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I am a paragraph AFTER the figure. I should not be included as the caption for the above figure.
But I am a caption for the figure above.
Some images use a special CSS class to wrap a fake browser around them:
And RST figures use a different syntax to define their custom CSS classes:


All this stuff was for fun (except Steam keys). But now we know:
Satisfactory will
be released to Early Access in
Epic Games store at March 19!
<- Press this to load ALL Simon data
For results there are different key counts,
pictures of Simon, key decrements, Epic keys (??) and
Steam keys. Simon says different phrases.


Balance Tracker
Your browser does not support HTML5 tags. Please use the last version of Chrome, Safari, Firefox or Opera.
Amount:
Date:
↑ Increment ↑
↓ Decrement ↓


Rachee Singh Travels to Google Networking Research Summit
Thanks to the support of Women for UMass Amherst, we have awarded out first Travel Grant! Rachee Singh received $500 to cover the costs of her visit to Google Networking Research Summit. Below is her impression of the event.
This February, Google organized a Networking Research Summit to engage with academics performing research in various areas of computer networking. My PhD advisor, Dr. Phillipa Gill, was invited to attend the summit to present recent work from our lab, Calipr at UMass Amherst. Google also invited a few PhD students and on my advisor’s nomination I received an invite to attend the Networking Research Summit at Google Mountain View!
The summit was a congregation of very well known and accomplished networking researchers. The discussion began with an outline of milestones in the development of Google’s networking infrastructure and the open challenges that they face. This spurred an engaging discussion where academics asked questions and gave ideas to solve the open problems. This style of understanding the practical problems and proposing solutions was the overall theme of the entire summit.
In the next two days, the essence of the bleeding edge of networking research was presented to an audience comprising of students, professors and Googlers through a series of talks and panel discussions. Talks by Google engineers and scientists were interspersed among talks by professors from various universities in the world.
The coffee breaks offered a great opportunity to meet Googlers who gave us a peek into the kind of problems they work on. I found this perspective unique and thought provoking. Also, being fairly new to the networking research community, I got the chance to interact with many PhD students from other institutions. Google had also organized a poster session for PhD students. I presented our work on PathCache, a network path prediction toolkit. People had great suggestions and feedback for us that we are actively working on.
The event was enjoyable and beneficial to me and I hope Google continues to organize it. I am also thankful to Women for UMass Amherst and CSWomen group for providing their generous financial support. It helped cover some of the travel expenses to attend this wonderful event!


Net Neutrality
The importance of freedom
March 21, 2015
Technology is only a child. And, as every kid, it is evolving to a more mature stage. At this phase, several subjects raise and need to be clarified for a brighter future. And, today, I chose to talk about a major concern on the table: Net neutrality.
Net neutrality concept states that internet service providers should enable access to all content and applications regardless of the source, and without favoring or blocking particular products or websites. That means that there cannot be any sort of traffic differentiation by user, content, platform, application or mode of communication.
The importance of it is huge. Net neutrality is what guarantees that public information that exists on the web has to be equally available to everyone. And that nobody can have privileged access to it over anybody else. It guarantees that we won't be overcharged to access any sort of public content, and that we won't be overcharged to have easier access to it. Access to information should be, by principle, everybody's right, indiscriminately.
Believe it or not, Brazil is pioneer and an example to be followed on this matter. It's been one year now that we had approved on congress the Brazilian Civil Right Framework for the Internet. This framework was built under a tripod: freedom, privacy and neutrality of the internet. Brazil was the first country in the world to have this kind of guidelines approved, and it was a big victory that this move was disassociated to the national telecommunication regulation organ and telecom companies in general.
US has just had approved its guideline on net neutrality. This is to be celebrated, no doubts. The biggest issue on that move is that these guidelines are under FCC. Even though they assure the level of neutrality needed, it seems a little uncomfortable to have it controlled by the telecommunication regulation organ. That's why it's vital that US citizens keep engaged and vigilant on this matter, to prevent that conflicts of interests might end up distorting this essential pilar for the future of the web.


--- layout: null section-type: about title: About --- ## Hi There! I'm Rich, currently working as a Python and Unity developer at Cisco in the UK. Software development Emerging technologies VR/AR Video Games All the above at once


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◆ WaypointList
| List<WayPoint> WaypointMover.WaypointList |
|
getset |
The documentation for this class was generated from the following file:


About The ANTLR Parser Generator
Check out the testimonials.
Excerpt from the preface of the ANTLR v4 book:
ANTLR is a powerful parser generator that you can use to read,
process, execute, or translate structured text or binary files. It’s
widely used in academia and industry to build all sorts of languages,
tools, and frameworks. Twitter search uses ANTLR for query parsing,
with over 2 billion queries a day. The languages for Hive and Pig, the
data warehouse and analysis systems for Hadoop, both use ANTLR. Lex
Machina uses ANTLR for information extraction from legal
texts. Oracle uses ANTLR within SQL Developer IDE and their migration
tools. NetBeans IDE parses C++ with ANTLR. The HQL language in the
Hibernate object-relational mapping framework is built with ANTLR.
Aside from these big-name, high-profile projects, you can build all
sorts of useful tools like configuration file readers, legacy code
converters, wiki markup renderers, and JSON parsers. I’ve built little
tools for object-relational database mappings, describing 3D
visualizations, injecting profiling code into Java source code, and
have even done a simple DNA pattern matching example for a lecture.
From a formal language description called a grammar, ANTLR generates a
parser for that language that can automatically build parse trees,
which are data structures representing how a grammar matches the
input. ANTLR also automatically generates tree walkers that you can
use to visit the nodes of those trees to execute application-specific
code.
There are thousands of ANTLR downloads a month and it is
included on all Linux and OS X distributions. ANTLR is widely used
because it's easy to understand, powerful, flexible, generates
human-readable output, comes with complete source under the BSD
license, and is actively supported.
ANTLR has contributed to the theory and practice of parsing including:
- linear approximate lookahead
- semantic and syntactic predicates
- ANTLRWorks
- tree parsing
- LL(*)
- Adaptive LL(*) in ANTLR v4 (paper coming soon)
Terence Parr is the maniac behind ANTLR and has been working on ANTLR since 1989. He is a professor of computer science at the University of San Francisco.


As an experienced ketamine user in the north of England, I had been finding the recent drought somewhat irritating (although it was nice to be able to use my nose again...)All sorts of dubious substances were being bandied about under the name 'k', and I had little to no confidence in any of them, so stayed away. A phone call from a trusted friend got me a bit excited - ''Kevin's back!' he said.
After rushing round there imagine my disappointment to hear everyone grumbling that this stuff was something other than ketamine, with a similar but 'different' effect. I had a look (and a little sniff) and realised it was MXE, a legal 'alternative' which I had tried a couple of times before, with unimpressive results. In the interests of science I decided to study the new drug more thoroughly, and so racked up a line of about 100mg (smaller than I'd make a line of k, I'd heard this stuff was quite strong), and of course, sniffed it.
After about 20 minutes I started to feel a bit strange, it came on much more gently than the white horse does and is a bit frustrating if (like me) you like that 'sliding towards a hole' feeling. Soon after that I was experiencing mild confusion, slight difficulties with motor functions as well as sensitivity towards light. So far, so-so.
About an hour after that I was offered another (slightly bigger) line, and out of boredom accepted. The effects were exactly the same, although a little more pronounced. As the night wore on I realised there was a mild stimulant action, as I didn't feel tired at all and I'm usually one for going home to bed! Over the whole night (from about 9pm - 6am) I sniffed about a gram in several small lines. I never experienced any other effects than the ones mentioned above. After the final line I would say the effects lasted for about 3 of 4 hours, lessening slowly as time went on.
After I went home and had a sleep, (which was difficult, the mephedrone-like buzz clung on and it was only when I closed my eyes that I experienced anything close to an interesting visual effect - lots of flickering)I awoke feeling a bit groggy (which is normal), I didn't have a 'hangover' but I did have difficulty with getting my words out. I also felt a bit low, but can't necessarily put that down to the MXE. One interesting thing I did notice was that throughout the night in question and the next day I had an almost constant need to urinate, then when I went to the toilet it wouldn't happen. This is a problem reported by many heavy k users, but one which I had never experienced myself. It would have put me off if I hadn't already decided that the drug was crap and not worth doing again.
I remain unimpressed with this drug for several reasons. The first is that it has (in my experience) none of the interesting qualities of ketamine - no real visuals, no psychic links, no out of body stuff, no feeling of movement, no hole to speak of. The confusion, physical difficulty and snot well known to all k users were there, but on their own they're not much fun. The second is the difficulty pissing, which I found really irritating. Another reason I don't like it is that it is being sold as k, although I suppose that's a problem with unscrupulous dealers rather than the drug itself.
After doing a bit more research about MXE I read that it lowers the breathing and the heart rate of the user, in contrast to ketamine which raises the heart rate and doesn't affect breathing. I find this quite worrying in a drug which can induce ketamine-like loss of consciousness, and this is the final reason why I won't touch the stuff again.
Unless I'm REALLY bored...
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.


Image Puzzle
Steps:
0
Time Taken:
secs
Re-arrange to create a picture like this.
Easy
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Instructions
Another Photo
About
Game Over!!
Bravoooo!!
As Usual You are AWESOME..!!!
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steps.
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ROBObutton - miniMAC Experiments Lab
ROBObutton for every insanely Social Network - Powered by
miniMAC
- COPYRIGHTS miniMAC


{% extends "test_data_show.html" %} {% block app_content %}
Are you sure you want to delete the test data?
This action cannot be undone.


| {$c{org}->generic_print_switch('Edit Assessment Groups', 'Publish Test Paper')} |
| $period |
| Year $year - $name |
| $name |
| Level Breakdown | ||
| Levels: $levels | ||
| Total Marks: $marks | ||
| Marks | Info | |
| $title | $marks | |
| -- $topic$ratiost | $marks | $ratio_marks |
| $level | $total | $percent% = $required |
+++
}
if($c{org}->is_generic_nctest)
{
$OUT.=<<"+++";
Press the publish button to finalize the test paper and start assigning students to it
+++
}
}


Pray About It
List below the people whose friendship has had a significant impact on your life. Can you see God at work in bringing such wonderful people across your path?
Acknowledge that each of these persons came into your life as a gift from God. Say a prayer of thanksgiving, giving God thanks for sending you these people as instruments of His friendship and love for you.
Act on It
Beside each description, write the name or initials of someone in your life who fits this category:
Someone who has been a covenant friend to you.
Take time to reach out to your covenant friend and let him or her know how meaningful the friendship has been to you.
Someone you’d like to develop a deeper and more meaningful friendship with.
Plan an outing or activity with this person to develop stronger ties.
Someone who needs a friend.
List below ways you can support and show care for this person.
Day 3: Love in Action
Read God’s Word
1 Now Naomi had a relative on her husband’s side, from the clan of Elimelech, a man of standing, whose name was Boaz.


Performance measurement
Image resolution
Canvas resolution
Create GL context
Upload texture
Pre-filter X (ms)
Pre-filter Y (ms)
Cubic filtering (ms)


Asleep Eidolon was known for their mermaid bjd dolls, but now, they release these latest beautiful fairies, Agnes, Sylph, and Freya. Kalika, Glitter, and Rogue are also available. Don't miss this chance!
Agnes Fullset
Set Content
DOLL Main Body(Assembled)
Make Up
Acryl eyes(random)
Outfit
Certificate
Cushion
Box
Option
Additional Hand Part
Skin Color
High Heel Leg
Body Type
Price: $360
Agnes
Set Content
DOLL Main Body(Assembled)
Make Up
Acryl eyes(random)
Outfit
Certificate
Cushion
Box
Option
Additional Hand Part
Skin Color
High Heel Leg
Body Type
Price: $280
Sylph Fullset
Set Content
DOLL Main Body(Assembled)
Make Up
Acryl eyes(random)
Outfit
Certificate
Cushion
Box
Option
Additional Hand Part
Skin Color
High Heel Leg
Body Type
Price: $350
Sylph
Set Content
DOLL Main Body(Assembled)
Make Up
Acryl eyes(random)
Outfit
Certificate
Cushion
Box
Option
Additional Hand Part
Skin Color
High Heel Leg
Body Type
Price: $280
Freya Fullset
Set Content
DOLL Main Body(Assembled)
Make Up
Acryl eyes(random)
Outfit
Certificate
Cushion
Box
Option
Additional Hand Part
Skin Color
High Heel Leg
Body Type
Price: $360
Freya
Set Content
DOLL Main Body(Assembled)
Make Up
Acryl eyes(random)
Outfit
Certificate
Cushion
Box
Option
Additional Hand Part
Skin Color
High Heel Leg
Body Type
Price: $280
Kalika Fullset
Set Content
DOLL Main Body(Assembled)
Make Up
Acryl eyes(random)
Outfit
Certificate
Cushion
Box
Option
Additional Hand Part
Skin Color
High Heel Leg
Body Type
Price: $615
Kalika
Set Content
DOLL Main Body(Assembled)
Make Up
Acryl eyes(random)
Outfit
Certificate
Cushion
Box
Option
Additional Hand Part
Skin Color
High Heel Leg
Body Type
Price: $425
Glitter Fullset
Set Content
DOLL Main Body(Assembled)
Make Up
Acryl eyes(random)
Outfit
Certificate
Cushion
Box
Option
Additional Hand Part
Skin Color
High Heel Leg
Body Type
Price: $630
Set Content
DOLL Main Body(Assembled)
Acryl eyes(random)
Certificate
Cushion
Box
Option
Hand Part
Skin Color
Make Up
Outfit
Price: $425
Rogue Fullset
Set Content
DOLL Main Body(Assembled)
Make Up
Acryl eyes(random)
Outfit
Certificate
Cushion
Box
Option
Additional Hand Part
Skin Color
High Heel Leg
Body Type
Price: $630
Set Content
DOLL Main Body(Assembled)
Acryl eyes(random)
Certificate
Cushion
Box
Option
Hand Part
Skin Color
Make Up
Outfit
Price: $425


{% block head %}
This is Braggi's Admin Dashboard
Here, you can make carry out some simple tasks without restarting the server.
Learn moreLoad and View all conversations carried out through Braggi's API
View ChatsDownload all conversations carried out through Braggi's API
Download ChatsThis is Braggi's Wiki for Developers
Here, you can find all information about the various tasks you can perfom using Braggi's Admin Dashboard
Get Started


Let suppose you have a clickstream data
and you store it in non-aggregated form.
You need to generate reports for your customers on the fly.
This is typical ClickHouse use case.
Most customers are small, but some are rather big.
You want to get instant reports even for largest customers.
Solution: define a sample key in your MergeTree table.
CREATE TABLE ... ENGINE = MergeTree
ORDER BY (CounterID, Date, intHash32(UserID))
PARTITION BY toYYYYMM(Date)
SAMPLE BY intHash32(UserID)
SELECT uniq(UserID) FROM hits_all
WHERE CounterID = 76543
AND EventDate BETWEEN '2018-03-25' AND '2018-04-25'
┌─uniq(UserID)─┐
│ 47362335 │
└──────────────┘
1 rows in set. Elapsed: 4.571 sec.
Processed 1.17 billion rows, 16.37 GB
(255.88 million rows/s., 3.58 GB/s.)
SELECT uniq(UserID) FROM hits_all
SAMPLE 1/10
WHERE CounterID = 76543
AND EventDate BETWEEN '2018-03-25' AND '2018-04-25'
┌─uniq(UserID)─┐
│ 4742578 │
└──────────────┘
1 rows in set. Elapsed: 0.638 sec.
Processed 117.73 million rows, 1.65 GB
(184.50 million rows/s., 2.58 GB/s.)
Must be:
— included in the primary key;
— uniformly distributed in the domain of its data type:
Bad: Timestamp;
Good: intHash32(UserID);
— cheap to calculate:
Bad: cityHash64(URL);
Good: intHash32(UserID);
— not after high granular fields in primary key:
Bad: ORDER BY (Timestamp, sample_key);
Good: ORDER BY (CounterID, Date, sample_key).
Sampling is:
— deterministic;
— works in a consistent way for different tables;
— allows to read less amount of data from disk;
SAMPLE 1/10
— select data for 1/10 of all possible sample keys;
SAMPLE 1000000
— select from about (not less than) 1 000 000 rows on each shard;
— you can use _sample_factor virtual column to determine the relative sample factor;
SAMPLE 1/10 OFFSET 1/10
— select second 1/10 of all possible sample keys;
SET max_parallel_replicas = 3
— select from multiple replicas of each shard in parallel;
-If
-Array
-ForEach
-Merge
-State
Example: sumIf(x, cond)
SELECT
uniqIf(UserID, RefererDomain = 'yandex.ru')
AS users_yandex,
uniqIf(UserID, RefererDomain = 'google.ru')
AS users_google
FROM test.hits
┌─users_yandex─┬─users_google─┐
│ 19731 │ 8149 │
└──────────────┴──────────────┘
SELECT
uniq(arr),
uniqArray(arr),
groupArray(arr),
groupUniqArray(arr),
groupArrayArray(arr),
groupUniqArrayArray(arr)
FROM
(
SELECT ['hello', 'world'] AS arr
UNION ALL
SELECT ['goodbye', 'world']
)
FORMAT Vertical
Row 1:
──────
uniq(arr): 2
uniqArray(arr): 3
groupArray(arr): [['hello','world'],['goodbye','world']]
groupUniqArray(arr): [['hello','world'],['goodbye','world']]
groupArrayArray(arr): ['hello','world','goodbye','world']
groupUniqArrayArray(arr): ['goodbye','world','hello']
... they may be combined
Example: sumArrayIf, sumIfArray.
... they may be combined
Example: sumForEachStateForEachIfArrayIfState.
Obtain Intermediate state with -State combiner;
Example: uniqState(user_id) AS state;
— it will return a value of AggregateFunction(...) data type;
— you can store them in tables;
— merge them back with -Merge combiner;
Example: uniqMerge(state) AS result;
SELECT
avg(x),
uniq(x)
FROM
(
SELECT 123 AS x
UNION ALL
SELECT 456
)
┌─avg(x)─┬─uniq(x)─┐
│ 289.5 │ 2 │
└────────┴─────────┘
SELECT
avgState(x),
uniqState(x)
FROM
(
SELECT 123 AS x
UNION ALL
SELECT 456
)
┌─avgState(x)─────┬─uniqState(x)─┐
│ C\0\0\0\0\0\0 │ \0▒�P���a� │
└─────────────────┴──────────────┘
SELECT
toTypeName(avgState(x)),
toTypeName(uniqState(x))
FROM
(
SELECT 123 AS x
UNION ALL
SELECT 456
)
FORMAT Vertical
Row 1:
──────
toTypeName(avgState(x)): AggregateFunction(avg, UInt16)
toTypeName(uniqState(x)): AggregateFunction(uniq, UInt16)
CREATE TABLE t
(
users_state AggregateFunction(uniq, UInt64),
...
) ENGINE = AggregatingMergeTree ORDER BY ...
SELECT uniqMerge(uniq_state)
FROM t GROUP BY ...
Main use case:
Incremental data aggregation
with AggregatingMergeTree table engine
of MATERIALIZED VIEW.
— versioning of state serialization format;
— identify the cases when different aggregate functions have the same state (sumState, sumIfState must be compatible);
— allow to create aggregation state with a function (now it's possible to use arrayReduce for that purpose);
— allow to insert AggregateFunction values into a table directly as a tuple of arguments;
— adaptive index_granularity;
By default, ClickHouse implements:
asynchronous, conflict-free, multi-master replication.
Asynchronous:
INSERT is acknowledged after being written on a single replica
and the replication is done in background.
Some replicas may lag and miss some data;
All replicas may miss some different parts of data.
By default, you have only eventual consistency.
You can enable strong consistency.
SET insert_quorum = 2;
— each INSERT is acknowledged by a quorum of replicas;
— all replicas in quorum are consistent: they contain data from all previous INSERTs (INSERTs are linearized);
SET select_sequential_consistency = 1;
— allow to SELECT only acknowledged data from consistent replicas
(that contain all acknowledged INSERTs).
You can simply increase max_memory_usage
Also you can enable aggregation with external memory:
max_bytes_before_external_group_by
distributed_aggregation_memory_efficient
— pointInPolygon;
— pointInEllipses;
— greatCircleDistance;
SELECT pointInPolygon((lat, lon),
[(6, 0), (8, 4), (5, 8), (0, 2), ...])
SELECT modelEvaluate('name', f1, ... fn)
AS ctr_prediction
https://www.altinity.com/blog/2018/1/18/clickhouse-for-machine-learning
How we can make it better:
— add simple regression models;
— train models in ClickHouse directly;
— online training of models;
— parametrized models (dictionaries of multiple models);
clickhouse-local tool
$ clickhouse-local \
--input-format=CSV --output-format=PrettyCompact \
--structure="SearchPhrase String, UserID UInt64" \
--query="SELECT SearchPhrase, count(), uniq(UserID)
FROM table \
WHERE SearchPhrase != '' GROUP BY SearchPhrase \
ORDER BY count() DESC LIMIT 20" < hits.csv
┌─SearchPhrase────────────┬─count()─┬─uniq(UserID)─┐
│ интерьер ванной комнаты │ 2166 │ 1 │
│ яндекс │ 1655 │ 478 │
│ весна 2014 мода │ 1549 │ 1 │
│ фриформ фото │ 1480 │ 1 │
│ анджелина джоли │ 1245 │ 1 │
Bonus: SELECT and process data from an offline server.
How we can make it better?
— add more supported formats for Date and DateTime values in text form;
— add formats like Avro, Parquet;
— customizable CSV format;
— "template" and "regexp" formats for more freeform data;
Web site: https://clickhouse.com/
Google groups: https://groups.google.com/forum/#!forum/clickhouse
Maillist<EMAIL_ADDRESS>Telegram chat: https://telegram.me/clickhouse_en and https://telegram.me/clickhouse_ru (now with over 1500 members)
GitHub: https://github.com/ClickHouse/ClickHouse/
Twitter: https://twitter.com/ClickHouseDB
+ meetups. Moscow, Saint-Petersburg, Novosibirsk, Ekaterinburg, Minsk, Nizhny Novgorod, Berlin, Palo Alto, Beijing, Sunnyvale, San Francisco...


Warning: HELB to publish names and photos of loan defaulters
by Miriam MwendeAll who have defaulted loans from 1975 to be on list of shame
The Higher Education Loans Board (HELB) has announced a new measure against all Kenyan beneficiaries who have defaulted on repayment from 1975.
In an undated notice seen by Pulselive.co.ke, HELB noted that it will begin placing the names and photos of all loan defaulters on the local dailies in the next one month.
"Please take not that the names and pictures of HELB loan beneficiaries who have defaulted repayment of the loan from 1975 to date shall be published in the leading newspapers after expiry of 30 days from the date of this notice and thereafter legal action may ensue against each defaulter as stipulated in the HELB act," the notice read in part.
Ignoring messages from HELB
HELB explained that the notorious defaulters had failed to respond to various attempts by the loans board to reach them.
"Some beneficiaries who are in default have not responded to previous communication addressed to them yet the funds received from loan repayment are used to support the current needy students therefore sustained default hinders funding of other deserving Kenyan youth.
"HELB wishes to notify the general public and loan defaulters that the HELB student loan is a government debt which should be repaid as per the terms and conditions on which it was advanced," the notice elaborated.


If you are operating a local business there is something you can do right now to gain an advantage over your competition. It is called schema markup. Taking advantage of this early on will benefit you. What is schema markup? Basically it is code you can add to your page so that search engines like Google and Bing can better identify the data and be more likely to serve that information up in SERPs.
There is a great article on KissMetrics about How to Boost Your SEO by Using Schema Markup. I suggest reading through that if you want to know more about how schema markup helps you and then come back to our page. I will show you how to quickly add some schema markup for a local business location to a page in WordPress.
There is a WordPress plugin out there called Schema Creator by Raven, however I was not happy with the results.  I needed to add location data, specifically the LocalBusiness attribute. So I asked my brother Brett who is a phenomenal PHP programmer and he whipped up something in a matter of minutes. So I thought I would share it with you as an alternative to using a plugin or simply pasting the HTML from a schema generator.
Step 1
First we are going to add a function for our schema itemtypes, specifically LocalBusiness and PostalAddress, to WordPress. So in your WordPress dashboard browse to Appearance and click on on “Editor.”
Step 2
Click into your WordPress functions.php file. (please don’t edit this file if you are unsure of what you are doing, or please make a backup first!)
Step 3
Now scroll to the bottom and we are going to add this code. What we are doing is creating a shortcode we can use later. Why do we do a shortcode? Simply because if you have multiple people editing sometimes they will switch out out of the text view and you lose your schema markup.
function shortcode_printaddress($atts) { $val.= '
'; $val.= ''; $val.= '
'; $val.= '' . $atts["name"] . ''; $val.= '
'; $val.= ''; $val.= '
'; $val.= ''; $val.= $atts["address1"]; if(!empty($atts["address2"])) { $val.= '
' . $atts["address2"]; } $val.= '
'; $val.= '' . $atts["city"] . ', ' . $atts["state"] . ' ' . $atts["zip"] . ''; $val.= '
'; $val.= '
'; return $val; } add_shortcode('printaddress', 'shortcode_printaddress');
Step 4
Now we can go to our page and add the shortcode. Obviously you will want to change the details below with your address.
[printaddress name="OKay Marketing" address1="8275 E Bell RD" address2="Suite 250" city="Scottsdale" state="AZ" zip="85260"]
The shortcode will display like our address below.
Suite 250
Scottsdale, AZ 85260
The reason we like this method better is because it is much easier to style, there aren’t any line breaks and to the general user, it looks just like a normal address listing.
Step 5
Now you will want to copy the URL of the page you have the shortcode on and go test it in the Google Structured Data Testing Tool.
At the very bottom you will want to verify that you have the localbusiness item type and that the address points to Item 1 which in turn points to Item 1 for the address.
There are many more things you could add too, like phone number, hours of business, etc. To read more about the available schema types visit the official schema website.


The World Puzzle Championship (WPC) is the World Championship Puzzle Competition run by the World Puzzle Federation and is held annually in a different country.
All the puzzles in the competition are designed to be playable regardless of language or culture. The first WPC was held in 1992 in New York City.
wpc-unofficial.org is an online resource for past and upcoming World Puzzle Championships. It is unofficial and non-profit, and does not hold any rights over its contents. The data is taken from various resources including WPF Newsletters.
It is a fork of ipho-unofficial.org. ipho-unofficial.org on Github.
The theme and design of the webpage is based on imo-official.org.
The theme and design of the webpage is based on imo-official.org.


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{{ define "main" }}
Hi, I'm Leon! I'm a machine learning engineer at Dexter Energy, working on forecasting renewable (solar/wind) electricity generation. Previously I was at Plumerai, researching binarized neural networks and working on Larq. Before that, I studied computer science at TU Delft (BSc) and artificial intelligence at the University of Edinburgh (MSc).
Use the links below to contact me in English or Dutch, or check out the rest of this site for my current projects, past projects, skills, and photography.
Here's what I'm working on, mostly in the form of side projects.
These are some projects I've done in the past, that are either finished or no longer active. There are {{ len (where .Pages "Section" "projects") }} projects, which broadly fall into categories like {{ $list := (.Site.Taxonomies.categories) }} {{ $len := (len $list) }} {{ range $key, $value := $list.ByCount }} {{ humanize .Name }} {{- if lt (add $key 2) $len }}, {{end}} {{- if eq (add $key 2) $len }}, and {{end}} {{- if eq (add $key 1) $len }}.{{end}} {{end}}
{{ range (where .Site.RegularPages "Section" "projects").GroupByDate "2006" }}Here are some of the things I have experience with. Each category is sorted by how many projects I've used the skill for, and each skill links to related projects.
Programming languages: {{ range .Site.Taxonomies.tags.ByCount }} {{ if hasPrefix .Page.Title "language" }} {{ replace (strings.TrimPrefix "language-" .Page.Title) "-" " " }} ({{ .Count }}) {{ end }} {{ end }}
Technical tools: {{ range .Site.Taxonomies.tags.ByCount }} {{ if hasPrefix .Page.Title "tool" }} {{ replace (strings.TrimPrefix "tool-" .Page.Title) "-" " " }} ({{ .Count }}) {{ end }} {{ end }}
Machine learning packages: {{ range .Site.Taxonomies.tags.ByCount }} {{ if hasPrefix .Page.Title "ml" }} {{ replace (strings.TrimPrefix "ml-" .Page.Title) "-" " " }} ({{ .Count }}) {{ end }} {{ end }}
Machine learning models: {{ range .Site.Taxonomies.tags.ByCount }} {{ if hasPrefix .Page.Title "model" }} {{ replace (strings.TrimPrefix "model-" .Page.Title) "-" " " }} ({{ .Count }}) {{ end }} {{ end }}
Web APIs: {{ range .Site.Taxonomies.tags.ByCount }} {{ if hasPrefix .Page.Title "api" }} {{ replace (strings.TrimPrefix "api-" .Page.Title) "-" " " }} ({{ .Count }}) {{ end }} {{ end }}
Creative + video skills: {{ range .Site.Taxonomies.tags.ByCount }} {{ if hasPrefix .Page.Title "skill" }} {{ replace (strings.TrimPrefix "skill-" .Page.Title) "-" " " }} ({{ .Count }}) {{ end }} {{ end }}
These are some of my favorite photo's I've taken over the years, mostly on family vacations with my Canon EOS 60D digital SLR.
You can see more of my photos on my Unsplash profile.


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|
Capturing the Friedmans: Will it change anything?
by Rob Waring
Capturing the Friedmans, now in theaters, is a likely Oscar
contender for best documentary. The film tracks the disintegration
of a family whose patriarch is arrested for child molestation.
Director Andrew Jarecki's skill makes the film an emotional roller
coaster for the viewer. After one interview, you believe one
interpretation of what happened, and a few minutes later you
find your loyalties shifting to the other side - just until the
next bombshell hits. (Another great documentary, American
Movie, toyed with the viewer's perspective on the subjects'
morality in much the same way.)
One
of the key questions raised by the film is the extent to which
films should serve as a means to correct injustice. If it is
assumed that the system is not perfect and that mistakes will
be made, do films about mistakes in specific cases serve some
broader purpose that makes the system better or do they simply
decrease people's confidence in the system without leading to
changes that increase the chances of better results? Should The
Innocence Project have a film division to spur change in the
criminal justice system through the political process? Is the
media so filled with scary stories about child molesters that
Capturing the Friedmans is unlikely to have any measurable
effect?
After the preview I attended,
the director answered questions from the audience. If you decide
to explore the issues in the film further with friends or in
discussion group, here are some thought-provoking facts he shared,
along with others gleaned from radio interviews that aired after
the film's release.
Jarecki started to make film
about clowns for hire at New York children's birthday parties.
He was many months into that project when he learned David's
family history and began to turn the film into a story about
the Friedmans. The turning point came when Jarecki took David
back to visit the family home in Great Neck, and the family now
living there let David spend some time in his old bedroom. David
began to open up to Jarecki after that, apparently realizing
that the story of the events in Great Neck needed to be told.
Susan Orlean, who wrote The
Orchid Thief, which became the movie Adaptation, wrote
a New Yorker article about David being the most popular children's
clown in New York City in 1994, and only learned that he was
the son of a convicted child molester after the article was published.
She said in a recent interview that because of David's family
history, she would not hire him to perform at a party for her
children. David also has a clown act for adult parties, where
he specializes in making balloons into genitalia. Should these
facts have been included in the film?
When I asked the director how
the making the film changed his view of the justice system, he
talked about the how the story was reported in the press at the
time, and how the reporting was so prejudicial. A lawyer who
was with me commented that the director had not answered my question.
But he did, I replied; the case was tried in the media. Once
the charges were brought, the community's reaction made the outcome
of the trial a foregone conclusion. Even the judge admits her
mind was made up from the Friedmans' first court appearance.
Jarecki regards each screening of the film as the trial the defendants
never had. He has a version five and a half hours long ready
for the DVD release.
The judge refused to grant
a change of venue motion, but did grant scores of press requests
for cameras in the courtroom. She remains absolutely convinced
that she did everything right in the cases shown in the film,
and has asked the director for a signed publicity poster for
the film. She attended early previews of the film before conducting
a hearing concerning Jesse's sex offender registration status.
Convinced that his insistence that he has not molested anyone
made him at risk of repeating his crimes, she assigned him to
the highest risk category upon his release from prison. It took
him six months to find a place to live because of fears about
his past. For the rest of his life, his movements will be constantly
monitored by an electronic bracelet. He cannot go to parks or
any place where there are children. He must be home at night
after 7 PM. He must attend twice weekly sex offender group therapy
where the entire subject of discussion is molestation. The judge
recently retired and teaches ballroom dancing on cruise ships.
Three friends of Jesse's were
prosecuted, convicted and sent to jail for the alleged molestations.
The police went to each boy in turn and told him that the others
had confessed and implicated him. Each refused to cooperate,
insisting on their innocence. Finally, the youngest was offered
a deal that he would be treated as a juvenile, serve six months
in a youth facility and have his record sealed if he testified
against the others. He agreed and testified as requested, but
then the judge rejected his deal and sentenced him as an adult
to two years. The young snitch appealed and the appellate court
ordered the judge to honor his deal. The others, who maintained
their innocence, received six years.
The pleas made by the defendants
occurred before two hung juries in the contemporaneous McMartin
day care center molestation case caused those charges to be dismissed.
Under a pseudonym, the postal
inspector wrote to Arnie, the father, many times over a two-year
period until Arnie finally sent him some child pornography as
requested.
Jarecki says everyone in the
film told him things he later discovered were not true, except
Jesse Friedman. Jesse says he hopes that the film will cause
some of the computer class students to recant their previous
statements implicating him, but so far that has not happened.
Posted August 12, 2003
|


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Australia’s inter-generational divide is centred on housing
by Unconventional EconomistTony Walker, columnist at Fairfax, has done a good job dissecting the resentment towards the baby boomer generation, which has ridden Australia’s long housing boom at the expense of their children and grandchildren:
A lucky generation in a lucky country in danger of scorching its dumb luck has had bestowed on itself the sort of once-in-a-hundred-year windfall that is unlikely to be repeated in all our lifetimes, Millennials included.
The housing boom is the Baby Boomer Klondike. I’m talking about a period of supercharged wealth accumulation that has produced the sort of intergenerational-riches bulge that has distorted the benefits of a record-setting economic expansion to the advantage of one generation and the disadvantage of others…
There are 1218 words left in this subscriber-only article.


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
real_input = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], name="real_input")
z_input = tf.placeholder(tf.float32, [None, z_dim], name="z_input")
learning_rate = tf.placeholder(tf.float32, name="learning_rate")
return real_input, z_input, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def leaky_relu(input, alpha=0.2, name="leaky_relu"):
return tf.maximum(input, alpha * input, name=name)
def discriminator(images, reuse=False, keep_prob=0.9):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
with tf.variable_scope("discriminator", reuse=reuse):
# convert 28x28x(input_dimension) into 14x14x64
layer1 = tf.layers.conv2d(images, 64, 5, 2, 'same', kernel_initializer=tf.contrib.layers.xavier_initializer(True))
layer1 = leaky_relu(layer1)
#layer1 = tf.nn.dropout(layer1, keep_prob=keep_prob)
#layer1 = tf.Print(layer1, [tf.shape(layer1)], "discriminator layer 1 shape", summarize=100)
# convert 14x14x64 into 7x7x128
layer2 = tf.layers.conv2d(layer1, 128, 5, 2, 'same', kernel_initializer=tf.contrib.layers.xavier_initializer(True))
layer2 = tf.layers.batch_normalization(layer2, training=True)
layer2 = leaky_relu(layer2)
#layer2 = tf.nn.dropout(layer2, keep_prob=keep_prob)
#layer2 = tf.Print(layer2, [tf.shape(layer2)], "discriminator layer 2 shape", summarize=100)
# removed a layer as suggested by reviewer
# convert 7x7x128 into 4x4x256
layer3 = tf.layers.conv2d(layer2, 256, 5, 2, 'same', kernel_initializer=tf.contrib.layers.xavier_initializer(True))
layer3 = tf.layers.batch_normalization(layer3, training=True)
layer3 = leaky_relu(layer3)
#layer3 = tf.maximum(alpha * layer3, layer3)
#layer3 = tf.nn.dropout(layer3, keep_prob=keep_prob)
#layer3 = tf.Print(layer3, [tf.shape(layer3)], "discriminator layer 3 shape", summarize=100)
# flatten to linear array
convolutions_output = tf.reshape(layer2, (-1, 4 * 4 * 256))
logits = tf.layers.dense(convolutions_output, 1) # 1 single output
output = tf.sigmoid(logits)
return logits, output
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True, keep_prob=0.5):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
reuse = not is_train
with tf.variable_scope('generator', reuse=reuse):
# transform input into a tensor of 4x4x256
layer1 = tf.layers.dense(z, 4 * 4 * 256)
layer1 = tf.reshape(layer1, (-1, 4, 4, 256))
layer1 = tf.layers.batch_normalization(layer1, training=is_train)
layer1 = leaky_relu(layer1)
#layer1 = tf.nn.dropout(layer1, keep_prob=keep_prob)
#layer1 = tf.Print(layer1, [tf.shape(layer1)], "generator layer 1 shape", summarize=100)
# convert 4x4x256 into 8x8x128
layer2 = tf.layers.conv2d_transpose(layer1, 128, 4, 1, 'valid', kernel_initializer=tf.contrib.layers.xavier_initializer(True))
layer2 = tf.layers.batch_normalization(layer2, training=is_train)
layer2 = leaky_relu(layer2)
#layer2 = tf.nn.dropout(layer2, keep_prob=keep_prob)
#layer2 = tf.Print(layer2, [tf.shape(layer2)], "generator layer 2 shape", summarize=100)
# convert 8x8x128 into 16x16x64
layer3 = tf.layers.conv2d_transpose(layer2, 64, 5, 2, 'same', kernel_initializer=tf.contrib.layers.xavier_initializer(True))
layer3 = tf.layers.batch_normalization(layer3, training=is_train)
layer3 = leaky_relu(layer3)
#layer3 = tf.nn.dropout(layer3, keep_prob=keep_prob)
#layer3 = tf.Print(layer3, [tf.shape(layer3)], "generator layer 3 shape", summarize=100)
# convert 16x16x64 into 32x32x(out_channel_dim)
logits = tf.layers.conv2d_transpose(layer3, out_channel_dim, 5, 2, 'same', kernel_initializer=tf.contrib.layers.xavier_initializer(True))
#layer4 = tf.Print(logits, [tf.shape(logits)], "generator logits shape", summarize=100)
output = tf.tanh(logits)
return output
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss).  Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
trainable_variables = tf.trainable_variables()
discriminator_variables = [variable for variable in trainable_variables if variable.name.startswith('discriminator')]
generator_variables = [variable for variable in trainable_variables if variable.name.startswith('generator')]
# choo chooo
discriminator_train = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=discriminator_variables)
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='generator')):
generator_train = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=generator_variables)
return discriminator_train, generator_train
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
image_width = data_shape[1]
image_height = data_shape[2]
image_channels = data_shape[3]
input_real, input_z, learning_rate_tensor = model_inputs(image_width, image_height, image_channels, z_dim)
discriminator_loss, generator_loss = model_loss(input_real, input_z, image_channels)
discriminator_training, generator_training = model_opt(discriminator_loss, generator_loss, learning_rate, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
iterations = 0
for batch_images in get_batches(batch_size):
batch_images = batch_images * 2.0 # normalize to -1, 1
iterations += 1
#batch_z_input = np.random.normal(0, scale=1.0, size=(batch_size, z_dim))
batch_z_input = np.random.uniform(-1, 1, size=(batch_size, z_dim))
network_feed_dict = {
input_real: batch_images,
input_z: batch_z_input,
learning_rate_tensor: learning_rate
}
_ = sess.run(discriminator_training, feed_dict=network_feed_dict)
_ = sess.run(generator_training, feed_dict=network_feed_dict)
if iterations % 10 == 0:
training_loss_discriminator = discriminator_loss.eval(network_feed_dict)
training_loss_generator = generator_loss.eval(network_feed_dict)
print("Iteration {:3} -- Epoch {}/{} -- Discriminator loss: {:5.5f} -- Generator loss: {:5.5f}".format(
iterations,
epoch_i + 1,
epoch_count,
training_loss_discriminator,
training_loss_generator
))
if iterations % 100 == 0:
show_generator_output(sess, show_n_images, input_z, image_channels, data_image_mode)
show_generator_output(sess, show_n_images, input_z, image_channels, data_image_mode)
print("Done!")
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 32
z_dim = 128
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


| IE6 | N/A |
|---|---|
| IE7 | N/A |
| IE8 | N/A |
| IE9 | Some Property CSS Not Supported |
| IE10 | Some Property CSS Not Supported |
| IE11 | Some Property CSS Not Supported |
| Edge | Supported |
| Opera | Supported |
| Safari | Supported |
| Firefox | Supported |
| Chrome | Supported |
| Android | Supported |
|---|---|
| iOS | Supported |
Core Version
| Version 1 | N/A | Removed |
|---|---|---|
| Version 2 | Navoon | Removed |
| Version 3 | Pop Green | Removed |
| Version 4 | Magnum | Removed |
| Version 5 | Neo | Developed [Create] 18/12/2017 |


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We see the Protagorean tradition as a many-colored multifaceted portrait gallery. We collect, edit and annotate these portraits: we aim to reconstruct historical representations of the person Protagoras, his thought and his (alleged) works: an edition of a network of “versions of Protagoras”. We focus on stories, notions and concepts attributed to or associated with Protagoras —a kaleidoscopic variety that we will try to do justice to in a digital edition of sources and their intricate relations.
Read more about our principles, mission statement and guidelines.


We got networks!
- synthesize seamless networks
- recontextualize holistic action-items
- grow vertical bandwidth
- matrix viral metrics
- morph holistic interfaces
- e-enable global deliverables
- syndicate proactive web-readiness
- innovate holistic e-tailers
- expedite intuitive synergies


Simple Toast Demo
Toast Message:
Toast Type:
Info
Success
Warning
Error
Options:
Auto Close
Duration:
Toast Is Closeable:
Closeable (
Prevents the autoClose property
)
Show Toast
Clear All Toast
Destroy Toast List


The tests in the scored section are independent of the type of the modeled organism, the complexity of the model itself or the types of identifiers that are used to describe the model components. Calculating a score for these tests allows for the quick comparison of any two given models at a glance. The unscored section provides general statistics and covers specific aspects of a model that are not universally applicable. For instance, dedicated quality control of the biomass equation only applies to metabolic models which are used to investigate cell growth.
The variety of constraints-based modeling approaches and the fundamental differences between various organisms compound the assessment of GEMs. For instance, authors may publish metabolic networks, which are constrained to reflect one experimental condition or publish unconstrained metabolic databases, which need to be initialized before simulation. Both can be encoded in SBML. With having a scored test section, we attempt to normalize each type of model such that they become comparable.
Each test provides a relative measure of completeness with regard to the tested property. The final score is the weighted sum of all individual test results normalized by the maximally achievable score i.e. all individual results at 100%. Individual tests can be weighted, but it is also possible to apply weighting to entire test categories. Hence the final score is calculated:
a, b denote section weights, w1, w2, w3, w4 denote weights applied to individual tests.


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vignettes/how-to-install.Rmd
how-to-install.Rmd
Also some package needs install from sources which needs compilation and it needs proper version of Rtools installed
It’s recommended to install devtools
, BiocManager
package first, to install following packages in Github / Bioconductor (not in CRAN).
As noted in README
CellEnrich
needs some packages to be installed before use it.
Bioconductor Packages
SingleCellExperiment
scran
Other Packages
waiter
ggbiplot
devtools::install_github('JohnCoene/waiter') devtools::install_github('vqv/ggbiplot')
After install these 3 packages,
running this Script will install most of dependent packages.
install_github('unistbig/CellEnrich') library(CellEnrich)
CellEnrich provides example dataset and gene-sets.
using this function, user can download them in current working directory.
CellEnrich::DownloadData()
Here are list of data.
Human Gene-sets
Mouse Gene-sets
Human Data
“pbmcClustInfo.RData”
“pbmcData.RData”
“koh.RData”
“kohInfo.RData”
Mouse Data


Working throughout the whole software development cycle – including, but not limited to, solution design, technical specification writing, coding, testing, deployment and ongoing maintenance – on new and existing Wholesale systems and applications.
-
Liaise with Business Systems and Applications team members, other HLO departments and external customers to determine system requirements
-
Identify potential enhancements and improvements to systems and applications
-
Liaise with other internal and external application designers/developers and managers to develop Technical Specifications
-
Assist in the design, implementation and maintenance of new and existing applications
-
Maintain and enhance personal knowledge of market products and practice
-
Share information with team members
-
Other duties as assigned by the Project Delivery Manager
Relative Skills:
-
PHP web frameworks: Symfony, Laravel, etc
-
Database management technologies: PostgreSQL, NoSQL databases
-
Code versioning: Git, SVN
-
Issue tracking: Jira
-
Front-end technologies: HTML5, CSS, jQuery, ReactJS, AngularJS
-
Testing: Spring, Hibernate and unit testing
-
Database/caching technologies: HAProxy, Memcache(d), Redis, Elasticsearch,
PgPool, AWS


EU Might Issue Digital Currency In Response to Libra
by Anatol AntonoviciThe European Central Bank (ECB) might issue a public digital currency, Reuters reported on Tuesday, citing a draft document. The move is meant to counteract the influence of Libra, the upcoming stable coin developed by Facebook.
EU to Ban High-Risk Crypto Projects
According to a draft text prepared by the Finnish EU presidency, the European Union might create a common regulatory framework aimed at cryptocurrencies and even ban projects regarded as “high-risk.”
The first candidate in this category of perilous crypto projects is Libra. Facebook first unveiled its stable coin plan in June. However, the social media giant has faced opposition in many jurisdictions. France and Germany voiced prompt opposition to such a digital currency that could undermine the influence of the ECB.
ECB to Issue Digital Currency
Besides the common crypto regulation, the draft text hints to the creation of a digital currency that would have a legal base, unlike Bitcoin and Libra. The document reads:
The ECB and other EU central banks could usefully explore the opportunities as well as challenges of issuing central bank digital currencies including by considering concrete steps to this effect.
EU finance ministers will meet this Friday and likely discuss the draft. Eventually, they can adopt it at their next meeting on December 5.
Judging by the draft excerpt published by Reuters, the EU is only pondering this move as a possibility, but the chances are that the plan will become a reality soon.
It seems that the ECB is lobbying its own digital currency as it seeks more control over the monetary flow. An ECB official revealed that the project could enable consumers to use electronic cash that would be deposited directly at the central bank. This would reduce the need for banking intermediaries.
Without banking accounts and banking intermediaries, the ECB may take over the banks’ functions by managing digital payments at low costs. Obviously, banks will oppose to such a scenario, so the “digital euro” will likely look different from this ambitious version.
If the EU launches its own digital coin indeed, this will be a u-turn from the ECB’s stance in 2018. Last year, former ECB President Mario Draghi said that there was no need for digital currency.
Do you expect the ECB to issue digital currency soon? Share your thoughts in the comments section!
Image via Shutterstock
The Rundown


University of Nevada, Reno, BA Studio Art and American History
Areas of specialization: Sculpture, Ceramics and Printmaking
Bushwick Open Studios, Brooklyn, New York- 2013-2015
Small Art Show Vol.2, Kiyomi Yamagishi Gallery, Nagano, Japan. Studio 34, Brooklyn, New York- 2015
Text & Material, Mt. Rose Space, Brooklyn, New York- 2014
Work and Painting, Ed. Varie Gallery, New York, New York- 2012
Society and Solitude, Saffron, Brooklyn, New York- 2012
Go Brooklyn Open Studios, Brooklyn, New York- 2012
Small Art Show Vol.1, Kiyomi Yamagishi Gallery, Nagano, Japan. Studio 34, Brooklyn, New York- 2012
A Concise History of the World, Front Door Gallery, Reno, Nevada- 2007
Work and Painting Brings Reno to Manhattan’s Ed. Varie Gallery BULLETT MEDIA
Off the Beaten Path: Bushwick Alternative Art Venue, Mt. Rose Space BUSHWICK DAILY


Juventus midfield star gives Man Utd green light for January move
Emre Can has admitted that a lack of football at Juventus has been a frustration, as Manchester United consider a move for the German.
The 25-year-old quit Liverpool in 2018 and moved to the Serie A champions for free when Massimiliano Allegri was in charge.
But it would appear that current Juve boss Maurizio Sarri clearly does not see Can as part of his plans and even left him out of his 25-man Champions League squad this season.
The defensive midfielder has been strongly linked with a Premier League return and maintains he has the ambition and drive to further his career.
Can told Kicker: “I’m just an ambitious guy who always wants to compete at the highest level, but I’m also learning from this tough time when things are not going the way I was hoping.
“I’m not playing games from the start, so I’m not happy, but I will stay strong and keep working on myself. I’m often in the gym before or after training, doing extra shifts. I want to be ready.
“A lot can always happen in football.”
Can has made just one start for Juventus all season, with three further substitute appearances, and it’s marked difference from the 29 times he played for the club as they won Serie A in 2018-19.
Bayern Munich, where he started his career, have expressed an interest as have fellow Bundesliga giants Borussia Dortmund.
But United appear to be the firm favourites to bring Can back to England, with Ole Gunnar Solakjaer looking to create more competition in his midfield engine for the likes of Scott McTominay, Fred and Paul Pogba, and Can’s latest comments could well ignite a new year switch to Old Trafford.


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Massa discusses the 2021 F1 car: "I don't like the rear wing"
by Matt GrettonFelipe Massa has given his opinion on the 2021 Formula 1 cars. The former F1 driver isn't a fan of the new rear wings. Bosses and Liberty Media announced the changes ahead of last weekend's United States Grand Prix.
In an interview with the Italian branch of Motorsport, Massa believes those in charge might make changes to the rear wings.
"Some solutions are interesting, but I don't like the rear wing, I would have done something different. I believe, however, that the definitive single-seater will be a bit different from the one we saw in the presentation, maybe those guidelines will be, but I expect some news," Massa said.
"But I believe that the car is only a part, you need a championship that works for the public, in the end, this will be the goal."