Processed numbers
As soon as you start sending messages this table will get filled with the numbers to which the message
has been sent.
Important instructions
Follow the below instructions to get started
-
Important
Make sure the all the popups are allowed for
https://whatsend.defcon007.com
. If you don't know how to do it, follow the steps mentioned here.
- Important
Make sure you can access WhatsApp web in the current browser.
Click here to know how to do it.
-
Open WhatsApp web in a new tab by clicking here.
-
Log in with the account you wish to send message from, if not already logged in.
-
If you are logged in at other places too, you will see option to use WhatsApp web
here.
Click `Use here` option.
-
Close the tab.
- Download the helper chrome extension from here and load
it
in the browser by following the instructions from here.
-
You are good to go now. Just select the corresponding CSV file and then select the column for
mobile number, type your message and click Submit.
Adding receiver's name to message
- Click on Add custom field to add a new custom field.
- Select the column from the dropdown which has the names of the receivers.
- In Key to be replaced box, type a unique key which will be present in the message.
Note: All the instances of this key from the message will be replaced by the value from the column you selected.
- The key is case sensitive and should be unique, Some examples keys could be {NAME}, "NAME", etc. Using { and " toi provide uniqueness.
And Voilà! All your messages will now have the dynamic names.
The above method can be used to add any number of custom fields to create dynamic messages from the CSV file and add unique receiver specific attributes in the messages.
For example, if the key you selected is NAME which maps to name column in the CSV file and the message you type is,
Hi NAME,
Thank you being a valuable customer. Please use the code NAME-123 as coupon code for 10% off.
If the name column has the value John for a customer, following will be the actual message that will be sent.
Hi John,
Thank you being a valuable customer. Please use the code John-123 as coupon code for 10% off.


Version Control to Major Tom:
A Stellar Coding Adventure
Back to Blog
More About Me
Back to Home Page
Oct. 26: JavaScript, Not Just a Pretty Object
Ruby Hashes vs. JavaScript Objects
This week's new focus, JavaScript, was less of a mountain to climb simply because the language has so many similarities with Ruby. While the two languages remain distinct, they still use a number of tools which, despite different naming conventions, accomplish more or less the same role.
JavaScript's Objects has functionality that can be easily compared to Ruby's Hash.
A hash, as we know, is a collection of key-value pairs linking a key such as "name" to the value "Charlotte" or "Age" to the number 25.
Objects are JavaScript's way of organizing key-value pairs.
The expression of those relationships is, of course, different.
For instance, JavaScript would express that relationship thus:
var woman = {name: "Charlotte", age: "35"}
Ruby will instead say:
woman = {"name" => "Charlotte", "age" => 35}
To access the data, the idea is the same but the methods vary: in JavaScript, you would learn Charlotte's age by calling woman.age; whereas in Ruby, you would write woman[age].
Modifying the content of an object or a hash is equally simple. If I were to add the key "Hometown", I would do this:
woman.hometown = "New York"; in JavaScript,
and simply write:
woman['hometown'] = "New York" in Ruby.
Same ideas, slight differences in syntax and punctuation.
Back to Home Page


---
layout: page
title:
subtitle:
use-site-title: false
---
I am a climate scientist with interests in tropospheric and stratospheric chemical, radiative and dynamical processes, understanding links and interactions with other parts of the Earth system, machine learning (ML) techniques applied to model evaluation and development, as well as using Earth System Models to address the scientific challenges of global change.
Research
I am Research Associate of the Earth System Model Evaluation and Analysis Department, Institute of Atmospheric Physics, at the German Aerospace Center (DLR). Within the “Understanding and Modelling the Earth System with Machine Learning (USMILE)” project, I am focused on developing new methods and approaches via ML means to eliminate long-standing systematic errors in climate models and to provide more robust climate projections.
Education
- 2017 PhD Atmospheric Science, Lancaster University, Lancaster, UK. Dissertation: Stratospheric ozone in the Earth system.
Supervisors: Dr Paul J. Young and Prof Oliver Wild.
- 2011 MSc Climate Change, University of East Anglia (UEA), Norwich, UK. Dissertation: Investigating geoengineering solutions to meet the 2ºC target. Supervisors: Dr Naomi Vaughan and Prof Timothy Osborn.
- 2004 BSc Environmental Science, Universidad Pablo de Olavide, Seville, Spain.
Research and employment
- 2019– Research Associate, DLR, Oberpfaffenhofen, Germany
- 2017–2019 Research Associate, Spanish National Research Council, Madrid, Spain.
- 2016 Research Associate, Lancaster University Management School, Lancaster, UK.
- 2012 Research Assistant, Centro de Investigação em Gestão, Lisbon, Portugal.
- 2005–2010 Environmental Officer, Comisiones Obreras de Andalucia, Seville, Spain.
- 2004 Undergraduate Research Assistant, AIMCRA, Seville, Spain.
Awards and honours
- 2016 Award Winner for Best Presentation at the Royal Meteorological Society (RMetS) Student Conference, Manchester, UK.
- 2015 Award of Excellence, Community Earth System Model Tutorial, Boulder, CO, US.
- 2015 Visiting PhD student, National Center for Atmospheric Research, Boulder, CO, US.
- 2011 Best Debate in ‘Understanding climate change in society’, UEA, Norwich, UK.
Proffesional service and activities
- 2018– Early Career Scientists representative, European Geosciences Union (EGU) Atmospheric Science (AS) Division.
- 2016– Member of Voice of Young Science, Sense about Science, UK.
- 2016–2018 Early Career Scientists team representative EGU AS Division.
- 2013–2017 Meteorological observer, Hazelrigg Weather Station, Met Office, Lancaster, UK.
Publications
Peer reviewed articles (Google scholar)
- Iglesias-Suarez, F., Badia, A., Fernandez, R. P., Cuevas, C. A., Kinnison, D. E., Tilmes, S., Lamarque, J.-F., Long, M. C., Hossaini, R., and Saiz-Lopez, A.: Natural halogens buffer tropospheric ozone in a changing climate. Nat. Clim. Chang, doi:10.1038/s41558-019-0675-6, 2020
- Barrera, J. A., Fernandez, R. P., Iglesias-Suarez, F., Cuevas, C. A., Lamarque, J.-F., and Saiz-Lopez, A.: Seasonal impact of biogenic VSL bromine on the evolution of mid-latitude lowermost stratospheric ozone during the 21st century. Atmos. Chem. Phys. Discuss., 1, 1680-7375, doi: 10.5194/acp-2019-109, 2020
- Yumashev, D., Hope, C., Schaefer, K., Riemann-Campe, K., Iglesias-Suarez, F., Jafarov, E., Burke, E. J., Young, P. J., Elshorbany, Y., and Whiteman, G.: Climate policy implications of nonlinear decline of Arctic land permafrost and other cryosphere elements. Nat. Commun., 10, 1900, doi:10.1038/s41467-019-09863-x, 2019
- Iglesias-Suarez, F., Kinnison, D. E., Rap, A., Wild, O., and Young, P. J.: Key drivers of ozone change and its radiative forcing over the 21st century. Atmos. Chem. Phys., 18, 6121-6139, doi:10.5194/acp-18-6121-2018, 2018
- Iglesias-Suarez, F., Young, P. J., and Wild, O.: Stratospheric ozone change and related climate impacts over 1850–2100 as modelled by the ACCMIP ensemble. Atmos. Chem. Phys., 16, 343-363, doi:10.5194/acp-16-343-2016, 2016
- Iglesias-Suarez, F.: Investigating geoengineering solutions to meet the 2ºC target. IBER Journal, 5, 87-108, ISSN: 1647-1989, 2014
Non-peer reviewed articles
- Iglesias-Suarez, F.: El Ártico, un termómetro para el cambio climático. The Conversation (Spain), ISSN: 2201-5639, 2019


# rb-inotify
This is a simple wrapper over the [inotify](en.wikipedia.org/wiki/Inotify) Linux kernel subsystem for monitoring changes to files and directories. It uses the [FFI](wiki.github.com/ffi/ffi) gem to avoid having to compile a C extension.
[API documentation is available on rdoc.info](rdoc.info/projects/nex3/rb-inotify).
## Basic Usage
The API is similar to the inotify C API, but with a more Rubyish feel. First, create a notifier:
notifier = INotify::Notifier.new
Then, tell it to watch the paths you’re interested in for the events you care about:
notifier.watch("path/to/foo.txt", :modify) {puts "foo.txt was modified!"} notifier.watch("path/to/bar", :moved_to, :create) do |event| puts "#{event.name} is now in path/to/bar!" end
Inotify can watch directories or individual files. It can pay attention to all sorts of events; for a full list, see [the inotify man page](www.tin.org/bin/man.cgi?section=7&topic=inotify).
Finally, you get at the events themselves:
notifier.run
This will loop infinitely, calling the appropriate callbacks when the files are changed. If you don’t want infinite looping, you can also block until there are available events, process them all at once, and then continue on your merry way:
notifier.process
## Advanced Usage
Sometimes it’s necessary to have finer control over the underlying IO operations than is provided by the simple callback API. The trick to this is that the {INotify::Notifier#to_io Notifier#to_io} method returns a fully-functional IO object, with a file descriptor and everything. This means, for example, that it can be passed to `IO#select`:
# Wait 10 seconds for an event then give up if IO.select([notifier.to_io], [], [], 10) notifier.process end
It can even be used with EventMachine:
require 'eventmachine' EM.run do EM.watch notifier.to_io do notifier.process end end
Unfortunately, this currently doesn’t work under JRuby. JRuby currently doesn’t use native file descriptors for the IO object, so we can’t use the notifier’s file descriptor as a stand-in.


---
layout: default
title: Team
description: "Tens of volunteer organisers are helping their local communities in emerging and developed countries to embrace their inner entrepreneur, and adopt new AI technologies to reply to world challenges."
image: /assets/images/logo_small.png
permalink: /team
---


Bible says about: Ghosts
What are ghosts
The Bible says that one third of the angels rebelled against God (see my page on Satan for more info). Satan (Lucifer) was the angel in charge of this rebellion. These angels were punished by being banished from heaven.
Satan hates man. Man has a relationship with God that angels could never have. Man was made in the image and likeness of God. Satan is jealous of this and hates us for it. He wants to deceive human kind into rejecting God, so that man will share the same punishment that he is destined for.
Satan knows that one single lie wont be effective in deceiving everyone. So he came up with several. Some men would be deceived by Ghosts and reincarnation. Others would adopt an evolutionary philosophy, that the world created itself without God. Others would be deceived into believing in pagan gods, etc...
I believe (and the Bible says) that Satan sends fallen angels to earth to deceive us. Satan wants us to doubt the existence of God. He wants man to share in his punishment.
Part of this deception is the appearance of "ghosts". Satan wants man to believe that these are the spirits of people who have died. That they have overcome the chains of death and returned.


Make teacher’s lives easier, one peer review at a time. Think of Bookr as the Yelp for textbooks.
Bookr helps education professionals find what they need. Educators can browse a list of textbooks with trusted reviews from their peers, making it easy to select the right one for their needs. By clearly laying out the resources and providing a rating and reviews system, educators can use Bookr as their #1 source for material.


This is the container for all assets that are to be used in game.
More...
#include <AssetManager.h>
This is the container for all assets that are to be used in game.
| AssetManager::~AssetManager |
( |
| ) |
|
| void AssetManager::AddFont |
( |
std::string |
fontId, |
|
|
const char * |
filePath, |
|
|
int |
fontSize |
|
) |
| |
| void AssetManager::AddTexture |
( |
std::string |
textureId, |
|
|
const char * |
filePath |
|
) |
| |
| void AssetManager::ClearData |
( |
| ) |
|
| TTF_Font * AssetManager::GetFont |
( |
std::string |
fontId | ) |
|
| SDL_Texture * AssetManager::GetTexture |
( |
std::string |
textureId | ) |
|
The documentation for this class was generated from the following files:


The hook filename is comprised of: OPERATION_{ASSET_}POSITION, e.g.
.loft_deploy/hooks/
├── fetch_db_post.sh
├── fetch_db_pre.sh
├── fetch_files_post.sh
├── fetch_files_pre.sh
├── fetch_post.sh
├── fetch_pre.sh
├── pull_db_post.sh
├── pull_db_pre.sh
├── pull_files_post.sh
├── pull_files_pre.sh
├── pull_post.sh
├── pull_pre.sh
├── reset_db_post.sh
├── reset_db_pre.sh
├── reset_files_post.sh
├── reset_files_pre.sh
├── reset_post.sh
└── reset_pre.sh
You may create .sh
files that will execute before or after an operation. These are called hooks and should be created in .loft_deploy/hooks
. An example is a hook to be executed after a reset
operation, you need only create a file at using the pattern OPERATION_{ASSET_}POSITION
. The variables from loft_deploy.sh are available to your hook files, e.g., $config_dir
. If you want the same file to be executed for multiple operations you should use symlinks.
.loft_deploy/hooks/reset_post.sh
Then create a symlink:
cd .loft_deploy/hooks/ && ln -s reset_post.sh pull_post.sh
The contents of the file could look like this, where $1 is a verbose comment about calling the hook, you should echo it if you care to have it displayed.
#!/bin/bash
#
# @file
# Clears the drupal cache after the database has been reset
# Verbose statement about this hook
echo $1
# Leverage the $relative location and then do a drush cc all
echo "`tty -s && tput setaf 3`Clearing the drupal cache...`tty -s && tput op`"
(cd "$(dirname $config_dir)/public_html" && drush cc all)
You can add mysql commands against the local environment in a hook using loft_deploy_mysql
like this:
#!/bin/bash
#
# @file
# Clears the drupal cache after the database has been reset
# Verbose statement about this hook
echo $1
loft_deploy_mysql "DROP TABLE cache_admin_menu;"
See also sanitization using hooks.
echo_green
, echo_yellow
, and echo_red
.return
0, or a non-zero if the hook fails.exit
in a hook file.| var | description |
|---|---|
| $ROOT | Path to the directory containing loft_deploy.sh |
| $INCLUDES | Path to the loft deploy includes directory |
| arg | definition | example |
|---|---|---|
| $1 | operation | push |
| $2 | production server |<EMAIL_ADDRESS>|
| $3 | staging server |<EMAIL_ADDRESS>|
| $4 | local basepath as defined in config.yml | |
| $5 | path to the copy stage directory | |
| $6 | role of the server being handled | prod, staging |
| $7 | operation status | true or false |
| $8 | local role | dev, staging, prod |
| ${13} | path to hooks dir | /do/re/hooks |


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“Go put the raiment on. … You'll find it in a chamber on whose door a star is turning black. Don it and return. I'll think of beetles while you're gone, and things like that.” — James Thurber, The Thirteen Clocks


--- layout: default title: Get Involved - La Brigada de la Basura backgroundImg: 4 bodyClass: getInvolvedPage js: ['js/yvonne.js'] ---
"Find out what you can do to help La Brigada"
There are many ways you can help out La Brigada. We always need more adult supervisors - spanish language skills are helpful but not neccessary. You are more than welcome to come with us when we clean the streets and help us keep an eye on the kids. After the pickup we have had successful afternoon activies like basketball, a choir, and english lessons. The availability of these activies depends almost entirely on our volunteers so if there's something you'd like to do please let us know!
Donations of Toys, Trash Bags, Candy, Gloves etc. are more than welcome. School supplies are usually a big hit with the kids and really help out their families. One of our volunteers once brought in two boxes of toothbrushes. Turns out most of the kids were sharing theirs at home with siblings and/or parents and just loved getting a toothbrush of their own. It goes to show that even the most simple things can make a major difference in the kid's lives, anything you choose help them with will be greaty appreciated.
We usually start our Christmas Toy Drive around Thanksgiving time. We'd love for you to bring in a toy, unwrapped, for either a boy or a girl toddler to teenager - Santa and his helpers will hand them all out on Christmas Eve.
Of course cash donations are more than welcome as well. They help us greatly with buying trash bags, gloves, hot dogs, chips, toys etc. every week. Eventually we hope to get enough support that we can purchase permanent trash receptacles for the barrio.
Just come on by any Saturday morning and meet the wonderful children of La Brigada de la Basura. We would all love to see you!


Home page
Welcome to my web site!
Personal introduction
I studied Multilingual Communication at the University of Geneva and now I am a student at the MA in Multilingual Communication Technology (MATIM).
I am of Russian origin, but I also consider Italian to be my mother tongue because most of my life I have lived in Italy. I also studied English, French and German during my academic career.
I have always been attracted by languages and technology, indeed this is what motivated me to attend this Master.


lind.design.randomization package¶
Submodules¶
lind.design.randomization.md5 module¶
md5: Hashing can be used to generate reproducible pseudo-randomization. This can be useful in contexts where the user does not want to store a fixed seed to ensure replicability of test randomization.
Examples
>>> # numpy randomization with fixed seed
>>> random_state = np.random.RandomState(42)
>>> random_state.choice(["Lauren", "Sam", "Ben"], size=1)
>>> # hd5 random sample (no seed required)
>>> md5shuffle(["Lauren", "Sam", "Ben"])[0]
-
lind.design.randomization.md5.
md5shuffle
(arr: numpy.ndarray, salt: str = None) → numpy.ndarray¶ Will shuffle the input array pseudo-randomly in a deterministic manner using MD5 hashing.
- Parameters
arr (list, ndarray) – The array of values that you want shuffled
salt (str) – A sting to append to sample ids to avoid collisions across experiments testing on the same population. If None, then no salt is applied.
- Returns
the input array in a shuffled order
- Return type
ndarray
Examples
>>> md5shuffle( >>> arr=[i for i in range(1000)], >>> salt="whale" >>> )
-
lind.design.randomization.md5.
draw_percentile
(arr: Union[List, numpy.ndarray], lb: float = 0.25, ub: float = 0.75, salt: str = None) → numpy.ndarray¶ Draw array values that fall within a certain percentile of the hash space.
- Parameters
arr (list, ndarray) – An array of objects that you want to sample from
lb (float, optional) – The lower bound of the percentile; must be between 0 and 1
ub (float, optional) – The upper bound of the percentile; must be between 0 and 1; must be greater than lb
salt (str) – A sting to append to sample ids to avoid collisions across experiments testing on the same population. If None, then no salt is applied.
- Returns
an array of values from arr that fall within the specified percentile of the hash space
- Return type
ndarray
Examples
>>> draw_percentile([i for i in range(1000)], lb=0.25, ub=0.75) # sample 50% of inputs
Module contents¶
randomization / pseudo-randomization utilities for treatment assignment and segmentation


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I had dropped Morning Glory once before---4.5 grams---I was disappointed by the effects. Iím driving to a friendís party after having a good night, so I was in a good mindset. Right before I get to his house, I dropped two packets (Heavenly Blue) without any preparation method. I got lost a couple of times on the way and got pretty frustrated. I arrived 15 minutes later and immediately dropped four more packets (1.5 g each). When I was eating the last packet, my gag reflex kicked in, and I spat out around 1g of the seeds. This was around midnight. I hadnít eaten since 4:30 that afternoon.
15 minutes later, and Iím already feeling it. I know this is going to be big. I run out to accompany a friend, S, whoís smoking a bowl. I donít partake. Iím sitting in her car listening to music when I look down at my hand thatís resting on my thigh. It slowly blends into my pants and disappears completely. Iím ecstatic that Iím tripping and also surprised at how quick it took. The other time I did it, on a full stomach, it took 2 hours to feel the threshold effects.  I go sit down on a couch and listen to/ watch a Flaming Lips DVD. As I sat there, the morning glory kicked in harder and, the pain went away.
It was amazing. I had a great buzz going and was having a great time. I looked up at the ceiling and noticed some of the beams were blending into the top of the ceiling. I looked out the window at the ground which was situating itself into geometric patterns. My sense of touch felt amazingly sensitive. The next thing I know, I look at the clock and it was 1:30. It had already been more than an hour. I realized I had absolutely no concept of time. I got tired of the DVD, so I went out to the fire my friend had set up. I wasnít seeing or feeling anything, so we eventually went up to his pool room. There, I sat down, and for no reason felt depressed and apathetic to life. I donít know how long I was sitting there, but we eventually went back to fire, where I stared down at the ground and immediately felt extremely giddy again.
At the fire, I stared at trees, whose leaves would shift left to right. Looking at a tree, I saw faces morphing in it. I felt like I could talk to it. I looked up at the moon and noticed a rainbow halo surrounded it. I blinked and immediately, I saw the trees again, but they were completely made out of tiny circles. When I looked at the ground, I felt as if my head were resting on a wall and the ground itself was another vertical wall. I then went to my friendís garage (which is on the way to the pool) and stared at his car. The surface of it looked like it was water. Itís body looked entirely liquid.
We then went to play some more pool, and this is where I started peaking (around 4 AM). I looked at the checkered tiles of the floor, and they would rise and fall and move around. I saw an added dimension to them. The flat surface looked 3d. I played some pool, which I was surprisingly good at, and watched the balls as they would move around at their leisure after I hit them. The pattern on the felt jumped out at me, something I had never noticed before. There was a geometric pattern embroidered on the felt. I checked the next day and sure enough, it was still there.
Colors became brighter, my friendís blond hair became a neon green. At one point we listened to The Whoís ďTommy.Ē When the song ďHoliday CampĒ came on, it sounded so amazing that I couldnít stop smiling and jumping around. I feel as if that was the very height of my trip. We then walked back to the house where doors were shifting and dancing. We played a board game from 6 till 7 AM but I couldnít concentrate on it because the grain of the wood on the table kept moving. In my peripheral vision, aluminum cans danced around, but when I turned to look at them, they stopped. I then went to go brush my teeth.
Staring at my reflection in the mirror was amazing. My face turned entirely to stretched out rubber and became huge. It also detached from my head and started floating around left and right, as my gray shirt turned green. Also, when looking at my reflection in a window, my mouth became that of a sad clown. I started laughing and decided I needed some sleep desperately. When I went to the bed and put on the covers, I felt as if the covers had melded to my body. I looked up at them and they were situated so it looked like a giant turtle was lying on top of me.
I was rudely awakened 3 hours later at 10:30. For a couple of hours after that, colors were somewhat brighter, and for the rest of the day I was worthless. This may be due to the lack of sleep I got or the MGís, Iím not sure. This was the first time I ever outright tripped. I had had a brief but amazing shroom flirtation but it didnít yield hallucinations. I had an incredible time, and I believe morning glory is an excellent place to start out for hallucinating.
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.


Copyright (c) 1992 Hans van Maanen
Trianon is a game played on a 5x5 board:
|
| An example
White's turn. In this position, every White drop makes a 3 in-a-row and that implies losing the game. |
The board should not have edges with even size, because a symmetry strategy for the second player exist. There's a website with information about the game (in Dutch).


There are 7 invisible buttons:
Bottom-Left - Add a pitched light-pole
Bottom-Right - Add a beating oscillator
Top-Left - Remove a pitched light-pole
Top-Right - Remove a beating oscillator
Center-Right-Edge - Start Crescendo/Descrendos
Center-Left-Edge - Stop Crescendo/Descrendos
Center-Middle - Kill All Audio (cannot be undone)
Once the score is loaded, touch anywhere on the screen to activate buttons and start piece.


OnTrack works with you to track your water, protien, and excercise goals during your illness and recovery. Maintaining adequate hydration and protein is vital to help your body endure your treatment.
After you register, OnTrack uses the information you provide to set up daily goals. As you enter information you will see charts about your daily progress on the Daily page. You can view weekly and monthly progress on the Charts page.


This is a simple card without actions. The entire element has a .card class. The content is enclosed in a .card-content class.
This is a simple card without actions. The entire element has a .card class. The content is enclosed in a .card-content class.
This is a simple card with actions. The entire element has a .card class. The content is enclosed in a .card-content class. The action links below are styled using a .card-action class.
This is a simple card with actions. The entire element has a .card class. The content is enclosed in a .card-content class. The action links below are styled using a .card-action class.


FAQs¶
This section includes FAQs for creating and updates DOIs for PDS data sets, bundles, and products. If you require additional support, please contact the PDS Operator for assistance.
- FAQs
- How do I know whether I should reserve or release a DOI?
- How can I specify multiple authors in the DOI reserve spreadsheet?
- The DOI reserve spreadsheet seems very terse, can I provide more information?
- What PDS4 products should we be assigning DOIs to?
- What does it mean to have data registered?
- How do I register my PDS4 product with Engineering Node?
- How do I get a DOIs for PDS3 data sets and documents?
How do I know whether I should reserve or release a DOI?¶
The short answer:
If the data is not yet publicly available -> Reserve
If the data is publicly available -> Release
The longer answer:
To “reserve” a DOI, is to get a DOI from our DOI Provider associated with a data set for a future release. This is the ideal start for getting DOIs so you can get the DOI, and then add it to the PDS4 label metadata prior to release.
To “release” a DOI is to make the
https://doi.org/<my_doi>
URL live. This requires that the data is registered at EN, available online, and the landing pages are live (automated by EN Registry).
How can I specify multiple authors in the DOI reserve spreadsheet?¶
You can just put 1 author in the spreadsheet for now. The only part of that spreadsheet that really matters is the LIDVID. That is what ties to the DOI to the product. You will have a chance to update the DOI metadata with as many authors as you’d like at the “Release DOI” step.
The DOI reserve spreadsheet seems very terse, can I provide more information?¶
The “Reserve DOI” step is really just a placeholder to request a DOI from our DOI Provider. The only information that is really critical at this step is the LIDVID. Everything else will be updated when you submit a request to “release” the DOI using the PDS4 label.
What PDS4 products should we be assigning DOIs to?¶
Per the PDS DOI Policy, bundles are the “default” product to receive a DOI, but it is up to the Discipline Node if you would like DOIs for collections or documents.
What does it mean to have data registered?¶
In order for the DOI Landing Pages to be generated, the products must be registered with PDS Engineering Node.
How do I register my PDS4 product with Engineering Node?¶
Contact the PDS Operator with the URL(s) to the applicable PDS4 product(s) you would like registered.
How do I get a DOIs for PDS3 data sets and documents?¶
The PDS has gone through the exercise with each Discipline Node to mint DOIs for most PDS3 Data Sets. If we missed one or there is a new version available, please complete the following:
Send an email with the DS.CAT for all applicable PDS3 Data Sets to PDS Operator and Ron
Joyner with subject Release DOI for PDS3 Data Set: some identifying info
. Be sure to indicate whether the data set is new to the system, the DS.cat been updated, and if it has already been registered at PDS Engineering Node.


|
Keraflow
Deep Learning for Python.
|
Utility class for flexible user input assiging optimizers, regularizers, numpy input... More...
Public Member Functions | |
| def | __init__ |
| def | __call__ |
Utility class for flexible user input assiging optimizers, regularizers, numpy input...
To bring both flexibly and convince to argument passing in Keraflow, for some arguments of some functions, users can pass a single value, a dictionary, or a list. We normalize the input to an ordered list of arrays (same order as legal_names
). For more information, please refer to Argument Passing section of Tutorials"
| def keraflow.utils.user_input_utils.UserInput.__init__ | ( | self, | |
| legal_names | |||
| ) |
| legal_names | list of str. Ordered target names of user's input, e.g. ['W', 'b'] for setting layer weight or ['output1', 'output2'] for setting outputports' attributes (loss, metric etc.). |
| def keraflow.utils.user_input_utils.UserInput.__call__ | ( | self, | |
| user_input, | |||
array = False , |
|||
allow_missing = False , |
|||
no_single_value = True , |
|||
error_context = '' |
|||
| ) |
| user_input | dict/list/single object(s) |
| array | boolean. Whether each target accepts an numpy array. If only single target, a list is also acceptable. The list well be automatically transformed to a numpy array. |
| allow_missing | False or 'skip', 'fill_none'. If False, len(user_inputs) should be equal to len(legal_names). Return list of values If 'skip', return a list of (name, value) tuples, target that is not specified is not returned. If 'fill_none', return a list of (name, value) tuples, target that is not specified is filled with None. |
| no_single_value | boolean. Whether user_input could be a single value |
| error_context | str. Debug messages when user_ipnut is not acceptable. |
allow_missing
=False) or list of (name, value) tuples (otherwise).


DocOnce supports a variety of Bootstrap styles for HTML
documents. To get the journal
Bootswatch style, for instance, run
Terminal> doconce format html mydoc --html_style=bootswatch_journal
The title, optionally with authors, date, table of contents, abstract and/or a short introduction is typeset using the Jumbotron environment from Bootstrap, resulting in large fonts on a gray background.
If a !split
command is used before the first section heading or
TOC: on
instruction, a button with Read...
and a link to the next
page is inserted if the document is split using doconce split_html
.
This construction results in a short Bootstrap main page that has
become very popular on web. The --html_bootstrap_jumbotron=value
option can be used to adjust the jumbotron: value
can be on
(default), off
(to turn the gray background and large fonts off), or
h2
(to replace the large font in the document title by a smaller
font).


--- layout: default title: mfX categories: - research ---
The nucleation of a new phase within another occurs in many natural phenomena as well as engineering processes of relevance to industry. In particular, we look at:
Interfacial phenomena is the study of the physics involving the boundary between two phases, which determines the dynamics of the phases, as well as transport between them. In particular, we look at:
Porous media flows is the study of fluid flow through porous structures. Many open problems involving porous flows are highly relevant to next-generation industrial applicatons, such as:
Particle-laden/particle flows ubiquitously occur in environmental, biological, and industrial systems. Through cutting-edge modelling and numerical simulations, we try to advance our understanding of such system and shed light on the complex micro-scale interactions of particles with each other and with the boundaries which determines the characteristics of the system at large scales. Currently, we are tackling challenging research/technical questions in the following application areas:
Rarefied gas dynamics involves the study of molecular effects in non-equilibrium fluid transport. We develop gas kinetic models, efficient numerical methods based on the direct simulation Monte Carlo method and discrete velocity method, with application to:
Fluid-structure interaction deals with the response of obstacles and structures to fluid (compressible/incompressible) flow forces as the flow interacts with them. Here we deal with such interactions at small scales, which has received much less attention. Currently we are actively working on the following areas:


ff-foreach
< Back To DOCS
Purpose
The ff-foreach attribute duplicates a section of markup for each entry in an array.
Sample: simple list
HTML
Result
- {{_key}} number {{_value}}
Firebase emulator
Expand the sample list in the Firebase emulator and notice the key value pairs. To access the key use
{{_key}}. To access a primitive
type (string, number, boolean, ...) use
{{_value}}. If the value is an object, you can use the objects keys as in the example below.
Sample: list of objects
HTML
Result
- {{firstName}} {{lastName}} - {{city}}
Firebase emulator
Future Features
Firefight does not currently support nesting input or other elements that write to firebase inside ff-foreach.
Other resources
< Back To DOCS


Querying data with the InfluxDB API
The InfluxDB API is the primary means for querying data in InfluxDB (see the command line interface and client libraries for alternative ways to query the database).
Note: The following examples use
curl
, a command line tool that transfers data using URLs. Learn the basics ofcurl
with the HTTP Scripting Guide.
To perform a query send a GET
request to the /query
endpoint, set the URL parameter db
as the target database, and set the URL parameter q
as your query.
You may also use a POST
request by sending the same parameters either as URL parameters or as part of the body with application/x-www-form-urlencoded
.
The example below uses the InfluxDB API to query the same database that you encountered in Writing Data.
curl -G 'http://localhost:8086/query?pretty=true' --data-urlencode "db=mydb" --data-urlencode "q=SELECT \"value\" FROM \"cpu_load_short\" WHERE \"region\"='us-west'"
InfluxDB returns JSON.
The results of your query appear in the "results"
array.
If an error occurs, InfluxDB sets an "error"
key with an explanation of the error.
{
"results": [
{
"statement_id": 0,
"series": [
{
"name": "cpu_load_short",
"columns": [
"time",
"value"
],
"values": [
[
"2015-01-29T21:55:43.702900257Z",
2
],
[
"2015-01-29T21:55:43.702900257Z",
0.55
],
[
"2015-06-11T20:46:02Z",
0.64
]
]
}
]
}
]
}
Note: Appending
pretty=true
to the URL enables pretty-printed JSON output. While this is useful for debugging or when querying directly with tools likecurl
, it is not recommended for production use as it consumes unnecessary network bandwidth.
Multiple queries
Send multiple queries to InfluxDB in a single API call. Simply delimit each query using a semicolon, for example:
curl -G 'http://localhost:8086/query?pretty=true' --data-urlencode "db=mydb" --data-urlencode "q=SELECT \"value\" FROM \"cpu_load_short\" WHERE \"region\"='us-west';SELECT count(\"value\") FROM \"cpu_load_short\" WHERE \"region\"='us-west'"
returns:
{
"results": [
{
"statement_id": 0,
"series": [
{
"name": "cpu_load_short",
"columns": [
"time",
"value"
],
"values": [
[
"2015-01-29T21:55:43.702900257Z",
2
],
[
"2015-01-29T21:55:43.702900257Z",
0.55
],
[
"2015-06-11T20:46:02Z",
0.64
]
]
}
]
},
{
"statement_id": 1,
"series": [
{
"name": "cpu_load_short",
"columns": [
"time",
"count"
],
"values": [
[
"1970-01-01T00:00:00Z",
3
]
]
}
]
}
]
}
Other options when querying data
Timestamp format
Everything in InfluxDB is stored and reported in UTC.
By default, timestamps are returned in RFC3339 UTC and have nanosecond precision, for example 2015-08-04T19:05:14.318570484Z
.
If you want timestamps in Unix epoch format include in your request the query string parameter epoch
where epoch=[h,m,s,ms,u,ns]
.
For example, get epoch in seconds with:
curl -G 'http://localhost:8086/query' --data-urlencode "db=mydb" --data-urlencode "epoch=s" --data-urlencode "q=SELECT \"value\" FROM \"cpu_load_short\" WHERE \"region\"='us-west'"
Authentication
Authentication in InfluxDB is disabled by default. See Authentication and Authorization for how to enable and set up authentication.
Maximum row limit
The max-row-limit
configuration option allows users to limit the maximum number of returned results to prevent InfluxDB from running out of memory while it aggregates the results.
The max-row-limit
configuration option is set to 0
by default.
That default setting allows for an unlimited number of rows returned per request.
The maximum row limit only applies to non-chunked queries. Chunked queries can return an unlimited number of points.
Chunking
Chunking can be used to return results in streamed batches rather than as a single response by setting the query string parameter chunked=true
. Responses will be chunked by series or by every 10,000 points, whichever occurs first. To change the maximum chunk size to a different value, set the query string parameter chunk_size
to a different value.
For example, get your results in batches of 20,000 points with:
curl -G 'http://localhost:8086/query' --data-urlencode "db=deluge" --data-urlencode "chunked=true" --data-urlencode "chunk_size=20000" --data-urlencode "q=SELECT * FROM liters"
InfluxQL
Now that you know how to query data, check out the Data Exploration page to get acquainted with InfluxQL. For more information about querying data with the InfluxDB API, please see the API reference documentation.


Ensure you understand the basic usage of the modeling engine prior to proceeding.
Developing classifiers using the Syberia modeling engine follows one primary tenet: development equals production. When a web developer experiments with a new website layout or feature, they are targeting a production system. After they are satisfied with their work, they push a button and deploy the code base so it is live and others can interact with it.
Feature engineering and statistical modeling (as we will see, inseparable components of machine learning) should belong to the same class of work. When an architect designs a skyscraper their work has to be translated to reality through a construction team by replaying the design using a physical medium. This is the current industry standard for machine learning: prototype the model in one language, typically a Python or R "notebook," and then write it in a "solid production-ready" language so it can survive the harsh winds of the real world.
This is wrong. Prior to the invention of HTML, construction of UIs was a laborious design process that proceeded from the lab to the factory. HTML, and later JavaScript, introduced a syntax, a grammar, for rapidly experimenting with UIs and quickly pushing them to the real world.
The tenet of using different languages for development and production is fundamentally flawed. Good syntax will always win because it records the critical mathematical abstractions relevant to the domain and retroactive optimization becomes possible. Recoding your hardly fought experimental fruit from R to Scala or from Python to C is a waste. Before HTML, inter-network UIs were written in C and other lower languages. The world wide web only exploded because it gave the power of rapid, iterative UI development to all through a simple and straightforward syntax.
Teaching all the world's statisticians and applied mathematicians C, Java, or Scala to "productionize" their work is not a viable long-term strategy. The only correct approach is to take the syntax, the mathematical notation, most comfortable to these experts and then wielding a new world from it: one that is alive.
Whether any aspect of the current Syberia framework or its de facto modeling engine survives is irrelevant, in the same way that VBScript ended up irrelevant, but it is important to take that first step. Do not be afraid to see real-time production-ready hybrid stream-and-batch data processing and statistical prediction as an experimental process that culminates in a finished product the same way experimenting with a web app culminates in a deployable real world tool without additional code or re-implementation work.
We have bothered with a philosophical prelude because we believe the statistician and scientist should come first: there should be no "data engineer" trained in computer science and difficult-to-master functional languages and distributed systems to rewrite the work from scratch to make it "ready," at least to the same extent there is no "web engineer" that takes a UI/UX expert's JavaScript work and translates it to a C++ application every single time. All this should be the corollary of a well-designed syntax, and the data engineering task should be a one-time fully general abstract undertaking that implements that syntax in the medium of streaming distributed systems. We are trying to find that syntax, and R's homoiconicity and interactive nature forms the perfect testbed for finding that grail.
Naturally, some organizations and performance-intensive applications will continue to require full rewrites of the experimental designs, but there is no reason why a well-designed syntax should not be capable of auto-transpiling to the performance-optimized description, in the same way that assembly languages were subsumed by higher abstract tools.
With all that said, let's jump into what a primordial first iteration of a complete modeling grammar might look like. If you like this approach to statistical modeling, maybe you can help us design its future.
The first analogy that came to my mind is of immersing the nut in some softening liquid, and why not simply water? From time to time you rub so the liquid penetrates better, and otherwise you let time pass. The shell becomes more flexible through weeks and months—when the time is ripe, hand pressure is enough, the shell opens like a perfectly ripened avocado!
— Alexander Grothendieck
Finding well-designed syntax is hard. Historically, many achievements in math and physics were a direct result thereof. Nevertheless, it is worth doing because a successful execution allows you to think at a higher level of abstraction. We start with the simplest possible grammar so that we may extend and build atop it to achieve more complexity. We will do so without getting distracted by the latest and shiniest database tool or complex ensemble topology, but instead with the meticulous patience of a mathematician, until the formerly daunting seems trivial.
For now, we stick with the standard supervised learning problem on a binary or continuous predictor. Any instance of this machine learning problem requires us to get data to perform analysis on. On the other hand, typically "export" is not embodied as a single button but as a process: re-coding the results of the experimental sandbox analysis into a production codebase. The Syberia approach is to turn this around and demand that all metadata relevant to reproducing the modeling process in a real-time setting be attached to a single serializable object. Fortunately, R makes this easy.
The first grammatic abstraction is a fairly commonplace one: that of
I/O adapters. We need a way to read in data, typically from a file
or a database, and a place to store our results, typically a file or
cloud storage. We will see how defining short snippets of code in the
lib/adapters
directory in a Syberia modeling
project allows us to import arbitrary data and export models arbitrarily.
What happens in the middle? Once we have the data, we typically have to clean it. This is because the real world is messy and we rarely get exactly what we expect: we may have missing values, mislabeled points, insufficiently massaged features, and a whole host of other problems. Determining which variables to keep after arduous experimentation can be first-order approximated using sure independence screening. The process of data preparation in the Syberia modeling approach is presently performed using mungebits, atomic production-ready feature engineering templates that allow us to quickly parametrize the end-to-end cleaning process without having to write what usually boils down to fairly repetitive code.
The other, perhaps primary, purpose of mungebits is to encode any metadata we may need to replay our feature engineering steps in production. For example, if we imputed certain columns with the mean, we will have to pre-record the means of each imputed column, as this information will not be available in production when a new record streams in.
After the data is "clean," in the sense that it forms a nearly complete primarily numerical matrix without incorrectly labeled signals, we can try a host of machine learning techniques on it: generalized linear models, support vector machines, neural networks, clustering, random forests, and so on.
Typically, recording the model is done in a format like PMML, but this is too restrictive. It forces us into a very limited set of feature engineering steps and statistical parameters. Instead, we leverage the power of R's LISP-like computation and store any code and data required to reproduce the predictions within a single R object called a tundra container (named tundra because it freezes the model for production use :).
To determine whether we are satisfied with the model, we have to apply some sort of validation. Typically, this may be measuring the AUC of the predictions against a target threshold. Note that both feature engineering and choice of statistical learning parameters may heavily influence performance.
Together with importing data and exporting results, the iterative process of experimenting with feature engineering and statistical classifier parameters forms the heart and intersection of most approaches to modeling problems, so we will focus on solving this grammatical exercise until it becomes a trivial feat before proceeding with higher grammars.
Throughout, we aim to make the description of the modeling process as concise as possible while retaining the tip of the iceberg composing the critical information. If a model file ever grows to longer than 100 lines of code, it is a good sign you should split it up and refactor. Let's revisit our earlier trivial example.
list(
import = list(R = "iris"),
data = list(
"Create dep var" = list(renamer, c("Sepal.Length" = "dep_var")),
"Create ID var" = list(multi_column_transformation(seq_along), "dep_var", "id")
),
model = list("lm", .id_var = "id"),
export = list(R = "model")
)
The last expression of every model file is a list.
A list itself is a static object, but the assembly
of a list into a living, breathing avatar of the modeling process occurs
internally when typing run("example")
.
Let's break down the four stages.
Importing data
is done through the import stage. This stage looks at
lib/adapters
to find the necessary adapter.
The modeling engine comes with several built-in adapters,
including the R, file, and S3 adapters for importing from and exporting to
R's global environment, files, and Amazon's S3 cloud storage, respectively.
Reading the name of the list element "R"
from list(R = "iris")
allows the import stage
to pluck out lib/adapters/R
.
You can define a new way to import data by placing a file with
read
and/or write
local
variables holding functions in lib/adapters/your_new_adapter.R
and then replacing the first stage with
import = list(your_new_adapter = ...)
where the
right-hand side is the parametrization that will be passed to the
read
function. Later, we will examine further
how to do this (and test that it works as expected).
Munging the data is achieved through the data stage. Each stage accepts its own parametrization and convention. While the import stage is relatively simple, describing merely the external location of the data, the data stage can be as complicated as you need it to be.
The vanilla data stage is parametrized by a named list, with each list element name representing a very brief summary of the feature engineering or cleaning step, and the list element value representing a grammar of data preparation we will explain down the road.
The nice thing about this
grammar is that it allows us to write almost any operation on the data
set in one line of code and separate the abstract parametrized code
for performing the operation into a separate place, lib/mungebits
,
where all of its inputs and outputs can be thoroughly tested and
ensured of correctness.
Running the model! This step is frequently and incorrectly viewed as the most important component of the modeling process: the secret sauce is in finding the correct parameters to a magical mathematical method. This is wrong. Once you start writing some Syberia modeling files, you will see that the meat often lies primarily in the data preparation, not in the statistical method. It is a pity that most books on machine learning do not make this acknowledgement.
The parametrization for the model stage is simple: a list whose first element describes
the name of the classifier we will use, which convention dictates it fetches
from lib/classifiers/lm.R
. Any file in
that directory will be a short R file containing two local variables,
train
and predict
,
where the former records the metadata required to play the predictions
on any subsample of the original dataset (in particular, single rows of
streaming data). Typically, this is what we refer to when we say we are
estimating the "parameters" of a statistical learning function.
The rest of the list elements will be fed directly to the train
function after the data has been cleaned. The Syberia modeling engine
provides the shortcut M
to represent the model
object so that we can preview some predictions using
M$predict(validation_data)
.
Hand-in-hand with some function validate(M)
,
we can downsample the import stage prior to experimentation and
use the re-running functionality to play run(, 3)
(i.e., re-run the model stage) to make a succession of plots or estimates
of the efficacy of various parameters, before settling on a choice for the
final dataset. This is a typical approach to experimenting with data sets that
fit into memory, but there is a way to make it work on arbitrarily large data sets as well.
Exporting
the model. Borrowing from the adapters introduced in the import stage,
this stage follows a similar parametrization: the name of each list
element should correspond to an entry in lib/adapters
with a write
local variable determining
how to save the model object based off the parametrizations provided
on the right-hand side.
In our trivial example, we exported to the R global environment so we can inspect
our model. If we are happy, we could add file = "~/tmp/model.RDS"
(or even better, use an S3 key) indicating we are using the file
adapter to export the model object for later use.
Typing run(, 4)
will re-run the export stage with
this new export adapter, recording our model permanently.
The example we described is a trivial one. What if we wanted to do something more complicated? Imagine we would like to import data from a database, run three complex feature engineering steps (say principal component analysis, some time series aggregation, and sure independence screening), followed by running a model with a new package we found on CRAN, and then exporting the serialized object to a Mongo database.
How much work would we have to do?
# models/dev/fancy_model.R
list(
import = list(database = list(table = "my_data")),
data = list(
"Aggregate time series" = list(aggregate_ts, vars = c("timevar1", "timevar2")),
"Sure indep screening" = list(sis, pval_threshold = 0.05),
"Principal components" = list(pca, center = TRUE, scale = TRUE)
),
model = list("fancy_model", param1 = 0.7, param2 = "foo")
export = list(mongo = "models/experimental/fancy_model/1")
)
database.R
and mongo.R
file with a read
and write
method, respectively,
placing them in lib/adapters
.
(And write tests! More on this later.)
sis.R
, aggregate_ts.R
,
and pca.R
in lib/mungebits
. We will
examine later what goes into writing mungebits, but it isn't much.lib/classifiers/fancy_model.R
file.In total, we had to touch six files, excluding the model file, with some huge benefits: (1) as we will see later, each of our files requires tests for the project to pass continuous integration, ensuring we are confident the results are correct, and (2) we have made the fruits of our work accessible to anyone else working on the project!
This latter point is crucial:
if we had written a linear, serial script, someone would later have to go
dig through our model, potentially sprawled across several files,
to figure out what to copy and where to inject new parameters to
appropriate to their particular data set. If six months later we have written
100 models that used pca
and discovered
a subtle bug, we could fix all models simultaneously instead of wading
through old files or notebooks that will inevitably be forgotten.
Forget notebooks. By thinking like developers even when experimenting, we can grow stronger. We can have the best of both worlds. By writing the modeling process in this way, the Syberia modeling engine is forcing the work we write to be modular, testable, and reproducible, preferably in a versioned codebase that records all of history.
This is what gave great power to frameworks like Ruby on Rails, tools that were not hailed theoretical results yet empowered formerly siloed developers to harness the benefits of convention over configuration and reach developer productivity and project iteration speeds that were formerly inaccessible.
The dream of the modeling engine is to reach and surpass this level of productivity as applied to the domain of, well, whatever it is that R does! Machine learning is only the beginning.


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Please note – this map should be used for reference purposes only. Refer to parking signage for official policy and regulations.
Parking rates are listed in $ per hour unless otherwise noted.
Please submit questions or comments at ParkBurlington.com.


My Little Business
The most creative ideas
Hello,
{{emailUser}}
Home Page
Sales Events
My Favorites
About Us
Contact Us
>>
The man with the hat
Master Designer Balloons Balloons which transmits events and activities for the whole family.


Number of Distributed Pixels
Size of the Pixels
Number of Distinct Colors
Minimum Threshold for Similar Neighbor Frequency


?Flora
Domingensis.
.!.$.?f...........’a-
i/wujiiijv
fy*
Prov.
de
la-Vega
1912
JcS.
(UA+^
leg.
Padre
Miguel
Fuertes.
01333936
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Summer time is that time of year that a lot of us look forward to. Spending time with our friends and family can be loads of fun. Though with all the summer activities going on, we can tend to let safety slip through the cracks. By educating ourselves, as parents, we can help prevent summer time injuries and illnesses.


Tag Matcher Demo
Put the cursor on or inside a pair of tags to highlight them. Press Ctrl-J to jump to the tag that matches the one under the cursor.
Put the cursor on or inside a pair of tags to highlight them. Press Ctrl-J to jump to the tag that matches the one under the cursor.


|
PerSt Pile
version 0.0.1
Persistent storage pile used for storing key/value pairs
|
Factory class that produces PerSt instances. More...
#include <perst_factory.h>
Public Types | |
| typedef PerSt *(* | kbCreate) (const PERST_STRING &name, const PERST_STRING &value, void *user_data) |
| callback used to create PerSt instances by providers. | |
| typedef bool(* | kbForEach) (int index, const PERST_STRING &name, kbCreate kb, void *user_data, void *for_each_user_data) |
| callback used to iterate all providers. | |
Static Public Member Functions | |
| static void | init () |
| Start-up. More... | |
| static void | end () |
| End-up. More... | |
| static PerSt * | create (const PERST_STRING &name, const PERST_STRING &value) |
| Create a new instance. More... | |
| static bool | addProvider (const PERST_STRING &name, kbCreate kb, void *user_data) |
| Register a new kind. More... | |
| static bool | delProvider (const PERST_STRING &name) |
| Unregister a custom kind. More... | |
| static bool | hasProvider (const PERST_STRING &name) |
| Tell if a name is registered. More... | |
| static bool | forEachProvider (kbForEach kb, void *for_each_user_data) |
| Iterate all providers (includes build-ins). More... | |
Factory class that produces PerSt instances.
|
static |
Register a new kind.
The method creates the singleton if it does not already exists.
|
static |
Create a new instance.
The method creates the singleton if it does not already exists.
|
static |
Unregister a custom kind.
The method creates the singleton if it does not already exists.
|
static |
End-up.
Deletes the singleton.
|
static |
Iterate all providers (includes build-ins).
The method creates the singleton if it does not already exists.
|
static |
Tell if a name is registered.
The method creates the singleton if it does not already exists.
|
static |
Start-up.
Creates the singleton.


This week marked the start of learning Javascript. While HTML and CSS determine what is displayed on a web page, Javascript adds another layer, allowing the web page responds to events that happen. Without JS, the web would look great but would be nowhere near as useful. Google, Facebook, Youtube, Wikipedia and most other major websites all use JavaScript as their front-end language.
Javascript uses "control flow" and "loops" to perform tasks. Here is an example of how javascript would achieve the task of reminding the user to mow the lawns. If the grass is taller than ten centimetres, it should be mowed.
function mowLawns(grassHeight) {
var result = "";
if (val > 9) {
result = "Time to mow the lawns";
}
else {
result = "Your lawn is looking great";
}
return result;
}
Two ways of handling data in JS are "Arrays[]" and Objects {}:
Array:var myGarden = ["pohutakawa", "mount cook lily", "kowhai", "lavender"]
Object:
var myFlower = {
"name": "Mount Cook Lily",
"latin": "Ranunculus lyallii",
"location discovered": ["South Island", "Stewart Island"]
};
}
Accessing data from the above array would involve var myGarden[1]
which would return "mount cook lily".
Accessing data from the above object (ie. properties) would involve using the dot operator (when you know the name of the property you're trying to access): myFlower.name;
or using bracket notation: myFlower["location discovered"];
Functions in JS are blocks of code that perform certain tasks. They are useful because they organise tasks into seperate packages, so can be executed or halted without other code being effected.


Linked Data Graphic and Visualization Components for Angular
Initially, this library is designed as an Angular Component and released as an npm package, for which Angular Development Environment and an npm package manager is prerequisites.
Open the module file (e.g app.module.ts
) and import the
LinkedDataGraphicModule
symbol from linked-data-graphic
library. Then add LinkedDataGraphicModule
to the @NgModule
metadata's imports
array.
Prepare a simple list of linked data with nodes and relations information
in your application component(e.g app.component.ts
) as a property
for binding into ngld-canvas
component later.
Add a <ngld-canvas>
element,
the <ngld-canvas>
component selector, into your
application's template, where your linked data is intended to be shown.
debug
directive, the graphic should be rendered like this:


Major hourly trends in New York City taxi dropoffs, midnight to midnight, via multi-dimensional clustering.
Checkout geo-time-series on GitHub.


fn_framedisplay
Contents
Description
[im/ha =] function fn_framedisplay([x,[y,...]]data[,clip][,'singleaxes|multaxes'] ... [['ncol',]ncol][,'in',hf|ha][,'display'][,'normalaxis'][,'nocolor']) Input (except for data, order of inputs can be changed): - x,y x and y range - data 3D or 4D data; NOT using Matlab image convention (i.e. first dim is x and second dim is y) if the third dimension is of length 3, it is assumed to represent color; to avoid this assumption to take place, place the additional flag 'nocolor' in the argument list - clip clipping range or 'fit' - axflag 'singleaxes' will create a large image by putting each frame next to each other and display this image 'multaxes' will display each frame in a separate axes If axflag is not specified, 'multaxes' will be used iff the number of frames is small. - ncol specify the number of columns to use; the 'ncol' flag is not necessary - hf|ha handle of figure or axes (implicit'singleaxes' option) for figure handle, it is necessary to precede it by the 'in' flag (otherwise the argument is ambiguous with ncol), for axes handle, this is not necessary - 'display' by default, nothing is displayed if an output is requested, this option is used to display even if there is an output - 'normalaxis' do not set the axes aspect ratio - 'nocolor' explicitely tell the function not to try to detect a color channel
Output: - im a large image obtained by concatenating all images, note that compared to the large image created with the 'singleaxes' option, this image is slighlty larger since there is a 1-pixel wide separation between all frames - ha if axflax is 'multaxes', the output is the set of axes handles
Source
Thomas Deneux
Copyright 2011-2017


Raeven Lynn M. Clockston
Democrat
I am currently employed as a contact tracer and case investigator for COVID-19 through the Colorado Department of Public Health and Environment. I am also employed as a teaching assistant and graduate research assistant through the Colorado State University and University of Colorado Denver, Anschutz Medical campus.
Bachelor of Science in Nutrition and Food Science earned in 2018 Master of Public Health candidate, 2021 NASM Certified Personal Fitness Trainer, 2016
80249
I have been a registered democrat since 2015 and have participated in all general and local elections since then.
I am a strong advocate for health equity in my communities, and most recently I have been working with the Inclusive Excellence Committee (a tri-campus group) to promote more diversity, equity, and inclusion in Colorado higher education institutions.
I would like to serve on the commission because I would like to bring a new perspective as a young Black woman who is a fourth generation Colorado native. I know how important policy and civic engagement are and I am looking to get involved and serve where possible.
I will promote consensus and contribute to creating agreement among the commissioners through thoughtful conversation and willingness to learn from others as well as share my knowledge. It is imperative that all voices are heard, represented, and respected. I am able to be fair and I enjoy working with people from diverse backgrounds.
Through my time spent studying analytical epidemiology in my master's degree program, and my time spent working as a member of the COVID-19 response, I have strengthened my skills in the area of problem solving and critical thinking. I am able to think deeply, considering all sides of a problem, and act in a way that is beneficial for most people. I am especially tuned into the needs of communities that have been historically disenfranchised.
Female


Anime Quiz
your score is
0%
On with the quetions..
Naruto tail beast name is ?
shukaku
kurama
who is best ?
Naruto
Sasuke
who is more beautiful ?
sakura
hinata
who is best ?
Madara
Hashirama


Class: Aws::Proton::Types::CreateEnvironmentInput
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-proton/lib/aws-sdk-proton/types.rb |
Overview
Constant Summary
Instance Attribute Summary
-
#codebuild_role_arn ⇒ String
rw
The Amazon
::Aws::Proton::Resource
Name (ARN) of the::Aws::IAM
service role that allows::Aws::Proton
to provision infrastructure using CodeBuild-based provisioning on your behalf. -
#component_role_arn ⇒ String
rw
The Amazon
::Aws::Proton::Resource
Name (ARN) of the::Aws::IAM
service role that::Aws::Proton
uses when provisioning directly defined components in this environment. -
#description ⇒ String
rw
A description of the environment that's being created and deployed.
-
#environment_account_connection_id ⇒ String
rw
The ID of the environment account connection that you provide if you're provisioning your environment infrastructure resources to an environment account.
-
#name ⇒ String
rw
The name of the environment.
-
#proton_service_role_arn ⇒ String
rw
The Amazon
::Aws::Proton::Resource
Name (ARN) of the::Aws::Proton
service role that allows::Aws::Proton
to make calls to other services on your behalf. -
#provisioning_repository ⇒ Types::RepositoryBranchInput
rw
The linked repository that you use to host your rendered infrastructure templates for self-managed provisioning.
-
#spec ⇒ String
rw
A YAML formatted string that provides inputs as defined in the environment template bundle schema file.
-
#tags ⇒ Array<Types::Tag>
rw
An optional list of metadata items that you can associate with the
::Aws::Proton
environment. -
#template_major_version ⇒ String
rw
The major version of the environment template.
-
#template_minor_version ⇒ String
rw
The minor version of the environment template.
-
#template_name ⇒ String
rw
The name of the environment template.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#codebuild_role_arn ⇒ String
(rw)
The Amazon ::Aws::Proton::Resource
Name (ARN) of the ::Aws::IAM
service role that allows
::Aws::Proton
to provision infrastructure using CodeBuild-based
provisioning on your behalf.
To use CodeBuild-based provisioning for the environment or for any
service instance running in the environment, specify either the
environmentAccountConnectionId
or codebuildRoleArn
parameter.
# File 'gems/aws-sdk-proton/lib/aws-sdk-proton/types.rb', line 821
class CreateEnvironmentInput < Struct.new( :codebuild_role_arn, :component_role_arn, :description, :environment_account_connection_id, :name, :proton_service_role_arn, :provisioning_repository, :spec, :, :template_major_version, :template_minor_version, :template_name) SENSITIVE = [:description, :spec] include Aws::Structure end
#component_role_arn ⇒ String
(rw)
The Amazon ::Aws::Proton::Resource
Name (ARN) of the ::Aws::IAM
service role that ::Aws::Proton
uses when provisioning directly defined components in this
environment. It determines the scope of infrastructure that a
component can provision.
You must specify componentRoleArn
to allow directly defined
components to be associated with this environment.
For more information about components, see Proton components in the Proton User Guide.
# File 'gems/aws-sdk-proton/lib/aws-sdk-proton/types.rb', line 821
class CreateEnvironmentInput < Struct.new( :codebuild_role_arn, :component_role_arn, :description, :environment_account_connection_id, :name, :proton_service_role_arn, :provisioning_repository, :spec, :, :template_major_version, :template_minor_version, :template_name) SENSITIVE = [:description, :spec] include Aws::Structure end
#description ⇒ String
(rw)
A description of the environment that's being created and deployed.
# File 'gems/aws-sdk-proton/lib/aws-sdk-proton/types.rb', line 821
class CreateEnvironmentInput < Struct.new( :codebuild_role_arn, :component_role_arn, :description, :environment_account_connection_id, :name, :proton_service_role_arn, :provisioning_repository, :spec, :, :template_major_version, :template_minor_version, :template_name) SENSITIVE = [:description, :spec] include Aws::Structure end
#environment_account_connection_id ⇒ String
(rw)
The ID of the environment account connection that you provide if you're provisioning your environment infrastructure resources to an environment account. For more information, see Environment account connections in the Proton User guide.
To use Amazon Web Services-managed provisioning for the environment,
specify either the environmentAccountConnectionId
or
protonServiceRoleArn
parameter and omit the
provisioningRepository
parameter.
# File 'gems/aws-sdk-proton/lib/aws-sdk-proton/types.rb', line 821
class CreateEnvironmentInput < Struct.new( :codebuild_role_arn, :component_role_arn, :description, :environment_account_connection_id, :name, :proton_service_role_arn, :provisioning_repository, :spec, :, :template_major_version, :template_minor_version, :template_name) SENSITIVE = [:description, :spec] include Aws::Structure end
#name ⇒ String
(rw)
The name of the environment.
# File 'gems/aws-sdk-proton/lib/aws-sdk-proton/types.rb', line 821
class CreateEnvironmentInput < Struct.new( :codebuild_role_arn, :component_role_arn, :description, :environment_account_connection_id, :name, :proton_service_role_arn, :provisioning_repository, :spec, :, :template_major_version, :template_minor_version, :template_name) SENSITIVE = [:description, :spec] include Aws::Structure end
#proton_service_role_arn ⇒ String
(rw)
The Amazon ::Aws::Proton::Resource
Name (ARN) of the ::Aws::Proton
service role that
allows ::Aws::Proton
to make calls to other services on your behalf.
To use Amazon Web Services-managed provisioning for the environment,
specify either the environmentAccountConnectionId
or
protonServiceRoleArn
parameter and omit the
provisioningRepository
parameter.
# File 'gems/aws-sdk-proton/lib/aws-sdk-proton/types.rb', line 821
class CreateEnvironmentInput < Struct.new( :codebuild_role_arn, :component_role_arn, :description, :environment_account_connection_id, :name, :proton_service_role_arn, :provisioning_repository, :spec, :, :template_major_version, :template_minor_version, :template_name) SENSITIVE = [:description, :spec] include Aws::Structure end
#provisioning_repository ⇒ Types::RepositoryBranchInput (rw)
The linked repository that you use to host your rendered
infrastructure templates for self-managed provisioning. A linked
repository is a repository that has been registered with ::Aws::Proton
. For
more information, see CreateRepository.
To use self-managed provisioning for the environment, specify this
parameter and omit the environmentAccountConnectionId
and
protonServiceRoleArn
parameters.
# File 'gems/aws-sdk-proton/lib/aws-sdk-proton/types.rb', line 821
class CreateEnvironmentInput < Struct.new( :codebuild_role_arn, :component_role_arn, :description, :environment_account_connection_id, :name, :proton_service_role_arn, :provisioning_repository, :spec, :, :template_major_version, :template_minor_version, :template_name) SENSITIVE = [:description, :spec] include Aws::Structure end
#spec ⇒ String
(rw)
A YAML formatted string that provides inputs as defined in the environment template bundle schema file. For more information, see Environments in the Proton User Guide.
# File 'gems/aws-sdk-proton/lib/aws-sdk-proton/types.rb', line 821
class CreateEnvironmentInput < Struct.new( :codebuild_role_arn, :component_role_arn, :description, :environment_account_connection_id, :name, :proton_service_role_arn, :provisioning_repository, :spec, :, :template_major_version, :template_minor_version, :template_name) SENSITIVE = [:description, :spec] include Aws::Structure end
#tags ⇒ Array
<Types::Tag> (rw)
An optional list of metadata items that you can associate with the
::Aws::Proton
environment. A tag is a key-value pair.
For more information, see Proton resources and tagging in the Proton User Guide.
# File 'gems/aws-sdk-proton/lib/aws-sdk-proton/types.rb', line 821
class CreateEnvironmentInput < Struct.new( :codebuild_role_arn, :component_role_arn, :description, :environment_account_connection_id, :name, :proton_service_role_arn, :provisioning_repository, :spec, :, :template_major_version, :template_minor_version, :template_name) SENSITIVE = [:description, :spec] include Aws::Structure end
#template_major_version ⇒ String
(rw)
The major version of the environment template.
# File 'gems/aws-sdk-proton/lib/aws-sdk-proton/types.rb', line 821
class CreateEnvironmentInput < Struct.new( :codebuild_role_arn, :component_role_arn, :description, :environment_account_connection_id, :name, :proton_service_role_arn, :provisioning_repository, :spec, :, :template_major_version, :template_minor_version, :template_name) SENSITIVE = [:description, :spec] include Aws::Structure end
#template_minor_version ⇒ String
(rw)
The minor version of the environment template.
# File 'gems/aws-sdk-proton/lib/aws-sdk-proton/types.rb', line 821
class CreateEnvironmentInput < Struct.new( :codebuild_role_arn, :component_role_arn, :description, :environment_account_connection_id, :name, :proton_service_role_arn, :provisioning_repository, :spec, :, :template_major_version, :template_minor_version, :template_name) SENSITIVE = [:description, :spec] include Aws::Structure end
#template_name ⇒ String
(rw)
The name of the environment template. For more information, see Environment Templates in the Proton User Guide.
# File 'gems/aws-sdk-proton/lib/aws-sdk-proton/types.rb', line 821
class CreateEnvironmentInput < Struct.new( :codebuild_role_arn, :component_role_arn, :description, :environment_account_connection_id, :name, :proton_service_role_arn, :provisioning_repository, :spec, :, :template_major_version, :template_minor_version, :template_name) SENSITIVE = [:description, :spec] include Aws::Structure end


--- layout: post title: "This Week's Menu, Minus CSA" date: 2022-09-07 12:00:00 +0000 tags: menu image: imagecredits: ---
Next week marks a significant change in this household's food habits, as Kaia leaves to return to college. It's her birthday today and we're dining out tonight at a Korean restaurant in The City. We'll eat out on Saturday at the Chuseok Festival and possibly on Sunday, her last night here.
I have a cheesecake in the oven for her birthday. It's been much too hot to cook or bake, and I'm already regretting turning the oven on as I'm typing this at the kitchen table and it's hot in here.
Here's what I'll pick up in the CSA boxes this afternoon:
I still have one cucumber left in the fridge from last week — the painted serpents were longer than my arm. And I froze two tomatoes, which is heartbreaking, but they'll definitely get used up this winter.
It's too hot to think about how I'll cook with these. "Salads" is all that I can say right now (even though the heatwave is supposed to end on Thursday or Friday).


Card Panel Hello
Word
!!
Cores dinâmicas
Cores estáticas
done
chat
backup
delete
Ao passar o mause em cima adiciona um efeito de sombra acima !!
Texto grande sendo truncado industry. Lorem Ipsum has been the
industry's standard dummy text ever since the 1500s, when an unknown
printer took a galley of type and scrambled it to make a type specimen book.
Ocultando para despositivos móveis.
Ocultando para tablets.
Ocultando para PC.
Texto sendo alinhado à esquerda.
Testo sendo alinhado à direita
Texto sendo alinhado ao centro.
Seção 1
Está é uma seção.
seção 2
Está é uma seção.
the printing and typesetting industry. Lorem Ipsum has been the
industry's standard dummy text ever since the 1500s, when an unknow
the printing and typesetting industry. Lorem Ipsum has been the
industry's standard dummy text ever since the 1500s, when an unknown
printer took a galley of type and scrambled it to make a type specimen book.
the printing and typesetting industry. Lorem Ipsum has been the
industry's standard dummy text ever since the 1500s, when an unknown
printer took a galley of type and scrambled it to make a type specimen book.
the printing and typesetting industry. Lorem Ipsum has been the
industry's standard dummy text ever since the 1500s, when an unknown
printer took a galley of type and scrambled it to make a type specimen book.
the printing and typesetting industry. Lorem Ipsum has been the
industry's standard dummy text ever since the 1500s, when an unknown
printer took a galley of type and scrambled it to make a type specimen book.
the printing and typesetting industry. Lorem Ipsum has been the
industry's standard dummy text ever since the 1500s, when an unknown
printer took a galley of type and scrambled it to make a type specimen book.
the printing and typesetting industry. Lorem Ipsum has been the
industry's standard dummy text ever since the 1500s, when an unknown
printer took a galley of type and scrambled it to make a type specimen book.
Heading h1
Heading h2
Heading h3
Heading h4
Heading h5
Isto é uma citação.


The Yomega Raider is one of the premier yoyos in the advanced players’ world. With a precise bearing system, the Raider is engineered to provide the ultimate balance in response, speed, and spin time. Among world and national Champions, the Raider is used for extreme looping play and is the recommended choice for any progressing player.
The Yomega Maverick is a high grade, all aluminum, laser etched yoyo that is designed for advanced play. This yoyo combines a highly rated steel ball bearing with a smooth rubber return system, a weighted perimeter for long spins, and a wide gap profile for advanced string play. The Maverick is a high performance, high value yo-yo that is incredibly popular. This yoyo promotes individuality in yoyo play.
The Yomega Pro Speed is a heavy (70 grams) non responsive yoyo that requires a bind in order to return to the hand. Designed specifically for string tricks, it features a wing shape design with a wide gap making it easier to catch the yoyo on the string. This yoyo is perfect for the advanced player to push the limits to perform complex string tricks and winning moves.


<lazy-image>
is a custom element that contains an image that has the
option to be loaded only on-demand, for performance reasons. If a <lazy-image>
is inactive, then its source is not loaded (the XHR won't be made until the
active
attribute is set on the element). See the
code on GitHub.
<lazy-image>
has the following attributes:
active
, whether the image should be loaded or notalt
, the alternate text for the image the image source (like you would use with a
)src
, the image source (like you would use with a
)srcset
, the image source (like you would use with a
)active
property set to true
.
In the example below, the images will only load when clicked (you can check the network tab in
your favourite developer tools to see that there's no initial request for these files).
This happens because they each start off with the active
property set to false
,
and have a click
event listener, that
sets it to true
:
<lazy-image src="..." alt="..." id="i"></lazy-image>
<lazy-image alt="..." srcset="... 1x, ... 2x"></lazy-image>

Demo using src(Click on an image to load it)
<lazy-image>
on the page (i.e. activates or deactivates all of them), you can set the
window.LazyImageSiteDefaultActive
global before loading the
lazy-image.js
script.
<lazy-image>
, this lets you only load
images that are scrolled into view, while leaving images at the bottom
of the page that haven't been seen inactive.
// Create an observer.
var observer = new IntersectionObserver(onChange, {
threshold: [0.5] // rootMargin: '50% 0%'
});
// That observes all the random images we've created.
els.forEach(el => observer.observe(el));
// Whenever we scroll...
function onChange(changes) {
changes.forEach(change => {
var el = change.target;
if (!el.active)
el.active = true;
observer.unobserve(el); // Don't care anymore.
});
}
IntersectionObserver demo (this demo only works in Chrome, Edge and FF nightly.
👀 caniuse for
updates)


The DisseminatorC component holds and synchronizes a single value of a chosen type, and identifies that value by a chosen 16-bit key. Different nodes should use the same key for the same value. See TEP118 - Dissemination for details.
t
- the type of the object that will be disseminated
key
- the 16-bit identifier of the disseminated object


Next Film: In the Night Garden and The Curse of the Ware Rabbit
Showing at 19:45
The synchronization may not be perfect! It's a work in progress, blame archie if anything breaks
Change the mode for a different bitrate and less stuttering


Chemical
An Erlenmeyer flask set $35.50
A type of laboratory flask which features a flat bottom. There are four size of flasks in the set which are 25 ml, 50 ml, 125 ml, 250 ml.
- 25ml $8.50
- 50ml $9.50
- 125ml $11.50
- 150ml $12.50
A type of laboratory flask which features a flat bottom. There are four size of flasks in the set which are 25 ml, 50 ml, 125 ml, 250 ml.
A simple container for stirring, mixing and heating liquids commonly used in many laboratories. Also, cylindrical in shape, with a flat bottom. There are five size of beaker in the set which are 50 ml, 100 ml, 250 ml, 500 ml,and 1000 ml.
A thin glass tube closed at one end, used to hold small amounts of material for laboratory testing or experiments.
This set include 10 test tubes and the test tube rack
A common piece of laboratory equipment that produces a single open gas flame, which is used for heating, sterilization, and combustion. This set include the bunsen burner and the connecting tube.
A short, small plastic tube that is put into your child's vein using a needle. The plastic tube is then left in so that fluids and medicines can be given directly into the blood via the vein.
A medical device intended to assist medical personnel during a surgical procedure by illuminating a local area or cavity of the patient. A combination of several surgical lights is often referred to as a “surgical light system”.
A medical instrument for listening to the action of someone's heart or breathing, typically having a small disk-shaped resonator that is placed against the chest and two tubes connected to earpieces.


Kehong Li
Contact
(current)
Portfolio
About
(current)
Portfolio
COVID-tracker
Quick access to each states data of COVID-19, and compare data with other countries in the world.
Go to page
Repo page


During the period of economic prosperity, purchasing real-estate for Airbnb rental has been a popular form of investment in major cities. Warsaw has been no exception, with strong economic incentives for short-term accommodation provision in recent years: Airbnb hosts benefited from the growing number of tourists, as well as from the rising flat prices.
These months a quick return of investment has become less secure. The market for short-term home rental, as well as the entire tourism sector has turned upside down. Tourists are trapped in their homes and cancelled reservations, while short-term rental even became prohibited for around a month (from the 31.03 to 04.05), with the exception of certain groups (business travellers, medical personnel and people under quarantine). As of today, the strictest restrictions are slowly lifted in many countries, but the chances of open borders, quick trips and cheap flights remain low.
In an exciting post (avaialble in Polish) Łukasz Nawaro showed that a considerable share of Airbnb hosts has already decided to pivot to the long-term rental market. We can also expect that more and more flats from Airbnb will re-appear on the real-estate market.
What was the state of the market at the end of March? We checked to what extent hosts adjusted their offers to the changing conditions. A previous experiment from 2019 was repeated between the 27-29 in March: using web-scraping, we collected offers for 2 persons for a stay in the period 17-19 in April. Our sample from 2019 concerns stays for the period 12-14 in April. The collected prices refer to the full cost of the accommodation, including the service and cleaning fees.
Did prices change with the dramatic shift in demand? Table 1. presents the number of the analysed offers, along with statistics on the price distribution. A decrease of prices can be observed: in relation to the previous year, the median price has decreased by 8%. Comparing the samples to each other (Figure 1), it can be also noted that the distribution of the 2020 prices (red) is shifted downwards in relation to the 2019 rates. The values of the first quartile (25% of offers has same or smaller price), median and third quartile are all lower. However, the average price has decreased by a lower extent due to the stronger presence of high-end offers (listings costing 2000 PLN for a weekend).
Tabel 1. Price statistics for offers in 2019 and 2020 (in PLN)
| Year | Number | 1st quartile | Median | 3rd quartile | Average |
|---|---|---|---|---|---|
| 2020 | 2748 | 350 | 448 | 587 | 513 |
| 2019 | 2387 | 389 | 487 | 623 | 531 |
Figure 1. Price distribution
How did prices evolve in the various districts of Warsaw? Figure 2. introduces median prices in in the districts with at least 30 offers. The data suggests that a significant fall in prices occurred in the districts with the greatest density of Airbnb listings: City Centre (Śródmieście)- decrease by 13% and Wola – by 9%. On the other hand, in various districts prices did not change (Wilanów, Mokotów, Praga Południe) or even increased (Włochy).
Figure 2. Median prices in various districts
What is the strategy of the different types of hosts? We checked the listings offered by hosts owning:
- 1
- 2 or 3
- at least 4 listings (“professional” hosts)
and calculated the median prices for 2019 and 2020. The numbers show that the corresponding decrease of prices was at the level of 5%, 6%, and 13%. This suggests that the professional hosts adjusted their offers to the greatest extent.
Finally, 798 offers available at both time periods were identified in our samples. In the case of 358 listings, prices decreased (in the case of 237 by more than 10%), while in the case of 438 prices increased (for 153 by more than 10%).
Conclusions
By the end of March, Airbnb rates decreased by around 8%. This change is primarily driven by professional hosts owning flats in the city centre. In the districts characterized by a lower density of Airbnb offers and lower share of professional hosts, prices have not changed yet. This may suggest that non-professional hosts reacted slower or turned to the long-term real estate market.
All signs point towards a significant fall in the revenues from short-term home rental. This may be the first year since 2015 with a falling number of Airbnb listings, potentially increasing the number of available flats for local residents.


Climate crisis has the potential to change humanity and the natural world forever. As such, climate crisis demands our attention.
In 2017, multidisciplinary artist, Deborah Carruthers, served as the inaugural Artist in Residence at the Peter Wall Institute for Advanced Studies, University of British Columbia (UBC). Carruthers teamed up with science researchers at the institution as well as the UBC School of Music to create an interdisciplinary work combining sound, science, and visual art for the purposes of increasing public awareness of the climate crisis and inciting action.
Carruthers found inspiration for the project on an earlier research trip to Athabasca Glacier in the Canadian Rockies. The artist was determined to experience glaciers firsthand, motivated by their importance to our living planet; glaciers provide fresh water for irrigation and domestic use, sustain animal and plant life, and determine the level of earth’s oceans. Scientists continue to monitor glacial retreat or melting to determine the impact of atmospheric greenhouse gases released as a result of human activity. Athabasca Glacier is currently melting at a rate of over five meters a year.
Lying on her back on top of the ice field, Carruthers listened to the voice of the glacier – the natural environment underneath her, above her, and around her, capturing initial experiences through the lens of her camera and notetaking.
The result of Carruthers’ concern for the natural environment, the field trip to Athabasca, and tenure as a Wall Scholar is ‘slippages’– a 27-page graphic score with an accompanying streaming visual component. For the unfamiliar, graphic scores use visual symbols to represent music rather than traditional music notation. Because of their emphasis on the visual, graphic scores are frequently considered works of art in and of themselves.


This document discusses how to create a Magic Lantern title using the Magic Lantern Java2D Studio from Wizzer Works. These instructions assume that you are familiar with the Eclipse Integrated Development Environment or IDE upon which the Magic Lantern Studio is based. This version of the Magic Lantern Java2D Studio, or The Studio, is built upon version 4.16 of Eclipse.
The document first discusses how to launch The Studio using the Windows or Linux desktop shortcut. It then identifies how to create a project using a simple HelloWorld template. The guide will also address how to test and debug your application within the context of the IDE. Finally, this document presents a mechanism for bundling the Java2D application for distribution.
Launching the Magic Lantern Studio
Creating a Magic Lantern Project
Title Structure
Title Mastering Process
Executing the Title
Executing a Title from the Magic Lantern Studio
Creating a Launch Configuration
Stopping Title Execution
Executing a Title from the Title Application Bundle
Debugging the Title
Bundling the Title for Distribution


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What is sharepoint ?
Microsoft SharePoint is a browser-based collaboration and document management platform from Microsoft - Wikipedia. Microsoft's content management system.
It allows groups to set up a centralized, password protected space for document sharing.
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What is Microsoft SharePoint Used For?
Over three quarters of Fortune 500 companies use SharePoint, but they’re not all using it for the same things. The tricky thing about explaining what SharePoint does is that it isn’t any one software program but rather a platform for several different kinds of programs.
SharePoint isn’t something you buy and install on your own desktop but rather a back-end system that ties all your employees’ PCs and mobile devices together, allowing them all to communicate and synchronize their efforts.
The basic goal is to make it possible for a company with a hundred employees spread all over the region to work with the same level of agility and coordination as a company with ten people working out of a single office.
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What is SharePoint in Office 365?
SharePoint Online is a cloud-based service that helps organizations share and collaborate with colleagues, partners, and customers.
With SharePoint Online, you can access internal sites, documents, and other information from anywhere—at the office, at home, or from a mobile device.


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