|
Nabu-asr
|
contains functions to compute the training loss More...
Functions | |
| def | nabu.neuralnetworks.trainers.loss_functions.factory (loss_function) |
| factory method for the loss function More... | |
| def | nabu.neuralnetworks.trainers.loss_functions.marigin_loss (targets, logits, logit_seq_length, target_seq_length) |
| marigin loss More... | |
| def | nabu.neuralnetworks.trainers.loss_functions.cross_entropy (targets, logits, seq_length) |
| compute the cross entropy for all sequences in the batch More... | |
| def | nabu.neuralnetworks.trainers.loss_functions.sigmoid_cross_entropy (targets, logits, seq_length) |
| compute the sigmnoid cross entropy for all sequences in the batch More... | |
| def | nabu.neuralnetworks.trainers.loss_functions.sum_cross_entropy (targets, logits, logit_seq_length, target_seq_length) |
| cross entropy summed over timesteps | |
| def | nabu.neuralnetworks.trainers.loss_functions.average_cross_entropy (targets, logits, logit_seq_length, target_seq_length) |
| cross entropy averaged over timesteps | |
| def | nabu.neuralnetworks.trainers.loss_functions.average_sigmoid_cross_entropy (targets, logits, logit_seq_length, target_seq_length) |
| sigmoid cross entropy averaged over timesteps | |
| def | nabu.neuralnetworks.trainers.loss_functions.CTC (targets, logits, logit_seq_length, target_seq_length) |
| CTC loss. More... | |
contains functions to compute the training loss
| def nabu.neuralnetworks.trainers.loss_functions.cross_entropy | ( | targets, | |
| logits, | |||
| seq_length | |||
| ) |
compute the cross entropy for all sequences in the batch
| targets | a dictionary of [batch_size x time x ...] tensor containing the targets |
| logits | a dictionary of [batch_size x time x ...] tensor containing the logits |
| seq_length | a dictionary of [batch_size] vectors containing the sequence lengths |
| def nabu.neuralnetworks.trainers.loss_functions.CTC | ( | targets, | |
| logits, | |||
| logit_seq_length, | |||
| target_seq_length | |||
| ) |
CTC loss.
| targets | a dictionary of [batch_size x time x ...] tensor containing the targets |
| logits | a dictionary of [batch_size x time x ...] tensor containing the logits |
| logit_seq_length | a dictionary of [batch_size] vectors containing the logit sequence lengths |
| target_seq_length | a dictionary of [batch_size] vectors containing the target sequence lengths |
| def nabu.neuralnetworks.trainers.loss_functions.factory | ( | loss_function | ) |
factory method for the loss function
| loss_function | the required loss function |
| def nabu.neuralnetworks.trainers.loss_functions.marigin_loss | ( | targets, | |
| logits, | |||
| logit_seq_length, | |||
| target_seq_length | |||
| ) |
marigin loss
| targets | a dictionary of [batch_size x time x ...] tensor containing the targets |
| logits | a dictionary of [batch_size x time x ...] tensor containing the logits |
| logit_seq_length | a dictionary of [batch_size] vectors containing the logit sequence lengths |
| target_seq_length | a dictionary of [batch_size] vectors containing the target sequence lengths |
| def nabu.neuralnetworks.trainers.loss_functions.sigmoid_cross_entropy | ( | targets, | |
| logits, | |||
| seq_length | |||
| ) |
compute the sigmnoid cross entropy for all sequences in the batch
| targets | a dictionary of [batch_size x time x ...] tensor containing the targets |
| logits | a dictionary of [batch_size x time x ...] tensor containing the logits |
| seq_length | a dictionary of [batch_size] vectors containing the sequence lengths |


The gradation of steps between the contour lines can be done either in multiples of even distribution with a logarithmic gradation or in a user defined gradation. In the first case five contour lines are calculated and automatically plotted. The steps between the contour lines are arranged in a logarithmic scale between 1 and the maximum counting density. in the first option the size of the counting calotte is fitted to the number of data, in the second option it is defined manually.
In the menu 'Colour\Contour lines' you can also choose different grades of gray for the plot of contour lines.
In the menu Lines the line type for each contour line can be changed.
In the menu Contouring methods further methods for the visualisation of counting densities are offered.
The menu smoothing factor is used only for the Scalar product-method .
The Menu Diagram ist the same as in the other programs.


//= includes/head.html
//= ../blocks/main/main.html //= ../blocks/sidebar/sidebar.html //= includes/scripts.html


This is a simple geographic quiz game.
In Play
mode, depending on the selected options in the Settings
dialog,
you will be shown some information about a country, and asked others, among :
Note that text queries require an exact match against the reference data, including casing, accents (even on uppercase letters), spaces, quotes, dashes.
In Explore
mode, clicking on a country on the map will display all its information.


{% extends theme %} {% block slider %}
{% endblock slider %} {% block content %}For those heroes who like what they hear and want to take a little thrum to their next scrum, Awolnation’s ‘Magic Sticks of Dynamite’ music pack is also available in the MuOnline store.
For those heroes who like...
For those heroes who like what they hear and want to take a little thrum to their next scrum, Awolnation’s ‘Magic Sticks of Dynamite’ music pack is also available in the MuOnline store.
For those heroes who like...
For those heroes who like what they hear and want to take a little thrum to their next scrum, Awolnation’s ‘Magic Sticks of Dynamite’ music pack is also available in the MuOnline store.
For those heroes who like...
For those heroes who like what they hear and want to take a little thrum to their next scrum, Awolnation’s ‘Magic Sticks of Dynamite’ music pack is also available in the MuOnline store.
For those heroes who like...
For those heroes who like what they hear and want to take a little thrum to their next scrum, Awolnation’s ‘Magic Sticks of Dynamite’ music pack is also available in the MuOnline store.
For those heroes who like...


2020 Land Rover Defender Gets Pushed to the Limit on No Time to Die Movie Set
by Elena GorganThat the 2020 Land Rover Defender would be making an appearance in the upcoming, 25th James Bond installment is never a secret – or, at least, it wasn’t anymore after photos of the muddy car on location leaked on social media last August.
Here’s official confirmation, along with a teaser from of what we’re to expect from the Defender. Land Rover USA doesn’t show actual content from the movie, but the footage shows the Defender being properly put through its paces. Stunt driver and W Series racing driver Jess Hawkins and stunt coordinator Lee Morrison roll it, jump it and test its off-roading capabilities thoroughly.
Hawkins’ satisfied conclusion is that she “could do this all day” because everything she threw at the Defender, it was able to take. And, while her car and the other Defenders used in the film are most likely adapted to withstand rougher handling than the commercial version, there is no doubt that the Defender will be fun to drive. This video is very eloquent on that.
No Time to Die is the 25th James Bond installment and probably the last one starring Daniel Craig. For the record, Craig himself is nowhere to be seen in this video, for at least a couple of reasons off the top of our head: he doesn’t do his own stunts and this was probably a second unit.
Shooting for the film actually wrapped a while back and it’s now in post-production, ahead of an April 8, 2020 release date. Expect more info on the cars that appear in it (and it’s quite an impressive roster, including 4 Aston Martin models) to start popping up as the release date draws near and the promo tour kicks off in full force.
Craig too will have a story or two to tell by then, with or without cars. 🏁


Realtime Bitcoin transaction visualizer. (Formerly 'Listen To Bitcoin').
Most donations get highlighted: 1istendqWJ1mKvrdRUQZDL2F3tVDDyKdj
By Maximillian Laumeister • Contact • Source Code
Select Instrument: Celesta Planet Wikki
Scale pitch with transaction amount(bigger transaction = deeper sound)
Show units in USD instead of BTC
Please enable Javascript, or try a different web browser.
Sorry, no support for Internet Explorer.
Try Chrome, Firefox or Safari.
(Click Here to Suppress this warning.)


A translation of the Visual Studio Code Keyboard Shortcuts for macOS Cheat Sheet
When I first came across the VSCode shortcut cheat sheet, I couldn't make out any of their gobbledy-gook. I'm referring to the shortcut symbols that appeared everywhere on the sheet with no explanation of what each represented.
I put together a translation of all the shortcuts represented in the VSCode shortcut cheat sheet. Enjoy!
Whenever you see the plus sign, it is just a replacement for and then
. This is
standard
procedure in technical documentation.
Shift + Command + P || F1 -
Show Command Palette
Command + P -
Quick Open, Go to File
Shift + Command + N -
New Window/Instance
Command + W -
Close Window/Instance
Command + , -
User Settings
Command + K Command + S -
Keyboard Shortcuts
Command + X -
cut line (empty selection)
Command + C -
Copy Line (empty selection)
Option + Down Arrow/Option + Up Arrow -
Move line down/up
Shift + Option + Down Arrow/Shift + Option + Up Arrow -
Copy line down/up
Shift + Command + K -
Delete line
Command + Enter/ Shift + Command + Enter -
Insert line down/up
Shift + Command + \ -
Jump to matching bracket
Command + ]/Command + [ -
Indent/Outdent line
Home / End -
Go to beginning/end of line.
Command + Up Arrow -
go to the beginning of the file.
Command + Down Arrow -
go to the end of the file.
Option + Command + [ / Option + Command + ] -
Fold/Unfold region.
Command + K + Command + [ / Command + K + Command + ] -
Fold/Unfold all sub-regions.
Command + K + Command + 0 / Command + K + Command + J -
Fold/Unfold all regions.
Command + K + Command + C -
Add line comment.
Command + K + Command + U -
Remove line comment.
Command + / -
Toggle line comment.
Shift + Option + A -
Toggle block comment.
Option + Z -
Toggle word wrap
Option + click -
Insert cursor.
Option + Command + Up Arrow -
Insert cursor above.
Option + Comment + Down Arrow -
Insert cursor
below.
Command + U -
Undo last cursor operation.
Shift + Option -
Insert cursor at end of each line selected.
Command + L -
Select current line.
Shift + Command + L -
Select all occurrences of current
selection.
Command + F2 -
Select all occurrences of current word.
Control + Shift + Command + Right Arrow/Left Arrow -
Expand/Shrink selection.
Shift + Option + Drag Mouse -
Column (box) selection.
Shift + Option + Command + Up Arrow/Down Arrow -
Column
(box) selection
up/down
Shift + Option + Command + Left Arrow/Right Arrow -
Column
(box) selection
left/right
Shift + Option + Command + PgUp -
Column (box) selection page up.
Shift + Option + Command + PgDn -
Column (box) selection page down.
Command + F -
Find
Option + Command + F -
Replace
Command + G/Shift + Command + G -
Find next/previous.
Option + Enter -
Select all occurrences of Find match
Command + D -
Add selection to next Find match
Command + K + Command + D -
Move last selection to next Find match
Control + Space -
Trigger suggestion
Shift + Command + Space -
Trigger parameter hints
Shift + Option + F -
Format document
Command + K + Command + F -
Format selection
F12 -
Go to definition
Command + K + F12 -
Open definition in the side
Command -
Quick Fix
Shift F12 -
Show References
F2 -
Rename Symbol
Command + K + Command + X -
Trim trailing whitespace
Command + K + M -
Change file language
Command + T -
Show all Symbols
Control + G -
Got to Line
Command + P -
Go to file
Shift + Command + O -
Go to Symbol
Shift + Command + M -
Show Problems panel
F8/Shift + F8 -
Go to next/previous error or warning
Control + hyphen/Control + Shift + hyphen -
Go back/go forward
Control + Shift + M -
Toggle Tab moves focus
Command + W -
Close editor
Command + K + F -
Close Folder
Command + \ -
Split editor
Command + 1 / Command + 2 / Command + 3 -
Focus into 1st, 2nd, 3rd editor group.
Command + K + Command + Left Arrow / Command + K + Command + Right Arrow -
focus into
previous/next editor group.
Command + K + Shift + Command + Left Arrow / Command + K + Shift +
Command + Right Arrow -
Move editor left/right
Command + K + Left Arrow / Command + K + Right Arrow -
Move
active editor group
Command + N -
New file
Command + O -
Open file
Command + S -
Save
Shift + Command + S -
Save as
Option + Command + S -
Save All
Command + K + Command + W -
Close All
Shift + Command + T -
Reopen closed editor
Command + K + Enter -
Keep preview mode editor open
Control + Tab / Control + Shift + Tab -
Open
next/previous
Command + K + P -
Copy path of active file
Command + K + R -
Reveal active file in Finder
Command + K + O -
Show active file in new window/instance
Control + Command + F -
Toggle full screen
Option + Command + 0 -
Toggle editor layout
(horizontal/vertical)
Command + Equal/Shift + Command + Hyphen -
Zoom in/out
Command + B -
Toggle Sidebar visibility
Shift + Command + E -
Show Explorer/Toggle focus
Shift + Command + F -
Show Search
Control + Shift + G -
Show Source Control
Shift + Command + D -
Show Debug
Shift + Command + X -
Show Extensions
Shift + Command + H -
Replace in files
Shift + Command + F -
Show Search
Control + Shift + G -
Show Source Control
Shift + Command + D -
Show Debug
Shift + Command + X -
Show Extensions
Shift + Command + H -
Replace in files
Shift + Command + J -
Toggle Search details
Shift + Command + U -
Show Output Panel
Shift + Command + V -
Open Markdown preview
Command + K + Z -
Zen Mode (Esc Esc to exit)
F9 -
Toggle Breakpoint
F5 -
Start/Continue
F11/Shift + F11 -
Step into/out
F10 -
Step over
Shift + F5 -
Stop
Command + K + Command + I -
Show hover
Control + backtick -
Show Integrated Terminal
Control + Shift + backtick -
Create new Terminal
Command + C -
Copy selection
Command + Up Arrow / Down Arrow -
Scroll up/down
PgUp/PgDn -
Scroll page up/down
Command + Home / End -
scroll to top/bottom
⌘ -
Command Key
⌥ -
Option Key
⌃ -
Control Key
⇧ -
Shift Key
⇪ -
Caps Lock Key
⌫ -
Backspace/Delete Key
⌦ -
Forward/Delete Key (fn + backspace)
⎋ -
Escape Key
← -
Left Arrow Key
→ -
Right Arrow Key
↑ -
Up Arrow Key
↓ -
Down Arrow Key
⇥ -
Tab Key
⇤ -
Back Tab Key (Shaft + Tab)
↩ -
Return Key
⌤ -
Enter Key
⇞ -
Page Up Key
⇟ -
Page Down Key
↖ -
Home
↘ -
End
⌧ -
Clear
␣ -
Space
⏏ -
Eject
fn -
Function Key
§ -
Section Mark
Home
and End
Keys (I
don't), Then You Can Do the Following:Fn + left arrow -
jump to top of the page
Fn + right arrow -
jump to end of the page
Command + down arrow key -
jump to end of page
Command + up arrow key -
jump to top of page


For the complete list of the tutorial on this blog, you can check these following links:
1. Introduction
2. First C-Program
3. Types and Variables
4. Conditional Statements
5. Function and Math Operations
6. Arrays
7. Pointers
8. I/O in C
Probably, you will see C-language as nothing special. You might even say C sucks compared to Java, Ruby or Python nowadays. But, is it? Well, if you start coding from 90s, probably you’ve encountered a hard time developing with Fortran
, or Assembly
and C came as a saviour from the darkness, it’s like a programming heaven.
There are several top reasons why C is still a good language to learn, here I’ve compiled some.
C has nice syntax
As a language that often interacts with the machine, C is considered to be a pretty language with beautiful syntax.
As we can see, Pygments
rendered C-syntax (with line numbers) beautifully. Now, time for a trivia. Can you guess, how many programmers which are saved by C-syntax from obfuscating codes? Count me one!
C macros are the best
Macros is one of the most important preprocessor #
implementation in C. It can be used to create a fast function without putting everything on the stack. Check this out,
In line:4, we define the macro function MACRO_OR
that takes a and b as an input. Then, this macro will return the logical OR
result from a and b. Super neat!
In line:12, the macro is invoked to compute the logical OR
between a and b and return the result to c.
C is small
C utilised merely variables, macros, functions and structures. Therefore, C is considered small in size, complexity and that much of it. Due to this, C has been heavily used on embedded system, modern microprocessor, even modern Arduino
.
More about this, lot of parallel programming languages, OpenMP, OpenCL, CUDA, etc, are based on C language due to its relative connection to lower level hardware compared with other languages.
C is root of all goodness
You can pretty much stand on opinion that C is different compared to C++ or ObjectiveC, but from pedagogical approach, lot of programming expert will tell you to learn basic things from C, e.g. overflow, memory addressing, pointer, profiling and debugging. A true concept of those languages can be learned from C.
C is alive and sound
The number one reason C is the best programming language today is still the fact that it simply powers everything. From your phone to your Wii
, no other language provides the level of hardware interaction with the practicality of a concise and expressive syntax. Unless you’re still using an early IBM
(please switch soon, if so), the screen you’re reading this article on is probably powered by C.
With C, we can forget writing thousands of complex Assembly routines, thousands of processor-specific instructions, and spaghetti like code. Because of C, the CPU, memory, and graphics cards have turned from their messy, low-level pasts into an easier, readable format
Well, I hope this is quite enough motivation for you to start learning C more than just for gaining basic programming language skill. Then when it comes the time when you woke up at the wrong side of the bed, you aren’t rambling about how bad, stressful, and obfuscated low-level programming is.
So, are you ready now? If yes, then let’s get started on writing your first program on the next tutorial.


A | |
| Anim |
Animation curves.
|
| App [Useri] |
Application
|
D | |
| Default [G2048] | |
| Drop [Useri_jsoo] |
User drag and drop.
|
| Drop [Useri] |
User drag and drop.
|
F | |
| File [Useri_jsoo.Drop] | |
| File [Useri.Drop] |
Dropped files
|
G | |
| G2048 |
2048 game logic.
|
| Gl [Useri.Surface] |
The type for OpenGL surface specification.
|
H | |
| Handle [Useri_jsoo.Surface] |
Surface handles.
|
| Human [Useri] |
Human factors.
|
K | |
| Key [Useri_jsoo] |
User keyboard.
|
| Key [Useri] |
User keyboard.
|
M | |
| Make [Render] | |
| Make [Test] | |
| Make [G2048] | |
| Mouse [Useri] |
User mouse.
|
R | |
| Render |
2048 game board renderer
|
S | |
| Surface [Useri_jsoo] |
Surface.
|
| Surface [Useri] |
Rendering surface.
|
T | |
| Test |
Tests
|
| Text [Useri] |
User textual input and clipboard.
|
| Time [Useri] |
Monotonic time.
|
| Touch [Useri_jsoo] |
User touch.
|
| Touch [Useri] |
User touches.
|
U | |
| Useri |
Simple declarative user interaction with
React .
|
| Useri_jsoo |
Useri
js_of_ocaml backend specific functions.
|
| Utils |
Utility functions.
|


Image Dehazing with DCP algorithm. Upload image by clicking the upload button. After the image is processed it will be displayed to the right.


Before Change
import versioneer
with open("requirements.txt", "r") as req:
requirements = req.read().split("\n")
// Copy over notebooks from examples to docs for build
notebooks = glob.glob("examples/*.ipynb")
for nb in notebooks:
After Change
<EMAIL_ADDRESS>url="http://github.com/bashtage/linearmodels",
long_description=description,
install_requires=open("requirements.txt").read().split("\n"),
include_package_data=True,
keywords=["linear models", "regression", "instrumental variables", "IV",
"panel", "fixed effects", "clustered", "heteroskedasticity",


{{< layout}} {{$propositionHeader}}{{/propositionHeader}} {{$pageTitle}}Error{{/pageTitle}} {{$header}}
The link you’ve entered doesn’t exist. Please check the link and try again.
If this keeps happening, contact the company who provided your digital photo.
Try again {{/content}} {{/ layout}}


A demo of the popular MineSweeper game I wrote ages ago!!
This will not work in chrome due to unavailability of NPAPI. Try in Firefox. Also add the url of this page to the exception list in the Java Control Panel (in system preferences)


From<EMAIL_ADDRESS>To: Me
Invite to collaborate
You have been invited to join a collaborative application for the competition 'Technology Inspired'.
Lead organisation: Worth Systems
Lead applicant: John Smith
The lead applicant has invited you to join this application on behalf of the following organisation: Nomensa
If this is not the correct organisation email the application lead


The directory structure in the NT SDK differs significantly from that of the DOS SDK. The change reflects standardization across platforms and facilitates multi-engineer development in a network environment.
Branches
Main Directories
Target Directories
Developing In Your Working Branch
Multi-Developer Projects


— Brian Moeskau (@bmoeskau)
http://edgeATX.github.io/slides
— Andrew Thornton (@encodingpixels)
Thanks to our sponsor:
Includes most of our slide decks from the past 2 years:
A monthly-ish update of the latest and greatest in:
Anything else that might be interesting to web app devs!
Last version check — August 2014 (2 months ago):
| 37 | 31 | 23 | 7 | 11 |
As of October 23, 2014:
| 37 38 |
31 33 |
23 25 |
7 8 |
11 |
http://www.w3.org/TR/css-shapes/
CSS Shapes allow web designers to wrap content around custom paths, like circles, ellipses and polygons, thus breaking free from the constraints of the rectangle.
This is a live example — select text or resize the browser!

Code for the previous example:

We're no strangers to love...

I just wanna tell you how I'm feeling...

Compare the previous slide in browsers that do and do NOT support CSS shapes.
The shapes CSS class gets added to the  tag by Modernizr if supported

// Example HTML and code

Key concept: target shapes must be floated
Built-in shapes: circle, ellipse, polygon
Additional layout properties, e.g. shape-margin: 1em;
* Surprisingly, Firefox typically leads on W3C spec support, but does NOT currently support Shapes.
There is also a Level 2 spec (currently in Editor's Draft status)
Will extend Level 1:
Windows is currently called Windows 8.
The next planned version is Windows 10.
Official party line:
Windows 10 carries Windows forward into a new way of doing things. It is not an incremental change, but a new Windows that will empower the next billion users.
— "Microsoft Spokesman"
Official party line:
Windows 10 carries Windows forward into a new way of doing things. It is not an incremental change, but a new Windows that will empower the next billion users.
— "Microsoft Spokesman"
Most likely real answer:
I bet the real reason it's called Windows 10 is because some enterprise app written 20 years ago checks for "Windows 9x"
— Anil Dash (@anildash)
The first operating system powered by npm
A next generation web framework for the Perl programming language
All the modern features you expect: REST, routes, plugins, even WebSockets
"Perl-ish" templates
Example of parsing a web page:
use Mojo::Base -strict;
use Mojo::ByteStream 'b';
use Mojo::UserAgent;
# Extract named character references from HTML spec
my $tx = Mojo::UserAgent->new->get('https://html.spec.whatwg.org');
b($_->at('td > code')->text . ' ' . $_->children('td')->[1]->text)->trim->say
for $tx->res->dom('#named-character-references-table tbody > tr')->each;
1;
A minimal, ui-focused programming language for web designers

You have 3 unread messages.

<button class="hide">Hide</button>

clicking on ".hide" adds class "hidden" on "#notification"

http://paypal.github.io/accessible-html5-video-player/ (Overview)
Massive matrix of browser-specific feature support
Includes known issues, links to specs and polyfills and more
https://cdn.polyfill.io/v1/ (Overview)
Hosted HTML5 / CSS3 / ES6 polyfill delivery service
A way of sending one single copy of each of the best polyfills, in the right order (respecting dependencies), to just the browsers that lack a native implementation.
/* Detected: chrome/37.0.2062
*
* - Array.isArray, License: CC0 (required by modernizr:es5array)
* - Array.prototype.every, License: CC0 (required by modernizr:es5array)
* - Array.prototype.filter, License: CC0 (required by modernizr:es5array)
* etc...
*/
2,600+ high-quality, royalty-free and customizable icons
Keyword search and curated collections
Available as of FF 33+
Useful for validating event handler setup, debugging memory leaks, etc.
Easily test standard and custom mobile layout sizes
Take mobile-sized fullscreen screenshots
In recent FF, go to Developer Toolbox Options and check "3D View"
Click the tiny 3D box icon bottom-right and enjoy
/
#


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Magic Kingdom, USA
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---
layout: default_child
title: Contact Me
previous_url: /Michael/
---
Contact Me
{% include picture.html url='/assets/icons/venmo.png' title='Venmo Icon' %}
Venmo: @mettle
{% include picture.html url='/assets/icons/discord.png' title='Discord Icon' %}
Discord: Mettle#8129
{% include picture.html url='/assets/icons/nintendo.png' title='Nintendo Icon' %}
Nintendo Switch: SW-7755-9487-8676


Q1. You are part of data science team that is working for a national fast-food chain. You create a simple report that shows trend: Customers who visit the store more often and buy smaller meals spend more than customers who visit less frequently and buy larger meals. What is the most likely diagram that your team created?
[ ] multiclass classification diagram
[ ] linear regression and scatter plots
[ ] pivot table
[x] K-means cluster diagram
Q2. You work for an organization that sells a spam filtering service to large companies. Your organization wants to transition its product to use machine learning. It currently a list Of 250,00 keywords. If a message contains more than few of these keywords, then it is identified as spam. What would be one advantage of transitioning to machine learning?
[ ] The product would look for new patterns in spam messages.
[ ] The product could go through the keyword list much more quickly.
[ ] The product could have a much longer keyword list.
[x] The product could find spam messages using far fewer keywords.
Q3. You work for a music streaming service and want to use supervised machine learning to classify music into different genres. Your service has collected thousands of songs in each genre, and you used this as your training data. Now you pull out a small random subset of all the songs in your service. What is this subset called?
[ ] data cluster
[ ] Supervised set
[ ] big data
[x] test data
Q4. In traditional computer programming, you input commands. What do you input with machine learning?
[ ] patterns
[ ] programs
[ ] rules
[x] data
Q5. Your company wants to predict whether existing automotive insurance customers are more likely to buy homeowners insurance. It created a model to better predict the best customers contact about homeowners insurance, and the model had a low variance but high bias. What does that say about the data model?
Q6. You want to identify global weather patterns that may have been affected by climate change. To do so, you want to use machine learning algorithms to find patterns that would otherwise be imperceptible to a human meteorologist. What is the place to start?
[ ] Find labeled data of sunny days so that the machine will learn to identify bad weather.
[x] Use unsupervised learning have the machine look for anomalies in a massive weather database.
[ ] Create a training set of unusual patterns and ask the machine learning algorithms to classify them.
[ ] Create a training set of normal weather and have the machine look for similar patterns.
Q7. You work in a data science team that wants to improve the accuracy of its K-nearest neighbor result by running on top of a naive Bayes result. What is this an example of?
[ ] regression
[ ] boosting
[ ] bagging
[x] stacking
Q8. \_\_\_\_ looks at the relationship between predictors and your outcome.
[x] Regression analysis
[ ] K-means clustering
[ ] Big data
[ ] Unsupervised learning
Q9. What is an example of a commercial application for a machine learning system?
[ ] a data entry system
[ ] a data warehouse system
[ ] a massive data repository
[x] a product recommendation system
Q10. What does this image illustrate?
[x] a decision tree
[ ] reinforcement learning
[ ] K-nearest neighbor
[ ] a clear trendline
Q11. You work for a power company that owns hundreds of thousands of electric meters. These meters are connected to the internet and transmit energy usage data in real-time. Your supervisor asks you to direct project to use machine learning to analyze this usage data. Why are machine learning algorithms ideal in this scenario?
[ ] The algorithms would help the meters access the internet.
[ ] The algorithms will improve the wireless connectivity.
[x] The algorithms would help your organization see patterns of the data.
[ ] By using machine learning algorithms, you are creating an IoT device.
Q12. To predict a quantity value. use \_\_\_\_.
[x] regression
[ ] clustering
[ ] classification
[ ] dimensionality reduction
Q13. Why is naive Bayes called naive?
[ ] It naively assumes that you will have no data.
[ ] It does not even try to create accurate predictions.
[x] It naively assumes that the predictors are independent from one another.
[ ] It naively assumes that all the predictors depend on one another.
Q14. You work for an ice cream shop and created the chart below, which shows the relationship between the outside temperature and ice cream sales. What is the best description of this chart?
[x] It is a linear regression chart.
[ ] It is a supervised trendline chart.
[ ] It is a decision tree.
[ ] It is a clustering trend chart.
Q15. How is machine learning related to artificial intelligence?
[ ] Artificial intelligence focuses on classification, while machine learning is about clustering data.
[x] Machine learning is a type of artificial intelligence that relies on learning through data.
[ ] Artificial intelligence is form of unsupervised machine learning.
[ ] Machine learning and artificial intelligence are the same thing.
Q16. How do machine learning algorithms make more precise predictions?
[ ] The algorithms are typically run more powerful servers.
[x] The algorithms are better at seeing patterns in the data.
[ ] Machine learning servers can host larger databases.
[ ] The algorithms can run on unstructured data.
Q17. You work for an insurance company. Which machine learning project would add the most value for the company!
[ ] Create an artificial neural network that would host the company directory.
[x] Use machine learning to better predict risk.
[ ] Create an algorithm that consolidates all of your Excel spreadsheets into one data lake.
[ ] Use machine learning and big data to research salary requirements.
Q18. What is the missing information in this diagram?
[ ] Training Set
[ ] Unsupervised Data
[x] Supervised Learning
[ ] Binary Classification
Q19. What is one reason not to use the same data for both your training set and your testing set?
[ ] You will almost certainly underfit the model.
[ ] You will pick the wrong algorithm.
[ ] You might not have enough data for both.
[x] You will almost certainly overfit the model.
Q20. Your university wants to use machine learning algorithms to help sort through incoming student applications. An administrator asks if the admissions decisions might be biased against any particular group, such as women. What would be the best answer?
[ ] Machine learning algorithms are based on math and statistics, and so by definition will be unbiased.
[ ] There is no way to identify bias in the data.
[ ] Machine learning algorithms are powerful enough to eliminate bias from the data.
[x] All human-created data is biased, and data scientists need to account for that.
Explanation: While machine learning algorithms don't have bias, the data can have them.
Q21. What is stacking?
[x] The predictions of one model become the inputs another.
[ ] You use different versions of machine learning algorithms.
[ ] You use several machine learning algorithms to boost your results.
[ ] You stack your training set and testing set together.
Q22. You want to create a supervised machine learning system that identifies pictures of kittens on social media. To do this, you have collected more than 100,000 images of kittens. What is this collection of images called?
[x] training data
[ ] linear regression
[ ] big data
[ ] test data
Q23. You are working on a project that involves clustering together images of different dogs. You take image and identify it as your centroid image. What type machine learning algorithm are you using?
[ ] centroid reinforcement
[ ] K-nearest neighbor
[ ] binary classification
[x] K-means clustering
Explanation: The problem explicitly states "clustering".
Q24. Your company wants you to build an internal email text prediction model to speed up the time that employees spend writing emails. What should you do?
[x] Include training email data from all employees.
[ ] Include training email data from new employees.
[ ] Include training email data from seasoned employees.
[ ] Include training email data from employees who write the majority of internal emails.
Q25. Your organization allows people to create online professional profiles. A key feature is the ability to create clusters of people who are professionally connected to one another. What type of machine learning method is used to create these clusters?
[x] unsupervised machine learning
[ ] binary classification
[ ] supervised machine learning
[ ] reinforcement learning
Q26. What is this diagram a good example of?
[ ] K-nearest neighbor
[ ] a decision tree
[ ] a linear regression
[x] a K-means cluster
Note: there are centres of clusters (C0, C1, C2).
Q27. Random forest is modified and improved version of which earlier technique?
[ ] aggregated trees
[ ] boosted trees
[x] bagged trees
[ ] stacked trees
Q28. Self-organizing maps are specialized neural network for which type of machine learning?
[x] semi-supervised learning
[ ] supervised learning
[ ] reinforcement learning
[ ] unsupervised learning
Q29. Which statement about K-means clustering is true?
[x] In K-means clustering, the initial centroids are sometimes randomly selected.
[ ] K-means clustering is often used in supervised machine learning.
[ ] The number of clusters are always randomly selected.
[ ] To be accurate, you want your centroids outside of the cluster.
Q30. You created machine learning system that interacts with its environment and responds to errors and rewards. What type of machine learning system is it?
[ ] supervised learning
[ ] semi-supervised learning
[x] reinforcement learning
[ ] unsupervised learning
Q31. Your data science team must build a binary classifier, and the number one criterion is the fastest possible scoring at deployment. It may even be deployed in real time. Which technique will produce a model that will likely be fastest for the deployment team use to new cases?
[ ] random forest
[x] logistic regression
[ ] KNN
[ ] deep neural network
Q32. Your data science team wants to use the K-nearest neighbor classification algorithm. Someone on your team wants to use a K of 25. What are the challenges of this approach?
[ ] Higher K values will produce noisy data.
[ ] Higher K values lower the bias but increase the variance.
[ ] Higher K values need a larger training set.
[x] Higher K values lower the variance but increase the bias.
Q33. Your machine learning system is attempting to describe a hidden structure from unlabeled data. How would you describe this machine learning method?
[ ] supervised learning
[x] unsupervised learning
[ ] reinforcement learning
[ ] semi-unsupervised learning
Q34. You work for a large credit card processing company that wants to create targeted promotions for its customers. The data science team created a machine learning system that groups together customers who made similar purchases, and divides those customers based on customer loyalty. How would you describe this machine learning approach?
[x] It uses unsupervised learning to cluster together transactions and unsupervised learning to classify the customers.
[ ] It uses only unsupervised machine learning.
[ ] It uses supervised learning to create clusters and unsupervised learning for classification.
[ ] It uses reinforcement learning to classify the customers.
Q35. You are using K-nearest neighbor and you have a K of 1. What are you likely to see when you train the model?
[x] high variance and low bias
[ ] low bias and low variance
[ ] low variance and high bias
[ ] high bias and high variance
Q36. Are data model bias and variance a challenge with unsupervised learning?
[ ] No, data model bias and variance are only a challenge with reinforcement learning.
[x] Yes, data model bias is a challenge when the machine creates clusters.
[ ] Yes, data model variance trains the unsupervised machine learning algorithm.
[ ] No, data model bias and variance involve supervised learning.
Q37. Which choice is best for binary classification?
[ ] K-means
[x] Logistic regression
[ ] Linear regression
[ ] Principal Component Analysis (PCA)
Explanation: Logistic regression is far better than linear regression at binary classification since it biases the result toward one extreme or the other. K-means clustering can be used for classification but is not as accurate in most scenarios. Source:
Q38. With traditional programming, the programmer typically inputs commands. With machine learning, the programmer inputs
[ ] supervised learning
[x] data
[ ] unsupervised learning
[ ] algorithms
Explanation: This one is pretty straight forward and a fundamental concept. Source:
Q39. Why is it important for machine learning algorithms to have access to high-quality data?
[ ] It will take too long for programmers to scrub poor data.
[ ] If the data is high quality, the algorithms will be easier to develop.
[ ] Low-quality data requires much more processing power than high-quality data.
[x] If the data is low quality, you will get inaccurate results.
Q40. In K-nearest neighbor, the closer you are to neighbor, the more likely you are to
[x] share common characteristics
[ ] be part of the root node
[ ] have a Euclidean connection
[ ] be part of the same cluster
Q41. In the HBO show Silicon Valley, one of the characters creates a mobile application called Not Hot Dog. It works by having the user take a photograph of food with their mobile device. Then the app says whether the food is a hot dog. To create the app, the software developer uploaded hundreds of thousands of pictures of hot dogs. How would you describe this type of machine learning?
[ ] Reinforcement machine learning
[ ] unsupervised machine learning
[x] supervised machine learning
[ ] semi-supervised machine learning
Q42. You work for a large pharmaceutical company whose data science team wants to use unsupervised learning machine algorithms to help discover new drugs. What is an advantage to this approach?
[ ] You will be able to prioritize different classes of drugs, such as antibiotics.
[ ] You can create a training set of drugs you would like to discover.
[x] The algorithms will cluster together drugs that have similar traits.
[ ] Human experts can create classes of drugs to help guide discovery.
Explanation: This one is similar to an example talked about in the Stanford Machine Learning course. Source:
Q43. In 2015, Google created a machine learning system that could beat a human in the game of Go. This extremely complex game is thought to have more gameplay possibilities than there are atoms of the universe. The first version of the system won by observing hundreds of thousands of hours of human gameplay; the second version learned how to play by getting rewards while playing against itself. How would you describe this transition to different machine learning approaches?
[x] The system went from supervised learning to reinforcement learning.
[ ] The system evolved from supervised learning to unsupervised learning.
[ ] The system evolved from unsupervised learnin9 to supervised learning.
[ ] The system evolved from reinforcement learning to unsupervised learning.
Q44. The security company you work for is thinking about adding machine learning algorithms to their computer network threat detection appliance. What is one advantage of using machine learning?
[x] It could better protect against undiscovered threats.
[ ] It would very likely lower the hardware requirements.
[ ] It would substantially shorten your development time.
[ ] It would increase the speed of the appliance.
QTraining1. You work for a hospital that is tracking the community spread of a virus. The hospital created a smartwatch app that uploads body temperature data from hundreds of thousands of participants. What is best technique to analyze the data?
[ ] Use reinforcement learning to reward the system when a new person participates
[ ] Unsupervised machine learning to cluster together people based on patterns the machine discovers
[ ] Supervised machine learning to sort people by demographic data
[x] supervised ml to classify people by body temperature
QTraining2. Man of the advances in ml have come from improved
[ ] statistics
[x] structured data
[ ] availability
[ ] algorithms
Q45. What is this diagram a good example of?
[ ] unsupervised learning
[ ] complex cluster
[ ] multiclass classification
[x] k-nearest neighbour
Q46. The supervisor asks to create a ml system that will help your hr dep. classify job applicants into well-defined groups.What type of system are more likely to recommend?
[ ] deep learning artificial neural network that relies on petabytes of data
[ ] unsupervised ml system that clusters together the best candidates
[ ] Not recommend ml for this project
[x] supervised ml system that classifies applicants into existing groups // we do not need to classify best candidates we just need to classify job applicants in to existing categories
Q47. Someone of your data science team recommends that you use decision trees, naive Bayes and K-nearest neighbor, all at the same time, on the same training data, and then average the results. What is this an example of?
[ ] regression analysis
[ ] unsupervised learning
[ ] high -variance modeling
[x] ensemble modeling
Q48. Your data science team wants to use ml to better filter out spam messages. The team has gathered a database of 100,000 messages that have been identified as spam or not spam. If you are using supervised ml, what would you call this data set?
[ ] ml algorithm
[x] training set
[ ] big data test set
[ ] data cluster
Q49.  Your data model uses 5 characteristics to match people to their foto: color, eye, gender, eyeglasses and facial hair. Your customers have been complaining that get tens of thousands of fotos without them. What is the problem?
[ ] You are overfitting the model to the data
[ ] You need a smaller training set
[x] You are underfitting the model to the data
[ ] You need a larger training set
Q50. Your supervisor asks you to create a machine learning system that will help your human resources department classify jobs applicants into well defined groups. What type of system are you more likely to recommend?
[ ] an unsupervised machine learning system that clusters together the best candidates.
[ ] you would not recommend a machine learning system for this type of project.
[ ] a deep learning artificial neural network that relies on petabytes of employment data.
[x] a supervised machine learning system that classifies applicants into existing groups.
Q51. You and your data science team have 1 TB of example data. What do you typically do with that data?
[ ] you use it as your training set.
[x] You label it big data.
[ ] You split it into a training set and test set.
[ ] You use it as your test set.
Q52. Your data science team is working on a machine learning product that can act as an artificial opponent in video games. The team is using a machine learning algorithm that focuses on rewards: If the machine does some things well, then it improves the quality of the outcome. How would you describe this type of machine learning algorithm?
[ ] semi-supervised machine learning
[ ] supervised machine learning
[ ] unsupervised machine learning
[x] reinforcement learning
Q53. The model will be trained with data in one single batch is known as ?
[ ] Batch learning
[ ] Offline learning
[x] Both A and B
[ ] None of the above
Q54. Which of the following is NOT supervised learning? ?
[ ] Decision Tree
[ ] Linear Regression
[x] PCA
[ ] Naive Bayesian
Q55. Suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate? ?
[ ] Decision Trees
[ ] K-means clustering
[x] Density-based clustering
[ ] Model-based clustering
Q59. The error function most suited for gradient descent using logistic regression is
[ ] The entropy function.
[ ] The squared error.
[x] The cross-entropy function.
[ ] The number of mistakes.
Q58. Compared to the variance of the Maximum Likelihood Estimate (MLE), the variance of the Maximum A Posteriori (MAP) estimate is ____
[ ] Higher
[ ] same
[x] Lower
[ ] it could be any of the above
Q57. **___** refers to a model that can neither model the training data nor generalize to new data.
[ ] good fitting
[ ] overfitting
[x] underfitting
[ ] all of the above
Q58. How would you describe this type of classification challenge?
[x] This is a multiclass classification challenge. Explanation: Shows data being classified into more than two categories or classes. Thus, this is a multi-class classification challenge.
[ ] This is a multi-binary classification challenge.
[ ] This is a binary classification challenge.
[ ] This is a reinforcement classification challenge.
Q59. What does it mean to underfit your data model?
[ ] There is too little data in your training set.
[ ] There is too much data in your training set.
[x] There is not a lot of variance but there is a high bias. // Underfitted data models usually have high bias and low variance. Overfitted data models have low bias and high variance.
[ ] Your model has low bias but high variance.
Q60. Asian user complains that your company's facial recognition model does not properly identify their facial expressions. What should you do?
[ ] Include Asian faces in your test data and retrain your model.
[ ] Retrain your model with updated hyperparameter values.
[ ] Retrain your model with smaller batch sizes.
[x] Include Asian faces in your training data and retrain your model. // The answer is self-explanatory: if Asian users are the only group of people making the complaint, then the training data should have more Asian faces.
Q61. You work for a website that helps match people up for lunch dates. The website boasts that it uses more than 500 predictors to find customers the perfect date, but many costumers complain that they get very few matches. What is a likely problem with your model?
[ ] Your training set is too large.
[ ] You are underfitting the model to the data.
[x] You are overfitting the model to the data. Explanation: // This question is very similar to Q49 but involves a polar opposite scenario.
[ ] Your machine is creating inaccurate clusters.
// I find that answer somewhat vague and unsettled. Small number of matchings does not necessarily implies that the model overfits, especially given 500 (!) independent variables. To me, it sounds more reasonable that the threshold (matching) criterion might be too tight, thus allowing only a small number of matching to occur. So a solution can be either softening the threshold criterion or increasing the number of candidates.
Q62. (Mostly) whenever we see kernel visualizations online (or some other reference) we are actually seeing:
[x] What kernels extract
[ ] Feature Maps
[ ] How kernels Look
Q62. The activations for class A, B and C before softmax were 10,8 and 3. The different in softmax values for class A and class B would be :
[x] 76%
[ ] 88%
[ ] 12%
[ ] 0.0008%
Q63. The new dataset you have just scraped seems to exhibit lots of missing values. What action will help you minimizing that problem?
[ ] Wise fill-in of controlled random values
[ ] Replace missing values with averaging across all samples
[ ] Remove defective samples
[x] Imputation
Q64. Which of the following methods can use either as an unsupervised learning or as a dimensionality reduction technique?
[ ] SVM
[x] PCA
[ ] LDA
[ ] TSNE
Q65. What is the main motivation for using activation functions in ANN?
[x] Capturing complex non-linear patterns
[ ] Transforming continuous values into "ON" (1) or "OFF" (0) values
[ ] Help avoiding the vanishing/exploding gradient problem
[ ] Their ability to activate each neurons individually.
Q66. Which loss function would fit best in a categorical (discrete) supervised learning ?
[ ] kullback-leibler (KL) loss
[x] Binary Crossentropy
[ ] Mean Squared Error (MSE)
[ ] Any L2 loss
Q67. What is the correct option?
| no. | Red | Blue | Green | | — | — | — | — | | 1. | Validation error | Training error | Test error | | 2. | Training error | Test error | Validation error | | 3. | Optimal error | Validation error | Test error | | 4. | Validation error | Training error | Optimal error |
[ ] 1
[ ] 2
[ ] 3
[x] 4
Q68. You create a decision tree to show whether someone decides to go to the beach. There are three factors in this decision: rainy, overcast, and sunny. What are these three factors called?
[ ] tree nodes
[x] predictors // these nodes decide whether the someone decides to go to beach or not, for example if its rainy people will mostly refrain from going to beach
[ ] root nodes
[ ] deciders
Q69. You need to quickly label thousands of images to train a model. What should you do?
[ ] Set up a cluster of machines to label the images
[ ] Create a subset of the images and label then yourself
[x] Use naive Bayes to automatically generate labels.
[ ] Hire people to manually label the images
Q70. The fit line and data in the figure exhibits which pattern?
[ ] low bias, high variance
[ ] high bias, low variance
[ ] high bias, high variance
[x] low bias, low variance // since the data is accurately classified and is neither overfitting or underfitting the dataset
Q71. Many of the advances in machine learning have come from improved?
[x] structured data
[ ] algorithms
[ ] time
[ ] computer scientists
Q72. You need to select a machine learning process to run a distributed neural network on a mobile application. Which would you choose?
[ ] Scikit-learn
[ ] PyTorch
[x] Tensowflow Lite
[ ] Tensorflow
Q73. Which choice is the best example of labeled data?
[ ] a spreadsheet
[ ] 20,000 recorded voicemail messages
[x] 100,000 images of automobiles
[ ] hundreds of gigabytes of audio files
Q74. In statistics, what is defined as the probability of a hypothesis test of finding an effect - if there is an effect to be found?
[ ] confidence
[ ] alpha
[x] power
[ ] significance
Q75. You want to create a machine learning algorithm to identify food recipes on the web. To do this, you create an algorithm that looks at different conditional probabilities. So if the post includes the word flour, it has a slightly stronger probability of being a recipe. If it contains both flour and sugar, it even more likely a recipe. What type of algorithm are you using?
[ ] naive Bayes classifier
[ ] K-nearest neighbor
[ ] multiclass classification
[x] decision tree
Q76. What is lazy learning?
[ ] when the machine learning algorithms do most of the programming
[ ] when you don't do any data scrubbing
[x] when the learning happens continuously
[ ] when you run your computation in one big instance at the beginning
Q77. What is Q-learning reinforcement learning?
[ ] supervised machine learning with rewards
[ ] a type of unsupervised learning that relies heavily on a well-established model
[ ] a type of reinforcement learning where accuracy degrades over time
[x] a type of reinforcement learning that focuses on rewards


-   Prise:  $22
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Force Sensor
Product Features
This simple force sensor can be used as a button for human input or to sense the presence of a small object.
Product Description
The sensor reads 0 when no force is applied. As force increases on the circular button the value increases towards 1000.
It is intended as a user input device (i.e. recognizing that someone is pushing a button).
The sensor is not accurate enough to be used as a weight measurement device and is not designed to have force applied constantly over time.
-   Prise:  $22
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FlexiForce Adapter
Product Features
The FlexiForce adapter allows you to connect one FlexiForce FSR to an analog adapter input.
Product Description
The FlexiForce Adapter makes it easy to interface a Tekscan FlexiForce Force Sensor to your PC. Just use the cable included with the adapter. We even give you a round plastic disc for the sensing pad.
The FlexiForce sensor is an ultra-thin and flexible printed circuit, which can be easily integrated into most applications. With its paper-thin construction, flexibility and force measurement ability, the FlexiForce force sensor can measure force between almost any two surfaces and is durable enough to stand up to most environments.
FlexiForce has better force sensing properties, linearity, hysteresis, drift, and temperature sensitivity than any other thin-film force sensors.
The "active sensing area" is a 0.375” diameter circle at the end of the sensor.
-   Prise:  $30
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Thin Force Sensor
Product Features
This FSR comes with an adapter board already attached for convenience. Measures up to 2 kg of force.
Product Description
With its paper-thin construction, flexibility and force measurement ability, the Thin Force sensor can measure force between almost any two surfaces, up to 2 kg.
To ensure the most accuracy, a small disc (included) can be placed directly on the sensing pad before applying force to the disc. This will ensure that all the force is applied directly to the sensing pad and not to the surrounding surface.
Force Sensing Resistors (FSRs) are very thin, robust, polymer thick film (PTF) devices that decrease in resistance when increased pressure is applied to the surface of the sensor. FSRs are not a load cell or strain gauge devices though they have many similar properties.
They are more appropriate for qualitative rather than precision measurements.
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8″ FlexiForce 0-1lb. Resistive Force Sensor
Product Features
This thin FlexiForce sensor is compact and easy to integrate. Requires the Flexiforce adapter and measures up to 1 lb of force.
Product Description
The FlexiForce sensor is an ultra-thin and flexible printed circuit, which can be easily integrated into most applications. With its paper-thin construction, flexibility and force measurement ability, the FlexiForce force sensor can measure force between almost any two surfaces and is durable enough to stand up to most environments.
FlexiForce has better force sensing properties, linearity, hysteresis, drift, and temperature sensitivity than any other thin-film force sensors.
The "active sensing area" is a 0.375” diameter circle at the end of the sensor.
-   Prise:  $46
-   In Stock:  by request
8″ FlexiForce 0-25lb. Resistive Force Sensor
Product Features
This thin FlexiForce sensor is compact and easy to integrate. Requires the Flexiforce adapter and measures up to 1 lb of force.
Product Description
The FlexiForce sensor is an ultra-thin and flexible printed circuit, which can be easily integrated into most applications. With its paper-thin construction, flexibility and force measurement ability, the FlexiForce force sensor can measure force between almost any two surfaces and is durable enough to stand up to most environments.
FlexiForce has better force sensing properties, linearity, hysteresis, drift, and temperature sensitivity than any other thin-film force sensors.
The "active sensing area" is a 0.375” diameter circle at the end of the sensor.
-   Prise:  $46
-   In Stock:  by request
8″ FlexiForce 0-100lb. Resistive Force Sensor
Product Features
This thin FlexiForce sensor is compact and easy to integrate. Requires the Flexiforce adapter and measures up to 1 lb of force.
Product Description
The FlexiForce sensor is an ultra-thin and flexible printed circuit, which can be easily integrated into most applications. With its paper-thin construction, flexibility and force measurement ability, the FlexiForce force sensor can measure force between almost any two surfaces and is durable enough to stand up to most environments.
FlexiForce has better force sensing properties, linearity, hysteresis, drift, and temperature sensitivity than any other thin-film force sensors.
The "active sensing area" is a 0.375” diameter circle at the end of the sensor.
-   Prise:  $13
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Interlink Electronics 0.2″ Circular 20N FSR
Product Features
The smallest of our Interlink FSRs, this 0.2" sensor measures up to 20 N.
Product Description
The Interlink Force Sensing Resistor has an active sensing area of 0.2” (5mm) to sense applied force.
Force Sensing Resistors (FSRs) are very thin, robust, polymer thick film (PTF) devices that decrease in resistance when increased pressure is applied to the surface of the sensor. FSRs are not a load cell or strain gauge devices though they have many similar properties.
They are more appropriate for qualitative rather than precision measurements.
-   Prise:  $15
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Interlink Electronics 0.5″ Circular 20N FSR
Product Features
This medium-sized Interlink FSR is 0.5" in diameter and measures up to 20 N.
Product Description
The Interlink Force Sensing Resistor has an active sensing area of 0.5” (12.7mm) to sense applied force.
Force Sensing Resistors (FSRs) are very thin, robust, polymer thick film (PTF) devices that decrease in resistance when increased pressure is applied to the surface of the sensor. FSRs are not a load cell or strain gauge devices though they have many similar properties.
They are more appropriate for qualitative rather than precision measurements.
-   Prise:  $20
-   In Stock:  by request
Interlink Electronics 1.5″ Square 20N FSR
Product Features
This large, square Interlink FSR has a 1.5" sensing area and measures up to 20 N.
Product Description
The Interlink Force Sensing Resistor has an active sensing area of 1.5” (12.7mm) to sense applied force.
Force Sensing Resistors (FSRs) are very thin, robust, polymer thick film (PTF) devices that decrease in resistance when increased pressure is applied to the surface of the sensor. FSRs are not a load cell or strain gauge devices though they have many similar properties.
They are more appropriate for qualitative rather than precision measurements.
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srec - S-record file and record format
DESCRIPTION
An S-record file consists of a sequence of specially formatted ASCII character strings. An S-record will be less than or equal to 78 bytes in length.The order of S-records within a file is of no significance and no particular order may be assumed.
The general format of an S-record follows:
+-------------------//------------------//-----------------------+ | type | count | address | data | checksum | +-------------------//------------------//-----------------------+
type -- A char[2] field. These characters describe the type of record (S0, S1, S2, S3, S5, S7, S8, or S9).count -- A char[2] field. These characters when paired and interpreted as a hexadecimal value, display the count of remaining character pairs in the record.
address -- A char[4,6, or 8] field. These characters grouped and interpreted as a hexadecimal value, display the address at which the data field is to be loaded into memory. The length of the field depends on the number of bytes necessary to hold the address. A 2-byte address uses 4 characters, a 3-byte address uses 6 characters, and a 4-byte address uses 8 characters.
data -- A char [0-64] field. These characters when paired and interpreted as hexadecimal values represent the memory loadable data or descriptive information.
checksum -- A char[2] field. These characters when paired and interpreted as a hexadecimal value display the least significant byte of the ones complement of the sum of the byte values represented by the pairs of characters making up the count, the address, and the data fields.
Each record is terminated with a line feed. If any additional or different record terminator(s) or delay characters are needed during transmission to the target system it is the responsibility of the transmitting program to provide them.
S0 Record. The type of record is 'S0' (0x5330). The address field is unused and will be filled with zeros (0x0000). The header information within the data field is divided into the following subfields.
mname is char[20] and is the module name.Each of the subfields is composed of ASCII bytes whose associated characters, when paired, represent one byte hexadecimal values in the case of the version and revision numbers, or represent the hexadecimal values of the ASCII characters comprising the module name and description.
ver is char[2] and is the version number.
rev is char[2] and is the revision number.
description is char[0-36] and is a text comment.
S1 Record. The type of record field is 'S1' (0x5331). The address field is intrepreted as a 2-byte address. The data field is composed of memory loadable data.
S2 Record. The type of record field is 'S2' (0x5332). The address field is intrepreted as a 3-byte address. The data field is composed of memory loadable data.
S3 Record. The type of record field is 'S3' (0x5333). The address field is intrepreted as a 4-byte address. The data field is composed of memory loadable data.
S5 Record. The type of record field is 'S5' (0x5335). The address field is intrepreted as a 2-byte value and contains the count of S1, S2, and S3 records previously transmitted. There is no data field.
S7 Record. The type of record field is 'S7' (0x5337). The address field contains the starting execution address and is intrepreted as 4-byte address. There is no data field.
S8 Record. The type of record field is 'S8' (0x5338). The address field contains the starting execution address and is intrepreted as 3-byte address. There is no data field.
S9 Record. The type of record field is 'S9' (0x5339). The address field contains the starting execution address and is intrepreted as 2-byte address. There is no data field.
EXAMPLE
Shown below is a typical S-record format file.
S00600004844521B
S1130000285F245F2212226A000424290008237C2A
S11300100002000800082629001853812341001813
S113002041E900084E42234300182342000824A952
S107003000144ED492
S5030004F8
S9030000FCThe file consists of one S0 record, four S1 records, one S5 record and an S9 record.
The S0 record is comprised as follows:
The first S1 record is comprised as follows:
- S0 S-record type S0, indicating it is a header record.
- 06 Hexadecimal 06 (decimal 6), indicating that six character pairs (or ASCII bytes) follow.
- 00 00 Four character 2-byte address field, zeroes in this example.
- 48 44 52 ASCII H, D, and R - "HDR".
- 1B The checksum.
- S1 S-record type S1, indicating it is a data record to be loaded at a 2-byte address.
- 13 Hexadecimal 13 (decimal 19), indicating that nineteen character pairs, representing a 2 byte address, 16 bytes of binary data, and a 1 byte checksum, follow.
- 00 00 Four character 2-byte address field; hexidecimal address 0x0000, where the data which follows is to be loaded.
- 28 5F 24 5F 22 12 22 6A 00 04 24 29 00 08 23 7C Sixteen character pairs representing the actual binary data.
- 2A The checksum.
The second and third S1 records each contain 0x13 (19) character pairs and are ended with checksums of 13 and 52, respectively. The fourth S1 record contains 07 character pairs and has a checksum of 92.
The S5 record is comprised as follows:
- S5 S-record type S5, indicating it is a count record indicating the number of S1 records
- 03 Hexadecimal 03 (decimal 3), indicating that three character pairs follow.
- 00 04 Hexadecimal 0004 (decimal 4), indicating that there are four data records previous to this record.
- F8 The checksum.
The S9 record is comprised as follows:
- S9 S-record type S9, indicating it is a termination record.
- 03 Hexadecimal 03 (decimal 3), indicating that three character pairs follow.
- 00 00 The address field, hexadecimal 0 (decimal 0) indicating the starting execution address.
- FC The checksum.


Step card
Activate Sass component
In order to have the styling of this component available, you need to include it in your project. After including the Radius library in your main ./index.scss
file, add this snippet:
@include radius-component-step-card;
Description
The step card is a pattern built for forms with multiple logical parts but
also has a static modifier to be used without its javascript counterpart.
The main components are:
step-card__wrap
: This wrapper is needed if there are multiple cards in a series. This is not needed forstep-card--static
.step-card
: The main element. This can have the modifiers such as--narrow
and--static
.step-card__content
: The meat of the content. Contains the__title
,__steps
,__def-list
,__intro
and general content.step-card__buttons
: The action buttons to continue through steps
If the content is narrower than full width, such as for a nested form, you you can apply thestep-card--narrow
modifier.
For the static card you can usestep-card--static
.
There is astep-card--warning
class to apply a yellowborder-left
. This can be used to draw attention to a specific card. For example, in transfer ins we use it to emphasise the 'Check details' step.

Check your personal details

Name

Craig David

 Edit my details These are correct

Usage example
Check your personal details
- Name
- Craig David
- Date of birth
- 26/05/1980
- Address
- Fancy House, This Lane, That City, BA447SS
Please make sure these details are the same as those of the bank account you want to add.
Takes 1-2 business days
Bank transfer
- Set up a transfer or standing order with your bank
- Use these payee details
- Add your unique reference so we can credit your account quickly
Transfer these funds back before 2016 to make the most of its tax-free status.


Just In: ICPC Sends Names Of Corrupt Public Officials In Nigeria To FG
by Philips SundayThe Independent Corrupt Practices and other related offences Commission (ICPC), says it has sent the names of corrupt public officials in Nigeria to President Muhammadu Buhari’s Federal Government.
Naija News reports that this was disclosed by the Chairman of the ICPC, Prof. Bolaji Owasanoye, at an event organised by the agency and the Office of the Secretary to the Government of the Federation in Abuja, Nigeria’s capital, on Tuesday, November 20.
The ICPC Chairman said this after he had earlier recommended the suspension of public office holders under investigation.
Owasanoye said names of such persons had been forwarded to the Federal government.
The ICPC Chairman maintained that the commission discovered that some civil servants served conviction terms and returned to work as if nothing had happened.
His words: “Such persons should be flushed out of the system in accordance with extant laws. We have communicated Iist of such persons to government”, NAN quoted him as saying.
“We advise digital information sharing between anti-corruption agencies and financial services sector critical to preventing money laundering and other corruption prevention measures.
“All Anti-Corruption Agencies (ACAs) as a policy ought to have seamless access to existing databases of suspects and the profile, biometric of persons of interest.
“Inter-agency collaboration is essential to avoid duplication and waste of resources in the investigation and prosecution of cases”, he said.


Client side Refresh
|
Client side group
| Server-side Refresh |
Client side latest update
Content
FromWho
Kind
ReceivedTime
Subject
ToMe
Sentiment


FITSIO Home
Next: 5.8.2 Date and Time
Up: 5.8 Utility Routines
Previous: 5.8 Utility Routines
Contents
The following routines either compute or validate the checksums for the
CHDU. The DATASUM keyword is used to store the numerical value of the
32-bit, 1's complement checksum for the data unit alone. If there is
no data unit then the value is set to zero. The numerical value is
stored as an ASCII string of digits, enclosed in quotes, because the
value may be too large to represent as a 32-bit signed integer. The
CHECKSUM keyword is used to store the ASCII encoded COMPLEMENT of the
checksum for the entire HDU. Storing the complement, rather than the
actual checksum, forces the checksum for the whole HDU to equal zero.
If the file has been modified since the checksums were computed, then
the HDU checksum will usually not equal zero. These checksum keyword
conventions are based on a paper by Rob Seaman published in the
proceedings of the ADASS IV conference in Baltimore in November 1994
and a later revision in June 1995. See Appendix B for the definition
of the parameters used in these routines.
- 1
- Compute and write the DATASUM and CHECKSUM keyword values for the CHDU
into the current header. If the keywords already exist, their values
will be updated only if necessary (i.e., if the file
has been modified since the original keyword
values were computed).
int fits_write_chksum / ffpcks
(fitsfile *fptr, > int *status)
- 2
- Update the CHECKSUM keyword value in the CHDU, assuming that the
DATASUM keyword exists and already has the correct value. This routine
calculates the new checksum for the current header unit, adds it to the
data unit checksum, encodes the value into an ASCII string, and writes
the string to the CHECKSUM keyword.
int fits_update_chksum / ffupck
(fitsfile *fptr, > int *status)
- 3
- Verify the CHDU by computing the checksums and comparing
them with the keywords. The data unit is verified correctly
if the computed checksum equals the value of the DATASUM
keyword. The checksum for the entire HDU (header plus data unit) is
correct if it equals zero. The output DATAOK and HDUOK parameters
in this routine are integers which will have a value = 1
if the data or HDU is verified correctly, a value = 0
if the DATASUM or CHECKSUM keyword is not present, or value = -1
if the computed checksum is not correct.
int fits_verify_chksum / ffvcks
(fitsfile *fptr, > int *dataok, int *hduok, int *status)
- 4
- Compute and return the checksum values for the CHDU
without creating or modifying the
CHECKSUM and DATASUM keywords. This routine is used internally by
ffvcks, but may be useful in other situations as well.
int fits_get_chksum/ /ffgcks
(fitsfile *fptr, > unsigned long *datasum, unsigned long *hdusum,
int *status)
- 5
- Encode a checksum value
into a 16-character string. If complm is non-zero (true) then the 32-bit
sum value will be complemented before encoding.
int fits_encode_chksum / ffesum
(unsigned long sum, int complm, > char *ascii);
- 6
- Decode a 16-character checksum string into a unsigned long value.
If is non-zero (true). then the 32-bit sum value will be complemented
after decoding. The checksum value is also returned as the
value of the function.
unsigned long fits_decode_chksum / ffdsum
(char *ascii, int complm, > unsigned long *sum);
FITSIO Home
Next: 5.8.2 Date and Time
Up: 5.8 Utility Routines
Previous: 5.8 Utility Routines
Contents


Tool/Status/Dialog
The example just got better
You can now create 3 types of
Toolbars
from the
menu
.
Statusbar
Displays
Date
and
Time


| Modifier and Type | Method and Description |
|---|---|
void |
Device.commit()
Commit the candidate configuration.
|
void |
Device.commitConfirm(long seconds)
Commit the candidate configuration, temporarily.
|
void |
NetconfSession.commitFull()
Commit the candidate configuration and rebuild the config database.
|
void |
Device.commitFull()
Commit full is an unsupported Juniper command that will commit the config and then signal all processes to
check the configuration for changes.
|
void |
Device.commitThisConfiguration(String configFile,
String loadType)
Loads and commits the candidate configuration, Configuration can be in
text/xml format.
|
Copyright 2018, Juniper Networks, Inc.


Footballer called "pathetic little man" by opposition boss after being racially abused
Ahmad Mendes Moreira left the field as the game against Den Bosch was halted before returning to score. But coach of the opposition, Erik van der Ven, criticised the player
by Darren Wells, https://www.mirror.co.uk/authors/darren-wells/Dutch footballer Ahmad Mendes Moreira has told of his "hurt" after being labelled a "pathetic little man" in the aftermath of receiving racist abuse.
The Excelsior striker lined up for his team away at Den Bosch in the Dutch first division on Sunday, but left the field after being abused by the home fans.
He stormed off as the referee called a temporary halt to the clash at Den Bosch's De Vliert stadium, but returned soon after to score for his team.
But Den Bosch coach Erik van der Ven blasted Moreira as a "pathetic little man" for his actions during the match.
Speaking in a post match press conference, Moreira said: "That just hurts me a lot."
The match eventually ended in a 3-3 draw, after Excelsior fought back from 2-1 down following Moreira's goal, and then levelling late on through Ruben Rodrigues.
With the scores at 1-1 earlier in the match, Moreira gave away a foul over by the touchline, before he was shouted at by Den Bosch supporters sitting nearby.
Moreira - whose parents hail from the West African nations of Bissau-Guinea and Guinea respectively - naturally reacted angrily to the abuse.
He was escorted off the pitch by sympathetic teammates before a lengthy break, during which the match looked as though it could be abandoned.
However the game soon resumed and Moreira quickly provided the best response, scoring and cupping his ears in celebration.
Objects were hurled at the 24-year-old and his teammates as they celebrated.
Former Man Utd star Memphis Depay reacted to the incident on social media, tweeting: "I'm sick and tired to see these images over and over! When is it going to stop!!? #SayNoToRacism."
He then tweeted the Dutch FA, saying: "What we doing to do? Specially with the upcoming Euro 2020?"
Den Bosch released a statement after the fixture condemning the behaviour of their fans.
It read: "Let one thing be clear; FC Den Bosch distances itself from everything that has to do with this and acts strongly against stadium visitors who are guilty of this."
It is the latest occurrance of racism in football following a recent spate of similar incidents.
The England national team were subjected to abuse in Bulgaria last month, while an FA Cup tie between Haringey Borough and Yeovil Town was called off days later.


rust_bin
APIGET /
Return this page.
GET /ip
Return the origin IP of the user.
GET /user-agent
Return the User-Agent
header or 400 if it was not found.
GET /stream-bytes/<n>
Stream n
number of bytes in chunks.
GET /status/<s>
Return the specified HTTP status s
or 400 if it is invalid.


---
layout: post
title: Oracle SQL Developer 1.1.3 is out
date: '2007-05-10T14:21:00.000-05:00'
author: Don Seiler
tags:
- oracle
- sql developer
modified_time: '2010-11-19T22:48:46.542-06:00'
blogger_id: tag:blogger.com,1999:blog-7032512792942232766.post-358689140316477656
blogger_orig_url: http://www.seiler.us/2007/05/oracle-sql-developer-113-is-out.html
---
Kris Rice announced the release of Oracle SQL Developer 1.1.3, with "about 200 bug fixes in this build."
Full list of bug fixes is here.


| CN CPI Monthly Y/Y (%) | ||||||
| Date | For Month | Market Expectation | Kshitij Expectation | Previous | Released | Revised |
| 9-Dec-17 | Nov-17 | 1.80 | - | 1.90 | 1.70 | - |
| 9-Jan-18 | Dec-17 | 1.90 | - | 1.70 | 1.80 | - |
| 9-Feb-18 | Jan-18 | 1.50 | - | 1.80 | 1.50 | - |
| 9-Mar-18 | Feb-18 | 2.40 | - | 1.50 | - | - |
| Copyright: Kshitij Consultancy Services |


Each of the DIV elements below will receive a tooltip.
North w/header.
South w/header.
East w/header.
West w/out header.
North West w/header.
North East w/header.
South West w/header.
South East w/header.
West South w/out header.
East South w/header.
West North w/out header.
East North w/header.


This is a demo app, made by Deven Blackburn and is not intended for real world use.
Data stored here is periodically erased without notice.


Preparation time
less than 30 mins
Cooking time
no cooking required
Serves
Makes 1.5litres/2¾pints
Dietary
Homemade elderflower cordial is so much better than anything you can buy. Serve with sparkling water for a refreshing drink or sparkling wine for a delicious cocktail.
Gently rinse over the elderflowers to remove any dirt or little creatures.
Pour the boiling water over the sugar in a very large mixing bowl. Stir well and leave to cool.
Add the citric acid, the orange and lemon slices, and then the flowers.
Leave in a cool place for 24 hours, stirring occasionally.
Strain through some muslin and transfer to sterilised bottles.


Access Statistics
Current Activity
{content}
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phpOpenTracker
{pot_version}.
Copyright © 2000-2003 Sebastian Bergmann
All Rights Reserved.
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Made in
Elm
Elm-Redex
The interactive lambda calculus interpreter
Liam O'Connor
Last modified: Fri Aug 22 10:32:22 EST 2014


?PLANTS
OF
BAHAMA
ISLANDS
Aristida
vilfifolia
Henr,
Grand
Bahama:
in
open
pinela
Çis
along
Qpeen's
Highway
going
east
to
North
Biding
PointÇ
August
19.
197^
d.
s.
Coneu
&
Eobert
Krai
^3038
Herbarium
FAIRCHILD
TROPICAL
GARDEN
1659710
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


<%- article.title %>
<%- article.extract %>
These scenarios describe the type of businesses which implement Auth0, and how Auth0 can help in each of those business scenarios
<% _.forEach(_.sortBy(articles.findByHash('architecture-scenarios/business').items, 'order'), function(article) { %><%- article.extract %>
These scenarios describe the different type of technology architectures you application may use, and how Auth0 can help for each of those
<% _.forEach(_.sortBy(articles.findByHash('architecture-scenarios/application').items, 'order'), function(article) { %><%- article.extract %>


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Talk is cheap. Show your potential clients that you know your stuff by uploading a few photos of your work.
Load up to three photos for your free trial. Paid subscriptions are allowed more. Upgrade today!


{% extends "account/base.html" %} {% load i18n %} {% load account %} {% block local-account %}
{% trans "Note" %}: {% blocktrans %}you are already logged in as {{ user_display }}.{% endblocktrans %}
{% blocktrans %}We have sent you an e-mail. Please contact us if you do not receive it within a few minutes.{% endblocktrans %}


+++ title = "Otavalo market splurge" slug = "otavalo-market-splurge" date = 2007-08-25T10:00:00+10:00 tags = ["chess", "shopping", "big-events", "markets", "for-sale"] locations = ["ecuador/otavalo"] draft = false summary = """
I woke up this morning, to find Otavalo completely transformed. Outside my hostel was a market. Down the street was more market. In fact, the entire town had been converted into one giant, sprawling, souvenir-riddled market! So the rumours were true: it is indeed a big deal, here in Otavalo on Saturday. And since this was my last full day in Ecuador, and in the Central Andes as a whole, it was time to embark on a serious splurge of souvenir and present shopping. I now have a very overstuffed bag, and a rather lean pocket.
""" +++I woke up this morning, to find Otavalo completely transformed. Outside my hostel was a market. Down the street was more market. In fact, the entire town had been converted into one giant, sprawling, souvenir-riddled market! So the rumours were true: it is indeed a big deal, here in Otavalo on Saturday. And since this was my last full day in Ecuador, and in the Central Andes as a whole, it was time to embark on a serious splurge of souvenir and present shopping. I now have a very overstuffed bag, and a rather lean pocket.
Unfortunately, I don't have the photos to prove it (more on that sad issue tomorrow, folks) — but here's what I bought in Otavalo this morning:
Not sure exactly how much all that cost me, but it must have been at least US$70. No small spend, eh? Oh well, it's my last chance to buy proper, South American souvenirs — none of this "indigenous stuff" down in Chile / Argentina / Brazil.
Well, I wonder who's getting what? 'Cause all of this was present shopping. At any rate, the "pink, little-girl-sized alpaca jumper" is certainly not for me. :P
It was great fun, wandering through the endless market stalls this morning, checking out what was on offer, and of course engaging in plenty of good, old-fashioned bargaining. Wasn't too packed with tourists, either: at least, not with gringo tourists — I think the majority of shoppers were Quiteños, just come down for the morning on the bus.  Once I was in this state, I decided it was time to grab some lunch, and to hop on a bus back to Quito, for my final night in Ecuador.


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The most signature food in Melaka would be the Nyonya cuisine, or Peranakan cuisine.
Peranakan or Nyonya cuisine comes from the Peranakans, descendants of early Chinese migrants who settled in Penang, Malacca, Singapore and Indonesia inter-marrying with local Malays and combines Chinese, Malay and other influences.
We were going to visit this restaurant claiming to serve authentic Nyonya food in town, the Nancy’s Kitchen.
娘惹餐是马六甲的招牌菜色。娘惹文化可以追溯到早期近1800，中国华人南下移民定居于槟城，马六甲，新加坡和印尼，和当地的马来土著的结合而衍生出来的文化。所以娘惹等于是华人和马来人的结合体。今日来到马六甲城中有名的娘惹餐馆，Nancy’s Kitchen。
The typical Nyonya style lantern, which is much different from the typical Chinese lantern. The price of making one was costly since the procedure is complicated and hand-made only.
典型的娘惹式灯笼，很明显的和传统的华人灯笼不一样。听说由于纯手工制作，程序繁重，价格也不菲。
The mixed culture of Malay and Chinese can be clearly seen in the decorations as well.
墙上的装饰也可看出结合了中华和马来文化。
The Food
总的来说，这间店的性价比还是不错的，七个人消费 RM100 。味道也和从小吃到大的没太大出入，不熟悉娘惹餐的我表示原来平常吃的家常菜就是娘惹风味了啊。
Address: 13, Jalan KL 3/8, Taman Kota Laksamana, 75200 Melaka, Malaysia
Lastly, happy birthday to my dad!
This page is synchronized from the post: ‘Melaka Nyonya Food 马六甲娘惹餐’


I've been screwing around with snuffs, in an attempt to make the perfect cheap snuff to replace marijuana. I think I've finally found it. For a while I've been using the DMT snuff during daily activities. Most people say it sends them into a trance, but for me it makes me much more active, and much more a part of the real world. I've used it at work when there isn't a private bathroom provided to smoke weed, even while taking long bus rides. But, it's not something I like to relax to, so I knew I needed to add something.
A few weeks ago I got my hands on 2lb of morning glory seeds. I made an extract, removing the bad stuff with a non-polar solvent first, threw most of the liquid form in a vial and used salt to help evap the rest. For some reason, without the salt, the extract REFUSED to evap. It was still not dry completely when I decided to scrape up some of the salt. I wasn't sure what was running through my mind, but I mixed a few mg of it with a few mg of dmt and insufflated the mix.
It was incredible. Auditory hallus came on quick. I needed to turn off my tv IMMEDIATELY. The lights were next, as they were repressing visuals. My mind was swarming with all the knowledge I've ever gained, all the memories I've ever had, I fell into a trance, ocassionally launching my entire body from one place to another, which is actually quite normal to me, as I am a contortionist. I could see creationism, I could see evolutionism, I watched both happen as if I was there. I got answers to questions about life, about love, about happiness. I watched figures form in the darkness with quiet admiration, I heard voices, some loving, some hateful, some memories from times long past and dearly missed. I wondered if this was what people meant by the old cliche 'Life was passing before my eyes.'
I drifted off into sleep, enjoying incredibly vivid dreams until I was ripped out of sleep by a knock at the door. I dealt with it, and went back to laying down. The voices, the images seems to carress my body, my mind, my very soul. I drifted off again, more incredibly vivid dreams. This isn't a good thing for everyone, but I strive for vivid dreams, without differentiating between nightmare and dream. Another knock at the door. My heart was running at at least 80 beats per minute. Mine usually runs at a cool 50, with my temp usually being a cool 96.5.
I couldn't remember which way the door was, and the dream shifted seemlessly into visuals. I called out 'What do you need?' They needed a ride, or for me to cook dinner, or for me to to go back to dreaming.. I wasn't sure which voice to listen to so I told them I just awoke from a nightmare and didn't have the ability to. It had to be at least 6 in the morning, and I had taken the tiny combo at around 1. Back to sleep, to more vivid dreams, until about 8 AM when my dog woke me up. I was sober, and felt incredible. My mind buzzing with the events of the past few hours. I let him in, and drifted back to sleep holding him gently.
In conclusion, I think I've found my replacement for the overpriced marijuana, and I knew I had to share this mixture with the world. I'll be continuing to make a decent amount of this snuff, and probably will end up carrying it around with me wherever I go. For me it was a great experience. I've been experimenting with, basically, random things since I was 12, marijuana being a constant the whole time. I yearn to expand my mind at every possible oppurtunity.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


class: center, middle, inverse, title-slide # Week 4: ‘Tidying’ data 2 ### Joshua Rosenberg, Alex Lishinski ### February 11, 2021 --- # Welcome! Welcome to *week 4*! **Record the meeting** --- # Breakout rooms! Starting with whomever has the least total letters in their first middle and last names... **Two questions** - What is one particular "messy data" problem have you encountered (or do you anticipate encountering) in your own work? - What is an example of multiple, separate datasets that you might wish to combine together into a single dataset in your work? If you cannot immediately think of one, consider information that might be recorded or created in two different files, but which could be useful to combine. (10 minutes) --- ### Review of last week's class **Last week we discussed wrangling and tidying data**: 1. Reading in Data 1. Tidying data 1. Our tidy data tools --- ### Homework highlights **Effective and ineffective ways of filtering the data** ```r data %>% filter(content == 1, content == 2, content == 3, content == 4, content == 5) ``` -- ```r data %>% filter(content == "1", content == "2", content == "3", content == "4", content == "5") ``` -- ```r data %>% filter(content == "1" | content == "2" | content == "3" | content == "4" | content == "5") ``` -- ```r data %>% filter(between(content, 1, 5)) ``` -- ```r data %>% filter(content %in% c("1", "2", "3", "4", "5")) ``` -- ```r data %>% filter(str_detect(content, "[1-5]")) ``` --- ## Homework reminder Check the output of functions you write carefully; you may find yourself *constantly* looking at and viewing your data! **How do I check/view my data?** There are many ways: - `my_data` (just typing the name of your data will print info about it!) - `glance(my_data)` - `str(my_data)` - `View(my_data)` (do not include in `.Rmd` document) - `skimr::skim(my_data)` (must install "skimr" first) - `psych::describe()` (must install "psych" first) --- ### This week's topics **Overview** 1. Tidy data: Data reshaping 1. Working with multiple data frames: joins 1. Grouped data operations with dplyr We are by no means done with the data tidying tools we discussed last week, so don't feel like you should already have those completely mastered yet. --- ### 1. Data reshaping **Outline** What is reshaping? Why would you reshape? How do you reshape? --- ### 1. Data reshaping ** What is reshaping? ** * Reshaping involves moving data between "long"er and "wide"er formats + Wide data has more columns and fewer rows + Long data has more rows and fewer columns + This is often a choice you can make with your data --- ### 1. Data reshaping ** What is reshaping? ** * Reshaping involves moving data between "long"er and "wide"er formats <img src="img/original-dfs-tidy.png" width="65%" /> --- ### 1. Data reshaping ** Why would you reshape? ** One reason to reshape is to get data in a proper 'tidy' format. The broader reason is that data can be in multiple shapes and you may need different ones for different purposes. 'Tidy' data doesn't always fully determine what you should do, particularly in the case of repeated measures data. --- ### 1. Data reshaping ** How do you reshape? ** The `tidyr` package offers the `gather()` and `spread()` functions. These are meant to be replaced by the "pivot" functions from `dplyr`, which are `pivot_longer()` and `pivot_wider()` --- ### 1. Data reshaping ** How do you reshape? ** ![Join gif](img/tidyr-longer-wider.gif) --- ### 2. Data joining **What are joins?** Why are joins important? What are the different types? How do we know we're doing them right? --- ### 2. Data joining **What are joins?** - When you have multiple data frames that you want to combine - Need to have overlap Why are joins important? - They are the way we will use to integrate data from different sources for analysis - Doing them incorrectly can substantially change your data set --- ### 2. Data joining **Different types of joins** * Main types + left join + right join + full join + inner join * Less common + semi join + anti join --- ### 2. Data joining `dplyr::left_join()` * Columns from both * Matching rows from both * non-matching rows from X only ![Join gif](img/left-join.gif) --- ### 2. Data joining `dplyr::right_join()` * Columns from both * Matching rows from both * non-matching rows from Y only ![Join gif](img/right-join.gif) --- ### 2. Data joining `dplyr::full_join()` * Columns from both * Matching rows from both * non-matching rows from both ![Join gif](img/full-join.gif) --- ### 2. Data joining `dplyr::inner_join()` * Columns from both * Matching rows from both * non-matching rows from neither ![Join gif](img/inner-join.gif) --- ### 2. Data joining `dplyr::semi_join()` * Columns from X * Matching rows from both * non-matching rows from neither ![Join gif](img/semi-join.gif) --- ### 2. Data joining `dplyr::anti_join()` * Columns from X * No matching rows * Only non-matching rows from X ![Join gif](img/anti-join.gif) --- ### 2. Data joining How do we know we're doing them right? - Short answer: Carefully inspecting your data before and after How do we know which one to choose? - Short answer: Knowing what the different types do and knowing your data - It all depends on what you want. --- ### 2. Data joining Matching variables: * Need to make sure matching variables are correct * Join functions by default will match all names, but you can specify Need to align variable names: * Can do this by renaming variables * Can also specify corresponding pairs in the join functions Other issues to consider: * Multiple matches * Matching NAs * Duplicate variable labels --- ### 3. Grouped data operations and summary ** How do we look at summary information about our variables? ** What do we mean by grouping? How do we group? What can you do with grouped data? --- ### 3. Grouped data operations and summary ** How do we look at summary information about our data? ** `summarize()` from `dplyr` allows us to look at various sorts of summary info. --- ### 3. Grouped data operations and summary ** What do we mean by grouping? ** Grouping is when we create groups in our data based on a categorical variable (or something that can be transformed into one). --- ### 3. Grouped data operations and summary ** How do we group? ** The `dplyr` function `group_by()` allows us to create grouped data frames. ```r library(dplyr) storms %>% group_by(name) ``` ``` ## # A tibble: 5 x 13 ## # Groups: name [1] ## name year month day hour lat long status category wind pressure ## <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <chr> <ord> <int> <int> ## 1 Amy 1975 6 27 0 27.5 -79 tropi… -1 25 1013 ## 2 Amy 1975 6 27 6 28.5 -79 tropi… -1 25 1013 ## 3 Amy 1975 6 27 12 29.5 -79 tropi… -1 25 1013 ## 4 Amy 1975 6 27 18 30.5 -79 tropi… -1 25 1013 ## 5 Amy 1975 6 28 0 31.5 -78.8 tropi… -1 25 1012 ## # … with 2 more variables: ts_diameter <dbl>, hu_diameter <dbl> ``` --- ### 3. Grouped data operations and summaries ** What can we do with grouped data? ** We can combine `summarize()` with grouped data frames and get summary information by group. We can also do different sorts of data cleaning operations on grouped data. --- ### 3. Grouped data operations and summaries ** Finding the mean wind speed for storms ** ```r library(dplyr) storms %>% group_by(name) %>% summarize(mean_wind_speed = mean(wind)) ``` ``` ## # A tibble: 198 x 2 ## name mean_wind_speed ## * <chr> <dbl> ## 1 AL011993 27.5 ## 2 AL012000 25 ## 3 AL021992 29 ## 4 AL021994 24.2 ## 5 AL021999 28.8 ## 6 AL022000 29.2 ## 7 AL022001 25 ## 8 AL022003 30 ## 9 AL022006 38 ## 10 AL031987 21.2 ## # … with 188 more rows ``` --- ### 3. Grouped data operations and summaries ** Finding the mean wind speed for storms and arranging ** ```r library(dplyr) storms %>% group_by(name) %>% summarize(mean_wind_speed = mean(wind)) %>% arrange(desc(mean_wind_speed)) ``` ``` ## # A tibble: 198 x 2 ## name mean_wind_speed ## <chr> <dbl> ## 1 Wilma 91.9 ## 2 Luis 89.2 ## 3 Hugo 88.1 ## 4 David 86.8 ## 5 Gonzalo 86.1 ## 6 Ike 83.2 ## 7 Igor 81.8 ## 8 Joaquin 81.8 ## 9 Rita 80.1 ## 10 Gilbert 79.6 ## # … with 188 more rows ``` --- # Course Logistics * Exam 1: Available - Tomorrow; Due - Before our next class begins * Homework 4: (small change; post _one_ comment, feedback, or reflections on your homework when you post it to #homework on Slack) * [Personal Data Ethics Statement](https://making-data-science-count.github.io/s21-intro-to-data-sci-methods-in-ed/assignment-data-ethics.html): Check the assignments page; due 3/4; see assignments details for more information * Reading on tidying data: https://r4ds.had.co.nz/tidy-data.html --- # Notes on asking questions Your #questions are great; keep it up; a few suggestions: - First, check to see whether someone else asked a question about a similar issue - When possible and relevant, provide either a) a screen shot or b) a link to your `.Rmd` document along with your question - Provide as much detail as possible; it's much more helpful to us and others if you explain what's going on, even if you're not certain about terminology - Consider Googleing the error message. - If you know the answer, feel free to respond to others! - If your question is resolved, be sure to post in Slack that you figured it out! --- # Wrapping up In your base group's Slack channel: - What is one thing you took away from today? - What is something you want to learn more about?


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A project-driven Service Learning course in which advanced Communication Design students analyze
and solve a design or technical problem for their community partners and their constituents.
Students address social injustice issues and combine design and technical skills with professional
knowledge to solve the problem. Requirement: concurrent enrollment in a 400-level Communication
Design course.
Service Learning is a course taken in conjunction with the Web Production course, CST 451, to not only
give students some experience in a work environment, but also a taste of what it is like to use one's
abilities and skill sets to give back to the community to make a long term impact. The majority of the
volunteer hours are subtracted from the work done during the class period, because we are creating
a website for an actual non-profit organization as a client, with the exception of 20 hours to be
completed outside of class. The best was I felt I could assist the organization was to help make the
tutorial videos on how to use their drupal-built CMS website, as well as an active tech support for any
questions that may arise.


java.util.concurrent.ScheduledThreadPoolExecutor
instances in which
corePoolSize
is set to zero via the setCorePoolSize
method or
the object constructor.
A ScheduledThreadPoolExecutor
with zero core threads will run nothing.
Example:
void foo(int corePoolSize) {
if (corePoolSize != 0) return;
ThreadPoolExecutor executor = new ScheduledThreadPoolExecutor(corePoolSize); // warning
executor.setCorePoolSize(corePoolSize); // warning
}