Making Movies with Google Earth
Note - Movie making features are available to users of the Google Earth
Pro and Google Earth EC products.
Learn
more.
You can use the Movie Maker feature of Google Earth to record 3D viewer imagery and save
the recording as a movie file. You can either set the recorder to record your
interactions with the 3D viewer in real-time, or you can set up a tour and record the
entire tour without interruption.
Once you finish recording and have saved your file, you can make the file available on a
web site, use it in a presentation, or send it via email. For instance, you might want to
create a movie of your property to present to clients or at trade shows where visitors
can see satellite views of your property without having to use Google Earth.
The Movie Maker feature supports different movie file
formats:
-
Windows Media Video (WMV) - (Windows and Linux only) This output file
format is compressed and optimized for presentations. These WMV files can be further
configured for the best type of delivery, such as via a T1 web connection or over a 56K
modem connection.
-
Audio Video Interleave (AVI) - (Windows and Linux only) Movies created
in AVI format are uncompressed and as a result, generate extremely large files, making
it almost impossible for a standard movie player to play back the recording. This is
useful only if you want to edit the movie clips using a movie editing software.
-
Image Stream (JPG) - Movies can be captured as images in variable or
fixed length. This format is useful if you want to control the speed artificially and
edit each screenshot (frame by frame).
-
QuickTime - (Mac only) Movies can be captured in Apple QuickTime .mov
format.
Movie Quality Levels
The Movie Maker feature also supports two types of movie quality levels:
-
Standard quality movie (real-time) - Use this option when you want to
adjust the visibility of icons and overlays while the movie is recording. For example,
use this option to display an overlay of a site plan or additional layers of
information as you record. Keep in mind that with this option, the frame rate is
limited to the performance of your graphics card, which is generally slowed by making
movies.
-
High-quality movie (tour-mode only) - In general, high quality
recordings work best when you want to display the movie on a large screen. Use this
option to achieve the best possible movie recording. With this option, each frame is
completely downloaded to the Google Earth client before it is recorded, maximizing
image quality. For this reason, recording time is comparatively slow. Unlike the
standard quality movie, you do not need to monitor the actual recording of the movie.
Note - Keep in mind that high quality movies can only play back tour
points. You cannot manually interact with the 3D viewer when using the high quality
option.
Tips for Recording Movies
This section covers a number of tips to help you create quality movies. We recommend you
familiarize yourself with these tips before beginning your recording in order to produce
the best possible movie for your needs.
Note: The time slider is not
available when you record movies.
Update Your Movie Player
Having the latest version of Windows Media Player or Apple QuickTime installed is
absolutely essential to making good quality movies, especially if you are having issues
with the graphics quality of your movie. We highly recommend that you download Windows
Media Player 9 or above:
http://www.microsoft.com/windows/windowsmedia/player/9series/default.aspx
Or for the Mac:
http://www.microsoft.com/windows/windowsmedia/player/mac/default.aspx
You can find the latest version of Apple QuickTime here:
http://www.apple.com/quicktime/
Update Your Graphics Card Driver
Although graphics card drivers have no impact on the actual recording of your
movie, a faulty graphics card driver can produce anomalies in your 3D viewer that then
transfer into the movie itself. Therefore, it's always good practice to update your
driver to avoid any possible rendering issues. We recommend that you use a graphics card
with at least 64 MB of video RAM or above.
Increase Your Memory Cache
By increasing your memory cache size, your movie recording experience will go faster
(although it will have no impact on the final quality of your movie). To increase your
memory cache, see Memory and Disk Cache
Preferences.
Increase the Detail Area of the 3D Viewer
Increasing the area of detail for your 3D viewer helps to capture all the pixels in more
detail. As a result, the resolution of your movie is improved. See Viewing Preferences for instructions.
Note - Set Detail Area to Large if you have 64 MB of
graphics card memory or more. If you have less than 64 MB of graphics card memory, set
Detail Area to Medium.
Adjust the Touring Speed
When recording, it is best to use a relatively slow tour speed. However, feel free to
experiment with different speeds and then play the movie back to evaluate whether the
speed is appropriate. See Using Tours for details.
Note - The tour speed you choose will be consistent throughout the
movie. If you want to set different tour speeds for different Placemarks in the same
movie, you will need to create separate movie files for each tour and then edit them in a
movie making software.
Adjust the Elevation Exaggeration
If you want to enhance the appearance of terrain in your movie, you can adjust the
Elevation Exaggeration as described in Viewing Preferences.
Showing or Hiding Items in the 3D Viewer
To hide or show the compass, status bar, scale legend or grid, simply uncheck or check these items in the View
menu. Likewise, in this same menu, you can check or uncheck Atmosphere to hide the
atmosphere around the globe when viewed at full horizon or from space. See also:
Preview Your Tour Setup Before Recording
It's a good idea to set up and review the tour points you want to visit in your movie.
This involves two simple actions:
- In the Places panel, select either an entire folder to view all the
placemarks within it, or select individual items in the folder to view only selected
ones. See Using Tours for details.
- Click the Play button that appears below the Places panel to play
the tour. This tour is the same tour that will be followed if you use the High
Quality Movie option, so you can have a sense of what your movie will look like by
playing the tour. See Using Tours for details.
After previewing your movie, be sure to make any adjustments necessary before recording.
For example, if you want to change the view of a particular placemark on the tour, you
can always right-click (CTRL click on the Mac) on the placemark, then select Snapshot
View in the menu. This will overwrite your existing placemark view. See Editing Places and Folders for more details.
Set Up Your System for Maximum Efficiency
Making movies is a memory-intensive and graphics-intensive process. It's best not to have
multiple large applications running on your computer during recording. Disable both
screen savers and pop-up blockers.
Steps for Recording Movies
Follow these steps to record your movie:
- From the Tools menu, select Movie Maker. The Movie Maker dialog box appears.
-
From the Supported Compression Formats selector, choose the desired
compression format for your movie.
If you want to have a movie format other than WMV (Windows Media Video) or .mov
(QuickTime), check the Advanced checkbox and choose the format. If you choose
another movie format, only standard AVI compression is available. Note that AVI formats
are not compressed and therefore will result in large files.
-
Select High Quality or Standard Quality as the movie
quality. See Movie Quality Levels for details.
-
Choose the Frames Per Second (FPS) you desire for your movie. The
highest FPS setting, 60, offers the smoothest movie viewing experience, but at
the cost of a large file size. For instance, if you want to record a 800 x 600 movie at
60 FPS, you will not achieve the best results. In this case, choose a lower setting in
order to produce a smaller file. The lower the FPS setting, the more jerky the movie
will be. You'll want to find the best compromise between frame transition and file
size. Typically, a setting of 30 works fairly well for movies that you want to provide
over the Internet.
-
Choose the resolution for your movie. You'll want to choose a
resolution that is appropriate for your final movie display. For example, you might
have a computer screen projector that only supports a 800 x 600 format. You can specify
the following resolutions, which indicate the width and height of the movie in pixels:
- 320 x 240
- 640 x 480
- 800 x 600
- 720 x 480 (NTSC) - This setting works well with large screen displays.
- 720 x 576 (PAL)
- 1280x720 (HD)
- 1920x1080 (HD)
-
Specify a name for your movie. Click on the Browse button and
navigate to a location on your computer where you want to save the movie file when you
are finished recording. Type in a name for the file in the file dialog box and click on
the OK button.
Alternatively, you can select an existing movie file in the same format as the one you
are about to create, and that file will be overwritten with the contents of your new
recording.
Note - If you are recording your movie as an image stream, it's best to
choose a new filename or folder location rather than overwriting an existing image stream
selection.
-
Click the Record Tour button. The button is available only
after you enter a valid filename for your movie. When the movie begins recording, the
Movie Maker dialog box appears below so you can visualize the recording of the movie.
Note - Recording time in high quality movies occurs very slowly, since
each frame is fully processed before the next one is viewed.
When You Are Finished
When you are finished the steps of recording movies, to stop
recording, click the Stop Recording button in the Movie Maker dialog box. Once
you finish recording your movie, you can open the movie file in a media player that
supports the movie format you selected and play back the movie to view the results. If
you are satisfied with the results, you can post the file to a local server or use it in
a presentation.


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That’s one of the viral phrases going around to raise awareness about how some workers—like health care workers, first responders and cleaning staff—cannot work from home or self-quarantine during this coronavirus outbreak.
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Plain of Tears [as36], plain, in Mandoria, peasants: 136
Last updated Turn #145
Plain of Tears [as36], plain, in Mandoria, peasants: 136
------------------------------------------------------------------------
8: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails, arrived
8: from Ocean [as37], owner:
8: Dorothy [v1b], Pen, with one worker, 94 sailors
8: Apollo [b5e], Imperial Empire, with 11 workers, six sailors, 63 lt.
8: xbowmen, five warmounts, accompanied by:
8: Lutra [a4w], Imperial Empire, with 11 paladins, 21 pikemen
8: Lungor [y4r], Imperial Empire
8: Merbit [e2m], Imperial Empire
8: Pink Blazer [n8e], Imperial Empire
8: Hera [g3f], Imperial Empire, with three soldiers, 28 pikemen
8: Thor [r4g], Imperial Empire, with 32 pikemen
8: Eos [a2t], Imperial Empire, with ten workers, 48 lt. xbowmen
8: Khaki Blazer [w4j], Imperial Empire, with four soldiers,
8: four pikemen, 25 lt. foot
8: Banian [e7n], Imperial Empire, duke, the High Priest of the
8: Church of Eres [151], with one soldier, ten angels
8: Black Blazer [s8u], Mandor Confederacy, prisoner
8: Baltor [v5s], Mandor Confederacy, prisoner
8: Rafa El [i0p], Mandor Confederacy, prisoner
8: Andrea Sorvetao [f4f], Mandor Confederacy, prisoner
8: Kiki Ghelfond [p2w], Mandor Confederacy, prisoner
8: De Pif [p2n], Mandor Confederacy, prisoner
9: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails,
9: departed for Ocean [ar36].
14: It has become quite foggy.
30: The population grows by one peasant.
Routes leaving Plain of Tears:
North, to Ocean [ar36], The Inner Sea, 2 days
East, to Ocean [as37], The Inner Sea, 2 days
South, plain, to Plain of Tears [at36], 7 days
West, plain, to Plain of Tears [as35], 7 days
Inner locations:
Sacred grove [ae0r], sacred grove, 1 day, owner:
Lion [fz7i], lions, number: 7
The province is blanketed in fog.
No one can be seen through the fog.


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| Module | Babelphish::HtmlTranslator |
| In: |
lib/babelphish/html_translator.rb
|
Generate a file name for the newly translated content
# File lib/babelphish/html_translator.rb, line 95 def get_translated_file(page, to) new_page = page.gsub('.html', ".#{to}.html") new_page.gsub!('text.html', "text.#{to}.html") new_page.gsub!('text.plain', "text.#{to}.plain") new_page end
Translates all files in the given path from the language specififed by ‘translate_from’ into the languages in ‘translate_tos’. Translations that already exist will not be overwritten unless overwrite = true
# File lib/babelphish/html_translator.rb, line 9 def translate(path, translate_tos, translate_from = 'en', overwrite = false) @path = path if !Babelphish::GoogleTranslate::LANGUAGES.include?(translate_from) STDERR.puts "#{translate_from} is not one of the available languages." return end if !File.exists?(path) STDERR.puts "#{path} does not exist." return end translate_and_write_pages(path, translate_tos, translate_from, overwrite) end
Translate a single page from the language specified in ‘from’ into the languages specified by ‘tos‘
# File lib/babelphish/html_translator.rb, line 34 def translate_and_write_page(source_page, tos, from, overwrite) if File.exist?(source_page) STDERR.puts "Translating: #{source_page}" else STDERR.puts "Could not find file: #{source_page}" return end if !translate_file?(source_page) STDERR.puts "Not translating file: #{source_page}" return end text = IO.read(source_page) begins_with_html = text['<html>'] # Pull out all the code blocks so Google doesn't mess with those pattern = /\<\%.+\%\>/ holder = '{{---}}' replacements = text.scan(pattern) text.gsub!(pattern, holder) # Pull out all the new lines so Google doesn't mess with those pattern = /\n/ newline_holder = '<brr />' newline_replacements = text.scan(pattern) text.gsub!(pattern, newline_holder) # Send to Google for translations translations = Babelphish::Translator.multiple_translate(text, tos, from) # Put the code back translations.each_key do |locale| replacements.each do |r| translations[locale].sub!(holder, r) end end # Put the newlines back in translations.each_key do |locale| newline_replacements.each do |r| translations[locale].sub!(newline_holder, r) if translations[locale]['<html>'] # Google translate can insert '<html>' at the beginning of the result. Remove it. translations[locale]['<html>']= '' unless begins_with_html end end end # Write the new file translations.each_key do |locale| translated_filename = get_translated_file(source_page, locale) if (locale != from) && (overwrite || !File.exists?(translated_filename)) File.open(translated_filename, 'w') { |f| f.write(translations[locale]) } end end end
# File lib/babelphish/html_translator.rb, line 22 def translate_and_write_pages(path, tos, from, overwrite) Dir.glob("#{path}/*").each do |next_file| if File.directory?(next_file) translate_and_write_pages(next_file, language) else translate_and_write_page(next_file, tos, from, overwrite) end end end
This is a hack but all the translated files live in the same directory as the original file so we have to have some way of not translating the translated files. this should return true for test.html.erb, test.text.html.erb and test.text.plain.erb and false for test.es.html.erb, test.text.es.html.erb and test.text.es.plain.erb
# File lib/babelphish/html_translator.rb, line 109 def translate_file?(page) test = page test = test.gsub('./', '') if page[0..1] == './' test = test.gsub('.text.html.erb', '') test = test.gsub('.text.plain.erb', '') test = test.gsub('.html.erb', '') test.split('.').length == 1 end


This document contains installation instructions and other notes that may help you use this software library more effectively. For more information, see the JAXP Reference Implementation Project web site.
Note:
If you are reading this page online, this is the most current
version of the release notes. If this page was downloaded as part
of the release bundle, please see the JAXP Reference Implementation Project web site.
An important technical change in JAXP 1.4 is the addition of the
javax.xml.stream.*
packages from StAX, the Streaming API for XML
(JSR 173). With this addition, JAXP 1.4 now supports validation and
transformation of documents with the streaming API. Since JAXP 1.4
is integrated in Java SE, StAX support is transparent on any
system running the latest Java platform.
JAXP 1.4 changed its XML Schema validation logic for gMonth to reflect the changes made in the XML Schema 1.0 Specification Errata: E2-12 Error. Specifically, it will now reject gMonths with a lexical representation of "--MM--" as invalid.
The following sections summarize the significant changes introduced in JAXP 1.4. For additional details, please see the appropriate JavaDoc.
Changed DATATYPEFACTORY_IMPLEMENTATION_CLASS
to
com.sun.org.apache.xerces.internal.jaxp.datatype.DatatypeFactoryImpl
.
Added newInstance(String,
ClassLoader)
method.
Return false
if the argument to
equals()
is null
instead of throwing NullPointerException
.
Added the javax.xml.stream package to support StAX. The package consists of the following interfaces:
EventFilter
Location
StreamFilter
XMLEventReader
XMLEventWriter
XMLReporter
XMLResolver
XMLStreamConstants
XMLStreamReader
XMLStreamWriter
Classes:
XMLEventFactory
XMLInputFactory
XMLOutputFactory
Exceptions:
XMLStreamException
And errors:
FactoryConfigurationError
Added the javax.xml.stream.events package to support StAX. The package consists of the following interfaces:
Attribute
Characters
Comment
DTD
EndDocument
EndElement
EntityDeclaration
EntityReference
Namespace
NotationDeclaration
ProcessingInstruction
StartDocument
StartElement
XMLEvent
Clarified the semantics of getOutputProperty()
with respect to properties
that have not been set explicitly with either setOutputProperty()
or xsl:output
in the stylesheet.
Clarified that the features set on the SchemaFactory
should be passed to the
Validator
created with newValidator()
.
Clarified that the features set on the SchemaFactory
should be passed to the
ValidatorHandler
created with
newValidatorHandler()
.
Added newInstance(String,
ClassLoader)
method.
Clarified that the features set on the SchemaFactory
should be passed to the
Schema
s created with newSchema()
. Included a note to implementors
and developers about the subtleties associated with newSchema()
in this context.
Clarified that the inputs to newSchema(Source[])
are expected to be XML
documents or elements.
Updated documentation of setFeature()
to clarify that the features set
on the SchemaFactory
should be
passed to the Schema
s created with
this factory and by extension to the Validator
and ValidatorHandler
s created from that Schema
.
The SchemaFactoryLoader
class was
created during the JAXP 1.3 development process. Shortly before
JAXP 1.3 was finished, the factory mechanisms associated with
validation were changed, and this class was removed. Unfortunately,
the file was left in a repository and it slipped into the
Java Platform, Standard Edition 5.0 API
Specification.
Since the class can neither be added to JAXP 1.3 nor removed from the Java 5.0 API Specification due to backwards compatibility issues, it is being added to JAXP 1.4. The class is harmless and should not be used. It is being added simply to avoid the confusion that arises when developers notice that it's defined in the platform but not in JAXP.
Do not use this class.
Extended the semantics of getElementTypeInfo()
, allowing it to be called
from either the startElement event or the endElement event. This allows the
API to support W3C XML Schema union types more usefully.
When W3C XML Schema validation is being performed, in the case
where an element has a union type, the TypeInfo
returned by a call to getElementTypeInfo()
from the startElement
event will be the union type. The TypeInfo
returned by a call from the endElement
event will be the actual member type used to validate the
element.


Full Name
Full Name is required
Email Address
Email Address is required
Phone Number
Phone Number is required
Amount
Amount is required
Payment Type
Verve Card
Visa
MasterCard
PocketMoni
POS
ATM
BANK BRANCH
BANK INTERNET
Remita Account Transfer
Payment Type is required


Contributing¶
tl;dr¶
Make the code better
Make the code pass tests
Create a Pull Request
How to Get Help¶
We have a Google Group, cloudtools-dev, where you can ask questions and engage with the troposphere community. Issues and pull requests are always welcome!
How to Test Your Code¶
The latest test scripts can be found at Travis-CI. If you look at the details of a job, you can see what automated tests will be run against any commits to the project.
Create a virtualenv (e.g. virtualenv ~/virtualenv/troposphere)
Activate it: source ~/virtualenv/troposphere/bin/activate
pip install –upgrade pip setuptools wheel
pip install -r requirements-dev.txt
- Run tests
pycodestyle .
pyflakes .
python setup.py test
Alternatively, make can be used to run the tests, i.e. make test.
Tests are run against Python 2.7, 3.5, and 3.6.
Contributing Example Code¶
New example code should go into troposphere/examples. The expected CloudFormation Template should be stored in troposphere/tests/examples_output/. When tests are run the output of the code in the examples directory will be compared with the expected results in the example_output directory.
Releasing¶
Steps to release a new version¶
Change version in troposphere/__init__.py
Update CHANGELOG.md with changes made since last release
Verify release installs on Python2 and Python 3:
make release-test
Create a signed tag:
git tag --sign -m "Release 1.1.1" 1.1.1
Create PyPI release:
python setup.py sdist upload --sign
Push commits:
git push
Push tag:
git push --tags
Update github release page: https://github.com/cloudtools/troposphere/releases
Helper to create CHANGELOG entries¶
git log --reverse --pretty=format:"%s" | tail -100 | sed 's/^/* /'
Helper to list supported resources¶
grep -h 'resource_type = "AWS::' troposphere/* | sed 's/[ ]*resource_type = "'// | cut -f1-3 -d: | sort | uniq | sed 's/^/- /'


The Emerald City of Oz
by L. Frank Baum
Chapter 18: “How Ozma Looked into the Magic Picture”
Additional Information
- Year Published: 1910
- Language: English
- Country of Origin: United States of America
- Source: Baum, L. F. (1910). The Emerald City of Oz. Chicago, IL: Reilly and Britton.
-
Readability:
- Flesch–Kincaid Level: 6.0
- Word Count: 888
- Genre: Fantasy
- ✎ Cite This
Downloads
Princess Ozma was a very busy little ruler, for she looked carefully after the comfort and welfare of her people and tried to make them happy. If any quarrels arose she decided them justly; if any one needed counsel or advice she was ready and willing to listen to them.
For a day or two after Dorothy and her companions had started on their trip, Ozma was occupied with the affairs of her kingdom. Then she began to think of some manner of occupation for Uncle Henry and Aunt Em that would be light and easy and yet give the old people something to do.
She soon decided to make Uncle Henry the Keeper of the Jewels, for some one really was needed to count and look after the bins and barrels of emeralds, diamonds, rubies and other precious stones that were in the Royal Storehouses. That would keep Uncle Henry busy enough, but it was harder to find something for Aunt Em to do. The palace was full of servants, so there was no detail of housework that Aunt Em could look after.
While Ozma sat in her pretty room engaged in thought she happened to glance at her Magic Picture.
This was one of the most important treasures in all the Land of Oz. It was a large picture, set in a beautiful gold frame, and it hung in a prominent place upon a wall of Ozma’s private room.
Usually this picture seemed merely a country scene, but whenever Ozma looked at it and wished to know what any of her friends or acquaintances were doing, the magic of this wonderful picture was straightway disclosed. For the country scene would gradually fade away and in its place would appear the likeness of the person or persons Ozma might wish to see, surrounded by the actual scenes in which they were then placed. In this way the Princess could view any part of the world she wished, and watch the actions of any one in whom she was interested.
Ozma had often seen Dorothy in her Kansas home by this means, and now, having a little leisure, she expressed a desire to see her little friend again. It was while the travelers were at Fuddlecumjig, and Ozma laughed merrily as she watched in the picture her friends trying to match the pieces of Grandmother Gnit.
“They seem happy and are doubtless having a good time,” the girl Ruler said to herself; and then she began to think of the many adventures she herself had encountered with Dorothy.
The image of her friends now faded from the Magic Picture and the old landscape slowly reappeared.
Ozma was thinking of the time when with Dorothy and her army she marched to the Nome King’s underground cavern, beyond the Land of Ev, and forced the old monarch to liberate his captives, who belonged to the Royal Family of Ev. That was the time when the Scarecrow nearly frightened the Nome King into fits by throwing one of Billina’s eggs at him, and Dorothy had captured King Roquat’s Magic Belt and brought it away with her to the Land of Oz.
The pretty Princess smiled at the recollection of this adventure, and then she wondered what had become of the Nome King since then. Merely because she was curious and had nothing better to do, Ozma glanced at the Magic Picture and wished to see in it the King of the Nomes.
Roquat the Red went every day into his tunnel to see how the work was getting along and to hurry his workmen as much as possible. He was there now, and Ozma saw him plainly in the Magic Picture.
She saw the underground tunnel, reaching far underneath the Deadly Desert which separated the Land of Oz from the mountains beneath which the Nome King had his extensive caverns. She saw that the tunnel was being made in the direction of the Emerald City, and knew at once it was being dug so that the army of Nomes could march through it and attack her own beautiful and peaceful country.
“I suppose King Roquat is planning revenge against us,” she said, musingly, “and thinks he can surprise us and make us his captives and slaves. How sad it is that any one can have such wicked thoughts! But I must not blame King Roquat too severely, for he is a Nome, and his nature is not so gentle as my own.”
Then she dismissed from her mind further thought of the tunnel, for that time, and began to wonder if Aunt Em would not be happy as Royal Mender of the Stockings of the Ruler of Oz. Ozma wore few holes in her stockings; still, they sometimes needed mending. Aunt Em ought to be able to do that very nicely.
Next day, the Princess watched the tunnel again in her Magic Picture, and every day afterward she devoted a few minutes to inspecting the work. It was not especially interesting, but she felt that it was her duty.
Slowly but surely the big, arched hole crept through the rocks underneath the deadly desert, and day by day it drew nearer and nearer to the Emerald City.


Channel 1 Offset
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raises 'oe-show-success' event with data as string message
raises 'oe-show-message' event with data as string message
raises 'oe-show-warning' event with data as string message
raises 'oe-show-error' event with data as string message
raises 'oe-show-message' event with data as {message:..., forever:true}
raises 'oe-show-confirm' event with data as {message:..., ok:okCallbackFunc, cancel:cancelCallbackFunc}
raises 'oe-show-message' event with data as {message:..., ok:callbackFunc}
raises 'oe-show-message' event with data as {code:'valueMissing'}
raises 'oe-show-message' event with data as {code:'invalid-credentials',message:'Fallback message...'}


QBSOLV(1)
User Commands
QBSOLV(1)
NAME
qbsolv – minimize the objective function represented by a QUBO
SYNOPSIS
qbsolv -i <input_file.qubo> [-o <output_file> [-w]] [-m] [-T <target_value>] [-a <algorithm>] [-t <num_seconds>] [-n <num_repeats>] [-S <subproblem_size>] [r <seed>] [-v <verbosity_level>] [-q] [-V] [-h]
DESCRIPTION
qbsolv executes the quadratic unconstrained binary optimization (QUBO) problem in the specified file. It provides bit-vector results that minimize or, optionally, maximize the value of the objective function represented by the QUBO problem. qbsolv typically executes in a hybrid fashion on a classical CPU and a quantum processing unit (QPU).
The problem must be in the QUBO(5) file format and is not
limited to the size (number of variables), connectivity pattern, or numerical
precision of the D-Wave system on which it will be executed.
OPTIONS
-i <input_file.qubo>
Name of the file (relative or absolute pathname) containing the
input QUBO. The “.qubo” extension
is by convention.
-o
<output_file>
Optional. Name of
the file (relative or absolute pathname) to which qbsolv will write
results. If no file is specified,
results are written to standard output.
-a
algorithm
Optional. Chooses
nuances of the outer loop algorithm.
The default is 'o' for the original qbsolv method, which selects
the submatrix based upon change in energy. An alternative is 'p' for path
relinking, which selects the submatrix based upon differences of solutions.
-w
Optional, in which case -o must also be specified. Prints the input QUBO matrix and the output result in .csv format to the specified output file.
-m
Optional. Specifies that qbsolv is to return the maximum value of the objective function instead of the minimum (default).
-T
<target_value>
Optional. Target value of the objective function, expressed as an integer or floating-point number. When target value is reached, qbsolv stops the optimization process. Usually beneficial to specify only when the optimal value of the objective function has been analytically derived.
-t
<num_seconds>
Optional. Number of seconds of elapsed CPU time consumed by the classically executing portion of qbsolv after which it stops the optimization process. The value may be specified as an integer or floating-point value. The elapsed CPU time is only checked after completion of the main loop, so the actual stop time may be more than the value. If options –T or –n are also specified, those limits may (depending on their values) stop qbsolv execution before the timeout is reached. The default value is 2,592,000.0 seconds (30 days).
-n
<num_repeats>
Optional. Number of times that qbsolv is to repeat the main loop of the algorithm after a new optimal value is found. This value must be a positive integer. The default value is 40.
-r <seed>
Optional. Used to
set the seed for random number generation.
-S <subproblem_size>
This optional argument indicates the size of the subproblems into which the QUBO will be decomposed. A "-S 0" argument or "-S" argument not present indicates to use the size specified in the embedding file found in the workspace set up by dw. If a dw environment has not been established, the value will default to (47) and will use the tabu solver for subproblem solutions. If a value is specified, qbsolv uses that value to create subproblems and solve with the tabu solver. The ability to establish a dw environment is dependent upon the proper installation of qOp tools on your system.
-v <verbosity_level>
Optional. Level of
verbosity of the output. Values are as follows:
- 0 (default) outputs the number of
bits in the solution, the solution itself, and the energy of the solution.
- 1 outputs the same information as
above for multiple solutions, if found.
- 2 outputs more detailed
information at each step of the algorithm.
-q
Optional. Prints the format of an arbitrary
QUBO file and exits without running the program.
-V
Optional. Prints the version number of the qbsolv program and exits without running the program.
-h
Optional. Prints the help message for qbsolv and exits without running the program.
BUGS
Report any bugs to<EMAIL_ADDRESS>COPYRIGHT
© 2016 D-Wave Systems Inc.
SEE ALSO
qubo(5)


Lesson learnt from Kaggle - Bengali Image Classification Competition
python
kaggle
My first silver medal on Kaggles Competition - Bengali Image Classification Competition. 10 lessons learn!
Published
March 21, 2020
I have teamed up with a friend to participate in the Bengali Image Classification Competition. We struggled to get a high rank in the Public leaderboard throughout the competition. In the end, the result is a big surprise to everyone as the leaderboard shook a lot.
Public Leaderboard
The final private score was much lower than the public score. It suggests that most participants are over-fitting Public leaderboard.
The Classification Task
This is an image classification competition. We need to predict 3 parts of Bengali characters root, consonant and vowel. It is a typical classification tasks like the MNIST dataset.
Grapheme example
Evaluation Metrics
The competition use macro-recall as the evaluation metric. In general, people get >96% recall in training, the tops are even getting >99% recall.
We start with xresnet50, which is a relatively small model. As we have the assumption that this classification task is a very standard task, therefore the difference of model will not be the most important one. Thus we pick xresnet50 as it has a good performance in terms of accuracy and train relatively fast.
Near the end of the competition, we switch to a larger model se-resnext101. It requires triple training time plus we have to scale down the batch size as it does not fit into the GPU memory. Surprisingly (maybe not surprising to everyone), the bigger model did boost the performance more than I expected with ~0.3-0.5% recall. It is a big improvement as the recall is very high (~0.97), in other words, it reduces ~10% error solely by just using a better model, not bad!
Augmentation
There are never “enough” data for deep learning, so we always try our best to collect more data. Since we cannot collect more data, we need data augmentation. We start with rotation + scale. We also find MixUp and CutMix is very effective to boost the performance. It also gives us roughly 10% boost initially from 0.96 -> 0.964 recall.
Mixup is simple, if you know about photography, it is similar to have double exposure of your photos. It overlays two images (cat+dog in this case) by sampling weights. So instead of prediction P(dog) = 1, the new target could become P(dog) = 0.8 and P(cat) = 0.2.
CutMix shares a similar idea, instead of overlay 2 images, it crops out a certain ratio of the image and replaces it with another one.
It always surprises me that these augmented data does not make much sense to a human, but it is very effective to improve model accuracy and reduce overfitting empirically.
Logging of Experiment
I normally just log my experiment with a simple CSV and some printing message. This start to get tedious when there are more than 1 people to work. It is important to communicate the results of experiments. I explore Hydra and wandb in this competition and they are very useful.
It is often a good idea to make your experiment configurable. We use Hydra for this purpose and it is useful to compose different configuration group. By making your hyper-paramters configurable, you can define an experiment by configuration files and run multiple experiments. By logging the configuration with the training statistics, it is easy to do cross-models comparison and find out which configuration is useful for your model.
wandb (Weight & Biases) does a few things. It provides built-in functions that automatically log all your model statistics, you can also log your custom metrics with simple functions.
Compare the configuration of different experiments to find out the model with the best performance.
Built-in function for logging model weights and gradient for debugging purpose.
Log any metrics that you want
All of these combined to make collaboration experience better. It is really important to sync the progress frequently and getting everyone results in a single platform makes these conversations easier.
This is a simple yet effective technique which gives about 0.3-0.4% boost to my model. In simple words, it takes snapshots of the model weights during training and takes an average at the end. It provides a cheap way to do models ensemble while you are only training 1 model. This is important for this competition as it allows me to keep training time short enough to allow feedback within hours and reduce over-fitting.)
Larger is better (image size)
We downsample our image size to 128x128 throughout the competition, as it makes the model train faster and we believe most technique should be transferable to larger image size. It is important to keep your feedback loop short enough (hours if not days).  Once we scale our image to full size, it takes almost 20 hours to train a single model, and we only have little chance to tune the hyper-parameters before the competition end.
Debug & Checkpoint
There was a time we develop our model separately and we didn’t sync our code for a while. We refactor our code during the time and it was a huge mistake. It turns out our pre-refactor code trains much better model and we introduce some unknown bug. It is almost impossible to find out as we change multiple things. It is so hard to debug a neural network and testing it thoroughly is important. Injecting a large amount of code may help you to run an experiment earlier, but you may pay much more time to debug it afterwards.
I think this is applicable even if you are working alone. * Keep your changes small. * Establish a baseline early, always do a regression test after a new feature introduced (especially after code refactoring) * Create checkpoint to rollback anytime, especially if you are not working on it every day.
Implementation is the key of Kaggle competition (in real life too). It does not matter how great your model is, a tiny little bug could have damaged your model silently
Use auxiliary label
As mentioned earlier, this competition requires to predict the root, vowel and the consonant part. In the training data, they actually provide the grapheme too. Lots of people saying that if you train with the grapheme, it improves the model greatly and get the recall >98% easily.
This is something we could not reproduce throughout the competition, we tried it in the very last minute but it does not seem to improve our model. It turns out lots of people are overfitting the data, as the testing dataset has much more unseen character.
But it is still a great remark that training with labels that is not your final desired output could still be very useful.
Weight loss
The distribution of the training dataset is very imbalance, but to get a good result, we need to predict every single class accurately (macro recall). To deal with this issue, we choose to use class weights, where a higher weight would be applied to rare samples. We don’t have an ablation study for this, but it seems to help close the gap between accuracy & recall and allows us to train the model slightly better.
Find a teammate!
Lastly, please go and find a teammate if you can. It is very common to start a Kaggle competition, but not so easy to finish them. I have stopped for a month during the competition due to my job. It is really hard to get back to the competition after you stopped for so long. Getting a teammate helps to motivate you and in the end, it is a great learning experience for both of us.
Pretrain Model
We also tried to use a pretrained model, as it allows shorter training and gives better performance by transfer learning (Using weights learn from a large dataset to as initial weight). It also gives our model a bit of improvement.
Finetune the model head, while keeping other layers freeze (except BatchNorm layer).
Unfreeze the model, train all the layers together.
I also tried training the model directly with discriminating learning rate while not freezing any layer at all. It performs similarly to freezing fine-tuning , so I end up just start training the entire model from the beginning.
If the code works, don’t touch it
This is probably not a good habit usually, but I suggest not to do it for a competition. We spent lots of time for debugging our code after code refactoring and end up just rolling back to an older commit and cherry-picks new features. In a competition, you don’t have enough time to test everything. You do not need a nice abstract class for all your features, some refactoring to keep your function/class clean is probably needed, but do not overspend your time on it. It is even common to jump between frameworks (you may find other’s Kernel useful), so it is not possible to structure your code perfectly.
If someone has create a working submission script, use it!
If someone has create a working pre-processing function, use it!
Don’t spend time on trying to optimize these code unless it is necessary, it is often not worth it in a competition context. You should focus on adding new features, trying out new model, testing with new augmentation technique instead.
Summary
This is a great learning experience and refreshes some of my outdated computer vision model knowledge. If you have never joined a competition, find a friend and get started. If you have just finished one, try writing it out and share your experience. 😉


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variety
eula arbol plkplk


4
Description: Multiplayer Battleship-like game with a social focus to help connect people.
Technologies Used:
14
Description: A social platform for software developers to connect and collaborate. Built using React, Redux, Firebase, and the GitHub API.
1
Description: React + Redux application for the store manager of Fast Reaction Coffee, Taproom hosting local coffee brews from all over Oregon.
5
Description: Automated data mining from Reddit's front page using Node.js
Description: A full-stack application using the .NET core API backend and a JavaScript front-end to help users find out more information about Grocery Stores.
7
Description: 2D Platformer game using JavaScript and the Phaser3 engine to create a multi-level game.
Projects:
Skills Acquired:
Major: Microbiology | Minor: Chemistry
Awards:
Curriculum:
Software: FL Studio 12 & 20 - Digital Audio Workstation by Image-Line
Skills:
Software: Blender 2.8 - 3D Creation Suite by Ton Roosendaal
Favorite Books:


Usage and examples¶
The command line interface for neuropredict is strongly recommended (given its focus on batch processing multiple comparisons). If the installation was successful, options could be obtained by typing one of the following commands:
neuropredict
neuropredict -h
Those options are also shown below (may not always show up due to problems with software for auto generation of docs). Check the bottom of this page for examples.
Easy, standardized and comprehensive predictive analysis.
usage: neuropredict [-h] [-m META_FILE] [-o OUT_DIR] [-f FS_SUBJECT_DIR]
[-y PYRADIGM_PATHS [PYRADIGM_PATHS ...]]
[-u USER_FEATURE_PATHS [USER_FEATURE_PATHS ...]]
[-d DATA_MATRIX_PATHS [DATA_MATRIX_PATHS ...]]
[-a ARFF_PATHS [ARFF_PATHS ...]] [-p POSITIVE_CLASS]
[-t TRAIN_PERC] [-n NUM_REP_CV]
[-k NUM_FEATURES_TO_SELECT]
[-s [SUB_GROUPS [SUB_GROUPS ...]]]
[-g {none,light,exhaustive}]
[-e {randomforestclassifier,extratreesclassifier}]
[-z MAKE_VIS] [-c NUM_PROCS] [-v]
Named Arguments¶
| -m, --meta_file | |
Abs path to file containing metadata for subjects to be included for analysis. At the minimum, each subject should have an id per row followed by the class it belongs to. E.g. .. parsed-literal: sub001,control
sub002,control
sub003,disease
sub004,disease
| |
| -o, --out_dir | Output folder to store gathered features & results. |
| -f, --fs_subject_dir | |
Absolute path to E.g. |
Input data and formats¶
Only one of the following types can be specified.
| -y, --pyradigm_paths | |
Path(s) to pyradigm datasets. Each path is self-contained dataset identifying each sample, its class and features. | |
| -u, --user_feature_paths | |
List of absolute paths to user’s own features. Format: Each of these folders contains a separate folder for each subject (named after its ID in the metadata file) containing a file called features.txt with one number per line. All the subjects (in a given folder) must have the number of features (#lines in file). Different parent folders (describing one feature set) can have different number of features for each subject, but they must all have the same number of subjects (folders) within them. Names of each folder is used to annotate the results in visualizations. Hence name them uniquely and meaningfully, keeping in mind these figures will be included in your papers. For example, --user_feature_paths /project/fmri/ /project/dti/ /project/t1_volumes/
Only one of | |
| -d, --data_matrix_paths | |
List of absolute paths to text files containing one matrix of size N x p (num_samples x num_features). Each row in the data matrix file must represent data corresponding to sample in the same row of the meta data file (meta data file and data matrix must be in row-wise correspondence). Name of this file will be used to annotate the results and visualizations. E.g. ``–data_matrix_paths /project/fmri.csv /project/dti.csv /project/t1_volumes.csv `` Only one of
| |
| -a, --arff_paths | |
List of paths to files saved in Weka’s ARFF dataset format.
|
Cross-validation¶
Parameters related to training and optimization during cross-validation
| -p, --positive_class | |
Name of the positive class (e.g. Alzheimers, MCI etc) to be used in calculation of area under the ROC curve. Applicable only for binary classification experiments. Default: class appearing last in order specified in metadata file. | |
| -t, --train_perc | |
Percentage of the smallest class to be reserved for training. Must be in the interval [0.01 0.99]. If sample size is sufficiently big, we recommend 0.5. If sample size is small, or class imbalance is high, choose 0.8. | |
| -n, --num_rep_cv | |
Number of repetitions of the repeated-holdout cross-validation. The larger the number, more stable the estimates will be. | |
| -k, --num_features_to_select | |
| |
| -s, --sub_groups | |
This option allows the user to study different combinations of classes in a multi-class (N>2) dataset. For example, in a dataset with 3 classes CN, FTD and AD,
two studies of pair-wise combinations can be studied separately
with the following flag Format: Different subgroups must be separated by space, and each sub-group must be a comma-separated list of class names defined in the meta data file. Hence it is strongly recommended to use class names without any spaces, commas, hyphens and special characters, and ideally just alphanumeric characters separated by underscores. Any number of subgroups can be specified, but each subgroup must have atleast two distinct classes. Default: | |
| -g, --gs_level | Possible choices: none, light, exhaustive Flag to specify the level of grid search during hyper-parameter optimization on the training set. Allowed options are : ‘none’, ‘light’ and ‘exhaustive’, in the order of how many values/values will be optimized. More parameters and more values demand more resources and much longer time for optimization.
|
Predictive Model¶
Parameters related to pipeline comprising the predictive model
| -e, --classifier | |
Possible choices: randomforestclassifier, extratreesclassifier String specifying one of the implemented classifiers. (Classifiers are carefully chosen to allow for the comprehensive report provided by neuropredict). Default: ‘RandomForestClassifier’ More options will be implemented in due course. |
Visualization¶
Parameters related to generating visualizations
| -z, --make_vis | Option to make visualizations from existing results in the given path. This is helpful when neuropredict failed to generate result figures automatically e.g. on a HPC cluster, or another environment when DISPLAY is either not available. |
Computing¶
Parameters related to computations/debugging
| -c, --num_procs | |
Number of CPUs to use to parallelize CV repetitions. Default : 4. Number of CPUs will be capped at the number available on the machine if higher is requested. | |
| -v, --version | show program’s version number and exit |
If you don’t see any command line usage info shown above, click here:
A rough example of usage can be:
neuropredict -m meta_data.csv -f /work/project/features_dir
Example for meta-data
For example, if you have a dataset with the following three classes: 5 controls, 6 disease_one and 9 other_disease, all you would need to do is produce a meta data file as shown below (specifying a class label for each subject):
3071,controls
3069,controls
3064,controls
3063,controls
3057,controls
5004,disease_one
5074,disease_one
5077,disease_one
5001,disease_one
5002,disease_one
5003,disease_one
5000,other_disease
5006,other_disease
5013,other_disease
5014,other_disease
5016,other_disease
5018,other_disease
5019,other_disease
5021,other_disease
5022,other_disease
and neuropredict will produce the figures (and numbers in a CSV files) as shown here:
The higher resolution PDFs are included in the docs folder.
The typical output on the command line would like something like:
neuropredict -y *.MLDataset.pkl -m meta_FourClasses.csv -o ./predictions -t 0.75 -n 250
Requested features for analysis:
get_pyradigm from chebyshev.MLDataset.pkl
get_pyradigm from chebyshev_neg.MLDataset.pkl
get_pyradigm from chi_square.MLDataset.pkl
get_pyradigm from correlate_1.MLDataset.pkl
get_pyradigm from correlate.MLDataset.pkl
get_pyradigm from cosine_1.MLDataset.pkl
get_pyradigm from cosine_2.MLDataset.pkl
get_pyradigm from cosine_alt.MLDataset.pkl
get_pyradigm from cosine.MLDataset.pkl
get_pyradigm from euclidean.MLDataset.pkl
get_pyradigm from fidelity_based.MLDataset.pkl
Different classes in the training set are stratified to match the smallest class!

CV repetition n
pyradigm here is the python class to ease your ML workflow - check it out here: pyradigm.readthedocs.io
I hope this user-friendly tool would help you get started on the predictive analysis you’ve been wanting to do for a while.


SQL injection attacks represent a serious threat to any database-driven site. The methods behind an attack are easy to learn and the damage caused can range from considerable to complete system compromise. Despite these risks, an incredible number of systems on the internet are susceptible to this form of attack.
The form below allows a user to view weather data. Try to inject an SQL string that results in all the weather data being displayed.


Nokia 7.2 is a CES 2020 Innovation Award Honoree
by NayanIn a great recognition for Nokia Mobile’s prowess with smartphones, Nokia 7.2 has been chosen as one of the CES 2020 Innovation Award Honoree. Talking about why Nokia 7.2 was chosen, the official CES page mentions,
Nokia 7.2 offers fans advanced tools to express their creativity with a powerful 48MP triple camera featuring ZEISS Optics. Nokia 7.2 combines PureDisplay technology, always-on HDR and Google Assistant into a device with a timeless Nordic design.
You can check the official CES award page for Nokia 7.2 by clicking here. Reacting to this good news, HMD CPO Juho expressed his happiness on Twitter.
Nokia 7.2 comes with 48 MP (Zeiss) triple-lens rear cameras with 120-degree wide-angle photography and a round camera module like Lumias. Nokia 7.2 Camera also boasts of big sensor size and some exclusive imaging features. Read our detailed Nokia 7.2 review by clicking here.
You can read our complete 7.2 coverage by clicking here. Read detailed Nokia 7.2 specs and other information on its dedicated page.


Best Regards, Julia Van Cleve Customer Success Manager Email<EMAIL_ADDRESS>FAQ: help.thedataloom.com


This app was built with JavaScript to provide a fast search experience. The underlying data this app presents is available at https://github.com/operator-framework/community-operators (and you can contribute new operators there).
The source code for this site is available at https://github.com/operator-framework/operatorhub.io.


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Home
About
Skills
Projects
Resume
Skills
I am quite passionate about data science and web development, the tools and technologies for which I am currently exploring.
My skill levels can be divided into 2 categories: proficient and familiar.
Under "proficient" are the skills that I can comfortably use in projects (personal or otherwise). The only difference is the amount of familiarity I have and the amount of time I have spent using them.
Under "familiar" are mostly those that I have simply yet to use in a real project (real as in "not just a tutorial"), but there are also a few that I'm just not that fond of (yeah, I'm talking to you, android dev).
Now that I've described the categories in a qualitative fashion, let me present a list of my skills
Proficient
- Python (General, Pandas, Matplotlib)
- JavaScript (Native, Node.js, Bootstrap)
- HTML5/CSS3
- MATLAB
- Java
- Git
Familiar
- Python (SQLite, Seaborn)
- SQL (SQL Server, PostgreSQL)
- JavaScript (React.js, Vue.js)
- C
- C++
- Ruby
- OCaml
- Arduino
- Android Development


Maintain¶
Web document generation
- Installation
pip install recommonmark
git clone https://github.com/sphinx-doc/sphinx
cd sphinx
pip install . --user
- Build
docs/gh_pages/sphinx-build -b html . ..
Web document generation
pip install recommonmark
git clone https://github.com/sphinx-doc/sphinx
cd sphinx
pip install . --user
docs/gh_pages/sphinx-build -b html . ..


Mulvaney Backs Off Attempt To Glom On To Kupperman Impeachment Testimony Lawsuit
After speaking with a judge Monday afternoon, lawyers for acting White House chief of staff Mick Mulvaney backed off their attempt to join a lawsuit filed by former deputy NSA Charles Kupperman.
According to a transcript of the conversation obtained by Politico, U.S. District Court Judge Richard Leon said that he was disinclined to allow the motion for Mulvaney to intervene in the lawsuit.
Mulvaney’s lawyers will reportedly file a lawsuit of their own to determine if he has to comply with the House subpoena to testify as part of the impeachment inquiry.
The acting chief of staff’s move to join the lawsuit last week shocked and dismayed Kupperman’s, and by extension, former NSA John Bolton’s, allies not least because Mulvaney has been increasingly implicated as a central player in the Ukraine pressure campaign by witnesses in the inquiry.


Source File
path_unix.go
Belonging Package
os
package os
const (
PathSeparator = '/' // OS-specific path separator
PathListSeparator = ':' // OS-specific path list separator
)
func ( uint8) bool {
return PathSeparator ==
}
for ; > 0 && [] == '/'; -- {
= [:]
for --; >= 0; -- {
if [] == '/' {
= [+1:]
break
}
}
return
}
:= "."
for ; > 0 && [] == '/'; -- {
= [:]
}
:=
| The pages are generated with Golds v0.3.2-preview. (GOOS=darwin GOARCH=amd64) Golds is a Go 101 project developed by Tapir Liu. PR and bug reports are welcome and can be submitted to the issue list. Please follow @Go100and1 (reachable from the left QR code) to get the latest news of Golds. |


Testing text.As created by A is for Awesome
I will present a famous quote now.
Strive not to be a success, but rather to be of value.


We sequenced three lines of zea mays, using paired-end sequencing. This sequencing was done by our sequencing core and we received the data on 2013-05-10. Each variety should have two sequences files, with suffixes _R1.fastq
and _R2.fastq
, indicating which member of the pair it is.
All raw FASTQ sequences are in data/seqs/
:
$ find data/seqs -name "*.fastq"
data/seqs/zmaysA_R1.fastq
data/seqs/zmaysA_R2.fastq
data/seqs/zmaysB_R1.fastq
data/seqs/zmaysB_R2.fastq
data/seqs/zmaysC_R1.fastq
data/seqs/zmaysC_R2.fastq
After the sequencing data was received, our first stage of analysis was to ensure the sequences were high quality. We ran each of the three lines' two paired-end FASTQ files through a quality diagnostic and control pipeline. Our planned pipeline is:
Recommended trimming programs:


You can see sample configuration files and their corresponding Phenote screenshots in Sample Configuration 1 and Sample Configuration 2 . The easiest way to get comfortable with editing your own configuration file is to simply edit an existing configuration. You can copy the text from the sample configurations in this help file, or you can copy an existing configuration file that is distributed with Phenote. As a precaution, be sure to save your edited configuration file with a new filename to prevent any accidental overwriting by our server when you upgrade to a new Phenote version.
Before you try editing a configuration file, make sure you've launched Phenote at least once. This should create a directory on your computer called ".phenote".
The configuration files used by Phenote are located in the "conf" directory in your .phenote directory.
Within this directory you can store any number of configuration files. The configuration files that come with Phenote are named "flybase.cfg", "zfin-standalone.cfg", or "human.cfg", etc. Any configuration file you create/modify should be placed in this directory.
Once you have placed your new or modified configuration file in the .phenote/conf directory, you need to tell Phenote to use that particular configuration file.
See Changing your configuration for instructions.
If you have created your own custom configuration file with a ".cfg" extension, it should be shown in the list of known configurations. Any changes to your configuration settings will take effect the next time you launch Phenote
To use a custom ontology file in Phenote, copy the file into .phenote/obo-files/. Any reference to this ontology within your configuration file must match the filename.


{% extends "layouts/_block_content.html" %} {% set page_title = "Something went wrong" %} {% block main %}
You can return to Sign in or call us on 0300 1234 931.


* All the fields are required
Total Cost : {{ totalAmount | currency: "R$" }}
Threshold: {{ debtThreshold | currency: "R$" }}
To Spend: {{ debtThreshold - totalAmount | currency: "R$" }}


Machine Learning to Revit
Sketch pad to Revit
Description
Machine learning to Revit is using a Convolution Neural Network (CNN) prototype that uses an image classifier to predict a sketch of someone in Revit. The image is classified and a 3D object is placed in the Revit project.
The architecture for this CNN is two layers with a RELU, max-pooling between each layer and Adam optimizer. Trained on 4 images each with 10,000 images downloaded from Google Quick Draw. The use of 7,000 for training and 3,000 for testing.
Revit has a small sketch interface that saves the image file in a folder that is being monitored by a .h5 prediction file. The classification is sent to Revit using a hidden terminal. Revit is monitoring the terminal and reads the payload. It gets the correct family from the Revit model and places it in the Revit project.


CnC makes it easy to write C++ programs which take full advantage of the available parallelism. Whether run on multicore systems, Xeon Phi™ or clusters CnC will seamlessly exploit the performance potential of your hardware. Through its portabilty and composability (with itself and other tools) it provides future-proof scalability.
Intel® Concurrent Collections for C++ is a C++ template library for letting C++ programmers implement CnC applications which run in parallel on shared and distributed memory.
| Easy parallelism | There is no need to think about lower level parallelization techniques like threading primitives or message passing; no need to understand pthreads, MPI, Windows threads, TBB,... Intel® Concurrent Collections for C++ provides a separation of concerns between what the application means and how to tune it for a specific platform. |
| Portability | The same source runs on Windows and Linux. The same binary runs on shared memory multi-core systems and clusters of workstations. |
| Efficiency | Intel® Concurrent Collections for C++ provides a way to express an algorithm with minimal scheduling constraints. this makes it very efficient. |
| Scalability | Intel® Concurrent Collections for C++ achieves scalable performance on a wide range of configurations from small multicore systems to large clusters. No need to re-write or re-compile application in order to target a new configuration. |


{% load static %}
Research Assistant
Rogelio is a Research Assistant co-mentored by Gustavo Stolovitzky and Pablo Meyer. Rogelio earned his BS in Genomic Sciences from the National Autonomous University of Mexico.


In industry the most common attachment for a tele-handler is pallet forks and the most common application is to move loads to and from places unreachable for a conventional forklift.
For example, telehandlers have the ability to remove palletised cargo from within a trailer and to place loads on rooftops and other high places. The latter application would otherwise require a crane, which is not always practical or time-efficient.
In agriculture the most common attachment for a tele-handler is a bucket or bucket grab, again the most common application is to move loads to and from places unreachable for a 'conventional machine' which in this case is a wheeled loader or backhoe loader. For example, telehandlers have the ability to reach directly into a high-sided trailer or hopper. The latter application would otherwise require a loading ramp, conveyor, or similar.
The telehandler can also work with a crane jib along with lifting loads, that attachments that include on the market are dirt buckets, grain buckets, rotators, power booms. The agricultural range can also be fitted with three point linkage and power take-off. This make the telehandler a truly universal piece of equipment.
The advantage of the telehandler is also its biggest limitation: as the boom extends or raises while bearing a load, it acts as a lever and causes the vehicle to become increasingly unstable, despite counterweights in the rear.
Technically speaking, a telehandler is considered a (ITA*) class 7 powered industrial truck, which means it is a rough terrain forklift, but a forklift in name only. Any similarity between a telehandler and a vertical mast type of forklift literally ends on sight, not to mention in operation. Let’s run through a few of the more significant differences between the two.


DH_AUTO_INSTALL(1) Debhelper DH_AUTO_INSTALL(1) NAME dh_auto_install - automatically runs make install or similar SYNOPSIS dh_auto_install [buildÂ systemÂ options] [debhelperÂ options] [--Â params] DESCRIPTION dh_auto_install is a debhelper program that tries to automatically install built files. It does so by running the appropriate command for the build system it detects the package uses. For example, if there's a Makefile and it contains a install target, then this is done by running make (or MAKE, if the environment variable is set). If there is a setup.py or Build.PL, it is used. Note that the Ant build system does not support installation, so dh_auto_install will not install files built using Ant. Unless --destdir option is specified, the files are installed into debian/package/ if there is only one binary package. In the multiple binary package case, the files are instead installed into debian/tmp/, and should be moved from there to the appropriate package build directory using dh_install(1). DESTDIR is used to tell make where to install the files. If the Makefile was generated by MakeMaker from a Makefile.PL, it will automatically set PREFIX=/usr too, since such Makefiles need that. This is intended to work for about 90% of packages. If it doesn't work, or tries to use the wrong install target, you're encouraged to skip using dh_auto_install at all, and just run make install manually. OPTIONS See "BUILD SYSTEM OPTIONS" in debhelper(7) for a list of common build system selection and control options. --destdir=directory Install files into the specified directory. If this option is not specified, destination directory is determined automatically as described in the "DESCRIPTION" section. -- params Pass params to the program that is run, after the parameters that dh_auto_install usually passes. SEE ALSO debhelper(7) This program is a part of debhelper. AUTHOR Joey Hess<EMAIL_ADDRESS>9.20120909 2011-10-19 DH_AUTO_INSTALL(1)


--- layout: post title: "World’s smartest mask - Project Hazel" subtitle: "Wireless charging enabled N95 transparent mask with voice modulation and a airtight seal" date: 2021-01-13 19:00:13 +0530 background: '/img/posts/06.jpg' published: true categories: [ Technology ] tags: [ technology, mask, offbeat, products, futuristic, smart mask, modulation ] ---
Razer is no stranger no making offbeat products, whether it’s the Project Breadmaker toaster or the Respawn energy drink.
But the company envisions Project Hazel entirely differently — this smart mask is a serious attempt to do something positive in the world. Razer announced the project at CES 2021, alongside a number of more conventional product updates and releases.
Razer calls Project Hazel the “world’s smartest mask,” referring to some of its unique smart features. First off, it has something called Razer Voiceamp technology, which listens to your voice and then intelligently reproduces it through the two speakers.
I, of course, asked if voice modulation would eventually be an option, but the intent is to help break down the social barriers that mask-wearing creates.
Normally, plastic masks don’t create a thorough seal up top, which can be problematic for those who wear glasses. But Razer’s mask includes a silicone grip along the top that creates an “unobtrusive, airtight seal.” Speaking of the seal, Razer says Project Hazel qualifies as a surgical N95 respirator that includes both active ventilation and a self-sterilization function.
No word on pricing or availability was announced. As of now, Project Hazel is just that — a concept. Though it’s not a sellable product just yet, Razer says it’ll be showing off the advanced face masks at its live CES 2021 showcase, based out of its Las Vegas storefront.


$38/56: Constant 7, ID mark for start of data block ROM-Reference: LDA $38 : $F4F0 STA $38 : $F60F $F8F9 Previous Page / Next Page


Home
Projects
Paintings
Contacts
Paintings
While this is not all my paintings these are quite a few of them.


Leslie Thompson Team Leader
Creative Front-end Developer React, responsive,
interactive websites. Currently in Lambda School's Full Stack Web Dev Program!
Dallas James
Coding since 14 yo. HTML, CSS3, JS, LESS, REACT.
Currently in Lambda School's Full Stack Web Dev Program!
https://github.com/dallasjames
Cale Alexander Haug
HTML, CSS3, JS, LESS, REACT.
Currenty in Lambda School's Full Stack Web Dev Program!
https://github.com/cahaug
Elijah Conley
HTML, CSS3, JS, LESS, REACT. Currently in Lambda School's
Full Stack Web Dev Program!
https://github.com/revsoldest
Reginald Miller
HTML, CSS3, JS, LESS. Currently in Lambda School's
Full Stack Web Dev Program!
https://github.com/Regbmiller
Michael Lowe
HTML, CSS3, JS, LESS. Currently in Lambda School's
Full Stack Web Dev Program!
https://github.com/Zuess666


CharacterController.detectCollisions 检测碰撞
var detectCollisions : bool
Description描述
Should other rigidbodies or character controllers collide with this character controller (By default always enabled)
其他的刚体和角色控制器是否能够与本角色控制器相碰撞（默认值始终启用）
This method does not affect collisions detected as part of the character movement. Instead it controls collisions between the controller and other objects. For example a box will block the movement of the controller, but the box can fall through the controller as part of the simulation. This is useful to temporarily disable the character controller. Eg. you might want to mount a character into a car and disable collision detection until you exit the car again. Note that this only affects other bodies to not collide with r detectCollisions is not serialized. This means, it doesn't show up in the inspector and when Instantiate'ing the controller or saving it in a scene it will not be saved.
这个方法不影响被检测为角色运动部分的碰撞。相反，它控制控制器与其他物体之间的碰撞。例如，一个盒子将阻碍控制器的运动，但是盒子作为部分模拟能够穿过控制器。这对于暂时禁用角色控制器来说是非常有用的。例如，你可能想把一个人物放进一个汽车里并且禁用碰撞检测，直到你又走出汽车。注意，这只影响其他的物体，使它不与没有序列化的r检测碰撞相碰撞。它的意思是，它并不在检视面板中显示，而且当在场景中实例化或者保存控制器时，它将不被保存。
using UnityEngine;
using System.Collections;
public class example : MonoBehaviour {
public CharacterController c;
public void Awake() {
c = GetComponent<CharacterController>();
c.detectCollisions = false;
}
}
var c : CharacterController;
c = GetComponent(CharacterController);
c.detectCollisions = false;


Understanding what delights users & customers represents what I strive to ensure through research, web design & insights: effortless, deeply enjoyable experiences.
I love to dive into and understand what drives a user’s joy.
I love building great products employing Lean, user-centered approaches. I am based out of Los Angeles and available for hire.
I can be easily reached via several methods below.


@(manufacturers: Seq[Manufacturer]) @for(manufacturer <- manufacturers) { @manufacturer.name_manufacturer Edit Delete }


Test-driven development (TDD) is a software development process that relies on the repetition of
a very short development cycle: requirements are turned into very specific test cases, then the code is improved
so that the tests pass.
Why we use angular for Test Driven Development?
In this project, I’m not trying to convince you that writing tests is important. I’m over that point already. What
I do believe is working on a solution in a very structured way leads to the best results. Let me show you how to
accomplish this. I’ll show you how I do this by applying the software-development process known as test-driven
development (TDD). Let’s start with the definition, and after that, I’ll show you how to write code like this in
Angular, using Jasmine as our testing framework.
Why Test Driven Development for Testing ?
Basically when we write code this way it all comes down to this: We write a test, and it fails because it’s not
implemented yet. We then write just enough code to make this test pass. When the test passes, we check if this is
satisfiable code. If it’s not, we clean it up using the refactor step.
After the first iteration, we’re 100% certain we got exactly what we want. Even more, this stays the case when we continue this way. Every test we wrote in the past will be something we’re 100% certain about.
Want to check if anything we wrote in the past still works? Just run the tests and they got your back.
 However, when you work at a bigger organisation, chances increase that you’re assuming something works when it doesn’t. Tests give you and the team something to fall back on in the case something does go wrong — i.e. during an external package upgrade or a large refactoring (or it could even be a missing feature).
After the first iteration, we’re 100% certain we got exactly what we want. Even more, this stays the case when we continue this way. Every test we wrote in the past will be something we’re 100% certain about.
Want to check if anything we wrote in the past still works? Just run the tests and they got your back.
 However, when you work at a bigger organisation, chances increase that you’re assuming something works when it doesn’t. Tests give you and the team something to fall back on in the case something does go wrong — i.e. during an external package upgrade or a large refactoring (or it could even be a missing feature).


[define title]What is MFK?[end] [include "head.html"]
[include "header.html"]Marry-Fuck-Kill (MFK) is a simple game. You are given three things. Choose one to marry, one to fuck, and one to kill. These things could be people, places, concepts, or commom household objects. The harder the choice, the more interesting the MFK!
The real life version of the game comes in handy when your flight out of Costa Rica is delayed three hours and the airport staff won't let you buy more beer with your food vouchers.
The original game is as old as time itself.
Web game concept by
Anna
and
Michael.
Programming by Hunter
and Michael.
All code is on GitHub, Apache 2.0 license.


多謝 (
doh-jie) is a voice-recognition based narrative which retells a distinct and difficult memory I had of my dad.
I began this project with a comic I drew in high school—which depicted the moment I told my dad I wanted to study art.
I wanted to ask my dad about his perspective on that conversation and if his stance had changed 4 years later.
I wrote and rewrote how I felt, what I would say to my dad, but I couldn't seem to make the call from LA to SF.
Two weeks before graduation, I called, laughed, and hurriedly translated the conversation from Cantonese to English.
A day before graduation, I got to see the impact of my struggle on my friends and peers.
Nearly all of them had shared the same difficulty of pursuing a creative career with their parent's support.
The conclusion to this project was an uplifting one. Overall, it was an incredibly rewarding experience.


{% extends "base.html" %} {% load static from staticfiles %} {% block title %}{{ block.super }} | Error {% endblock title %} {% block content %}
I have no idea what you are talking about.


---
layout: post
title: kateoplis nope this is not a still from blade
date: '2013-01-23T23:40:41+00:00'
tags: []
tumblr_url: http://backlog.robbevan.com/post/41313315681/kateoplis-nope-this-is-not-a-still-from-blade
---
Nope, this is not a still from Blade Runner. It’s smog in Beijing.