This article discusses a new framework that works with the AWS SDK for iOS to allow apps to use Amazon DynamoDB as a persistent back-end for the Apple Core Data Framework. The article assumes familiarity with both technologies. If you haven't done so already, please review the User Preferences sample app to familiarize yourself with using Amazon DynamoDB with the AWS SDK for iOS. You should also review Apples's Core Data Introduction.
The Apple Core Data Framework provides generalized and automated solutions to common tasks associated with object life-cycle and object graph management, including persistence. You can read more about Core Data in the Apple documentation here
Amazon DynamoDB is a fast, highly scalable, highly available, cost-effective, non-relational database service. Amazon DynamoDB removes traditional scalability limitations on data storage while maintaining low latency and predictable performance.
This sample app is based on Apple's example iOS Core Data app, Locations. Our version adds another entity to capture checkins at a particular location and adds a relationship between the two entities. Finally, we use the AWS Persistence Framework for Core Data to allow our app to save the entities to Amazon DynamoDB.
Where the Locations example
from Apple has only a single entity, our version expands the model to have two entities,
Location and Checkin, with a 1-to-many relationship between
them.
Note: The AWS Persistence Framework for Core Data has been tested
with 1-to-1 and 1-to-many relationships.
For each object, we
defined the property types to correspond to an appropriate Core Data
type.
Note: The AWS Persistence Framework for Core Data supports String,
Numeric (Integer 16/32/64, Decimal, Double, and Float), Date, and Boolean types. For
properties that are hash keys, only String and Numeric types are supported. Primitives
are not supported as entity properties.
Given this entity and relationship model, you need to configure the app to use the AWS Persistence Framework for Core Data.
The AppDelegate also implements the
AWSPersistenceDynamoDBIncrementalStoreDelegate
protocol, which requires
you to implement a single selector:
- (AmazonCredentials *)credentials { return [AmazonClientManager credentials]; } |
Note: This selector is called every time the framework requires a connection to Amazon DynamoDB; it should return valid credentials. The supplied sample uses TVM to gather these credentials.
The next step is to register that you want to use Amazon DynamoDB as your back-end store.
This happens in your AppDelegate as part of the persistentStoreCoordinator
selector.
[NSPersistentStoreCoordinator registerStoreClass:[AWSPersistenceDynamoDBIncrementalStore class] forStoreType:AWSPersistenceDynamoDBIncrementalStoreType]; persistentStoreCoordinator = [[NSPersistentStoreCoordinator alloc] initWithManagedObjectModel:[self managedObjectModel]]; |
Next, you need to create a number of mappings to allow the framework to understand how to handle your managed objects. These include:
NSDictionary *hashKeys = [NSDictionary dictionaryWithObjectsAndKeys: @"locationId", @"Location", @"checkinId", @"Checkin", nil]; |
version
of the object being stored.
NSDictionary *versions = [NSDictionary dictionaryWithObjectsAndKeys: @"version", @"Location", @"version", @"Checkin", nil]; |
NSDictionary *tableMapper = [NSDictionary dictionaryWithObjectsAndKeys: @"AWS-Locations", @"Location", @"AWS-Checkins", @"Checkin", nil]; |
With the mappings created, you next create a larger options dictionary and include the delegate for your object manager, which is set to be the AppDelegate itself.
NSDictionary *options = [NSDictionary dictionaryWithObjectsAndKeys: hashKeys, AWSPersistenceDynamoDBHashKey, versions, AWSPersistenceDynamoDBVersionKey, self, AWSPersistenceDynamoDBDelegate, tableMapper, AWSPersistenceDynamoDBTableMapper, nil]; |
Finally, you pass the options dictionary to the persistence store coordinator and check for errors.
if(![persistentStoreCoordinator addPersistentStoreWithType:AWSPersistenceDynamoDBIncrementalStoreType configuration:nil URL:nil options:options error:&error]) { // Handle the error. NSLog(@"error: %@", error); } |
When fetching objects in Core Data, you create an instance of the NSFetchRequest
class and specify the type of entity you are fetching:
NSFetchRequest *request = [[[NSFetchRequest alloc] init] autorelease]; NSEntityDescription *entity = [NSEntityDescription entityForName:@"Location" inManagedObjectContext:managedObjectContext]; [request setEntity:entity]; |
While it is not shown in our sample app, you can also specify an NSPredicate
to
your NSFetchRequest
to restrict the entities returned. In the initial beta
release of the AWS Persistence Framework for Core Data, NSPredicate
is
supported only for hash key properties.
Note: Fetching without a predicate
will execute as many scan operations as necessary to load the entire contents of the
underlying Amazon DynamoDB table.
Once you've created your fetch request, you ask your managed object context to run it and return the results. You can then work with the returned array as you normally would.
NSArray *fetchedResults = [managedObjectContext executeFetchRequest:request error:&error]; NSMutableArray *mutableFetchResults = [fetchedResults mutableCopy]; |
When creating new objects in Core Data, you must request they be created from the managed object context. After the object has been created, you can interact with it normally, setting properties on the object.
Checkin *checkin = (Checkin *)[NSEntityDescription insertNewObjectForEntityForName:@"Checkin" inManagedObjectContext:managedObjectContext]; checkin.checkinId = [Utilities getUUID]; checkin.checkinTime = [NSDate date]; checkin.comment = @"First Checkin"; |
Making changes to managed objects does not cause them to be persisted until the application
makes a call to explicitly save the changes.
Note: This saves any and all
changes to managed objects created or modified during this session or since the previous
save.
NSError *error; if (![managedObjectContext save:&error]) { // Handle the error. NSLog(@"error: %@", error); } |
The app can also choose to undo changes.
Note: It is possible to undo
persisted changes made during the current session, but you'll need to save the undo.
[[[self managedObjectContext] undoManager] undo]; |
Deleting objects also requires notifying the managed object context. You cannot remove objects from the back-end store until you've saved the changes.
[managedObjectContext deleteObject:locToDelete]; |
When using the AWS Persistence Framework for Core Data on multiple threads or devices, you
may encounter a situation where you need to resolve inconsistencies between data on
Amazon DynamoDB tables and your local copy in memory. When the conflicts are detected,
NSManagedObjectContext
returns an NSError
object and your
change will not be persisted. You can change this behavior by setting a merge policy
to your NSManagedObjectContext
:
managedObjectContext.mergePolicy = [[[NSMergePolicy alloc] initWithMergeType:NSMergeByPropertyStoreTrumpMergePolicyType] autorelease]; |
There are five NSMergePolicyTypes available: NSErrorMergePolicyType
(default), NSMergeByPropertyStoreTrumpMergePolicyType
,
NSMergeByPropertyObjectTrumpMergePolicyType
, NSOverwriteMergePolicyType
, and
NSRollbackMergePolicyType
. The AWS Persistence Framework for Core Data can
handle all five merge policy types. For more information about
NSMergePolicy
, please read NSMergePolicy Class Reference.
Because NSManagedObject
instances returned by Core Data are lazily loaded, it is
possible that when you access an attribute of an object, the actual row in the Amazon
DynamoDB table has been already deleted. In this situation, the AWS Persistence
Framework for Core Data will return an empty NSManagedObject
(the accessor
will return nil) and sends out
AWSPersistenceDynamoDBObjectDeletedNotification
. You can register to
observe AWSPersistenceDynamoDBObjectDeletedNotification
by calling the
following method:
[[NSNotificationCenter defaultCenter] addObserver:self selector:@selector(mergeDeletedObject:) name:AWSPersistenceDynamoDBObjectDeletedNotification object:nil]; |
When you receive the notification, you should remove any invalid objects from local cache and
refresh the NSManagedObject
instance as necessary. The notification object
has a userInfo
dictionary, which you can use to identify the deleted
object, containing:
AWSPersistenceDynamoDBObjectDeletedNotificationHashKey
keyAWSPersistenceDynamoDBObjectDeletedNotificationEntityName
keyNSManagedObjectID
of the deleted object with AWSPersistenceDynamoDBObjectDeletedNotificationObjectID
keyThe following line shows how to refresh an NSManagedObject
. Please note that
refreshing an NSManagedObject
will send a getItem request to the Amazon
DynamoDB.
[managedObjectContext refreshObject:managedObject mergeChanges:YES]; |
Under heavily concurrent environments, periodically calling the refreshObject
method may reduce inconsistent states. Also, calling the refreshObject
method before saving the change will minimize inconsistent states to be persisted to the
Amazon DynamoDB tables.
Although the AWS Persistence Framework for Core Data was developed to meet the widest use case possible, it does have certain limitations.
NSError
object.NSManagedObject
subclasses, make sure this
option is not enabled.NSPredicates
are
limited to only the properties that are mapped to hash keys.| "hashKey1=value1" | supported, one hashKey and one value |
| "hashKey1 = value1 OR hashkey1 = value2" | not supported, one haskKey, but two values |
| "hashKey1 IN (value1, value2)" | not supported, one haskKey, but two values |
The code in this article demonstrates how to use the AWS Persistence Framework for Core Data to create a simplified object model backed by a scalable cloud database. We are offering the AWS Persistence Framework for Core Data as beta software that you can use in addition to the AWS SDK for iOS. You can download the SDK and review the documentation here.
For more information about using AWS credentials with mobile apps, see the following article:
Please feel free to ask questions or provide comments in the Mobile Development Forum.


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MUSACA
Products
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Pesho
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Product
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Price
"MishMash" in "burkan"
$20.00
Salam
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Beer Nuts
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Coca Cola 2l.
$2.50
Cashout
Total: $26.75


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string title of the event list (ex. "Events For Today")
Date to fetch events for, 0 being today, 1 being tommorrow, and so on. See UIUtil.CalParams.dayStart.
Whether or not to display the schedule event ("A schedule")
Storage document item (selects wrapper div)
Storage array of the EventRowUI objects
All day time template
Heading template ("Thursday")
Normal time template
Generic wrapper div template for use in an extending class
Store EventGraphic.elem, and setup array of EventRowUI.
Build HTML based on the events recieved.
The DataManage generated data, containing CalDataManage data
The event graphic HTML
Called when a signifigant time change (one minuete as of writing) has occured.Implementing class should update the HTML to reflect this new time using document queries.
If new data has been recieved, rebuild the HTML and use EventGraphic.elem to swap out the old HTML.
The DataManage generated data, containing CalDataManage data
Create array of EventRowUI based on the event data, day, and whether or not to display the schedule.
The CalDataManage data in format of {epoch : Array<EventData.EventInterface>}
The epoch time of the begining of the day, used to select the event row from eventData
Whether or not to display the schedule
An array of EventRowUI
Generated using TypeDoc
Events list graphic.
Includes clickable events with dropdown descriptions, auto updating, and way to many strings. See also: EventRowUI.


Setting the Default Control Panel
How to set the control panel that will be used across all sites within this DNN installation. DNN 7+ comes packaged with the ControlBar and RibbonBar control panels. The Iconbar is a legacy Control Panel that will only available to sites being upgraded to DNN 7+ that are currently using the Iconbar.
- Navigate to Host > Host Settings.
- Select the Other Settings tab.
- At Control Panel, select the appearance of the Control Panel from these options:
- ControlBar: The ControlBar Control Panel displays a single row of options above your site with drop down lists to access grouped features.
- Iconbar: Display all tools on one interface. This option is only available for DNN installations that are currently using this toolbar which was removed in DNN6+. If you are using the Iconbar, it is recommended that you switch to the latest ControlBar.
- RibbonBar: RibbonBar tools are grouped by the headings Admin, Host, Modules, Pages and Tool. Hovering your mouse over a heading reveals the related features and tools. Note: The below image is the RibbonBar for DNN Platform.
-
Click the Update button.
Setting the Control Panel
-
See "Overview of the ControlBar"
-
See "Overview of the RibbonBar"
-
See "Overview of the Iconbar"


TV prequel to Polanski's 'Chinatown' in works
by AFPThe Oscar-winning writer of Roman Polanski's "Chinatown" is working on a television series set before the events of the 1974 movie, a spokeswoman told AFP on Tuesday.
The show, which is in early development, will serve as a prequel to the noir classic about corruption in Los Angeles, confirmed the spokeswoman for 84-year-old screenwriter Robert Towne.
"The Social Network" director David Fincher is also attached to the project being produced by Netflix, according to trade publication Variety, which broke the news.
Netflix did not immediately respond to a request for comment.
Polanski, who has been a fugitive from US justice since admitting to the statutory rape of a 13-year-old girl in 1977, is not involved in the new project.
This month the French-Polish director was accused of raping another young woman in 1975. Polanski, 86, denies attacking Valentine Monnie, and has threatened to sue his accusers.
A French guild of more than 200 filmmakers this week said it will propose new rules for members charged or convicted of sexual violence, which could lead to Polanski's suspension.
Polanski made "Chinatown" on his return to Hollywood five years after his heavily pregnant wife Sharon Tate was murdered by the Charles Manson gang.
The movie was nominated for 11 Oscars, including best film, best director, best actor (Jack Nicholson) and best actress (Faye Dunaway.)
It won one Academy Award -- best original screenplay for Towne.
A sequel, "Two Jakes," was released in 1990. Directed by Nicholson, who reprised his role as private detective Jake Gittes, the film was also written by Towne.


Plugin Information |
|---|
View VncRecorder on the plugin site for more information. |
Older versions of this plugin may not be safe to use. Please review the following warnings before using an older version:
This plugin records screen of current build or other configured VNC session as Shock Wave Flash (swf) file during a build and stores it as an artifact. This plugin can be used in combination with VncViewer and Xvnc plugin.
After the build, the video file gets also published as a HTTP link in the "Console output" of the build. The generated HTML file embeds a video and makes it possible to navigate within a recorded video directly in browser.
Requirements
- Linux on Jenkins master node
- vnc2swf on Jenkins master node
How you use the plugin in a job
Example for usage together with Xvnc plugin
Result: video in your Browser
Link in "Console output"
Store as build artifact
The generated HTML file MyTest.html embeds video MyTest_2.swf and makes it possible to navigate within a video directly in browser.
Path of vnc2swf
Path of used vnc2swf can be customized under "Jenkins System configuration / Global settings". Default is the vnc2swf found in the PATH variable.
JIRA issues
Attachments:
vncrecorder2.png (image/png)
vncrecorder3.png (image/png)
vncrecorder4.png (image/png)
MyTest_2.swf (application/x-shockwave-flash)
MyTest.html (text/html)
Xvnc.png (image/png)
vncrecorder2.png (image/png)
vncrecorder2_new.xcf (image/x-xcf)
vncrecorder2_new.png (image/png)
globalSettings.png (image/png)
recordVncSession.png (image/png)


THE WINSLOW BOY (1999) (Sony Classics) Starring: Nigel Hawthorne, Jeremy Northam, Rebecca Pidgeon, Gemma Jones, Guy Edwards, Colin Stinton, Aden Gillett, Matthew Pidgeon. Screenplay: David Mamet, based on the play by Terence Rattigan. Producer: Sarah Green. Director: David Mamet. MPAA Rating: G Running Time: 100 minutes. Reviewed by Scott Renshaw.
Depending upon one's point of view, THE WINSLOW BOY is either as unlikely a piece of material as one could expect from David Mamet or a perfect fit in the Mamet oeuvre. Certainly it's surprising to find Mamet directing a script by another writer, even more surprising to find the ultra-contemporary Mamet directing a period piece, and more surprising yet to find the profanity-happy Mamet directing a G-rated script. There's also something perfectly Mamet-like in the film's themes of people seeking the truth of things behind the formalized dance of conversation. Mamet isn't straying far from comfortable ground, but he strays far enough to find an intriguing new perspective on film-making. THE WINSLOW BOY may be his finest work as a film director, perhaps because he's working from a less tightly-controlled script.
The story, set in the early 1910's, concerns an upper-middle class English family called the Winslows. Father Arthur (Nigel Hawthorne), a banker, is comfortable with the life he has made, and even with the lives of his underachieving son Dicky (Matthew Pidgeon) and Suffragette daughter Catherine (Rebecca Pidgeon). He is not at all comfortable, however, with youngest son Ronny's (Guy Edwards) dismissal from military school when he is accused of stealing a five-shilling postal order. Determined to clear his son's name, Arthur throws all his resources into Ronny's defense, including retaining famed lawyer Sir Robert Morton (Jeremy Northam). But the Winslows face an uphill battle trying to obtain a civil trial before either their money or Sir Robert's political connections run out.
A contemporary movie-goer might expect THE WINSLOW BOY to turn into a courtroom drama, chock full of witness stand backpedaling and banging gavels. In fact, there's not a second of courtroom footage in THE WINSLOW BOY; the closest it gets is Morton's tense interview with Ronnie before deciding to take the case. Rattigan's text is more concerned with subtler matters than the melodrama of legal confrontations, including issues still resonant fifty years after the play's debut. As the Winslows' resources dwindle, Arthur begins to wonder whether he can afford to pursue the case indefinitely, inspiring questions on the modern connection between wealth and justice. The story also attacks the casual dismissal of pursuits of justice, not on the basis of right or wrong, but on the basis of how annoying it is to have normalcy disrupted.
As thought-provoking as THE WINSLOW BOY may be, however, it's often far too sedate in its interpersonal relationships. Rattigan's narrative generally focuses not on the people involved in the Winslow case, but on all the business surrounding the case: the intense press scrutiny (including a "women's issues" reporter more interested in curtains than Ronnie's guilt or innocence); the vagaries of public opinion; the machinations required even to bring a case against the Admiralty. The characters in THE WINSLOW BOY face consequences for their choices, but the choices are more interesting to Rattigan than those consequences. There's a Merchant Ivory vibe to the repressed emotions without the Merchant Ivory readiness to show the intensity behind them.
Mamet has never been known for his emotionalism, but the fault here appears to lie far more with Rattigan. Indeed, Mamet takes some of the simplest scenes and charges them with more feeling than they might deserve (notably Arthur's demand of honesty from Ronnie). He has always been a remarkably efficient film director, and THE WINSLOW BOY certainly doesn't stray in that regard. He does, however, find a facility with actors not always evident when he's working from his own words. Hawthorne is brilliant, Northam is appropriately enigmatic, and even Rebecca Pidgeon seems vaguely human. There's little Mamet can do with the huge chunks of dialogue involving exposition of off-stage events, or to fill in the spaces between the people, but he does a lot with a story that's more conceptual than actual. THE WINSLOW BOY is an interesting challenge to which Mamet rises, applying his intricate construction as well to a stately manor as he does to a skyscraper.
On the Renshaw scale of 0 to 10 trials of honor: 7.
Visit Scott Renshaw's Screening Room http://www.inconnect.com/~renshaw/ *** Subscribe to receive new reviews directly by email! See the Screening Room for details, or reply to this message with subject "Subscribe".
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


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Do a quick background check: run a wide scope search for rings with
the properties of the ring you want to add. If you don't notice anything that looks like your ring, proceeed
to the next steps.
Make a record for your ring
On the admin home page, click the "add" option in the row for Ring (or Commutative ring, if
applicable. A commutative ring can be created in either place.)
Fill in as much information for the ring as you can (use the keyword selector and ignore kwd.)
Hopefully you had some key properties of the ring in mind. You ought to add
properties to the ring via the following steps.
The bottom of the form has a few rows to associate properties with the ring.
Select a property you are interested in from the drop-down in the row.
In "has property," put a 1 if the ring HAS the property, and a 0 if it does NOT have the property.
Use the "reason" field if the reason is something very straightforward. Use the "source"
field to include a citation if you can. Please use at least one of these fields!
Hit "save." Your ring is now in the database, and is associated with the properties you entered.
On the admin home page, click the "processor" link. Select your ring from the dropdown, then hit "process."
The processor will draw all conclusions that it possibly can with current data. Running the processor twice in
a row will not add anything, but if you manually add new properties to the ring, then processing again may
add new entries.
You will probably get a "success" message, but if instead you get an error message, get in contact with me.
Tips:
If the form demands content in one of the text fields, you can skirt it by submitting whitespace.
Remember that you don't have to supply all properties you know about the ring, just "the best" ones.
How to modify properties on an existing ring
Click the "change" link in the Ring row (or Commutative ring row, if you want to modify its commutative
properties.)
The form for the ring will appear, and at the bottom will be the currently associated properties, along with
a few empty rows for you to add items. You can remove currently existing items (if you have permissions to do
so.
Once done modifying the associations, you hit the save button.
How to add a citation
Click the "add" link in the Citations row on the admin homepage. (In the Theorem adder, you can also click
the square next to the citation dropdown field to reach this form. It will soon be the same for the ring adder.)
Select the publication from the dropdown, or if your publication is not in the dropdown already, use
the square next to the dropdown as a shortcut to the publication adder.
Include the page number and other descriptors as appropriate into the location field. (For example "Theorem 10.9")
Hit save, and your citation will now be in the database.
How to add a publication
Click the "add" link in the Citations row on the admin homepage. (You may also have gotten here from the square
next to the publication dropdown in the citation adder.)
Enter the publication title, author(s) and date, using January 1 as a default, if necessary.
Anything else about the publication (like edition, volume, publisher) goes into the details field.
Click "save" and the publication will now appear in the database.


The main concepts of the book stem explore how to create Reliable, Scalable, and Maintainable software.
Reliability - The ability to tolerate hardware and software faults or failures.
Scalability - The ability to adapt to system growth under specific load parameters.
Maintainability - The ability to be operable, simple, and evolvable.
Common System Requirements
Today's software applications are more data intensive than compute intensive.
- Store Data
- Cache Data
- Search Indexes
- Stream Processing
- Batch Processing
Reliability
How do you ensure data remains correct and complete when things go wrong internally?
Hardware errors
- Hardware errors are usually mitigated through redundancy.
Human errors
- Decouple places where people make mistakes
- Well designed abstractions prevent bad patterns (must not be too strict or practice will not be followed)
- Test thoroughly at all levels (unit … integration)
- Monitor (telemetry) smartly to give advanced warning of faults of failures to come
- Quick recoveries from errors
Reliability Testing
- Isolate services and make them fail. Determine what other unrelated services are impacted from failure.
- System alerts if component is not meeting it's guarantees (messages in > messages out === future fault or failure)
Scalability
If the system grows in a particular way what are our options for dealing with the growth.
- Active Users
- Cache Hits
- Reads vs Writes
- Server Requests
- Simultaneous Active Users
Latency vs Response Time vs Throughput
Maintainabilty
- Operability: Ability to operate easily
- Simplicity: Ability for newcomers to understand easily
- Evolvability: Ability to make changes easily
Simplicity
- Have good defaults
- Predictable
- Documented
- Abstractions


Syntax:
parse-integer string &key start end radix junk-allowed => integer, pos
Arguments and Values:
string---a string.
start, end---bounding index designators of string. The defaults for start and end are 0 and nil, respectively.
radix---a radix. The default is 10.
junk-allowed---a generalized boolean. The default is false.
integer---an integer or false.
pos---a bounding index of string.
Description:
parse-integer parses an integer in the specified radix from the substring of string delimited by start and end.
parse-integer expects an optional sign (+ or -) followed by a a non-empty sequence of digits to be interpreted in the specified radix. Optional leading and trailing whitespace[1] is ignored.
parse-integer does not recognize the syntactic radix-specifier prefixes #O, #B, #X, and #nR, nor does it recognize a trailing decimal point.
If junk-allowed is false, an error of type parse-error is signaled if substring does not consist entirely of the representation of a signed integer, possibly surrounded on either side by whitespace[1] characters.
The first value returned is either the integer that was parsed, or else nil if no syntactically correct integer was seen but junk-allowed was true.
The second value is either the index into the string of the delimiter that terminated the parse, or the upper bounding index of the substring if the parse terminated at the end of the substring (as is always the case if junk-allowed is false).
Examples:
(parse-integer "123") => 123, 3 (parse-integer "123" :start 1 :radix 5) => 13, 3 (parse-integer "no-integer" :junk-allowed t) => NIL, 0
Side Effects: None.
Affected By: None.
Exceptional Situations:
If junk-allowed is false, an error is signaled if substring does not consist entirely of the representation of an integer, possibly surrounded on either side by whitespace[1] characters.
See Also: None.
Notes: None.


WITNESS LIST Appropriations Committee (Subcommittee on Health & Human Services) February 5, 1997-8:00A Budget Overview/Health & Human Ser For: Anderson, Patty (UCP of Texas, Inc) Anh, Ngo Minh (UCP class program) Bower,Bruce (Citizen) Cole, Pat (Council on Family Viole) Cranston, Ron (Citizen) Dieter, Alison (Gray Panthers) Dillander, Glenn (UCP class program) Dougherty, Russell (CLASS) Gauthier, James (UCP Class PRogram) Graves, Tim (TX Health Care Assn) Latimer, David (Assn of Homes for Aging) McPhail, Jennifer (ADAPT) Penry, Kellie Jo (UCP class program) Schnall, Rona (ADAPT) Thomas, Stephanie (ADAPT) Tompkins, Kathi Jo (UCP Class program) Wall, Raymond (UCP) Wittie, David (ADAPT) Wyss, Michael (UCP Class Program) Against: Gonzalez, Freddy (AD apt) Kafka, Bob (ADAPT) Spahn, Wayne (ADAPT) Wisdom, Marie (Advocates for Home Refo) On: Greebon, Karen (ADAPT) Halfmann, Bobby (DHS) Hayes, Aaryce (Advocacy, Inc.) Herndon, David (DHS) Johnson, Jacqueline (DHS) McGiffert, Lisa (Consumers Union) McKinney, Mike (HHSC) Saenz, Danny (Citizen) Templeton, J.T. (ADAPT) Trimble, Terry (DHS) Venza, Tony (DHS)


Did you [activity] with anyone on [date]?
We are looiking for the names of people who are not part of your household, who have taken part in activities with you.
Do
Important:
Enter contacts who:
-
you have had face to face contact (within 1 metre for any length of time) or skin to skin contact with or someone you coughed on OR
-
have been within 1 metre of you for 1 minute or longer OR
-
have been within 2 metres of you on one or more occasions during a single day for at least 15 minutes in total (when all times added up together)
If a person does not meet this definition, you DO NOT need to add them
Don't
Do not enter:
-
Health and Social Care Workers wearing appropriate Personal Protective Equipment (PPE).
- This includes doctors, nurses, social care workers, or other care workers who visited your home


BeoLiving Intelligence Privacy Policy
Data privacy is important to our company (referred in this document as “us”) because we respect the privacy rights of users and recognize the importance of protecting information collected about you. This Privacy Policy explains how information is collected, used and disclosed when using a BeoLiving Intelligence (“home controller”). The BeoLiving Intelligence provides the user (“you”) with features for your smart home products such as remote access and product control. It also acts as an integrator between third-party services and home systems such as Twitter, Nest and Philip Hue. It also uses the Khimo service to provide remote access. This policy is closely related to the BeoLiving App Privacy Policy, since the BeoLiving App is basically a user interface for the BeoLiving Intelligence.
Who we are?
Bang & Olufsen Group consists of Bang & Olufsen a/s, Peter Bangs vej 15, DK-7600 Struer, Denmark and its subsidiaries, including B&O Play a/s, Peter Bangs vej 15, 7600 Struer, Denmark.
What will we do with your personal information?
By using our service you knowingly provide personal information that is stored in your home controller. This information will only be used in order to provide you with the features of our service and always in ways you would reasonably expect.
Log files of all the system activity are kept in the home controller and you have the option to download them and then send them to us. These log files help us to debug and fix any possible errors and will not be used for any other purpose than that. If you choose to share it with anyone else, you do it under your own responsibility. The logs could contain any of the personal data listed below and any relevant system information. In addition to creating the logs, the information will be also processed for the purposes that are specified in each item:
-
**Home controller’s credentials**: Your home controller’s user and password will be requested when you log in to the service. This will be stored in order to identify you as a user.
-
**Credentials and tokens from third parties**: The credentials and tokens of the third parties you connect to the home controller are stored to be able to send and retrieve information from them.
-
**IP of third parties:** The IP addresses of each third-party system connected to the home controller are stored in order to establish and maintain communication with them.
-
**Description of the house**: This information is used to create macros, send commands to the resources and share it with the BeoLiving App.
-
**Home controllers commands and changes of status**: Each command you execute as well as any status change and events that occurs in your home controllers are stored for local monitoring and fixing. This information is not persistent and will be deleted if you reboot the home controller.
All this information is stored in the home controller until you reset it, except the logs that are kept until reaching a certain amount of data stored.
Do we share information with third parties?
In some cases, the home controller will need to share personal information with third parties.
When you link a third-party to the home controller, it might share the credentials you have previously set for that third-party and any other information that is necessary to be able to use the service that it provides. We understand that you have previously read and agreed with that third-party’s privacy policy and terms and conditions.
In particular, the service is always connected to Khimo cloud services in order to provide remote access. For this purpose, information such as your home controller’s IP and your home description is shared. We make sure that Khimo does not store any information unless what you accept in Khimo’s Privacy Polciy.
Also, in order to be able to use the BeoLiving App the home controller will share the home description with it.
Your personal information may be also disclosed to a third-party if we are required to do so because of an applicable law, court order or governmental regulation.
Do we receive information from third parties?
When using third-party home systems, the home controller might get some information from them such as configured devices, changes of state and logged in credentials and tokens. This information is necessary to be able to control the products and will be used for that purpose only.
How can you access, erase or update your personal information?
If you are an EU citizen the General Data Protection Regulation (GDPR) gives you rights regarding your personal information. More specifically, you can access, review, update and delete any personal information we hold. If you have sent us personal information by emailing the service’s logs, you can exercise these rights by writing to<EMAIL_ADDRESS>with your requests and we will get back to you in less than 5 working days.
In addition to you being able to request the deletion of your data, if you stop using our services for a period of 5 years and you have accepted this policy, then your data will be automatically deleted.
Where do we store your information?
The information is stored locally in the home controller, however it might be transferred to a third-party as specified in the “Do we share information with third parties” section.
What about children?
We do not knowingly solicit or collect any personal information from children under 16. If notified by a parent or discovered by any means that we have gathered information from a child under 16 we will delete it as soon as possible.
Could this policy change?
We might change this policy in case of any change on what or how the data is processed. If we do change this policy we will communicate with you so you can choose whether to agree to the new use.
What am I accepting?
By accepting this policy you let us know that you have read and understood this policy, and that you agree with our legitimate interest in processing your personal data.
What if you are not satisfied with this policy?
If you have any doubts or concerns about this policy please send us a message to<EMAIL_ADDRESS>and we will answer you in less than 5 working days. If you are not satisfied with the outcome of our communication, you may refer your complaint to the Danish Data Protection Authorities at dt@datatilsynet.dk.


Tip: length in 3 ~ 64, word characters only.
Tip: length should be less than 32 characters.
Tip: length between 6 ~ 20.
Tip: length should be less than 256 characters.
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It seems that {{authService.nickName}} has logged in, you can go to the main page to explore Jigsaw Any Badge with this accoun, or logout this account if it is not yours.


Module: ActiveSupport::Callbacks::CallTemplate
Do not use. This module is for internal use only.
| Relationships & Source Files | |
| Namespace Children | |
|
Classes:
| |
| Defined in: | activesupport/lib/active_support/callbacks.rb |
Overview
A future invocation of user-supplied code (either as a callback, or a condition filter).
Class Method Summary
-
.build(filter, callback)
Filters
support:
Class Method Details
.build(filter, callback)
Filters
support:
Symbols:: A method to call.
Procs:: A proc to call with the object.
Objects:: An object with a {before_foo} method on it to call.
All of these objects are converted into a CallTemplate
and handled the same after this point.
# File 'activesupport/lib/active_support/callbacks.rb', line 533
def self.build(filter, callback) case filter when Symbol MethodCall.new(filter) when Conditionals::Value ProcCall.new(filter) when ::Proc if filter.arity > 1 InstanceExec2.new(filter) elsif filter.arity > 0 InstanceExec1.new(filter) else InstanceExec0.new(filter) end else ObjectCall.new(filter, callback.current_scopes.join("_").to_sym) end end


Aerospace engineer Senior mulls selling aerostructures division
by Alex DanielSenior, an engineering supplier to the likes of Boeing and Daimler, has admitted it is thinking about selling off its aerostructures division, in a deal that could be worth up to £450m.
Shares in the FTSE 250 company rose 5.5 per cent today after it confirmed reports that emerged over the weekend that a sale was under consideration, as one of several options amid a review of the business.
Read more: Risks pile on for aerospace industry as 737 Max saga continues
Senior posted a 16 per cent drop in first-half profit in August, as margins in the aerospace business were hit by Boeing’s decision to temporarily halt production of its best-selling 737 Max planes after two fatal crashes that involved the aircraft.
Directors have not made a final decision, and the division may yet remain part of the business. However, it has appointed adviser Lazard on the potential sale, according to Bloomberg.
The aerostructures business makes aeroplane parts as part of the company’s wider aerospace division, which makes up nearly three quarters of revenue.
Last month, Senior said it was gearing up to restructure the business, in a process which wil include closing plants and cutting jobs.
“It is Senior’s policy to review its portfolio on an ongoing basis and evaluate all its operating businesses in terms of their strategic fit within the Group,” it said.
“Senior can confirm that it has been reviewing all strategic options for its aerostructures business, which includes an early stage assessment of a potential divestment of the division.
Read more: Aerospace engineer Senior’s profits shrink after Boeing 737 Max grounding
“There can be no certainty that this will lead to a transaction.”
It comes as US private equity fund Advent takes over UK defense supplier Cobham in a £4bn deal.


Michael Healy-Rae brought to hospital after fire in his shop in Co Kerry
by NewstalkKerry TD Michael Healy-Rae has been brought to hospital following a fire in his shop.
The blaze is believed to have broken out around the fuse board of the filling station in Kilgarvan this morning.
The fire services were notified just before 9am, and one unit attended.
Danny Healy-Rae told Radio Kerry his brother is fine, but has gone to hospital as a precaution.
He says Michael extinguished the fire and inhaled smoke in the process.
Deputy Healy-Rae explained: "He's fine, but it was decided he should go for an x-ray to check himself out.
"It was a shock, because it was a bit scary for a few minutes until [the fire] was brought under control."


‘Spandana’ evokes good response
Complainants get a chance to interact with SP, thanks to video link
by Special CorrespondentThanks to video conferencing facility, people from far-off places, including Hyderabad and Bengaluru, complained to the Prakasam police to get their grievances addressed quickly, during the ‘Spandana’' programme held here on Monday.
Superintendent of Police Siddarth Kaushal patiently listened to one Lakshmaiah from Hyderabad who had been running from pillar to post for a solution to a land dispute in Kandukur town in the district. The SP entrusted the case for quick resolution to Kandukur Deputy Superintendent of Police B. Ravichandra.
Another petitioner, Preethi from Hyderabad, poured out her woes to the SP complaining that a case had been foisted' on her by one Raja Chandrika in Addanki town. The SP directed the Addanki police to handle the case diligently and provide relief to the complainant.
Yet another petitioner, Sailesh Babu pleaded with the SP for bringing to fruition hearing in a case in a local court relating to a murder in Medermetla in the year 2008 got prolonged due to non-appearance of witnesses.
 The entire proceedings was live cast on social media to ensure transparency and build confidence in the petitioners.


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title: Envy Nail paints from Health & Glow
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I was standing at H&G counter and saw these bottles for some offer (buy 2, get 1 free), they looked good and for just Rs.33 within a few seconds I grabbed these shades..
Price: Rs. 49 for one... currently some offers going on..
 Can't say much about brush though as all three all of them are different size :P
I just wish Nail paints disclose their ingredients.. since they come for so less.. who knows what goes in :(
My Favorite shade of the three...
These are a good deal. Except the Blue one(Running stream), rest 2 were nicely pigmented.
Are you going to get these budget favorites ?


Toggle navigation
BlueShift
View Running Jobs
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Jobs Running Currently
Namespace
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Actions
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BSVSP in 2D SnTe thin film. a Band structure and the y components of spin polarizations of 2D SnTe thin film calculated by DFT. Both x and z components vanish and are not shown here. b Local spin polarizations of Sn and Te atoms for the Bloch states marked with the red points I and II in a. The blue and green spheres are Sn and Te atoms, respectively


Verifying the index using xapian-delve¶
Xapian comes with a handy utility called xapian-delve which can be used to inspect a database, so let’s look at the one you just built. If you just pass a database path as a parameter you’ll get an overview: how many documents, average term length, and some other statistics:
$ xapian-delve db
UUID = 1820ef0a-055b-4946-ae73-67aa4ef5c226
number of documents = 100
average document length = 100.58
document length lower bound = 33
document length upper bound = 251
highest document id ever used = 100
has positional information = true
You can also look at an individual document, using Xapian’s docid (-d
means output document data as well):
$ xapian-delve -r 1 -d db # output has been reformatted
Data for record #1:
{
"MEASUREMENTS": "",
"DESCRIPTION": "Ansonia Sunwatch (pocket compas dial)",
"PLACE_MADE": "New York county, New York state, United States",
"id_NUMBER": "1974-100",
"WHOLE_PART": "WHOLE",
"TITLE": "Ansonia Sunwatch (pocket compas dial)",
"DATE_MADE": "1922-1939",
"COLLECTION": "SCM - Time Measurement",
"ITEM_NAME": "Pocket horizontal sundial",
"MATERIALS": "",
"MAKER": "Ansonia Clock Co."
}
Term List for record #1: Q1974-100 Sansonia Scompas Sdial Spocket
Ssunwatch XDansonia XDcompass XDdial XDpocket XDsunwatch ZSansonia
ZScompas ZSdial ZSpocket ZSsunwatch ZXDansonia ZXDcompas ZXDdial
ZXDpocket ZXDsunwatch Zansonia Zcompass Zdial Zpocket Zsunwatch
ansonia compass dial pocket sunwatch
You can also go the other way, starting with a term and finding both statistics and which documents it indexes:
$ xapian-delve -t Stime db
Posting List for term `Stime' (termfreq 4, collfreq 4, wdf_max 4):
41 56 58 65
This means you can look documents up by identifier:
$ xapian-delve -t Q1974-100 db
Posting List for term `Q1974-100' (termfreq 1, collfreq 1, wdf_max 1):
1
xapian-delve
is frequently useful if you aren’t getting the behaviour you
expect from a search system, to check that the database contains the
documents and terms you expect.


This is Words and Buttons Online — a collection of interactive #tutorials, #demos, and #quizzes about #mathematics, #algorithms and #programming.
Disclaimer. The trick I am about to show is not an example of good engineering. It depends on implementation-specific optimization and as such is unreliable. In some contexts, it may give you some noticeable performance gains, but in other, it may even lead to performance degradation.
However, quoting Donald Knuth, “in established engineering disciplines a 12% improvement, easily obtained, is never considered marginal; and I believe the same viewpoint should prevail in software engineering.”
C++ has tail call optimization. It's when a recursive function that ends with a self-call is replaced with a simple loop. Let me show you how it works
|
From disassembly, you can see that there is no actual self-call in the “recursive” sum_of_first function. From the Valgrind report, you can also see why this is important.
Normally, every time you call a function, you have to store its return address in a stack. Even if the address remains the same. Which means that if you call your function 65536 times in a nested way, you will be writing the same address 65536 times only to read it all back on returns. It is pure overhead. But if the self-call is a tail call, meaning that all the nested calls will eventually return to the same place, you don't have to do the calls at all. A simple loop will be enough.
Compilers can do that for you. They rewrite your code to minimize overhead whenever they can. But what if they can't?
|
When the self-call is not a tail call, and can't even be made a tail call, the compiler skips the TCO optimization. And yes, you get all the overheads: all the amount of needless reads and writes.
What if instead of a self-call, we would instantiate a copy of the original function and call that instead? The copy then will call the original. In theory, this allows a compiler to inline the copy, so instead of all 65536 nested calls, we will only have 32768. Let's try it out.
|
Well, the theory and compiler optimizations don't always work together. Yes, we have fewer calls, but the code itself became larger, so the instruction count remained almost as is was. Although the data read and write count lowered, it is still far from the desired TCO-like result.
But it is something.
Besides, who said we should stop there? Let's try the same principle, but with more instances.
It's not trivial to relevantly micro-benchmark this kind of functions. The thing is, if we make the operation larger, it will just overflow the stack. We can request a larger stack, but then the operation itself will become unrealistic. Let's not measure the time then. Let's thick to instruction counts and memory operations instead.
This is not the same thing as the time of execution, but it more or less correlates with the performance expectations.
Here are the instruction counts for (almost) the same code with TCO, without TCO, and with self-calls spread through 2, 4, 8, 16, and 32 instances.
Now let's measure total data reads and writes for the same family of implementations.
The trick shows some performance gain, and it's safe to use. It does rely on compiler's implementation details, but in the worse case, it will merely not give you the gain you wish.
On the other hand, it is merely a cheap trick. If you have a bottleneck in the recursion, it is still best to rewrite it in an iterative way or in the manner that enables the TCO. It is more work, but the benefit will not then depend on the current compiler's optimizations.
All the measurements conducted on Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz, the code is compiled with g++ 5.4.0 (-O2). The source code for the benchmark is available on Github.
| Index #programming #demos | ← there's more. |
+
So You Think You Know C (free book) + Geometry for Programmers (paid book) + Github & RSS |


| Name | Job | Age |
|---|---|---|
| Art Vandelay | Architect | 33 |
| H.E. Pennypacker | Wealthy industrialist, and philanthropist | 35 |
| Kel Varnsen | Importer/exporter of fine latex goods | 30 |
| Name | Job | Age |
|---|---|---|
| Art Vandelay | Architect | 33 |
| H.E. Pennypacker | Wealthy industrialist, and philanthropist | 35 |
| Kel Varnsen | Importer/exporter of fine latex goods | 30 |
| Name | Job | Age |
|---|---|---|
| Art Vandelay | Architect | 33 |
| H.E. Pennypacker | Wealthy industrialist, and philanthropist | 35 |
| Kel Varnsen | Importer/exporter of fine latex goods | 30 |


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| Package | Description |
|---|---|
| be.ac.ulb.crashcoin.common | |
| be.ac.ulb.crashcoin.common.net |
| Modifier and Type | Class and Description |
|---|---|
class |
Address |
class |
Block
Block that compose BlockChain.
|
class |
BlockChain
Represents the CrashCoin BlockChain as a list of Block instances.
|
class |
MerkleTree
Merkle tree used to check whether a transaction is part of a block by hashing
it along its merkle branch and checking if it alters the Merkle root.
|
class |
Message
Class representing a network request.
|
class |
Transaction |
class |
TransactionInput
Input of a transaction, from the doc
https://en.bitcoin.it/wiki/Transaction
|
class |
TransactionOutput
Output of a transaction, from the doc
https://en.bitcoin.it/wiki/Transaction
|
| Constructor and Description |
|---|
Message(String request,
JSONable option) |
| Modifier and Type | Class and Description |
|---|---|
class |
TestStrJSONable |
| Modifier and Type | Method and Description |
|---|---|
protected abstract void |
AbstractConnection.receiveData(JSONable data)
Called when an object was successfully created from data received.
|
void |
AbstractConnection.sendData(JSONable jsonData) |
Copyright © 2017. All rights reserved.


{% if shower %} {% else %} {% endif %}
Meteor showers on Earth are caused by streams of meteoroids hitting our atmosphere. These meteoroids are sand- and pebble-sized bits of rock that were once released from their parent comet. Some comets are no longer active and are now called asteroids.
This visualization shows these meteoroid streams orbiting the Sun, some stretching to the outer regions of the solar system. Select the meteor shower in the menu to see the corresponding meteoroid stream in space. Their meteoroid orbits are based on those measured by NASA's CAMS video camera surveillance network, and were calculated by meteor astronomer Peter Jenniskens of the SETI Institute and NASA Ames Research Center.
This visualization is developed and hosted by Ian Webster.
Meteor showers are best seen on certain dark and clear nights. To see how many meteors per hour are visible from your location, go to this website and select the date, the shower, and your location.
If you like these sorts of interactive visualizations, take a look at Ian Webster's other tools, including Pluto, ancient Earth, or asteroids!
Sure! Use the following code to load the interactive visualization in an iframe. You must know the IAU number of your meteor shower, which you can look up here.
<iframe src="https://www.meteorshowers.org/view/iau-7" width="500" height="500" frameBorder="0"></iframe>
Please remember to attribute this site!
The visualization engine used for MeteorShowers.org has been released as SpaceKit.js. Give it a try!


weex-template-compiler
This package is auto-generated. For pull requests please see
src/platforms/weex/entry-compiler.js
.


LabLua is a research laboratory at PUC-Rio, affiliated with its Computer Science Department. It is dedicated to research about programming languages, with emphasis on research involving the Lua language. LabLua was founded on May 2004 by Prof. Roberto Ierusalimschy.
In these pages you can find information about current and past projects, publications and members. Please follow the links above to go to the other sections of the web site.
LabLua is available for training in Lua and consulting work in projects that use the Lua language. Contact Prof. Ierusalimschy for more details.
You can contact LabLua at<EMAIL_ADDRESS>You can also contact LabLua by phone: +55-21-3527-1497 Ext. 4523
In 2021, LabLua is participating Google Summer of Code (GSoC). This will be our eight year in this awesome program! If you are a student you can check out our ideas list for project ideas and ways to get in touch with us.
The blog posts for our previous GSoC participations can be found here: 2021, 2020, 2019, 2018, 2017, 2016, 2015.


The Housing Rebound Continues, Strong Evidence Of Recession Risk Receding In 2020
by New Deal DemocratSummary
- Housing permits and starts are long leading indicators.
- Permits made new expansion highs in October.
- The three-month average of starts also made a new expansion high.
- Since interest rates have been declining through most of this year, expect the housing rebound to continue.
- The positive news in these long leading indicators is strong evidence that the economy will strengthen in 2020.
Introduction
This morning’s release of housing permits and starts shows that the housing rebound continues.
Important metrics made new expansion highs
Starts increased, although not to an expansion high. More importantly, the more leading and less volatile permits *did* increase to a new expansion high:On a three-month average basis, starts also made a new expansion high.
When we turn to the even less volatile single-family permits and starts, we see the same thing:The rebound in single-family permits is particularly impressive.
Only once in the past 50 years has a recession started after housing bottomed. That was the producer-led 2001 recession, which started 8 months after the bottom, and there the pre-recession rebound was less than 10%:By contrast, since their recent bottom 6 months ago, housing permits have rebounded by more than 15% as of this month.
Expect the rebound to continue
Further, because interest rates lead housing, usually by about 6 months or so, and interest rates have been declining all year until recently, we can expect the housing data to continue to improve. The below graph is one I have run many times over the past 5+ years, showing the YoY change in mortgage interest rates (blue, inverted) vs. the YoY% change in single-family permits (red):
Conclusion
Both mortgage interest rates and housing permits and starts are long leading indicators. While it isn’t conclusive (no single indicator is), this is a very strong argument that recession risk is receding, particularly as we go through 2020.
Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.


Price drop alert: PlayStation Plus 12-month codes are discounted again if you hurry
by Maren EstradaAttention PlayStation 4 owners: we have good news and bad news for you. The good news is that PlayStation Plus 12-Month Membership Digital Codes are discounted again on Amazon after having sold out in about an hour earlier this week. The bad news is that the discount isn’t quite as deep as it was last time. 12 months of PS Plus for $44.99 instead of $60 is still a solid deal though, and there’s no telling if or when it’ll be discounted again. The only thing we can say for certain is that there’s a good chance this deal will sell out fast just like the last one, so hurry up if you want to save some cash!
PlayStation Plus: 12 Month Membership [Digital Code]
$44.99
Available from Amazon
BGR may receive a commission
Buy Now
Here’s what you need to know from the product page:
- 12 Months – Play All Year Long
- Enables online multiplayer on PS4, so you can play games online with friends
- FREE PlayStation 4 games every month
- Access to exclusive PlayStation Store sales and discounts
PlayStation Plus: 12 Month Membership [Digital Code]
$44.99
Available from Amazon
BGR may receive a commission
Buy Now
Follow @BGRDeals on Twitter to keep up with the latest and greatest deals we find around the web. Prices subject to change without notice. BGR may receive a commission on orders placed through this article.


Dashboard
45,000
total sharing
2,105
Sharing today
2,105
Empathizes today
3,339
Sharing yesterday
8,100
Empathizes yesterday


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Celia's Portfolio
See all my projects.
OddJobs.ca
Professional hub for all odd jobs. Niche job site for job recruiters and seekers.
Rent-a-Lot
Parking app to find a lot to rent and list your vacant spot for profit.
Beerz
Convenient app to find the breweries and pubs near you.
Peanuts Clicky Game
Clicky game to test your memory with the Peanuts gang from the comic book.
Tech News Scraper
This app scrapes the current news from the TechCrunch website.
Friend Finder Survey
App for finding your best matched friend based on compatibility in personality.
Eat-Da-Burger!
Restaurant app that lets users input the names of burgers they'd like to eat
Train Schedule App
App to view and add train schedule with real time arrivals and departures.
Crystal Collector
A game of collecting crystals to earn points.
The Psychic Game
A game that allows you to guess the letter the computer chooses.


{% extends "parleys_creek_management/base.html" %} {% load staticfiles %} {% block app_content %} {# #}
The Parleys Watershed is one of four drainages that are included in Salt Lake City's "Protected Watershed" Canyons. Parley's Creek Basin, located on western slope of Wasatch Mountains, includes two reservoirs, Little Dell and Mountain Dell. The reservoirs were developed with the primary use of municipal and industrial water supply and secondary use of flood control. The primary inflows are generated from Lamb's and Dell Creek.
This application can be used to evaluate various management scenarios for the Parley's Creek system to give this ability to managers, stakeholders, and users to test different alternatives. This also can be used to test climate change scenarios (uncertain future extreme climate conditions) to evaluate the reservoirs' performance. Click on the "New Scenario" button to get started.
{% endblock %} {% block app_actions %} {% endblock %}


To download our software, select the proper version for your system, and install the same as you would for Bitcoin Core. To connect to public nodes, do the following:
Click 'Help' -> Debug Window -> Console - Paste
addnode "<IP_ADDRESS>:6703" "add"
addnode "<IP_ADDRESS>:6703" "add"
addnode "<IP_ADDRESS>:6703" "add"
If everything works properly, then you will download until 530,501 blocks. At this point close Bitcoin 1776 and restart. Now you will be on the 6703 port (instead of 8333). From here you should be able to begin mining (after opening port 6703).
Ubuntu Install Instructions
ONLY WORKS WITH GUI SUCH AS GNOME NO CMDOpen Package, extract, and open a terminal(CTRL + T)
cd Downloads
ls
sudo chmod +X bitcoin1776-bin
./bitcoin1776qt


Filtering. Using content of the book "JavaScript for Kids"
Custom matcher. Also we hide children if node has ones. Select complexity:
{{ item }}
{{ node.text }}
{{ node.data.complexity }}


parts¶
attention¶
This module implements attention mechanisms described in “Attention is All You Need” https://arxiv.org/abs/1706.03762
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parts.attention.
get_future_masking_bias
(Q, K)[source]¶ Performs future masking for decoder, by setting everything > current position to -INF (-1e9). It asserts that Q and K are of the same shape :param Q: Queries tensor [batch, num_heads, Q_length, dk] :param K: Keys tensor [batch, num_heads, K_length, dk] :return: tensor with same dtype as Q and of shape [batch, num_heads, Q_length, K_length], where for all batch_ind and head_ind:
[batch_ind, head_ind, :, :] is an upper diagonal (without diagonal) and all non zero entries are -INF
-
parts.attention.
multi_head_attention_fn
(Q, K, V, d_model, dk=None, dv=None, h=2, mask_future=False, additional_bias=None, initializer=None)[source]¶ Computes multi-head attention (sess Section 3.2. in the paper above) :param Q: Queries tensor [batch, Q_length, orig_dq] :param K: Keys tensor [batch, K_length, orig_dk] :param V: Values tensor [batch, V_length, orig_dv] :param d_model: model dimensionality :param dk: (default: d_model/h) Q and K will be projected to dk :param dv: (default: d_model/h) V will be projected to dv :param h: (default: 2) number of heads in attention :param mask_future: (default: False) whether to mask future steps :param additional_bias: (default: None) additional bias, such as pad masking bias should be a tensor of shape [batch, heads, Q_length, K_length] :param initializer: (default: None) initializer for projection :return:
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parts.attention.
scaled_dot_attention_fn
(Q, K, V, sqrt_normalize=True, bias=None)[source]¶ Computes scaled dot attention (formula 1 from Section 3.2.1 in the paper above). Per batch per head Contains no trainable parameters :param Q: Queries tensor [batch, num_heads, Q_length, dk] :param K: Keys tensor [batch, num_heads, K_length, dk] :param V: Values tensor [batch, num_heads, V_length, dv] :param sqrt_normalize: (default: True) whether to normalize by sqrt(dk) :param bias: (default: None) masking bias :return: scaled dot attention tensor of shape [batch, num_heads, length_q, dv]
attention_wrapper¶
A powerful dynamic attention wrapper object.
-
class
parts.attention_wrapper.
AttentionMechanism
[source]¶ Bases:
object
-
alignments_size
¶
-
state_size
¶
-
-
class
parts.attention_wrapper.
AttentionWrapper
(cell, attention_mechanism, attention_layer_size=None, alignment_history=False, cell_input_fn=None, output_attention=True, initial_cell_state=None, name=None)[source]¶ Bases:
tensorflow.python.ops.rnn_cell_impl.RNNCell
Wraps another RNNCell with attention.
-
__init__
(cell, attention_mechanism, attention_layer_size=None, alignment_history=False, cell_input_fn=None, output_attention=True, initial_cell_state=None, name=None)[source]¶ Construct the AttentionWrapper.
NOTE If you are using the BeamSearchDecoder with a cell wrapped in AttentionWrapper, then you must ensure that:
- The encoder output has been tiled to beam_width via @{tf.contrib.seq2seq.tile_batch} (NOT tf.tile).
- The batch_size argument passed to the zero_state method of this wrapper is equal to true_batch_size * beam_width.
- The initial state created with zero_state above contains a cell_state value containing properly tiled final state from the encoder.
An example:
``` tiled_encoder_outputs = tf.contrib.seq2seq.tile_batch(
encoder_outputs, multiplier=beam_width)- tiled_encoder_final_state = tf.conrib.seq2seq.tile_batch(
- encoder_final_state, multiplier=beam_width)
- tiled_sequence_length = tf.contrib.seq2seq.tile_batch(
- sequence_length, multiplier=beam_width)
- attention_mechanism = MyFavoriteAttentionMechanism(
- num_units=attention_depth, memory=tiled_inputs, memory_sequence_length=tiled_sequence_length)
attention_cell = AttentionWrapper(cell, attention_mechanism, ...) decoder_initial_state = attention_cell.zero_state(
dtype, batch_size=true_batch_size * beam_width)- decoder_initial_state = decoder_initial_state.clone(
- cell_state=tiled_encoder_final_state)
Parameters: - cell – An instance of RNNCell.
- attention_mechanism – A list of AttentionMechanism instances or a single instance.
- attention_layer_size – A list of Python integers or a single Python integer, the depth of the attention (output) layer(s). If None (default), use the context as attention at each time step. Otherwise, feed the context and cell output into the attention layer to generate attention at each time step. If attention_mechanism is a list, attention_layer_size must be a list of the same length.
- alignment_history – Python boolean, whether to store alignment history from all time steps in the final output state (currently stored as a time major TensorArray on which you must call stack()).
- cell_input_fn – (optional) A callable. The default is: lambda inputs, attention: array_ops.concat([inputs, attention], -1).
- output_attention – Python bool. If True (default), the output at each time step is the attention value. This is the behavior of Luong-style attention mechanisms. If False, the output at each time step is the output of cell. This is the beahvior of Bhadanau-style attention mechanisms. In both cases, the attention tensor is propagated to the next time step via the state and is used there. This flag only controls whether the attention mechanism is propagated up to the next cell in an RNN stack or to the top RNN output.
- initial_cell_state – The initial state value to use for the cell when the user calls zero_state(). Note that if this value is provided now, and the user uses a batch_size argument of zero_state which does not match the batch size of initial_cell_state, proper behavior is not guaranteed.
- name – Name to use when creating ops.
Raises: TypeError
– attention_layer_size is not None and (attention_mechanism is a list but attention_layer_size is not; or vice versa).ValueError
– if attention_layer_size is not None, attention_mechanism is a list, and its length does not match that of attention_layer_size.
-
_item_or_tuple
(seq)[source]¶ Returns seq as tuple or the singular element.
Which is returned is determined by how the AttentionMechanism(s) were passed to the constructor.
Parameters: seq – A non-empty sequence of items or generator. Returns: Either the values in the sequence as a tuple if AttentionMechanism(s) were passed to the constructor as a sequence or the singular element.
-
call
(inputs, state)[source]¶ Perform a step of attention-wrapped RNN.
- Step 1: Mix the inputs and previous step’s attention output via cell_input_fn.
- Step 2: Call the wrapped cell with this input and its previous state.
- Step 3: Score the cell’s output with attention_mechanism.
- Step 4: Calculate the alignments by passing the score through the normalizer.
- Step 5: Calculate the context vector as the inner product between the alignments and the attention_mechanism’s values (memory).
- Step 6: Calculate the attention output by concatenating the cell output and context through the attention layer (a linear layer with attention_layer_size outputs).
Parameters: - inputs – (Possibly nested tuple of) Tensor, the input at this time step.
- state – An instance of AttentionWrapperState containing tensors from the previous time step.
Returns: - attention_or_cell_output depending on output_attention.
- next_state is an instance of AttentionWrapperState
- containing the state calculated at this time step.
Return type: A tuple (attention_or_cell_output, next_state), where
Raises: TypeError
– If state is not an instance of AttentionWrapperState.
-
output_size
¶
-
state_size
¶ The state_size property of AttentionWrapper.
Returns: An AttentionWrapperState tuple containing shapes used by this object.
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zero_state
(batch_size, dtype)[source]¶ Return an initial (zero) state tuple for this AttentionWrapper.
NOTE Please see the initializer documentation for details of how to call zero_state if using an AttentionWrapper with a BeamSearchDecoder.
Parameters: - batch_size – 0D integer tensor: the batch size.
- dtype – The internal state data type.
Returns: An AttentionWrapperState tuple containing zeroed out tensors and, possibly, empty TensorArray objects.
Raises: ValueError
– (or, possibly at runtime, InvalidArgument), if batch_size does not match the output size of the encoder passed to the wrapper object at initialization time.
-
-
class
parts.attention_wrapper.
AttentionWrapperState
[source]¶ Bases:
parts.attention_wrapper.AttentionWrapperState
namedtuple storing the state of a AttentionWrapper.
Contains:
- cell_state: The state of the wrapped RNNCell at the previous time step.
- attention: The attention emitted at the previous time step.
- time: int32 scalar containing the current time step.
- alignments: A single or tuple of `Tensor`(s) containing the alignments
- emitted at the previous time step for each attention mechanism.
- alignment_history: (if enabled) a single or tuple of `TensorArray`(s)
- containing alignment matrices from all time steps for each attention mechanism. Call stack() on each to convert to a Tensor.
- attention_state: A single or tuple of nested objects
- containing attention mechanism state for each attention mechanism. The objects may contain Tensors or TensorArrays.
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clone
(**kwargs)[source]¶ Clone this object, overriding components provided by kwargs.
The new state fields’ shape must match original state fields’ shape. This will be validated, and original fields’ shape will be propagated to new fields.
Example:
`python initial_state = attention_wrapper.zero_state(dtype=..., batch_size=...) initial_state = initial_state.clone(cell_state=encoder_state) `
Parameters: **kwargs – Any properties of the state object to replace in the returned AttentionWrapperState. Returns: A new AttentionWrapperState whose properties are the same as this one, except any overridden properties as provided in kwargs.
-
class
parts.attention_wrapper.
LuongAttention
(num_units, memory, memory_sequence_length=None, scale=False, probability_fn=None, score_mask_value=None, dtype=None, name='LuongAttention')[source]¶ Bases:
parts.attention_wrapper._BaseAttentionMechanism
Implements Luong-style (multiplicative) attention scoring.
This attention has two forms. The first is standard Luong attention, as described in:
Minh-Thang Luong, Hieu Pham, Christopher D. Manning. “Effective Approaches to Attention-based Neural Machine Translation.” EMNLP 2015. https://arxiv.org/abs/1508.04025
The second is the scaled form inspired partly by the normalized form of Bahdanau attention.
To enable the second form, construct the object with parameter scale=True.
-
__init__
(num_units, memory, memory_sequence_length=None, scale=False, probability_fn=None, score_mask_value=None, dtype=None, name='LuongAttention')[source]¶ Construct the AttentionMechanism mechanism.
Parameters: - num_units – The depth of the attention mechanism.
- memory – The memory to query; usually the output of an RNN encoder. This tensor should be shaped [batch_size, max_time, ...].
- memory_sequence_length – (optional) Sequence lengths for the batch entries in memory. If provided, the memory tensor rows are masked with zeros for values past the respective sequence lengths.
- scale – Python boolean. Whether to scale the energy term.
- probability_fn – (optional) A callable. Converts the score to probabilities. The default is @{tf.nn.softmax}. Other options include @{tf.contrib.seq2seq.hardmax} and @{tf.contrib.sparsemax.sparsemax}. Its signature should be: probabilities = probability_fn(score).
- score_mask_value – (optional) The mask value for score before passing into probability_fn. The default is -inf. Only used if memory_sequence_length is not None.
- dtype – The data type for the memory layer of the attention mechanism.
- name – Name to use when creating ops.
-
-
class
parts.attention_wrapper.
BahdanauAttention
(num_units, memory, memory_sequence_length=None, normalize=False, probability_fn=None, score_mask_value=None, dtype=None, name='BahdanauAttention')[source]¶ Bases:
parts.attention_wrapper._BaseAttentionMechanism
Implements Bahdanau-style (additive) attention.
This attention has two forms. The first is Bahdanau attention, as described in:
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio. “Neural Machine Translation by Jointly Learning to Align and Translate.” ICLR 2015. https://arxiv.org/abs/1409.0473
The second is the normalized form. This form is inspired by the weight normalization article:
Tim Salimans, Diederik P. Kingma. “Weight Normalization: A Simple Reparameterization to Accelerate
Training of Deep Neural Networks.”https://arxiv.org/abs/1602.07868
To enable the second form, construct the object with parameter normalize=True.
-
__init__
(num_units, memory, memory_sequence_length=None, normalize=False, probability_fn=None, score_mask_value=None, dtype=None, name='BahdanauAttention')[source]¶ Construct the Attention mechanism.
Parameters: - num_units – The depth of the query mechanism.
- memory – The memory to query; usually the output of an RNN encoder. This tensor should be shaped [batch_size, max_time, ...].
- memory_sequence_length (optional) – Sequence lengths for the batch entries in memory. If provided, the memory tensor rows are masked with zeros for values past the respective sequence lengths.
- normalize – Python boolean. Whether to normalize the energy term.
- probability_fn – (optional) A callable. Converts the score to probabilities. The default is @{tf.nn.softmax}. Other options include @{tf.contrib.seq2seq.hardmax} and @{tf.contrib.sparsemax.sparsemax}. Its signature should be: probabilities = probability_fn(score).
- score_mask_value – (optional): The mask value for score before passing into probability_fn. The default is -inf. Only used if memory_sequence_length is not None.
- dtype – The data type for the query and memory layers of the attention mechanism.
- name – Name to use when creating ops.
-
-
parts.attention_wrapper.
hardmax
(logits, name=None)[source]¶ Returns batched one-hot vectors.
The depth index containing the 1 is that of the maximum logit value.
Parameters: - logits – A batch tensor of logit values.
- name – Name to use when creating ops.
Returns: A batched one-hot tensor.
-
parts.attention_wrapper.
safe_cumprod
(x, *args, **kwargs)[source]¶ Computes cumprod of x in logspace using cumsum to avoid underflow.
The cumprod function and its gradient can result in numerical instabilities when its argument has very small and/or zero values. As long as the argument is all positive, we can instead compute the cumulative product as exp(cumsum(log(x))). This function can be called identically to tf.cumprod.
Parameters: - x – Tensor to take the cumulative product of.
- *args – Passed on to cumsum; these are identical to those in cumprod.
- **kwargs – Passed on to cumsum; these are identical to those in cumprod.
Returns: Cumulative product of x.
-
parts.attention_wrapper.
monotonic_attention
(p_choose_i, previous_attention, mode)[source]¶ Compute monotonic attention distribution from choosing probabilities.
Monotonic attention implies that the input sequence is processed in an explicitly left-to-right manner when generating the output sequence. In addition, once an input sequence element is attended to at a given output timestep, elements occurring before it cannot be attended to at subsequent output timesteps. This function generates attention distributions according to these assumptions. For more information, see ``Online and Linear-Time Attention by Enforcing Monotonic Alignments’‘.
Parameters: - p_choose_i – Probability of choosing input sequence/memory element i. Should be of shape (batch_size, input_sequence_length), and should all be in the range [0, 1].
- previous_attention – The attention distribution from the previous output timestep. Should be of shape (batch_size, input_sequence_length). For the first output timestep, preevious_attention[n] should be [1, 0, 0, ..., 0] for all n in [0, ... batch_size - 1].
- mode –
How to compute the attention distribution. Must be one of ‘recursive’, ‘parallel’, or ‘hard’.
- ‘recursive’ uses tf.scan to recursively compute the distribution. This is slowest but is exact, general, and does not suffer from numerical instabilities.
- ‘parallel’ uses parallelized cumulative-sum and cumulative-product operations to compute a closed-form solution to the recurrence relation defining the attention distribution. This makes it more efficient than ‘recursive’, but it requires numerical checks which make the distribution non-exact. This can be a problem in particular when input_sequence_length is long and/or p_choose_i has entries very close to 0 or 1.
- ‘hard’ requires that the probabilities in p_choose_i are all either 0 or 1, and subsequently uses a more efficient and exact solution.
Returns: A tensor of shape (batch_size, input_sequence_length) representing the attention distributions for each sequence in the batch.
Raises: ValueError
– mode is not one of ‘recursive’, ‘parallel’, ‘hard’.
-
class
parts.attention_wrapper.
BahdanauMonotonicAttention
(num_units, memory, memory_sequence_length=None, normalize=False, score_mask_value=None, sigmoid_noise=0.0, sigmoid_noise_seed=None, score_bias_init=0.0, mode='parallel', dtype=None, name='BahdanauMonotonicAttention')[source]¶ Bases:
parts.attention_wrapper._BaseMonotonicAttentionMechanism
Monotonic attention mechanism with Bahadanau-style energy function.
This type of attention encorces a monotonic constraint on the attention distributions; that is once the model attends to a given point in the memory it can’t attend to any prior points at subsequence output timesteps. It achieves this by using the _monotonic_probability_fn instead of softmax to construct its attention distributions. Since the attention scores are passed through a sigmoid, a learnable scalar bias parameter is applied after the score function and before the sigmoid. Otherwise, it is equivalent to BahdanauAttention. This approach is proposed in
Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck, “Online and Linear-Time Attention by Enforcing Monotonic Alignments.”
Parameters: - num_units – The depth of the query mechanism.
- memory – The memory to query; usually the output of an RNN encoder. This tensor should be shaped [batch_size, max_time, ...].
- memory_sequence_length (optional) – Sequence lengths for the batch entries in memory. If provided, the memory tensor rows are masked with zeros for values past the respective sequence lengths.
- normalize – Python boolean. Whether to normalize the energy term.
- score_mask_value – (optional): The mask value for score before passing into probability_fn. The default is -inf. Only used if memory_sequence_length is not None.
- sigmoid_noise – Standard deviation of pre-sigmoid noise. See the docstring for _monotonic_probability_fn for more information.
- sigmoid_noise_seed – (optional) Random seed for pre-sigmoid noise.
- score_bias_init – Initial value for score bias scalar. It’s recommended to initialize this to a negative value when the length of the memory is large.
- mode – How to compute the attention distribution. Must be one of ‘recursive’, ‘parallel’, or ‘hard’. See the docstring for tf.contrib.seq2seq.monotonic_attention for more information.
- dtype – The data type for the query and memory layers of the attention mechanism.
- name – Name to use when creating ops.
-
-
class
parts.attention_wrapper.
LuongMonotonicAttention
(num_units, memory, memory_sequence_length=None, scale=False, score_mask_value=None, sigmoid_noise=0.0, sigmoid_noise_seed=None, score_bias_init=0.0, mode='parallel', dtype=None, name='LuongMonotonicAttention')[source]¶ Bases:
parts.attention_wrapper._BaseMonotonicAttentionMechanism
Monotonic attention mechanism with Luong-style energy function.
This type of attention encorces a monotonic constraint on the attention distributions; that is once the model attends to a given point in the memory it can’t attend to any prior points at subsequence output timesteps. It achieves this by using the _monotonic_probability_fn instead of softmax to construct its attention distributions. Otherwise, it is equivalent to LuongAttention. This approach is proposed in
Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck, “Online and Linear-Time Attention by Enforcing Monotonic Alignments.”
Parameters: - num_units – The depth of the query mechanism.
- memory – The memory to query; usually the output of an RNN encoder. This tensor should be shaped [batch_size, max_time, ...].
- memory_sequence_length (optional) – Sequence lengths for the batch entries in memory. If provided, the memory tensor rows are masked with zeros for values past the respective sequence lengths.
- scale – Python boolean. Whether to scale the energy term.
- score_mask_value – (optional): The mask value for score before passing into probability_fn. The default is -inf. Only used if memory_sequence_length is not None.
- sigmoid_noise – Standard deviation of pre-sigmoid noise. See the docstring for _monotonic_probability_fn for more information.
- sigmoid_noise_seed – (optional) Random seed for pre-sigmoid noise.
- score_bias_init – Initial value for score bias scalar. It’s recommended to initialize this to a negative value when the length of the memory is large.
- mode – How to compute the attention distribution. Must be one of ‘recursive’, ‘parallel’, or ‘hard’. See the docstring for tf.contrib.seq2seq.monotonic_attention for more information.
- dtype – The data type for the query and memory layers of the attention mechanism.
- name – Name to use when creating ops.
-
common¶
-
parts.common.
dropout_normalize_add_NTC
(x, residual_x=None, norm_type='layer_norm', drop_prob=0.0, training=True)[source]¶ Performs dropout on output, :param x: output of the block. A tensor of shape [batch, Time, dim] :param residual_x: (default: None) input to the block. If None, will be ignored :param drop_prob: dropout drop probability :return: A tensor of shape [batch, Time, dim]
-
parts.common.
embed_and_maybe_add_position_signal
(inpt, emb_W, num_timescales, heads)[source]¶ Performs embedding and adds sinusoid signals :param inpt: a tensor of shape [batch, T] :param emb_W: embedding matrix :param num_timescales: set this to d_model/2 :param heads: number of heads :return: a tensor of shape [batch, T, dim] and a tensor of shape [bath, heads, T, dim]
-
parts.common.
ffn_and_layer_norm
(inputs, inner_dim, resulting_dim, norm_type, drop_prob=0.0, training=True, inner_activation=<function relu>)[source]¶ Position-wise fully connected feed-forward network with layer norm and residual connection :param inpt: input tensor :param inner_dim: bottleneck dimension :param resulting_dim: output dimensionality :param drop_prob: dropout probability of drop :param inner_activation: inner activation function :return:
t2t_timing_signal¶
-
parts.t2t_timing_signal.
add_timing_signal
(x, min_timescale=1, max_timescale=10000.0, num_timescales=2)[source]¶ Adds a bunch of sinusoids of different frequencies to a Tensor.
This allows attention to learn to use absolute and relative positions. The timing signal should be added to some precursor of both the source and the target of the attention.
The use of relative position is possible because sin(x+y) and cos(x+y) can be experessed in terms of y, sin(x) and cos(x).
In particular, we use a geometric sequence of timescales starting with min_timescale and ending with max_timescale. For each timescale, we generate the two sinusoidal signals sin(timestep/timescale) and cos(timestep/timescale). All of these sinusoids are concatenated in the depth dimension, padded with zeros to be the same depth as the input, and added into input.
Parameters: - x – a Tensor with shape [?, length, depth]
- min_timescale – a float
- max_timescale – a float
- num_timescales – an int <= depth/2
Returns: a Tensor the same shape as x.
-
parts.t2t_timing_signal.
get_timing_signal
(length, min_timescale=1, max_timescale=10000.0, num_timescales=16, dtype=tf.float32)[source]¶ Create Tensor of sinusoids of different frequencies.
Parameters: - length – Length of the Tensor to create, i.e. Number of steps.
- min_timescale – a float
- max_timescale – a float
- num_timescales – an int
Returns: Tensor of shape (length, 2*num_timescales)
utils¶
-
parts.utils.
create_rnn_cell
(cell_type, cell_params, num_layers=1, dp_input_keep_prob=1.0, dp_output_keep_prob=1.0, residual_connections=False, wrap_to_multi_rnn=True)[source]¶ TODO: MOVE THIS properly to utils. Write doc :param cell_type: :param cell_params: :param num_layers: :param dp_input_keep_prob: :param dp_output_keep_prob: :param residual_connections: :return:


About Me
Hello! My name is Ilyana Smith, and I enjoy being in this class because of its focus on design and engineering. This class is very
appealing to me because of my plans to major in aerospace engineering.
I have also had fun exploring the WIC in my own free time, so I know I will enjoy
learning about the various machines in more depth.
Here is a picture of me in front of one of the testing towers for the hydrogen tanks of NASA's Space Launch System
rocket at Marshall Spaceflight Center in Huntsville, Alabama. I am very interested in space exploration, and I plan to work at
NASA after
I graduate from college.


--- title: "Recipe of Favorite Chicken-Bacon-Ranch Pasta Bake Delicious" slug: chicken-bacon-ranch-pasta-bake description: "Chicken-Bacon-Ranch Pasta Bake Recipe - A delicious and easy pasta bake that is full bacon ranch flavor. Simple Way to Prepare Ultimate Chicken-Bacon-Ranch Pasta Bake Delicious" date: 2021-04-18T02:46:58Z image: https://cdn.statically.io/img/images.media-allrecipes.com/userphotos/6882202.jpg thumbnail: https://cdn.statically.io/img/images.media-allrecipes.com/userphotos/6882202.jpg cover: https://cdn.statically.io/img/images.media-allrecipes.com/userphotos/6882202.jpg author: Chelsea ratingvalue: 4.6 reviewcount: 503 recipeingredient: - "cooking spray" - "1 (16 ounce) package farfalle (bow tie) pasta" - "½ cup butter" - "1 (8 ounce) package cream cheese, cubed" - "2 tablespoons ranch dressing mix" - "2 cups milk" - "1 (8 ounce) package shredded Monterey Jack cheese" - "8 slices cooked bacon, divided" - "2 cooked chicken breasts, shredded" - "1 cup shredded Cheddar Jack cheese" recipeinstructions: - "Preheat the oven to 350 degrees F (175 degrees C). Coat a 9x13 baking dish with cooking spray." - "Bring a large pot of lightly salted water to a boil. Cook bow-tie pasta at a boil, stirring occasionally, until tender yet firm to the bite, about 12 minutes." - "Meanwhile, melt butter in a saucepan over medium heat. Add cream cheese and ranch dressing mix; whisk until smooth. Slowly whisk in milk. Stir in Monterey Jack cheese; cook until cheese has melted and sauce has thickened, 5 to 10 minutes. Crumble 6 slices bacon and stir into sauce." - "Combine cooked pasta and shredded chicken in a large bowl. Pour sauce on top and mix until well combined. Transfer to the prepared baking dish. Top with Cheddar Jack cheese and crumble remaining 2 slices of bacon on top." - "Bake in the preheated oven until cheese has melted, about 20 minutes." categories: - "Meat and Poultry Recipes" - "Pork" - "Recipes" tags: - "meat and poultry recipes" - "pork" katakunci: chicken-bacon-ranch pasta bake, meat and poultry recipes, pork, . nutrition: 785.9 calories recipecuisine: Western preptime: "P0DT0H15M" cooktime: "P0DT0H40M" totaltime: "P0DT0H55M" recipeyield: "8 servings" recipecategory: - Meat and Poultry Recipes - Pork quotes: If you do not eat well, then you wont be able to think well, love well, and sleep well. ---
Chicken-Bacon-Ranch Pasta Bake - Hey everyone, I hope youre having an incredible day today. It is me again Chelsea , welcome to our recipe site. Today, we are going to make a special dish, Chicken-Bacon-Ranch Pasta Bake. It is one of my favorites recipes. This time, I am going to make it a little bit unique. This will be really delicious.
Chicken-Bacon-Ranch Pasta Bake is A delicious and easy pasta bake that is full bacon ranch flavor.
Chicken-Bacon-Ranch Pasta Bake is one of the most popular of recent trending meals on earth. its simple, its quick, it tastes yummy. It is appreciated by millions daily. It is simple, its fast, it tastes delicious. Chicken-Bacon-Ranch Pasta Bake is something which I ve loved my whole life. They re fine and they look fantastic. They are nice and they look fantastic. Chicken-Bacon-Ranch Pasta Bake is something that I have loved my entire life.
To get started with this particular recipe, we have to first prepare a few components. You can have Chicken-Bacon-Ranch Pasta Bake using 10 ingredients and 5 steps. Here is how you cook it.
So thats going to wrap it up with this special food Chicken-Bacon-Ranch Pasta Bake recipe. Thank you very much for reading. Im confident that you will make this at home. There is gonna be more interesting food at home recipes coming up. Remember to bookmark this page in your browser, and share it to your loved ones, friends and colleague. Thanks again for reading. Go on get cooking!


2.2.10. Soft-story Building Classifier¶
The Soft-story Building Classifier is a module built upon the Generic Image Classifier module.
The module is shipped with BRAILS, so you don’t have to install it standalone if you’ve installed BRAILS following the Installation instruction.
It takes a list of street view images of buildings as the input, and classify the buildings into two categories: soft-story building and other building.
<IP_ADDRESS>. Use the module¶
A pretrained model is shipped with BRAILS. So you can use it directly without training your own model.
The first time you initialize this model, it will download the model from the internet to your local computer.
The images used in the example can be downloaded from here.
# import the module
from brails.modules import SoftstoryClassifier
# initilize a soft-story classifier
ssModel = SoftstoryClassifier()
# define the paths of images in a list
imgs = ['image_examples/Softstory/Others/3110.jpg',
'image_examples/Softstory/Softstory/901.jpg']
# use the model to predict
predictions = ssModel.predict(imgs)
The predictions look like this:
Image : image_examples/Softstory/Others/3110.jpg Class : others (96.13%)
Image : image_examples/Softstory/Softstory/901.jpg Class : softstory (96.31%)
Results written in file softstory_preds.csv
Sample images used in this example are:
Note
The classifier takes an image as the input and will always produce a prediction. Since the classifier is trained to classify only a specific category of images, its prediction is meaningful only if the input image belongs to the category the model is trained for.
<IP_ADDRESS>. Retrain the model¶
You can retrain the existing model with your own data.
# Load images from a folder
ssModel.loadData('folder-of-images')
# Re-train it for only 1 epoch for this demo. You can increase it.
ssModel.retrain(initial_epochs=1)
# Test the re-trained model
predictions = ssModel.predict(imgs)
# Save the re-trained model
ssModel.save('myCoolNewModelv0.1')


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Contiki 2.5
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The phase1 MAC implemented to support the IPv6/6LoWPAN stack within the Contiki project is a light weight yet adequate beginning. This phase supports point to point data connectivity between a router device and an end device. The router is the RZ USB stick from the ATAVRRZRAVEN kit. The end node is the AVR Raven from the ATAVRRZRAVEN kit. The picture below shows the complete ATAVRRZRAVEN kit.
The next phases will implement a commissioning concept including scan, and beacon generation. These kinds of primitives will allow dynamic network formation. Additionally, routing and low power/sleep will be implemented in following phases.
See the Running Contiki with uIPv6 and SICSlowpan support on Atmel RAVEN hardware for required systems setup configuration.
This MAC follows the recommendations of RFC4944 with respect to data frames and acknowledgements (i.e. all data frames are acknowledged). At the time of this writing (phase 1) beacons (frames) and association events are not implemented. Additionally, data frames always carry both source and destination addresses. PANID compression (intra-pan) is not used so both source and destination PANID's are present in the frame.
The SICSLoWMAC supports the IEEE 802.15.4 Data Request primitive and the Data Request Indication primitive. The data request primitive constructs a proper 802.15.4 frame for transmission over the air while the data indication parses a received frame for processing in higher layers (6LoWPAN). The source code for the mac can be found in the sicslowmac.[c,h] files.
To assemble a frame a MAC header is constructed with certain presumptions:
These and other variables are defined in mac.h.
Given this data and the output of the 6LoWPAN function, the MAC can construct the data frame and the Frame Control Field for transmission.
An IEEE 802.15.4 MAC data frame consists of the fields shown below:
The Frame Control Field (FCF) consist of the fields shown below:
The output function of the 6LoWPAN layer (sicslowpan.c) is the input function to the MAC (sicslowmac.c). The output function of the MAC is the input function of the radio (radio.c). When the radio receives a frame over the air it processes it in its TRX_END event function. If the frame passes address and CRC filtering it is queued in the MAC event queue. Subsequently, when the MAC task is processed, the received frame is parsed and handed off to the 6LoWPAN layer via its input function. These relationships are depicted below:
The source code for the MAC, Radio and support functions is located in the path:
The source code for the Raven platforms is located in the path:
There are two projects that utilize the Logo Certified IPv6 and 6LoWPAN layers contributed to the Contiki project by Cisco. These are ping-ipv6and webserver-ipv6 applications. They are located in the following paths:
The ping-ipv6 application will allow the USB stick to ping the Raven board while the webserver-ipv6 application will allow the raven board to serve a web page. When the ravenlcd-3290 application is programmed into the ATmega3290P on the Raven board, the Raven board can ping the USB stick and it can periodically update the temperature in the appropriate web page when served.