Estimation of prevalence ratios using logistic models and confidence intervals with delta and bootstrap methods.
| Version: | 1.2 |
| Depends: | R (≥ 2.10.0), boot, methods, stats, stats4, lme4, Hmisc |
| Published: | 2013-09-19 |
| Author: | Raydonal Ospina and Leila D. Amorim. |
| Maintainer: | Raydonal Ospina <raydonal at de.ufpe.br> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | ChangeLog |
| CRAN checks: | prLogistic results |
| Reference manual: | prLogistic.pdf |
| Package source: | prLogistic_1.2.tar.gz |
| Windows binaries: | r-devel: prLogistic_1.2.zip, r-release: prLogistic_1.2.zip, r-oldrel: prLogistic_1.2.zip |
| OS X binaries: | r-release: prLogistic_1.2.tgz, r-oldrel: prLogistic_1.2.tgz |
| Old sources: | prLogistic archive |
Please use the canonical form https://CRAN.R-project.org/package=prLogistic to link to this page.


This tutorial introduces various features in Maqetta that allow for quick sketching of mobile user interfaces. The sketching features are often useful in the early stages of designing a user interface when you want to create a visual mockup quickly and solicit early feedback from colleagues and customers.
Before running this tutorial, be sure to first go through the more general "Quick UI Sketching" tutorial. That tutorial introduces the various sketching features found in Maqetta. This tutorial supplements the "Quick UI Sketching" tutorial with some features and techniques specific to mobile UI sketching.
In the previous tutorial, "Tutorial - Mobile User Interfaces", you learned how to use Maqetta's drag/drop authoring features to visually compose an actual running mobile application, all within a life-size replica of the target device (e.g., silhouette of an iPhone). This tutorial shows how to use an alternate technique for mocking up a mobile user interface using Maqetta's quick UI sketching features. With these techniques, you will create a WYSIWYG drawing of a proposed user interface, using actual mobile widgets, but the result will be a static image of the proposed user interface rather than an actual working application.
Here are some advantages/disadvantages between mobile visual composition and mobile sketching:
The steps below introduce mobile sketching.
Go to the Create menu at the top of the application, and issue a "Create Sketch (high-fidelity)" command.
You can either accept the default filename
(e.g., file1.html
) or enter a custom filename. Click on the Create button.
In the Palette, open up the Clip Art section of the Widgets palette, then click on Device Silhouttes to open the Device Silhouttes subsection. You should see various clip art entries for mobile devices (e.g., Android_340x480, Android_480x800, AndroidTablet, Blackberry, iPad, iPhone).
Drag/drop a iPhone clip art widget onto the page canvas, dropping it near the top/left corner of the canvas. Your canvas should now look like this:
In the Palette, open up the "Heading" section and then drag/drop a Heading widget into the picture of the iPhone. Attempt to drop the widget at the top/left corner of the inner rectangle of the iPhone clip art widget. When the prompt appears for the Heading widget's text, enter ""Mobile UI Sketch".
Click/drag on the interior of the Heading widget to move it so that it is repositioned such that its top/left corner is exactly aligned with the top/left corner corner of the inner rectangle of the iPhone clip art widget. You might want to first move the Heading down and to the right so that you can see where the top/left inner corner is and then move the Heading back into alignment with the top/left inner corner.
You can also use the arrow keys to move widgets one pixel in a particular direction. You can also go to the Layout section of the Properties palette to adjust the 'left' and 'top' properties there. The "+" and "-" icons can be convenient for pixel tweaking operations.
Once the top/left corner is in the right location, now drag the right edge of the Heading widget to the right until the right edge of the Heading widget is exactly aligned with the right edge of the iPhone's inner rectangle.
You can also go to the Layout section of the Properties palette to adjust the 'width' property there. The "+" and "-" icons can be convenient for pixel tweaking operations.
Your canvas should now look like this:
If you check the Outline palette, you will see that the Heading is a child of the device clip art widget, and therefore positioned "absolutely, but relative to its parent". In other words, the Heading is at a fixed (x,y) location relative to the top/left of its parent widget (i.e., the device clip art widget). What this means is that if you move the device clip art widget, the Heading will move along with it.
Drag/drop a RoundRectList widget (found in the Lists section of the Palette) into the picture of the iPhone below the Heading widget. When the prompt appears, type in 4 lines of text:
Active Pending Approved Deleted
With the RoundRectList widget selected, drag it to a position such that it is directly under the Heading and centered horizontally within the iPhone's inner rectangle.
Drag/drop a TabBar widget (found in the ToolBars, ButtonBars subsection of the Controls section of the Palette) into the picture of the iPhone and drop near the bottom of the iPhone's inner rectangle. When the prompt appears, type in this text:
Tasks, Alerts, Messages
With the TabBar widget selected, drag it to a position such that its bottom-left corner is exactly aligned with the bottom-left corner of the iPhone's inner rectangle.
With the TabBar widget selected, drag on the right edge of the TabBar so that the right edge of the TabBar is exactly aligned with the right edge of the iPhone's inner rectangle.
Your canvas should now look like this:
Your mobile sketch is now complete.
The device clipart widgets (e.g., Android_340x480, Android_480x800, AndroidTablet, Blackberry, iPad, iPhone) can be changed to landscape orientation if you need to do quick mobile UI sketching for how user interfaces will look in landscape mode. To do so, select the clipart widget on the canvas, then go to the Widget-Specific section of the Properties palette. You can then change the value of the 'Orientation' property of the given device clipart widget to 'landscape'. But if you do this, only that one widget will change its orientation. Any other widgets that might have been added to the page will not automatically reposition and resize themselves because Maqetta is in quick sketching mode. (Note: if instead of quick UI sketching, you had composed an actual mobile page in Maqetta, the child widgets would indeed automatically reposition and resize themselves.)


2017 has been the most intense year in my life so far.
Taught me a hell lot of new things.
Things went wrong in ways I had never even thought of. And things started to get right again after I learned to focus on the right things.
Now I know..
Things go wrong for a reason.
I’m more awake and less day dreaming.
More realistic, yet optimistic.
I’m alive.
println!(“Hello World!”);


Peer Pressure is by far the hardest thing to escape from in high school, simply because there are so many things that are demanded from you and so many strong influences all around you. The influences range from consuming drugs to changing your flair. The point is that you have to be believe in who you are and not change, even when you feel like you do not fit anywhere.
The goal of freshman year is to find the place where you can be who you are, without changing anything about yourself. It might be easier to change and adjust to fit in but nothing that is easy is worth in the long run.


Members
(static) propTypes
PropTypes:
variables: An array of variable objects.
setVariable: A function that passes the newly selected Variable to the parent component.
selectedVariable: A variable object (or array of two objects) that represent the currently selected Variable.
fetchData: A function that is called when new data is needed from the API.
unfilteredHospitals: A complete list of all (-> "unfiltered") hospital objects in an array.
filteredByEnum: A function that passes a list of hospital objects filtered by enum to the parent component.
filteredByType: A function that passes a list of hospital objects filtered by Type to the parent component (when in Map view).
filteredLinRegByType: A function that passes a list of hospital objects filtered by Type to the parent component (when in Graph view).
year: The currently selected year.
hasLoaded: A boolean that signifies that all data has been fetched by the API and is ready to be manipulated.
view: A number that represents the currently selected view (Map, Table, Graph)
setView: A function that passes the newly selected view to the parent component.
mapView: A number that represents the currently selected tab on the Map view.
setMapView: A function that passes the newly selected Map view to the parent component
graphView: A number that represents the currently selected tab on the Graph view.
setGraphView: A function that passes the newly selected Graph view to the parent component.
csvData: An array that represents the data shown in the table when on Table view, to export as CSV.
- Source:
fetchData
Called when asking for a Variable to be displayed on the map
Prepares correct query to ask App.js
- Source:
resetEnum
resetEnum - resets the enum
- Source:
setEnum
Sets the state for selected Enum variable
Gets data after changing it
- Source:
setTabView
sets the view to be displayed on the ControlPanel.
- Source:
setVariable
Sets the state variable selectedVariable to the selected variable from a DropdownMenu Component,
then calls fetchData to fetch data from the API.
- Source:
Methods
componentDidUpdate(prevProps)
componentDidUpdate - called after the component was updated
Parameters:
| Name | Type | Description |
|---|---|---|
prevProps |
type | previous props |
- Source:
render() → {JSX}
render - renders the component
- Source:
Returns:
JSX of the component
- Type
- JSX


Twitter
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Reddit
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Github
Web App:
Stable
-
Beta
Chrome Extension:
Stable
-
Beta
Time Saved with Destiny Item Manager


If you need additional assistance please see the RSVP page for a point of contact.
Set your starting point using this link.
Set your starting point using this link.
From I-77: Take Exit 75 for Porter Rd or Exit 77 for Sutton Rd.
Set your starting point using this link.


This newly rebuild harbour is on the Mediterranean coast, due south of Malaga in Spain.
Position 35° 14.9'N, 03° 55.1'W.
Clearance can be completed here, but not that the berths outside the Customs building are for impounded vessels only.
Yachts can dock on the south quay. There is good shelter here.
Some provisions available.


PICS: Kenyan Prophetess visits ED, blesses him with prophecy
by Sharon ChirisaKenyan Prophetess Reverend Lucy Natasha visits ED, blesses him with prophecy
Kenyan Prophetess, Reverend Lucy Natasha, leader of the Prophetic Latter Glory Ministries International was in Zimbabwe recently and she got a chance to meet up with President Emmerson Mnangagwa.
She took to social media to share the pictures accompanied by a Prophetic message for president Emmerson Mnangagwa
OTHER INTERESTING STORIES TO READ
The Real Reason Behind Steamy Photographs Of Plaxedes & Ben 10 Finally revealed
PIC: KuLocker Mastreets……White ZRP traffic officer breaks Zim Twitter
CRAZY TIMES:This is How Much a 10kg Bag Of Mealie Meal Now Costs in Zimbabwe
To View and Apply for the latest Jobs and Vacancies in Zimbabwe, Please Visit our Sister website iHarare Jobs


Website Design
Each of my designs begins with a blank canvas which allows your content to guide the layout.
Development
Responsive development will help make your website easily accessible across all devices.
Newsletter
A matching newsletter is the perfect way to complement your website & keep in contact.
Social Media
It's important to keep your brand consistent and recognizable across all platforms.
Website Design
Each of my designs begins with a blank canvas which allows your content to guide the layout.
Development
Responsive development will help make your website easily accessible across all devices.
Newsletter
A matching newsletter is the perfect way to complement your website & keep in contact.
Social Media
It's important to keep your brand consistent and recognizable across all platforms.


First use of FaceTime with good friends the Herlings. Surprisingly seamless but for our spotty wifi.
8/27/2011, 11:19:17 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
First use of FaceTime with good friends the Herlings. Surprisingly seamless but for our spotty wifi.
8/27/2011, 11:19:17 PM
Favs: 0
Retweets: 0
link


The following constraints are placed on the multi-author weights (MAWs):
- Sum of weights equals 1.
- Changes in one weight are compensated equally by the weights of all other authors.
- No weight can be less than 0.001% or exceed 100% - (numAuthors x 0.001%).


Methods
append(element)
Append an element to the list.
Parameters:
| Name | Type | Description |
|---|---|---|
element |
* | Element to be appended |
back()
Set the position to facilitate traversal from the back of the list.
clear()
Remove all elements from the list.
contains(element) → {boolean}
Determine whether or not a list contains a given element.
Parameters:
| Name | Type | Description |
|---|---|---|
element |
* | The given element of interest |
Returns:
True if the list contains the element, false otherwise
- Type
- boolean
currPos() → {number}
Get the current position in the list.
Returns:
The index of the current position in the list
- Type
- number
find(element) → {number}
Find the given element in the list.
Parameters:
| Name | Type | Description |
|---|---|---|
element |
* | The element to be found |
Returns:
The index of the given element or -1 if element not found
- Type
- number
front()
Set the position to facilitate traversal from the front of the list.
getCurrent() → {*}
Get the current element of the list.
Returns:
The current element of the list
- Type
- *
insert(element, after)
Insert an element in the list.
Parameters:
| Name | Type | Description |
|---|---|---|
element |
* | The element to be inserted |
after |
* | The element after which the given element will be inserted |
isEmpty() → {boolean}
Determine whether or not the list is empty.
Returns:
True if empty, false if not
- Type
- boolean
length() → {number}
Get the number of elements in the list.
Returns:
The number (integer) of elements in the list
- Type
- number
moveTo(position)
Move to the given position in the list.
Parameters:
| Name | Type | Description |
|---|---|---|
position |
number | The index associated with the new position in the list |
next() → {*}
Move the current position to the next element.
Returns:
The next element
- Type
- *
prev() → {*}
Move the current position to the previous element.
Returns:
The previous element or -1 if there is no previous element
- Type
- *
remove(element) → {boolean}
Remove a given element from the list.
Parameters:
| Name | Type | Description |
|---|---|---|
element |
* | Element to be removed |
Returns:
True if the item is found and removed, false otherwise
- Type
- boolean
toString() → {string}
Get a string representation of the list.
Returns:
Return string representation of the list (using Object.prototype.toString())
- Type
- string


{% extends "base.html" %} {% block container %}
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Guide.Me
Logout
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Name:
Alex
Email:
<EMAIL_ADDRESS>Skills:
Web Dev, Python, Psychology
Name:
William
Email:
<EMAIL_ADDRESS>Skills:
DevOps, Game Development
Name:
James
Email:
<EMAIL_ADDRESS>Skills:
Java, Networking
Name:
Nina
Email:
<EMAIL_ADDRESS>Skills:
Data Structures, Programming, Shell, ios
Name:
Evelyn
Email:
<EMAIL_ADDRESS>Skills:
Python, Psychology, Programming
Name:
Ella
Email:
<EMAIL_ADDRESS>Skills:
DBMS, Mathematics, Frontend
Name:
Avery
Email:
<EMAIL_ADDRESS>Skills:
Web Dev, Python, Psychology, DevOps, Java
Name:
Jackson
Email:
<EMAIL_ADDRESS>Skills:
Game Development, Data Structures
Name:
Jack
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<EMAIL_ADDRESS>Skills:
Design, Testing
Name:
Scarlett
Email:
<EMAIL_ADDRESS>Skills:
Project Management, Social Media Management
Name:
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<EMAIL_ADDRESS>Skills:
Research, Big Data
Name:
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Email:
<EMAIL_ADDRESS>Skills:
HTML, CSS, JS, React, Project Management
Name:
Johnson
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<EMAIL_ADDRESS>Skills:
Benchmarking, Operations, Scheduling, Design
Name:
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<EMAIL_ADDRESS>Skills:
Video Creation, Information Security, Cybersecurity


{% extends "publicgoods/base_generic.html" %} {% block content %}
Hobbes enables large classes to participate in public goods games in real-time.

If you are a student, select the game you'd like to participate in from the list to the right.


This page demonstrates generating text from a Markov chain using the RiTa library. The program reads this text file and generates new names based on character-based "n-gram" probabilities from the source text. File issues and view source on github.
rename itp


[[css|/main.css]]
The requested page does not exist. If you think this is a mistake, please report an issue.


INTJ
: interjection
The English INTJ
corresponds to the PTB UH.
Treebank Statistics (UD_English)
There are 109 INTJ
lemmas (1%), 112 INTJ
types (1%) and 803 INTJ
tokens (0%).
Out of 17 observed tags, the rank of INTJ
is: 10 in number of lemmas, 10 in number of types and 16 in number of tokens.
The 10 most frequent INTJ
lemmas: please, yes, no, well, hi, ok, like, hey, lol, oh
The 10 most frequent INTJ
types: please, yes, no, well, hi, ok, like, hey, lol, oh
The 10 most frequent ambiguous lemmas: please (INTJ 298, VERB 1, ADJ 1), yes (INTJ 73, NOUN 2), no (DET 281, INTJ 48, ADV 31, NOUN 1, X 1), well (ADV 183, INTJ 47, ADJ 10, NOUN 3), ok (ADJ 39, INTJ 25, ADV 3), like (ADP 189, VERB 187, SCONJ 93, INTJ 24, ADJ 9, ADV 1), oh (INTJ 17, ADV 1), anyway (ADV 21, INTJ 8), sorry (ADJ 27, INTJ 8), okay (ADJ 11, INTJ 7)
The 10 most frequent ambiguous types: please (INTJ 133, VERB 1, ADJ 1), yes (INTJ 28, NOUN 1), no (DET 229, INTJ 33, ADV 25, VERB 2, NOUN 1, X 1), well (ADV 175, INTJ 14, ADJ 10, NOUN 2), ok (ADJ 22, ADV 3, INTJ 3), like (ADP 179, VERB 158, SCONJ 91, INTJ 18, ADJ 9, ADV 1), oh (INTJ 4, ADV 1), anyway (ADV 13, INTJ 1), sorry (ADJ 15, INTJ 4), okay (ADJ 11, INTJ 3)
- please
- INTJ 133: If you can make it , please come !
- VERB 1: In a Hadith the Messenger of Allah explains that the souls of the martyrs are in the hearts of green birds that fly wherever they please in the Paradise .
- ADJ 1: I was very happy with the customer service and even more please with the portion size , to go box set up and quality of the food for the price .
- yes
- no
- DET 229: i think they are all bark and no bite .
- INTJ 33: Er , no ?
- ADV 25: Stylish and contemporary , no matter your size or personality type .
- VERB 2: I du n no how they did it , but Scottish friends — this is THE REAL DEAL .
- NOUN 1: It was a no brainer really .
- X 1: she knows she is invading someone else ‘s territory , but ca n’t help it , and has no where to go .
- well
- ADV 175: Please clarify “ all “ do you intend 10MM for ENA as well ?
- INTJ 14: well i d just come straight out and tell them how you feel
- ADJ 10: Glad to hear all is well .
- NOUN 2: This financing activity was primarily started to help ENA secure long term well head and pipeline natural gas supplies to supplement the gas trading desk activities .
- ok
- like
- ADP 179: They actively excluded State Department Iraq hands like Tom Warrick .
- VERB 158: Please let me know how you would like to proceed .
- SCONJ 91: they look like they were doberman pinchers who were shrunk .
- INTJ 18: like 2 day
- ADJ 9: More like the French than he would care to admit
- ADV 1: I can rent another machine for like $ 60 that will give it a finished look .
- oh
- anyway
- ADV 13: It settled anyway , a long time ago .
- INTJ 1: Wow he does sound interested in you , my farrier never gives me hugs like that ( probably a good thing as he has a wife and 2 kids ) anyway , I find that they can be very busy so I would wait a week lo9nger and if still no reply give him a call but do n’t leave a message so you do n’t have to necessarily wait for him to call .
- sorry
- okay
Morphology
The form / lemma ratio of INTJ
is 1.027523 (the average of all parts of speech is 1.181137).
The 1st highest number of forms (2) was observed with the lemma “oops”: OOPS, Oops.
The 2nd highest number of forms (2) was observed with the lemma “plz”: PLZ, Plz.
The 3rd highest number of forms (2) was observed with the lemma “ugh”: UGH, Ugh.
INTJ
does not occur with any features.
Relations
INTJ
nodes are attached to their parents using 10 different relations: en-dep/discourse (680; 85% instances), en-dep/root (102; 13% instances), en-dep/ccomp (6; 1% instances), en-dep/advcl (4; 0% instances), en-dep/conj (3; 0% instances), en-dep/obj (3; 0% instances), en-dep/nmod (2; 0% instances), en-dep/advmod (1; 0% instances), en-dep/nmod:poss (1; 0% instances), en-dep/parataxis (1; 0% instances)
Parents of INTJ
nodes belong to 14 different parts of speech: VERB (504; 63% instances), ROOT (102; 13% instances), NOUN (74; 9% instances), ADJ (46; 6% instances), INTJ (26; 3% instances), PROPN (23; 3% instances), ADV (14; 2% instances), PRON (5; 1% instances), NUM (3; 0% instances), AUX (2; 0% instances), ADP (1; 0% instances), CCONJ (1; 0% instances), DET (1; 0% instances), PART (1; 0% instances)
683 (85%) INTJ
nodes are leaves.
81 (10%) INTJ
nodes have one child.
24 (3%) INTJ
nodes have two children.
15 (2%) INTJ
nodes have three or more children.
The highest child degree of a INTJ
node is 11.
Children of INTJ
nodes are attached using 13 different relations: en-dep/punct (123; 63% instances), en-dep/discourse (22; 11% instances), en-dep/vocative (21; 11% instances), en-dep/advmod (6; 3% instances), en-dep/conj (6; 3% instances), en-dep/mark (4; 2% instances), en-dep/cc (3; 2% instances), en-dep/obl (3; 2% instances), en-dep/case (2; 1% instances), en-dep/parataxis (2; 1% instances), en-dep/cop (1; 1% instances), en-dep/nmod:poss (1; 1% instances), en-dep/nsubj (1; 1% instances)
Children of INTJ
nodes belong to 13 different parts of speech: PUNCT (123; 63% instances), INTJ (26; 13% instances), PROPN (13; 7% instances), NOUN (9; 5% instances), ADV (7; 4% instances), SCONJ (4; 2% instances), CCONJ (3; 2% instances), DET (3; 2% instances), ADP (2; 1% instances), VERB (2; 1% instances), ADJ (1; 1% instances), AUX (1; 1% instances), PRON (1; 1% instances)
Treebank Statistics (UD_English-ESL)
There are 1 INTJ
lemmas (6%), 1 INTJ
types (6%) and 67 INTJ
tokens (0%).
Out of 17 observed tags, the rank of INTJ
is: 7 in number of lemmas, 7 in number of types and 15 in number of tokens.

The 1st highest number of forms (1) was observed with the lemma “_”: _.
INTJ
does not occur with any features.
Relations
INTJ
nodes are attached to their parents using 8 different relations: en-dep/discourse (57; 85% instances), en-dep/dobj (3; 4% instances), en-dep/root (2; 3% instances), en-dep/advmod (1; 1% instances), en-dep/compound (1; 1% instances), en-dep/conj (1; 1% instances), en-dep/nmod (1; 1% instances), en-dep/xcomp (1; 1% instances)
Parents of INTJ
nodes belong to 7 different parts of speech: VERB (52; 78% instances), NOUN (6; 9% instances), ADJ (3; 4% instances), ADV (2; 3% instances), ROOT (2; 3% instances), INTJ (1; 1% instances), PRON (1; 1% instances)
61 (91%) INTJ
nodes are leaves.
1 (1%) INTJ
nodes have one child.
3 (4%) INTJ
nodes have two children.
2 (3%) INTJ
nodes have three or more children.
The highest child degree of a INTJ
node is 5.
Children of INTJ
nodes are attached using 9 different relations: en-dep/punct (7; 47% instances), en-dep/amod (1; 7% instances), en-dep/case (1; 7% instances), en-dep/cc (1; 7% instances), en-dep/conj (1; 7% instances), en-dep/det (1; 7% instances), en-dep/mwe (1; 7% instances), en-dep/nmod (1; 7% instances), en-dep/vocative (1; 7% instances)
Children of INTJ
nodes belong to 9 different parts of speech: PUNCT (7; 47% instances), ADJ (1; 7% instances), ADP (1; 7% instances), CONJ (1; 7% instances), DET (1; 7% instances), INTJ (1; 7% instances), NOUN (1; 7% instances), PRON (1; 7% instances), PROPN (1; 7% instances)
Treebank Statistics (UD_English-LinES)
There are 1 INTJ
lemmas (6%), 20 INTJ
types (0%) and 122 INTJ
tokens (0%).
Out of 17 observed tags, the rank of INTJ
is: 7 in number of lemmas, 13 in number of types and 15 in number of tokens.
The 10 most frequent INTJ
lemmas: _
The 10 most frequent INTJ
types: yes, well, no, oh, please, eh, Ah, Adieu, good-by, Yeah
The 10 most frequent ambiguous lemmas: _ (NOUN 12161, PUNCT 8085, VERB 8020, ADP 6788, DET 6429, PRON 6303, ADJ 4270, ADV 3700, AUX 3539, PROPN 2257, CCONJ 2081, PART 1703, SCONJ 1231, NUM 462, INTJ 122, X 41, SYM 5)
The 10 most frequent ambiguous types: well (ADV 32, INTJ 6), no (DET 82, ADV 19, INTJ 7, PRON 7), please (INTJ 5, VERB 1)
- well
- no
- please
Morphology
The form / lemma ratio of INTJ
is 20.000000 (the average of all parts of speech is 527.705882).
The 1st highest number of forms (20) was observed with the lemma “_”: Adieu, Ah, Ave, Blimey, Er, HURRAH, Hey, MUUUUUUM, So-o-o, Yeah, ach, boom, eh, good-by, hello, no, oh, please, well, yes.
INTJ
does not occur with any features.
Relations
INTJ
nodes are attached to their parents using 8 different relations: en-dep/discourse (85; 70% instances), en-dep/parataxis (12; 10% instances), en-dep/obj (11; 9% instances), en-dep/root (8; 7% instances), en-dep/advmod (2; 2% instances), en-dep/amod (2; 2% instances), en-dep/appos (1; 1% instances), en-dep/conj (1; 1% instances)
Parents of INTJ
nodes belong to 11 different parts of speech: VERB (69; 57% instances), NOUN (12; 10% instances), ADJ (8; 7% instances), ROOT (8; 7% instances), AUX (7; 6% instances), ADV (4; 3% instances), PRON (4; 3% instances), INTJ (3; 2% instances), PROPN (3; 2% instances), ADP (2; 2% instances), PUNCT (2; 2% instances)
53 (43%) INTJ
nodes are leaves.
58 (48%) INTJ
nodes have one child.
6 (5%) INTJ
nodes have two children.
5 (4%) INTJ
nodes have three or more children.
The highest child degree of a INTJ
node is 4.
Children of INTJ
nodes are attached using 12 different relations: en-dep/punct (72; 82% instances), en-dep/parataxis (3; 3% instances), en-dep/cc (2; 2% instances), en-dep/discourse (2; 2% instances), en-dep/vocative (2; 2% instances), en-dep/advcl (1; 1% instances), en-dep/advmod (1; 1% instances), en-dep/amod (1; 1% instances), en-dep/case (1; 1% instances), en-dep/conj (1; 1% instances), en-dep/det (1; 1% instances), en-dep/nmod (1; 1% instances)
Children of INTJ
nodes belong to 10 different parts of speech: PUNCT (72; 82% instances), INTJ (3; 3% instances), ADP (2; 2% instances), CCONJ (2; 2% instances), NOUN (2; 2% instances), PROPN (2; 2% instances), VERB (2; 2% instances), ADJ (1; 1% instances), DET (1; 1% instances), X (1; 1% instances)
Treebank Statistics (UD_English-ParTUT)
There are 2 INTJ
lemmas (0%), 2 INTJ
types (0%) and 5 INTJ
tokens (0%).
Out of 17 observed tags, the rank of INTJ
is: 16 in number of lemmas, 16 in number of types and 17 in number of tokens.
The 10 most frequent INTJ
lemmas: yes, etc.
The 10 most frequent INTJ
types: yes, etc.
The 10 most frequent ambiguous lemmas:
The 10 most frequent ambiguous types:
Morphology
The form / lemma ratio of INTJ
is 1.000000 (the average of all parts of speech is 1.187751).
The 1st highest number of forms (1) was observed with the lemma “etc.”: etc..
The 2nd highest number of forms (1) was observed with the lemma “yes”: yes.
INTJ
does not occur with any features.
Relations
INTJ
nodes are attached to their parents using 4 different relations: en-dep/discourse (2; 40% instances), en-dep/advcl (1; 20% instances), en-dep/conj (1; 20% instances), en-dep/root (1; 20% instances)
Parents of INTJ
nodes belong to 4 different parts of speech: VERB (2; 40% instances), ADJ (1; 20% instances), PROPN (1; 20% instances), ROOT (1; 20% instances)
0 (0%) INTJ
nodes are leaves.
3 (60%) INTJ
nodes have one child.
0 (0%) INTJ
nodes have two children.
2 (40%) INTJ
nodes have three or more children.
The highest child degree of a INTJ
node is 6.
Children of INTJ
nodes are attached using 8 different relations: en-dep/punct (5; 36% instances), en-dep/cop (2; 14% instances), en-dep/nsubj (2; 14% instances), en-dep/advmod (1; 7% instances), en-dep/amod (1; 7% instances), en-dep/det (1; 7% instances), en-dep/mark (1; 7% instances), en-dep/nmod (1; 7% instances)
Children of INTJ
nodes belong to 7 different parts of speech: PUNCT (5; 36% instances), NOUN (3; 21% instances), AUX (2; 14% instances), ADJ (1; 7% instances), ADV (1; 7% instances), DET (1; 7% instances), SCONJ (1; 7% instances)
INTJ in other languages: [am] [ar] [bg] [bxr] [ca] [ckb] [cop] [cs] [cu] [da] [de] [el] [en] [es] [et] [eu] [fa] [fi] [fo] [fr] [ga] [gl] [got] [grc] [he] [hi] [hr] [hu] [id] [it] [ja] [kk] [kmr] [ko] [la] [lv] [mr] [nl] [no] [pl] [pt] [ro] [ru] [sa] [sk] [sla] [sl] [so] [sr] [sv] [swl] [ta] [tr] [ug] [uk] [u] [urj] [ur] [vi] [yue] [zh]


Anjali Vijayvargiya
About Myself
Experienced Junior Researcher with a demonstrated history of working in the higher education industry. Skilled in Computer Science, Full-Stack Development,
Django Skills, Machine Learning, and Deep Learning Deployment, HTML, Research, and Cascading Style Sheets (CSS). Strong research professional experience with
a Dual Degree M.Tech B.Tech focused on Information and Communication Technology from the University of Rajasthan.
Current Work Status: Junior Research Fellow
Research Project: Child face Age Progression and Regression to trace Missing Children
Sponsored by: Meity, Govt of India
Tasks:
- To develop a responsive, user-friendly, intuitive website for the project.
- To develop a back-end image database with a front end web portal for the project.
- Handling GPU systems to work with Image database.
- Contribution to research work related to the project which is based on Generative Models(GAN).
- Machine Learning and Deep Learning program/model development and deployment in Website
- Full Stack Development = FrontEnd + BackEnd + Deployment ML model = HTML+CSS+JS+PHP/Django+Python
Education: Dual Degree M.Tech B.Tech
College: Centre for Converging Technologies
Qualification: Dual Degree M.Tech B.Tech
Branch: Information and Communication Technology
Specialization in: "DEVELOPMENT OF ANIMATION AND GAMEPLAY OF
3D PACMAN FOR ANDROID PLATFORM" (Developed a Unity Game)


With query running disabled, this panel receives its hit count from a histogram panel. If multiple queries are running this will show the total of all queries.
This shows a simple count of how many records match your filtered query. If multiple queries are sent from a single panel the first query will be displayed


| Interface | Description |
|---|---|
| CopyStreamListener |
The CopyStreamListener class can accept CopyStreamEvents to keep track
of the progress of a stream copying operation.
|
| Class | Description |
|---|---|
| CopyStreamAdapter |
The CopyStreamAdapter will relay CopyStreamEvents to a list of listeners
when either of its bytesTransferred() methods are called.
|
| CopyStreamEvent |
A CopyStreamEvent is triggered after every write performed by a
stream copying operation.
|
| CRLFLineReader |
CRLFLineReader implements a readLine() method that requires
exactly CRLF to terminate an input line.
|
| DotTerminatedMessageReader |
DotTerminatedMessageReader is a class used to read messages from a
server that are terminated by a single dot followed by a
<CR><LF>
sequence and with double dots appearing at the begining of lines which
do not signal end of message yet start with a dot.
|
| DotTerminatedMessageWriter |
DotTerminatedMessageWriter is a class used to write messages to a
server that are terminated by a single dot followed by a
<CR><LF>
sequence and with double dots appearing at the begining of lines which
do not signal end of message yet start with a dot.
|
| FromNetASCIIInputStream |
This class wraps an input stream, replacing all occurrences
of <CR><LF> (carriage return followed by a linefeed),
which is the NETASCII standard for representing a newline, with the
local line separator representation.
|
| FromNetASCIIOutputStream |
This class wraps an output stream, replacing all occurrences
of <CR><LF> (carriage return followed by a linefeed),
which is the NETASCII standard for representing a newline, with the
local line separator representation.
|
| SocketInputStream |
This class wraps an input stream, storing a reference to its originating
socket.
|
| SocketOutputStream |
This class wraps an output stream, storing a reference to its originating
socket.
|
| ToNetASCIIInputStream |
This class wraps an input stream, replacing all singly occurring
<LF> (linefeed) characters with <CR><LF> (carriage return
followed by linefeed), which is the NETASCII standard for representing
a newline.
|
| ToNetASCIIOutputStream |
This class wraps an output stream, replacing all singly occurring
<LF> (linefeed) characters with <CR><LF> (carriage return
followed by linefeed), which is the NETASCII standard for representing
a newline.
|
| Util |
The Util class cannot be instantiated and stores short static convenience
methods that are often quite useful.
|
| Exception | Description |
|---|---|
| CopyStreamException |
The CopyStreamException class is thrown by the org.apache.commons.io.Util
copyStream() methods.
|
Copyright © 2001-2013 The Apache Software Foundation. All Rights Reserved.


Here at Blackfoot Motorsports, we are passionate about providing quality service to all of our customers. Our maintainence team provides fast service, at costs that accomadate every costomers needs. Our sales team puts the customer first and always provides the best options for anything that you could ask for.


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We're just a regular ol' Pacific Northwest family with incredible teeth, who loves cookies, french cuisine and global domination. Our children are fluent in 7 languages, which will make the longevity of our domination more sustainable. Join us in our determination to revolutionize the way the world sees salmon AND cookies and find a store near you, today!


{% extends 'base_email.html' %} {% load i18n %} {% block heading %}Thank you for donating to PARI{% endblock %} {% block email_body %}
We are delighted to accept your donation of Rs.{{ donated_amount }} every {{ donation_frequency|lower|cut:"ly" }} for {{ donation_term }}. We will soon send you a receipt for your donation with 80G certificate details.
At any time, if you wish to cancel your recurring donation, please write to<EMAIL_ADDRESS>and we will immediately cancel future recurring donations from you.
{% endblock %}


Hampi is famous for its ruins belonging to the erstwhile medieval Hindu kingdom of Vijaynagar and it is declared a World Heritage site. The temples of Hampi, its monolithic sculptures and monuments, attract the traveler because of their excellent workmanship.
The best time to visit Hampi is the winter season from October to February. The weather remains moderately cool except for peak afternoons but mostly perfect for exploring the World Heritage Site. Some of the most important festivals including Vijaya Utsav (Festival of Hampi) is celebrated in the month of November.
The best way to visit Hampi is to rent a motorbike and drive all around the area. You can visit the temples like this, but also find hidden places in the countryside. It is beautiful to just getting lost in the rice fields. Renting a motorbike in Hampi cost only 500 INR, a push bike cost only 200 INR per day.


| Home | ClipperLib |
Del.»
{$IFDEF use_int32}
cInt = Int32;
{$ELSE}
cInt = Int64;
{$ENDIF}
C++ »
#ifdef use_int32
typedef int cInt;
#else
typedef signed long long cInt;
#endif
C# »
#if use_int32
using cInt = Int32;
#else
using cInt = Int64;
#endif
cInt is the integer type used by the Clipper Library to represent vertex coordinate values. (See also IntPoint.)
cInt是Clipper库用于表示顶点坐标值的整数类型。（另请参见IntPoint。）
The library uses integers instead of floating point values to preserve numerical robustness. (Very early versions of the library used floating point coordinates, but it became apparent that floating point imprecision was always going to cause occasional errors.)
Clipper库使用整数而不是浮点值来保留数值稳定性。（早期版本的库使用浮点坐标，但很明显，浮点不精确总是会导致偶尔的错误。）
By default cInt represents a signed 64bit integer and polygon coordinates can have any value in the range ± 9.2e+18. This accommodates the scaling of floating point coordinate values to very large integers so that very high degrees of precision can be retained during clipping. However, if coordinate values can be kept within the range ± 3.0e+9, then by avoiding large integer math, a modest ~10% improvement in clipping performance is achieved.
默认情况下，cInt表示一个有符号的64位整数，多边形坐标可以有范围±9.2e的任何值+18。这适应浮点坐标值到非常大的整数的缩放，使得在剪切期间可以保持非常高的精度。然而，如果坐标值可以保持在±3.0e + 9的范围内，那么通过避免大的整数数学，实现了适度〜10％的削波性能的改进。
If the preprocessor directive use_int32 is defined, cInt will represent a signed 32bit integer. This improves clipping performance by 20-30% but the trade-off is that coordinate values are restricted to the much narrower range of ± 46340.
如果定义了预处理器指令use_int32，则cInt将表示有符号的32位整数。这提高了20-30％的剪切性能，但是折衷是坐标值被限制在非常窄的范围±46340。
Copyright ©2010-2014 Angus Johnson - Clipper 6.2.1 - Help file built on 1-November-2014


TAB
Field
Normalized
ParserTester
TBoolean
FieldType
TDateMillis
FieldType
TDateString
FieldType
TDouble
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TNumeric
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TSV2Normalized
Normalized
TString
FieldType
TimeLog
Timer
Timer
utils
Timer
TokenizeRule
structs
timestamp
Header3164
Header5424
SyslogHeader
tokenizeRuleId
ArrangeRule
tokenizeRules
ParserRef
tokenizeRules_fn
ParserRefFiles
tokrul
ConfZkPaths
topic
Conf
Opts
KfPaths
topicConfig
KfPaths
topicConfigChanges
KfPaths
topicConfigs
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ParserHelper
tuple
FieldKey


Everyone does better when women do better
Women have diverse and valuable experiences. Women in leadership are catalyst for change.
Women achieve greater change when they work together as their authentic selves.
In fact, Indiana was given a C+ grade overall for women in management positions and elected office. The gap is even greater for women of color.
Despite contributing 58% of the volunteer hours nationwide, women hold less than XX% of the positions on non-profit governing boards.
Indiana was given an F grade and ranked 45th of all states for the well-being of women based on 36 factors related to women's economic security, leadership, and health.
We are creating progressive change in Hoosier communities by connecting high potential women leaders. We believe that a progressive future in Indiana must be powered by strong, diverse, and effective women leaders who have the skills and knowledge ready to build solutions as great as the challenges we face.
WPP trains and connects motivated women who unabashedly support one another in positively impacting the areas where they work and live. Our network of leaders drives collaboration across sectors in order to improve the quality of our institutions and amplify the voices of women in policy making.
Join Us!


The Brown Fairy Book
by Andrew Lang
Kisa the Cat
by Andrew Lang
Additional Information
- Year Published: 1904
- Language: English
- Country of Origin: England
- Source: Lang, A. (Ed.). (1904). The Brown Fairy Book. London: Longmans, Green & Co.
-
Readability:
- Flesch–Kincaid Level: 6.7
- Word Count: 1,945
- Genre: Fairy Tale/Folk Tale
- ✎ Cite This
Downloads
Once upon a time there lived a queen who had a beautiful cat, the colour of smoke, with china-blue eyes, which she was very fond of. The cat was constantly with her, and ran after her wherever she went, and even sat up proudly by her side when she drove out in her fine glass coach.
‘Oh, kitty,’ said the queen one day, ‘you are happier than I am! For you have a dear kitten just like yourself, and I have nobody to play with but you.’
‘Don’t cry,’ answered the cat, laying her paw on her mistress’s arm. ‘Crying never does any good. I will see what can be done.’
The cat was as good as her word. As soon as she returned from her drive she trotted off to the forest to consult a fairy who dwelt there, and very soon after the queen had a little girl, who seemed made out of snow and sunbeams. The queen was delighted, and soon the baby began to take notice of the kitten as she jumped about the room, and would not go to sleep at all unless the kitten lay curled up beside her.
Two or three months went by, and though the baby was still a baby, the kitten was fast becoming a cat, and one evening when, as usual, the nurse came to look for her, to put her in the baby’s cot, she was nowhere to be found.  The servants, each anxious to find her, as the queen was certain to reward the lucky man, searched in the most impossible places. Boxes were opened that would hardly have held the kitten’s paw; books were taken from bookshelves, lest the kitten should have got behind them, drawers were pulled out, for perhaps the kitten might have got shut in. But it was all no use. The kitten had plainly run away, and nobody could tell if it would ever choose to come back.
Years passed away, and one day, when the princess was playing ball in the garden, she happened to throw her ball farther than usual, and it fell into a clump of rose-bushes. The princess of course ran after it at once, and she was stooping down to feel if it was hidden in the long grass, when she heard a voice calling her: ‘Ingibjorg! Ingibjorg!’ it said, ‘have you forgotten me? I am Kisa, your sister!’
‘But I never HAD a sister,’ answered Ingibjorg, very much puzzled; for she knew nothing of what had taken place so long ago.
‘Don’t you remember how I always slept in your cot beside you, and how you cried till I came? But girls have no memories at all! Why, I could find my way straight up to that cot this moment, if I was once inside the palace.’
‘Why did you go away then?’ asked the princess. But before Kisa could answer, Ingibjorg’s attendants arrived breathless on the scene, and were so horrified at the sight of a strange cat, that Kisa plunged into the bushes and went back to the forest.
The princess was very much vexed with her ladies-in-waiting for frightening away her old playfellow, and told the queen who came to her room every evening to bid her good-night.
‘Yes, it is quite true what Kisa said,’ answered the queen; ‘I should have liked to see her again. Perhaps, some day, she will return, and then you must bring her to me.’
Next morning it was very hot, and the princess declared that she must go and play in the forest, where it was always cool, under the big shady trees. As usual, her attendants let her do anything she pleased, and sitting down on a mossy bank where a little stream tinkled by, soon fell sound asleep. The princess saw with delight that they would pay no heed to her, and wandered on and on, expecting every moment to see some fairies dancing round a ring, or some little brown elves peeping at her from behind a tree. But, alas! she met none of these; instead, a horrible giant came out of his cave and ordered her to follow him. The princess felt much afraid, as he was so big and ugly, and began to be sorry that she had not stayed within reach of help; but as there was no use in disobeying the giant, she walked meekly behind.
They went a long way, and Ingibjorg grew very tired, and at length began to cry.
‘I don’t like girls who make horrid noises,’ said the giant, turning round. ‘But if you WANT to cry, I will give you something to cry for.’  Then he went away.
Poor Ingibjorg lay on the grass in terrible pain, and wondering if she should stay there till she died, as no one would know where to look for her. How long it was since she had set out in the morning she could not tell—it seemed years to her, of course; but the sun was still high in the heavens when she heard the sound of wheels, and then, with a great effort, for her throat was parched with fright and pain, she gave a shout.
‘I am coming!’ was the answer; and in another moment a cart made its way through the trees, driven by Kisa, who used her tail as a whip to urge the horse to go faster.
In the corner of the room was a pile of cushions, and these Kisa arranged as a bed. Ingibjorg, who by this time was nearly fainting from all she had gone through, drank greedily some milk, and then sank back on the cushions while Kisa fetched some dried herbs from a cupboard, soaked them in warm water and tied them on the bleeding legs. The pain vanished at once, and Ingibjorg looked up and smiled at Kisa.
 I will lock the door, and no one can hurt you.’ But before she had finished the princess was asleep. Then Kisa got into the cart, which was standing at the door, and catching up the reins, drove straight to the giant’s cave.
Leaving her cart behind some trees, Kisa crept gently up to the open door, and, crouching down, listened to what the giant was telling his wife, who was at supper with him.
‘The first day that I can spare I shall just go back and kill her,’ he said; ‘it would never do for people in the forest to know that a mere girl can defy me!’ And he and his wife were so busy calling Ingibjorg all sorts of names for her bad behaviour, that they never noticed Kisa stealing into a dark corner, and upsetting a whole bag of salt into the great pot before the fire.
‘Dear me, how thirsty I am!’ cried the giant by-and-by.
‘So am I,’ answered the wife. ‘I do wish I had not taken that last spoonful of broth; I am sure something was wrong with it.’
‘If I don’t get some water I shall die,’ went on the giant. And rushing out of the cave, followed by his wife, he ran down the path which led to the river.
Then Kisa entered the hut, and lost no time in searching every hole till she came upon some grass, under which Ingibjorg’s feet were hidden, and putting them in her cart, drove back again to her own hut.
Ingibjorg was thankful to see her, for she had lain, too frightened to sleep, trembling at every noise.
‘Oh, is it you?’ she cried joyfully, as Kisa turned the key.
‘In two minutes they shall be as tight as they ever were!’ said Kisa. And taking some strings of the magic grass which the giant had carelessly heaped on them, she bound the feet on to the legs above.
‘Of course you won’t be able to walk for some time; you must not expect THAT,’ she continued. ‘But if you are very good, perhaps, in about a week, I may carry you home again.’
And so she did; and when the cat drove the cart up to the palace gate, lashing the horse furiously with her tail, and the king and queen saw their lost daughter sitting beside her, they declared that no reward could be too great for the person who had brought her out of the giant’s hands.
‘We will talk about that by-and-by,’ said the cat, as she made her best bow, and turned her horse’s head.
The princess was very unhappy when Kisa left her without even bidding her farewell. She would neither eat nor drink, nor take any notice of all the beautiful dresses her parents bought for her.
‘She will die, unless we can make her laugh,’ one whispered to the other. ‘Is there anything in the world that we have left untried?’
‘Nothing except marriage,’ answered the king. And he invited all the handsomest young men he could think of to the palace, and bade the princess choose a husband from among them.
It took her some time to decide which she admired the most, but at last she fixed upon a young prince, whose eyes were like the pools in the forest, and his hair of bright gold. The king and the queen were greatly pleased, as the young man was the son of a neighbouring king, and they gave orders that a splendid feast should be got ready.
When the marriage was over, Kisa suddenly stood before them, and Ingibjorg rushed forward and clasped her in her arms.
‘I have come to claim my reward,’ said the cat. ‘Let me sleep for this night at the foot of your bed.’
‘Is that ALL?’ asked Ingibjorg, much disappointed.
‘It is enough,’ answered the cat. And when the morning dawned, it was no cat that lay upon the bed, but a beautiful princess.
‘My mother and I were both enchanted by a spiteful fairy,’ said she, ‘we could not free ourselves till we had done some kindly deed that had never been wrought before.
Then they were all more delighted than before, and the princess lived in the court until she, too, married, and went away to govern one of her own.


Long-serving member of NamPol Deputy Commissioner Jurie Johannes Vermeulen dies
A long-serving member of the Namibian Police Force (NamPol) Deputy Commissioner Jurie Johannes Vermeulen has died.
This was announced by the Inspector General of NamPol Sebastian Ndeitunga today.
The 55-year-old Vermeulen, who served the force for 29 years, died on Sunday in the Gobabis Private Hospital.
Vermeulen joined NamPol as a sergeant in 1990 and through his dedication to policing service, worked his way up to the position of Deputy Commissioner in 2017, a rank he held until the time of his death.
Ndeitunga described Vermeulen as a disciplined, devoted, skilful and hardworking senior police officer who upheld and defended the rule of law throughout his policing career.
He will be buried on Saturday.


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Omni isn’t better, just different. It’s another option for the billion Android users out there. Android (vs. iOS and every other mobile OS) has thrived on options as well as the gigantic, talented development community that has emerged to build those options. That’s the beauty of Android – that you can pick and choose from a smorgasbord of devices with varying features and functionality.
Omni is a chance to get involved, no matter who you are. Developers, whether you’ve been developing apps for a week, or ROM features for 3 years, you’re welcome. Users, we know you want to help out, and now you can - we encourage all levels of contribution, from code to reporting bugs to squashing bugs to (what developer's tend to hate) documentation.
The core of Omni is the community, and we recognize that everyone has a role to play. So we have a number of ways for you to get involved:
Github – our codebase where we build from.
Gerrit – view our project at the code level with the ability to see what’s merged, what’s open and what’s in review.
Forum – participate in general discussion, Q&A, and Features Development on XDA. Also find device-specific builds in their relevant forums on XDA
IRC – get involved with Omni in realtime on IRC Freenode:


Universal update guide for patch versions
Select Version to Install
Make sure you view this update guide from the tag (version) of GitLab you would like to install. In most cases this should be the highest numbered production tag (without rc in it). You can select the tag in the version dropdown in the top left corner of GitLab (below the menu bar).
0. Backup
It's useful to make a backup just in case things go south: (With MySQL, this may require granting "LOCK TABLES" privileges to the GitLab user on the database version)
cd /home/git/gitlab
sudo -u git -H bundle exec rake gitlab:backup:create RAILS_ENV=production
1. Stop server
sudo service gitlab stop
2. Get latest code for the stable branch
In the commands below, replace LATEST_TAG
with the latest GitLab tag you want
to update to, for example v8.0.3
. Use git tag -l 'v*.[0-9]' --sort='v:refname'
to see a list of all tags. Make sure to update patch versions only (check your
current version with cat VERSION
).
cd /home/git/gitlab
sudo -u git -H git fetch --all
sudo -u git -H git checkout -- Gemfile.lock db/schema.rb
sudo -u git -H git checkout LATEST_TAG -b LATEST_TAG
3. Update gitlab-shell to the corresponding version
cd /home/git/gitlab-shell
sudo -u git -H git fetch
sudo -u git -H git checkout v`cat /home/git/gitlab/GITLAB_SHELL_VERSION` -b v`cat /home/git/gitlab/GITLAB_SHELL_VERSION`
4. Update gitlab-workhorse to the corresponding version
cd /home/git/gitlab
sudo -u git -H bundle exec rake "gitlab:workhorse:install[/home/git/gitlab-workhorse]" RAILS_ENV=production
5. Install libs, migrations, etc.
cd /home/git/gitlab
# PostgreSQL
sudo -u git -H bundle install --without development test mysql --deployment
# MySQL
sudo -u git -H bundle install --without development test postgres --deployment
# Optional: clean up old gems
sudo -u git -H bundle clean
# Run database migrations
sudo -u git -H bundle exec rake db:migrate RAILS_ENV=production
# Clean up assets and cache
sudo -u git -H bundle exec rake assets:clean assets:precompile cache:clear RAILS_ENV=production
6. Start application
sudo service gitlab start
sudo service nginx restart
7. Check application status
Check if GitLab and its environment are configured correctly:
sudo -u git -H bundle exec rake gitlab:env:info RAILS_ENV=production
To make sure you didn't miss anything run a more thorough check with:
sudo -u git -H bundle exec rake gitlab:check RAILS_ENV=production
If all items are green, then congratulations upgrade complete!


As a UX intern at FICO, I designed an enterprise web application that allows organizations to view complex cybersecurity data and perform security risk assessment || Visualizing and organizing multi dimensional data in an intuitve way.
As a Software Engineer at Microsoft, I worked on designing a Windows Phone 8 application that allows sales users to quickly contact account managers and team members of an account.
As a Software Engineer at Microsoft, I worked on designing a Windows 10 Desktop application that that facilitates OEM partners to access multiple accounts and perform critical administrative tasks quickly and efficiently.
Collaborated with Arbor Insight on a mobile app to anonymously report safety, security, and ethics concerns in a workplace || Uncovering potential user needs, assessing implementation oppotunities and challenges, identifying usability issues.


--- title: "The Backdoor Criterion and Basics of Regression in R" subtitle: "Our first steps in statistical modeling in R" type: book weight: 5 output: blogdown:html_page: theme: journal highlight: tango df_print: paged toc: yes toc_float: collapsed: false ---
Welcome to our fourth tutorial for the Statistics II: Statistical Modeling & Causal Inference (with R) course.
During this week's lecture you reviewed bivariate and multiple linear regressions. You also learned how Directed Acyclic Graphs (DAGs) can be leveraged to gather causal estimates.
In this lab session we will:
ggdag
and dagitty
packages to assess your modeling strategy# These are the libraries we will use today. Make sure to install them in your console in case you have not done so previously.
set.seed(42) #for consistent results in randomization
library(wooldridge) # To get our example's dataset
library(tidyverse) # To use dplyr functions and the pipe operator when needed
library(ggplot2) # To visualize data (this package is also loaded by library(tidyverse))
library(ggdag) # To dagify and plot our DAG objects in R
library(dagitty) # To perform analysis in our DAG objects in R
library(stargazer) # To render nicer regression output
data("wage1") # calls the wage1 dataset from the woorldridge package
Last week we learned about the general syntax of the ggdag
package:
ggdag::dagify()
ggdag::ggdag()
Today, we will learn how the ggdag
and dagitty
packages can help us illustrate our paths and adjustment sets to fulfill the backdoor criterion
Let's take one of the DAGs from our review slides:
coord_dag <- list(
x = c(d = 0, p = 0, b = 1, a = 1 , c = 2, y = 2),
y = c(d = 0, p = 2, b = 1, a = -1, c = 2, y = 0)
)
our_dag <- ggdag::dagify(d ~ p + a, # p and a pointing at d
b ~ p + c, # p and c pointing at b
y ~ d + a + c, # d, a, and c pointing at y
coords = coord_dag, # our coordinates from the list up there
exposure = "d", # we declare out exposure variable
outcome = "y") # we declare out outcome variable
ggdag::ggdag(our_dag) +
theme_dag() # equivalent to theme_void()
As you have seen, when we dagify a DAG in R a dagitty object is created. These objects tell R that we are dealing with DAGs.
This is very important because in addition to plotting them, we can do analyses on the DAG objects. A package that complements ggdag
is the dagitty
package.
Today, we will focus on two functions from the dagitty
package:
dagitty::paths()
: Returns a list with two components: paths, which gives the actual paths, and open, which shows whether each path is open (d-connected) or closed (d-separated).dagitty::adjustmentSets()
: Lists the sets of covariates that would allow for unbiased estimation of causal effects, assuming that the causal graph is correct.We just need to input our DAG object.
Let's see how the output of the dagitty::paths
function looks like:
dagitty::paths(our_dag)
## $paths
## [1] "d -> y" "d <- a -> y" "d <- p -> b <- c -> y"
##
## $open
## [1] TRUE TRUE FALSE
We see under $paths
the three paths we declared during the manual exercise:
Additionally, $open
tells us whether each path is open. In this case, we see that the second path is the only open non-causal path, so we would need to condition on a to close it.
We can also use ggdag
to present the open paths visually with the ggdag_paths()
function, as such:
ggdag::ggdag_paths(our_dag) +
theme_dag()
In addition to listing all the paths and sorting the backdoors manually, we can use the dagitty::adjustmentSets()
function.
With this function, we just need to input our DAG object and it will return the different sets of adjustments.
dagitty::adjustmentSets(our_dag)
## { a }
For example, in this DAG there is only one option. We need to control for a.
We can also use ggdag
to present the open paths visually with the ggdag_adjustment_set()
function, as such:
Also, do not forget to set the argument shadow = TRUE
, so that the arrows from the adjusted nodes are included.
ggdag::ggdag_adjustment_set(our_dag, shadow = T) +
theme_dag()
ggdag
documentation: https://ggdag.malco.io/dagitty
vignette: https://cran.r-project.org/web/packages/dagitty/dagitty.pdfNOW LET'S MOVE TO REGRESSION
Linear regression is largely used to predict the value of an outcome variable based on one or more input explanatory variables. As we previously discussed, regression addresses a simple mechanical problem, namely, what is our best guess of y given an observed x.
In bivariate regression, we are modeling a variable \(y\) as a mathematical function of one variable \(x\). We can generalize this in a mathematical equation as such:
\[y = \beta_{0} + \beta{1}x + ϵ\]
In multiple linear regression, we are modeling a variable \(y\) as a mathematical function of multiple variables \((x, z, m)\). We can generalize this in a mathematical equation as such:
\[y = \beta_{0} + \beta_{1}x + \beta_{2}z + \beta_{3}m + ϵ\]
Let's illustrate this with an example
We will use the wage1
dataset from the wooldridge
package. These are data from the 1976 Current Population Survey used by Jeffrey M. Wooldridge with pedagogical purposes in his book on Introductory Econometrics.
If you want to check the contents of the wage1
data frame, you can type ?wage1
in your console
We can start by exploring the relationship visually with our newly attained ggplot2
skills:
ggplot(wage1, aes(x = educ, y = wage)) +
geom_point(color = "grey60") +
geom_smooth(method = "lm", se = F, color = "#CC0055") +
theme_minimal() +
labs(x = "Years of education",
y = "Hourly wage (USD)")
lm()
functionThis question can be formalized mathematically as:
\[Hourly\ wage = \beta_0 + \beta_1Years\ of\ education + ϵ\]
Our interest here would be to build a model that predicts the hourly wage of a respondent (our outcome variable) using the years of education (our explanatory variable). Fortunately for us, R provides us with a very intuitive syntax to model regressions.
The general syntax for running a regression model in R is the following:
your_model_biv <- lm(outcome_variable ~ explanarory_variable, data = your_dataset) #for a bivariate regression
your_model_mult <- lm(outcome_variable ~ explanarory_variable_1 + explanarory_variable_2, data = your_dataset) #for multiple regression
Now let's create our own model and save it into the model_1
object, based on the bivariate regression we specified above in which wage
is our outcome variable, educ
is our explanatory variable, and our data come from the wage1
object:
model_1 <- lm(wage ~ educ, data = wage1)
summary()
and broom::tidy()
We have created an object that contains the coefficients, standard errors and further information from your model. In order to see the estimates, you could use the base R function summary()
. This function is very useful when you want to print your results in your console.
Alternatively, you can use the tidy()
function from the broom
package. The function constructs a data frame that summarizes the model’s statistical findings. You can see what else you can do with broom by running: vignette(“broom”). The broom::tidy()
function is useful when you want to store the values for future use (e.g., visualizing them)
Let's try both options in the console up there. You just need to copy this code below the model_1
code.
summary(model_1)
broom::tidy(model_1)
\[Hourly\ wage = \beta_0 + \beta_1Years\ of\ education + ϵ\]
| Dependent variable: | |
| Hourly wage | |
| Years of education | 0.541*** |
| (0.053) | |
| Constant | -0.905 |
| (0.685) | |
| Observations | 526 |
| R2 | 0.165 |
| Adjusted R2 | 0.163 |
| Residual Std. Error | 3.378 (df = 524) |
| F Statistic | 103.363*** (df = 1; 524) |
| Note: | p<0.1; p<0.05; p<0.01 |
How would you interpret the results of our model_1
?
educ
coefficient mean?As we have discussed in previous sessions we live in a very complex world. It is very likely that our exploration of the relationship between education and respondents' salaries is open to multiple sources of bias.
Looking back at 1976 US, can you think of possible variables inside the mix?
How about the sex or the ethnicity of a worker?
ggplot(wage1, aes(x = educ, y = wage, color = as.factor(female))) +
geom_point(alpha = 0.3) +
geom_smooth(method = "lm", se = F) +
theme_minimal() +
labs(x = "Years of education",
y = "Hourly wage (USD)",
color = "Female") +
theme(legend.position = "bottom")
Check what happens when we replace the color = as.factor(female)
for color = female
This question can be formalized mathematically as:
\[Hourly\ wage = \beta_0 + \beta_1Years\ of\ education + \beta_2Female + ϵ\]
Our interest here would be to build a model that predicts the hourly wage of a respondent (our outcome variable) using the years of education and their sex (our explanatory variables).
Let's remember the syntax for running a regression model in R:
your_model_biv <- lm(outcome_variable ~ explanarory_variable, data = your_dataset) #for a bivariate regression
your_model_mult <- lm(outcome_variable ~ explanarory_variable_1 + explanarory_variable_2, data = your_dataset) #for multiple regression
Now let's create our own model, save it into the model_2
object, and print the results based on the formula regression we specified above in which wage
is our outcome variable, educ
and female
are our explanatory variables, and our data come from the wage1
object:
model_2 <- lm(wage ~ educ + female, data = wage1)
summary(model_2)
##
## Call:
## lm(formula = wage ~ educ + female, data = wage1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9890 -1.8702 -0.6651 1.0447 15.4998
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.62282 0.67253 0.926 0.355
## educ 0.50645 0.05039 10.051 < 2e-16 ***
## female -2.27336 0.27904 -8.147 2.76e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.186 on 523 degrees of freedom
## Multiple R-squared: 0.2588, Adjusted R-squared: 0.256
## F-statistic: 91.32 on 2 and 523 DF, p-value: < 2.2e-16
\[Hourly\ wage = \beta_0 + \beta_1Years\ of\ education + \beta_2Female + ϵ\]
| Dependent variable: | |
| Hourly wage | |
| Years of education | 0.506*** |
| (0.050) | |
| Female | -2.273*** |
| (0.279) | |
| Constant | 0.623 |
| (0.673) | |
| Observations | 526 |
| R2 | 0.259 |
| Adjusted R2 | 0.256 |
| Residual Std. Error | 3.186 (df = 523) |
| F Statistic | 91.315*** (df = 2; 523) |
| Note: | p<0.1; p<0.05; p<0.01 |
How would you interpret the results of our model_2
?
educ
coefficient mean?female
coefficient mean?As we discussed previously, when we do not have our causal inference hats on, the main goal of linear regression is to predict an outcome value on the basis of one or multiple predictor variables.
R has a generic function predict()
that helps us arrive at the predicted values on the basis of our explanatory variables.
The syntax of predict()
is the following:
predict(name_of_the_model, newdata = data.frame(explanatory1 = value, explanatory2 = value))
Say that based on our
model_2
, we are interested in the expected average hourly wage of a woman with 15 years of education.
predict(model_2, newdata = data.frame(educ = 15, female = 1))
## 1
## 5.946237
female
to 0? What does the result mean?predict(model_2, newdata = data.frame(educ = 16, female = 0)) - predict(model_2, newdata = data.frame(educ = 15, female =0))
## 1
## 0.5064521
Here are some questions for you. Note that there are multiple ways to reach the same answer:
What is the expected hourly wage of a male with 15 years of education?
How much more on average does a male worker earn than a female counterpart?",
How much more is a worker expected to earn for every additional year of education, keeping sex constant?
As we can remember from our slides, we were introduced to a set of key rules in understanding how to employ DAGs to guide our modeling strategy.
In this portion of the tutorial we will demonstrate how different bias come to work when we model our relationships of interest.
As it is showcased from our DAG, we assume that earning celebrity status is a function of an individuals beauty and talent.
We will simulate data that reflects this assumptions. In our world, someone gains celebrity status if the sum of units of beauty and celebrity are greater than 8.
# beauty - 1000 observations with mean 5 units of beauty and sd 1.5 (arbitrary scale)
beauty <- rnorm(1000, 5, 1.5)
# talent - 1000 observations with mean 3 units of talent and sd 1 (arbitrary scale)
talent <- rnorm(1000, 3, 1)
# celebrity - binary
celebrity_status <- ifelse(beauty + talent > 8, "Celebrity" , "Not Celebrity") # celebrity if the sum of units are greater than 8
celebrity_df <- dplyr::tibble(beauty, talent, celebrity_status) # we make a df with our values
head(celebrity_df, 10)
## # A tibble: 10 x 3
## beauty talent celebrity_status
## <dbl> <dbl> <chr>
## 1 7.06 5.33 Celebrity
## 2 4.15 3.52 Not Celebrity
## 3 5.54 3.97 Celebrity
## 4 5.95 3.38 Celebrity
## 5 5.61 2.00 Not Celebrity
## 6 4.84 2.40 Not Celebrity
## 7 7.27 3.17 Celebrity
## 8 4.86 0.0715 Not Celebrity
## 9 8.03 2.15 Celebrity
## 10 4.91 3.80 Celebrity
In this case, as our simulation suggest, we have a collider structure. We can see that celebrity can be a function of beauty or talent. Also, we can infer from the way we defined the variables that beauty and talent are d-separated (ie. the path between them is closed because celebrity is a collider).
Say you are interested in researching the relationship between beauty and talent for your Master's thesis, while doing your literature review you encounter a series of papers that find a negative relationship between the two and state that more beautiful people tend to be less talented. The model that these teams of the researchers used was the following:
\[Y_{Talent} = \beta_0 + \beta_1Beauty + \beta_2Celebrity\]
Your scientific hunch makes you believe that celebrity is a collider and that by controlling for it in their models, the researchers are inducing collider bias, or endogenous bias. You decide to move forward with your thesis by laying out a criticism to previous work on the field, given that you consider the formalization of their models is erroneous. You utilize the same data previous papers used, but based on your logic, you do not control for celebrity status. This is what you find:
true_model_celebrity <- lm(talent ~ beauty, data = celebrity_df)
summary(true_model_celebrity)
##
## Call:
## lm(formula = talent ~ beauty, data = celebrity_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9225 -0.6588 -0.0083 0.6628 3.5877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.962209 0.107595 27.531 <2e-16 ***
## beauty 0.006545 0.020755 0.315 0.753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9865 on 998 degrees of freedom
## Multiple R-squared: 9.964e-05, Adjusted R-squared: -0.0009023
## F-statistic: 0.09945 on 1 and 998 DF, p-value: 0.7526
ggplot(celebrity_df, aes(x=beauty,
y=talent)) +
geom_point() +
geom_smooth(method = "lm", se = F) +
theme_minimal() +
theme(legend.position = "bottom") +
labs(x = "Beauty",
y = "Talent")
Let's see:
biased_model_celibrity <- lm(talent ~ beauty + celebrity_status, data = celebrity_df)
summary(biased_model_celibrity)
##
## Call:
## lm(formula = talent ~ beauty + celebrity_status, data = celebrity_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4244 -0.5394 0.0110 0.5064 2.9429
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.37834 0.13983 38.46 <2e-16 ***
## beauty -0.32668 0.02265 -14.43 <2e-16 ***
## celebrity_statusNot Celebrity -1.51375 0.06808 -22.24 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.807 on 997 degrees of freedom
## Multiple R-squared: 0.3316, Adjusted R-squared: 0.3302
## F-statistic: 247.3 on 2 and 997 DF, p-value: < 2.2e-16
ggplot(celebrity_df, aes(x=beauty, y=talent, color = celebrity_status)) +
geom_point() +
geom_smooth(method = "lm", se = F) +
theme_minimal() +
theme(legend.position = "bottom") +
labs(x = "Beauty",
y = "Talent",
color = "")
As we can see, by controlling for a collider, the previous literature was inducing to a non-existent association between beauty and talent, also known as collider or endogenous bias.
# sex - replicate male and female 500 times each
sex <- rep(c("Male", "Female"), each = 500)
# shoe size - random number with mean 38 and sd 4, plus 4 if the observation is male
shoesize <- rnorm(1000, 38, 2) + (4 * as.numeric(sex == "Male"))
# salary - a random number with mean 25 and sd 2, plus 5 if the observation is male
salary <- rnorm(1000, 25, 2) + (5 * as.numeric(sex == "Male"))
salary_df <- dplyr::tibble(sex, shoesize, salary)
head(salary_df, 10)
## # A tibble: 10 x 3
## sex shoesize salary
## <chr> <dbl> <dbl>
## 1 Male 42.5 28.6
## 2 Male 41.4 28.4
## 3 Male 38.6 29.2
## 4 Male 38.0 27.7
## 5 Male 39.4 32.2
## 6 Male 42.7 28.2
## 7 Male 41.7 32.6
## 8 Male 40.5 27.1
## 9 Male 40.4 31.7
## 10 Male 43.1 28.8
Say now one of your peers tells you about this new study that suggests that shoe size has an effect on an individuals' salary. You are a bit skeptic and read it. The model that these researchers apply is the following:
\[Y_{Salary} = \beta_0 + \beta_1ShoeSize\]
Your scientific hunch makes you believe that this relationship could be confounded by the sex of the respondent. You think that by failing to control for sex in their models, the researchers are inducing omitted variable bias.  This is what you find:
\[Y_{Salary} = \beta_0 + \beta_1ShoeSize + \beta_2Sex\]
true_model_salary <- lm(salary ~ shoesize + sex, data = salary_df)
summary(true_model_salary)
##
## Call:
## lm(formula = salary ~ shoesize + sex, data = salary_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2341 -1.3698 -0.0501 1.3595 6.4303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.73879 1.15886 22.210 <2e-16 ***
## shoesize -0.02030 0.03044 -0.667 0.505
## sexMale 5.05924 0.17616 28.720 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.981 on 997 degrees of freedom
## Multiple R-squared: 0.6129, Adjusted R-squared: 0.6121
## F-statistic: 789.2 on 2 and 997 DF, p-value: < 2.2e-16
ggplot(salary_df, aes(x=shoesize, y=salary, color = sex)) +
geom_point() +
geom_smooth(method = "lm", se = F) +
theme_minimal() +
theme(legend.position = "bottom") +
labs(x = "Shoe size",
y = "Salary",
color = "")
biased_model_salary <- lm(salary ~ shoesize, data = salary_df)
summary(biased_model_salary)
##
## Call:
## lm(formula = salary ~ shoesize, data = salary_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.8777 -1.9101 -0.0511 1.8496 7.9774
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.68865 1.17280 3.145 0.00171 **
## shoesize 0.59429 0.02925 20.319 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.676 on 998 degrees of freedom
## Multiple R-squared: 0.2926, Adjusted R-squared: 0.2919
## F-statistic: 412.9 on 1 and 998 DF, p-value: < 2.2e-16
ggplot(salary_df, aes(x=shoesize, y=salary)) +
geom_point() +
geom_smooth(method = "lm", se = F) +
theme_minimal() +
labs(x = "Shoe size",
y = "Salary")
As we can see, by failing to control for a confounder, the previous literature was creating a non-existent association between shoe size and salary, incurring in ommited variable bias.


Name: {{ owner.first_name }}
Surname: {{ owner.last_name }}
Passport (number): {{ owner.person.passport }}
Lives at: {{ owner.person.address }}
Nationality: {{ owner.person.nationality }}
License number and type: {{ license.number }}, {{ license.type }}
Has/had cars:
{%for owns_one in owns%}
- {{owns_one.car.make}} {{owns_one.car.model}}
{%endfor%}


Revamp the roadmap website for corporate strategy
Web design
Working in a design team, I designed mockups, defined site map, collaborated with engineers to deliver for roll-out
Jul 2019 - Nov 2019
Information architecture, rapid prototyping, redlining, React, visual design
One major initiative of SAP in 2019 is to make the long-lasting B2B company more "consumable". Roadmap, encompassing complex product portfolio and catering to a wide range of audience, is looking for a revamp for the customers to access the information in an efficient, but appealing way.


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Stephen Kelley
Greater Worcester County
Congratulations on your engagement! I have been marrying happy couples for over 22 years and I loved every year of it. If you are looking for a Justice of the Peace in the greater Worcester area, I would love to join you on your special day. I have been a part of many unique ceremonies and weddings and I am flexible to fit the needs of your ceremony.


Investment & Loan Management Solution
Developed by Monika Gautam & Shashank Srivastava
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Messi Reveals His Favourite Ballon d'Or Triumph
by David KappelWith Lionel Messi having done battle with Cristiano Ronaldo for many Ballons d'Or over the past decade, the Argentine has now revealed his favourite Golden Ball triumph.
Read: The 10 Richest Footballers In The World
With the two superstars are once again among the leading candidates to lift the 2019 version of the award, Messi has explained that his first triumph is his most memorable.
"It is a beautiful recognition and perhaps the most special was the first," he told OTRO, as cited by AS.
"It is something that comes from the collective. Without having achieved all the collective successes that we had to achieve, I wouldn't have been able to get these individual awards."
Choose the left winger for SA's Real Madrid Dream Team & win a replica shirt!
Messi also spoke about why his victory in 2010 will always stay in his memory.
"The image of the three nominees (Messi, Andres Iniesta and Xavi in 2010) for the Ballon d'Or, it was special for us because of the friendship we have," he added.
"Being so long in the same club, all three living practically the same during our whole careers and special for the club. For what it means, the truth is it was impressive."
Read: Ronaldo Extends Ballon d'Or Record Over Messi
Messi won the Ballon d'Or in 2009, 2010, 2011, 2012 and 2015, and could become the first player in history to lift the Golden Ball six times this year – Ronaldo has won it five times as well, and could achieve the same feat.


Given an image of a formula (typeset or handwritten) this package provides calls to the 'Mathpix' service to produce the 'LaTeX' code which should generate that image, and pastes it into a (e.g. an 'rmarkdown') document. See <https://docs.mathpix.com/> for full details. 'Mathpix' is an external service and use of the API is subject to their terms and conditions.
| Version: | 0.3.0 |
| Depends: | R (≥ 3.3.0) |
| Imports: | purrr, base64enc, httr, rstudioapi, texPreview, magick |
| Suggests: | testthat, covr |
| Published: | 2018-04-27 |
| Author: | Jonathan Carroll [aut, cre] |
| Maintainer: | Jonathan Carroll <rpkg at jcarroll.com.au> |
| BugReports: | https://github.com/jonocarroll/mathpix/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/jonocarroll/mathpix |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | WebTechnologies |
| CRAN checks: | mathpix results |
| Reference manual: | mathpix.pdf |
| Package source: | mathpix_0.3.0.tar.gz |
| Windows binaries: | r-devel: mathpix_0.3.0.zip, r-release: mathpix_0.3.0.zip, r-oldrel: mathpix_0.3.0.zip |
| OS X binaries: | r-release: mathpix_0.3.0.tgz, r-oldrel: mathpix_0.3.0.tgz |
| Old sources: | mathpix archive |
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A PL/SQL package is a named collection of functions, procedures, variables, cursors, user-defined types, and records that are referenced using a common qualifier - the package name.
In the next few pages, you will be creating PL/SQL packages for the e-Commerce application described in this workshop. If you have not already done so, please create the database schema presented in the "Schema Enablement" section. The right pane should match the following list of tables.


Beware
Once upon a time in a land far away, thousands of years ago, millions of Care Bears thrived across the land we know as Care de Casa…. Care Bears lived here with rainbows, positivity, and chocolate trees…
Then one day out of the sky the suited man landed on the land of positivity and made the thirteen colonies. What was once a land full of sunshine and rainbows, became thirteen sectors of concrete, raining taxi cabs, and suited man… a.k.a. presidents… with mystical powers. What has happened to the Care Bear population, is unfathomable…
A moment of silence....
Today, there only remains five Care Bears who have been in hiding, unknown as to where the rest of their Care Bear Crew has vanished to… while the thirteen colonies have prospered in unity of a grand patriotic ceremony.
The Presidents know nothing of what is about to come. The fiery and passion of the Care in the Bears is back. The Care Bears are ready to use their powers and stares that scare… to take back the land that was once lollipops and chocolate trees… and maybe just maybe… find the rest of the Care Bears and declare their independence to have the land of Care de Casa free of the suited man again...


[1]
Good quality pictures, removable lenses, beautiful camera; aa batteries
[2]
great auto settings; annoyingly slow flash cycle time
[3]
Uses 2 AA batteries; slower than some but not by much


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By creating an account you will be able to shop faster, be up to date on an order's status, and keep track of the orders you have previously made.
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| Product Name | Model | Quantity | Unit Price | Total |
| aenean dignissim - Delivery Date: 2011-04-22 |
Product 21 | 1 | $48.00 | $48.00 |
| Sub-Total: | $48.00 | |||
| Flat Shipping Rate: | $5.00 | |||
| Total: | $53.00 |


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Topics
Video Tutorials
Slides
Links
Combinatorial Optimization
Algorithms, Analyzing & Designing Algorithms
Correctness of Algorithms
Greedy Algorithms - Introduction to Greedy Algorithms
Greedy Choice Property
Greedy vs. Dynamic Programming
Fractional Knapsack Problem, Activity Selection Problem
Huffman Encoding
Task Scheduling Problem
Coin Changing Problem
Kruskal’s and Prim’s Minimum Spanning Tree Algorithms
Divide and Conquer Algorithms
Quick Sort
Merge Sort
Proof of Correctness, and Run Time Analysis
ntroduction to Dynamic Programming
Principle of Optimality
Optimal Substructure Property
Assembly Line Scheduling
Matrix Chain Multiplication
Traveling Salesperson Problem and Runtime Analysis
Graph Searching and Shortest Path Problems
Breadth First Search
Depth First Search
Single Source and All Pair Sho
Flow Networks
Formulating Problem as Linear Programs
Computational Geometry
Number Theoretic
String Matching Algorithms
Simplex Algorithm and Integer Linear Programming
NP Completeness and Approximation Algorithms
Polynomial Time Verification
NP Completeness and Reducibility
NP Complete Problems and Approximation Algorithms


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Sloop Debug Menu
Query Data Store
- Allows you to query keys and values from the badger store
Sloop Keys Histogram
- View the keys histogram
Config
- View the current active config for Sloop
Tables
- View Badger LSM Table Info
Badger Requests
Badger Events
Badger Metrics
Sloop Metrics


Sword - A Discord Library for Swift
Requirements
- macOS, Linux, or iOS (no voice for iOS)
- Swift 3.1
- libsodium (brew update && brew install libsodium)
Adding Sword
In order to add Sword as a dependency, you must first create a Swift executable in a designated folder, like so swift package init --type executable
. Then in the newly created Package.swift, open it and add Sword as a dependency
import PackageDescription
let package = Package(
name: "yourswiftexecutablehere",
dependencies: [
.Package(url: "https://github.com/Azoy/Sword", majorVersion: 0, minor: 8)
]
)
After that, open Sources/main.swift and remove everything and replace it with the example below.
import Sword
let bot = Sword(token: "Your bot token here")
bot.on(.ready) { [unowned bot] _ in
bot.editStatus(to: "online", playing: "with Sword!")
}
bot.on(.messageCreate) { data in
let msg = data as! Message
if msg.content == "!ping" {
msg.reply(with: "Pong!")
}
}
bot.connect()
Running the bot
Build the libraries with swift build
, then type .build/debug/yourswiftexecutablehere
Running the bot in Xcode
To run the bot in Xcode, you first have to compile the libraries with swift build
. Then to build the xcode project, type swift package generate-xcodeproj
. Finally, type open yourswiftexecutablehere.xcodeproj
, look at the top and follow the steps below
Then click the play button!
Links
Documentation - (created with Jazzy)
Join the API Channel to ask questions!


PubSubService
public final class PubSubService
Opens a connection to Cogswell pub/pub system.
-
Creates and configures a pub/sub connection.
Declaration
Swift
public static func connect(keys: [String], options: PubSubOptions?) -> PubSubConnectionHandle
Parameters
keys
The provided project keys.
options
The connection options.
Return Value
Returns a configured pub/sub connection handler to manage the connection.


This package contains the code for the JReleaseInfo code generator.
The code generator is implemented as a JavaBean. This JavaBean can be used from an AntTask or build in in other environments.


The Enumerable module is a required module for collection objects (e.g.,hashes, arrays, ranges). Enumerable methods build upon the each method to iterate over a collection object in multiple ways. For a quick reference to see what methods are included in the Enumerable model, run this code:
Enumerable.instance_methods.sort
You'll see the following returned:
[
[ 0] :all?,
[ 1] :any?,
[ 2] :chunk,
[ 3] :collect,
[ 4] :collect_concat,
[ 5] :count,
[ 6] :cycle,
[ 7] :detect,
[ 8] :drop,
[ 9] :drop_while,
[10] :each_cons,
[11] :each_entry,
[12] :each_slice,
[13] :each_with_index,
[14] :each_with_object,
[15] :entries,
[16] :find,
[17] :find_all,
[18] :find_index,
[19] :first,
[20] :flat_map,
[21] :grep,
[22] :group_by,
[23] :include?,
[24] :inject,
[25] :lazy,
[26] :map,
[27] :max,
[28] :max_by,
[29] :member?,
[30] :min,
[31] :min_by,
[32] :minmax,
[33] :minmax_by,
[34] :none?,
[35] :one?,
[36] :partition,
[37] :reduce,
[38] :reject,
[39] :reverse_each,
[40] :select,
[41] :slice_before,
[42] :sort,
[43] :sort_by,
[44] :take,
[45] :take_while,
[46] :to_a,
[47] :zip
]
Boom! 48 methods for your viewing and using pleasure (sorted alphabetically)!
Let's focus on the Enumerable#cycle method. Simply put the #cycle method offers you the ability to cycle through your collection object for however many times you ask it. If you don't tell the method how many times to cycle through the object, it will run forever. The #cycle method will return an Enumerator if you do not give it a code block. With a code block it will return nil and execute whatever you tell it to execute in the code block. Here's an example of what you'll see when an Enumerator is returned:
array = ["apples", "oranges", "bananas"]
array.cycle(3)
<Enumerator: ["apples", "oranges", "bananas"]:cycle(3)>
And here's what you'll see when you run it with a code block:
array.cycle(3){|x| puts x }
apples
oranges
bananas
apples
oranges
bananas
apples
oranges
bananas
nil
Above you can see how because the code block 'puts x' 3 times, the array outputs three times and then returns nil.
A cool technique to use is to store a collection object with the #cycle method called upon it in a variable. This allows you to cycle through your collection object as needed by using the #next method and iterating through it however many times. Here's an example of a program that implements these ideas to illustrate:
school_days = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"]
days = school_days.cycle
activities = ["volleyball", "dodgeball", "basketball", "capture the flag"]
gym_activities = activities.cycle
10.times do
day = days.next
activity = gym_activities.next
puts "On #{day} 5th graders will play #{activity}."
end
The code above will print out a list of activities for two weeks worth of an activity schedule. Where the focus is on playing four different games. Here's the output:
On Monday 5th graders will play volleyball.
On Tuesday 5th graders will play dodgeball.
On Wednesday 5th graders will play basketball.
On Thursday 5th graders will play capture the flag.
On Friday 5th graders will play volleyball.
On Monday 5th graders will play dodgeball.
On Tuesday 5th graders will play basketball.
On Wednesday 5th graders will play capture the flag.
On Thursday 5th graders will play volleyball.
On Friday 5th graders will play dodgeball.
Cool! So, now this 5th grade class has an activity schedule for the next two weeks!
Resources:
http://www.globalnerdy.com/2008/07/06/enumerating-enumerable-enumerablecycle/
Black, David A. The Well-Grounded Rubyist pg. 286-328


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