Share your #CoEpi status today to save a life from Coronavirus
Late Saturday evening, Leap Day Feb 29, 2020, we learned from the genetic sequence of their coronaviruses that someone north of Seattle who was just diagnosed the day before with COVID-19 had (with high probability) a virus directly descended from that of the first Washington case, sampled Jan 19. The same day, we learned that another person just south of Seattle died of COVID-19, and that dozens of others are likely infected there. Since then, public officials have been uncovering more and more previously hidden cases, and will be discovering far more in coming days.
This outbreak has been spreading exponentially undetected through our community for 6 weeks now, and epidemiological estimates put the total likely infections in the Seattle area somewhere in the range of hundreds to thousands.
This is an example of something that emerges from the unforgiving math of exponential growth of a deadly disease outbreak like this: every single case, if left undetected and unchecked, spreads to hundreds of people after a few generations, thousands after a couple more, and tens of thousands by about a dozen. And COVID-19 has an infection fatality rate somewhere around 0.5% for people with well-functioning non-overloaded health care systems, so someone is likely to die for every 200 people infected.
What now?
What that means for each of us is that there is no time to wait: we must stop as many of the chains of transmission of the COVID-19 coronavirus as possible today.  If our communities don't take appropriate social distancing measures (shutting down schools, canceling nonessential meetings and travel, having everyone nonessential work remotely, etc.) until it's too late, every chain of transmission stopped today will save a life in about 1-2 months, and every infection allowed to occur will (on average) likely kill someone.
If our local public health officials take the threat seriously and immediately start canceling everything they can and testing and isolating all contacts of suspected cases, things won't get worse quite as fast. As shown by Singapore's rigorous contact tracing and social distancing measures, it's possible to bring down the number of people infected on average by each case, called R, from the virus's natural level (R0) of ~2.5 to something closer to 1.1. So far, only China has managed to bring R down below 1, by completely locking down entire cities and provinces.
Even just slowing the outbreak is tremendously valuable: if too many people get infected too fast, as occurred in Wuhan and is now occurring in Iran and possibly parts of Italy, health systems will be strained past their capacity and not everyone with moderate illness will be able to get the care they need, and some of those will progress to critical condition and die who wouldn't have otherwise. In such a situation, the case fatality rate can appear (and be) much higher: Wuhan measured a 2% death rate, and likely only missed about half the total infections, meaning that twice as many infected people likely died there as are currently dying in places like Korea and Singapore (closer to 0.5%).
It's likely that a vaccine and/or effective therapeutics will be developed and tested against COVID-19, but it's also likely that it'll take a year (or anywhere from 6 months to 2 years) for an effective prophylactic countermeasure to come into widespread use and further slow down transmission enough to force the outbreak to die down. During that time, our only defenses are non-pharmaceutical interventions (NPI): testing, isolation, quarantine, awareness, and social distancing to limit spread.
So, what can you do?
To start with, follow all the advice being given by local public health officials, and: wash and sanitize your hands frequently, don't touch your face in public, stay home and avoid close contact with family if you're sick, work from home and cancel all nonessential travel and meetings if you can, don't shake hands, avoid being within 6 feet of anyone coughing, avoid close face-to-face conversations entirely (sit/stand farther apart), and sanitize surfaces frequently.
But you can (and must) do more. In order to maintain such constant vigilance over the long term, and be able to relax it only when it's safe to do so, we all need to know how potentially infectious all of our contacts are, so we can practice extra care and avoid participating in one of those transmissions that will eventually kill someone vulnerable.
How?
We (a rapidly growing diverse team, with experience building communities to create open-source digital health tools, rapidly building mobile apps using location and proximity technologies, rapidly scaling cloud services, and fighting infectious disease through the healthcare and public health systems) have started a project we call CoEpi, short for Community Epidemiology in Action. We're working on a mobile app that will make it easy for everyone to privately track who they've been in contact with, and anonymously share reports and update on their own symptoms and likely contagiousness, whether they have a cold, flu, COVID-19, or any other contagious disease. It's not ready yet, but we're trying to build it as fast as we can. (If you or anyone in your network might be able to help, we could use the assistance.)
But #WeCannotWait - you need to start now
But even if we get an app together in record time, it will be too late to save some of the lives we could be saving today. So we need your help to implement #CoEpi, manually and socially.
 They need to know when you first came down with symptoms, when you have a record of interacting with them face to face (look at your calendar, Google Maps timeline, ask your family, or whatever you need to do to figure it out), and what the progression of symptoms was. (Did you get a sore throat, congestion, or a cough first? Did you develop a fever? Anything else?) Use a spreadsheet (Google Sheets can be shared with your contacts), write everything down, or otherwise make sure you don't forget the important details. Keep your contacts posted as those details change, so they know what to expect.
If you're not experiencing symptoms, ask everyone you talk to what their #CoEpi status is. If any of your contacts were symptomatic while, or within a day or two after, you saw them face to face, be on the lookout for similar symptoms yourself a few days later, and start being extra careful about hygiene and personal space even before you feel anything.
Spread the word
And if you're willing to help spread the word, you can help protect yourself by announcing your #CoEpi status on social media and asking all your contacts to do the same. Every one of your friends and coworkers who practices good #CoEpi communication and transmission-prevention behaviors lowers the risk that you personally will catch COVID-19, and that you might pass it along in a long chain that eventually ends up killing someone in your community's parent, grandparent, or vulnerable relative.
We need the message and practices of #CoEpi, of community epidemiology and social-network hygiene, to spread virally, and faster than this COVID-19 coronavirus, so we can slow it down long enough to keep too many people from dying before a vaccine or prophylactic drugs get into mass production and distribution.


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LAST_ITEM
- Static variable in interface it.essepuntato.earmark.core.
EARMARKPropertyURI
LaunchTests
- Class in
it.essepuntato.earmark.core.test
The class running the entire test suite.
LaunchTests()
- Constructor for class it.essepuntato.earmark.core.test.
LaunchTests
LIST
- Static variable in interface it.essepuntato.earmark.core.
EARMARKClassURI
List
- Class in
it.essepuntato.earmark.core
List()
- Constructor for class it.essepuntato.earmark.core.
List
LIST_ITEM
- Static variable in interface it.essepuntato.earmark.core.
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- Class in
it.essepuntato.facility.list
A class defining facilities for lists.
ListFacility()
- Constructor for class it.essepuntato.facility.list.
ListFacility
load(File)
- Static method in class it.essepuntato.earmark.core.
EARMARKDocument
This method loads an EARMARK document from either an RDF/XML or a Turtle representation of it given through a file.
load(String)
- Static method in class it.essepuntato.earmark.core.
EARMARKDocument
This method loads an EARMARK document from either an RDF/XML or a Turtle representation of it given through a string.
load(URL)
- Static method in class it.essepuntato.earmark.core.
EARMARKDocument
This method loads an EARMARK document from either an RDF/XML or a Turtle representation of it given through an URL.
load(Model)
- Static method in class it.essepuntato.earmark.core.
EARMARKDocument
This method loads an EARMARK document from a Jena model of it.
LoadTest
- Class in
it.essepuntato.earmark.core.test
A test aims to check the loading of EARMARK documents.
LoadTest()
- Constructor for class it.essepuntato.earmark.core.test.
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An R Package for Making a Project Computationally Reproducible Before Sharing it
Author: Jae Yeon Kim
File an issue if you have problems, questions or suggestions.
## Install the current development version from GitHub devtools::install_github("jaeyk/makereproducible", dependencies = TRUE) library(makereproducible)
make_here
Creating self-contained projects is great to increase research reproducibility and make your life easier. To make a project self-contained, one of the things you need to do is STOP using setwd()
and START using alternatives like here()
. The problem is people, including, and especially me, are lazy. We know the best practice, but we don’t follow it.
The make_here()
function nudges you to make your project self-contained before sharing it with other people. It does this by saving typing time to turn the relative or absolute path in your R code into the arguments in the here()
function.
To learn how to use it, see the examples below.
read_csv("/home/jae/validating-two-linked-fates/raw_data/sub-data.txt")
read_csv(make_here("/home/jae/validating-two-linked-fates/raw_data/sub-data.txt"))
Only two conditions are needed to make the make_here()
function work.
foo
) and divided different types of files into different subdirectories (data
, R
, etc.).here
package:
.git
.).here
in the main project directory (minimal effort).here::dr_here()
helps to check whether these two conditions were met. If setup correclty, you should be able to see something like the following: here() starts at [your main project directory], because it [satisfies one of the conditions listed above].


The 1st Spanish Young Statisticians and Operational Researchers Meeting (SYSORM) will take place in Granada in the 13th-15th of November 2017. The meeting, held under the auspice of the Spanish Society of Statistics and Operations Research (SEIO), is organised for and by young researchers (young: with less than three years of postdoctoral experience).
The scope of the meeting is to represent and give visibility to the newer generations of talented researchers in statistics and operations research, ranging from methodological to applied research topics, and to foster professional communication between them.
The meeting gathers four renowned plenaries, 30 invited speakers and allows for a limited number of contributed talks. Every participant is expected to submit an abstract of his/her talk in English and to give a presentation on his/her research field to a wide audience. There will not be parallel sessions and listeners will be allowed.
If you are a young researcher in statistics or operations research, then you have to be at SYSORM 2017. Either as a speaker or as an attendee, you will find this meeting as one of the conferences you will make the most of it, both scientifically and socially.
Contact e-mail<EMAIL_ADDRESS>https://congresos.ugr.es/sysorm17/


Do co-ops need more support? Do you?
Co-ops are all about working together, but good help is hard to find.
CoLab is working with the International Cooperative Alliance to prototype a way of connecting co-ops, capital, and support. In talking with dozens of co-op stakeholders in North America, Europe, and Southeast Asia, we’ve learned co-op entrepreneurs struggle to use capital strategically. Our research points to two reasons: no real culture of risk-taking, and no ecosystem of mentorship.
To get updates on our project to connect co-ops, capital, and support, fill in the form below:


{% extends 'layouts/application.html' %} {% from 'macros/helpers.html' import sprite %} {% import "items/cards/card.html" as cards %} {% block content %}


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Welcome to the YorkTest app
Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like)
Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like)
Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text .
What do you want to do?
My test results
[Name]
Gluten is a general name for the proteins found,Gluten can be found in many types of foods.


Hi! I'm
Predicate
, and I like to tinker with the WoW UI. I also created a tool called
AddonDoc
, which I use to automatically generate
documentation for my addons
.


The girl I love…
I love you more than I can tell,
Be happy this New Year, my belle.


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{{< layout}} {{$title}}Delivering your {{^values.veteran}}old passport{{/values.veteran}}{{#values.veteran}}passports{{/values.veteran}}{{/title}} {{$header}}
We’ll send your old and new passports separately and from different locations.
Both passports will be sent by special delivery to: {{/values.veteran}} {{^values.veteran}}
We recommend that you have your old passport returned by special delivery ({{#currency}}3{{/currency}} extra).
{{#radio-group}}secure-return{{/radio-group}}We’ll send your old passport to: {{/values.veteran}} {{values.address1}}, {{#values.address2}} {{values.address2}}, {{/values.address2}} {{values.town}}, {{values.postcode}}.
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--- layout: post title: no title date: '2014-09-29T07:30:38+02:00' categories: - Sketches - Sketchbooks picture_url: https://film-Storyboards.tumblr.com/post/98719865712 ---


My sister lives in the United States (7 words -> 7 terms)
2017-03-07
last modified: 2023-04-10
== !
I organize online workshops and personalized trainings for Gephi, for beginners and experts. To schedule one or to get more information<EMAIL_ADDRESS>This tutorial explains how to draw "semantic networks" like this one:
We call "semantic network" a visualization where textual items (words, expressions) are connected to each others, like above.
We will see in turn:
why are semantic networks interesting
how to create a semantic network
tips and tricks to visualize semantic networks in the best possible way in Gephi
Tip | All the operations described in this tutorial are now available as a click-and-point, free web application developed by the author of this tutorial. Upload a text file and get a semantic network in a few seconds. You can then open and explore the network in Gephi. Visit this online service here. |
A text, or many texts, can be hard to summarize.
Drawing a semantic network highlights what are the most frequent terms, how they relate to each other, and reveal the different groups or "clusters" they form.
Often, a cluster of terms characterizes a topic. Hence, converting a text into a semantic network helps detecting topics in the text, from micro-topics to the general themes discussed in the documents.
Semantic networks are regular networks, where:
nodes are words ("USA") or groups of words ("United States of America")
relations are, usually, signifying co-occurrences: two words are connected if they appear in the same document, or in the same paragraph, or same sentence… you decide.
It means that if you have a textual network, you can visualize it with Gephi just like any other network.
Yet, not everything is the same, and this tutorial provides tips and tricks on why textual data can be a bit different than other data.
The starting point can be: a term is a single word. So in this sentence, we would have 7 terms:
My sister lives in the United States (7 words -> 7 terms)
This means that each single term is a meaningful semantic unit.
This approach is simple but not great. Look again at the sentence:
My sister lives in the United States
My
, in
, the
are frequent terms which have no special significance: they should probably be discarded
United
and States
are meaningful separately, but here they should probably be considered together: United States
lives
is the conjugated form of the verb to live
. In a network, it would make sense to regroup live
, lives
and lived
as one single node.
Analysts, facing each of these issues, have imagined several solutions:
To remove these little terms without informational value, the most basic approach is to keep a list of them, and remove any word from the text which belongs to this list.
You can find a list of these useless terms in many languages, called "stopwords", on this website.
So, United States
should probably be a meaningful unit, not just United
and States
.
Because United States
is composed of 2 terms, it is called a "bi-gram".
Trigrams are interesting as well obviously (eg, chocolate ice cream
).
People often stop there, but quadrigrams can be meaningful as well, if less frequent: United States of America
, functional magnetic resonance imaging
, The New York Times
, etc.
Many tools exist to extract n-grams from texts, for example these programs which are under a free license.
Another approach to go beyond single word terms (United
, States
) takes a different approach than n-grams. It says:
"delete all in the text except for groups of words made of nouns and adjectives, ending by a noun"
→ (these are called, a bit improperly, "noun phrases")
Take United States
: it is a noun (States
) preceded by an adjective (United
). It will be considered as a valid term.
This approach is interesting (implemented for example in the software Vosviewer), but it has drawbacks:
you need to detect adjectives and nouns in your text. This is language dependent (French put adjectives after nouns, for instance), and the processing is slow for large corpora.
what about verbs, and noun phrases comprising non adjectives, such as "United States of America"? These are not going to be included in the network.
live
, lives
, lived
: in a semantic network, it is probably useless to have 3 nodes, one for each of these 3 forms of the same root.
Stemming consists in chopping the end of the words, so that here, we would have only live
.
Lemmatization is the same, but in a more subtle way: it takes grammar into account. So, "good" and better" would be reduced to "good" because there is the same basic semantic unit behind these two words, even if their lettering differ completely.
A tool performing lemmatization is TextGrid. It has many functions for textual analysis, and lemmatization is explained there.
We have seen that some words are more interesting than others in a corpus:
stopwords should be removed,
some varieties of words (lived
, lives
) could be grouped together (live
).
sequences of words (baby phone
) can be added because they mean more than their words taken separately (baby
, phone
)
Once this is done, we have transformed the text into plenty of words to represent. Should they all be included in the network?
Imagine we have a word appearing just once, in a single footnote of a text long of 2,000 pages. Should this word appear? Probably not.
Which rule to apply to keep or leave out a word?
A starting point can be the number of words you would like to see on a visualization. A ball park figure is 300 words max:
it already fills in all the space of a computer screen.
300 words provides enough information to allow micro-topics of a text to be distinguished
More words can be crammed in a visualization, but in this case the viewer would have to take time zooming in and out, panning to explore the visualization. The viewer transforms into an analyst, instead of a regular reader.
If ~ 300 words would fit in the visualization of the network, and the text you start with contains 5,000 different words: which 300 words should be selected?
To visualize the semantic network for a long, single text the straightforward approach consists in picking the 300 most frequent words (or n-grams, see above).
In the case of a collection of texts to visualize (several documents instead of one), two possibilities:
Either you also take the most frequent terms across these documents, like before
Or you can apply a more subtle rule called "tf-idf", detailed below.
The idea with tf-idf is that terms which appear in all documents are not interesting, because they are so ubiquitous.
Example: you retrieve all the webpages mentioning the word Gephi
, and then want to visualize the semantic network of the texts contained in these webpages.
→ by definition, all these webpages will mention Gephi, so Gephi will probably be the most frequent term.
→ so your network will end up with a node "Gephi" connected to many other terms, but you actually knew that. Boring.
→ terms used in all web pages are less interesting to you than terms which are used frequently, but not uniformly accross webpages.
Applying the tf-idf correction will highlight terms which are frequently used within some texts, but not used in many texts.
(to go further, here is a webpage giving a simple example: http://www.tfidf.com/)
So, should you visualize the most frequent words in your corpus, or the words which rank highest according to tf-idf?
Both are interesting, as they show a different info. I’d suggest that the simple frequency count is easier to interpret.
tf-idf can be left for specialists of the textual data under consideration, after they have been presented with the simple frequency count version.
We now have extracted the most interesting / meaningful terms from the text. How to decide which connections make sense between them?
Connections between terms are usually drawn from co-occurrences: two terms will be connected if they appear next to each other in some pre-defined unit of text:
in the same sentence
in the same paragraph
in the same document (if the corpus is made of several documents)
(note on vocabulary: in the following, we will call this a "unit of text").
For example, in bibliometrics (the study of the publications produced by scientists), this could give:
collect abstracts (short summaries) of all scientific articles discussing "nano-technologies".
so, abstracts are our units of text here.
two terms will be connected if they frequently appear in the same abstracts.
An edge between two terms will have:
weight of "1" if these two terms co-occur in just one unit of text.
weight of "2" if they co-occur in two units of text.
etc…
The logic is simple, and yet there are some refinements to discuss. It will be up to you to decide what’s preferable:
An example to clarify. Let’s imagine that we are interested in webpages discussing nanotechnology. We want to draw the semantic network of the vocabulary used in these web pages.
A co-occurrence is: when 2 terms are used on the same web page.
Among the pages we collected, there is the Wikipedia page discussing nanotechnology:
Nanotechnology ("nanotech") is manipulation of matter on an atomic, molecular, and supramolecular scale. The earliest, widespread description of nanotechnology referred to the particular technological goal of precisely manipulating atoms and molecules for fabrication of macroscale products, also now referred to as molecular nanotechnology
The question is:
should I count only one co-occurrence between molecular
and nanotechnology
, because it happened on this one web page? This is called binary counting
or should I consider that molecular
appears twice on this page, and nanotechnology
three times, so multiple co-occurrences between these 2 terms should be counted, just on this page already? This is called full counting
There is no exact response, and you can experiment with both possibilities.
Example:
Chun-Yuen Teng, Yu-Ru Lin and Lada Adamic have studied (using Gephi!) the pairing of ingredients in cooking recipes.
So, in their study the unit of text was the "recipe", and the terms in the semantic network where ingredients in all these recipes.
Just because they are so common, some ingredients (like flour
, sugar
, salt
) are bound to appear more frequently in the same recipes (to co-occur), than infrequent ingredients.
The authors of this study chose to highlight complementary ingredients: some ingredients appear often used together in the same recipes, even if they are ingredients which are quite rarely used.
"Complementary" here means that these ingredients have some interesting relationship: when one is used, the other "must" be used as well.
If we just count co-occurrences, this special relationship between infrequent complementary ingredients will be lost: by definition, 2 infrequent ingredients can’t co-occurr often.
To fix this, a solution consists in comparing how many times the 2 ingredients co-occur, with how frequent they are in all recipes:
→ ingredients co-occurring each and every time they are used will have a large edge weight,
→ ingredients co-occuring many times, but also appearing many times in different recipes, will get a low edge weight.
A simple formula does this operation. For ingredients A and B:
weight of edge between A and B = nb of recipes where A & B co-occur divided by (total nb of recipes where A appear x total nb of recipes where B appear)
Logs are often added to this formula, which is called "Pointwise mutual information":
We now have nodes and their relations: a semantic network. Let’s see now how to visualize it in Gephi.
We need a dataset to practice. This is a semantic network of 250 terms and 19,613 relations:
download this zip file and unzip it on your computer.
The network was built from the short summaries ("abstracts") of 1484 research articles from the PubMed database of scientific reports, retrieved by conducting this query:
"social neuroscience" OR "neuroeco*" OR "decision neuroscience"
→ The query can be seen at online here. (it comprises more than 1484 results, because some articles have no abstract).
We used Cowo to create the network from these 1484 short pieces of text, based on co-occurrences.
Open the file inside the zip (pubmed_abstracts_network.gml
) in Gephi:
Several steps will make this network intelligible
Showing the labels of the nodes:
Making the edges (relations) invisible, because they clutter the view
Reducing node size to the minimum (0.5) because we just need labels
Detect communities with the "modularity" function in the statistics panel
see tutorial 'simple project from A to Z' for this step
Give a different color to each community
→ each group of terms, distinguished by a color, will represent a topic.
We then need to assign this node color to their labels:
Spatializing the network with Force Atlas 2 will place related terms next to each other, because they co-occur:
Some parameters have been modified:
The network so far:
Apply 2 more layouts to enhance readability:
"Expansion" to spread nodes (just select it and click on Run a couple of times)
"Label Adjust" to move labels around so that they don’t overlap
Switching to the preview panel
A number of parameters must be modified (to show Labels, hide edges, etc.)
The network is now ready to be exported to pdf, png or svg file formats.
Other software / web apps to visualize texts as networks:
Visit the Gephi group on Facebook to get help,
or visit the website for more tutorials
Give a try to nocodefunctions.com, the web application I develop to create networks for Gephi. Click-and-point, free, no registration needed.


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3
Human Resource Policies
Annexure A
–
Acceptance of the terms and conditions of Assets Policy by a new user
To,
(NAME)
(DESIGNATION)
(COMPANY)
(PLACE)
Dear Sir,
Subject:
Declaration to accept the terms and condition
s stated under Company’s Assets
Policy
This is to inform you that I have read and understood the Asset Policy of our Company, Digital IP
Insights Pvt Ltd, and will abide by the same.
I declare that I will adher
e to the Company’s Assets
Policy in the manner as stated in the Policy.
Any negligence on my part regarding any clause of the policy shall be addressed as per the terms and
conditions mentioned in the policy.
Thanking you,
Name
:
Designation
:
Employee code
:
Date of issue
:


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AB&D Office / Solo Arquitetos
- Curated by Matheus Pereira
- about 14 hours
Offices Interiors · Bigorrilho, Brazil
- version="1.0" encoding="UTF-8"? Architects: Solo Arquitetos
- AreaArea of this architecture projectArea: 50.0 m2
- version="1.0" encoding="UTF-8"?YearCompletion year of this architecture project Year: 2019
- version="1.0" encoding="UTF-8"?PhotographsPhotographs: Eduardo Macarios
- Coordination João Gabriel Küster Cordeiro
- Woodwork Moldare Móveis
- Light Glass Partitions Esquadria Santos
- Construction Neuri Batista
- Production Elements Ôda Design Club, Heloísa Galvão, Alex Rocca
- Blinds Inovart Persianas
- Lighting E.LED Light Studio, DsgnSelo
- Furniture Inove Design, Bruno Faucz
- Artistic Tiling Rômulo Lass
More Specs Less Specs
Text description provided by the architects. AB&D’s new office emerges from the integration between two neighboring commercial rooms that were combined in a single space. The project was designed to express the personality of the practice and its brand, while still providing a comfortable working space for the team’s countless hours of work.
The office has a workspace, meeting room, reception and kitchen, all organized around the volume of bathrooms marked by the green color - the highlight of the project. In contrast to this strong compositional element, the material palette adopted is short and simple, with white elements, woodwork in Freijó timber and cementitious porcelain flooring. Another highlight is the artistic tiles by the local artist Rômulo Lass in the back wall of the kitchen.
The layout of the space is basically organized into two zones. On one side, the hall and meeting room welcome people when they arrive at the office, and, on the other, the workroom and pantry complete the program in a fully integrated way. The only compartment of the project is the meeting room due to the need for privacy of customers, such a point stands out because between this room and the volume of bathrooms ends up forming a small corridor marked by the linearity of the ceiling and its indirect lighting.


“The neohumanist paradigm seeks radical change, emancipation, and potentiality, and stresses the role that different social and organizational forces play in understanding change” Hirscheim and Klein
"a deliberation about the anticipated and desirable outcomes worth pursuing" Constantinides et al
Immateriality Bitner et al.,Vallgårda and Redström,Ozenc et al
Palpability Buscher et al
Processuality Bratteteig
Reflection-in-action...-on-action Schon
Sketching Craft and Cairns,Fallman,Arvola and Artman
Bricolage Louridas,Turkle
Ready-to-hand Heidegger,Dreyfus
Boundary, -objects, -spanners Star and Greisemer,Levina and Vaast
Order of worth, Community of practice Boltanski and Thevenot, Patriotta et al, Wenger
"transferring a design from designers to developers would be ...improved by ...a boundary object that allows [them] to engage in an effective conversation" Ozenc et al
| Dec 13 | Jan 13 | Feb 14 | Mar 14 | Apr 14 | Jun 14 | Jul 14 | Aug 14 | Sep 14 | Oct 14 | ... | ... |
| Method Review | Informal Recruitment | Audiovisual Transcription and Analysis #0
|
Recruitment - Users, Designers #1 | Audiovisual Transcription and Analysis #1
|
Recruitment - Users, Designers #2 | Audiovisual Transcription and Analysis #2
|
|||||
Exploratory End User Workshops
|
Firmware Blueprint position paper
|
||||||||||
| Design scope definitions and preparations#1 | Boundary Object Design #1 | Boundary Object Trial Workshops #1
|
Firmware Implementation and Testing #1 | Pilots; Instrument Designers case study
|
|||||||
| Design scope definitions and preparations #2 | Boundary Object Design Phase 2 | Boundary Object Trial Workshops Phase #2
|
Firmware Implementation and Testing#2 | Meta-design paper
|


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zxcvbn4Chrome
Password Strength Estimator |
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| Using personal information in the password make it very guessable and hence very weak. Filling information on this page will help zxcvbn4Chrome to estimate the password strength correctly when personal information is used in password. | |||
| * Please note, this information is stored locally in the browser's local storage and we do not share this information with any third party organization. | |||
| First Name: | |||
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| Options saved. |


Basile scores 19 to lift Wright St. past Urbana 88-51
DAYTON, Ohio (AP) - Grant Basile had a career-high 19 points off the bench to lead Wright State to an 88-51 win over Urbana on Wednesday night.
Cole Gentry had 13 points for Wright State (4-1). Loudon Love added 12 points.
Shaunn Monroe had 11 points for the Blue Knights.
Wright State faces Weber State on Monday.
For more AP college basketball coverage: https://apnews.com/Collegebasketball and http://twitter.com/AP_Top25
Elements of this story were generated by Automated Insights, http://www.automatedinsights.com/ap, using data from STATS LLC, https://www.stats.com
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


This project has been started with one goal, to help people. For us nothing is bigger than humanity and thus we are trying to initiate the courage and determination to the young blood of our country. Please help in our mission to help you.


Site under construction
Mandhri Dushyanthi Abeysooriya
Mandhri earned her Bachelor’s degree in a combination of biotechnology, biochemistry and genetics from Bangalore university, India in year 2017 and her Master’s degree in Biotechnology and Bioinformatics from Deakin university, Australia in 2021. Mandhri joined the Ziemann lab in 2020 to complete her Master’s research project to investigate the effect of Homocysteine on DNA methylation and gene expression. She was able to achieve distinction for her Master’s thesis project and was able to first author a manuscript entitled “Gene Name Errors: Lessons Not Learned”. During her Bachelor’s degree, she was also able to first author another publication related to microbiology under the title “A Study on VRSA Prevalence in Hospital Settings-Bangalore India". After her Bachelor’s she worked as a quality assurance intern in a leading pharmaceutical company in Sri Lanka. She was recently selected to do her PhD at Deakin University after completing her Master’s degree in July 2021 where she will be pursing her further studies in related to epigenetic mechanisms of sex differences in aging. As for leisure time activities, Mandhri likes to play chess and League of Legends (online game). She also enjoys cloud watching and listening to instrumental music.
Links
Kingsley Isaac Ogamanya
Kingsley earned his bachelor degree in biochemistry in 2018 from The Federal University of Technology in Nigeria. Kingsley joined the group in February 2021 to complete a masters project investigating the impacts of genetic variation among inbred mice on experimental findings. During his Bachelor’s degree, Kingsley co-authored a publication on Biochemical and liver histological changes in rats exposed to sub-lethal dose of Uproot-pesticide and the protective potentials of nutritional supplements. Kingsley will be completing his Masters project in November 2021 and will be looking for opportunities to start a PhD project or work as a bioinformatics or biotechnology research assistant in Australia or abroad. Kingsley loves the beach, dogs, and enjoys playing snooker.
Portfolio
Ujowundu CO, Ogamanya KI, Ujowundu FN, Adejoh VO, Iheme CI, Igwe KO. Biochemical and liver histological changes of Wistar albino rats exposed to uproot-pesticide and the protective potentials of nutritional supplements. J Appl Biol Biotech, 2020;8(04):026-032. DOI: https://dx.doi.org/10.7324/JABB.2020.80404
Contact
Twitter: @isaackingsley85
Linkedin: www.linkedin.com/in/kingsley-isaac-ogamanya-3b146a19b
Email: isaackingsley85[αt]yahoo.com
Aaron Kovacs
Aaron earned his Bachelor of Biomedical Science with Distinction in 2020 from Deakin University. Aaron joined the group in 2020 to undertake a placement with Mark Ziemann. During this placement, Aaron collaboratively completed a bioinformatics project which involved creating accessible gene signatures for a number of diseases including diabetes, epilepsy, SARS, SARS-CoV-2, MERS and heart disease. In 2021, Aaron began his Honours project investigating the role of epigenetic changes in contributing to phenotypic adaptations in guppies bred for many generations under altered light conditions. Aaron will be completing his Honours in November of 2021 and is interested in undertaking a PhD project in 2022.
Contact
email: akovacs[αt]deakin.edu.au


Level
Functions
Database
`available_studies()`
Study
`get_genetics_by_study()`
Molecular Profile
`get_genetics_by_study()`
Sample
`get_genetics_by_sample`


SCAPE Planning Suite - Feedback
Submit feedback
Dear user, please help us to improve Plato by sending us your ideas, comments and feedback.
Your message will contribute to the further development of the planning suite.
Thank you!


Returns a SaveableObject for saving/restoring iterator state using Saver.
tf.compat.v1.data.experimental.make_saveable_from_iterator
tf.compat.v2.data.experimental.make_saveable_from_iterator
tf.data.experimental.make_saveable_from_iterator(iterator)
iterator
: Iterator.A SaveableObject for saving/restoring iterator state using Saver.
ValueError
: If iterator does not support checkpointing.with tf.Graph().as_default(): ds = tf.data.Dataset.range(10) iterator = ds.make_initializable_iterator() # Build the iterator SaveableObject. saveable_obj = tf.data.experimental.make_saveable_from_iterator(iterator) # Add the SaveableObject to the SAVEABLE_OBJECTS collection so # it can be automatically saved using Saver. tf.compat.v1.add_to_collection(tf.GraphKeys.SAVEABLE_OBJECTS, saveable_obj) saver = tf.compat.v1.train.Saver() while continue_training: ... Perform training ... if should_save_checkpoint: saver.save()
Note: When restoring the iterator, the existing iterator state is completely discarded. This means that any changes you may have made to the Dataset graph will be discarded as well! This includes the new Dataset graph that you may have built during validation. So, while running validation, make sure to run the initializer for the validation input pipeline after restoring the checkpoint.
Note: Not all iterators support checkpointing yet. Attempting to save the state of an unsupported iterator will throw an error.


Summing up the relationship between these languages in a simple analogy would be the makeup of the human body. HTML represents the Skeletal structure of the body. It determines the rough form of what the body will look like but there is not much else more to it. CSS is the skin and exterior features of the body that establish the visual Identity.  Finally, JavaScript is the brain, and nervous system of the body that allows everything to work and interact together. The brain is constantly sending messages throughout the body to perform actions, much like JavaScript would on a website. Without it, the body would be rather lifeless and static.
Control flow simply put is the order in which code is executed based on what has been written. In its most basic form, code will tick off tasks line by line. However, there are many instances in which conditions are given that change the order and sequential structure of a code. For example, conditional statements such as If else statements require a certain condition to determine if a block of code is run or ignored. A comparable example of this in our day to day life is a doing the laundry. The task itself is straight forward but encounters a few conditions that will alter the process.
For example If the clothes being washed are delicates, run the delicate cycle on the washing machine, if not then run the standard setting. When it comes to loop conditionals, the weather could impact the drying process. While it is raining outside, put all clothes in the dryer and if the rain stops then the clothes can be hung outside to dry.
The DOM (Document Object Model) is the structural interface for your HTML document. The main purpose of the DOM is to make an HTML structure accessible for other programming languages such as JavaScript. It provides a family tree like view that shows the elements such as the root of the Document, Div Containers, and all the HTML Elements.
In a more detailed look, you can access the attributes, classes, and content inside each element. By being able to access the DOM using other programming languages, the ability to make more complex and unique websites becomes much easier. A common use of DOM manipulation on a website is a click event. Because JavaScript can now access an element through the DOM, it can run blocks of code according to specific interactions with HTML elements (“if the user clicks this button, run this code”).
The key difference when accessing arrays or Objects are that when you access an object, you are accessing a property and receive a return value.
var menu = {
"steak" : {
"price" : 20,
"serves" : 1
}
}
menu.steak.price;
20
When accessing an array you either return the entire array, or access specific parts of an array through indexing.
var myArray = [“dog”, “cat”, “bird”]
myArray[2];
“bird”
In a more complex data structure. You will find Objects nested inside of other objects or Arrays. This makes accessing data slightly trickier requiring a combination of both methods to access the correct data.
var menu = [{"porkbelly" : {
"price" : 19,
"serves" : 2,
}},
{"steak" : {
"price" : 20,
"serves" : 1 }}
]
menu[1].steak.price
20
Functions are contained blocks of code that are run when the name of the function is called in. What is written inside of a function is completely up to the developer but what makes functions ‘functional’ is their ability to be called anywhere by simply writing their name “myFunction()”. What makes this beneficial is the time saved on rewriting the same code repeatedly, as well as saving frustration by not having to change 30 instances of the same code block when you make a tweak. Functions are your friend and could be the difference between 500 lines of code or 1000.
var shoppingList = [];
shoppingList.push("Lettuce");
console.log(shoppingList);
shoppingList.push("Ham");
console.log(shoppingList);
shoppingList.push("Cheese")
console.log(shoppingList);
var shoppingList = [];
function addGroceries(item) {
shoppingList.push(item);
console.log(shoppingList);
}
addGroceries("ham")


ORAJEL ANTISEPTIC RINSE FOR MOUTH SORES - hydrogen peroxide liquid
Church & Dwight Co., Inc.
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
Active ingredient Purpose
hydrogen peroxide 1.5% Oral debriding agent
Uses for temporary use in cleansing minor wounds or minor gum inflammation resulting form:
minor dental procedures accidental injury
orthodontic appliances canker sores
dentures other irritations of the mouth and gums
Warnings
Do not use this product for more than 7 days unless directed by a dentist or doctor
When using this product do not swallow
Stop use and see your dentist or doctor promptly if swelling, rash or fever develops;' irritations, pain or redness persists or worsens; sore mouth symptoms do not improve in 7 days
Directions remove imprinted safety seal from bottle cap; to remove child-resistant cap, squeeze smooth sides of cap while turning. Reclose tightly. Ready to use, no mixing needed
Adults and children Swish one-half capful (2teaspoons - 10mL) around the
2 years of age and older mouth over the affected area for at least 1 minute and Then spit out. Use up to 4 times daily after meals and at bedtime or as directed by a dentist or doctor
Children under 12 year of age Should be supervised in the use of this product
Children under 2 years of age Consult a dentist or doctor
Other information cap tightly; keep away from heat or direct sunlight; do not use if safety seal is broken or missing
Inactive ingredients alcohol (4.1% by volume), blue 1, disodium EDTA, methylparaben, methyl salicylate, phosphoric acid, poloxamer 338, purified water, sodium saccharin, sorbitol
|
ORAJEL ANTISEPTIC RINSE FOR MOUTH SORES
hydrogen peroxide liquid | ||||||||||||||||||||||
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| Labeler - Church & Dwight Co., Inc. (001211952) |
| Establishment | |||
| Name | Address | ID/FEI | Business Operations |
|---|---|---|---|
| Accupac, Inc. | 071609663 | manufacture |


Penn Relays 20 km
Philadelphia PA/USA
Website: www.thepennrelays.com 1:21:12 1:28:20 1:14:09 15 Apr
1984
2:17:16
2:52:11
17 Apr
1983
2:26:00
2:54:52
18 Apr
1982
2:22:28
2:51:04
12 Apr
1981
2:17:34
2:54:55
20 Apr
1980
2:22:56
3:06:15
21 Apr
1979
2:22:14
3:07:21
24 Apr
1978
2:23:51
3:14:49
26 Apr
1977
2:21:13
3:10:07
20 Apr
1976
2:28:19
22 Apr
1975
2:27:32
3:37:31
23 Apr
1974
2:33:03
3:42:13
24 Apr
1973
2:32:07
4:00:34
Race Winners (40 runnings)
Notes: The inaugural year was 1973. The course started and finished at Plaisted hall, ran three loops of the Art
Museum and East River Dr., crossed the Falls Bridge, ran down the hill and returned to Plaisted Hall. The race
was shortened to 20 km for 1985 to date.
22 Apr
2012
1:04:53
Mike Rolek (NJ/USA)
Justyna Wilson (PA/USA)
17 Apr
2011
1:06:21
Brian Lang (PA/USA)
Jocelyn Sui-Lee Wong (DE/USA)
18 Apr
2010
1:05:02
John Butler (26,PA)
Carol Jefferson (MI/USA)
2009
---
n/a
---
n/a
20 Apr
2008
1:08:02
Pat Hayburn (PA/USA)
1:17:43
Andrea Niggemeier-2
22 Apr
2007
1:06:02
Christopher Nirschel (PA/USA)
1:16:35
Abby Deon (PA/USA)
23 Apr
2006
1:05:43
Todd Witzleben (NY/USA)
1:16:22
Andrea Niggemeier (GER)
17 Apr
2005
1:06:59
Fred Klevan (PA/USA)
1:20:24
Kelly Dworak (NY/USA)
18 Apr
2004
1:07:20
Paul Rades (MD/USA)
1:20:01
Veena Reddy (PA/USA)
13 Apr
2003
1:05:32
Matthew diPretore (PA/USA)
1:16:06
Lori Hageman-2
21 Apr
2002
1:05:16
Stephen Walsh (PA/USA)
1:14:52
Connie Buckwalter (PA/USA)
22 Apr
2001
1:06:04
Wally Dietrich (PA/USA)
1:20:13
Lori
Hageman (NJ/USA)
16 Apr
2000
1:04:26
Jerry Lawson (FL/USA)
1:17:22
Jenae Strader (PA/USA)
1999
---
n/a
---
n/a
1998
---
n/a
---
n/a
1997
---
n/a
---
n/a
1996
---
n/a
---
n/a
1995
---
n/a
---
n/a
24 Apr
1994
1:04:18
Sydney Maree (PA/USA)
1993
---
n/a
---
n/a
1992
---
n/a
---
n/a
1991
---
n/a
---
n/a
22 Apr
1990
1:12:23
Veronique Collard (BEL)
1989
---
n/a
---
n/a
1988
---
n/a
---
n/a
1987
---
n/a
---
n/a
1986
---
n/a
---
n/a
1985
---
n/a
---
n/a
Robert Schlau (SC/USA)
Diane Miller-4
Vince Touey (USA)
Lena Hollmann-2
Jeffrey Brandt-2
Diane Miller-3
Terry Stanley (USA)
Lena Hollmann (SWE)
Jeffrey Brandt (PA/USA)
Diane Miller-2
Tim Backenstose (USA)
Diane Miller (USA)
Marty Sudzina-2
Ann Davis (USA)
Mark Stevenson (USA)
Gillian Adams-Horovitz (ENG)
Marty Sudzina (USA)
.
Art Hall (USA)
Eileen Disken-3
Dave Patterson (PA/USA)
Eileen Disken-2
Karl Thornton (PA/USA)
Eileen Disken (NJ/USA)
Most Wins
(1973/date)
Men
2
Jeffrey Brandt (PA/USA)
1980,1982
Women
4 Prize Money
Diane Miller (USA)
1979-80,1982,1984
No prize money has been awaded at this race.
Race Time Bias
(sec/km)Competitive Level
(1990-date)
Quality Performances
Men <2:182 <2:19 3 <2:20 3 Women No women under 2:50
Last Updated on 24 Apr 2012
Contributors: Dave Johnson, Chris Tatreau


--- layout: post title: Learn by teaching date: 2010-12-14 22:25:31.000000000 +02:00 categories: [] tags: [] status: draft type: post published: false post_id: learn-by-teaching meta: _edit_last: '1' author: login: admin email<EMAIL_ADDRESS>display_name: Radu first_name: '' last_name: '' ---
It might sound strange. But this is a great way to learn things in depth.


--- ---
The National Board for Higher Mathematics (NBHM) was formed by the Department of Atomic Energy to foster the development of higher mathematics in the country, help in the establishment and development of mathematics centres and give financial assitance to research projects and to doctoral and post-doctoral scholars. NBHM has recognised the Institute Library as the Regional Centre for Mathematics for the Southern region. This regional centre was set up in 1992 at JRD Tata Memorial Library.
The main aim of NBHM is to provide library facilities to research workers in mathematics and the faculty of the Universities and academic institutions for reference, to provide facility for xeroxing of research papers and inter library loan facility.
The amount sanctioned by NBHM is fully utilised for acquiring books and journals in Mathematics. JRD Tata Memorial Library is acting as a service library for all mathematicians of the Southern region.
|
|
||
| Indian Institute of Science | Contact the Administrator | Disclaimer |


Do you often feel suspicious that the posts in your favorite subreddit have a political slant that you just can't put your finger on?
Even worse, are you worried that the topic is slipping towards The Other party?
Are you looking for a safe echo chamber where everyone will agree with you?
Maybe you prefer a battleground to test your convictions and hone your debate skills?*
If you answered yes to any of these questions, you're in luck! I present: the HUAC 9000!
Color based on identified party leaning of sub (Red for Republican, Blue for Democrat). Hover over circles to see additional detail.
The HUAC 9000 trained on posts in the official Democrats and Republican subreddits. Once trained, it looked at the subreddits shown here to give a Democratic and Republican strength score to each post. Whichever party scored higher on that post 'won', and whoever won the most posts in each subreddit won it for that party.
Some interesting clusters showed up
In the lower left - Cats, Aww, and HumansBeingBros (a derivative of AnimalsBeingBros) form a triumvirate of the warm and fuzzies.
Towards the upper right, TodayILearned and AskScience (not far from Science itself), form a little informational corner.
Movies, Books, Videos, and music form a tight group of media consumption right at the center of the graph.
News was very low overall in the political intensity, almost the opposite of WorldNews. Consider which you prefer.
Both Sides Can't Stop Talking About Eachother
The Communism discussion subreddit (for communists, by communists) scores as Republican. Presumably, they are talking about the things that people on /r/Republican are worried about.
Conversely, The_Donald (a formerly satirical fan page for our current president) showed up as Democrat, presumably for the same reasons.
Take a look at the overlap in most topic words for each:
So there you have it
There's nowhere to hide from politics these days, not even when looking for fun outfits on MaleFashionAdvice. With the HUAC 9000, you can at least ensure its the right kind of politics.
Happy browsing.
Additional Detail about interpreting this chart
- 500 posts were analyzed, on a scale of 0 to 1 for Democrat and Republican
- A 500 would indicate that every post in the sample was identified as one party or the other with 100% confidence
- Init intensity is a rough measure of how political the subreddit is in general
- Trendline shows the average ratio of Rep score to Dem score, and indicates a small bias towards Republican. More detail on this will be in a forthcoming blog
- Score vs. Party determination
- Subreddits are charted on total Democrat and Republican confidences for each post
- Party leaning was based on the total posts which scored for one party or another, i.e. the stronger of the two
- This leads to edge cases where one party scored higher overall in confidence but lost in the absolute number of posts (basically where red circles are to lower than blue circles or vice versa)
- Backtested data - r/Democrat and Republican both scored higher than average for eachother (i.e. the Democrat subreddit had a strong strength score for Republican), indicating a high conversational overlap
For code used to generate this chart, as well as the data itself, see my GitHub.
 Nobody debates honestly on the internet.