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--- layout: archive title: Zulip Chat Archive permalink: /stream/144837-PR-reviews/topic/.232303.20refactor.20algebra.html ---
#2303 reduces git grep 'is_\(semi_\)\?ring_hom' | wc -l
from 263 to 219
@Johan Commelin suggests using extends ring_hom
in algebra
instead of to_fun : ring_hom _ _
. Is it OK for a class
to extend a non-class?
I haven't seen that before, but I don't think it would be a problem; the parent coercion is simply not an instance. The other direction, a structure extending a class, might be more problematic
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


Note: loginname stands for the SourceForge user login name.
/home/groups/s/sm/smlnj/This directory contains subdirectories
htdocs
and cgi-bin
.
ssh -l loginname shell.sourceforge.netYour home directory will look something like:
/home/users/m/ma/macqueen
.
You can abbreviate the command to (e.g.)
ssh sf-shellby defining sf-shell as a host alias in your .ssh/config file:
Host = sf-shell HostName = shell.sourceforge.net User = loginname
scp localfile macqueen@smlnj.sourceforge.net:/home/users/m/ma/macqueenor simply
scp localfile sf-shell:if you have defined host sf-shell in your ssh config file as illustrated above. Similarly, to copy a file or files to the top-level directory of the www.smlnj.org web site, you could execute the command:
scp localfile sf-shell:/home/groups/s/sm/smlnj/htdocs
export CVS_RSH=ssh
eval `ssh-agent` ssh-add -p <<EOF your pass phrase EOFThese last three commands can be, and probably should be, added to your .profile or .login file. "your pass phrase" refers to the pass phrase that you gave when you created your ssh identity files with ssh-keygen. Note that some versions of ssh do not support the -p flag, in which case you will have to run ssh-add interactively and type in your pass phrase. Unfortunately, the version of ssh available on the SourceForge shell servers is one of these, so ssh-add has to be run manually after login.
Host = sf-smlnj HostName = cvs.sourceforge.net User = loginnameThis defines a standard alias for the cvs host that agrees with that used the CVS/Root files (in htdocs, for instance).
export CVSROOT=sf-smlnj:/cvsroot/smlnjNote the use of the alias "sf-smlnj" defined in the config file from the previous item.
cvs update bugs
http://www.smlnj.orgThe htdocs directory is a CVS working directory created by checking out the htdocs module from the project CVS repository. The Bell Labs copy (in /home/sml/Doc/WWW/smlnj) is also checked-out from the repository ("htdocs" and "smlnj" are both aliases of the pages module in the repository). If you have extensive edits to perform on the web pages, we recommend that you check out your own private working copy, make the edits, test them, and commit the changes. After the changes are commited, "cvs update" must be performed in the two directories
http://smlnj.sourceforge.net/index.html
/home/groups/s/sm/smlnj/htdocs (at SF)to synchronize them with the changed repository. You can do the update on the htdocs directory yourself as described in the next paragraph, but you may need to send mail to Bell Labs to have the update done on that copy until we set up automatic email notifications of commits to the web pages repository.
/stage/w3serv/smlnj.cs.uchicago.edu (at U of C)
To actually update the pages at SF, you login to your SF account and do (e.g.)
cd /home/groups/s/sm/smlnj/htdocs ... set up cvs ... export CVSROOT=sf-smlnj:/cvsroot/smlnj cvs update htdocsThe definition of host sf-smlnj for your SF shell account should look like this:
Host = sf-smlnj Hostname = cvs1 User = usernameNote the Hostname definition; "cvs1" is the local name for the cvs server at SF.


{% extends "layout.html" %} {% block page_title %} Apprenticeships {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} {% include "includes/secondary-nav-finance.html" %} {% include "includes/breadcrumbs/register-back.html" %} Search results There are 162 apprenticeships matching your search for 'customer service'. Showing search results for: Customer service Level: 3 (Equivalent to A Levels at grades A to E) Typical length: 12 months {% endblock %}
There are 162 apprenticeships matching your search for 'customer service'.
Level: 3 (Equivalent to A Levels at grades A to E)
Typical length: 12 months


JPEG
The most popular among the image formats used on the web. JPEG files are an example of a 'lossy format', This is because JPEG discards information to keep the image file size small; which means some degree of quality is also lost.
JPEG compression of varying levels, from highest to lowest.
Pros of JPEG:
- 24-bit color, with up to 16 million colors (2^24)these rich colors make it great for photographs that needs fine attention to color detail.
- Almost every digital camera can shoot and save in the JPEG format.
- JPEG is very web friendly because the file is smaller, which means it takes up less room, and requires less time to transfer to a sites.
- Most used and most widely accepted image format
Cons of JPEG:
- They tend to discard a lot of data
- The stronger the compression, JPEG tends to create pixelation (see my cover image above)
- Cannot be animated
- Do not support transparency
GIF
GIF, short for Graphics Interchange Format, is limited to an 8 bit colour palette with only 256 colors. GIF is still a popular image format on the internet because image size is relatively small compared to other image types and are easily animatable.
Using simple GIF animations you can even simulate glasses free 3D!
GIF compresses images in two ways: first, by reducing the number of colors in rich color images, thus reducing the number of bits per pixel. Second, GIF replaces multiple occurring colours. So instead of storing five shades of blue, it stores only one blue.
Compared to JPEG, it is lossless and thus more effective with compressing images with a single color, but not with detailed pictures. In other words, GIF is lossless for images with 256 colors and below. So for an image with a higher colour range allot of information will be lost.
Pros of GIF:
- Can support transparency
- Can do small animation effects
- 'Lossless' quality–they contain the same amount of quality as the original, except of course it now only has 256 colors
- Great for images with limited colors, or with flat regions of color
Cons of GIF:
- Only supports 256 colors
- Sometimes the file size is larger than PNG
BMP
These files are large and uncompressed, but the images are rich in color, high in quality, simple and compatible in all Windows OS and programs. BMP files are also known as raster or paint images.
BMP files are not great in my opinion being large and hard to scale
Example BMP Image
Pros of BMP:
- Works well within most Windows programs
Cons of BMP:
- Does not scale or compress well
- Again, very huge image files making it not web friendly
- No real advantage over other image formats


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| Ranking Criteria for Location Selection for Industries | ||||
| LOGISTICS | COMMUNITY | |||
| Highways/Motorways | 3.15% | Education level | 2.19% | |
| Within 2 KMs | 100% | Number of university/Colleges in 50 KMs | ||
| 2-5 KMs | 75% | Above 5 KMs | 100% | |
| 5-8 KMs | 50% | 4 KMs | 75% | |
| 8-10 KMs | 25% | 3 KMs | 50% | |
| Above 10 KMs | 0% | 2 KMs | 25% | |
| Railroad/Station | 1.58% | below 2 KMs | 0% | |
| Within 5 KMs | 100% | Hospital | 2.92% | |
| 5-15 KMs | 75% | General Hospital Distance in KMs | ||
| 15-30 KMs | 50% | Within 10 KMs | 100% | |
| 30-45 KMs | 25% | 10-25 KMs | 75% | |
| Above 45 KMs | 0% | 25-40 KMs | 50% | |
| Airport | 1.05% | 40-50 KMs | 25% | |
| Commercial/Logistics | Above 50 KMs | 0% | ||
| Within 25 KMs | 100% | Hotel/Motel | 2.19% | |
| 25-40 KMs | 75% | 3 star hotel/Guest house in KMs | ||
| 40-60 KMs | 50% | Within 5 KMs | 100% | |
| 60-100 KMs | 25% | 5-10 KMs | 75% | |
| Above 100 KMs | 0% | 10-20 KMs | 50% | |
| Trucking Stations/Interchanges | 1.05% | 20-40 KMs | 25% | |
| 0-5 KMs | 100% | Above 40 KMs | 0% | |
| 5-8 KMs | 75% | Total Community Weight | 7% | |
| 8-15 KMs | 50% | |||
| 15-25 KMs | 25% | INDUSTRIAL SITE | ||
| Above 25 KMs | 0% | Land Cost | 2.72% | |
| Shipping Cost/DryPort | 1.58% | Per Acre Cost | ||
| Within 20 KMs | 100% | Very Low | 100% | |
| 20-40 KMs | 75% | Low | 75% | |
| 40-60 KMs | 50% | Medium | 50% | |
| 60-100 KMs | 25% | High | 25% | |
| Above 100 KMs | 0% | Very High | 0% | |
| Warehouse/Storage(in Dist) | 1.05% | Developed Park/Zone | 3.63% | |
| Highly available | 100% | Ies/SEZs status | ||
| Medium Available | 75% | Highly Developed | 100% | |
| Available | 50% | Developed | 75% | |
| Low Available | 25% | Partly Developed | 50% | |
| No Available | 0% | Under dDeveloped | 25% | |
| Transport Time | 1.05% | Not Developed | 0% | |
| Carpted Road/Double with limited traffic | 100% | Quality (Risks involved) | 2.72% | |
| Carpted with Heavy Traffic | 75% | Soil Quality/Conditions | ||
| Non Carpted with low Traffic | 50% | Highly Suitable for Mega Constructions | 100% | |
| Non Carpted with heavy Traffic | 25% | Suitable for Mega Constructions | 75% | |
| No Proper Road/Connectivity | 0% | Low Suitable for Mega Constructions | 50% | |
| Total Logistics Weight | 11.0% | Very Low Suitable for Mega Constructions | 25% | |
| Not Suitable | 0% | |||
| Total Industrial Site Weight | 9.0% | |||
| LABOUR | ENVIRONMENT | |||
| Labour Proximity | 6.15% | Rainfall | 0.61% | |
| (0.3 million inhabitants in KMs) | ( Frequency and Intensity) | |||
| within 2 | 100% | Normal | 100% | |
| 2-5 | 75% | Heavy/Low | 50% | |
| 5-8 | 50% | Abnormal/No | 25% | |
| 8-10 | 25% | Humidity | 0.6% | |
| above 10 | 0% | Level of Humid | ||
| Labour Literacy level | 1.23% | Normal | 100% | |
| Literacy rate | Medium | 50% | ||
| Above average | 100% | Extreme | 25% | |
| average | 75% | Temprature | 1.23% | |
| below average | 50% | Temprature level | ||
| low | 25% | Normal | 100% | |
| very low | 0% | High | 75% | |
| Skilled Labour | 3.08% | Very High | 50% | |
| (Technical Institutes Turnover as % of Pop) | Extreme | 25% | ||
| Above average | 100% | Floods | 3.7% | |
| average | 75% | Floods frequency | ||
| below average | 50% | No floods | 100% | |
| low | 25% | Rare | 75% | |
| very low | 0% | Occasional | 50% | |
| Labour Cost | 1.85% | Normal | 25% | |
| (Wages data/LFS) | Floody | 0% | ||
| Highly available at MWR | 100% | Pollution | 3.68% | |
| Available at MWR | 75% | Existing polution level in the area | ||
| Medium available @ MWR | 50% | No Polution | 100% | |
| Low available at MWR | 25% | Low Polution | 50% | |
| Very Low available @ MWR | 0% | Highl Polution | 0% | |
| Total Labour Weight | 12% | Ecology | 2.45% | |
| Ecology level | ||||
| MARKETS | Green | 100% | ||
| Existing Consumer Market | 3.08% | Yellow | 50% | |
| 3 million Pop. Market distance in KMs | Red | 0% | ||
| Within 10 | 100% | Total Environment Weight | 12% | |
| 10-20 | 75% | |||
| 20-30 | 50% | RAW MATERIAL | ||
| 30-40 | 25% | Material Availability | 7.50% | |
| above 40 | 0% | Agri and Minerals hub within the area | ||
| Industry Concentration | 5.54% | Highly available/Rich | 100% | |
| Other Industrial Cluster/Zone in KMs | Available in the area | 75% | ||
| within 5 | 100% | Medium available | 50% | |
| 5-10 | 75% | Low available | 25% | |
| 10-15 | 50% | Very Low availablity | 0% | |
| 15-25 | 25% | Material Proximity | 4.50% | |
| above 25 | 0% | (Access of hub/Market in KMs) | ||
| Population Growth | 3.69% | Within 10 | 100% | |
| Adjacent City Growth | 10-20 | 75% | ||
| Above average | 100% | 20-30 | 50% | |
| average | 75% | 30-40 | 25% | |
| below average | 50% | above 40 | 0% | |
| low | 25% | Components/Supplies | 3.00% | |
| very low | 0% | DHQ/Major City distance in KMs | ||
| Total Market Weight | 12% | within 25 | 100% | |
| 25-60 | 75% | |||
| 60-100 | 50% | |||
| 100-200 | 25% | |||
| above 200 | 0% | |||
| Total Raw Material Weight | 15% | |||
| UTILITIES | INSTITUTIONS | |||
| Gas Network | 1.0% | Public Sector Dept/Organizations | 4.68% | |
| Pipeline access in KMs | Distance from DHQ in KMs | |||
| within 1 | 100% | Within 10 | 100% | |
| 2-5 | 75% | 10-20 | 75% | |
| 5-10 | 50% | 20-30 | 50% | |
| 10-15 | 25% | 30-50 | 25% | |
| above 15 | 0% | above 50 | 0% | |
| Electricity Network | 1.0% | Banks/DFIs | 1.17% | |
| Elecricity Grid distance in KMs | Availability of Bank Branches in KMs | |||
| within 1 | 100% | Within 10 | 100% | |
| 2-5 | 75% | 10-20 | 75% | |
| 5-10 | 50% | 20-30 | 50% | |
| 10-15 | 25% | 30-40 | 25% | |
| above 15 | 0% | above 40 | 0% | |
| Water Supply | 1.0% | Business Association/Chamber | 0.59% | |
| Water Supply network/or Surface water | Associations/Chambers offices in KMs | |||
| within 1 | 100% | Within 10 | 100% | |
| 2-5 | 75% | 10-20 | 75% | |
| 5-10 | 50% | 20-30 | 50% | |
| 10-15 | 25% | 30-40 | 25% | |
| above 15 | 0% | above 40 | 0% | |
| Sewerage Facility | 2.0% | Training Centre/R&D Centre, Labortories | 3.51% | |
| Drainages/Sanitation access in KMs | Tevta/Labortaory Centres in KMs | |||
| within 1 | 100% | Within 10 | 100% | |
| 2-5 | 75% | 10-20 | 75% | |
| 5-10 | 50% | 20-30 | 50% | |
| 10-15 | 25% | 30-50 | 25% | |
| above 15 | 0% | above 50 | 0% | |
| Electricity Supply Quality | 2.0% | Police Station | 1.76% | |
| Load shedding frequency | Police Station in KMs | |||
| No Loadshedding | 100% | Within 10 | 100% | |
| rare trippings | 75% | 10-20 | 75% | |
| Occasional LS | 50% | 20-30 | 50% | |
| Frequent LS | 25% | 30-40 | 25% | |
| Heavy LS | 0% | above 40 | 0% | |
| Gas Supply Quality | 1.0% | Total Institutions Weight | 12% | |
| Gas supply disconnection | ||||
| No curtailment | 100% | |||
| rare pressure issue | 75% | |||
| Seasonal disruptions | 50% | |||
| Occasional disruptions | 25% | |||
| Regular Intreptions | 0% | |||
| Ground Water Quality | 1.5% | |||
| Ground water access & quality | ||||
| Very Good | 100% | |||
| Good | 75% | |||
| Not Good | 50% | |||
| Not Useable | 25% | |||
| Not extractable | 0% | |||
| Fuel Station | 0.5% | |||
| Filling station distance in KMs | ||||
| within 5 | 100% | |||
| 5-10 | 75% | |||
| 10-20 | 50% | |||
| 20-30 | 25% | |||
| above 30 | 0% | |||
| Total Utilities Weight | 10% | |||


Xythos Enterprise Document Management Suite 7.1 - What's New
Connect more effectively
Customize the user experiense
Restore information when needed
Xythos Enterprise Document Manager 7.1 helps organizations connect more effectively, customize their user experience and more easily restore information when needed. Features such as application branding can help customize end user experiences and the new Drop Box helps and protect and track document submissions. Email, documents and files of all type can be managed with a common Web 2.0 interface providing organizations with a centralized and comprehensive solution for storing all unstructured content.
Planning your Xythos upgrade - listen to this webcast replay now.
Discover the five most important items to consider when you upgrade - and some tips we've learned along the way.
New Features
Connect
Xythos Drop Box
The Xythos Drop Box is a fast and easy way to collect time-sensitive material and preserve and verify its original state. Use the Xythos Drop Box to automate the document submittal process including bids and proposals, open job requests, team projects, and other types of time-sensitive content. With Xythos Drop Box there’s no question about when a submission was received or who delivered it. Drop Box works together with other Xythos features too – allowing you to receive instant notification of document deliveries. You can also create and customize as many Drop Boxes as needed without any programming requirements.
Email client integration
Choose between the available web-based email provided in EDMS or a preferred local email client when sharing and sending content from Xythos EDMS. Wizards guide the user to select the appropriate security for recipients so that the correct type of secure URL is sent. These new options help improve efficiency while providing users with an easy web-based mail alternative when they are away from their primary PC.
Wiki Templates
Integrated Wikis help users easily capture, organize and share information. Wiki templates enable users to standardize content delivery or help establish best practices. Wiki templates can include saved searches and RSS links helping automate content collection and secure content publication.
Wizards
Xythos wizards help take the guess-work out of safely managing documents and sharing them on the web. Users can stay focused on their tasks and let wizards quickly guide them through setting up workspaces or classifying documents.
Folder creation wizard. Xythos simplifies the process of creating secure shared workspaces. The Folder Creation Wizard is a step-by-step process to ensure users can create flexible and secure shared folders or workspaces. Want email notifications when team members access or update content? Need to maintain a project work history? Xythos’ easy-to-use folder wizard will help make this process a snap.
Classification wizard. Easily classify documents and folders with the new Classification Wizard. Choose from one of five pre-installed classes or create your own. Need to classify multiple documents easily without error? Want a more systematic approach to manage the documents required for compliance? The Classification wizard makes the process easy.
Customize
Personalized branding
Build stronger relationships with clients and users with the new personalized branding feature in EDMS. Add a custom logo, change the site title, even replace the logo URL, to remind customers or partners that they are benefiting from collaborating with your business.
Customized folder settings
Folder views. Create stronger knowledge sharing bonds by showing only those folder settings applicable to the project, team or initiative. Add or remove columns and select from standard properties or document classes. Customized folder views enable users to customize the view that’s right for their team.
Classification properties views. Easily view document metadata to find appropriate content without drilling down through extra screens. Apply classification properties to folders and sub-folders in addition to individual documents.
Restore
Accidentally deleted files can cause anguish and lost productivity. Ironically, one of the most common causes of deleted files are user overwrites. Files saved accidentally. Now Xythos EDMS makes restoring lost files easier than ever. When a file is deleted, it is sent to the Recycle Bin. To restore the document, simply navigate to the Xythos EDMS trash folder, right-click on the item and select the restore option. Not only is the file restored, but all of document properties, versions, log information, subscriptions and workflow. It is if the document was never deleted in the first place.
To restore a document that has been overwritten, the new sub-folder, “Overwritten Files”, enables the user to restore an overwritten file, and also select which version of the file should be restored. Any overwritten file is automatically sent to this special sub-folder and receives versioning information.
Other new features
Enhanced move permissions and owners assignment alleviates re-assigning permissions when folders or documents are moved.
Enhanced contacts screen makes it easier to locate users for shared activities.
Recycle Bin security enhancements ensures that even content in the Recycle Bin is managed securely.
Recycle Bin auto expiration lets you decide when to automatically delete content that resides in the Recycle Bin.
ADA-compliance for all current web applications.


Preparation time
less than 30 mins
Cooking time
1 to 2 hours
Serves
Serves 4
Turbot in a creamy sauce served with buttered spinach and orange scented carrots.
For the carrots and orange vinaigrette, preheat the oven to 150C/300F/Gas 2.

Once cooked, set aside to cool. Then cut the carrots lengthways and place in a shallow container.
Place the remaining vinaigrette ingredients in a small bowl and whisk to combine. Pour over the carrots and set aside to marinate for at least 30 minutes.
For the turbot steaks with cream, baked carrots, orange and wild sorrel, add a little butter to an ovenproof frying pan set over a medium heat. Add the shallots and mushrooms and place the turbot steaks on top. Pour in the wine and season with salt and freshly ground black pepper.  Turn the fish over half way through cooking.
Once cooked, remove the fish from the pan and keep warm until ready to serve. Place the frying pan over a medium heat and add the cream and sorrel. Simmer until the liquid has reduced by half. Season to taste with salt and freshly ground black pepper. Pass the sauce through a sieve and discard the vegetables.
To serve, fill a large pan with boiling water and plunge the spinach into the water then drain.
Place a frying pan over a medium heat and add the butter, once hot add the spinach and cook for two minutes.
Put the wilted spinach in the centre of serving plates and place the turbot on top. Add the carrots to the side. Spoon the sauce over the fish and finish with a drizzle of orange dressing and a few wild sorrel leaves.


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T-rate lets you anonymously rate Stenden teachers. You can also post comments to go along with your review, and see the public comments posted by others.
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Comments:
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Most popular teachers for the most recently finished module: {{shownCurrentMod}}.
{{topRatedAtmos.name}}
{{topRatedHelp.name}}
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See the 3 leading teachers of the day for each hall of fame category.


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{{title}}
Appoint agent to one or more properties
You can appoint ABC Agent (Agent ID: 12345) with the ability to submit checks and challenges to the following properties:
| Select | Address | Local authority reference | Actions |
|---|---|---|---|
| Clear search | |||
| 1 Chatsworth Rd, Stretford, Manchester M32 9QF |<PHONE_NUMBER>9 | ||
| 1 Rose Cottages, Penrose, Wadebridge PL27 7TB |<PHONE_NUMBER>9 | ||
| 1 Virginia St, St Katharine's & Wapping, London E1W |<PHONE_NUMBER>9 | ||
| 103 Fells Rigg, Livingston EH54 8PB |<PHONE_NUMBER>9 | ||
| 11 albert Square, Dundee DD1 1DD |<PHONE_NUMBER>9 | ||
| 13A Bridge St, Banchory AB31 5SX |<PHONE_NUMBER>9 | ||
| 14 Cardiff St, Millport, Isle of Cumbrae KA28 0AU |<PHONE_NUMBER>9 | ||
| 14 Hatley Ave, Ilford IG6 1EH |<PHONE_NUMBER>9 | ||
| 15 Milton Rd, Blacon, Chester CH1 5XE |<PHONE_NUMBER>9 | ||
| 171 Farringdon Rd, Clerkenwell, London EC1R 3AL |<PHONE_NUMBER>9 |
Why is a property not being shown in the list?
The list of properties you can select depends on whether you want an agent to carry out either a check, a challenge or both. A maximum of two agents can assigned to a property, with permissions to either check or challenge a property valuation.
- If you are assigning an agent with both check and challenge rights, then a list of properties that have no agents already assigned will be displayed.
- If you are assigning an agent with check rights only, then a list of properties with no agent assigned and properties where an agent has already been assigned with challenge rights will be displayed.
- If you are assigning an agent with challenge rights only, then a list of properties with no agent assigned and properties where an agent has already been assigned with check rights will be displayed.


{% include head.html %}
{% include nav_sub.html %}Motivation
If you ask a professional piano improvisor how he can arrange an accompaniment for a song instantly, he/she may well tell you: "I'm not arranging it myself. I'm just borrowing from Rahmaninov!" In many cases like this, musicians make improvisational accompaniment not from scratch, but by retrieving similar piano pieces they have mastered before, and playing the pieces with proper harmonic modification. Hence we propose, if we acquire a large dataset of music corpora (just as what the improvisor does in his/her brain), and a style transfer model which can finely modify a polyphonic input's harmonic structure, then we can generate high-quality arrangement for any lead melody simply by search and style transfer. This can be further developed to, say, an automatic accompaniment system, which can be applied on line, rendering amazing experience with real-time improvizational accompaniment generation.
Demos
At first, please play the following demos to see what we can do. Our proposed framework now can generate accompaniment for full-score melodies up to several minutes. We present accompaniment generation results for four lead melodies from the Nottingham Dataset, one at each line. In each line, the original melody (with a chord track) is followed by two accompanied versions in different texture style generated by our framework. We render the melody timbre by solo violin and the accompaniment by piano. Please enjoy!
Demo 1: Morning Star
Lead Melody
Accompaniment Result 1
Accompaniment Result 2
Demo 2: The 29th of May
Lead Melody
Accompaniment Result 1
Accompaniment Result 2
Demo 3: Barry's Favourite
Lead Melody
Accompaniment Result 1
Accompaniment Result 2
Demo 4: Boggy Bays
Lead Melody
Accompaniment Result 1
Accompaniment Result 2
Approach
As previously mentioned, we need a large dataset of music corpora and a style transfer model. We exploit the POP-909 Dataset for our work. POP-909 contains multiple versions of piano arrangements of 909 popular songs created by professional musicians. We process each song to acquire a library of melody-accompaniment pairs, which well aids our purpose for melody-accompaniment matching (more details later). We additionally utilize the phrase tags and segment each song into complete phrases. This helps maintain better rhythmic structure for our arrangement results.
We further extend the use of Poly-Disentanglement-VAE to conduct harmonic modification. For a piece of polyphonic arrangement, we believe its chord progression and texture are two style attributes that can be abstracted, disentangled, and exchanged. Poly-Disentanglement VAE is right for this purpose. Harmonic modification is essentially to substitute the chord attribute of the search result with that of the lead melody. We apply Poly-Disentanglement to realize this kind of style trasfer in the latent space.
The overall scheme of our work is shown in the following figure. Poly-Disentanglement VAE is powerful, but we can not rely solely on it because we must also control the rhthmic structure. Therefore we devise and test a series of rule-based criteria to make sure the searched accompaniment candidates are ideal. The search strategy will be discusssed in the following.
Overall Scheme of Algorithm Arrangement
Search Strategy
For a melody query QM, instead of searching for accompaniment targets directly, we first search for the melody target TM which is most "similar" to QM from POP-909. Our intuition is that, if TM and QM are similar in structures (i.e., progression of chord function, rhythm pattern, etc.), then TM's accompaniment will fit QM in rhythmic structure as well. Thanks to POP-909 which neatly aligns melody and accompaniment tracks in consistent order. When we have TM, we retrieve its accompaniment counterpart TA and manipulate TA's chord in the latent space for style transfer.
With both rule-based search strategy for locating proper candidate TA, and style transfer for harmonic manipulation, we can generate surprisingly natural and pleasing accompaniment for melody pieces up to several minitues long. To fascilitate our framework for more general scenarios, we will look into search methodologies by ancestral sampling in the latent space conditioned on given melodies for the next stage of this project.
Ablation Study
Our framework is based on the rule-base search strategy and style transfer. In this section do an ablation study on each of these two components to demonstrate our method's novelty and efficiency。 Here we arrange accompaniment for the song Boggy Bays from the Nottingham Dataset (the 4th song in the last demo section). We cut off the repeated phrases to make our demonstration more concise.
Search Strategy without Style Transfer
If we rely solely on the Rule-Based Search Strategy, the search targets are unlikely to perfectly match the melody in harmony. The longer the melody is, the more complexity its chord progressions can be. Truncating the search length, e.g., to 2 bars can partly relieve the problem, since it is easier to find similar 2-bars chord progressions. However, it will introduce unacceptable structure inconsistency in the longer range. The following demos are arranged by concatenating 2-bar pieces each searched from the dataset without any further manipulation. The result sounds okay within each piece, but not so on the whole range. We present the accompaniment result on the left and the direct search reference on the right.
Accompaniment Result (Baseline 1)
Search Reference (Concatenation of 2-Bar Pieces)
Style Transfer without Search Strategy
Style transfer can not settle everything on its own either. On the one hand, if accompaniment target TA's original chord varies too much from the lead chord, the manipulated results could be unsatisfactory. In fact the harmony is somewhat "colapsed" in this case and sounds rather insipid. On the other hand, the texture of TA can be totally unmatched with the melody's rhythm. The following demo illustrates both two cases. Its first half is an insipid result, and second an unmatched one.
Accompaniment Result (Baseline 2)
Search Reference
Search AND Style Transfer
The following demo is generated by our complete framework. The accompaniment target is first searched by the ranking of the rule-based criterion, and then style-transfered with lead target chord in the latent space. You will find the arrangement fits the melody well both in harmonic and rhythmic structure. Please enjoy!
Accompaniment Result 1 (Proposed Framework)
Search Reference
Below is another demo derived from our arrangement framework on the same song. It sounds natural and pleasing, with a totally different style from the first piece. This illustrates our framework's ability to generate style-varying arrangements.
Accompaniment Result 2 (Proposed Framework)
Search Reference


You will be given a random number at the start of the game.
There are four crystals below. By clicking on a crystal you
will add a specific amount of points to your total score.
You win the game by matching your total score to random
number, you lose the game if your total score goes above
the random number.
The values of each crystal is hidden from you until you
click on it.
Each time when the game starts, the game will change the
value of each crystal.


Grid Test
A1 This is some sample text.
B1 This is some sample text.
C1 This is some sample text.
D1 This is some sample text.
A2 This is some sample text.
B2 This is some sample text.
C2 This is some sample text.
D2 This is some sample text.


-
Projects
Here you can see my projects.
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Blog
My last blog post was: Super post.
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Tools
Tools and techniques that I use.
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Here you can see my projects.
My last blog post was: Super post.
Tools and techniques that I use.


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---
title: "Gridcoin - Step 1: BOINC Installation"
description: "Step 1 of the 'Pool Crunching' guide; Instructing users how to install the BOINC client and join a Gridcoin pool."
redirect_from:
- "/Guides/pool.htm"
---
⚠ Note: Solo crunching is the suggested method for most new users, please see our solo crunching guide.
Step 1
Step 2
{% include _boinc_1A.htm %}
Sign up to a Gridcoin pool
There is currently two pools:
GRCPOOL has created several video tutorials for new users:


Preparation time
30 mins to 1 hour
Cooking time
10 to 30 mins
Serves
Serves 4
This Mexican feast is full of flavour and perfect for sharing. The flanken cut ribs will need to be cut by a butcher, but seek them out as they taste so delicious.
Put the ribs into a large tray and add the remaining ingredients, reserving half the chilli for the salsa, and stir to combine. Leave to marinade at room temperature for at least 1 hour.
Meanwhile, for the salsa, place the corn onto a large griddle pan and cook until browned all over. Remove the kernels and place into a large bowl. Add the remaining ingredients with the reserved chilli and mix.
For the guacamole, put all the ingredients into a pestle and mortar and pound to combine.
For the tortilla chips, preheat the oven to 200C/180C Fan/Gas 6. Cut the tortillas into triangles, place on a baking tray and drizzle with the oil. Bake for 6–8 minutes.
For the ribs, heat a large griddle pan over a high heat, add the ribs and cook on each side of 1–2 minutes. Warm through the left over marinade in a small saucepan over a medium heat.
Place the cooked ribs, salsa, guacamole and tortilla chips onto a large serving plate. Drizzle the warm marinade over the ribs and serve.


ODBC Support - An LSX that implements ODBC connectivity from Prolog. It can be used from the IDE as well as applications written in any Windows development environment.
Sockets Support - An LSX that implements the Sockets protocol for implementing clients and servers in Prolog.
O.S. Specific Predicates - An LSX containing many operating-system dependent extended predicates.
Delphi Graphics Predicates - A collection of extended predicates that give Prolog direct access to Delphi forms and controls.
Command-Line Runtime Shell - The source code for the Windows console runtime. It is a relatively simple Logic Server application, that runs an .xpl file.
Copyright ©1987-2011 Amzi! inc. All Rights Reserved. Amzi! is a registered trademark and Logic Server is a trademark of Amzi! inc.


Modal
https://materializecss.com/modals.html
https://materializecss.com/getting-started.html
https://materializecss.com
The element below defines form elements
Click on the icon below to open an modal
Export this artboard individually or update the ID if it has been renamed
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Taking in summer guests was my parents' way of helping young academics revise a manuscript before publication.
For six weeks each summer I'd have to vacate my bedroom and move one room down the corridor into a much smaller room
that had once be¬longed to my grandfather.
During the winter months, when we were away in the city, it became a part-time toolshed, storage room, and attic where rumor had it my grandfather,
my name¬sake, still ground his teeth in his eternal sleep. Summer residents didn't have to pay anything, were given the full run of the house, and could basically do anything they pleased,
provided they spent an hour or so a day helping my father with his correspondence and assorted paperwork. They became part of the family, and after about fifteen years of doing this, we had gotten used to a shower of postcards and gift packages not only around Christmastime but all year long from people who were now totally de¬voted to our family and would go out of their way when they were in Europe to drop by B. for a day or two with their family and take a nostalgic tour of their old digs.
Taking ...
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that ...
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- Coffee
- Tea
- Black tea
- Green tea
- Milk
- Coffee
- Tea
- Milk
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.Display-1(font-size:6rem;font-weight:300;line-height:1.2)
.Display-2(font-size:5.5rem;font-weight:300;line-height:1.2)
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.Display-4(font-size:3.5rem;font-weight:300;line-height:1.2)
Border
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h1 (2.5rem = 40px) h1
h2 (2rem = 32px) h2
h3 (1.75rem = 28px) h3
h4 (1.5rem = 24px) h4
h5 (1.25rem = 20px) h5
h6 (1rem = 16px) h6
The blockquote element is used to present content from another source:
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer posuere erat a ante.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer posuere erat a ante.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer posuere erat a ante.
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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer posuere
erat a ante.
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erat a ante.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer posuere
erat a ante.
| # | First | Last | Handle |
|---|---|---|---|
| 1 | Mark | Otto | @mdo |
| 2 | Jacob | Thornton | @fat |
| 3 | Larry | the Bird |
| # | First | Last | Handle |
|---|---|---|---|
| 1 | Mark | Otto | @mdo |
| 2 | Jacob | Thornton | @fat |
| 3 | Larry | the Bird |
| Class | Heading | Heading |
|---|---|---|
| Active | Cell | Cell |
| Default | Cell | Cell |
| Primary | Cell | Cell |
| Secondary | Cell | Cell |
| Success | Cell | Cell |
| Danger | Cell | Cell |
| Warning | Cell | Cell |
| Info | Cell | Cell |
| Light | Cell | Cell |
| Dark | Cell | Cell |
Donec id elit non mi porta gravida at eget metus. Maecenas sed diam eget risus varius blandit.
Donec id elit non mi porta.Donec id elit non mi porta gravida at eget metus. Maecenas sed diam eget risus varius blandit.
Donec id elit non mi porta.Donec id elit non mi porta gravida at eget metus. Maecenas sed diam eget risus varius blandit.
Donec id elit non mi porta..btn-group-lg|sm to change size of buttons
This is a simple hero unit, a simple jumbotron-style component for calling extra attention to featured content or information.
It uses utility classes for typography and spacing to space content out within the larger container.
This is a simple hero unit, a simple jumbotron-style component for calling extra attention to featured content or information.
This is a modified jumbotron that occupies the entire horizontal space of its parent.
Learn moreSome quick example text to build on the card title and make up the bulk of the card's content.
Card link Another linkLorem ipsum dolor sit amet, consectetur adipiscing elit. Integer posuere erat a ante.
With supporting text below as a natural lead-in to additional content.
Go somewhereSome quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
See ProfileThis is a wider card with supporting text below as a natural lead-in to additional content. This content is a little bit longer.
Last updated 3 mins ago
See ProfileSome quick example text to build on the card title and make up the bulk of the card's content.
See ProfileSome quick example text to build on the card title and make up the bulk of the card's content.
See ProfileWith supporting text below as a natural lead-in to additional content.
Go somewhereWith supporting text below as a natural lead-in to additional content.
Go somewhereSome quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Aww yeah, you successfully read this important alert message. This example text is going to run a bit longer so that you can see how spacing within an alert works with this kind of content. Also here is an example alert link.
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Cras sit amet nibh libero, in gravida nulla. Nulla vel metus scelerisque ante sollicitudin. Cras purus odio, vestibulum in vulputate at
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.flex-nowrap:
.align-content-start (Default), .align-content-end,.align-content-center,.align-content-around
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.align-items-stretch (Default):
.align-self-start:
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.align-self-stretch (Default):


Wife of Mexico kingpin El Chapo to grace narco families TV show
by The Jakarta PostThe wife of convicted Mexican drug lord Joaquin "El Chapo" Guzman will appear on the VH1 show Cartel Crew alongside other family members of drug traffickers who grapple with notorious legacies, the television network said on Tuesday.
Emma Coronel, a former beauty queen and mother of Guzman's twin girls, attended nearly every day of his 11-week trial in New York, drawing attention for her elegant dress sense. In July, a federal judge sent the kingpin to prison for life.
In a video clip promoting the show, Coronel, 30, appears on the bow of a luxury yacht in a lacy white blouse and sunglasses, next to a table laid with flutes of Champagne. A man in dark clothing, who appears to be a bodyguard, stands nearby.
VH1 says Cartel Crew aims to offer an insider's look into how sons and daughters of cartel members are wrestling with their family histories.
"This docuseries follows their journeys as they set out to live legit lives and make names for themselves outside of the drug world," the show's website says.
Read also
Guzman's name carries heavy weight for Coronel.
Before his final capture in 2016 and extradition to the United States a year later, Guzman was the world's most infamous drug trafficker, shipping tons of drugs around the world and twice breaking out of maximum security prisons in Mexico.
Coronel was watching in February when a jury in a federal court in Brooklyn found the boss of the Sinaloa Cartel guilty of drug smuggling and various other criminal charges.
Coronel has not been charged with any crimes.
However, a witness at the gang leader's trial testified that Coronel played an important role in plotting Guzman's 2015 escape from a Mexican prison and tried to help him break out again after he was recaptured the following January.


Arcade
--> Closing Checklist
Always choose today's date.
Empty ticket stations and sweep them out.
Name
Wipe down counters and glass, get rid of any clutter.
Name
Restock redemption counter.
Name
Make sure every type of item has a visible ticket amount.
Name
Replace ALL stuffed animals on the walls.
Name
Make sure nothing is behind the white cabinet.
Name
Wipe down arcade tables and chairs.
Name
Sweep floors.
Name
Make sure all laser tag vests and remote are plugged in.
Name
Shut down Laser Tag and XD Theater.
Name
Vacuum Laser Tag and XD Theater.
Name
Declutter all counters and cabinets (including Laser Tag).
Name
Count Wizard of Oz coins and return to game.
Name
Empty Wizard of Oz drop trays.
Name
Stock all games with tickets.
Name
Make sure all games listed under your day have been cleaned.
Name
Record all game issues on the Arcade Error Reporting page.
Name
Turn off all breakers marked with an "A".

Name
Return promo card and key to cabinet.
Name
Return walkie to manager.
Name
Submit


Source code for pysal.explore.inequality.gini
"""
Gini based Inequality Metrics
"""
__author__ = "Sergio J. Rey<EMAIL_ADDRESS>"
import numpy as np
from scipy.stats import norm as NORM
__all__ = ['Gini', 'Gini_Spatial']
def _gini(x):
"""
Memory efficient calculation of Gini coefficient in relative mean difference form
Parameters
----------
x : array-like
Attributes
----------
g : float
Gini coefficient
Notes
-----
Based on http://www.statsdirect.com/help/default.htm#nonparametric_methods/gini.htm
"""
n = len(x)
try:
x_sum = x.sum()
except AttributeError:
x = np.asarray(x)
x_sum = x.sum()
n_x_sum = n * x_sum
r_x = (2. * np.arange(1, len(x)+1) * x[np.argsort(x)]).sum()
return (r_x - n_x_sum - x_sum) / n_x_sum
[docs]class Gini:
"""
Classic Gini coefficient in absolute deviation form
Parameters
----------
y : array (n,1)
attribute
Attributes
----------
g : float
Gini coefficient
"""
[docs] def __init__(self, x):
self.g = _gini(x)
[docs]class Gini_Spatial:
"""
Spatial Gini coefficient
Provides for computationally based inference regarding the contribution of
spatial neighbor pairs to overall inequality across a set of regions. See :cite:`Rey_2013_sea`.
Parameters
----------
y : array (n,1)
attribute
w : binary spatial weights object
permutations : int (default = 99)
number of permutations for inference
Attributes
----------
g : float
Gini coefficient
wg : float
Neighbor inequality component (geographic inequality)
wcg : float
Non-neighbor inequality component (geographic complement inequality)
wcg_share : float
Share of inequality in non-neighbor component
If Permuations > 0
p_sim : float
pseudo p-value for spatial gini
e_wcg : float
expected value of non-neighbor inequality component (level) from permutations
s_wcg : float
standard deviation non-neighbor inequality component (level) from permutations
z_wcg : float
z-value non-neighbor inequality component (level) from permutations
p_z_sim : float
pseudo p-value based on standard normal approximation of permutation based values
Examples
--------
>>> import pysal.lib
>>> import numpy as np
>>> from pysal.explore.inequality.gini import Gini_Spatial
Use data from the 32 Mexican States, Decade frequency 1940-2010
>>> f=pysal.lib.io.open(pysal.lib.examples.get_path("mexico.csv"))
>>> vnames=["pcgdp%d"%dec for dec in range(1940,2010,10)]
>>> y=np.transpose(np.array([f.by_col[v] for v in vnames]))
Define regime neighbors
>>> regimes=np.array(f.by_col('hanson98'))
>>> w = pysal.lib.weights.block_weights(regimes)
>>> np.random.seed(12345)
>>> gs = Gini_Spatial(y[:,0],w)
>>> gs.p_sim
0.04
>>> gs.wcg
4353856.0
>>> gs.e_wcg
4170356.7474747472
Thus, the amount of inequality between pairs of states that are not in the
same regime (neighbors) is significantly higher than what is expected
under the null of random spatial inequality.
"""
[docs] def __init__(self, x, w, permutations=99):
x = np.asarray(x)
g = _gini(x)
self.g = g
n = len(x)
den = x.mean() * 2 * n**2
d = g * den
wg = self._calc(x, w)
wcg = d - wg
self.g = g
self.wcg = wcg
self.wg = wg
self.dtotal = d
self.den = den
self.wcg_share = wcg / den
if permutations:
ids = np.arange(n)
wcgp = np.zeros((permutations, ))
for perm in range(permutations):
np.random.shuffle(ids)
wcgp[perm] = d - self._calc(x[ids], w)
above = wcgp >= self.wcg
larger = above.sum()
if (permutations - larger) < larger:
larger = permutations - larger
self.wcgp = wcgp
self.p_sim = (larger + 1.) / (permutations + 1.)
self.e_wcg = wcgp.mean()
self.s_wcg = wcgp.std()
self.z_wcg = (self.wcg - self.e_wcg) / self.s_wcg
self.p_z_sim = 1.0 - NORM.cdf(self.z_wcg)
def _calc(self, x, w):
sad_sum = 0.0
for i, js in w.neighbors.items():
sad_sum += np.abs(x[i]-x[js]).sum()
return sad_sum


I’ve been programming a lot lately, mainly to make my own life easier. I usually start something when I’m doing a menial task that a computer could do much more efficiently. As a result, I’ve learned just enough to know what should be possible, but not enough to know when something is a good idea and something is a really dumb idea. This one turned out to be a good idea.
Two months ago, I wrote up a way to automate rubric score reports using docappender in Google Drive. It worked great the first month.
Today, I went back to score a second set of writing prompts with the rubric. docappender works great to do this, but I have to go into Drive and create six separate documents with the class hour as the name for the form to work correctly. Google has no batch-create option, so I had to make each one individually. It didn’t take too long, but it’s something that really should take any quantity of time.
Major props to Ken Bauer and Alan Liddell for pointing me in the right direction. Here’s what to do…
Computers are good at making documents. One way to do this on a Mac (or Linux, for that matter) is to create a shell script which can be run from the terminal. Alan gave me this little snippet to get me started:
for i in $(seq 1 7);
do
touch file$i;
done
That worked great. But, what if I wanted more than seven files? I wanted to give myself a couple variables which would make the script more flexible:
echo "Number of files: ";
read number;
for i in (seq 1 $number);
do
touch file$i;
done
And that was all well and good…but what if I wanted a specific prefix? And, could I make it right into a Google Doc? Yes, and yes.
#! usr/bin/env bash
echo "You're about to create a series of files."
echo "Enter the number of files you'd like to create and then push [ENTER]: "
read number
echo "Enter a generic filename and then press [ENTER]: "
read file
for i in $(seq 1 $number);
do
touch $file$1.doc;
done
echo "Finished."
This was great. I could make files with less than three dozen keystrokes and clicks. But, like any good programmer, I wanted it to be done with even fewer – more automation was needed.
I wanted to make the script executable – I wanted it to run just by typing the name of the program into the terminal. So, I ran chmod +x makedoc
(makedoc is what I called the program…real original, eh?) and gave it permission to run. I also added #! /usr/bin/env bash
to the first line of the script to make sure it ran with the right kernel.
Then, I copied the script to my computer’s usr/local/bin
directory and voila! All I have to do is go to the folder where I want to make the files, open up the Terminal and type in makedoc
and make my sequence of files.

“That’s great to make them on your computer, but you need them on Google Docs to work with docappender. Wasn’t that the point?”
Google Drive allows me to sync certain folders to my computer. The script doesn’t know if I’m in a local folder or one synced from the web. So, all I have to do is cd
to the right spot in Terminal and run the script and BOOM – they’re all in Drive, ready to be used.
It’s. awesome.
Right now, it creates a Word document, which can be synced through the Drive folder. I would like to have a way to create a Google Document locally, but takes a little more computing power than I have time for tonight. Perhaps at a later date…
Written on April 1st , 2015 by Brian Bennett Categorized in: All Technology
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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)
"""
# TODO: Implement Function
real_inputs = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels),
'real_inputs')
z_data = tf.placeholder(tf.float32, (None, z_dim), 'z_data')
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
return real_inputs, z_data, 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 variables 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 discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: 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)
"""
# TODO: Implement Function
alpha = 0.01
keep_prob = 0.95
with tf.variable_scope('discriminator', reuse=reuse):
d1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', activation=None)
d1 = tf.maximum(alpha * d1, d1)
d2 = tf.layers.conv2d(d1, 128, 5, strides=2, padding='same', activation=None)
d2 = tf.layers.batch_normalization(d2, training=True)
d2 = tf.maximum(alpha * d2, d2)
d2 = tf.nn.dropout(d2, keep_prob=keep_prob)
d3 = tf.layers.conv2d(d2, 256, 5, strides=2, padding='same', activation=None)
d3 = tf.layers.batch_normalization(d3, training=True)
d3 = tf.maximum(alpha * d3, d3)
d3 = tf.nn.dropout(d3, keep_prob=keep_prob)
flat = tf.reshape(d3, (-1, 4 * 4 * 256))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
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 variables 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):
"""
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
"""
# TODO: Implement Function
alpha = 0.01
keep_prob = 0.95
with tf.variable_scope('generator', reuse=not is_train):
g1 = tf.layers.dense(z, 2 * 2 * 512)
g1 = tf.reshape(g1, (-1, 2, 2, 512))
g1 = tf.layers.batch_normalization(g1, training=is_train)
g1 = tf.maximum(alpha * g1, g1)
#print (g1.shape)
g2 = tf.layers.conv2d_transpose(g1, 256, 5, strides=2, padding='valid', activation=None)
g2 = tf.layers.batch_normalization(g2, training=is_train)
g2 = tf.maximum(alpha * g2, g2)
g2 = tf.nn.dropout(g2, keep_prob=keep_prob)
#print (g2.shape)
g3 = tf.layers.conv2d_transpose(g2, 128, 5, strides=2, padding='same', activation=None)
g3 = tf.layers.batch_normalization(g3, training=is_train)
g3 = tf.maximum(alpha * g3, g3)
g3 = tf.nn.dropout(g3, keep_prob=keep_prob)
#print (g3.shape)
logits = tf.layers.conv2d_transpose(g3, out_channel_dim, 5, strides=2, padding='same', activation=None)
out = tf.tanh(logits)
#print (out.shape)
return out
"""
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 the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. 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)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
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.
import matplotlib.pyplot as plt
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")
"""
# TODO: Build Model
_, image_width, image_height, image_channels = data_shape
input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)
d_loss, g_loss = model_loss(input_real, input_z, image_channels)
d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
saver = tf.train.Saver()
losses = []
step = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
# TODO: Train Model
step += 1
batch_images *= 2
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
if step == 1:
print('initial output:')
show_generator_output(sess, 36, input_z, image_channels, data_image_mode)
if step % 10 == 0:
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print(f"Step {step} - Epoch {epoch_i+1}/{epochs} - Gen Loss: {train_loss_g} - Disc Loss: {train_loss_d}")
losses.append((train_loss_d, train_loss_g))
if step % 100 == 0:
show_generator_output(sess, 36, input_z, image_channels, data_image_mode)
print('final epoch output:')
show_generator_output(sess, 36, input_z, image_channels, data_image_mode)
saver.save(sess, './checkpoints/generator.ckpt')
# Plot training losses
fig, ax = plt.subplots()
losses = np.array(losses)
plt.plot(losses.T[0], label='Discriminator', alpha=0.5)
plt.plot(losses.T[1], label='Generator', alpha=0.5)
plt.title("Training Losses")
plt.legend()
# with tf.Session() as sess:
# sess.run(tf.global_variables_initializer())
# for epoch_i in range(epoch_count):
# for batch_images in get_batches(batch_size):
# # TODO: Train Model
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 = 0.001
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 = 64
z_dim = 100
learning_rate = 0.001
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.


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NAACE and transformed education
Feb 16, 2006
I’m involved with NAACE’s project on transformed education, which is an attempt firstly to come up with some sort of consensus view of the characteristics of transformed learning, how technology can support this, and what a school needs to do to bring this transformation about. The intention is thento move on to produce CPD toolkits to help schools along this route, starting with one focussed on learning platforms.
Roger Broadie has done a fine job of weaving together the various contributions into broad clusters of characteristics, and the contribution that technology can make.
The most recent question has been about the “over-riding, educational, organisational,change management and/or education system issues” that the school needs to address before they start thinking about the what and the how. I used some of the work I’d done for the NCSL SLICT seminar series to provide a basis for my response, as the ideas seemed to have wider application than just learning platform implementation.
-
Individual – social
Does a school view learning as a social activity or a more idividual / personalized pursuit – in classroom terms, do they favour whole class interactive teaching or individualized schemes of work? -
Teacher – learner
Who’s the technology for? Who should have control of it? To what extent is learner voice and autonomy valued? This applies to curriculum design as much as technology. -
Resources – activities
Is the technology, or indeed the curriculum, seen as a series of knowledge to be presented, or rather as activities to be engaged in? -
Standard – bespoke
Obvious when applied to VLE provision, but the same applies to any technology systems (qv Buliding Schools for the Future), and indeed to school as a whole – is it the local branch of the DfES, or is an independent, autonomous institution, fitting its curriculum, policies, ethos to its pupils and community. -
Closed – open
How much contact or engagement does the school seek with the wider world – is it an ivory tower/walled garden or is it intimately engaged and embedded in its community and the global village? -
Behaviourist – constructivist
What sort of learning do the school want to see? Is knowledge a fixed body of facts or something which the community build for themselves? -
Control – autonomy
Moreover, where does the school see control of the technology and/or curriculum to be located – central government, local authority, SMT, subject leaders, teachers, pupils? -
Technology – pedagogy
What’s the leading force for the change – is it educational vision, or is it technological determinism? To what extent is the technology to be used to used to support a broader educational vision of transformation, or is the transformation to be led by what the tech. makes possible?
The other slide was about the topology of resources/activities in a VLE, but also has wider application – does the school see, for example, its curriculum as a linear sequence of objectives which all progress through, perhaps at differing paces, or does the curriculum branch (eg following 11+, or as vocational/academic etc), alternatively does the school see the curriculum as made up of richly connected areas of learning, through which many routes are possible, or do we have a highly personalized curriculum, centred on the learner, making use of learning from a number of different sources?
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./komodod -ac_name=KMDICE -ac_supply=10500000 -ac_reward=2500000000 -ac_halving=210000 -ac_cc=2 -addressindex=1 -spentindex=1 -addnode=<IP_ADDRESS> &
https://kmdice.pool-mining.xyz/ <- pool
https://pool.forkwitch.com/ <- another pool
https://minerpond.com <- another pool
100 messages
Before 05-Dec-19 12:00 AM
c2k19-Oct-18 05:13 PM
lil kids can wear anyone down
blackjok3r19-Oct-18 05:14 PM
and I had to quit smoking weed to learn KMD + blockchain ..
yes someone needs to do what I did and see if indeed it was a bug
jorian19-Oct-18 05:18 PM
i'm building now
blackjok3r19-Oct-18 05:18 PM
on VULTR 4 core it takes very little time, they are very fast
Alright19-Oct-18 05:18 PM
if you snapshot, make announcement of block height before hand please
1
ComputerGenie 👴👴👴19-Oct-18 05:18 PM
{
"result": "error",
"error": "only fund creator can add more funds (entropy)"
}
Who's silly idea was that?
jorian19-Oct-18 05:19 PM
*whose
no idea, but who in their right mind would fund something for free, while you can win the odds?
Alright19-Oct-18 05:20 PM
STAKED notary network is well suited for chains that are constantly snapshot/airdrop(edited)
just saying
jorian19-Oct-18 05:20 PM
ok ok, we love you guys, you did an amazing job with STAKED
ComputerGenie 👴👴👴19-Oct-18 05:21 PM
but who in their right mind would fund something for free, while you can win the odds?
Who in their right mind ever thought I was in my right mind?
jorian19-Oct-18 05:21 PM
now there's a good question
ComputerGenie 👴👴👴19-Oct-18 05:21 PM
Alright19-Oct-18 05:22 PM
sorry if I sound like a shill, but you could use STAKED network until it's stable if you like. We can push new assetchains.json and have notaries start notarizing automatically
that way you wouldn't have like a full day waiting for dpow between each(edited)
jorian19-Oct-18 05:22 PM
yes, i see the progress, it was a little joke, but for real: it looks good
@blackjok3r can i use the address as used in the README.MD?
i don't own that
wait that doesn't work
blackjok3r19-Oct-18 05:25 PM
You need to fill out config.ini with a valid address you own.
then do /TROLL/getfunds(edited)
send those coins to a new address
then delate wallet.dat
then change the pubkey in config.ini
and restert
it onyl checks if the config.ini is filled out
doesnt check the address/wif./pub match
jorian19-Oct-18 05:26 PM
i see, it only checks the length
blackjok3r19-Oct-18 05:26 PM
just to stop noobs complaing of inifnate loops
jorian19-Oct-18 05:27 PM
lol
blackjok3r19-Oct-18 05:27 PM
50% of the code is just to noob proof it
many advantages of letting noobs test
jorian19-Oct-18 05:29 PM
[blackjok3r] : tesing $
[blackjok3r] : I fixed it :D
[blackjok3r] : test
[blackjok3r] : chatting
lol
check chatbox for progress?
anyways. i send the funds i just got from faucet to a random address?
blackjok3r19-Oct-18 05:31 PM
yes make sure have private key
wait ..
you need to start the daemon with a pubkey that does not ahve its wif imported to the daemon
jorian19-Oct-18 05:32 PM
i need to have funds to do a bet
blackjok3r19-Oct-18 05:32 PM
thats all
jorian19-Oct-18 05:32 PM
yes i guess so
blackjok3r19-Oct-18 05:32 PM
you need to make a table ..
maybe do that first
then restart with wrong pubkey
random one
and bet
jorian19-Oct-18 05:34 PM
with table you mean dicefund?
blackjok3r19-Oct-18 05:35 PM
oh crap ... min dice fund is 100 ...faucet only gives 10 at a time sorry ... maybe should ahve used a diff chain... but that one is disposable
you can faucet get as many times as you like though
i disabled the new address thing
siu - Chainmakers19-Oct-18 05:51 PM
Any updates?
blackjok3r19-Oct-18 05:52 PM
I might have sent him on a bit of a job there ... and i still havent gone back to streamer ... did exactly what Alright told me not to do tonight
jorian19-Oct-18 05:54 PM
yeah it takes a bit of figuring out that staked repo
also blocks are getting in slower now
jl777c19-Oct-18 06:02 PM
@ComputerGenie 👴👴👴 the way the dice is provably random is that neither side knows both entropy values when they are betting
the dealer generates the entropy tx (no idea why the chainmakers dont make more entropy tx with 0.0001 values)
but it is only visible as the hash(entropy) the hentropy
the bettor then picks a random dealer hentropy utxo adds his entropy in the clear
now the dealer can tell if it is a win or loss, if he is honest he pays out the wins immediately
if he doesnt and it times out, it must be a loss of dealer, ie automatic win, otherwise the dealer can just never payout wins
blackjok3r19-Oct-18 06:04 PM
If my node was betting with a pubkey that did not have the WIF in the wallet (daemon started with wrong one ) would that cause an issue with dicefinish?
jl777c19-Oct-18 06:04 PM
so, given this, what is the attack vector by allowing users to create entropy tx?
your node wont be able to dicefinish properly, but the dealer node doesnt need any privkeys from the bettors
blackjok3r19-Oct-18 06:05 PM
ok so my node not being able to claim win/loss even after restarting with correct pubkey is not he issue ?
all bets are stuck in pending
jl777c19-Oct-18 06:06 PM
your node would only ever be able to claim a timeout forfeit win
blackjok3r19-Oct-18 06:06 PM
ahh ok got it
jl777c19-Oct-18 06:06 PM
since there are 7 entropy tx, once they are waiting for timeout, what else can happen
blackjok3r19-Oct-18 06:06 PM
hmm seems like an error check needed ... its ther for other contracts
"pubkey does not have wif imprted"
jl777c19-Oct-18 06:06 PM
why cant more entropy tx be created?
it does seem like a good error check, maybe tonyl can make a pull request


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The Donner Party (1992) 84m.
Absorbing documentary by Ric Burns follows the format of his successful television series THE CIVIL WAR and tells its story through the use of old letters, diaries, sepia-tone photographs, maps, paintings, and commentaries by writers/historians. It's to Burns' credit that he can sustain this approach for nearly an hour and a half - but then his subject matter is entirely engrossing. Film traces the progress west of the 1846 wagon train that became known as the Donner Party, which, after a terrible journey, became snowed in at the foothills of the Sierra Nevada.
While it is the trek's gruesome outcome that people are mostly familiar with, the Party's many hardships and dramas en route to California are equally gripping. There is enough material to fill a movie script of epic proportions. Curiously, the static appearance of the film's frames (the only motion Burns allows is from the elements - water, fire, clouds, and snow) may cause you to visualize your own 'action' and 'actors' and 'dialogue' as it goes on. This distant, yet respectful, approach has an authority which enables us to react to the emotional core of the story without feeling as manipulated as we might by a sensationalized Hollywood script (I know I shouldn't laugh in a couple of places, but there're a couple of narrated footnotes that are either too ironic or bizarre to resist). As in his earlier work, Burns employs actors to provide voice-overs for the various true-life emigrants (Amy Madigan, Timothy Hutton, and Eli Wallach, but to name a few). It's one of a number of 'medley' devices he weaves together to keep our attention from flagging, which also include his pan-scans of photographs (many of which are shown repeatedly) and frequent background music (ranging from languid Spanish guitars to brooding themes from TWIN PEAKS' Angelo Badalamenti). Keep an eye out for it.
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