| DOSE: |
2.0 g |
insufflated |
Catnip |
(plant material) |
Mine is a fairly normal story for legal drugs; I read about using catnip to get high on the internet, and decided to try it for myself. That was about a year ago, when I was pretty depressed and going through counseling (which was worthless). Helped a little, and I managed to keep myself down to a couple joints of catnip every month or so, until I no longer felt like I needed a lift every now and then.
Fast forward to February, 2003, the time of this writing. My teeth are in horrible shape so I need an upper jaw spreader thing to make room to straighten them (and hurts like crazy), I've been diagnosed with an active mono infection (probably had it for months), my back hurts constantly (has for as long as I can remember; it's apparently something I'll 'grow out of' eventually), I've been having panic attacks, and am on an SSRI (Zoloft) for that, as well as Clarinex to keep my face and hands from swelling up like a Human balloon.
Between all that, and some relationship difficulties (thankfully, mostly straightened out by now), I figured it was time to start up with the catnip again.
I smoked a little, and experienced the usual soothing, peaceful feeling of well-being (it also dulled the pain in my teeth and back, killed my stomach cramps, and made me less hungry, which was a _real_ plus, since my teeth have been killing me enough that I've been living on milk, milkshakes, breakfast drinks, multivitamins, and painkillers for the past few days).
Problem, though, was the parents being home a lot of the times I felt like smoking. My throat was also starting to really bug me (probably the mono). I didn't have enough of the stuff left to 'waste' it by making tea, and that was kind of a lengthy process anyway, so I decided 'why not snort the stuff?'
I took a little, put it in the middle of a piece of paper, and crushed it all up by folding and squeezing it in the paper. I picked out the bits of catnip that weren't reduced to powder, and took it into the bathroom with me (I was about to take a shower). I had read a bit about using snuff and other such things, and prepared myself for the worst as I snorted a pinch up my right nostril.
Didn't burn at all. Tickled a bit, actually, and didn't even make me feel like sneezing. I felt the effects after about 3 seconds (if that); I was a bit more alert--almost giddy, actually. I snorted the rest (about 3 more pinches), took my shower, dried off, and crumbled up some more, probably about 8 pinches worth.
After snorting all that, my sinuses felt a little dry and uncomfortable, and I could feel some of the stuff dripping down the back of my throat, but, as far as I could tell, the effects for me were comparable to smoking it, except that the feelings of tranquility and peacefullness were a bit less evident, the sensation of 'coolness' was in my sinuses instead of in my chest, and I seemed to be much more alert than I was before. I also had the constant urge to sneeze, which I managed to suppress. Duration was no different, though--about an hour or two.
It also smells _considerably_ better; catnip smoke tastes and smells, to me, like a combination of a freshly-mown lawn and burning rubber. Not as unpleasant as it sounds, and I actually almost like it, but it's not something that I'd want to experience constantly. Insufflated catnip, on the other hand, just smells minty--that's it.
If you've already tried catnip and liked it, but are looking for something faster than making tea and less conspicuous than smoking, I'd say to go for snorting it. It probably won't completely replace smoking for me, but it's something different to try.
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.


Search for the Incident you want to edit using the parameters provided. You can only edit the STATUS!
Change status to:


Get Started!
Watch this 90 second demo to see mu in action then get started with the mu wiki
Amazon ECS (EC2 Container Service) provides an excellent platform for deploying microservices as containers. However, there is a significant learning curve for developers to get their microservices deployed.
Mu knows when to stay out of the way. Mu only uses AWS resources for deploying your microservices.
Mu likes speed. Mu uses CodePipeline and CodeBuild to continuously test and delivery your microservice to production.
Mu doesn't have a favorite language. If you can get your microservice running with a Dockerfile, then mu can help!
Mu knows you might not need him anymore. Mu doesn't have any servers or databases running anywhere. Mu leverages CloudFormation to manage state for all AWS resources.
Mu makes sure you get what you want. You declare your configuration in a YAML file and commit with your source code. Mu takes care of setting up your AWS resources to meet your needs.
Mu is MIT licensed, so you can use it commercially. Mu is always looking to improve, so please consider contributing!
Watch this 90 second demo to see mu in action then get started with the mu wiki


--- layout: post-update current: post-update cover: assets/images/covers/artdirectcover.jpg navigation: True title: Art Director Solei date: 2018-06-13 16:30:00 class: post-template tags: [Web] author: raul subclass: 'post' category: home ---
Svitlana's work and trajectory are displayed on website for the public to admire her talent. Recently, she has refreshed her site's look with a new landing page.
Each of the icons is responsible for representing the meaning of the section. 4 divisions? 4 matching icons flavoured with a glowing yellow:
Yellow is the color of the sun. Of freshness. Of positivity. Of energy. And it contrasts perfectly alongside with a dark deep grey over a immaculate white.
All images and icons in this concept come from a free stock source.


Drilling Down – Assessing Digital Scholarship Options Available through Commercial Vendors
Joshua Been, Baylor University -- Michael Thompson, Baylor University -- Loretta Wallace, University of Texas
Ensuring the availability of rich digital content is a vital component of a library’s digital scholarship program. This session will explore the development of a rubric allowing libraries to evaluate the data mining potential within their current collections, as well as helping to inform the decision-making process for new acquisitions.


|
| static final double | kEps = 1E-9 |
| |
Represents a 2d pose (rigid transform) containing translational and rotational elements.
Inspired by Sophus (https://github.com/strasdat/Sophus/tree/master/sophus)
◆ Pose2d() [1/4]
| frc.lib.geometry.Pose2d.Pose2d |
( |
| ) |
|
◆ Pose2d() [2/4]
| frc.lib.geometry.Pose2d.Pose2d |
( |
double |
x, |
|
|
double |
y, |
|
|
final Rotation2d |
rotation |
|
) |
| |
◆ Pose2d() [3/4]
◆ Pose2d() [4/4]
| frc.lib.geometry.Pose2d.Pose2d |
( |
final Pose2d |
other | ) |
|
◆ distance()
| double frc.lib.geometry.Pose2d.distance |
( |
final Pose2d |
other | ) |
|
◆ epsilonEquals()
| boolean frc.lib.geometry.Pose2d.epsilonEquals |
( |
final Pose2d |
other, |
|
|
double |
epsilon |
|
) |
| |
◆ equals()
| boolean frc.lib.geometry.Pose2d.equals |
( |
final Object |
other | ) |
|
◆ exp()
◆ fromRotation()
◆ fromTranslation()
◆ getPose()
| Pose2d frc.lib.geometry.Pose2d.getPose |
( |
| ) |
|
◆ getRotation()
| Rotation2d frc.lib.geometry.Pose2d.getRotation |
( |
| ) |
|
◆ getTranslation()
◆ identity()
| static final Pose2d frc.lib.geometry.Pose2d.identity |
( |
| ) |
|
|
static |
◆ interpolate()
| Pose2d frc.lib.geometry.Pose2d.interpolate |
( |
final Pose2d |
other, |
|
|
double |
x |
|
) |
| |
Do twist interpolation of this pose assuming constant curvature.
◆ intersection()
Finds the point where the heading of this pose intersects the heading of another. Returns (+INF, +INF) if parallel.
◆ intersectionInternal()
◆ inverse()
| Pose2d frc.lib.geometry.Pose2d.inverse |
( |
| ) |
|
The inverse of this transform "undoes" the effect of translating by this transform.
- Returns
- The opposite of this transform.
◆ isColinear()
| boolean frc.lib.geometry.Pose2d.isColinear |
( |
final Pose2d |
other | ) |
|
Return true if this pose is (nearly) colinear with the another.
◆ log()
| static Twist2d frc.lib.geometry.Pose2d.log |
( |
final Pose2d |
transform | ) |
|
|
static |
Logical inverse of the above.
◆ mirror()
| Pose2d frc.lib.geometry.Pose2d.mirror |
( |
| ) |
|
◆ normal()
| Pose2d frc.lib.geometry.Pose2d.normal |
( |
| ) |
|
◆ toCSV()
| String frc.lib.geometry.Pose2d.toCSV |
( |
| ) |
|
◆ toString()
| String frc.lib.geometry.Pose2d.toString |
( |
| ) |
|
◆ transformBy()
| Pose2d frc.lib.geometry.Pose2d.transformBy |
( |
final Pose2d |
other | ) |
|
Transforming this RigidTransform2d means first translating by other.translation and then rotating by other.rotation
- Parameters
-
| other | The other transform. |
- Returns
- This transform * other
◆ kEps
| final double frc.lib.geometry.Pose2d.kEps = 1E-9 |
|
staticprivate |
◆ kIdentity
| final Pose2d frc.lib.geometry.Pose2d.kIdentity = new Pose2d() |
|
staticprotected |
◆ rotation_
| final Rotation2d frc.lib.geometry.Pose2d.rotation_ |
|
protected |
◆ translation_
The documentation for this class was generated from the following file:


A GPGPU CUDA powered library “CuCCAl”, acronym for “Cuda complex cellular automata library”, capable of hiding all the complexity behind the GPU programming is under development109. The aim of this library is to make life easier to researchers that need to parallelize on GPU cellular automata. The programmer has only to write the elementary processes that compose the transition function, register substates and set up some parameters (for example, the number of steps). 110. Memory management is completely behind the scene, letting the programmer concentrate only on the model he needs to write.


Olive XML importer¶
Custom classes¶
Classes to handle the Olive XML OCR format.
-
class
text_importer.importers.olive.classes.
OliveNewspaperIssue
(issue_dir: impresso_commons.path.path_fs.IssueDirectory, image_dirs: str, temp_dir: str)¶ Class representing a newspaper issue in Olive format.
Upon object initialization the following things happen:
the Zip archive containing the issue is uncompressed
the ToC file is parsed to determine the logical structure of the issue
page objects (instances of
OliveNewspaperPage
) are initialised.
- Parameters
issue_dir (IssueDir) – Description of parameter issue_dir.
image_dirs (str) – Path to the directory with the page images. Multiple paths should be separated by comma (“,”).
temp_dir (str) – Description of parameter temp_dir.
-
class
text_importer.importers.olive.classes.
OliveNewspaperPage
(_id: str, n: int, toc_data: dict, image_info, page_xml)¶ Class representing a page in Olive format.
- Parameters
_id (str) – Canonical page ID.
n (int) – Page number.
toc_data (dict) – Metadata about content items in the newspaper issue.
image_info (dict) – Metadata about the page image.
page_xml (str) – Path to the Olive XML file of the page.
-
add_issue
(issue: text_importer.importers.classes.NewspaperIssue)¶ Add to a page object its parent, i.e. the newspaper issue.
This allows each page to preserve contextual information coming from the newspaper issue.
- Parameters
issue (NewspaperIssue) – Newspaper issue contaning this page.
-
parse
()¶ Process the page file and transform into canonical format.
Note
This lazy behavior means that the page contents are not processed upon creation of the page object, but only once the
parse()
method is called.
Detect functions¶
-
text_importer.importers.olive.detect.
OliveIssueDir
¶ A light-weight data structure to represent a newspaper issue.
This named tuple contains basic metadata about a newspaper issue. They can then be used to locate the relevant data in the filesystem or to create canonical identifiers for the issue and its pages.
Note
In case of newspaper published multiple times per day, a lowercase letter is used to indicate the edition number: ‘a’ for the first, ‘b’ for the second, etc.
- Parameters
journal (str) – Newspaper ID
date (datetime.date) – Publication date
edition (str) – Edition of the newspaper issue (‘a’, ‘b’, ‘c’, etc.)
path (str) – Path to the directory containing OCR data
rights (str) – Access rights on the data (open, closed, etc.)
>>> from datetime import date >>> i = OliveIssueDir('GDL', date(1900,1,1), 'a', './GDL-1900-01-01/', 'open')
alias of
text_importer.importers.olive.detect.OliveIssueDirectory
-
text_importer.importers.olive.detect.
dir2olivedir
(issue_dir: impresso_commons.path.path_fs.IssueDirectory, access_rights: dict) → text_importer.importers.olive.detect.OliveIssueDirectory¶ Helper function that injects access rights info into an
IssueDir
.Note
This function is called internally by
olive_detect_issues()
.- Parameters
issue_dir (IssueDir) – Input
IssueDir
object.access_rights (dict) – Access rights information.
- Returns
New
OliveIssueDir
object.
-
text_importer.importers.olive.detect.
olive_detect_issues
(base_dir: str, access_rights: str, journal_filter: set = None, exclude: bool = False) → List[text_importer.importers.olive.detect.OliveIssueDirectory]¶ Detect newspaper issues to import within the filesystem.
This function expects the directory structure that RERO used to organize the dump of Olive OCR data.
- Parameters
base_dir (str) – Path to the base directory of newspaper data.
access_rights (str) – Path to
access_rights.json
file.journal_filter (set) – IDs of newspapers to consider.
exclude (bool) – Whether
journal_filter
should determine exclusion.
- Returns
List of OliveIssueDir instances, to be imported.
-
text_importer.importers.olive.detect.
olive_select_issues
(base_dir: str, config: dict, access_rights: str) → List[text_importer.importers.olive.detect.OliveIssueDirectory]¶ Detect selectively newspaper issues to import.
The behavior is very similar to
olive_detect_issues()
with the only difference thatconfig
specifies some rules to filter the data to import. See this section for further details on how to configure filtering.- Parameters
base_dir (str) – Path to the base directory of newspaper data.
config (dict) – Config dictionary for filtering.
access_rights (str) – Path to
access_rights.json
file.
- Returns
List of OliveIssueDir instances, to be imported.
Olive parsers¶
Functions to parse Olive XML data.
-
text_importer.importers.olive.parsers.
olive_image_parser
(text: bytes) → Optional[dict]¶ Parse the Olive XML file contaning image metadata.
- Parameters
text (bytes) – Content of the XML file to parse.
- Returns
A dictionary of image metadata.
- Return type
Optional[dict]
-
text_importer.importers.olive.parsers.
olive_parser
(text: str) → dict¶ Parse an Olive XML file (e.g. from Le Temps corpus).
The main logic implemented here was derived from <https://github.com/dhlab-epfl/LeTemps-preprocessing/>. Each XML file corresponds to one article, as detected by Olive.
- Parameters
text (string) – content of the xml file to parse
- Returns
A dictionary with keys:
meta
,r
,stats
,legacy
.- Return type
dict
-
text_importer.importers.olive.parsers.
olive_toc_parser
(toc_path: str, issue_dir: impresso_commons.path.path_fs.IssueDirectory, encoding: str = 'windows-1252') → dict¶ Parse the TOC.xml file (Olive format).
- Parameters
toc_path (str) – Path to the ToC XML file.
issue_dir (IssueDir) – Corresponding
IssueDir
object.encoding (str) – XML file encoding.
- Returns
A dictionary where keys are content item IDs and values their metadata.
- Return type
dict
-
text_importer.importers.olive.parsers.
parse_styles
(text: str) → List[dict]¶ Turn Olive style file into a dictionary.
Style IDs may be referred to within the
s
property of token elements as defined in the impresso JSON schema for newspaper pages (see documentation).- Parameters
text (str) – textual content of file styleGallery.txt
- Returns
A list of styles; each style has ID, font, font size, color (rgb).
- Return type
List[dict]
Helper methods¶
Helper functions used by the Olive Importer.
These functions are mainly used within (i.e. called by) the classes
OliveNewspaperIssue
and OliveNewspaperPage
.
-
text_importer.importers.olive.helpers.
combine_article_parts
(article_parts: List[dict]) → dict¶ Merge article parts into a single element.
Olive format splits an article into multiple components whenever it spans over multiple pages. Thus, it is necessary to recompose multiple parts.
- Parameters
article_parts (List[dict]) – one or more article parts
- Returns
A dictionary, with keys
meta
,fulltext
,stats
,legacy
.- Return type
dict
-
text_importer.importers.olive.helpers.
convert_image_coordinates
(image, page_xml, page_image_name, zip_archive, box_strategy, issue) → bool¶ Convert coordinates of an image.
NB: This conversions is necessary since the coordinates recorded in the XML file were computed on a different image than the one used for display in the impresso interface.
- Parameters
image (dict) – Image metadata.
page_xml (str) – Content of Olive page XML.
page_image_name (str) – Name of page image file.
zip_archive (ZipArchive) – Olive Zip archive.
box_strategy (str) – Conversion strategy to apply.
issue (OliveNewspaperIssue) – Newspaper issue the page belongs to.
- Returns
Whether the coordinate conversion was successful or not.
- Return type
bool
-
text_importer.importers.olive.helpers.
convert_page_coordinates
(page, page_xml, page_image_name, zip_archive, box_strategy, issue) → bool¶ Convert coordinates of all elements in a page that have coordinates.
NB: This conversions is necessary since the coordinates recorded in the XML file were computed on a different image than the one used for display in the impresso interface.
- Parameters
page (OliveNewspaperPage) – Page whose coordinates should be converted.
page_xml (str) – Content of Olive page XML.
page_image_name (type) – Name of page image file.
zip_archive (ZipArchive) – Olive Zip archive.
box_strategy (str) – Conversion strategy to apply.
issue (OliveNewspaperIssue) – Newspaper issue the page belongs to.
- Returns
Whether the coordinate conversion was successful or not.
- Return type
bool
-
text_importer.importers.olive.helpers.
merge_pseudo_tokens
(line: dict) → dict¶ Remove pseudo tokens from a line.
- Parameters
line (dict) – a line of OCR in JSON format.
- Returns
A new line object (with different number of tokens).
- Return type
dict
-
text_importer.importers.olive.helpers.
merge_tokens
(tokens: List[dict], line: str) → dict¶ Merge two or more tokens into one.
The resulting (merged) token will have new coordinates corresponding to the combination of coordinates of the input tokens.
- Parameters
tokens (List[dict]) – Tokens to merge.
line (str) – The line of text to which the input tokens belong.
- Returns
The new (merged) token.
- Return type
dict
-
text_importer.importers.olive.helpers.
normalize_hyphenation
(line: dict) → dict¶ Normalize end-of-line hyphenated words.
- Parameters
line (dict) – A line of OCR.
- Returns
A new line element.
- Return type
dict
-
text_importer.importers.olive.helpers.
normalize_line
(line: dict, lang: str)¶ Apply normalization to a line of OCR.
The normalization rules that are applied depend on the language in which the text is written. This normalization is necessary because Olive, unlike e.g. Mets, does not encode explicitly the presence/absence of whitespaces.
- Parameters
line (dict) – A line of OCR text.
lang (str) – Language of the text.
- Returns
A new line of text.
- Return type
dict
-
text_importer.importers.olive.helpers.
recompose_ToC
(original_toc_data: dict, articles: List[dict], images: List[dict]) → List[dict]¶ Recompose the ToC of a newspaper issue.
Function used by
OliveNewspaperIssue
.- Parameters
original_toc_data (dict) – ToC data.
articles (List[dict]) – List of content items in the issue.
images (List[dict]) – List of images in the issue.
- Returns
A new list of content items.
- Return type
List[dict]
-
text_importer.importers.olive.helpers.
recompose_page
(page_id: str, info_from_toc: dict, page_elements: dict, clusters: dict) → dict¶ Merge a list of page elements into a single one.
- Parameters
page_id (str) – page id
info_from_toc (dict) – a dictionary with page element IDs (articles, ads.) as keys, and dictionaries as values
page_elements (dict) – articles or advertisements
clusters (dict) – an inverted index of legacy ids; if an id is part of multipart article, the id is found not as a key but in one of the values.
- Returns
A new page element.
- Return type
dict
Note
It is here that an
n
attribute is assigned to each region/paragraph/line/token.


BlueInk Embed JS Library
A Javascript library to create embedded signing sessions with the BlueInk API.
Installation
From NPM:
npm install @blueink360/blueink-embed-js
Or if you prefer yarn:
yarn add @blueink360/blueink-embed-js
From CDN:
<script<EMAIL_ADDRESS>If including this library directly with a script tag, the BlueInkEmbed
class will be added to the global namespace.
Usage
To use the library, you will need a public API key from your BlueInk account. If you do not have a BlueInk account, you can create one here. You can send unlimited test Bundles for free while developing your eSignature integration.
Quickstart
import BlueInkEmbed from 'blueink-embed-js';
const embedUrl = 'EMBED-URL-RETURNED-BY-THE-SERVER';
const embed = new BlueInkEmbed('YOUR-PUBLIC-API-KEY');
// Mount the embedded signing iFrame in a div with ID 'iframe-container'
embed.mount(embedUrl, '#iframe-container');
// Optionally, unmount the signing iFrame when done
embed.unmount();
Fuller Example, with Error and Event Handling
This example shows fetching the embedUrl from the server via axios, and handling errors.
import BlueInkEmbed from 'blueink-embed-js';
const exampleRequestData = {
signerName: 'Frank Ricard',
signerEmail<EMAIL_ADDRESS>};
// This assumes that your server-side App can receive requests
// at /get-embed-url and will create a new Bundle and return
// an embedded signing URL. See the BlueInk API docs for examples.
axios.post('/get-embed-url', exampleRequestData)
.then(response => {
try {
const embedUrl = response.data.embedUrl;
const embed = new BlueInkEmbed('YOUR-BLUEINK-PUBLIC-API-KEY');
// Setup event listeners to respond to events emitted
// by the embedded signing iFrame
embed.on(BlueInkEmbed.EVENT.COMPLETE, () => {
console.log('signing complete!');
});
embed.on(BlueInkEmbed.EVENT.ERROR, (eventData) => {
console.log('Signing error occurred.');
console.log(eventData.message);
});
embed.mount(embedUrl, '#iframe-container');
} catch(error) {
// An error could be thrown if the public API key is invalid,
// or if no element was found with id 'iframe-container'
console.log(error.message);
}
})
.catch(error => {
// This catch block would handle if the /get-embed-url
// request returned a 5XX or 4XX error.
const message = error.response.data.error || error.message;
console.log(message);
});
More Resources
- BlueInkEmbed class and methods
- Events emitted by the BlueInkEmbed instance
- BlueInk API Documentation
Getting Help / Contributing
If you have a BlueInk Account and need help integrating this library into your API project, please contact our support team from your BlueInk account dashboard.
If you find issues in this codebase, or have any suggestions for improvement please create an issue here on github.
We welcome contributions and will consider any pull requests.
If you are making modifications to the library, or running the project,
locally the following commands should come in handy. We use yarn internally,
but all of the commands below should work with there npm
equivalents.
# Run the tests
$ yarn test
$ yarn test:coverage
# Build a production, minified version of the library
$ yarn run build
# Build a development, unminified version of the library
$ yarn run build:dev
# Run webpack-dev-server, so that the library will be accessible
# at http://localhost:8080/blueink-embed.js
$ yarn run devserver
# Build the docs
$ yarn run build:docs
License
MIT


This was quite a week for us at Utopian, with a new partnership, a witness upgrade, another new hire, some amazing task requests, and another huge episode of our radio show. Let’s catch up.
In this post:
- Welcoming Rocket.Chat to VIPO
- Big witness update
- Welcoming @Suesa
- Big Tasks, Big TRs
- Build Utopian.io v2 with us!
- Our radio show is back!
Welcoming Rocket.Chat to VIPO
For the second week in a row, we have a huge addition to our VIPO (Very Important Project Owners) club: Rocket.Chat. As covered in the announcement, they are a fast-growing communication and collaboration platform that is now installed on over 180k servers and counts over 10m users worldwide as well as having an active, passionate community of over 700 developer-contributors who help Rocket.Chat’s core team of developers to constantly improve the product.
Big witness update
As covered in this announcement post, We’ve recently upgraded our server’s hardware. As part of the upgrade, our main and backup servers are located in different countries with different providers in an effort to decentralise and remove single points of failure. We also doubled the RAM on the production server. The biggest news in regards to the Utopian-io witness is that we finally have a seed node at seed.utopian.io:2001. This is something we’ve wanted to do for some time, and we are very glad to have it up and running.
Welcoming @Suesa
This was also announced in the big witness announcement post, but it deserves its own headline here: We have a new COO! @Suesa isn’t just a future mad scientist, she is a Steem powerhouse who comes to us from @steemstem. During her trial period, she has already added order and discipline and a whole bunch of Jira tasks. That’s what happens when you put the self proclaimed “scary German” in charge. We love you, @Suesa!
Big Tasks, Big TRs
We’ve had a couple of huge task requests this week. @lemouth’s incredibly ambitious idea of running a full particle physics research project on Steem is starting to take shape with a TR about Managing particle physics model information in Python. The second one, from @surpassinggoogle, is for ulogs.org, and is about creating a bot which can vote and comment but shaped for communities. Click the links for all the info!
Build Utopian.io v2 with us!
As you probably know, our development team has been hard at work in creating the base for the next Utopian.io, with features and functionalities requested by the community. If you’re a developer and want to help, visit our repo and take a look.
Utopian.io Open Source Radio Show on MSP Waves
We had another huge, huge, huge radio show this week. Following up on last week’s addition of an Anti-abuse category to Utopian, we hosted some of Steem’s biggest abuse fighters for a talk about the state of the blockchain. We had @SteemCleaners’ founder @patrice, blacklist creator (and semi-regular guest) @themarkymark, our very own Anti-abuse category manager (and also @steemflagrewards powerhouse) @iamstan, @steemcleaners and Utopian abuse fighter @Naturicia, @steemcleaners abuse fighter and witness @guiltyparties, and @helpie founder and witness @meno. The discussion was lively, to say the least.
Be sure to tune in next week at 6PM UTC on Wednesday on MSP Waves!
Join the community on Discord: https://discord.gg/azdmM3v
First Time Contributing in Utopian.io?
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This page is synchronized from the post: Utopian.io Weekly - [October 19 2018] - New Partner, New COO, New Tasks!


While this can vary from person-to-person, we generally have the following recommendations:
- Dress in layers for both warm and cool weather
- Low-heeled rubber-soled shoes for on board the boat
- Seasickness medicine (take seasickness medicine the night before and the morning of departure if you have the tendency to get seasick)
- Swimsuit
- Towels
- Personal toiletries (soap, shampoo, toothbrush, toothpaste, etc.)
- Sunscreen
- Sunglasses
- Hat
- Camera (dry-bag or zip lock bag suggested)
- Alcoholic Beverages (if desired)
- Sleeping bag for extra comfort


All Purpose Flour
Blue Diamond Almond Breeze 1.88L
Carnation Evaporated Milk
Smuckers Goober Grape or Strawberry
Libby's Cornbeef
Pillsbury All Purpose Flour


Betsy Atkins named OYO independent director
by Our BureauOYO Hotels and Homes on Thursday announced the appointment of Betsy Atkins, CEO and founder of Baja Corporation, as an independent director on the company’s board of directors.
Betsy, as an independent member of the board, would play a supervisory role and provide guidance to the founder and the management on key business decisions.
“We are fortunate to have someone of Betsy’s calibre on our board of directors,” said Ritesh Agarwal, founder and group CEO, OYO Hotels and Homes.


Berka:
Who's there! Reveal yourself at once! Ah, you're after this Resonance Amulet, too, aren't you? YOu can try to take it by force if
you wish, but I warn you that I won't give it up so easily.
You...you're... a Soul Breaker! Why are you here?
I saw you in the Tablet of Vision and came to see you.


This demo is built for the CS 418/518 Networked Entertainment course. Video is captured and encoded by ffmpeg on a laptop from webcam with multiple bitrates for adaptive streaming, output of ffmpeg is redirected to our server in AWS by RTMP then nginx server on our AWS instance serves the video.


Version Control in Git
Version control, using a service like git, is an important tool for programmers. Version control allows for developers to view the entire history of revisions to one or more files, and merge revisions made by different people. Developers use version control in order to be able to track changes made to code, separate out (and branch off) changes made to different features, and work on different versions of the code at the same time.
Commits in Git
Git helps developers track changes by allowing them to create "save points" for their code, known as commits. Each time a change is made to code, a developer can create a new commit in git, while still retaining all of the previous commits. That way, if there is some kind of error, the developer can just revert to a previous version rather than having to root out the error before continuing with any other work.
Why Developers Use GitHub
Developers should use GitHub to store their code because GitHub makes that code available for any other GitHub user to see, download, and work on. That way, multiple users can work on the same code at the same time, and use their own experimentation to engage in a sort of dialogue with the developer and perhaps persuade developers to incorporate new features into their code.


Welcome to the Control Panel! To get started, move the slider to the desired position, and watch the camera's field of view change in response!
X-Servo Position: °
Y-Servo Position: °


# Introduction to Data scraping
*by Hrant Davtyan*
## Commercial/freeware tools
--------------------------
- [“Scraper” Google chrome extension](https://chrome.google.com/webstore/detail/scraper/mbigbapnjcgaffohmbkdlecaccepngjd?hl=en)
- [Mercury Reader](https://chrome.google.com/webstore/detail/mercury-reader/oknpjjbmpnndlpmnhmekjpocelpnlfdi)
- [www.downthemall.net](http://www.downthemall.net/)
- [www.import.io](www.import.io)
## A few scraping rules
--------------------------
- **You should check a site's terms and conditions before you scrape them.** It's their data and they likely have some rules to govern it.
- **Be nice** - A computer will send web requests much quicker than a user can. Make sure you space out your requests a bit so that you don't hammer the site's server.
- **Scrapers break** - Sites change their layout all the time. If that happens, be prepared to rewrite your code.
- **Web pages are inconsistent** - There's sometimes some manual clean up that has to happen even after you've gotten your data.
## Setting up the environment
--------------------------
1. Download and install [Anaconda (Python 2.7)](https://www.continuum.io/downloads). Make sure to download the version that satisfies your operating system (Windows 64-bit or Windows 32-bit or macOS etc.)
2. Download and install [Sublime text editor](https://www.sublimetext.com/3). Again, choose the correct version for your operating system.
3. Register at [www.github.com](https://github.com/).
4. Download and install [Google Chrome](https://www.google.com/chrome/browser/desktop/) web browser.
5. Create a “Data_Scraping” course folder (in home directory, e.g. C disk).
## Getting stock data with Python
--------------------------
```
import pandas_datareader.data as web
data = web.DataReader("IBM", 'yahoo')
data.head()
```
![img](http://i64.tinypic.com/15nojh3.png)
## HTML document structure
--------------------------
![img](http://i64.tinypic.com/2ztbx3o.png)
## Getting HTML table data
## with Python
--------------------------
```
import pandas as pd
import html5lib
url = "https://ideas.repec.org/top/top.person.all.html"
page = pd.read_html(url)
econ = page[1]
```
## Necessary libraries
--------------------------
- urllib2
- BeautifulSoup4
- lxml
- requests
- html5lib
- pandas-datareader
- regex
- json
##FAQ
--------------------------
- **How to install python packages?** Open the command line (cmd) and run the following command:
```
pip install packagename
```
- **How to run Jupyter notebooks?** Open the command line (cmd) and run the following command:
```
jupyter notebook
```
- **How to run a python file from the command line?** Move from the command line to the directory where your file is located and run the following command:
```
python filename.py
```


Javascript Labs
Search for an item within an array
Click here to search the array for a particular number


About Us
Mission Statement
Alm's Soup in Sixty Seconds:
Why a soup kitchen?
Still.. why a soup kitchen?
Building a business model like that of Goodwill Industries was an indication that I am on the right track. “Goodwill Industries International earns $5 billion revenue per year, and we spend about 83 cents of every dollar on programs and services for people in need.”
“build a stronger community by helping people with intellectual and developmental disabilities achieve individual goals for a better life.”
**BARC is an acronym for Bakersfield ARC1


WOMEN COLLECTION
MOSCHINO CHEAP AND CHIC
Compellingly actualize fully researched processes before proactive
outsourcing. Progressively syndicate collaborative architectures before cutting-edge services.
Completely visualize parallel core competencies rather than exceptional portals.
$561
CHOOSE COLOR
CHOOSE SIZE
QUANTITY
Add to cart
ELLERY X M'O CAPSULE
Known for her sculptural
takes
on traditional tailoring, Australian
arbiter of cool Kym Ellery teams up with Moda Operandi.
$54.69


Lazy Load XT is a jQuery plugin for images, videos and other media.
Work with AJAX-loaded content
Use effects from animate.css project for loaded images.
All images will be loaded after page's `load` event
Example of lazy loading of background images
Lazy loading with fade-in effect
Lazy loading for horizontal scroll
Infinite scroll with lazy loading
Work with jQuery Mobile framework
Example of markup for disabled JavaScript support
Lazy loading of responsive images
Example of loading images before print
Example of responsive images for different pixel densities
Tagging lazy elements with <script> tag
Lazy loading with spinner effect
Lazy loading of responsive images
Test of work on page with 1000 images
Lazy loading of <video> content
Lazy loading of YouTube videos
Work with Zepto library
Lazy loading of Facebook, Google+ Profile and Google Maps widgets as <iframe>s
Lazy loading of Facebook, Twitter, and other widgets


React
JavaScript
CSS3
HTML5
BootStrap
php
MySQL
Github
jQuery
Node
Webpack
apache
I am a developer who loves problem solving and learning. My journey started when I made my first blog post using HTML and CSS. Along the way, I learned JavaScript and many other frameworks and libraries.
I find programming to be very satisfying, by being able to build anything that comes to mind and just by simply being creative. I get a sense of fullfillment when I see my projects come to life and be able to share it with others.
In my personal life, I enjoy the outdoors, exploring and being with family and friends. I love to travel and I make it a goal to visit something new every year to appreciate what the nature has to offer.


A visual test paging + expandable rows caused problems in the expand events. This example is working if you go to the second page then expand any row. The row should show the current productId in the expandable area.


A relief removes some or all of the import duty for certain goods.
Anti-dumping duty
Anti-dumping duty is an amount of import duty applied in addition to any other duties that apply. It protects UK industry against the dumping of goods at a substantially lower value than normal.
Import controls
Import controls can be either prohibitions, where goods cannot be imported under any circumstances, or restrictions, where goods can be imported only under certain circumstances. Check the conditions related to this type of measure.


Vim: File Navigation
This is part 11 of a series of tutorial to Vim. You can read Part 10 here.
Till now we have seen several ways to navigate within a buffer in Vim. In this part we'll see how we can move across files, how to jump directly to function or variable definitions and jumping right in middle of a file at a predefined place.
:e
on steroids
You want to open a file, your file is deep down in the folder hierarchy, you don't want to type in full path and of course, you want to open it fast. Vim can help you with that too.
Press :e **/<file name initials>
and press <TAB>
to let Vim search for all
files starting with given initials and prepare a list of choices for you. You
can cycle through the list using <TAB>
and <enter>
to select a file to edit.
**
can also be used to search and open file at arbitrary location and not just
pwd
(present working directory). Suppose your pwd
is ~
and you want to open
a file somewhere deep inside /etc/lampp/config/...
, you can search in Vim by
typing :e /etc/lampp/config/**/file-name
, followed by <tab>
.
Figure 1: Using **/10
to open org-files/essays/vim/10.org
TAGS
Unix Philosophy
- Write programs that do one thing and do it well.
- Write programs to work together.
- Write programs to handle text streams, because that is a universal interface.
Installation size of Vim is less than size of an average quality 3 minute MP3.
Instead of having every feature, it relies on help from external programs. There
are several language specific plugins which give Vim IDE like features such
as jumping to function definition, REPL etc. We won't discuss all of these but
one program here which works for (almost) all languages: ctags
1.
ctags
generate a tags
file which has index of names found in source files2.
This index is used to jump to function, identifier definition. You can create
a tags
file by executing ctags -R .
3 from root directory. This will create
the tags
file in root of the project. You can start Vim without any arguments
from the root and use :e **/file-name
to open files so that tags
file is
loaded by Vim4.
Once tags
is loaded, jumping around is simple:
- you can jump to definition of the function name or identifier under cursor
by pressing
<ctrl>+]
. - or explicitly search for a name by
:tag <name>
. - After jumping to definition, if you search for another name you will build a deeper tag stack. Vim remembers your history of tag jumps using such tag stack.
- To go back to previous position from where you searched (that is up the tag stack)
press
<ctrl>+t
. - You can see the stack by executing
:tags
. - And, if there are multiple definition for same name (say because of function
overloading), Vim jumps to the first option if you press
<ctrl>+]
, to see all the options pressg
followed by<ctrl>+]
.
(Book)Marks
Marks does exactly what you might expect, it references specific location in file
so that you can jump directly to that position. You can set marks using m[a-zA-Z]
You can use a-zA-Z
for marking which are totally unrelated to the registers
which we saw in last part. A few more commands related to marking:
- Setting a new mark:
m[a-zA-Z]
- Jumping to a specific mark:
'<mark>
'
will take you to beginning of line`
will take you to the exact line and column
- List all the marks:
:marks
:delmarks <marks>
will delete marks
Upper case and lower case marks behave slightly differently. Lowercase marks are
local to each file, while upper case marks are global. That means if you assign
a uppercase mark to a position in file, say S
in .bashrc
, then whenever you
want to edit your .bashrc
you can hit 'S
to open and jump to saved position.
This can be very helpful if you have a file which needs to be edited often.
We'll conclude discussing marks with couple of special marks:
`.
: jump to position where last change occured``
: jump to last exited buffer''
and``
: jump back (to position where jumped from (using marks))- note this is different from
<ctrl> o
:``
jumps back to position where used marks to jump<ctrl> o
jumps back according to jump list (:help jump-motions
).
- note this is different from
Summary
| Command | Comment |
|---|---|
:e **/file-name-initials |
searches for file name recursively from current directory |
:e /dir1/dir2/**/file-name-initials |
same as above, but searches inside /dir1/dir2/ |
ctags -R . (shell command) |
generates index file (tags) |
<ctrl>+] |
jump to definition for name under cursor |
:tag <name> |
search definition for name |
<ctrl>+t |
go up the tag stack (go back to previous position from where jump to definition was made) |
:tags |
see the tag stack |
g <ctrl>+t |
go to definition (helpful when there are multiple definitions for same name |
m[a-zA-Z] |
set a new mark |
'<mark> |
jump to start of line of specified mark |
`<mark> |
jump to column and line of specified mark |
:marks |
list all marks |
:delmarks <marks> |
delete specified marks |
`. |
jump to position where last change occured |
`` |
jump to last exited buffer |
'' and `` |
jump back (jump taken using marks) |
Footnotes:
Similarly ctags
works with other editors too.
tags
is a text file which you easily open and play around with. It contains regex rules which helps in jumping.
-R
to generate recursively for files, you may also want to add options like --exclude
.
This step is necessary to make sure Vim loads the tags
file. But this is not the only way.


Magnitude 5.4 quake strikes southern Iran, no casualties reported: TV
DUBAI (Reuters) - A magnitude 5.4 earthquake on Wednesday struck Iran’s southern province of Hormozgan, state television reported. There were no immediate reports of casualties or damage.
Many houses in rural parts of the province are made of mud bricks and have been known to crumble easily under the impact of earthquakes.


SPARTACUS (1960) A Film Review Copyright Dragan Antulov 2000
1950s weren't the happiest of times for Hollywood talents with leftist sentiments, since in the atmosphere of Cold War hysteria many were considered to be Communists. Many of them were blacklisted by studios and forced to take pseudonyms in order to earn their living. It took great courage for executive producer Kirk Douglas to defy the Blacklist and hire a screenwriter from such blacklist. It took even more courage to use his talent to make adaptation of the novel written by radical leftist Howard Fast. And, to add insult to injury for any hysteric anti-leftists in these times, the subject of the film was life of a man who was considered one of great Communist martyrs and role models. It all happened in 1960 with SPARTACUS, the film that finally ended the era of Hollywood black lists and thus became one of landmark events in the history of American cinema. Unfortunately, its historical importance barely corresponded with the quality, and today it is often considered to be "the worst film ever directed by Stanley Kubrick".
The novel, as well as the film, is based on the events that took place in the first part of 1st Century B.C. Once democratic Roman Empire became the wealthiest and most powerful nation of the world, but with the wealth came corruption and widespread use of slavery. One of such slaves is Spartacus (played by Kirk Douglas), tough Thracian who was sentenced to spend the rest of his life working in Lybian mines, but even in such terrible conditions he didn't cease to defy his captors. Slave trader Batiatus (played by Peter Ustinov) thinks that such fighting spirit could be used, so he buys Spartacus for his gladiator school in Capua, Italy. Spartacus is there being subjected in intense combat training and also develops feelings for slave girl Varinia (played by Jean Simmons). One day the school is visited by powerful Roman politician Marcus Licinius Crassus (played by Laurence Olivier), and Batiatus, in order to entertain two of his lady companions, organises four of his best gladiator fighting to the death. In one of such fights Spartacus is defeated but his opponent Drabba (played by Woody Strode) decides to spare his life and instead sacrifices his own in vain attempt to escape. Spartacus is deeply moved by such event and when his beloved Varinia is being sold away, his personal anger leads to spontaneous act of rebellion that would be joined by all of the gladiators. Soon gladiators overwhelm their guards and the news of their revolt begins spreading like wildfire. Spartacus and his rebels begin plundering rich slave-owners' estates and thousands of escaped and liberated slaves join his band. Spartacus would use his combat skills and experience, as well as his charisma, to organise them into fighting force able to handle any Roman army sent after them. Spartacus begins his campaign towards port city of Brundisium, where he would find to transport slaves outside of Italy into freedom. In the meantime, slave revolt has some important political consequences in Roman Senate. While Crassus wants to use it as a pretext to impose his personal dictatorship, his main rival, liberal senator Crassus (played by Charles Laughton) wants to use the revolt as way to foil such plans and save the Republic.
SPARTACUS had a lot of potential to be one of the greatest films of its era, and the big box-office success and few "Oscars" prove that. Unfortunately, it failed in being anything more than historical spectacle because people behind the project had rather different creative visions. For screenwriter Dalton Trambo this historical story was an excellent opportunity to comment on the social condition of 1950s America, not so subtly disguised in his vision of 1st Century B.C. Roman Republic. Both countries were most powerful in the world, but their old republican institutions and democratic virtues had been corrupted by social injustice and class exploitation, and in both cases ambitious politicians were threatening to use society's paranoia in order to install their own personal dictatorship. It isn't very hard to imagine character of Crassus being inspired by Senator Joe McCarthy.
Unfortunately, when Stanley Kubrick replaced director Anthony Mann after his quarrel with Kirk Douglas, it turned out that Kubrick's vision was quite different. Kubrick, being perfectionist, checked the historical data about Spartacus and concluded that Trambo, just like many PC filmmakers of today, sacrificed historical accuracy for the sake of delivering political message. For example, many important details of Spartacus' campaign and his multiple attempts to lead slaves out of Italy, as well as infighting and desertions within his own ranks, remained outside the script. Furthermore, Trambo's idea to make huge contrast between rich, decadent and utterly depraved Romans on one side, and their hard-working, clean-cut and noble slaves was definitely not in line with Kubrick's own more pessimistic and misanthropic view on human affairs. After unsuccessful attempts to change the script, Kubrick lost interest in the film and did his job as "hired gun".
This becomes evident when we compare first third of the film, which was directed by Mann, with the segments later directed by Kubrick. The segment directed by Mann is the best in the film, because it deals almost exclusively with Spartacus and the events that led to revolt. The audience is being subjected to all harsh realities of the life of slaves and gladiators, and action dominates the words. But as soon as the plot is removed from the gladiator school and Kubrick takes over, film loses momentum. Spartacus and his gladiators are reduced to cardboard characters, and even charisma of Kirk Douglas can't make him as sympathetic as in the first segment of film. Hollywoodised romance between Spartacus and Varinia also doesn't help, being injected in the plot only to give some bright overtones to the dark, yet corny finale. In this segment, bad guys in the form of Romans are more interesting than the slave protagonists. Charles Laughton is simply grand as shrewd, cynical, hedonistic yet kind-hearted Roman Senator. In the case of Crassus, screenwriters obviously didn't know how to reconcile or explain the sharp contrast between Crassus' insistence on old Roman values on one side, and his depravity and ruthless dictatorial ambition on the other. But Laurence Olivier still shows his great talent by making this badly written character memorable. Finally, Peter Ustinov justly deserved his "Oscar" for the role of Batiatus - his character, despite being involved in one of the most deplorable professions of slave trade, comes out as nice guy at the end, not because of some sudden virtue, but because of his humanity. Tony Curtis as slave Antoninus, singer of songs we hear spoken as plain text, on the other hand, played his role very badly, and his character would be remembered simply as the dark object of Crassus' bisexual desire.
Script and characterisation problems aside, SPARTACUS is very attractive film because it doesn't lack spectacle. In the latter part of film, Kubrick obviously tried to compensate the lack of movie's quality with quantity - there are plenty of mass scenes with many interesting details and good use of colour photography by Russell Metty. Unfortunately, the most spectacular scene - the final battle between Spartacus and Roman legions under Crassus - is a failure. We actually don't see why and how the slaves had been beaten and Kubrick's effort to direct some 8,500 Spanish soldiers hired as extras weren't justified. After the last climactic battle, we are presented with its tragic aftermath, and subjected to many extra minutes of historical inaccuracy and pointless melodrama. The music by Alex North is also somewhat disappointing and explains why Kubrick in his later films relied mostly on non-original soundtrack.
For some people three hours spent in front of SPARTACUS might seem wasted, but despite being much overrated, this historical epic can be quite entertaining. It is especially interesting because of the insight given into one important and splendid era of American motion picture industry, as well into the early career of Stanley Kubrick.
RATING: 6/10 (++)
Review written on February 28th 2000
Dragan Antulov a.k.a. Drax Fido: 2:381/100 E-mail<EMAIL_ADDRESS>E-mail<EMAIL_ADDRESS>E-mail<EMAIL_ADDRESS>Filmske recenzije na hrvatskom/Movie Reviews in Croatian http://film.purger.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


---
layout: null
section-type: contact
title: Contact
---
## Contact
Asthma & Airway Disease Research Center
University of Arizona Biosciences Research Labs, Room 421
1230 N Cherry Avenue
Tucson, AZ 85721
Office:<PHONE_NUMBER>
Lab:<PHONE_NUMBER>
<EMAIL_ADDRESS>Or if you prefer, you can send a message here:


The Classes
HTMLLabel
Derived from: none
Declared in: HTMLFile.h
HTMLLabel represents a label in an html file. Use them with HTMLFile.
Constructor and Destructor
Remark: You shouldn't need to construct HTMLLabels yourself. The HTMLFile will create them for you when it finds a label or a link, in its Search() method.
|
|
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|
|
|
HTMLLabel(HTMLProject *proj, const char *fileName, const char *labelName, const char *labelText, unsigned int beacon=0)
| |
HTMLLabel(const HTMLLabel*)
| |
Creates a new HTMLLabel. The first version creates a new label, with these parameters:
- proj is the HTMLProject the label is included into.
- fileName is the name of the file that contains the label, relative to proj's home path.
- labelName is the name of the label, i.e. the italic string in one of these tags:  or .
- labelText. This is the name of the label as seen by the user, i.e. the string between the  and  tags, (or  and  for a label)
- beacon is the number of the last found beacon before this label (0 if no beacon was found before)
The second version is a copy constructor.
Destroys the HTMLLabel object.
Member Functions
|
|
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const char *FileName() const
| |
Returns the name of the html file that has the label.
This string is owned by the HTMLLabel. Don't destroy it.
Creates an HTMLFile associated to the file that contains the link. You keep the responsability for this object. You must destroy it after use.
|
|
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const char *LabelName() const
| |
Returns the name of the label, i.e. the italic string in one of these tags:  or .
This string is owned by the HTMLLabel. Don't destroy it.
|
|
|
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const char *LabelText() const
| |
Returns the label text. This is the name of the label as seen by the user, i.e. the string between the  and  tags (or  and  for a label).
This string is owned by the HTMLLabel. Don't destroy it.
Operators
!=, == (comparaison)
Compares the two HTMLLabels. == returns true if both HTMLLabel point to the same file, and have the same label.


Path Planning for 6 DoF Robotic Arm
This project implement Expansive Space Trees (EST) path planner to plan collision-free motions of an UR5 robot using C++ and ROS Integrated planning simulation with ROS, rviz and MoveIt! the implementation won the Expert Challenge with high success rate for diffcult planning problem on real UR5 robot
Monocular Depth Estimation of Endoscopic Image
This project trained a unsupervised neural network to estimate the disparity map of endoscopic image, then transform disparity information into depth. We were able to get satisfying depth estimation results in a unsupervised fashion
Constraint System Path Planning/Tracking
This project designed a Rapidly-exploring random tree(RRT) path planner under kinematics constraints. The path finding process was optimized with distance metric in the configuration space
Stereo Image Alignment and Depth Estimation
This project identify points of interest in two stereo images using the Harris corner detection method, and optimize detected features by non-maximum suppression. Then using normalized cross-correlation (NCC) to match features across the image. Finally, calculated transformation matrix to align images. Also, this project calculates Fundamental Matrix and Epipolar Line of the image pair to estimated the depth between camera and the object
ROS2 Introduction and Robot SLAM demo
This tutorial gives an overview of ROS2 (Robotic Operating System) status with an example of SLAM implementation. In this guide, we discuss and evaluate some of its new features by implementing SLAM in ROS2 in simulation and on a real robot and these new features are briefly introduced and some are tested in this implementation.
Program UR5 robot to complete a "place-and-draw" task
The project is to use the UR5 robot to complete a "place-and-draw" task using inverse kinematics joint angle control, using differential kinematics rate control, as well as Gradient-based control.
ROS based RC car with self-stabilized camera gimbal mounted
This RC car is armed with a stabilization platform for camera, to enhance the experience of comfortable and flexible video shooting. The car also has a 360 degree rotational robotic arm with four-bar mechanism to enable forward and backward motion, and provide extra degrees of freedom for camera shooting angle.


Orogenesis
Sharpstone Veil may be used more often, but it also becomes more energy-hungry.
^a${value} Second(s) Recharge
-1 -2 -3 -4 -5 -6 -7 -8 -9 -10
${value} Energy Cost
13 25 38 50 63 76 88 100 113 126
MaxLevel
6
UltimateLevel
10


Contains tools and features, which can be used for an Exploratory Landscape Analysis (ELA) of single-objective continuous optimization problems. Those features are able to quantify rather complex properties, such as the global structure, separability, etc., of the optimization problems.
| Version: | 1.7 |
| Depends: | R (≥ 3.0.0) |
| Imports: | BBmisc, checkmate, mlr |
| Suggests: | devtools, e1071, expm, ggplot2, lhs, MASS, Matrix, mda, mlbench, numDeriv, parallel, parallelMap, ParamHelpers, plotly, plyr, RANN, R.rsp, rpart, shape, shiny, smoof, testthat |
| Published: | 2017-06-14 |
| Author: | Pascal Kerschke [aut, cre], Christian Hanster [ctb], Jan Dagefoerde [ctb] |
| Maintainer: | Pascal Kerschke <kerschke at uni-muenster.de> |
| BugReports: | https://github.com/kerschke/flacco/issues |
| License: | BSD_2_clause + file LICENSE |
| URL: | https://github.com/kerschke/flacco |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | Optimization |
| CRAN checks: | flacco results |
| Reference manual: | flacco.pdf |
| Vignettes: |
flacco |
| Package source: | flacco_1.7.tar.gz |
| Windows binaries: | r-devel: flacco_1.7.zip, r-release: flacco_1.7.zip, r-oldrel: flacco_1.7.zip |
| OS X binaries: | r-release: flacco_1.7.tgz, r-oldrel: flacco_1.7.tgz |
| Old sources: | flacco archive |
Please use the canonical form https://CRAN.R-project.org/package=flacco to link to this page.


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multipass
CLI to manage Ubuntu virtual machines using native hypervisors.
More information: home page
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multipass launch -n instance_name --cloud-init configuration_file
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{% extends 'base.html'%} {% import 'macros.html' as macro %}
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These orbits are generated iterating this simple algorithm:
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where a, b, c are random parameters. This is known as the 'Hopalong Attractor'.
3D rendering done with three.js and vrrenderer.js
Original version by Iacopo Sassarini
WebVR port by Luke Groeninger


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--- layout: post date: 2010-10-21 time: "09:00:00" authors: ["Greg Wilson"] title: Slides Available as PDF and PPT tags: ["Content"] ---
The home pages of the following lectures now have links to PDF and PowerPoint versions of the slides used in each episode:
We haven't provided slides for the spreadsheets and databases lectures because they aren't slide-based. Please let us know if you find these useful, or if you notice any typos that we should fix.


This page was generated from docsrc/notebooks/geosilhouettes.ipynb. Interactive online version:
Geosilhouettes: geographical measures of cluster fit¶
Silhouette statistics (Rousseeuw, 1987)) are a nonparametric measure of an observation’s goodness of fit to a given cluster. Where clusters have a geographical interpretation, such as when they represent geographical regions, silhouette statistics can incorporate spatial thinking in order to provide more useful measures of cluster fit. The paper by Wolf, Knaap, & Rey (2019). (preprint on SocArXiv) defines two:
Path Silhouettes, which characterize the joint geographical and feature similarity in a clustering.
Boundary Silhouettes, which characterize how well-defined a geographical boundary is in a clustering.
Together, these two new measures provide new ways to measure the goodness of fit of clusters in clustering problems in geographic data science.
Below, we’ll dig a little into how these are implemented in the esda
package. First, though, let’s set up some data. We’ll be considering a single univariate dataset, the Gini index in 1989 in counties for the US Deep South.
[1]:
import libpysal
import numpy
import esda
import geopandas
import contextily
import matplotlib.pyplot as plt
%matplotlib inline
To focus in on only the deep south, we’ll build an array of states that fall within the deep south:
[2]:
focus_states = ["Alabama", "Georgia", "Louisiana",
"Mississippi", "South Carolina"]
Then, we’ll read in data for all of the south, using the example datasets in the libpysal
package:
[3]:
south = libpysal.examples.example_manager.load('South')
[4]:
south = geopandas.read_file(libpysal.examples.get_path('south.shp'))
Then, we’ll filter out only the states in the deep south:
[5]:
deep = south.query('STATE_NAME in @focus_states').reset_index()
[6]:
deep['state_label'] = deep.STATE_NAME.apply(lambda x: focus_states.index(x))
Finally, for mapping purposes, let’s get a basemap. This is done using the contextily
package, which expects our data in a specific coordinate projection system. For more, check out the `contextily
user guide <https://github.com/darribas/contextily/blob/master/contextily_guide.ipynb>`__.
[7]:
deep.crs = {'init':'epsg:4326'}
[8]:
deep = deep.to_crs(epsg=3857)
[9]:
basemap, extent = contextily.bounds2img(*deep.total_bounds, zoom=6,
url=contextily.tile_providers.ST_TONER_LITE)
[10]:
f,ax = plt.subplots(1,2, figsize=(12,3), sharex=True, sharey=True)
deep.plot('STATE_NAME', ax=ax[0], alpha=.6)
deep.plot('GI89', ax=ax[1], cmap='plasma', alpha=.6)
for ax_ in ax:
ax_.imshow(basemap, extent=extent, interpolation='bilinear')
ax_.axis(deep.total_bounds[[0,2,1,3]])
f.tight_layout()
plt.show()
The Silhouette Score¶
The standard silhouette score used in classification/grouping problems as measure of how well-fit an observation is to its current group. The measure is fairly robust, well-studied, and is common in many different problem areas.
Statistically, the silhouette score is composed of a few components: - \(c\), the cluster in which \(i\) is assigned - \(k\), another cluster in which \(i\) is not currently assigned - \(d_i(c)\): the dissimilarity between observation \(i\) and \(i\)’s own cluster \(c\) - \(d_i(k)\): the dissimilarity between observation \(i\) and a cluster \(k\) that \(i\) is not in.
Here, we define \(d\) as an arbitrary dissimilarity metric. For most cases, it’s normally the euclidean distance. So, if you see \(d_i(c)\), this is the average dissimilarity between observation \(i\) and all other observations \(j\) in cluster \(c\).
Then, we need to define the second best choice cluster, \(\hat{k}\), which is a cluster that \(i\) is most similar to, but of which \(i\) is not currently a member:
This lets us define the silhouette score as the relationship between the dissimilarity scores for \(i\) and its cluster \(c\), and \(i\) and the second-best choice cluster, \(\hat{k}\):
This value is close to 1 when \(i\) is much closer to its current cluster, \(c\), than the second-best cluster, \(\hat{k}\), indicating strong fit to its current cluster. And, when \(i\) is more similar to \(\hat{k}\) than to its own cluster \(c\), the value of \(s_i\) is close to -1. We can compute the score for each observation using sklearn
’s silhouette_samples
:
[11]:
from sklearn.metrics import silhouette_samples
silhouettes = silhouette_samples(deep[['GI89']].values, deep.state_label)
Here, we can visualize the silhouette scores by county. With this information, we can see that there is very strong heterogeneity in the Gini coefficient within states, because the silhouette statisic for each county is usually negative (i.e. blue). But, in some areas (e.g. areas of South Carolina and Louisiana), the silhouette statistics for counties are positive (red), suggesting those counties are well-fit to their surroundings.
[12]:
f,ax = plt.subplots(1,2,figsize=(12,3))
ax[0].hist(silhouettes)
deep.plot(silhouettes, ax=ax[1], cmap='bwr',
vmin=-.5, vmax=.5, alpha=.6)
ax[1].imshow(basemap, extent=extent, interpolation='bilinear')
ax[1].axis(deep.total_bounds[[0,2,1,3]])
f.tight_layout()
plt.show()
On the contrary, if we used a data-driven definition of regions, we might expect the silhouette scores to be more positive. For example, if we used \(k\)-means to define the best data-driven groups we could have for this data, we might have something like this:
[13]:
from sklearn.cluster import KMeans
data_driven_clustering = KMeans(n_clusters=len(focus_states)).fit(deep[['GI89']].values)
data_labels = data_driven_clustering.labels_
data_silhouettes = silhouette_samples(deep[['GI89']].values,
data_labels)
This yields the following geography of inequality in the deep south:
[14]:
deep.plot(data_labels, categorical=True, legend=True)
plt.show()
And, in this geography of inequality, each county is very well fit to its group:
[15]:
f,ax = plt.subplots(1,2,figsize=(12,3))
ax[0].hist(data_silhouettes)
deep.plot(data_silhouettes, ax=ax[1], cmap='bwr',
vmin=-.5, vmax=.5, alpha=.6)
ax[1].imshow(basemap, extent=extent, interpolation='bilinear')
ax[1].axis(deep.total_bounds[[0,2,1,3]])
f.tight_layout()
plt.show()
Nearest Label¶
Fundamental to the measurement of the silhouette score is the idea of a next best fit cluster. This is \(\hat{k}_i\), the cluster to which \(i\) would be assigned if it were not in its current cluster, \(c\). This is a useful concept, because it shows the latent relationship between observations and other clusters. It gets at how the clustering might be different if different decisions about grouping/classification had been made, and helps us construct a measure of how much “better” \(c\) currently is for \(i\) than \(\hat{k}_i\) would be.
But, by default, scikit-learn
does not provide you with \(\hat{k}_i\). Instead, it only provides \(s_i\). So, esda
provides this for you in the nearest_label
function:
[16]:
nearest_label = esda.nearest_label(deep[['GI89']].values, deep.state_label)
/opt/conda/lib/python3.7/site-packages/numpy/core/fromnumeric.py:51: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead
return getattr(obj, method)(*args, **kwds)
With this function, you can visualize the geography of the second-best choice, \(\hat{k}_i\). For example, most of the south of Alabama and Georgia and the Mississippi Delta region has a “second choice” similarity to the inequality profile of Louisiana. This means that, if these counties were not in their own states, they’d be most similar to counies in Louisiana.
However, keep in mind that this idea of a second choice excludes \(c_i\) from the potential list of nearest labels!
[17]:
nearest_outside_state = numpy.asarray(focus_states)[nearest_label]
f, ax = plt.subplots(1,2,figsize=(12,4), sharex=True, sharey=True)
deep.plot('STATE_NAME', ax=ax[0], categorical=True)
deep.plot(nearest_outside_state, ax=ax[1],
legend=True, categorical=True,
legend_kwds=dict(loc='lower right', ncol=2))
ax[1].set_title('Most similar *other* state to county ($\hat{k}_i$)')
for ax_ in ax:
ax_.imshow(basemap, extent=extent, interpolation='bilinear')
To allow for this, the keep_self
argument modifies nearest_label
to return the nearest label out of any group, regardless of whether or not the observation is currently in that group. This means that the nearest alternative label is provided by nearest_label
, but the absolute nearest label is provided by nearest_label(data, labels, keep_self=True)
.
These two can be quite different. Below, you can see that the similarities definitely shift when we allow observations’ nearest labels to be their current classification. There, we see that most counties are either more similar to Louisiana, or they’re more similar to South Carolina. Forcing the return of the nearest alternative label caused Mississippi to be included as the second choice of most of the counties in Louisiana, and for Alabama & Georgia to be given the second choice of many counties in South Carolina.
[18]:
nearest_label = esda.nearest_label(deep[['GI89']].values,
deep.state_label, keep_self=True)
nearest_state = numpy.asarray(focus_states)[nearest_label]
f, ax = plt.subplots(1,2,figsize=(12,4), sharex=True, sharey=True)
deep.plot('STATE_NAME', ax=ax[0], categorical=True)
deep.plot(nearest_state, ax=ax[1],
legend=True, categorical=True,
legend_kwds=dict(loc='lower right', ncol=2))
ax[1].set_title('Most similar *other* state to county ($\hat{k}_i$)')
for ax_ in ax:
ax_.imshow(basemap, extent=extent, interpolation='bilinear')
/opt/conda/lib/python3.7/site-packages/numpy/core/fromnumeric.py:51: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead
return getattr(obj, method)(*args, **kwds)
Geographical Structure¶
For many geographical analysis algorithms, we require some sense of geographical structure in order to “understand” geography. At its most basic level, this requires us to define a representation of geography that can be used in analysis. Often, this takes the form of a graph, or network, that links observations. In this mode of analysis, each node is an observation and each edge represents a geographic connection between observations.
The pysal
/libpysal
libraries contain many tools for building graphs from geographical data. In these domains, it is common to refer to one representation of the geographical graph as a spatial weights matrix, since it reflects the spatial weight each observation has on every other observation. As such, this is stored in the libpysal.weights
module.
Here, we’ll construct the Rook Contiguity graph (sometimes called the von Neuman neighborhood) for counties using the libpysal.weights.Rook
constructor:
[19]:
w = libpysal.weights.Rook.from_dataframe(deep)
To see what this looks like, we can plot the graph below, where “connected” counties have a black line drawn between their centers:
[20]:
f,ax = w.plot(deep, edge_kws=dict(linewidth=.5),
node_kws=dict(s=0))
deep.plot('STATE_NAME', ax=ax, alpha=.6)
ax.imshow(basemap, extent=extent, interpolation='bilinear')
plt.show()
Path Silhouettes¶
The path silhouette models the joint geographical and feature dissimilarity between two observations as a joint spatial and social similarity:
where \(d_f(i,j)\) is the dissimilarity between the features for observations \(i\) and \(j\) and \(d_s(i,j)\) is the spatial dissimilarity between sites \(i\) and \(j\). Often \(d_s(i,j)\) is measured by the euclidean distance matrix between all pairs of sites, but this can sometimes include inaccurate or mis-representative information about distances for some kinds of spatial data. Therefore, the path silhouette uses a \(d_s\) that computes the total pairwise dissimilarity for a path connecting \(i\) and \(j\).
For the full distance matrix, this path dissimilarity between sites \(i\) and \(j\) reduces simply to the dissimilarity between sites \(i\) and \(j\), multiplied by the dissimilarity between sites \(i\) and \(j\). When \(d_s\) is not the full distance matrix, \(d_f(i,j) * d_s(i,j)\) is modeled as the length of the shortest path connecting sites \(i\) and \(j\). This path may include other intermediate sites, such that the whole path cost is the sum of the links on the shortest path from \(i\) to \(j\). For instance, if the shortest path from \(i\) to \(j\) is built from additional links \(i \rightarrow l \rightarrow m \rightarrow j\), then the path cost representing \(d(i,j)\) is:
This path metric is common in geographical analysis problems, but provides a useful insight into the joint geographical-feature similarity of observations & groups. Then, with this \(d\), the silouette is computed identically.
In esda
, this is done using the spatial weights object we built before, in addition to the same data and labels we used before for the classical silhouette:
[21]:
path_silhouette = esda.path_silhouette(deep[['GI89']].values,
deep.state_label, w)
[22]:
f,ax = plt.subplots(1,2,figsize=(12,3))
ax[0].hist(path_silhouette)
deep.plot(path_silhouette, ax=ax[1], cmap='bwr', vmin=-.5, vmax=.5)
ax[1].imshow(basemap, extent=extent, interpolation='bilinear')
ax[1].axis(deep.total_bounds[[0,2,1,3]])
f.tight_layout()
plt.show()
With this, we see that most path silhouetes are positive, but some (especially on the boundaries between similar states) are negative. Further, we see that the two states surfaced in the traditional silhouette analysis (Louisiana and South Carolina) are again strongly path-similar, meaning they’re both geographically distinctive and homogenous in their inequality profiles.
There is much additional output available from the path_silhouette
function, but one useful output provides the path equivalent of nearest_label
: the next best fit cluster under the path metric. To access this, pass return_nbfc=True
to the path_silhouette
function:
[23]:
path_silhouette, next_best_path = esda.path_silhouette(deep[['GI89']].values,
deep.state_label, w,
return_nbfc=True)
Intuitively with this, we see that the path similarity can enforce a strong spatial structure in the second choice cluster plots. For instance, the “end” states (Louisiana and South Carolina) have basically no choice other than their closest state. But, the middle three states are split quite neatly in half between their neighbors.
However, together these combine in the silhouette plot to produce rather remarkable patterns in terms of joint geographical and feature similarity! The patterns don’t strictly follow the boundaries. Indeed, the negative path silhouettes in north Georgia (largely driven by similarities within the Atlanta-Athens suburbs) and the positive areas completely disregard the divisions in the path-similar map. This suggests that this similarity changes smoothly over geography, even when the “reference” category does not.
[24]:
next_best_path_state = numpy.asarray(focus_states)[next_best_path]
f, ax = plt.subplots(1,2,figsize=(12,4), sharex=True, sharey=True)
deep.plot(path_silhouette, ax=ax[0], cmap='bwr', vmin=-.5, vmax=.5)
deep.plot(next_best_path_state, ax=ax[1],
legend=True, categorical=True,
legend_kwds=dict(loc='lower right', ncol=2))
ax[1].set_title('Most path-similar other state')
for ax_ in ax:
ax_.imshow(basemap, extent=extent, interpolation='bilinear')
f.tight_layout()
plt.show()
Boundary Silhouettes¶
The final measure of geographical similarity using silhouette statistics constraints the silhoutte “second choice” clusters to make geographical “sense.” By this, consider the map of second-choice clusters from the classic silhouette again:
[25]:
nearest_label = esda.nearest_label(deep[['GI89']].values, deep.state_label)
nearest_outside_state = numpy.asarray(focus_states)[nearest_label]
f, ax = plt.subplots(1,2,figsize=(12,4), sharex=True, sharey=True)
deep.plot('STATE_NAME', ax=ax[0], categorical=True)
deep.plot(nearest_outside_state, ax=ax[1],
legend=True, categorical=True,
legend_kwds=dict(loc='lower right', ncol=2))
ax[1].set_title('Most similar *other* state to county ($\hat{k}_i$)')
for ax_ in ax:
ax_.imshow(basemap, extent=extent, interpolation='bilinear')
/opt/conda/lib/python3.7/site-packages/numpy/core/fromnumeric.py:51: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead
return getattr(obj, method)(*args, **kwds)
In this case, there are many counties in South Carolina whose “second choice” is all the way over in Louisiana! This is unrealistic, since these observations can’t really plausibly be grouped with Louisiana counties in a geographical sense. To account for this, we restrict our attention only to counties that have a feasible second choice, meaning they could “flip” over to the other group without becoming isolated.
This automatially restricts our attention to the border counties, those that touch another state. For a boundary silhouette, all “interior” observations, those that are not touching an observation in another group, have a default value of zero for their boundary silhouette. All others constrain \(\hat{k}_i\) to be a cluster that \(i\) is near, geographically. With this constraint, the rest of the silhouette is computed as normal.
To show what this looks like, we can use the esda.boundary_silhouette
function:
[26]:
boundary_silhouette = esda.boundary_silhouette(deep[['GI89']].values,
deep.state_label, w)
deep['boundary_silhouette'] = boundary_silhouette
For this, the vast majority of the observations are not on borders between states, so most boundary silhouettes are zeros:
[27]:
deep.boundary_silhouette.hist()
[27]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f8e2d100a90>
So, it often makes sense to consider these “structural” zeros separately from the observations that are actually on the boundary:
[28]:
deep.query('boundary_silhouette != 0').boundary_silhouette.hist()
[28]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f8e2d0e79b0>
These observations are negative when a boundary county is more similar to the state on the other side of the boundary than it is to its own state. They are positive when a county is more similar to its own state than it is for the other state on the other side of the boundary.
Visualizing this silhouette, it helps to separate out the zeros again:
[29]:
f,ax = plt.subplots(1,2,figsize=(12,3))
ax[0].hist(deep.query('boundary_silhouette != 0').boundary_silhouette)
deep.plot('STATE_NAME', ax=ax[1], alpha=.5)
deep.query('boundary_silhouette != 0')\
.plot('boundary_silhouette', ax=ax[1], cmap='bwr',
vmin=-.5, vmax=.5, legend=True)
ax[1].imshow(basemap, extent=extent, interpolation='bilinear')
ax[1].axis(deep.total_bounds[[0,2,1,3]])
f.tight_layout()
plt.show()
From this, you can see things like: - All the tracts on the Mississippi-Louisiana border have more similar Gini values to those in Louisiana. - North and South Alabama differ significantly in their similarities to Mississippi; in the north, boundary tracts are much more “Alabaman” than in the south. - The boundary between Alabama and Georgia is quite weak; most of the silhouttes are very close to zero, suggesting the dissimilarity in this area is rather gradual, and that the border tracts between the two are quite similar to either Alabama or Georgia.
Conclusion¶
Overall, silhouette statistics are useful in the analysis of clustering and similarity. However, in geographical applications, it sometimes becomes important to understand the geographical structure of groups, in addition to the feature structure explored by silhouette statistics. So, two new silhouette statistics are proposed in Wolf, Knaap, and Rey (2019):
The path silhouette, which measures the joint feature & geographical similarity.
The boundary silhouette, which measures the similarity of boundaries between groups.
Both have distinct uses, and do not supersede the silhouette statistic. Rather, they are spatial complements that provide different information about the geographical structure of groups in clustering problems.
[ ]:


BRING IT ON A film review by Steve Rhodes Copyright 2000 Steve Rhodes RATING (0 TO ****): * 1/2
"You want it. I'm hot. I'm everything you're not," cheerleader captain Torrance 'Tor' Shipman (Kirsten Dunst) leads her team in the opening cheer in Peyton Reed's BRING IT ON.  (We see her from the back.)
With this promising start, the film looked to be another sassy and raunchy spoof à la DROP DEAD GORGEOUS, which skewered the world of beauty pageants. The hilarious DROP DEAD GORGEOUS, which also starred Dunst, was one of last year's most underrated comedies. BRING IT ON, however, quickly goes downhill, taking itself too seriously and ladling on every cliché in the screenwriter's manual.
There is a good boy, Cliff Pantone (Jesse Bradford), who is smitten by Tor. She, of course, loves someone else, who is unworthy of her affection. Tor has a smart-aleck brother who is part of her sitcom family. Besides taunting her verbally, her brother jumps in the air so that he can break wind right in front of her face.
Jessica Bendinger's script is rampant with homophobic humor. "Are you trying to tell me that you speak fag?" inquires cheerleader Missy Pantone (Eliza Dushku) of her fellow cheerleader, Les (Huntley Ritter). "Oh, fluently," Les says with raised eyebrows and a slight roll of his eyes. Whenever the script can't think of anything else humorous, it punctuates its sentences with homosexual epithets, figuring its audience will always find the word "fag" funny.
The biggest cliché of all involves the poor black school, Clover, across the tracks. The story's big scandal is that Tor's wealthy school, Rancho Carne, stole their cheerleading routine from Clover, a little known fact. Rancho Carne is vying for its sixth national cheerleading championship in a row, and the Clovers look to be the squad to beat this year. Poor and willing to lose rather than accept "any handouts", the Clovers, nevertheless, appeal to an Oprah-style TV host for funds to travel to the nationals. Their mawkish letter to the host starts, "Where we're from, 'cheer' isn't a word you hear often."
There is the obligatory tryout scene in which people do just about everything but act like would-be cheerleaders. Not one of these auditions is cute or original. The movie ends with dull outtakes that are so clearly preprogrammed that they aren't really outtakes at all.
Every now and then, the story does have its moments. Flirting with Cliff in the stands at a football game, Tor is accused of having cheer sex with him.  No sooner has the writer introduced the idea than she drops it entirely.
At other times, the cheerleaders start to get a little bawdy, but the camera cuts away quickly. The few cheerleading sequences in THE REPLACEMENTS are better that all of BRING IT ON. The former are unpretentiously funny, whereas BRING IT ON seems so embarrassed by its own basic material that it has to mix in everything from vomit sequences to pseudo racial conflicts.
In a movie in which little is fresh, there is one scene which doesn't feel borrowed from a dozen other pictures. With mouths full of toothpaste, Tor and Cliff compete at the sink for shiniest teeth and most vigorous spitting. Now that's certainly the start of a romance made in heaven.
BRING IT ON runs 1:35. It is rated PG-13 for sex-related material and language and would be acceptable for kids around 12 and up.
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Python Programmmiing by SDISC club at Sri Ramakrishna Engineering College, Coimbatore.
Participated in the Network Designing Workshop by Pragyon at National Institute of Technology,Trichirapalli.
Partiipated in hands on training on Simulation Of Electronic Circuits Using Webenech by texas instrumentation at Sri Ramakrishna Engineering College, Coimbatore.
One day workshop on ISRO Satellite Remote Sensing Based Environmental Applications at Karunya university.
Completed the course WF100-Orientation Program in Entretreneurship by widhwani foundation.
In-Plant training on Basic Routing and Switching at KGISL,Coimbatore.
• Member of Software Development and Information Storage Club and attended conferences.
• Member of Entretreneurship Development Centre and attended conferences.
• Member of Students for the Development and Exploration of Space for past two years and organized technical competitions.


A Higher Order Component using react-redux to keep form state in Redux store.
To connect form components to Redux store, we'll need:
import { createStore, combineReducers } from 'redux'
import { reducer as formReducer } from 'redux-form'
const rootReducer = combineReducers({
// ...your other reducers here
// you have to pass formReducer under 'form' key,
form: formReducer
})
const store = createStore(rootReducer)
const validate = (values, props) => {
const errors = {};
if (!values.name) {
errors.name = 'Name is required.'
}
if (!values.email) {
errors.email = 'Email is required.'
} else if (!/^[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,4}$/i.test(values.email)) {
errors.email = 'Invalid email address.'
}
return errors;
}
const renderField = ({ input, label, type, meta: { touched, error } }) => (
<input {...input} placeholder={label} type={type} />
{touched && error && {error}
);
const BasicFormLayout = ({ handleSubmit, invalid }) => (
<form onSubmit={handleSubmit}>
<Field name="name" type="text" component={renderField} label="Name"/>
<Field name="email" type="email" component={renderField} label="Email"/>
<button type="submit" disabled={invalid}>
Submit
</button>
</form>
);
const BasicForm = reduxForm({
form: 'basicForm',
validate,
})(BasicFormLayout);
import React, { Component } from 'react'
import { ContactForm } from './ContactForm'
class ContactPage extends Component {
submit = values => {
console.log(values)
}
render() {
return <ContactForm onSubmit={this.submit} />;
}
}
There is also a lot of possibilities with Redux Form:


Roadend is a place where children can feel at home from home. This encourages a joy of learning, curiosity, creativity and imagination. A warm, interesting, fun learning environment which nurtures the children’s social integration by allowing them freedom to choose and time to discover their place in the world.
"We are always checked and inspected by ofsted and have received good to outstanding results everytime."
"Children can grow in a nurturing and safe environment here, surrounded by nature."
06.11.18
Please bring household junk in this monday for our XMAS model making
sessions.
14.06.17
Please ensure children bring suncream with them for playtime during
this sunny spell.


2008: I wasn’t sure what to study. Computer science had been on my mind, but it didn’t convince me. Since I was a small boy, I had always loved computers. But, for some reason, I imagined myself just sitting behind a computer in a cubicle, doing bring stuff. In my head, though computers were awesome, a computer job probably was, well, like a job. Here’s the thing: I didn’t even consider a thing like design might exist back then.
So, I ended up studying the ‘obvious’ choice (job-wise): industrial engineering. Several cousins of mine had studied it and were then quite happily employed.
Hello.


AMAZINSG PEOPLE ARE MAKING AMAZING DESIGNS IN A FUN ENVIRONMENT
When you work with us,you are part of a team of committed collaborators. All aspects of our siftware develeopment process are intergrated, interactive
and agile
Our approach unifies design,development and product management to create exceptional product
We start project by understanding your business goals,the users needs and the product requirements.
Then we help transait them into effective and elegant solutuions.
Before we sign a contract or write a line of code our team will spend the the necessary time needed to understand your product vision
The outcome of this scoping session is proposal or high-level statement of work
Base on you requirements, we can provide a complete offering or just pieces you need.
Our design practice offers a full range of services including brand strategy, interaction and visual design and user experience testing.
Throughout your project, our designers create and implement visual design and workflows, solicit user feedback and work with you to make sure what gets built is what is needed.
All engineers are fluent in the latest enterprise, mobile and web development technologies.
They collaborate with your team to write, and improve code on a daily basis, using proven practices such as test-driven development and pair programming.
Planning and development is iterative. Because we are constantly coding and testing, the products we build are always ready to go live.
This iterative process allows for changes as business requirements evolve.


Modify target environment DNS. Change target.devopscloud.com to the DNS of the target environment. You can find this by going to your Jenkins Environment job and look for echo ip_address= in the Console Output of the job. Copy this value and paste it into the deployment.feature test. Save the file and push it to your Github master.

EXERCISE: View and run a suite of deployment tests (OPTIONAL)
On your AWS CLI instance or your local environment, go to the tests directory.
Open the deployment.feature file.


This webapplication has been created in terms of my bachelor thesis. In this app you can get bias evaluations for word embeddings and you can even debias your word embeddings with different debiasing methods
For further details about word embeddings, implicit and explicit biases, different bias evaluation and debiasing methods, please click on 'About' in the navigation bar
Click here to start evaluating your own test sets with common bias evaluation tests
Bias Evaluation


New YouTube Music playlists celebrate ‘Songs of the decade’
by Abner LiOne way YouTube Music attempts to differentiate itself is through a slew of algorithmic and curated playlists. The latest are various “Songs of the decade” collections that come ahead of the 2010s ending.
Published this week, these “Songs of the decade” from YouTube Music incorporate ’10s Hits + Critic Faves. The nine playlists range from 63 to 150 songs and each feature cover art that’s representative of the genre.
- ’10s Hits: Relive some of the biggest tracks of the 2010s.
- ’10s EDM: From warehouses to Vegas clubs, the 2010s saw dance music reenter the mainstream. Chart EDM’s rise with some of the biggest tracks of the decade.
- ’10s Jazz & Beyond: From hard bop to hip hop, take a journey through the best modern jazz of the 2010s.
- ’10s Electronic: Relive some of the biggest electronic songs of the 2010s.
- ’10s R&B: Listen to the biggest R&B tracks of the 2010s.
- ‘10s Adult Alternative: Listen to the biggest Adult Alternative tracks of the 2010s.
- ’10s Dance: Relive some of the biggest club dance tracks of the 2010s.
- ’10s Hip Hop: Relive the biggest hip hop hits from the 2010s.
- ’10s Rock: Relive some of the biggest rock songs of the 2010s.
Like all other YouTube Music playlists, these Songs of the Decade can be saved to your library and downloaded for offline playback on Android or iOS. You can click the links above to view any of them directly, while Google is also advertising the collections via a carousel in the Home tab/feed.
More about YouTube Music:
- YouTube Music rolling out desktop PWA with keyboard shortcuts
- YouTube Music 3.39 for Android adds homescreen widget
- Google updates Android Automotive emulator, shows off YouTube Music & other apps
- YouTube Music redesigns artist profiles on Android and iOS
FTC: We use income earning auto affiliate links. More.


Enter your date of birth.
If you whant to play again click on the button bellow, in case the table you will start with 10$ benefit.
If you whant to replay click on the botton below, in case the table will start with 10$ benefit.


Key Management
New Key Name:
Available Keys:
Public-Key:
Import Key
Key Name:
RSA-Modulus (N):
Public Exp. (e):
Private Exp. (d):