Nav Drawer is the New QuickStart
With the release of Oracle JET 2.1.0, there are new “starters”–templates that make for quick setup on a new JET project and show some best practices for code structure. You use the new templates via the same Yeoman scaffolding process for a starting a new project.
yo oraclejet jet210navdrawer --template=navdrawer
[Edit:] If the navdrawer
option doesn’t work for you, you may need to update your Yeoman generators to get the latest generator-oraclejet
. Either do the update through Yeoman’s menu system by executing yo
at the command line, or use this command:
npm update generator-oraclejet -g
[/end Edit]
In the past there were only two templates: blank
for a bare-bones minimal JET scaffolded project with no router, navigation, or sample content views/viewmodels, and basic
for a QuickStart project. In JET v2.1.0, blank
and basic
are still there but now produce different assets–both rather basic–and they are joined by two newcomers: navbar
and navdrawer
. Descriptions and links to demos are under the ‘Starters’ section of the JET examples landing page.
The blank
template is now truly blank: it produces a JET project with no content at all and leaves it to you to implement routing and content components, but it does have the Alta styles, framework references, and Grunt tasks in place. The basic
template is no longer QuickStart and is pretty blank itself, although it does at least have a header and footer in the index.html.
So where did QuickStart go? It’s been replaced by two new templates: navbar
and navdrawer
. Nav Bar is the newest and transforms the navigation menu into a bar of icons when the smaller responsive breakpoints kick in. Nav Drawer will be more familiar to QuickStarters, as it uses the normally-hidden sidebar drawer to shrink down the menu at phone and tablet breakpoints.
These two templates are otherwise identical and scaffold out a router and a few content pages. The three-column main content area of QuickStart is now a single full-width flexbox that gets its content from where the router tells it to.
There are a few other changes under the hood. Application-wide configuration settings, such as for navigation and the router, have been moved into an appController.js
module that gets pulled in by main.js
. This is more modular and provides a pattern for your own global settings that could then be pulled into viewModels in your app as needed.
The individual views and viewModels such as for the Dashboard and Incidents tabs (no more Home tab to delete) don’t do much, but it’s interesting that each of the sample viewModels draw attention to the ojModule’s lifecycle handlers by implementing stub methods such as handleActivated
and handleAttached
. These are empty methods on a newly-instantiated ojModule so having them blank here doesn’t do anything different, but seeing them in your face draws attention to the fact that you can hook into them whenever you use an ojModule (which the router does to swap in your main content).
There’s a new themes
folder under the site root that gets leveraged by the grunt serve
tasks. Themes will let you define sets of styles that can be swapped in and out at build time. Look for more to come around the Theme Style Lab.
Another change is to the /scripts/grunt
folder. This has been massively simplified compared to earlier versions. The two tasks are still grunt serve
and grunt build
, but the Oracle JET-specific tasks are now imported from a grunt-oraclejet NPM module.
There are probably more cool things in these updated templates, but these stood out after a quick review. I really liked using the QuickStart as a seed for my JET projects, and am looking forward to upgrading our team’s seed repo with the 2.1.0 changes in the Nav Drawer template.


Maximum Screen Space Error
Attenuation
Geometric Error Scale
Maximum Attenuation
Base Resolution
Eye Dome Lighting
Eye Dome Lighting Strength
Eye Dome Lighting Radius


| Modifier and Type | Class and Description |
|---|---|
class |
InvalidMessageException
Specialized exception that is thrown when a message is not valid (some fields
are required or need to conform a particular format).
|
class |
MessageException
Base exception related to a message that is about to be sent.
|
class |
MessageNotSentException
This specialized exception indicates that the
Message has not been
send due to an error. |
| Modifier and Type | Class and Description |
|---|---|
class |
WaitException
Exception that is thrown when a pause or when waiting for something/some
state fails.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CleanableException
Specialized exception that keeps a reference to the
Cleanable
instance which Cleanable.clean() method has been called and failed. |
class |
CleanException
Some resources may be opened while Ogham runs.
|
class |
MultipleCleanException
Some resources may be opened while Ogham runs.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FillMessageException
The developer may not provide all fields of the message in its code.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ContentTranslatorException
Ogham uses a chain to transform original content into final content.
|
class |
CssInliningException
Ogham uses a chain to transform original content into final content.
|
class |
ImageInliningException
Ogham uses a chain to transform original content into final content.
|
class |
NoContentException
A specialized exception that is thrown when the content of the message is not
set or empty.
|
class |
TemplateNotFoundException
Ogham uses a chain to transform original content into final content.
|
class |
TemplateParsingFailedException
Ogham uses a chain to transform original content into final content.
|
| Modifier and Type | Class and Description |
|---|---|
class |
InvalidMimetypeException |
class |
MimeTypeDetectionException
Ogham is able to automatically detect mimetype of a resource.
|
class |
MimeTypeException
This is a general exception for mimetype handling and it has subclasses to
indicate additional information about the failure.
|
class |
NoMimetypeDetectorException |
class |
UnallowedMimeTypeException
Ogham is able to automatically detect mimetype of a resource.
|
| Modifier and Type | Class and Description |
|---|---|
class |
NoResolverException
|
class |
ResourceResolutionException
|
| Modifier and Type | Class and Description |
|---|---|
class |
BeanContextException
Specialized exception that indicates that the failure is due to an error
while trying to access a bean (or one of its properties).
|
class |
ContextException
General exception to indicate that the context for template parsing couldn't
be created.
|
class |
EngineDetectionException
Ogham is able to handle several template engines.
|
class |
NoEngineDetectionException
Ogham is able to handle several template engines.
|
class |
ParseException
General exception to indicate that the
TemplateParser couldn't parse
the template. |
| Modifier and Type | Method and Description |
|---|---|
void |
CleanableMessagingService.send(Message message) |
void |
MessagingService.send(Message message)
Sends the message.
|
void |
WrapExceptionMessagingService.send(Message message)
Sends the message.
|
void |
EverySupportingMessagingService.send(Message message)
Sends the message.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ResourceTranslatorException
Ogham uses a chain to transform original resource into final resource.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AttachmentResourceHandlerException
Attachments are referenced using a resource path.
|
class |
ContentHandlerException
Thrown when the content of the message can't be handled.
|
class |
NoAttachmentResourceHandlerException
Attachments are referenced using a resource path.
|
class |
NoContentHandlerException
When a message is about to be sent by a
MessageSender implementation,
the original message is converted to the format needed by the underlying
protocol/library. |
class |
UnresolvableAttachmentResourceHandlerException
Attachments are referenced using a resource path.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AddressingException
Sending Message Exception relative to any addressing policy issue (TON /
NPI).
|
class |
EncodingException
Sending Message Exception relative to any encoding policy issue .
|
class |
InvalidReferenceNumberException
When a SMS is split into segments, a reference number is shared for all
segments.
|
class |
NoSplitterAbleToSplitMessageException
The original message may be too long to fit in a single SMS.
|
class |
PhoneNumberTranslatorException
Ogham uses a chain to transform original phone number into final phone
number.
|
class |
ReferenceNumberGenerationException
When a SMS is split into segments, a reference number is shared for all
segments.
|
class |
SplitMessageException
The original message may be too long to fit in a single SMS.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ConnectionFailedException
Specialized exception that indicates that the session couldn't be bound
(connection failed).
|
class |
DataCodingException
Ogham tries to determine the value of the Data Coding Scheme according to the
message.
|
class |
GuessEncodingException
Ogham tries to guess the encoding of the
Sms . |
class |
KeepAliveException
In order to avoid creating a new session for each message to send, Ogha
provides different strategies to handle the sessions.
|
class |
MessagePreparationException
The
Sms message has to be converted to a SubmitSm PDU in
order to be sent by Cloudhopper. |
class |
SessionException
This exception indicates that the failure is due to the handling of the
session itself and not raised while sending the SMS.
|
class |
SmppException
Exception that is thrown when an error due to the SMPP protocol is raised
(either while trying to send a SMS or while connecting to the SMSC server or
anything else).
|
class |
UnsupportedCharsetException
Specific exception that is thrown when the encoding of the message has no
valid Data Coding Scheme value that can be handled by the SMSC.
|
| Modifier and Type | Class and Description |
|---|---|
class |
NoResolverAdapterException
Ogham provides
ResourceResolver s to resolve a resource from a
ResourcePath . |
class |
ResolverAdapterConfigurationException
Ogham provides
ResourceResolver s to resolve a resource from a
ResourcePath . |
class |
ResolverAdapterException
Ogham provides
ResourceResolver s to resolve a resource from a
ResourcePath . |
class |
TemplateVariantNotFoundException
Specialized exception that is thrown when the variant can be handled by Ogham
through a
VariantResolver but the ResolvedPath provided by
the VariantResolver points to an non-existing template. |
class |
UnknownVariantException
Specialized exception that indicates that the message references a variant
that is not known by Ogham.
|
class |
VariantResolutionException
When a
ResourcePath references a template, the path may be partial to
be able to load a template that has variants. |
Copyright © 2021. All rights reserved.


Kaş (pronounced "Kash") is a small fishing, diving, yachting and tourist town, situated midway between Finike and Fethiya on a hill running down to the sea. As a tourist town it is relatively unspoiled.
The port is attractive, although it can be rather airless in the hottest part of the summer.
For the full details on Turkey Formalities go here.
Customs, Police and Health Authorities are all conveniently situated near the waterfront, although visiting all the necessary offices can take a large part of the day.
A foreigners Police department has now opened in Kaş (located in Kas Police station), making residency applications and renewals, much more convenient. The Maliye office (where you pay your residency fees) is located in the same building.
Kas Marina, which opened in 2011, can provide agent services for formalities.
Last updated May 2015.
Approach to the entrance to Kas Harbour is not easy to identify until close up, and an underwater rock nearby is marked with a buoy.
To moor in the harbour go stern or bows-to. Holding in the harbour is reasonable and the shelter is good. Although full of charter, dive and tourist boats, space can usually be found on the southern quay.
The new Kas Marina opened for yachts in Spring 2011 and is five minutes from the centre of town.
Last updated May 2015.


These workshops are free to UVA faculty, staff and students and are offered in a single-session format, typically lasting one to three hours.
FIND YOUR NEXT SKILL ...


{% extends "base.html" %} {% block title %} Generate token | PTART{% endblock %} {% block page %}
A token is already configured for your user


-
surviving antifreeze poisoning and residual complications
Sora x Kairi Fanfic by ~KingOfSheeple on deviantART
May 19, 2008 . Sora x Kairi"So, you're still in love with Sora, right?" Kairi . I totally felt like doing a fanfic and I asked Tachii for an idea and this spat out.
http://kingofsheeple.deviantart.com/art/Sora-x-Kairi-Fanfic-86155573
-
discounts on avis rental cars
Sora loves Kairi Obsessions! | FanFiction
Set after the end of KH2, Kairi and Sora have to come to grips about the impending adventure, but they have to accept the fact that they will be apart again.
http://www.fanfiction.net/community/Sora-loves-Kairi-Obsessions/33063/
-
bad credit granteed loan personal credit
Kairi's Inner Darkness Chapter 1: A new girl moves in ... - Fanfiction.net
She was going to tell the boy of her dreams that she loves him, that is . . . until a . Rated: Fiction K+ - English - Romance/Adventure - Kairi & Sora - Chapters: 3 .
http://www.fanfiction.net/s/3045000/1/Kairi-s-Inner-Darkness
-
simple twist of fate chords
SQUARE ENIX > Thread > Sora & Kiari
Jul 7, 2012 . Sora and Kairi always had the classical fall in love type of relationship. (as proof remember in nightmare before christmas, in kingdom hearts II .
http://na.square-enix.com/go/thread/view/139243/29222529/Sora__Kiari
-
1903 desoto missouri mayor
Urban Dictionary: axel and kairi
I just read a kaxel fanfic!!! It BURNS! @_@" . She is a replica of Sora (the hero), made from his memories of Kairi. She is . "^YOU CAN'T DENY PLANT LOVE.
http://www.urbandictionary.com/define.php?term=axel%20and%20kairi
-
new bungalows in kitchener waterloo
head-canons
Riku/Kairi Sora/Kairi Seifer/Kairi Cloud/Kairi (alksdnfa this is such an old ship holy shit) Hayner/ . Was it a specific scene, a fanfic, or something gradual? Mm. This is a . Buuuuut I just kept going and eventually they just
kinda.. fell in love ?
http://kh-headcanons.tumblr.com/
-
horde population control garona
Kairi - Interests
Pimping good Kingdom Hearts fanfiction since the days of yore; living_tales . 4 months ago); kh_het - Kingdom Hearts Het love (Updated 4 months ago) . For fans of the pairing Sora and Kairi from the Kingdom Hearts series. ff_recs - Final .
http://www.livejournal.com/interests.bml?int=kairi
-
american academy of floriculture
fuzzy love | Tumblr
Kairi smiled happily at Riku. one of my favorite moments in the kh fanfic Fuzzy Love by Cold Silence. It's when Sora kissed Kairi at their school's homecoming .
http://www.tumblr.com/tagged/fuzzy%20love
-
loni h u ler
Kingdom Hearts Fanfiction Recommendations
Jan 30, 2013 . She was on FanFiction.Net for a . Or does love truly find a way to make exceptions? Featured . Cameos: Sora, Kairi (but not SoKai), Tifa, Lea .
http://khrecs.livejournal.com/
-
pigs with swine flu
Character analysis: Sora, Riku & Kairi - Hunted Witch
Character analysis: Sora, Riku & Kairi This won't be a character opinion. . Oh, and let me just point this out here: Riku was not in love with Kairi. . causing the swarm of fanarts and fanfiction featuring Riku as someone cold, guilty, and emo, .
http://huntedwitch.tumblr.com/post/8963150107
-
air charged lathe chucks
kairi-haters on deviantART
Death to Kairi by RikuGirl by ~kairi-haters . Fanfiction is readily and happily accepted! . i hate kairi so much! she gets in the way of riku and sora's love! Reply .
http://kairi-haters.deviantart.com/
-
could ghosts cause global warming
Kairi from Kingdom Hearts by *johnjoseco on deviantART
Mar 14, 2003 . Let Kairi take in charge now! . :iconkh-sora-love: #kh-sora- . Also if you know anyone with Fanfiction could you please tell them to check out .
http://johnjoseco.deviantart.com/art/Kairi-from-Kingdom-Hearts-1464675
-
clarence t jones rapidshare book
Kairi and Sora
You are viewing the community kairixsora. Find . Link: http://www.fanfiction.net/s/ 5556718/1/Dearly_Beloved . Does anyone has a Sora X Kairi=love colorbar?
http://kairixsora.livejournal.com/
-
examples ofdrug biologic products
Sora and Kairi's Wedding: KH Alternate Future 1 by *x-Destinys ...
Oct 6, 2010 . It was months ago that Kairi had first shown the bottled letter to Sora and . Any KH fanfiction writers! . "I love Kairi and all, but this is ridiculous!
http://x-destinys-force-x.deviantart.com/art/Sora-and-Kairi-s-Wedding-KH-Alternate-Future-1-181786765
-
barbie island princess mp3
3
. on the staff! English - Staff: 5 - Followers: 15 - Since: 06-26-05 - Founder: kairi clone . There's already a SoraRoxas community out there, so...why not? A collection of . Because I love it and there is just tons for Kingdom Hearts! Any pairing .
http://m.fanfiction.net/communities/game/Kingdom-Hearts/0/3/3/
-
native insights essay competion
KH - Sora and Kairi by =hikari-chan on deviantART
Mar 28, 2003 . This is my first ever drawing of Sora and Kairi together in color and its an oekaki! . love 25 25, joy 19 19, wow 12 12, mad 0 0, sad 0 0, fear 2 2, neutral 0 0 . and maybe even post their fanfiction there/spread the word so the .
http://hikari-chan.deviantart.com/art/KH-Sora-and-Kairi-1551127
-
destrehan plantation fall festival
Sora and Kairi's Wedding: KH Alternate Future 37 by *x-Destinys ...
Nov 13, 2010 . Any KH fanfiction writers! :iconkhfanatics: #KHFanatics . Sora and Kairi's Wedding: KH Alternate Future 37 . "Secret plan?! I love secrets!
http://x-destinys-force-x.deviantart.com/art/Sora-and-Kairi-s-Wedding-KH-Alternate-Future-37-186110141
-
apartment rentals in yountville
Kairi Sora Namine Roxas Fanart [Complete v1] - Avidityy - deviantART
Nov 18, 2012 . COLORS EVERYWHERE Fanart for my Kingdom Hearts fanfiction [[link] God, . love 1 1, joy 0 0, wow 0 0, mad 0 0, sad 0 0, fear 0 0, neutral 0 0 . a picture you created with Sora and Kairi kissing YEARS AND YEARS AGO!
http://avidityy.deviantart.com/art/Kairi-Sora-Namine-Roxas-Fanart-Complete-v1-338497767
-
technical education global issues and challenges
1
Mar 10, 2011 . Stories of Riku and Sora! Share with . This is where you can find all the best SoraRiku fanfics. You have . 121Sora loves Kairi Obsessions!
http://m.fanfiction.net/communities/game/Kingdom-Hearts/
-
chain of command of a hospital
SQUARE ENIX > Thread > Sora & Kiari
Jul 7, 2012 . Sora and Kairi always had the classical fall in love type of relationship. (as proof remember in nightmare before christmas, in kingdom hearts II .
http://na.square-enix.com/go/thread/view/139243/29222529/Sora__Kiari


The CEO’s Reading List: Nov 23, 2019
Missed all the important economy and policy news this week because you were neck-deep in work? Here’s a curated list of BL’s top stories on the subjects through the week for you to catch up.
Privatisation at last
In the first such attempt after 2002, the Government is kicking off strategic sales of large stakes in PSUs to private buyers while ceding management control. Of the five PSUs cleared for strategic sales by CCEA – BPCL, Concor, Shipping Corporation, Tehri Hydro Power and North Eastern Electric Power Corporation, the last two will be acquired by NTPC.
Under protest
But the move to disinvest profitable PSUs is meeting with stiff opposition from the employees and officers’ unions of PSUs who are likening it to the sale of “family silver.”
Red carpet to FDI
The DIPP is readying detailed plans to make a pitch to 324 global firms from Tesla to GSK and Eli Lilly to come up and set up factories in India. The investors will be provided land, power, water and other infrastructure access.
Readying for IBC
Using provisions under the new rules for financial firms under IBC, RBI superseded the Board of ailing DHFL, paving the way for an IBC reference.
Labour law revamp
As a part of its move to rationalise 44 labour laws into four Codes, the Centre approved the Industrial Relations Code Bill this week which ill introduced in the upcoming Parliament session. The bill seeks to subsume laws such as the Trade Unions Act, Industrial Employment Act and Industrial Disputes Act.
Low on charge
In a worrying sign for the economy, power generation slumped 13 percent in October on weak demand.
AGR relief
Responding to distress calls from the telecom companies, the Cabinet agreed to defer the AGR payments by two years, saving Reliance Jio, Airtel and Vodafone Idea Rs 42,000 crore
Could’ve done better
But could the telecom companies have done something to save themselves such large hits from the AGR ruling? Yes, they could have, argues this analysis.
Delayed dues
States are complaining about the Centre delaying compensation payouts to them under GST, even as the Centre grapples with slowing GST mop-ups.
Smart City update
81% of the 5151 projects proposed under the Smart City mission have been tendered but only 25 per cent are completed funds a status update on the initiative.
US-Bound
The new rules in the EB-5 Visa program for entrepreneurs to apply for permanent residence in the US came into effect on this week.
Compiled by Aarati Krishnan


Last Name
First Name
Address
City
State
Zip/Postal Code
Country
America
Amsterdam
Antalia
Bangladesh
Bulgaria
Siria
Japan
Jamaica
Phone(country code,
area code, number)
(
)
Email
Birth Date
Gender
male
female
Starting Date
Spring 2015
Summer 2015
Comments\Questions


Home
About Us
Page
Shop
Blog
Contact Us
Affordable paris tours
City / tours tickets / tour guides
New Collections
Blue Tshirt
$26
Woman Shirt
$31
Yellow Tshirt
$21
Cool Lingerie
$56


• Protocol
• Project
Relationship with protocol
Scroll to zoom
Drag to move
Click on the top left corner of each node to view fullsize wordcloud


| DOSE: T+ 0:00 |
300 mg |
oral |
DXM |
(liquid) |
| T+ 3:00 |
300 mg |
oral |
DXM |
(liquid) |
| T+ 6:00 |
900 mg |
oral |
DXM |
(liquid) |
| T+ 7:00 |
1200 mg |
oral |
DXM |
(liquid) |
| T+ 0:00 |
repeated |
oral |
Alcohol - Hard |
|
| T+ 0:00 |
repeated |
smoked |
Cannabis |
|
This is going to be a rather long report, but I think my experience is worth telling as it is a rather disturbing one of the darker sides of the substance. And it also shows how differently it acts on different people and that it can be a very dangerous drug contrary to many reports.
First a little bit about myself. I am 20 years old and I have been using alcohol (extensive amounts) since I was 15 and weed since I was 16 although weekly only for the last year. My smoking buddies and me had been looking for a psychedelic to use for a few months and we decided to use aminata muscaria, but be were unable to find any. We heard from a friend of us about Dexofan, a cough syrup that had psychedelic effects. This is a non-description drug in Estonia, where I live and so we got two 600 ml bottles of it with 30 mg/ml for the four of us.
Having drunk half a bottle per person for the four of us we waited. The results were very different.  I on the other hand had no ill effects but got great visuals and felt euphoric like never before. It was great!
After the experience I tried to learn about the substance, found the DXM FAQ and found out that DXM is rather safe and great for recreational use. And I used it A LOT. I changed to pills (30 mg/per pill) and took 20 to 40 pills almost daily. I guess I don’t have to mention that all other life stopped. The after effects in the mornings were far too great to ignore, I was unable to attend university and I had to either drink vodka or take the pills to get over them.
It started in the middle of September and now it is November already. My addiction culminated four days ago when I went to my cottage with 11 bottles of Dexofan (3300 Mg), 1 Litres of Vodka and 3 grams of cannabis. I had decided to experience the sigma plateau. I followed the advice I found on the net with 600 mg first on, followed by 600 after 3 hours and then 900 after another 3 hours. Perhaps my tolerance had already built up sufficiently, but I took the rest of the 1200 after another hour. I also had a lot of vodka down the way and smoked weed. And then it started!
I was barely conscious and saw only size distortions and had no thoughts. My mind was clear. The aliens came and were very real. But they ordered me to commit suicide, as I had seen them and it was unacceptable.  That was all I could think. And I must say that I was very close to actually doing it.
Then my brain reset itself in a very weird way. I no longer saw the aliens and I was not drunk, although I must have drunk at least ¾ L of vodka.  I went to the seaside and threw away my pipe.  Somehow I managed to get home, tell my parents about what has happened and go to psychiatrist in the next morning. I am still not sure if am mad or not, but I definitely have some brain damage as everything looks new and strange to me.
I never had any of the more visual and vivid visual hallucinations while using DXM, but the world definitely seemed strange to me and I had intense closed eye visuals. And the euphoria was also intense. I also never puked while using it.
I hope I will be normal again some time in the future, but all I can say is that it does cause addiction in some people and I personally wouldn’t recommend it to anyone. Ever.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Ads Campaign Management System
Login to your homepage
Remember me on this computer
Login
Forgot your password?
Click here to reset it
.


Note: Free trial doesn't have the organizations.
gcloud cp gs://myBucket test.txt
gsutil cp gs://myBucket test.txt
gcloud cp test.txt gs://myBucket
gsutil cp test.txt gs://myBucket
cf1
to your Bigtable table named my-table
?cbt createfamily cf1 my-table
cbt createfamily my-table cf1
gcloud createfamily cf1 my-table cf1
gcloud createfamily my-table cf1
gcloud ssh compute [instance_name] --zone=[zone]
compute ssh connect [instance_name] --zone=[zone]
gcloud compute ssh [instance_name] --zone=[zone]
compute connect ssh [instance_name] --zone=[zone]


This release provides some important bug fixes, some new features, and a new copyright.
New functionality:
One or more mapping objects can be passed to the MultiMapping constructor.
MultiMapping objects implement the has_key and get methods as defined for Python 1.5 dictionaries.
Bugs fixed:
When support was added for passing acquisition wrappers to methods of data-less C mix-in classes, C-based __call_method__ hooks were broken. The most notable example of this was the breaking of the Synchronized class.
Calling C-base-class special methods from overriding methods, as in::
class LowerMultiMapping(MultiMapping): def __getitem__(self, key): return MultiMapping.__getitem__(self, lower(key)) caused infinite loops.
A typo in the Acquisition probably caused __delitem__ calls on wrapped mapping objects to fail.
New functionality:
Changed the way that methods in pure mix-in classes are constructed. Now methods are wrapped in such a way that tricky wrapper objects (like Acquisition wrappers) can bind them to wrapped objects.
An "is subclass" test is provided via the macros
ExtensionClassSubclass_Check
, and
ExtensionClassSubclassInstance_Check
, which are
documented in ExtensionClass.h
.
Methods and Acquisition wrappers use free list to improve allocation and deallocation performance.
Bound methods have attributes who's values are stored in their instances.
Added __module__
attribute to ExtensionClasses to be
consistent with Python 1.5 classes and to work correctly
with 1.5 pickling.
Added the __basic__
new class method to allow
ExtensionClass instances to be unpickled without calling
their constructors.
Acquired acquiring objects can nor acquire from the object they were accessed in, in addition to the object they were acquired from.
Added new Acquisition
variable, Acquired
, to support
"Controlled Acquisition'.
Added a new aq_acquire
method for objects that subclass
Acquisition.Implicit
or Acquisition.Explicit
. This
supports explicit acquisition and provides an option
filter function to support "Filtered Acquisiition".
The acquire
method available in earlier releases is still
available, but is deprecated.
Bugs fixed:
There were some incorrect C-level error return values.
A bug in handling method chains caused "C inheritence" to fail. This only affected extension types that inherited from extension types using method chains, not extension subclasses defined in Python inheriting from extension base classes defined in C.
Expressions like not foo
or statements like::
if foo: ... often failed for non-collection types because of an incorrect attempt to take the 'len' of an object.
Comparisons of objects with different classes didn't work correctly.
Instances provided access to their class __bases__
attribute.
Bugs fixed:
Fixed bug in handling subclasses of Sequence objects.
Fixed comparison bug in Missing objects.
Added functionality to and fixed bug in Missing module
Fixed horible reference-counting bug
Changed so that Missing.Value.spam(a1,a2,whatever)
returns Missing.Value
for any method name (except
__reduce__
) and any arguments.
Changed so that missing values are picklable. Note that the special global, Missing.Value, is pickled in a slightly more efficient manner than other missing values.
First non-beta release
This release is the result of a major rewrite and "hardening" effort to increase performance and reliability. This version is being used in several Digital Creations products, so if parts are broken, we probably don't use them. :-)
This release also contains several new features and example modules, including:
Acquisition,
Custom method calls,
Class initialization protocol,
A class method that makes it possible to explicitly call Python base-class methods.
A sample application of custom method calls that provides Java-like synchronized classes that prevent more than one thread from accessing an object's methods at one time.
Note that there is one known incompatibility with previous
releases. In previouse releases, the method used to support
context wrapping was named __bind_to_object__
. The name of
this method was changed to __of__
in this release and I do
not expect this name to change in the future.


Search
My Garage
My Garage
Welcome to Your Garage!
To view each car's maintenance records, click "View Maintenance"
Add a new car to your garage:
Year:
Make:
Model:
Color:
Vin:


--- date: 2011-09-09T00:00:00Z published: true title: F-16 pilot was ready to give her life on Sept. 11 - The Washington Post url: /2011/09/09/f-16-pilot-was-ready-to-give-her-life-on-sept-11-the-washington-post/ ---
“I’m going to go for the cockpit,” Sasseville said.
She replied without hesitating.
“I’ll take the tail.”
It was a plan. And a pact.


--- layout: post authors: ["Greg Wilson"] title: "Van Lindberg's Keynote: Say Thanks" date: 2015-04-25 time: "08:00:00" tags: ["Noticed"] ---
Van Lindberg, the chair of the Python Software Foundation, gave a really insightful keynote at PyCon 2015 last week. In a nutshell:
The PSF's greatest challenge is that it's short of time: it has one full-time and three part-time employees.
What can you do to help?
How can a new organization grow up to be the PSF?
There's lots more, and you should watch the whole thing, but I think these are good guidelines for Software Carpentry (and most other things, too).


I look at the sky and wonder across the universe
I know
It's the machine I am but
Stronger than me, I'm called by the place of my self
The place I call home.
So everynight I write a part of you on the stars.
The milky way falling deep in your skin.
The rings of Saturn through your eyes.
The red dwarfs holding hands with your hair.

And through a thousand light-years
And seven trillion centuries
Any dust or grain left of me
Through the indivisible force of atoms
I'll hear you
I'll feel you
I'll touch you
I'll kiss you
I'll love you
Until God eats the last proton out the Universe
Unconditionally
Without reasons
Without limits


Home
Docs
Overview
Sources
About
Tweet
Download
Countries
in lockdown
0
People
affected
3M
“Proof of concept, no real data yet”


Clay Bully
问题描述 :
Ms. Terry is a pre-school art teacher who likes to have her students work with clay. One of her assignments is to form a lump of clay into a block and then measure the dimensions of the block. However, in every class, there is always one child who insists on taking some clay from some other child. Since Ms. Terry always gives every child in a class the same amount of clay to begin with, you can write a program that helps Ms. Terry find the bully and victim after she measures each child’s finished block.
输入:
There are one or more classes of students, followed by a final line containing only the value -1. Each class starts with a line containing an integer, n, which is the number of students in the class, followed by n lines of student information. Each line of student information consists of three positive integers, representing the dimensions of the clay block, followed by the student’s first name. There can never be more than 9 students nor less than 2 students in any class. Each student’s name is at most 8 characters. Ms. Terry always gives each student at most 250 cubic units of clay. There is exactly one bully and one victim in each class.
输出:
For each class print a single line exactly as shown in the sample output.
样例输入:
3 10 10 2 Jill 5 3 10 Will 5 5 10 Bill 4 2 4 10 Cam 4 3 7 Sam 8 11 1 Graham 6 2 7 Pam -1
样例输出:
Bill took clay from Will. Graham took clay from Cam.
解题代码:
import java.util.*; class Clay { private int l = 0; private int w = 0; private int h = 0; private String name; private int size = 0; public Clay(String l, String w, String h, String name) { this.l = Integer.valueOf(l).intValue(); this.w = Integer.valueOf(w).intValue(); this.h = Integer.valueOf(h).intValue(); this.name = name; this.size = this.l * this.w * this.h; } public int getSize() { return size; } public String getName() { return name; } } public class Main { public static void main(String[] args) { Scanner cin = new Scanner(System.in); while(true) { int num = Integer.valueOf(cin.nextLine()).intValue(); if(num == -1) break; List list = new ArrayList(); for(int i = 0; i < num; i++) { String[] str = cin.nextLine().split(" "); Clay clay = new Clay(str[0], str[1], str[2], str[3]); list.add(clay); } Iterator iter = list.iterator(); int max = 0; int min = 250; String maxName = null; String minName = null; while(iter.hasNext()) { Clay theone = (Clay)iter.next(); if(theone.getSize() > max) { max = theone.getSize(); maxName = theone.getName(); } if(theone.getSize() < min) { min = theone.getSize(); minName = theone.getName(); } } System.out.println(maxName + " took clay from " + minName + "."); } } }
(adsbygoogle = window.adsbygoogle || []).push({});
<EMAIL_ADDRESS>为什么暴力求解方法循环到sqrt(n) 就可以了呢
你指的哪个问题？
正确
原来欧几里德定理应用这么多
因为是要把从字符串s的start位到当前位在hash中重置，修改提交后能accept，但是不修改居然也能accept
这个也是算法导论的习题
赞一个！权限已更新:）
.net中char类型不是两个字节么
确实有点难度，搞了好久。int溢出要注意
这配图…..
第二个解法好像不对


Here are some of my favorite pizzas!
These are my three favorites, in order:
Thanks for reading about us!


Carrie Underwood
Some Hearts
-
Wasted
-
Don't Forget to Remember Me
- Some Hearts
- Jesus, Take the Wheel
- The Night Before (Life Goes On)
- Lessons Learned
- Before He Cheats
- Starts with Goodbye
- I Just Can't Live a Lie
- We're Young and Beautiful
- That's Where It Is
- Whenever You Remember
- I Ain't in Checotah Anymore
- Inside Your Heaven
Carnival Ride
-
Flat on the Floor
-
All-American Girl
- So Small
- Just a Dream
- Get Out of This Town
- Crazy Dreams
- I Know You Won't
- Last Name
- You Won't Find This
- I Told You So
- The More Boys I Meet
- Twisted
- Wheel of the World


| espn_potentialpoints {ffscrapr} | R Documentation |
This function calculates the optimal starters for a given week, using some lineup heuristics.
espn_potentialpoints(conn, weeks = 1:17)
conn |
the list object created by |
weeks |
a numeric vector for determining which weeks to calculate |
a tibble with the best lineup for each team and whether they were started or not
try({ # try only shown here because sometimes CRAN checks are weird conn <- espn_connect(season = 2020, league_id = 899513) espn_potentialpoints(conn, weeks = 1:3) }) # end try


Papers
- Philosophy Paper: This whitepaper provides an in-depth look at why we created ATT&CK, how we update and maintain it, and what the community commonly uses it for.
- Finding Cyber Threats with ATT&CK-Based Analytics: This paper presents a methodology for using ATT&CK to build, test, and refine behavioral-based analytic detection capabilities using adversary emulation.
Presentations
-
August 2019This presentation from Black Hat walks through the story of a fictional organization in order to explain how different teams can use ATT&CK as a powerful force to improve defenses.
-
This presentation from BSidesLV covers how to use ATT&CK to take cyber threat intelligence and operationalize it into behaviors that can drive relevant detections.
-
This keynote presentation from the SANS Security Operations Summit discusses a process to gauge a SOC’s detective capabilities as they relate to ATT&CK, including MITRE’s practical experiences and lessons learned.
-
This presentation from the Annual FIRST Conference presents different methods of using ATT&CK to find dependencies between adversary techniques to support defense.
-
This presentation from the SANS Enterprise Defense Summit explains how defenders can improve their security posture through the use of adversary emulation by performing their very own ATT&CK Evaluations.
-
This presentation from x33fcon takes a deep-dive into using ATT&CK for purple teaming, including lessons learned from ATT&CK Evaluations.
-
This presentation from Sp4rkcon presents an overview of ATT&CK as well as ideas for how you can put it into action for four use cases. Slides are also available.
-
April 2019This presentation from the SANS Blue Team Summit provides a red teamer’s perspective to show how ATT&CK is a valuable tool to help red and blue teams work together to improve their defenses. Slides are also available.
-
This presentation from the FIRST CTI Symposium discusses how you can use ATT&CK for threat intelligence as well as biases to be aware of as you do that.
-
This presentation from RSA covers an overview of ATT&CK as well as key use cases and tools that can be used to convert it into practice.
-
March 2019This presentation from BSides NOVA explores a number of different ways to analyze the ATT&CK knowledge base and how organizations might perform similar analyses with their own data.
-
This presentation from the SANS CTI Summit presents an overview of how two different organizations use ATT&CK to map adversary behavior and prioritize how you apply that intelligence to defenses.
-
his presentation from Shmoocon discusses the use case of evaluating security tools with ATT&CK. It provides an overview of the approach taken by the ATT&CK Evaluations initiative.
-
This presentation from BSides DC explores how you can apply ATT&CK to optimize and harmonize adversarial and defensive cyber operations.
-
This presentation from the FireEye Cyber Defense Summit covers the use of ATT&CK as a framework for understanding FIN7 behaviors. Slides are also available.
-
This presentation from the SANS Threat Hunting Summit shows how you can use ATT&CK to apply threat intelligence to adversary emulation.
-
August 2018This presentation from the DEFCON Blue Team Village shows how ATT&CK can be used for Security Operations Center (SOC) assessments.
-
August 2018This presentation from BSidesLV provides an overview of ATT&CK along with details on two use cases: threat intelligence and analytics. Slides are also available.
-
July 2018This presentation from HOPE provides perspective on how to use threat intelligence for ATT&CK-based adversary emulation. Slides are also available.
-
This presentation from x33fcon discusses how purple teams can use ATT&CK as a common language for adversary emulation. Slides are also available.
-
November 2016This presentation from BSides Delaware outlines the key features of ATT&CK, describing the tactics, techniques, groups, and software that make up ATT&CK along with a discussion on how it can be used.
Other ATT&CK Efforts
Building a community around sharing observations of ATT&CK techniques in the wild.
Plans that showcase the practical use of ATT&CK for offensive operators and defenders.
Evaluations of cybersecurity products using an open methodology based on ATT&CK.
Other Resources
- ATT&CK Update Log: Recent changes to the ATT&CK content.
- Interfaces for Working with ATT&CK: Tools we've developed for accessing and manipulating the ATT&CK content.


First look: Zaha Hadid Architects' flower-inspired lobby for Southbank Tower
by Helen CraneThe critically-acclaimed firm of architects founded by the late Zaha Hadid is responsible for London cultural landmarks like the London Aquatics Centre built for the 2012 Olympic Games and the Serpentine Sackler Gallery in Hyde Park.
Until now it had not worked on any residential projects in the UK, but it has just started a new commission to redesign the lobby of Southbank Tower – the 1972 Richard Seifert-designed office building which was converted into 191 apartments in 2015.
City A.M got an exclusive first look at its plans for the project, the centrepiece of which is a bold feature lighting scheme inspired by the intricate, organic forms of flower petals.
The sculptural petals, made from glass fibre reinforced gypsum, reflect the fluid shapes seen in lots of Hadid’s work, which led to her being dubbed ‘the Queen of the curve’. Her other major works included Guangzhou Opera House in China and Sheikh Zayed Bridge in Abu Dhabi.
“The design has evolved from our work reinventing the spaces of art museums and galleries around the world,” says Helmut Kinzler, project director at Zaha Hadid Architects (ZHA). “We took a sculptural approach to the space, and decided not to include any works of art because we see it as a canvas in itself. We also wanted to incorporate lots of light, because sculpture can only work in conjunction with light.
Read more: Clerkenwell controversy: The award-winning new building that could be torn down
The petal forms will be made in an off-site studio using 3D digital files, which ZHA says will allow it to create a level of detail usually associated with “intricate, hand-crafted design works.” The fluid curves of the structure will house the concierge on the ground floor, before peeling back at the top to reveal a new mezzanine level.“Residents will have a place to meet whoever comes in, and they can have small parties and gatherings there,” says Kinzler.
The lobby’s material and colour palette draws inspiration from the existing building design, marrying marble and concrete with walnut, leather and silks.
The work has been commissioned by a private investment firm which acquired 37 apartments in the 41-storey tower in early 2018. It will re-launch the apartments to the market following the completion of the lobby, and has appointed Knight Frank and JLL as sales agents.


Introduction¶
Why machine learning for the sciences?¶
Machine learning and artificial neural networks are everywhere and change our daily life more profoundly than we might be aware of. However, these concepts are not a particularly recent invention. Their foundational principles emerged already in the 1940s. The perceptron, the predecessor of the artificial neuron, the basic unit of many neural networks to date, was invented by Frank Rosenblatt in 1958, and even cast into a hardware realization by IBM.
It then took half a century for these ideas to become technologically relevant. Now, artificial intelligence based on neural-network algorithms has become an integral part of data processing with widespread applications. The reason for its tremendous success is twofold. First, the availability of big and structured data caters to machine learning applications. Second, the realization that deep (feed-forward) networks (made from many “layers” of artificial neurons) with many variational parameters are tremendously more powerful than few-layer ones was a big leap, the “deep learning revolution”. Machine learning refers to algorithms that infer information from data in an implicit way. If the algorithms are inspired by the functionality of neural activity in the brain, the term cognitive or neural computing is used. Artificial neural networks refer to a specific, albeit most broadly used, ansatz for machine learning. Another field that concerns iteself with inferring information from data is statistics. In that sense, both machine learning and statistics have the same goal. However, the way this goal is achieved is markedly different: while statistics uses insights from mathematics to extract information, machine learning aims at optimizing a variational function using available data through learning. The mathematical foundations of machine learning with neural networks are poorly understood: we do not know why deep learning works. Nevertheless, there are some exact results for special cases. For instance, certain classes of neural networks are a complete basis of smooth functions, that is, when equipped with enough variational parameters, they can approximate any smooth high-dimensional function with arbitrarily precision. Other variational functions with this property we commonly use are Taylor or Fourier series (with the coefficients as “variational” parameters). We can think of neural networks as a class or variational functions, for which the parameters can be efficiently optimized with respect to a desired objective.
As an example, this objective can be the classification of handwritten digits from ‘0’ to ‘9’. The input to the neural network would be an image of the number, encoded in a vector of grayscale values. The output is a probability distribution saying how likely it is that the image shows a ‘0’, ‘1’, ‘2’, and so on. The variational parameters of the network are adjusted until it accomplishes that task well. This is a classical example of supervised learning. To perform the network optimization, we need data consisting of input data (the pixel images) and labels (the integer number shown on the respective image).
Our hope is that the optimized network also recognizes handwritten digits it has not seen during the learning. This property of a network is called generalization. It stands in opposition to a tendency called overfitting, which means that the network has learned specificities of the data set it was presented with, rather than the abstract features necessary to identify the respective digit. An illustrative example of overfitting is fitting a polynomial of degree \(9\) to \(10\) data points, which will always be a perfect fit. Does this mean that this polynomial best characterizes the behavior of the measured system? Of course not! Fighting overfitting and creating algorithms that generalize well are key challenges in machine learning. We will study several approaches to achieve this goal.
Handwritten digit recognition has become one of the standard benchmark problems in the field. Why so? The reason is simple: there exists a very good and freely available data set for it, the MNIST database 2, see Fig. [fig:MNIST]. This curious fact highlights an important aspect of machine learning: it is all about data. The most efficient way to improve machine learning results is to provide more and better data. Thus, one should keep in mind that despite the widespread applications, machine learning is not the hammer for every nail. It is most beneficial if large and balanced data sets, meaning roughly that the algorithm can learn all aspects of the problem equally, in a machine-readable way are available.
This lecture is an introduction specifically targeting the use of machine learning in different domains of science. In scientific research, we see a vastly increasing number of applications of machine learning, mirroring the developments in industrial technology. With that, machine learning presents itself as a universal new tool for the exact sciences, standing side-by-side with methods such as calculus, traditional statistics, and numerical simulations. This poses the question, where in the scientific workflow, summerized in Fig. [fig:scientific_workflow], these novel methods are best employed.
Once a specific task has been identified, applying machine learning to the sciences does, furthermore, hold its very specific challenges: (i) scientific data has often very particular structure, such as the nearly perfect periodicity in an image of a crystal; (ii) typically, we have specific knowledge about correlations in the data which should be reflected in a machine learning analysis; (iii) we want to understand why a particular algorithm works, seeking a fundamental insight into mechanisms and laws of nature; (iv) in the sciences we are used to algorithms and laws that provide deterministic answers while machine learning is intrinsically probabilistic - there is no absolute certainty. Nevertheless, quantitative precision is paramount in many areas of science and thus a critical benchmark for machine learning methods.
[A note on the concept of a model]{}
In both machine learning and the sciences, models play a crucial role.
However, it is important to recognize the difference in meaning: In the
natural sciences, a model is a conceptual representation of a
phenomenon. A scientific model does not try to represent the whole
world, but only a small part of it. A model is thus a simplification of
the phenomenon and can be both a theoretical construct, for example the
ideal gas model or the Bohr model of the atom, or an experimental
simplification, such as a small version of an airplane in a wind
channel.
In machine learning, on the other hand, we most often use a complicated variational function, for example a neural network, to try to approximate a statistical model. But what is a model in statistics? Colloquially speaking, a statistical model comprises a set of statistical assumptions which allow us to calculate the probability \(P(x)\) of any event \(x\). The statistical model does not correspond to the true distribution of all possible events, it simply approximates the distribution. Scientific and statistical models thus share an important property: neither claims to be a representation of reality.
Overview and learning goals¶
This lecture is an introduction to basic machine learning algorithms for scientists and students of the sciences. We will cover
the most fundamental machine learning algorithms,
the terminology of the field, succinctly explained,
the principles of supervised and unsupervised learning and why it is so successful,
various architectures of artificial neural networks and the problems they are suitable for,
how we find out what the machine learning algorithm uses to solve a problem.
The field of machine learning is full of lingo which to the uninitiated obscures what is at the core of the methods. Being a field in constant transformation, new terminology is being introduced at a fast pace. Our aim is to cut through slang with mathematically precise and concise formulations in order to demystify machine learning concepts for someone with an understanding of calculus and linear algebra.
As mentioned above, data is at the core of most machine learning approaches discussed in this lecture. With raw data in many cases very complex and extremely high dimensional, it is often crucial to first understand the data better and reduce their dimensionality. Simple algorithms that can be used before turning to the often heavy machinery of neural networks will be discussed in the next section, Sec. [sec:structuring_data].
The machine learning algorithms we will focus on most can generally be divided into two classes of algorithms, namely discriminative and generative algorithms as illustrated in Fig. [fig:overview]. Examples of discriminative tasks include classification problems, such as the aforementioned digit classification or the classification into solid, liquid and gas phases given some experimental observables. Similarly, regression, in other words estimating relationships between variables, is a discriminative problem. More specifically, we try to approximate the conditional probability distribution \(P(y|x)\) of some variable \(y\) (the label) given some input data \(x\). As data is provided in the form of input and target data for most of these tasks, these algorithms usually employ supervised learning. Discriminative algorithms are most straight-forwardly applicable in the sciences and we will discuss them in Secs. [sec: linear methods for supervised learning] and [sec:supervised].
Generative algorithms, on the other hand, model a probability distribution \(P(x)\). These approaches are—once trained—in principle more powerful, since we can also learn the joint probability distribution \(P(x,y)\) of both the data \(x\) and the labels \(y\) and infer the conditional probability of \(y\). Still, the more targeted approach of discriminative learning is better suited for many problems. However, generative algorithms are useful in the natural sciences, as we can sample from a known probability distribution, for example for image denoising, or when trying to find new compounds/molecules resembling known ones with given properties. These algorithms are discussed in Sec. [sec:unsupervised]. The promise of artificial intelligence may trigger unreasonable expectations in the sciences. After all, scientific knowledge generation is one of the most complex intellectual processes. Computer algorithms are certainly far from achieving anything on that level of complexity and will in the near future not formulate new laws of nature independently. Nevertheless, researchers study how machine learning can help with individual segments of the scientific workflow (Fig. [fig:scientific_workflow]). While the type of abstraction needed to formulate Newton’s laws of classical mechanics seems incredibly complex, neural networks are very good at implicit knowledge representation. To understand precisely how they achieve certain tasks, however, is not an easy undertaking. We will discuss this question of interpretability in Sec. [sec:interpretability].
A third class of algorithms, which does not neatly fit the framework of approximating a statistical model and thus the distinction into discriminative and generative algorithms is known as reinforcement learning. Instead of approximating a statistical model, reinforcement learning tries to optimize strategies (actions) for achieving a given task. Reinforcement learning has gained a lot of attention with Google’s AlphaGo Zero, a computer program that beat the best Go players in the world. As an example for an application in the sciences, reinfocrment learning can be used to decide on what experimental configuration to perform next. While the whole topic is beyond the scope of this lecture, we will give an introduction to the basic concepts of reinforcement learning in Sec. [sec:RL].
A final note on the practice of learning. While the machine learning machinery is extremely powerful, using an appropriate architecture and the right training details, captured in what are called hyperparameters, is crucial for its successful application. Though there are attempts to learn a suitable model and all hyperparameters as part of the overall learning process, this is not a simple task and requires immense computational resources. A large part of the machine learning success is thus connected to the experience of the scientist using the appropriate algorithms. We thus strongly encourage solving the accompanying exercises carefully and taking advantage of the exercise classes.
Resources¶
While it may seem that implementing ML tasks is computationally challenging, actually almost any ML task one might be interested in can be done with relatively few lines of code simply by relying on external libraries or mathematical computing systems such as Mathematica or Matlab. At the moment, most of the external libraries are written for the Python programming language. Here are some useful Python libraries:
TensorFlow. Developed by Google, Tensorflow is one of the most popular and flexible library for machine learning with complex models, with full GPU support.
PyTorch. Developed by Facebook, Pytorch is the biggest rival library to Tensorflow, with pretty much the same functionalities.
Scikit-Learn. Whereas TensorFlow and PyTorch are catered for deep learning practitioners, Scikit-Learn provides much of the traditional machine learning tools, including linear regression and PCA.
Pandas. Modern machine learning is largely reliant on big datasets. This library provides many helpful tools to handle these large datasets.
Prerequisites¶
This course is aimed at students of the (natural) sciences with a basic mathematics education and some experience in programming. In particular, we assume the following prerequisites:
Basic knowledge of calculus and linear algebra.
Rudimentary knowledge of statistics and probability theory (advantageous).
Basic knowledge of a programming language. For the teaching assignments, you are free to choose your preferable one. The solutions will typically be distributed in Python in the form of Jupyter notebooks.
Please, don’t hesitate to ask questions if any notions are unclear.
References¶
For further reading, we recommend the following books:
[ML without neural networks]{}: The Elements of Statistical Learning, T. Hastie, R. Tisbshirani, and J. Friedman (Springer)
[ML with neural networks]{}: Neural Networks and Deep Learning, M. Nielson (http://neuralnetworksanddeeplearning.com)
[Deep Learning Theory]{}: Deep Learning, I. Goodfellow, Y. Bengio and A. Courville (http://www.deeplearningbook.org)
[Reinforcement Learning]{}: Reinforcement Learning, R. S. Sutton and A. G. Barto (MIT Press)
- 2
http://yann.lecun.com/exdb/mnist


|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |
| Packages that use TransformerImpl | |
|---|---|
| org.apache.xalan.extensions | Implementation of Xalan Extension Mechanism. |
| org.apache.xalan.lib | Extension elements and functions shipped with Xalan-Java, including EXSLT functions. |
| org.apache.xalan.templates | Implements the Templates interface,
and defines a set of classes that represent an XSLT stylesheet. |
| org.apache.xalan.trace | Implementation of Xalan Trace events, for use by a debugger. |
| org.apache.xalan.transformer | In charge of run-time transformations and the production of result trees. |
| Uses of TransformerImpl in org.apache.xalan.extensions |
|---|
| Fields in org.apache.xalan.extensions declared as TransformerImpl | |
|---|---|
private TransformerImpl |
XSLProcessorContext.transformer
An instance of a transformer |
| Methods in org.apache.xalan.extensions that return TransformerImpl | |
|---|---|
TransformerImpl |
XSLProcessorContext.getTransformer()
Get the transformer. |
| Methods in org.apache.xalan.extensions with parameters of type TransformerImpl | |
|---|---|
void |
ExtensionHandlerJavaPackage.processElement(java.lang.String localPart,
ElemTemplateElement element,
TransformerImpl transformer,
Stylesheet stylesheetTree,
java.lang.Object methodKey)
Process a call to this extension namespace via an element. |
void |
ExtensionHandlerJavaClass.processElement(java.lang.String localPart,
ElemTemplateElement element,
TransformerImpl transformer,
Stylesheet stylesheetTree,
java.lang.Object methodKey)
Process a call to this extension namespace via an element. |
void |
ExtensionHandlerGeneral.processElement(java.lang.String localPart,
ElemTemplateElement element,
TransformerImpl transformer,
Stylesheet stylesheetTree,
java.lang.Object methodKey)
Process a call to this extension namespace via an element. |
void |
ExtensionHandlerExsltFunction.processElement(java.lang.String localPart,
ElemTemplateElement element,
TransformerImpl transformer,
Stylesheet stylesheetTree,
java.lang.Object methodKey)
Required by ExtensionHandler (an abstract method). |
abstract void |
ExtensionHandler.processElement(java.lang.String localPart,
ElemTemplateElement element,
TransformerImpl transformer,
Stylesheet stylesheetTree,
java.lang.Object methodKey)
Process a call to this extension namespace via an element. |
| Constructors in org.apache.xalan.extensions with parameters of type TransformerImpl | |
|---|---|
XSLProcessorContext(TransformerImpl transformer,
Stylesheet stylesheetTree)
Create a processor context to be passed to an extension. |
| Uses of TransformerImpl in org.apache.xalan.lib |
|---|
| Methods in org.apache.xalan.lib with parameters of type TransformerImpl | |
|---|---|
SerializationHandler |
Redirect.createSerializationHandler(TransformerImpl transformer,
java.io.FileOutputStream ostream,
java.io.File file,
OutputProperties format)
A class that extends this one could over-ride this public method and receive a callback for the creation of the serializer used in the redirection. |
void |
Redirect.endRedirection(TransformerImpl transf)
A class that extends this class can over-ride this public method and receive a callback that redirection to the ContentHandler specified in the startRedirection() call has ended |
void |
Redirect.startRedirection(TransformerImpl transf,
org.xml.sax.ContentHandler formatter)
A class that extends this class can over-ride this public method and recieve a callback that redirection is about to start |
| Uses of TransformerImpl in org.apache.xalan.templates |
|---|
| Fields in org.apache.xalan.templates declared as TransformerImpl | |
|---|---|
private TransformerImpl |
XUnresolvedVariable.m_transformer
The transformer context for execution. |
| Methods in org.apache.xalan.templates with parameters of type TransformerImpl | |
|---|---|
void |
ElemUse.applyAttrSets(TransformerImpl transformer,
StylesheetRoot stylesheet)
Add the attributes from the named attribute sets to the attribute list. |
private void |
ElemUse.applyAttrSets(TransformerImpl transformer,
StylesheetRoot stylesheet,
QName[] attributeSetsNames)
Add the attributes from the named attribute sets to the attribute list. |
(package private) void |
ElemElement.constructNode(java.lang.String nodeName,
java.lang.String prefix,
java.lang.String nodeNamespace,
TransformerImpl transformer)
Construct a node in the result tree. |
(package private) void |
ElemAttribute.constructNode(java.lang.String nodeName,
java.lang.String prefix,
java.lang.String nodeNamespace,
TransformerImpl transformer)
Construct a node in the result tree. |
void |
ElemVariablePsuedo.execute(TransformerImpl transformer)
Execute a variable declaration and push it onto the variable stack. |
void |
ElemVariable.execute(TransformerImpl transformer)
Execute a variable declaration and push it onto the variable stack. |
void |
ElemValueOf.execute(TransformerImpl transformer)
Execute the string expression and copy the text to the result tree. |
void |
ElemUse.execute(TransformerImpl transformer)
Copy attributes specified by use-attribute-sets to the result tree. |
void |
ElemUnknown.execute(TransformerImpl transformer)
Execute an unknown element. |
void |
ElemTextLiteral.execute(TransformerImpl transformer)
Copy the text literal to the result tree. |
void |
ElemTemplateElement.execute(TransformerImpl transformer)
Execute the element's primary function. |
void |
ElemTemplate.execute(TransformerImpl transformer)
Copy the template contents into the result tree. |
void |
ElemPI.execute(TransformerImpl transformer)
Create a processing instruction in the result tree. |
void |
ElemParam.execute(TransformerImpl transformer)
Execute a variable declaration and push it onto the variable stack. |
void |
ElemNumber.execute(TransformerImpl transformer)
Execute an xsl:number instruction. |
void |
ElemMessage.execute(TransformerImpl transformer)
Send a message to diagnostics. |
void |
ElemLiteralResult.execute(TransformerImpl transformer)
Copy a Literal Result Element into the Result tree, copy the non-excluded namespace attributes, copy the attributes not of the XSLT namespace, and execute the children of the LRE. |
void |
ElemIf.execute(TransformerImpl transformer)
Conditionally execute a sub-template. |
void |
ElemForEach.execute(TransformerImpl transformer)
Execute the xsl:for-each transformation |
void |
ElemFallback.execute(TransformerImpl transformer)
This is the normal call when xsl:fallback is instantiated. |
void |
ElemExtensionCall.execute(TransformerImpl transformer)
Execute an extension. |
void |
ElemExsltFuncResult.execute(TransformerImpl transformer)
Generate the EXSLT function return value, and assign it to the variable index slot assigned for it in ElemExsltFunction compose(). |
void |
ElemElement.execute(TransformerImpl transformer)
Create an element in the result tree. |
void |
ElemCopyOf.execute(TransformerImpl transformer)
The xsl:copy-of element can be used to insert a result tree fragment into the result tree, without first converting it to a string as xsl:value-of does (see [7.6.1 Generating Text with xsl:value-of]). |
void |
ElemCopy.execute(TransformerImpl transformer)
The xsl:copy element provides an easy way of copying the current node. |
void |
ElemComment.execute(TransformerImpl transformer)
Execute the xsl:comment transformation |
void |
ElemChoose.execute(TransformerImpl transformer)
Execute the xsl:choose transformation. |
void |
ElemCallTemplate.execute(TransformerImpl transformer)
Invoke a named template. |
void |
ElemAttributeSet.execute(TransformerImpl transformer)
Apply a set of attributes to the element. |
void |
ElemApplyTemplates.execute(TransformerImpl transformer)
Apply the context node to the matching templates. |
void |
ElemApplyImport.execute(TransformerImpl transformer)
Execute the xsl:apply-imports transformation. |
void |
ElemExsltFunction.execute(TransformerImpl transformer,
XObject[] args)
|
void |
ElemFallback.executeFallback(TransformerImpl transformer)
Execute the fallback elements. |
private void |
ElemUnknown.executeFallbacks(TransformerImpl transformer)
Execute the fallbacks when an extension is not available. |
private void |
ElemExtensionCall.executeFallbacks(TransformerImpl transformer)
Execute the fallbacks when an extension is not available. |
(package private) void |
ElemTemplateElement.executeNSDecls(TransformerImpl transformer)
Send startPrefixMapping events to the result tree handler for all declared prefix mappings in the stylesheet. |
(package private) void |
ElemTemplateElement.executeNSDecls(TransformerImpl transformer,
java.lang.String ignorePrefix)
Send startPrefixMapping events to the result tree handler for all declared prefix mappings in the stylesheet. |
(package private) java.lang.String |
ElemNumber.formatNumberList(TransformerImpl transformer,
long[] list,
int contextNode)
Format a vector of numbers into a formatted string. |
java.lang.String |
ElemExtensionCall.getAttribute(java.lang.String rawName,
org.w3c.dom.Node sourceNode,
TransformerImpl transformer)
Return the value of the attribute interpreted as an Attribute Value Template (in other words, you can use curly expressions such as href="http://{website}". |
(package private) java.lang.String |
ElemNumber.getCountString(TransformerImpl transformer,
int sourceNode)
Given an XML source node, get the count according to the parameters set up by the xsl:number attributes. |
private void |
ElemNumber.getFormattedNumber(TransformerImpl transformer,
int contextNode,
char numberType,
int numberWidth,
long listElement,
FastStringBuffer formattedNumber)
Format the given number and store it in the given buffer |
(package private) java.util.Locale |
ElemNumber.getLocale(TransformerImpl transformer,
int contextNode)
Get the locale we should be using. |
private java.text.DecimalFormat |
ElemNumber.getNumberFormatter(TransformerImpl transformer,
int contextNode)
Get the number formatter to be used the format the numbers |
XObject |
ElemWithParam.getValue(TransformerImpl transformer,
int sourceNode)
Get the XObject representation of the variable. |
XObject |
ElemVariable.getValue(TransformerImpl transformer,
int sourceNode)
Get the XObject representation of the variable. |
void |
ElemTemplateElement.runtimeInit(TransformerImpl transformer)
This function will be called on top-level elements only, just before the transform begins. |
void |
ElemExtensionDecl.runtimeInit(TransformerImpl transformer)
This function will be called on top-level elements only, just before the transform begins. |
void |
ElemForEach.transformSelectedNodes(TransformerImpl transformer)
Perform a query if needed, and call transformNode for each child. |
void |
ElemApplyTemplates.transformSelectedNodes(TransformerImpl transformer)
Perform a query if needed, and call transformNode for each child. |
(package private) void |
ElemTemplateElement.unexecuteNSDecls(TransformerImpl transformer)
Send endPrefixMapping events to the result tree handler for all declared prefix mappings in the stylesheet. |
(package private) void |
ElemTemplateElement.unexecuteNSDecls(TransformerImpl transformer,
java.lang.String ignorePrefix)
Send endPrefixMapping events to the result tree handler for all declared prefix mappings in the stylesheet. |
| Constructors in org.apache.xalan.templates with parameters of type TransformerImpl | |
|---|---|
XUnresolvedVariable(ElemVariable obj,
int sourceNode,
TransformerImpl transformer,
int varStackPos,
int varStackContext,
boolean isGlobal)
Create an XUnresolvedVariable, that may be executed at a later time. |
| Uses of TransformerImpl in org.apache.xalan.trace |
|---|
| Fields in org.apache.xalan.trace declared as TransformerImpl | |
|---|---|
TransformerImpl |
TracerEvent.m_processor
The XSLT processor instance. |
TransformerImpl |
SelectionEvent.m_processor
The XSLT processor instance. |
TransformerImpl |
GenerateEvent.m_processor
The XSLT Transformer, which either directly or indirectly contains most needed information. |
private TransformerImpl |
TraceManager.m_transformer
A transformer instance |
TransformerImpl |
ExtensionEvent.m_transformer
|
| Constructors in org.apache.xalan.trace with parameters of type TransformerImpl | |
|---|---|
EndSelectionEvent(TransformerImpl processor,
org.w3c.dom.Node sourceNode,
ElemTemplateElement styleNode,
java.lang.String attributeName,
XPath xpath,
XObject selection)
Create an EndSelectionEvent. |
|
ExtensionEvent(TransformerImpl transformer,
java.lang.Class clazz)
|
|
ExtensionEvent(TransformerImpl transformer,
java.lang.reflect.Constructor constructor,
java.lang.Object[] arguments)
|
|
ExtensionEvent(TransformerImpl transformer,
java.lang.reflect.Method method,
java.lang.Object instance,
java.lang.Object[] arguments)
|
|
GenerateEvent(TransformerImpl processor,
int eventType)
Constructor for startDocument, endDocument events. |
|
GenerateEvent(TransformerImpl processor,
int eventType,
char[] ch,
int start,
int length)
Constructor for characters, cdate events. |
|
GenerateEvent(TransformerImpl processor,
int eventType,
java.lang.String data)
Constructor for comment and entity ref events. |
|
GenerateEvent(TransformerImpl processor,
int eventType,
java.lang.String name,
org.xml.sax.Attributes atts)
Constructor for startElement, endElement events. |
|
GenerateEvent(TransformerImpl processor,
int eventType,
java.lang.String name,
java.lang.String data)
Constructor for processingInstruction events. |
|
SelectionEvent(TransformerImpl processor,
org.w3c.dom.Node sourceNode,
ElemTemplateElement styleNode,
java.lang.String attributeName,
XPath xpath,
XObject selection)
Create an event originating at the given node of the style tree. |
|
TraceManager(TransformerImpl transformer)
Constructor for the trace manager. |
|
TracerEvent(TransformerImpl processor,
org.w3c.dom.Node sourceNode,
QName mode,
ElemTemplateElement styleNode)
Create an event originating at the given node of the style tree. |
| Uses of TransformerImpl in org.apache.xalan.transformer |
|---|
| Fields in org.apache.xalan.transformer declared as TransformerImpl | |
|---|---|
(package private) TransformerImpl |
NumeratorFormatter.m_processor
An instance of a transformer |
(package private) TransformerImpl |
NodeSortKey.m_processor
Transformer instance |
(package private) TransformerImpl |
XalanTransformState.m_transformer
|
(package private) TransformerImpl |
TreeWalker2Result.m_transformer
The transformer instance |
private TransformerImpl |
TrAXFilter.m_transformer
|
private TransformerImpl |
TransformerHandlerImpl.m_transformer
The transformer this will use to transform a source tree into a result tree. |
(package private) TransformerImpl |
StackGuard.m_transformer
|
private TransformerImpl |
MsgMgr.m_transformer
Transformer instance |
| Methods in org.apache.xalan.transformer that return TransformerImpl | |
|---|---|
TransformerImpl |
TrAXFilter.getTransformer()
Return the Transformer object used for this XML filter. |
| Methods in org.apache.xalan.transformer with parameters of type TransformerImpl | |
|---|---|
(package private) void |
TransformSnapshotImpl.apply(TransformerImpl transformer)
Deprecated. It doesn't look like this code, which is for tooling, has functioned propery for a while, so it doesn't look like it is being used. |
static void |
SerializerSwitcher.switchSerializerIfHTML(TransformerImpl transformer,
java.lang.String ns,
java.lang.String localName)
Switch to HTML serializer if element is HTML |
| Constructors in org.apache.xalan.transformer with parameters of type TransformerImpl | |
|---|---|
MsgMgr(TransformerImpl transformer)
Create a message manager object. |
|
NodeSortKey(TransformerImpl transformer,
XPath selectPat,
boolean treatAsNumbers,
boolean descending,
java.lang.String langValue,
boolean caseOrderUpper,
PrefixResolver namespaceContext)
Constructor NodeSortKey |
|
NumeratorFormatter(org.w3c.dom.Element xslNumberElement,
TransformerImpl processor)
Construct a NumeratorFormatter using an element that contains XSL number conversion attributes - format, letter-value, xml:lang, digit-group-sep, n-digits-per-group, and sequence-src. |
|
StackGuard(TransformerImpl transformerImpl)
Constructor StackGuard |
|
TransformerHandlerImpl(TransformerImpl transformer,
boolean doFragment,
java.lang.String baseSystemID)
Construct a TransformerHandlerImpl. |
|
TransformSnapshotImpl(TransformerImpl transformer)
Deprecated. It doesn't look like this code, which is for tooling, has functioned propery for a while, so it doesn't look like it is being used. |
|
TreeWalker2Result(TransformerImpl transformer,
SerializationHandler handler)
Constructor. |
|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |


Welcome to my first HTML!
This is my favorite Playboi Carti song
Playboi Carti's latest single
A link that goes nowhere
Bonus items!
How to make a bacon egg & cheese
- Toast 2 slices of bread, any of your liking
- After toasting, place ya cheese on!
- Now, place cooked eggs on top. (Make sure these eggs seasoned with salt & pepper)
- Top it off with Bacon, make sure they crispy
- Close it off with other piece of toast
- Enjoy!
More artists I like!
- Clairo
- Future
- Boy Pablo
- Summer Walker
- Rhye
| Animal Class |
Animal Name |
| Mammals |
Lions |
| Fish |
Goldfish |
| Reptiles |
Turtles |
| Amphibians |
Frog |


CRFTaskInfo
public struct CRFTaskInfo : RSDTaskInfo, RSDEmbeddedIconVendor
A task info object for the tasks included in this submodule.
-
The task identifier for this task.
Declaration
Swift
public let taskIdentifier: CRFTaskIdentifier
-
The task build for this info.
Declaration
Swift
public let task: CRFTaskObject
-
Default initializer.
Declaration
Swift
public init(_ taskIdentifier: CRFTaskIdentifier)
-
The identifier is the task identifier for this task.
Declaration
Swift
public var identifier: String { get }
-
The title for the task.
Declaration
Swift
public var title: String?
-
The subtitle for the task.
Declaration
Swift
public var subtitle: String?
-
The detail for the task.
Declaration
Swift
public var detail: String?
-
The image icon for the task.
Declaration
Swift
public var icon: RSDImageWrapper?
-
Estimated minutes to perform the task.
Declaration
Swift
public var estimatedMinutes: Int { get }
-
A schema associated with this task info.
Declaration
Swift
public var schemaInfo: RSDSchemaInfo?
-
Returns
task
.Declaration
Swift
public var resourceTransformer: RSDTaskTransformer? { get }
-
Declaration
Swift
public func copy(with identifier: String) -> CRFTaskInfo


A C D E F G H I J L M N O P R S T U V W
All Classes All Packages
All Classes All Packages
All Classes All Packages
I
- IllegalValueException - Exception in seedu.address.commons.exceptions
-
Signals that some given data does not fulfill some constraints.
- IllegalValueException(String) - Constructor for exception seedu.address.commons.exceptions.IllegalValueException
- IllegalValueException(String, Throwable) - Constructor for exception seedu.address.commons.exceptions.IllegalValueException
- incrementCompletedTask() - Method in class seedu.address.model.calendar.task.TaskCompletionStatistics
-
Increments the completed task count.
- incrementTotalTasks() - Method in class seedu.address.model.calendar.task.TaskCompletionStatistics
-
Increments the total task count.
- Index - Class in seedu.address.commons.core.index
-
Represents a zero-based or one-based index.
- init() - Method in class seedu.address.MainApp
- init(Config) - Static method in class seedu.address.commons.core.LogsCenter
-
Initializes with a custom log level (specified in the
config
object) Loggers obtained *AFTER* this initialization will have their logging level changed
Logging levels for existing loggers will only be updated if the logger with the same name is requested again from the LogsCenter. - initConfig(Path) - Method in class seedu.address.MainApp
-
Returns a
Config
using the file atconfigFilePath
. - initPrefs(UserPrefsStorage) - Method in class seedu.address.MainApp
-
Returns a
UserPrefs
using the file atstorage
's user prefs file path, or a newUserPrefs
with default configuration if errors occur when reading from the file. - isAddable(Activity) - Method in class seedu.address.model.calendar.activity.UniqueActivityList
-
Returns true if the activity can be added into the activity list.
- isAddable(Activity) - Method in class seedu.address.model.calendar.Calendar
- isAddable(Activity) - Method in class seedu.address.model.calendar.Day
-
Returns true if the activity can be added into the week.
- isAddable(Activity) - Method in interface seedu.address.model.calendar.ReadOnlyCalendar
-
Returns true if the activity can be added into the calendar.
- isAddable(Activity) - Method in class seedu.address.model.calendar.Week
-
Returns true if the activity can be added into the week.
- isAddable(Activity) - Method in interface seedu.address.model.Model
- isAddable(Activity) - Method in class seedu.address.model.ModelManager
- isAnyNonNull(Object...) - Static method in class seedu.address.commons.util.CollectionUtil
-
Returns true if
items
contain any elements that are non-null. - isCompleted - Variable in class seedu.address.model.calendar.task.Task
-
The completion status of the task.
- isCompleted() - Method in class seedu.address.model.calendar.task.Task
-
Is completed boolean.
- isEarlyAccess() - Method in class seedu.address.commons.core.Version
- isExit() - Method in class seedu.address.logic.commands.CommandResult
- isFileExists(Path) - Static method in class seedu.address.commons.util.FileUtil
-
Checks if file exists.
- isNonZeroUnsignedInteger(String) - Static method in class seedu.address.commons.util.StringUtil
-
Returns true if
s
represents a non-zero unsigned integer e.g. - isResetCalendar() - Method in class seedu.address.logic.commands.CommandResult
- isShowHelp() - Method in class seedu.address.logic.commands.CommandResult
- isShowing() - Method in class seedu.address.ui.HelpWindow
-
Returns true if the help window is currently being shown.
- isValid() - Method in class seedu.address.model.calendar.activity.Activity
-
Returns true if the activity is valid.
- isValidPath(String) - Static method in class seedu.address.commons.util.FileUtil
-
Returns true if
path
can be converted into aPath
via#get(String)
, otherwise returns false. - isValidStartDay(DayOfWeek) - Method in class seedu.address.model.calendar.Calendar
- isValidStartDay(DayOfWeek) - Method in interface seedu.address.model.calendar.ReadOnlyCalendar
-
Returns true if the day of the week matches with the one in the calendar.
- isValidStartDay(DayOfWeek) - Method in interface seedu.address.model.Model
- isValidStartDay(DayOfWeek) - Method in class seedu.address.model.ModelManager
- isWithinCalendarRange(LocalDate) - Method in class seedu.address.model.calendar.Calendar
- isWithinCalendarRange(LocalDate) - Method in interface seedu.address.model.calendar.ReadOnlyCalendar
-
Returns true if a date falls within the calendar range.
- isWithinCalendarRange(Activity) - Method in class seedu.address.model.calendar.Calendar
- isWithinCalendarRange(Activity) - Method in interface seedu.address.model.calendar.ReadOnlyCalendar
-
Returns true if an activity falls within the calendar range.
- isWithinCalendarTime(LocalDate) - Method in interface seedu.address.model.Model
- isWithinCalendarTime(LocalDate) - Method in class seedu.address.model.ModelManager
- isWithinCalendarTime(Activity) - Method in interface seedu.address.model.Model
- isWithinCalendarTime(Activity) - Method in class seedu.address.model.ModelManager
- iterator() - Method in class seedu.address.model.calendar.activity.UniqueActivityList
- iterator() - Method in class seedu.address.model.calendar.task.UniqueTaskList
All Classes All Packages


Guidance
Types of apprenticeships that you can apply for in the aviation operations on the ground sector.
The purpose of this apprenticeship is to attract new people into the aviation industry, as well as improving current workers’ skills.
Ground aviation operators help with all matters relating to the aircraft while it’s in the airport and on the runway. This can range from passenger support and baggage handling to working for the airport itself, maintaining the airfield or communicating with pilots as they come in to land.
Because of the security and safety measures surrounding airports and airlines, you will need to pass a Disclosure and Barring Service check , and must be able to prove 5 years’ continuous references (these can be from school, college or university, as well as from past employers).
There are 2 types of apprenticeships in this sector.
You could train in roles like:
You could train in roles like:
You can become an apprentice in England if you’re:
Published: 12 April 2012


uHangouts
Support
Contact
About
Change Password
Email does not exists.
The password change request has been sent to your email.
University Email


You can’t view this page
This is because the address doesn’t exist.
Go back a page and try again. If that doesn’t work, start a new application for a Budgeting Loan.
Try again by clicking the back button in the top left corner of your browser. It’s usually an arrow pointing left. If that doesn’t work, start a new application for a Budgeting Loan.


US official criticises countries 'opening their arms' to Chinese 5G
U.S. chief technology officer Michael Kratsios on Thursday criticised countries that "open their arms" to Chinese 5G networks and artificial intelligence technologies.
LISBON: U.S. chief technology officer Michael Kratsios on Thursday criticised countries that "open their arms" to Chinese 5G networks and artificial intelligence technologies.
He singled out Huawei, saying Chinese firms cannot be trusted because of laws compelling them to cooperate with their country's intelligence services.
Speaking at a tech conference in Lisbon, Kratsios said Europe must "take a stand" with America, which imposed export controls on Huawei in May and urged EU countries not to use its products.
"We may not see eye to eye on every aspect of technology policy, but we all agree on the principles that matter most," said Kratsios, who helps shape U.S. policies on technology and data.
Yet European companies do not seem deterred by Washington's concerns: of 65 commercial deals that Huawei has signed, half are with European customers building 5G networks, the company said last month.
EU lawmakers avoided explicitly singling out China and Huawei as security risks in a recent report on the danger of increased cyber attacks by state-backed entities.
Kratsios met Europe's antitrust chief Margrethe Vestager earlier in the day to discuss "digital issues", a European Commission official said.
In his speech, Kratsios reiterated allegations published by Le Monde last year that Beijing had transferred data from the African Union's headquarters in Addis Ababa to China for five years using Huawei IT equipment.
The US$200 million (£156.04 million) building was fully funded and built by China in 2012.
Huawei condemned Kratsios' comments as "hypocritical and manifestly false", stating that it did not have access to African Union data.
The African Union has previously rejected claims that Huawei was involved in any cyber security breach.
(Reporting by Victoria Waldersee; Editing by Giles Elgood)


Developing artist friendly graphics technologies to create compelling digital humans, creatures or ... anything that might pop on production...


One evening, he donned his beret and ventured out to an exciting cabaret, drawn in by the melodic sounds of the caret instruments. [G|D]
She wandered through the luscious meadows, admiring the graceful egret soaring above, in search of the elusive ferret which was swiftly darting across the claret-colored wildflowers. [G|D]
He anxiously paced around his garret, wringing his hands in fret, trying to interpret the signs and symbols he saw in his dreams. [G|D]
Looking up towards the majestic minaret in the distance, she couldn't help but regret the decisions she had made that led her to the turret in which she now stood. [G|D]


New Mexico public transportation project to begin operations
ALBUQUERQUE, N.M. (AP) - New Mexico city officials have announced a new public transportation project is expected to begin operations in Albuquerque this month.
City officials say the Albuquerque Rapid Transit project includes designated bus-only lanes and stops that would carry riders along Central Avenue every 10 minutes.
Officials say the ART project is set to launch Nov. 30 with free rides until Dec. 31. Riders must pay $1 starting Jan. 1.
Albuquerque Mayor Tim Keller says officials plan to time routes starting Sunday.
Law enforcement says there are plans to issue warning citations starting Nov. 30 to educate the community on safe driving practices around the buses before $80 fines are issued in January.
Officials say the $133 million project includes 20 new buses expected to replace the existing Rapid Ride lines.
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


ABOUT US
Yummy Scrummy Pizza was started in the year 2018, as a substitute to the tedious method of having to go to malls, supermarkets or restaurants to buy pizza. We simply made it easier for pizza lovers as they can now get a custom cooked pizza straight to their doorsteps. We are located in Thika near Thika Arcade building. Feel free to contact us when you reach Thika for any enquiries. We promise to make your pizza experience better as days go by as we open doors for anyone who has ideas on how to improve our customer service. As a customer we value you very much! All thanks for your support.


City of Zion dApps competition
DAPPS WINNERS
The City of Zion hosted their second dApps competition to further elevate the NEO community through collaborative engagement. Below are the winners and honourable mentions that we really felt stood out.
dAPPs Winners!
displayed below by rank
10 winners
5 Honourable mentions
1st: Treatail
By: gubanotorious
Treatail enables online shoppers to make offers to any online store and negotiate a personalized deal. With the addition of a NEP5 Token (TTL) to incent both buyers and sellers, in addition to creating Treatail Assets verified by the NEO blockchain, Treatail aims to bring the power of blockchain and cryptocurrency to the retail market to the average user.
2nd: Elight
By: ankarenkosergey, leonid
Elight is the project designed to automate thermo logistics processes. It includes the possibility of interaction between temperature data logger device, mobile app and blockchain, where Smart Contracts implementation is the key feature. This project can bring a part of Smart Economy into traditional market.
3rd: Precognition
By: ixje
The application presented here addresses the currently uncovered Prediction Markets segment that is envisioned for in the original NEO White paper (Note: exchanges are a separate category). Precognition is a decentralized prediction market application running on the NEO Blockchain which allows people to speculate on future events using any NEP5Token.
4th: Anno
By: jeffreylewis
Anno is a decentralized ticket reservation platform with the aim to improve the integrity of online ticket reservations by providing an easy solution for businesses to utilize the programmable logic and immutability of smart contracts on the NEO blockchain.
5th: Narrative
By: brianlenz
Our dApp is an end-to-end, fully functional NEP-5 token sale system for the token of Narrative's new content economy: NRVE. The token sale smart contract supports advanced sale features including multiple phases with different bonuses, separate token distribution mechanisms with vesting schedules, and more.
6th: Sendeo
By: slipo, l0c0luke
Sendeo is a smart contract on the NEO blockchain that makes it really easy to send NEO or GAS to anyone, even if they don’t have a NEO address.
7th: SciChain
By: igormcoelho, vncoelho, fabiocardoso, brunonazario
We propose SciChain: Science on the Blockchain. This DApp provides a transparent peer-review system that can become the new generation science management platform for scientific publishers and researchers.
8th: adX
By: applebananas
adX is a great novel tool for vendors (cafes, restaurants, stores, etc) to engage people in collecting ads and coupons. adX is a geolocation based Augmented Reality mobile application on NEO.
9th: Neoblog
By: drppr, legiit
Neoblog is a public blogging platform that's completely serverless. Reading and posting articles to our platform require no servers and runs completely on the NEO Blockchain and IPFS.
10th: The Construct
By: nickazg
The Construct implements a unique funding structure which relies on a planned project Funding Roadmap, specifying the exact process the project will take to reach the proposed output.
Honourable mentions
NEO Futures
By: wingyungchan
First multi-oracle approach to third party consensus for blockchain
NEO GAS Liquid (NGL)
By: amarillo
The NEO GAS Liquid smart contract is a secured loan contract.
Survey Token
By: mel
Have you ever filled a survey or review and not been rewarded for it? With Survey token we remove the middleman like SurveyMonkey and G2Crowd and redistribute the money to people taking the survey and adding reviews.
Generion
By: wollen, jknnr
Own your personal information, having it stored in safe place through a user-friendly app, to be aware of who has an access to your info, to safely share or sell your data.


da
3.5-Inch
4-Inch
4.7-Inch
5.5-Inch
de-DE
3.5-Inch
4-Inch
4.7-Inch
5.5-Inch
en-US
3.5-Inch
4-Inch
4.7-Inch
5.5-Inch
es-ES
3.5-Inch
4-Inch
4.7-Inch
5.5-Inch
fr-FR
3.5-Inch
4-Inch
4.7-Inch
5.5-Inch
it
3.5-Inch
4-Inch
4.7-Inch
5.5-Inch


June, 2008
December, 2008
June, 2009
December, 2009
June, 2010
December, 2010
June, 2011
December, 2011
June, 2012
December, 2012
June, 2013
Part-1 : General Astrology
Part-2 : Astronomy relevant to Astrology
Part-1 : Mathematical Astrology
Part-2 : Predictive Astrology
Part-1 : Deliniation and Dynamic Configuration
Part-2 : Dasa and Transits
Part-1 : Tajika Shastra
Part-2 : Muhurta


--- title: "Presentations" author: "Frederick Boehm" date: "2019-08-29 10:57:18" disable_comments: true ---
International Mammalian Genome Conference, November 11-14, 2018, Rio Mar, Puerto Rico
Joint Statistical Meetings, August 1, 2018, Vancouver, British Columbia, Canada
Complex Trait Community meeting, June 20, 2018, Glasgow, Scotland


404
There was a fail in the matrix... or whatever
This page is a demonstration of a beautiful 404 page using CSS to generate a glitch effect.


Night Theme
cloud
BCIT CST 2016 Fall Intake
Complete List Of Links and Resources
Open source on
GitHub
Notes
General
D2L Login
My BCIT Login
All CST Courses
BCIT Computing Site
Textbook Lookup
Beginner Java Videos (Free)
Course Outlines
COMM 2216
COMP 2121
COMP 2510
COMP 2526
COMP 2721
COMP 2714
Groups
Slack Team (Account Needed)
Get an invite to make an account
Set A Facebook Group
Set B Facebook Group
Set D Facebook Group
Set E Facebook Group
Downloads
Free Stuff/Student Discounts
Free Software Downloads
Eclipse Download
Filezilla Download
Java Runtime (Needed For Eclipse)
Books
Java Software Solutions
Murach's Html5 & Css3
Basics Of Web Design: Html5 & Css3
Comp 1113 Intro Math For Computer Science
Schedules
Set A Schedule
Set B Schedule
Set C Schedule
Set D Schedule
Set E Schedule
Prof Emails Term 2
Keith Tang
Goran Ruzic
Dennis Richards
Albert Wei
Jason Harrison
Sam Lee
Pope Kim
Trevor Lord
Other
Finacial Aid
BCIT News
Campus Map
Student Association
Book Study Rooms
Main BCIT Contacts


Fill this box with a list of links, each starting /r/TheMotte/comments..., newline-separated. Don't include any parameters like ?context=3 or the https://www.reddit.com part of the link. Open your browser's Javascript console to see logging.
This script still can't handle:


|
Graphitti
A toolkit/software architecture to ease creating high-performance neural network simulators
|
A container of all DS synapse data. More...
#include "AllSpikingSynapses.h"
Go to the source code of this file.
Classes | |
| class | AllDSSynapses |
A container of all DS synapse data.
/
The container holds synapse parameters of all synapses. Each kind of synapse parameter is stored in a 2D array. Each item in the first dimention of the array corresponds with each neuron, and each item in the second dimension of the array corresponds with a synapse parameter of each synapse of the neuron. Bacause each neuron owns different number of synapses, the number of synapses for each neuron is stored in a 1D array, edge_counts.
For CUDA implementation, we used another structure, AllDSSynapsesDevice, where synapse parameters are stored in 1D arrays instead of 2D arrays, so that device functions can access these data less latency. When copying a synapse parameter, P[i][j], from host to device, it is stored in P[i * max_edges_per_vertex + j] in AllDSSynapsesDevice structure.
The latest implementation uses the identical data struture between host and CUDA; that is, synapse parameters are stored in a 1D array, so we don't need conversion when copying data between host and device memory.
Phenomenological model of frequency-dependent synapses exibit dynamics that include activity-dependent facilitation and depression (Tsodyks and Markram 1997, Tsodyks et al. 1998). The model has two state variables: (the fraction of available synaptic efficacy), and (the running value of utilization of synaptic efficacy).
where is the time interval between nth and (n + 1)th AP, the two time constants and govern recovery from depression, and facilitation after a spike, and is utilization of synaptic efficacy (Markram et al. 1998).
The synaptic response that is generated by any AP in a train is therefore given by:
Definition in file AllDSSynapses.h.


Madams
Home
Recipes
Child Care
Fashion Tips
Family Counseling
Contact Us
First Name *
Last Name *
Email Address *
Telephone Number
Comments *


---
layout: slides
title: "On summarizing one's experience"
category: hidden
type: course
semester: Fall 2020
college: Stevens Institute of Technology
permalink: /thought-and-behavior/summarizing/
---
# On summarizing one's experience.
???
---
# Practice creating a causal graphical model
- [Air and Liquid Systems Corp. v. Devries](https://www.oyez.org/cases/2018/17-1104)
---
---
---
# Who are you?
- The experiencing self
- The remembering self
---
# Discussion 1
- Think of some real-world examples where the experiencing self's and remembering self's preferences diverge
- How might you separately measure these two selves in the lab?
---
# Discussion 2
A Marrian analysis of summarizing one's experience:
- Computational level
- Algorithmic / representational level
- Implementation level
---
class: center, middle
# Fin.