
 .small[ | variable | selected | | ------------ | :----------:| | rank | | | ethnicity | x | | gender | x | | language | x | | age | x | | cls_perc_eval| x | | cls_did_eval | | | cls_students | | | cls_level | | | cls_profs | | | cls_credits | x | | bty_avg | x | ] --- class: center, middle # Model validation --- ## Overfitting - The data we are using to construct our models come from a larger population. - Ultimately we want our model to tell us how the population works, not just the sample we have. - If the model we fit is too tailored to our sample, it might not perform as well with the remaining population. This means the model is "overfitting" our data. - We measure this using **model validation** techniques. - Note: Overfitting is not a huge concern with linear models with low level interactions, however it can be with more complex and flexible models. The following is just an example of model validation, even though we're using it in a scenario where the concern for overfitting is not high. --- class: center, middle # Prediction --- ## New observation To make a prediction for a new observation we need to create a data frame with that observation.
 Suppose we want to make a prediction for a 35 year old white woman professor who received her education at an English speaking country and who teaches a multi credit course. 80% of her classes tend to fill out evaluations, and she's average looiking (beauty score = 2.5).

 The following won't work. Why? How would you correct it?
 ```r new_prof <- data_frame(ethnicity = "white", sex = "woman", language = "English", age = 35, cls_perc_eval = 0.80, cls_credits = "multi-credit", bty_avg = 2.5) ``` ``` ## Warning: `data_frame()` is deprecated as of tibble 1.1.0. ## Please use `tibble()` instead. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_warnings()` to see where this warning was generated. ``` --- ## New observation, corrected ```r new_prof <- data_frame(ethnicity = "not minority", gender = "female", language = "english", age = 35, cls_perc_eval = 0.80, cls_credits = "multi credit", bty_avg = 2.5) ``` --- ## Prediction ```r predict(selected_model, newdata = new_prof) ``` ``` ## 1 ## 3.642981 ``` --- ## Uncertainty around prediction - Confidence interval around `\(\bar{y}\)` for new data (average score for profs with given characteristics): ```r predict(selected_model, newdata = new_prof, interval = "confidence") ``` ``` ## fit lwr upr ## 1 3.642981 3.406178 3.879785 ``` - Prediction interval around `\(\hat{y}\)` for new data (average score for profs with given characteristics): ```r predict(selected_model, newdata = new_prof, interval = "prediction") ``` ``` ## fit lwr upr ## 1 3.642981 2.626502 4.659461 ```


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Just to get a rough idea of what it might be worth to pay for Google, I made a little DCF valuation to see where it comes out at. I am actually very similar to Bambi’s valuation (what a cool name! :)) which I posted about earlier when I plug in the same number, which makes me rather happy as I it tells me that I am not doing anything terribly wrong. Picking the right numbers from a balance sheet and income statement can be a little bit tricky. So let’s see…
<!– more –>
We first need to take a look at the next few years of cash flows.  This actually makes us even more valuable, so ok.
The first thing I will have to say that tables are really bad to do in HTML and I am glad for easy suggestions. I know I could do wonderful CSS stuff arround all of this and I know the PRE tag, something else please. Easy.
Anyway. I looked at the next 10 years. Growth in sales will be 80%, 70%, 70%, 40%, 35%, 30%, 20%, 20%, 15%, 15% and then we will grow by 10% into perpetuity. This in itself is very very ambitious. It will also bring us to a sales level of $38 billion by 2015. Not bad really. In 2010 we are arround $20 billion. The problem is that this doesn’t tell us much because we have no real clue where this advertising market will move, if Google is even keeping with that market which I doubt anyway.
NOP/Sales will be at 35% for the next 5 years and then at 30%. This again, is fairly ambitious, especially in the advertising market and where they will have to rely more and more on their contextual advertising network and outside partners where they will have to pay out large percentages of their revenue. So I actually doubt that. But ok, let’s be optimistic.
NOA/Sales is at 30%, making NOA grow to $11 billion by 2015. Operating Cash to sales is at 25%, again fairly high of a number. But this gives us operating cash of almost $10 billion by 2015. Sales /NPPE is at 6 and Depreciation to NPP&E at 25%, for the lack of a better number. Taxes are at 30%, but again, there are mostly provisions available in their income statements which are at 50%, so I guessed the 30%.
At 10% growth after 10 years, the residual value is at $86 Billion.
We then take NOP, add back depreciation to get the EBITDA. Take out taxes, and expenditure on PP&E as well as the increase in NOA and the increase in operating cash. This brings us to the free cash flow which comes in at some interesting numbers, but anyway.
On we go to the WACC, which is hard to talk about. There is no debt as far as I know. Now what kind of return do we want from this? 15%? Sounds like a good number, let’s take that.

COMPANY VALUE
Present Value of Free Cash Flow 5.155,24
Present Value of Residual Value 33.255,80
Subtotal: 38.411,04
plus: Investments 0,00
Enterprise value 38.411,04
less:Total Debt 0,00
less: Minority interests 0,00
less: Other Equity 0,00
Shareholder value 38411,04
Number of shares (Millions) 400,00
Value per share 96,03

The share number is a guess there. But as with all those assumptions, things get tricky. Let’s presume that we have a higher discount rate of 20%. I mean we actually have no clue what we are doing here and as they said, there is actually no voting right so we can’t even do anything against not knowing ;)

COMPANY VALUE
Present Value of Free Cash Flow 3.613,08
Present Value of Residual Value 10.864,34
Subtotal: 14.477,42
plus: Investments 0,00
Enterprise value 14.477,42
less:Total Debt 0,00
less: Minority interests 0,00
less: Other Equity 0,00
Shareholder value 14477,42
Number of shares (Millions) 400,00
Value per share 36,19

This is just to show how volatile this can be.
Now change the growth rate after 10 years from 10 to 5%.

COMPANY VALUE
Present Value of Free Cash Flow 3.613,08
Present Value of Residual Value 6.913,67
Subtotal: 10.526,75
plus: Investments 0,00
Enterprise value 10.526,75
less:Total Debt 0,00
less: Minority interests 0,00
less: Other Equity 0,00
Shareholder value 10526,75
Number of shares (Millions) 400,00
Value per share 26,32

Now let’s change the growth rates in the next 10 years. I now went with a more moderate 50, 45, 40, 40, 35, 30, 25, 20, 15, 10%.

COMPANY VALUE
Present Value of Free Cash Flow 2.928,98
Present Value of Residual Value 5.388,23
Subtotal: 8.317,22
plus: Investments 0,00
Enterprise value 8.317,22
less:Total Debt 0,00
less: Minority interests 0,00
less: Other Equity 0,00
Shareholder value 8317,22
Number of shares (Millions) 400,00
Value per share 20,79

So there we go. A little more modest. A little more secure. With a hefty discount rate and little growth after 10 years, then if they have 400 million shares in total at time of issue, then $20 might be a good price. At least then, there is some good security that you make a killing, because you actually have no clue. A venture capitalist has more clue and they’d probably use a 40% discount rate.
In general this just goes to show how much will need to happen for a valuation of just $20 billion to be supportable. I will have to sit back and take a look how many shares they will issue and at what price. We’ll see.


{% extends "header.html" %} {% block body %}
import matplotlib import matplotlib.pyplot as plt import csv from mpl_toolkits.mplot3d import Axes3D fig = plt.figure() ax = fig.add_subplot(111,projection='3d') def graph(): with open('monteCarloLiberal.csv','r') as montecarlo: datas = csv.reader(montecarlo, delimiter=',') for eachLine in datas: percentROI = float(eachLine[0]) wagerSizePercent = float(eachLine[1]) wagerCount = float(eachLine[2]) pcolor = eachLine[3] ax.scatter(wagerSizePercent,wagerCount,percentROI,color=pcolor) ax.set_xlabel('wager percent size') ax.set_ylabel('wager count') ax.set_zlabel('Percent ROI') plt.show() graph()
So that's the monte carlo simulator in python. From here, you can use this for all sorts of things. At the end, we searched for variables that gave a result of something greater than something, or less than something. You can also search for vars that give you a range. An example of this is when monte carlo simulations are used to calculate pi. This is done by having the answer, and generating random samples, and selecting the ones that are within a range of the the known answer. From here, we can assume it is close to pi, which it usually is.
We made our simulator, and tested it against a few betting types, trying to find the best strategy, as well as strategy outcomes. Our findings seem plenty obvious to me, however, and that is that there's no way to beat odds to any large degree.
The d'Alembert strategy does do well in terms of steady reliable profit over time, if your wager size is small enough in comparison to your full funds. This means a wager size 1 one millionth of your total funds. In this case, you stand to gain a fraction of a percent of gain on a long term, which will take a very long time to play out, and the earnings would be significant compared to your starting amount.
In the end though, a wager size this small, and the time required to make a profit means you really are just wasting your time with the strategy.
Most people gamble for the excitement either knowingly or unknowingly. A lot of people gamble with the intention to make money. I imagine a few people watched this series and actually heard of the monte carlo strategy because of its relationship with odds and gambling, hoping to learn a new skill to make money. Maybe I'll have saved you some money, but that's the best I have besides teaching you some programming skills.
In the end, gambling should just be for the fun.
The most fascinating part of all of this to me is the degree randomness plays, especially when concerning outliers. The far outliers are heavily influenced by pure randomness in these examples. In today's world, the outliers are often looked towards with either admiration or discontent, depending on which direction they outlie, usually with very little appreciation towards the randomness that played a roll in their path.
I hope you all enjoyed the series, I had fun making it. Like with quite a few of my series, I just made this as I went, so there could very possibly be errors in the code. I encourage you to read it over, test, and develop your own code entirely.
Since we were testing betting strategies in this series, I have since appended another video, covering the Labouchere strategy.


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ContentServer service methods and parameters
Database
| METHOD clear |
| Description | Clears the whole content information database. |
| Parameters | in | sessionId | Session identifier |
| METHOD reload |
| Description | Reloads the content information into the memory (when using cache implementation). |
| Parameters | in | sessionId | Session identifier |
Data servers
| METHOD addDataServerInfo |
| Description | Adds a new ServerInfo record into the content database.
The DataServer calls this method when it wants to register itself during the start-up. |
| Parameters | in | sessionId | Session identifier |
| inout | serverInfo | ServerInfo record (in CSV). |
| METHOD deleteDataServerInfoById |
| Description | Deletes the ServerInfo record from the content information database.
The DataServer usually calls this method when it is shutting down. All the content registrations for the current DataServer will be removed. |
| Parameters | in | sessionId | Session identifier |
| in | serverId | Server identifier. |
| METHOD getDataServerInfoById |
| Description | Returns the ServerInfo record according to the DataServer identifier. |
| Parameters | in | sessionId | Session identifier |
| in | serverId | Server identifier. |
| out | serverInfo | ServerInfo record (in CSV). |
| METHOD getDataServerInfoByName |
| Description | Returns the ServerInfo record according to the DataServer name. |
| Parameters | in | sessionId | Session identifier |
| in | serverName | Server name. |
| out | serverInfo | ServerInfo record (in CSV). |
| METHOD getDataServerInfoByIor |
| Description | Returns the ServerInfo record according to the DataServer IOR. |
| Parameters | in | sessionId | Session identifier |
| in | serverIor | Server IOR. |
| out | serverInfo | ServerInfo record (in CSV). |
| METHOD getDataServerInfoList |
| Description | Returns all the ServerInfo records from the content information database. |
| Parameters | in | sessionId | Session identifier |
| out | producerInfo[] | ProducerInfo records (in CSV). |
Producers
The purpose of the producer information is to define "ownership" for the information stored into the content information database.
The producer information is stored in ProducerInfo records, which contains the following fields.
| RECORD ProducerInfo |
| Name | Description |
| producerId | Unique producer identifier. This is used as a technical key in other records (like GenerationInfo,FileInfo and ContentInfo). |
| name | Unique producer name, which is usually a short name of the actual producer (for example FMI). |
| title | Official name of the producer (for example, "Finnish Meteorological Institute"). |
| description | More information about the current producer. |
| flags | Additional information flags (32-bits) releated to the current producer. These flags can be used for example for categorizing different producers. |
| METHOD addProducerInfo |
| Description | Adds a new ProducerInfo record into the content database. |
| Parameters | in | sessionId | Session identifier |
| inout | producerInfo | ProducerInfo record (in CSV). |
| METHOD deleteProducerInfoById |
| Description | Deletes the producer information from the content information database.
This method deletes the ProducerInfo record, all GenerationInfo/FileInfo/ContentInfo recods releated to the current producer. |
| Parameters | in | sessionId | Session identifier |
| in | producerId | Producer identifier. |
| METHOD deleteProducerInfoByName |
| Description | Deletes the producer information from the content information database.
This method deletes the ProducerInfo record, all GenerationInfo/FileInfo/ContentInfo recods releated to the current producer. |
| Parameters | in | sessionId | Session identifier |
| in | producerName | Producer name. |
| METHOD getProducerInfoById |
| Description | Returns the ProducerInfo record according to the producer identifier. |
| Parameters | in | sessionId | Session identifier |
| in | producerId | Producer identifier. |
| out | producerInfo | ProducerInfo record (in CSV). |
| METHOD getProducerInfoByName |
| Description | Returns the ProducerInfo record according to the producer name. |
| Parameters | in | sessionId | Session identifier |
| in | producerId | Producer name. |
| out | producerInfo | ProducerInfo record (in CSV). |
| METHOD getProducerInfoList |
| Description | Returns all the ProducerInfo records from the content information database. |
| Parameters | in | sessionId | Session identifier |
| out | producerInfo[] | ProducerInfo records (in CSV). |
Generations
Forecast information is usually generated many times a day, which means that there can be several forecast generations in the same content
information base at the same time. The purpose of the generation information is to separate these generations from each other.
If we want to add a new generation data into the content information base, we have to first create a new GenerationInfo record and after that
we can add file and content information related to the current generation. The point is that each FileInfo and ContentInfo record must have
a valid "generationId" field value, which tells the generation where they belong to.
The generation information is stored in GenerationInfo records, which contains the following fields.
| RECORD GenerationInfo |
| Name | Description |
| generationId | Unique generation identifier. This field is automatically generated when a new GenerationInfo record is added.
The field is used as a technical key in other records (like FileInfo and ContentInfo). |
| producerId | The producer identifier, which links the current generation to a certain producer. |
| name | Generation name.The name should be unique and easy to remember. This makes it easier to access content information
in different generations. The recommendation is that the name is in format "producerName:originTime" (for example "HIRLAM:20170317T000000") |
| description | More information about the current generation. |
| status | The status of the current generation (0 = unknown, 1=valid, 2=incomplete). |
| flags | Additional information flags (32-bits) releated to the current producer. These flags can be used for example for categorizing
different producers. |
| METHOD addGenerationInfo |
| Description | Adds a new GenerationInfo record into the content database.
The method returns a new GenerationInfo record that contains the "generationId" field, which should be used as a technical key of the current record. |
| Parameters | in | sessionId | Session identifier |
| inout | generationInfo | GenerationInfo record (in CSV). |
| METHOD deleteGenerationInfoById |
| Description | Deletes the generation information from
the content information database. This method deletes the GenerationInfo record and FileInfo and ContentInfo recods releated to the current
generation. |
| Parameters | in | sessionId | Session identifier |
| in | generationId | Generation identifier. |
| METHOD deleteGenerationInfoByName |
| Description | Deletes the generation information from
the content information database. This method deletes the GenerationInfo record and FileInfo and ContentInfo recods releated to the current generation. |
| Parameters | in | sessionId | Session identifier |
| in | generationName | Generation name. |
| METHOD deleteGenerationInfoListByProducerId |
| Description | Deletes all the generation information related to the current producer.
This method deletes the all GenerationInfo, FileInfo and ContentInfo recods releated to the current producer. |
| Parameters | in | sessionId | Session identifier |
| in | producerId | Producer identifier. |
| METHOD deleteGenerationInfoListByProducerName |
| Description | Deletes all the generation information related to the current producer.
This method deletes the all GenerationInfo, FileInfo and ContentInfo recods releated to the current producer. |
| Parameters | in | sessionId | Session identifier |
| in | producerName | Producer name. |
| METHOD getGenerationInfoById |
| Description | Returns the GenerationInfo record according to the generation identifier. |
| Parameters | in | sessionId | Session identifier |
| in | generationId | Generation identifier. |
| out | generationInfo | GenerationInfo record (in CSV). |
| METHOD getGenerationInfoByName |
| Description | Returns the GenerationInfo record according to the generation name. |
| Parameters | in | sessionId | Session identifier |
| in | generationId | Generation name. |
| out | generationInfo | GenerationInfo record (in CSV). |
| METHOD getGenerationInfoList |
| Description | Returns all the GenerationInfo records
from the content information database. |
| Parameters | in | sessionId | Session identifier |
| out | generationInfo[] | GenerationInfo records (in CSV). |
| METHOD getGenerationInfoListByProducerId |
| Description | Returns all the GenerationInfo records related
to the given producer. |
| Parameters | in | sessionId | Session identifier |
| in | producerId | Producer identifier. |
| out | generationInfo[] | GenerationInfo records (in CSV). |
| METHOD getGenerationInfoListByProducerName |
| Description | Returns all the GenerationInfo records related to the given producer. |
| Parameters | in | sessionId | Session identifier |
| in | producerName | Producer name. |
| out | generationInfo[] | GenerationInfo records (in CSV). |
| METHOD setGenerationInfoStatusById |
| Description | Sets the status field of the GenerationInfo record. |
| Parameters | in | sessionId | Session identifier |
| in | generationId | Generation identifier. |
| in | status | Status. |
| METHOD setGenerationInfoStatusByName |
| Description | Sets the status field of the GenerationInfo record. |
| Parameters | in | sessionId | Session identifier |
| in | generationName | Generation name. |
| in | status | Status. |
Files
Forecast information is usually stored in several files. So, each generation contain most likely quite many files.
The file information is stored in FileInfo records, which contains the following fields.
| RECORD FileInfo |
| Name | Description |
| fileId | Unique file identifier. This field is automatically generated when a new FileInfo record is added.
The field is used as a technical key in other records (like ContentInfo). |
| producerId | The producer identifier, which links the current file to a certain producer. |
| generationId | The generation identifier, which links the current file to a certain generation. |
| fileType | The type of the forecast file (0 = unknown, 1 = GRIB1, 2 = GRIB2). |
| name | The filename with the directory path. This field must be unique in content information database. |
| groupFlags | A file can belong to 32 different groups, which are indicated by 32-bit group flags. The point is that
these groups gives us an opportunity to divide files into different groups according to some criteria. For example, a file
might belong to group X, which means that file must be kept in the memory all the time in order to guarantee a fast data access. |
| flags | Additional information flags (32-bits) releated to the current file. These flags can be used for example
to tell that the content of the current file is predefined, which means that the DataServers should use these predefinitions
and not to try to find out the content by themselves. |
| METHOD addFileInfo |
| Description | Adds a new FileInfo record
into the content database. The method returns a new FileInfo record that contains the "fileId" field, which should be used as a technical
key of the current record. |
| Parameters | in | sessionId | Session identifier |
| inout | fileInfo | FileInfo record (in CSV). |
| METHOD addFileInfoWithContentList |
| Description | Adds a new FileInfo record and its
predefined content information into the content information database. The method returns a new FileInfo record that contains the "fileId" field,
which should be used as a technical key of the current record. The "flags" field is used to indicate that the content of the current file is
predefined. |
| Parameters | in | sessionId | Session identifier |
| inout | fileInfo | FileInfo record (in CSV). |
| in | contentInfo[] | ContentInfo records (in CSV). |
| METHOD deleteFileInfoById |
| Description | Deletes the file information from the content
information database. This method deletes the FileInfo record and ContentInfo recods releated to the current file. |
| Parameters | in | sessionId | Session identifier |
| in | fileId | File identifier. |
| METHOD deleteFileInfoByName |
| Description | Deletes the file information from the content
information database. This method deletes the FileInfo record and ContentInfo recods releated to the current file. |
| Parameters | in | sessionId | Session identifier |
| in | fileName | File name. |
| METHOD deleteFileInfoListByProducerId |
| Description | Deletes all the file information related
to the current producer. This method deletes the all FileInfo and ContentInfo recods releated to the current producer. |
| Parameters | in | sessionId | Session identifier |
| in | producerId | Producer identifier. |
| METHOD deleteFileInfoListByProducerName |
| Description | Deletes all the file information related
to the current producer. This method deletes the all FileInfo and ContentInfo recods releated to the current producer. |
| Parameters | in | sessionId | Session identifier |
| in | producerName | Producer name. |
| METHOD deleteFileInfoListByGenerationId |
| Description | Deletes all the file information related
to the current generation. This method deletes the all FileInfo and ContentInfo recods releated to the current generation. |
| Parameters | in | sessionId | Session identifier |
| in | producerId | Producer identifier. |
| METHOD deleteFileInfoListByGenerationName |
| Description | Deletes all the file information related
to the current generation. This method deletes the all FileInfo and ContentInfo recods releated to the current generation. |
| Parameters | in | sessionId | Session identifier |
| in | producerName | Producer name. |
| METHOD deleteFileInfoListByGroupFlags |
| Description | Deletes all the file information related
to the given groups. This method deletes the all FileInfo and ContentInfo recods releated to the given groups. |
| Parameters | in | sessionId | Session identifier |
| in | groupFlags | Group flags. |
| METHOD getFileInfoById |
| Description | Returns the FileInfo record according
to the file identifier. |
| Parameters | in | sessionId | Session identifier |
| in | fileId | File identifier. |
| out | fileInfo | FileInfo record (in CSV). |
| METHOD getFileInfoByName |
| Description | Returns the FileInfo record according
to the file name. |
| Parameters | in | sessionId | Session identifier |
| in | fileId | File name. |
| out | fileInfo | FileInfo record (in CSV). |
| METHOD getFileInfoList |
| Description | Returns FileInfo records from the content information database. |
| Parameters | in | sessionId | Session identifier |
| in | startFileId | The records search starts from this file identifier. |
| in | maxRecords | The maximum number of the returned records . |
| out | fileInfo[] | FileInfo records (in CSV). |
| METHOD getFileInfoListByProducerId |
| Description | Returns FileInfo records related to the given producer. |
| Parameters | in | sessionId | Session identifier |
| in | producerId | Producer identifier. |
| in | startFileId | The records search starts from this file identifier. |
| in | maxRecords | The maximum number of the returned records . |
| out | fileInfo[] | FileInfo records (in CSV). |
| METHOD getFileInfoListByProducerName |
| Description | Returns FileInfo records related to the given producer. |
| Parameters | in | sessionId | Session identifier |
| in | producerName | Producer name. |
| in | startFileId | The records search starts from this file identifier. |
| in | maxRecords | The maximum number of the returned records . |
| out | fileInfo[] | FileInfo records (in CSV). |
| METHOD getFileInfoListByGenerationId |
| Description | Returns FileInfo records related to the given generation. |
| Parameters | in | sessionId | Session identifier |
| in | generationId | Generation identifier. |
| in | startFileId | The records search starts from this file identifier. |
| in | maxRecords | The maximum number of the returned records . |
| out | fileInfo[] | FileInfo records (in CSV). |
| METHOD getFileInfoListByGenerationName |
| Description | Returns FileInfo records related to the given generation. |
| Parameters | in | sessionId | Session identifier |
| in | generationName | Generation name. |
| in | startFileId | The records search starts from this file identifier. |
| in | maxRecords | The maximum number of the returned records . |
| out | fileInfo[] | FileInfo records (in CSV). |
| METHOD getFileInfoListByGroupFlags |
| Description | Returns FileInfo records related to the given groups. |
| Parameters | in | sessionId | Session identifier |
| in | groupFlags | Group flags. |
| in | startFileId | The records search starts from this file identifier. |
| in | maxRecords | The maximum number of the returned records . |
| out | fileInfo[] | FileInfo records (in CSV). |
Content
The actual forecast information is stored into GRIB files. Each GRIB files can contain one or several messages, which can have
different content. That's why each message in the file needs to have own ContentInfo record. Technically a ContentInfo record tell
what kind of information (= temperature, pressure, wind, etc.) the current message contains. The point is that when we want to find
some specific information (for example "temperature") during a given time interval then we go through all the ContentInfo records and
search records that match our criteria. After that we know exactly which files and which messages contains the information that we were looking for.
The content information is stored in ContentInfo records, which contains the following fields.
| RECORD FileInfo |
| Name | Description |
| fileId | The file identifier, which links the current content to a certain file. |
| fileType | The type of the forecast file (0 = unknown, 1 = GRIB1, 2 = GRIB2). |
| messageIndex | The index of the message, which links the current content to a certain message in the current file.
Notice that the first message in a file has index 0. |
| producerId | The producer identifier, which links the current content to a certain producer. |
| generationId | The generation identifier, which links the current content to a certain generation. |
| groupFlags | A content file can belong to 32 different groups, which are indicated by 32-bit group flags.
So, these groupFlags should be the same as the groupFlags in the releated FileInfo record. |
| startTime | The start time of the current content. The time format is "YYYYMMDDTHHMMSS". (for example, "20170317T123000") |
| endTime | The end time of the current content. The time format is "YYYYMMDDTHHMMSS". (for example, "20170317T123000") |
| fmiParameterId | The content parameter identifier used by FMI-Radon. |
| fmiParameterName | The content parameter name used by FMI-Radon. |
| gribParameterId | The content parameter identifier used by GRIB. |
| cdmParameterId | The content parameter identifier used by CDM. |
| cdmParameterName | The content parameter name used by CDM. |
| newbaseParameterId | The content parameter identifier used by FMI-Newbase. |
| newbaseParameterName | The content parameter name used by FMI-Newbase. |
| fmiParameterLevelId | The content parameter level identifier used by FMI-Radon. |
| grib1ParameterLevelId | The content parameter level identifier used by GRIB 1. |
| grib2ParameterLevelId | The content parameter level identifier used by GRIB 2. |
| gribParameterLevel | The content parameter level. |
| fmiParameterUnits | The content parameter units used by FMI-Radon. |
| gribParameterUnits | The content parameter units used by GRIB. |
| serverFlags | These flags indicates which DataServers have registered the current content. In other words, the content
is available in these DataServers. The field has 64 bits and each bit indicates wheather a DataServer has registered the
current content. For example, if a DataServer (serverId = 1) has registered the content then the first bit of the serverFlags
field should be "1". |
| flags | Additional information flags (32-bits) releated to the current content. |
| METHOD addContent |
| Description | Adds a new ContentInfo record
into the content database. The current record can be accessed by using its "fileId" and "messageIndex" field i.e. this combination
should be always unique. |
| Parameters | in | sessionId | Session identifier |
| inout | contentInfo | ContentInfo record (in CSV). |
| METHOD addContentList |
| Description | Adds a list of new ContentInfo records
into the content database. |
| Parameters | in | sessionId | Session identifier |
| inout | contentInfo[] | ContentInfo records (in CSV). |
| METHOD deleteContentInfo |
| Description | Deletes the ContentInfo record
according to its "fileId" and "messageIndex" fields. |
| Parameters | in | sessionId | Session identifier |
| in | fileId | File identifier. |
| in | messageIndex | The message index. |
| METHOD deleteContentListByFileId |
| Description | Deletes ContentInfo records linked into the given file. |
| Parameters | in | sessionId | Session identifier |
| in | fileId | File identifier. |
| METHOD deleteContentListByFileName |
| Description | Deletes ContentInfo records linked into the given file. |
| Parameters | in | sessionId | Session identifier |
| in | fileName | Filename. |
| METHOD deleteContentListByGroupFlags |
| Description | Deletes ContentInfo records that belongs to the given groups. |
| Parameters | in | sessionId | Session identifier |
| in | groupFlags | Group flags. |
| METHOD deleteContentListByProducerId |
| Description | Deletes ContentInfo records linked into the given producer. |
| Parameters | in | sessionId | Session identifier |
| in | producerId | Producer identifier. |
| METHOD deleteContentListByProducerName |
| Description | Deletes ContentInfo records linked into the given producer. |
| Parameters | in | sessionId | Session identifier |
| in | producerName | Producer name. |
| METHOD deleteContentListByGenerationId |
| Description | Deletes ContentInfo records linked into the given generation. |
| Parameters | in | sessionId | Session identifier |
| in | generationId | Generation identifier. |
| METHOD deleteContentListByGenerationName |
| Description | Deletes ContentInfo records linked into the given generation. |
| Parameters | in | sessionId | Session identifier |
| in | GenerationName | Generation name. |
| METHOD registerContentList |
| Description | DataServers use this method for registering the content information that they can deliver. |
| Parameters | in | sessionId | Session identifier |
| in | serverId | DataServer identifier, which is registering the content. |
| inout | contentInfo[] | ContentInfo records (in CSV). |
| METHOD registerContentListByFileId |
| Description | DataServers use this method for registering the content information of a file.
which ContentInfo records can already be found from the content information database. |
| Parameters | in | sessionId | Session identifier |
| in | serverId | DataServer identifier, which is registering the content. |
| in | fileId | File identifier. |
| METHOD unregisterContentList |
| Description | DataServers can use this method for removing all their content registrations.
This is usually done when a DataServer is reseting itself. |
| Parameters | in | sessionId | Session identifier |
| in | serverId | DataServer identifier, which is unregistering the content. |
| METHOD unregisterContentListByFileId |
| Description | DataServers use this method for unregistering the content information of a file.
In this way it indicates that the current content is not available in the current DataServer. |
| Parameters | in | sessionId | Session identifier |
| in | serverId | DataServer identifier, which is registering the content. |
| in | fileId | File identifier. |
| METHOD getContentInfo |
| Description | getContentInfo(in sessionId,in fileId,in messageIndex,out contentInfo) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentList |
| Description | getContentList(in sessionId,in startFileId,in startMessageIndex,in maxRecords,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentListByFileId |
| Description | getContentListByFileId(in sessionId,in fileId,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentListByFileName |
| Description | getContentListByFileName(in sessionId,in filename,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentListByGroupFlags |
| Description | getContentListByGroupFlags(in sessionId,in groupFlags,in startFileId,in startMessageIndex,in maxRecords,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentListByProducerId |
| Description | getContentListByProducerId(in sessionId,in producerId,in startFileId,in startMessageIndex,in maxRecords,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentListByProducerName |
| Description | getContentListByProducerName(in sessionId,in producerName,in startFileId,in startMessageIndex,in maxRecords,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentListByServerId |
| Description | getContentListByServerId(in sessionId,in serverId,in startFileId,in startMessageIndex,in maxRecords,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentListByGenerationId |
| Description | getContentListByGenerationId(in sessionId,in generationId,in startFileId,in startMessageIndex,in maxRecords,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentListByGenerationName |
| Description | getContentListByGenerationName(in sessionId,in generationName,in startFileId,in startMessageIndex,in maxRecords,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentListByGenerationIdAndTimeRange |
| Description | getContentListByGenerationIdAndTimeRange(in sessionId,in generationId,in startTime,in endTime,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentListByGenerationNameAndTimeRange |
| Description | getContentListByGenerationNameAndTimeRange(in sessionId,in generationName,in startTime,in endTime,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentListByParameter |
| Description | getContentListByParameter(in sessionId,in parameterKeyType,in parameterKey,in parameterLevelIdType,in parameterLevelId,in minLevel,in maxLevel,in startTime,in endTime,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentListByParameterAndGenerationId |
| Description | getContentListByParameterAndGenerationId(in sessionId,in generationId,in parameterKeyType,in parameterKey,in parameterLevelIdType,in parameterLevelId,in minLevel,in maxLevel,in startTime,in endTime,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD getContentListByParameterAndGenerationName |
| Description | getContentListByParameterAndGenerationName(in sessionId,in generationName,in parameterKeyType,in parameterKey,in parameterLevelIdType,in parameterLevelId,in minLevel,in maxLevel,in startTime,in endTime,out contentInfo[]) |
| Parameters | in | sessionId | Session identifier |
| METHOD addEventInfo |
| Description | addEventInfo(in sessionId,inout eventInfo) |
| Parameters | in | sessionId | Session identifier |
| METHOD getLastEventInfo |
| Description | getLastEventInfo(in sessionId,in requestingServerId,out eventInfo) |
| Parameters | in | sessionId | Session identifier |
| METHOD getEventInfoList |
| Description | getEventInfoList(in sessionId,in requestingServerId,in startEventId,in maxRecords,out eventInfo[]) |
| Parameters | in | sessionId | Session identifier |


Reload page if pctures are not equal.
If pictures are equal then service worker and Cache API works.


QtMultimedia¶
The QtMultimedia
module contains classes to handle
multimedia content and APIs to access camera and radio functionality.
The QtMultimedia
module contains classes to handle
multimedia content and APIs to access camera and radio functionality.


ETH but it's fashion
Save this image
Go to
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Use the image to make your own custom sweatshirt
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Google signs healthcare data and cloud computing deal with Ascension
REUTERS: Alphabet Inc's Google signed its biggest cloud computing customer in healthcare yet, according to an announcement on Monday (Nov 11), gaining with the deal datasets that could help it tune potentially lucrative artificial intelligence tools.
The Wall Street Journal earlier reported Google teaming up with Ascension to collect personal health-related information of millions of Americans across 21 states.
The partnership will also explore artificial intelligence and machine learning applications to help improve clinical effectiveness as well as patient safety, Ascension said in a statement.
Google Cloud Chief Executive Officer Thomas Kurian has made it a priority in his first year on the job to aggressively chase business from leaders in six industries, including healthcare.
The company previously had touted smaller healthcare clients, such as the Colorado Center for Personalized Medicine.
Google has spent several years developing artificial intelligence to automatically analyze MRI scans and other patient data to identify diseases and make predictions aimed at improving outcomes and reducing cost.
Ascension, which operates 150 hospitals and more than 50 senior living facilities across United States, said the partnership is in compliance with the U.S. data privacy act HIPPA (Health Insurance Portability and Accountability Act), which safeguards medical information.
The Journal reported that the data involved in the project includes lab results, doctor diagnoses and hospitalization records, among other categories, and amounts to a complete health history, complete with patient names and dates of birth.
The news follows an earlier announcement from Google that it would buy Fitbit Inc for US$2.1 billion, aiming to enter the wearables segment and invest in digital health.


GEOGRAPHY
INDIA
Match the name of the states of India with their location on a map
Quiz : States of India
Locate the rivers of India on a map (Match)
Quiz : Rivers of India
ASIA
Locate the States of Asia on a map
Quiz : States of Asia


Buy it
Per instance of SonarQube and per year, including upgrading and support.
When you purchase the plugin you agree with terms and conditions. Don't forget to include your email. We will send the license to that email.
Let us know if you need any further information by sending any issue to our help center.


com.zello.channel.sdk
/
OutgoingVoiceStreamError
/
NO_RESPONSE
NO_RESPONSE
NO_RESPONSE
The connection to the server timed out


Room Smell
Mirrors
Trash Can
TV Stand
TV
Lamps
Windows/Drapes
AC
Desk/Chair
Closet/Wardrobe
Beds
Bedside Table
Picture Frames
Easy Chair
Walls/Ceilings
Floors/Edges


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| 604 | (A) | thay kepe kenly witħ caste the kirnełłs one lofte |
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| 593 | (E) | ¶ Thei kepte kenely with cast . þe corners abowte |
| (Ex) | ---- no text ---- | |
| 622 | (L) | kepten kenly with caste þe kernels a-lofte |
| 562 | (P) | Keped kyndely þe caste with kyrnels on lofte |
| 601 | (U) | kept kenely witħ kast / the corners aboute |
| (V) | ---- no text ---- |


{% extends "layout.html" %} {% block title %}Page Not Found | {% endblock %} {% block content %}
Looks like that link is broken!
Let's take you back home.


{% if not group %}
{% endif %}
Click Left to Select Group
{% endif %} {% if group %} MAP MODE NORMAL MODEInformation
{% if group %}
| Centrality Rank | Name |
|---|---|
| {{ forloop.counter }} | {{ top.member_name }} |
| {{ my_rank }} | Me |
| Average Degree | {{ average_degree|floatformat:"2" }} | ||
|---|---|---|---|
| Network Not Connected | {% else %} | Average Distance | {{ average_distance|floatformat:"2" }} | {% endif %}
{% endif %}
- Your links cover {{ cover }}% of the global network
{% if bestfriend.member_name != None %}- Your best friend is {{ bestfriend.member_name }}, {{ bf_ratio }}% of the members have lined you two.
{% endif %} {% if heart.member_name != None %}- {{ heart.member_name }} is the member who knows you best.
{% endif %}


Approve your accounts
Which director approved the accounts?
Officer who approved the accounts
Change
Which director approved the accounts?
When were the accounts approved?
This is the date the accounts were approved by the director.
For example, 31 3 2017
Day
Month
Year
Save or print a copy of your accounts
(Opens in new tab/window)
(Opens in new tab/window)
Status: in-progress


{% extends 'templates/nhs_transaction_layout.html' %} {% block pageTitle %}NHS.UK prototyping kit{% endblock %} {% block content %} Prescription submissions Use this service to start a prescription submission claim Start {% endblock %}
Use this service to start a prescription submission claim


Introduction
What is Unum?
Unum is a decentralized cryptocurrency whose price is linked to the U.S. dollar. One Unum is worth $1.
Stable Price
Whenever a user buys or sells Unum through the Unum Smart Contract, the current US dollar value of the exchanged cryptocurrency is found on another smart contract, called the Price Oracle. The Price Oracle is constantly being updated with the current average price of crypto-currencies. This price oracle allows Unum to maintain it's link to the US dollar.
Auditable Reserves
Unum can be purchased for a variety of other cryptocurrencies. The cryptocurrency that is used to purchase Unum is held in the by Unum Smart Contract in reserve. The total value held by the Unum Smart Contract reserve is instantly auditable on the Etherum blockchain.
Decentralized
Exchanging cryptocurrencies for Unum, and vice-versa, occurs directly through the Unum Smart Contract deployed on the Ethereum blockchain. There is no intermediary organization to handle the exhange, no matching of buyers and sellers, and no approval needed.
Unum can be purchased on this website by users with an Ethereum blockchain plugin such as MetaMask, or through the use of external tools such as Mist.
Trustless
When we say Unum is trustless, we mean that the owner of the Unum Smart Contract cannot:
- Withdraw tokens or Ethereum from the Unum Smart Contract reserve
- Create or issue Unum
- Move Unum between accounts
- Destroy the Unum Smart Contract
- Prevent the sale of Unum for cryptocurrencies in the reserve
- Change the exchange price of Unum from $1
- Change the conversion fee for buying or selling Unum
All of this is enforced by the Unum Smart Contract, which cannot be altered, even by the owner. The Unum Smart Contract is open source and can be viewed on Github.
In addition, there was no pre-sale, pre-mine, or ICO of Unum.
Fees
The Unum Smart Contract charges a .05% conversion fee to purchase or sell Unum for any of the supported cryptocurrencies. The conversion fee goes to pay for the costs of maintaining the Price Oracle with a consistent feed of data.
Buying
There is a .05% conversion fee to purchase Unum. In other words, for every $100 worth of cryptocurrency sent in to purchase Unum, 5 cents is withheld as a fee.
For example, if the price of Ethereum is $320, and you traded 1 Ether for Unum, you would receive 319.84 Unum.
Selling
There is a .05% conversion fee to trade Unum for any cryptocurrency held in the Unum Smart Contract reserve. In other words, for every $100 worth of Unum converted into another cryptocurrency held in the Unum Reserve, 5 cents worth is held in fees.
For example, if you traded 320 Unum for Ether, and the price of Ether is $320, the conversion fee would equal 16 cents. The conversion fee is deducted from the amount of Unum sent in, and the resulting amount of Unum is then converted into Ether. Any Unum traded for cryptocurrencies held in the reserve is destroyed after the trade is complete.
Reserve Deficit Sell Penalty
Because the values of cryptocurrencies ar highly volatile, it is possible for the value of the cryptocurrencies held in reserve by the Unum Smart contract to become worth less than the total supply of Unum issued. In such times a Reserve Deficit Sell Penalty is applied to all exchanges of Unum into cryptocurrrencies held in the reserve. This penalty is meant to discourage Unum traders from increasing the reserve deficit.
The Reserve Deficit Sell Penalty is equal to 1/10th of the reserve deficit percent. If the US Dollar value of the reserve held by the Unum Smart Contract greater than the number of Unum in circulation, then no Reserve Deficit Sell Penalty is applied.
For example, if there are 1,000,000 Unum in circulation, but the US dollar value of the reserve held by the Unum Smart Contract is worth only $950,000, then the reserve has a deficit of 5%. This would equate to a .5% Reserve Deficit Sell Penalty, which is in addition to the conversion fee described above. Unlike the conversion fee, the Reserve Deficit Sell Penalty is not removed from the available Unum Smart Contract reserve balance.
In this case, if you traded 320 Unum for Ether, and the price of Ether is $320, the conversion fee of .05% would equal 16 cents and the Reserve Deficit Sale Penalty of 0.5% would equal $1.60.
Bonus Sales
The stability of Unum is increased when the value of the cryptocurrencies held in reserve by the Unum Smart Contract is increased. To encourage users to purchase Unum, and increase it's stability, occasionally Bonus Sale events are held.
During these events, and users who purchase Unum will receive an additional percentage bonus of Unum. These bonuses start at 1%, and increase by 1% each day until the Bonus Sale is over (no more than 10 days), or the Unum Cap for the sale is reached.
Any upcoming Bonus Sales will be announced in advance on our Blog.
More Info
Blog
To stay up-to-date with what's going on with Unum, check out our Blog.
Contact
We can be reached via email at support@unum.one.


public static class R.id
public static int accessibility_action_clickable_span
public static int accessibility_custom_action_0
public static int accessibility_custom_action_1
public static int accessibility_custom_action_10
public static int accessibility_custom_action_11
public static int accessibility_custom_action_12
public static int accessibility_custom_action_13
public static int accessibility_custom_action_14
public static int accessibility_custom_action_15
public static int accessibility_custom_action_16
public static int accessibility_custom_action_17
public static int accessibility_custom_action_18
public static int accessibility_custom_action_19
public static int accessibility_custom_action_2
public static int accessibility_custom_action_20
public static int accessibility_custom_action_21
public static int accessibility_custom_action_22
public static int accessibility_custom_action_23
public static int accessibility_custom_action_24
public static int accessibility_custom_action_25
public static int accessibility_custom_action_26
public static int accessibility_custom_action_27
public static int accessibility_custom_action_28
public static int accessibility_custom_action_29
public static int accessibility_custom_action_3
public static int accessibility_custom_action_30
public static int accessibility_custom_action_31
public static int accessibility_custom_action_4
public static int accessibility_custom_action_5
public static int accessibility_custom_action_6
public static int accessibility_custom_action_7
public static int accessibility_custom_action_8
public static int accessibility_custom_action_9
public static int action_container
public static int action_divider
public static int action_image
public static int action_text
public static int actions
public static int async
public static int blocking
public static int chronometer
public static int dialog_button
public static int forever
public static int icon
public static int icon_group
public static int info
public static int italic
public static int item_touch_helper_previous_elevation
public static int line1
public static int line3
public static int normal
public static int notification_background
public static int notification_main_column
public static int notification_main_column_container
public static int right_icon
public static int right_side
public static int tag_accessibility_actions
public static int tag_accessibility_clickable_spans
public static int tag_accessibility_heading
public static int tag_accessibility_pane_title
public static int tag_screen_reader_focusable
public static int tag_transition_group
public static int tag_unhandled_key_event_manager
public static int tag_unhandled_key_listeners
public static int text
public static int text2
public static int time
public static int title


{% include imports/favicon.html %} {% include imports/stylesheets.html %} {% include imports/preloads.html %} {% include imports/matomo.html %}


Meldonium Case
Meldonium (INN), trade-named as Mildronate among others, is a limited-market pharmaceutical, developed in 1970 by Ivars Kalviņš at the USSR Latvia Institute of Organic Synthesis, and now manufactured by the Latvian pharmaceutical company Grindeks and several generic manufacturers. It is primarily distributed in Eastern European countries as an anti-ischemia medication. Since 1 January 2016, it has been on the World Anti-Doping Agency (WADA) list of substances banned from use by athletes.However, there are debates over its use as an athletic performance enhancer. Some athletes are known to have been using it before it was banned. Meldonium may be used to treat coronary artery disease.These heart problems may sometimes lead to ischemia, a condition where too little blood flows to the organs in the body, especially the heart. Because this drug is thought to expand the arteries, it helps to increase the blood flow as well as increase the flow of oxygen throughout the body. Meldonium has also been found to induce anticonvulsant and antihypnotic effects involving alpha 2-adrenergic receptors as well as nitric oxide-dependent mechanisms.
Query Words: meldonium
Tweets per Day
Contains the count of tweets submitted every day during the researching period.
Sentiment per Month
Contains information about the scores of six basic sentiments (Anger, Disgust, Fear, Joy, Sadness & Surprise) on each month.
Infobox
6.332
6.332 distinct tweets
0
0 distinct Youtube comments
4.958
4.958 distinct users
10.473
10.473 distinct words
01/06/2016 to 01/08/2016
The time frame of the data is 2 months
Sentiment: Anger
The overall score of our sentiment analysis indicated the main sentiment of the users was Anger.
Most Frequent Words
Contains the most frequent distinct words as extracted from the final data during the researching period.
| # | Word | Frequency |
|---|---|---|
| 1 | meldonium | 6472 |
| 2 | sharapova | 2700 |
| 3 | maria | 1651 |
| 4 | banned | 769 |
| 5 | positive | 630 |
| 6 | doping | 607 |
| 7 | ban | 607 |
| 8 | years | 600 |
| 9 | drug | 593 |
| 10 | tennis | 566 |
Most Frequent Hashtags
Contains the most frequent distinct hashtags as extracted from the final data. The table shows the top-10 along with their frequencies.
| # | Hashtag | Frequency |
|---|---|---|
| 1 | #Meldonium | 811 |
| 2 | #Sharapova | 472 |
| 3 | #MariaSharapova | 343 |
| 4 | #doping | 106 |
| 5 | #tennis | 103 |
| 6 | #Doping | 50 |
| 7 | #Rio2016 | 38 |
| 8 | #WTA | 37 |
| 9 | #ITF | 36 |
| 10 | #WADA | 32 |
Most Frequent Users
Contains the most frequent distinct users as extracted from the final data. The table shows the top-10 users along with their frequencies.
| # | User | Frequency |
|---|---|---|
| 1 | @adrianaeloca | 86 |
| 2 | @l5iza | 47 |
| 3 | @arzillion | 26 |
| 4 | @LawInSport | 24 |
| 5 | @alexielsi | 23 |
| 6 | @tekno1radio | 23 |
| 7 | @shivanui | 22 |
| 8 | @TennisActu | 17 |
| 9 | @Sport_Integrity | 16 |
| 10 | @lluciz | 15 |
Most Frequent Mentions
Contains the most frequent distinct mentions as extracted from the final data. The table shows the top-10 mentions along with their frequencies.
| # | Mention | Frequency |
|---|---|---|
| 1 | @MariaSharapova | 140 |
| 2 | @ITF_Tennis | 41 |
| 3 | @wada_ama | 38 |
| 4 | @cnnsport | 36 |
| 5 | @MailSport | 19 |
| 6 | @USATODAY | 18 |
| 7 | @usantidoping | 17 |
| 8 | @NewsRepublic | 14 |
| 9 | @Nike | 12 |
| 10 | @WTA | 11 |


An interactive explorer for single-cell transcriptomics data
Quick start
Installation
Gallery
Cellxgene data portal
Demo datasets
Preparing your data
Launching cellxgene
Hosting cellxgene
Annotating data
Methods
Troubleshooting
Roadmap
Contributing (ideas or code)
Contact & finding help
Code
To use these datasets, run:
cellxgene launch [filename.h5ad] --open
Healthy human PBMCs (10X).
cellxgene launch https://cellxgene-example-data.czi.technology/pbmc3k.h5ad
20 organs and tissues from healthy mice (Smart-Seq2).
Rich metadata and annotations.
cellxgene launch https://cellxgene-example-data.czi.technology/tabula-muris.h5ad
22 organs and tissues from healthy mice at ages 3mo, 18mo, 21mo, and 24mo (Smart-Seq2).
Rich metadata and annotations.
cellxgene launch https://cellxgene-example-data.czi.technology/tabula-muris-senis.h5ad


SRSampling: take one or multiple simple random samples
SRSampling( x, size, replace = FALSE, rep = 1, show.SRS = FALSE, verbose = TRUE, na.rm = FALSE, trim = 0, ... )
| x | the assumed population from which the SRS are chosen. It must be a vector with length > 1. NA in x can also be chosen. |
|---|---|
| size | the sample size. the number of items to choose |
| replace | whether sampling should be with replacement |
| rep | the number of simulation runs. Default rep=1. |
| show.SRS | whether to show SRS for each run of simulations |
| verbose | whether to return verbose message |
| na.rm | whether to remove NA when calculate summary statistics for SRS. |
| trim | the fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed. Values of trim outside that range are taken as the nearest endpoint |
| ... | additional arguments to be passed to or from methods. |
a list a list containing the following components, conditional on the argument rep
:
When rep=1
(the default):
sample.mean
: a numeric number giving the mean of the SRS
sample.se
: a numeric number giving the SD of the SRS
SRS
: a matrix containing the values in the SRS with nrow=1
and ncol=size
. Available only if show.SRS=TRUE
When rep>1
:
sample.mean
: a list containing the following components about sample means from multiple SRS:
values
: a vector with a length of rep
giving sample mean for each of the multiple SRS
mean
: a numeric number giving the mean of values
se
: a numeric number giving the mean of values
sample.se
: a list containing the following components about sample sds from multiple SRS:
values
: a vector with a length of rep
giving sample sd for each of the multiple SRS
mean
: a numeric number giving the mean of values
se
: a numeric number giving the mean of values
SRS
: a matrix containing the values in each of the multiple SRS with nrow=rep
and ncol=size
. Available only if show.SRS=TRUE
.
pop.1 <- rnorm(1000,0,10) # take a SRS from x of size=10, show.SRS=TRUE SRSampling(x=pop.1, size=10, show.SRS=TRUE)#> A simple random sample of size 10 is drawn from x #> #> The SRS for each run of samplings are shown in the result #>#> $sample.mean #> [1] 4.703076 #> #> $sample.se #> [1] 11.22522 #> #> $SRS #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] 18.07087 0.3849955 -2.166291 4.768438 -6.570574 -9.974431 -3.470885 #> [,8] [,9] [,10] #> [1,] 8.767773 12.41263 24.80823 #># take 20 SRS from x of size=10, show.SRS=FALSE SRSampling(x=pop.1, size=10, rep=20)#> 20 runs of simple random sample of size 10 are drawn from x #> #> The SRS for each run of samplings are NOT included in the result #>#> $sample.mean #> $sample.mean$values #> [1] -10.5992995 -2.4645059 3.6621685 3.8722055 0.2039372 -0.3420757 #> [7] 0.7356245 4.0024178 -3.4364846 -0.1411582 1.1365655 -2.8541025 #> [13] 4.2445203 3.4185136 2.4451474 0.5074188 -2.6448420 -1.2266083 #> [19] 5.8116487 0.8172250 #> #> $sample.mean$mean #> [1] 0.3574158 #> #> $sample.mean$se #> [1] 3.707296 #> #> #> $sample.se #> $sample.se$values #> [1] 11.939111 12.067874 12.455150 12.793271 10.214753 8.293052 9.524084 #> [8] 13.522999 8.850345 7.068467 10.287013 6.353818 7.823334 12.781816 #> [15] 7.545748 10.582422 10.265528 9.853843 11.922616 6.719497 #> #> $sample.se$mean #> [1] 10.04324 #> #> $sample.se$se #> [1] 2.22555 #> #># size>length(pop.2) pop.2 <- rnorm(10,0,10) # SRSampling(x=pop.2, size=15, rep=20) # error # non-integer size SRSampling(x=pop.2, size=5.8) # warning#> Warning: size=5.8 is not an integer, and ceiling() function was applied. #> size=6 was used#> A simple random sample of size 6 is drawn from x #> #> The SRS for each run of samplings are NOT included in the result #>#> $sample.mean #> [1] -0.8715857 #> #> $sample.se #> [1] 8.114037 #># x=numeric(0) # SRSampling(x=numeric(0), size=1) # error # x with NA pop.3 <- c(1,2,3,4,NA) SRSampling(x=pop.3, size=5, na.rm=TRUE)#> Warning: #> size is equal to the length of x#> A simple random sample of size 5 is drawn from x #> #> The SRS for each run of samplings are NOT included in the result #>#> $sample.mean #> [1] 2.5 #> #> $sample.se #> [1] 1.290994 #>SRSampling(x=pop.3, size=5)#> Warning: #> size is equal to the length of x#> A simple random sample of size 5 is drawn from x #> #> The SRS for each run of samplings are NOT included in the result #>#> $sample.mean #> [1] NA #> #> $sample.se #> [1] NA #>