- getAccessToken(String, String, String) - Method in class service.TwitterOAuth
-
Gets the access token from the OAuth using the verifier token and token/secret key.
- getConsumerKey() - Method in class service.TwitterOAuth
-
Gets the ConsumerKey - key consumer/secret pair.
- getHomeTimeLineAsync(WSClient, String, String) - Method in interface service.TwitterService
-
Retrieves the HomeTimeLine in asynchronous mode.
- getHomeTimeLineAsync(WSClient, String, String) - Method in class service.TwitterServiceImpl
-
- getInstance() - Static method in class service.TwitterOAuth
-
Static 'instance' method
- getName() - Method in class models.Tweet
-
Gets the name
- getOAuth() - Method in class service.TwitterOAuth
-
Gets the OAuth
- getRequestToken(String) - Method in class service.TwitterOAuth
-
Retrieves the RequestToken from the OAuth.
- getSecret(Http.Session) - Static method in class util.SessionUtils
-
Gets the secret value from the HTTP session object.
- getSecret() - Method in class util.SessionUtilsTest
-
- getSignatureCalculator(String, String) - Method in class service.TwitterOAuth
-
Gets the OAuthCalculator from the RequestToken and ConsumerKey pair.
- getToken(Http.Session) - Static method in class util.SessionUtils
-
Gets the token value from the HTTP session object.
- getToken() - Method in class util.SessionUtilsTest
-
- getTweets(String) - Method in class controllers.Twitter
-
Searches the tweets based on the keywords.
- getTweetsAsync(WSClient, String, String, String) - Method in interface service.TwitterService
-
Retrieves a list of Tweets in asynchronous mode based on the search keywords.
- getTweetsAsync(WSClient, String, String, String) - Method in class service.TwitterServiceImpl
-
- getTweetsHastagAsync(WSClient, String, String, String) - Method in interface service.TwitterService
-
- getTweetsHastagAsync(WSClient, String, String, String) - Method in class service.TwitterServiceImpl
-
- getUserTimelineAsync(WSClient, String, String, String) - Method in interface service.TwitterService
-
Gets the information for a twitter user.
- getUserTimelineAsync(WSClient, String, String, String) - Method in class service.TwitterServiceImpl
-
- groupByCreatedAt(JsonNode) - Static method in class util.JsonHelper
-
Builds a Map
containing "created_at" as key and "text" as value
for the list present in the JsonNode
.
- groupByLocDesc(JsonNode) - Static method in class util.JsonHelper
-
Builds a Map
containing "location" as key and "description" as value
for the list of "user" present in the JsonNode
.
- groupByProfileImage(JsonNode) - Static method in class util.JsonHelper
-
Builds a Map
containing "profile_image_url" as key and "name" as value
for the list of "user" present in the JsonNode
.
- groupByText(JsonNode) - Static method in class util.JsonHelper
-
Builds a Map
containing "text" as key and "user" node element as value
for the list of "statuses" present in the JsonNode
.


Arend Features
Arend implements a version of homotopy type theory with an interval type, which syntax is similar to cubical type theory. This implies several nice properties of path types and allows for a simple and clean definition of higher inductive types (including recursive ones). To learn more about homotopy features implemented in Arend, see below.
Arend has a powerful class system, which supports Haskell-style instance inference. Class inheritance can be used to define various hierarchies of (algebraic) structures. For a discussion of the class system, see below.
Arend also has a simple and powerful mechanism of dealing with universe levels, see here.
Homotopy Features
Arend is based on homotopy type theory with an interval type. This means that it has a built-in contractible interval type together with two constructors left, right : I. We can use this type to define the type of n-dimensional cubes as \Sigma I … I, that is as the product of n itervals. These types are not very interesting by themselves since they are contractible, but they can be used as types of cells in the definition of a higher inductive type as discussed below.
Path Types
A path in a type A between points a, a’ : A is a function f : I -> A such that f left == a and f right == a’ (we use “==” to denote the definitional equality). The type of paths is denoted a = a’. To get a path from a function and vice verse, we can use the following functions:
path (f : I -> A) : f left = f right
@ (p : a = a') : I -> A
Function @ is usually written in infix form and pronounced “at”. Function path is actually the only constructor of the data type =. Functions path and @ are mutually inverse, that is, path f @ i == f i and path (\lam i => p @ i) == p.
This definition of path types has several nice properties. First, all the usual constructions (such as concatenation of paths, transport, and the J rule) are definable for them. Moreover, some of these constructions satisfy additional computational equalities. For example, we can define the function that applies a map to a path as follows:
\func pmap {A B : \Type} (f : A -> B) {a a' : A} (p : a = a') : f a = f a'
=> path (\lam i => f (p @ i))
This function is strictly functorial, that is we have equalities pmap id p == p and pmap (\lam x => g (f x)) p == pmap g (pmap f p).
It is also easy to prove functional extensionality:
\func funExt {A : \Type} (B : A -> \Type) (f g : \Pi (x : A) -> B x) (h : \Pi (x : A) -> f x = g x) : f = g
=> path (\lam i a => h a @ i)
This function also satisfies various definitional equalities, which means that we can pass easily between equality of functions and their pointwise equality.
Higher Inductive Types
A higher inductive type is a data type that has higher cells as its constructors. For example, the 1-dimensional sphere can be defined as the data type with one point base and one 1-dimensional constructor loop attached to this point:
\data S1
| base
| loop I \with {
| left => base
| right => base
}
Higher constructors such as loop work like ordinary functions: loop left and loop right evaluate to base. The syntax of such constructors is the same as the syntax of functions defined by pattern matching.
We can also attach cells of even higher dimensions. For example, the 2-dimensional sphere can be defined as the data type with one point base2 and one 2-dimensional constructor loop2 attached to this point. Since the boundary of 2-dimensional cube consists of four 1-dimensional cubes, we need to specify four conditions on constructor loop2:
\data S2
| base2
| loop2 I I \with {
| left, _ => base2
| right, _ => base2
| _, left => base2
| _, right => base2
}
Functions over higher inductive types can be defined by pattern matching as usual. The only difference is that it is required that they respect equations on constructors. For example, to define a function S2 -> T, we need to specify one point b : T and one function l : I -> I -> T such that l left _ == b, l right _ == b, l _ left == b, and l _ left == b:
\func f (x : S2) : T
| base2 => b
| loop2 i j => l i j
If this definition does not satisfy the conditions described above, an error message will be generated.
Truncated Data Types
Truncated data types can be defined as higher inductive types as usual, but there is a simpler way to do this. A data type can be defined as \truncated, which means that it will be truncated to the specified homotopy level. For example, set quotients can be defined as follows:
\truncated \data Quotient {A : \Type} (R : A -> A -> \Type) : \Set
| in~ A
| ~-equiv (x y : A) (R x y) (i : I) \elim i {
| left => in~ x
| right => in~ y
}
Such data types can be eliminated only into types of the corresponding homotopy level.
Pattern Matching
A pattern matching on a higher inductive type can be simplified if the goal has some finite homotopy level. To define a function into an n-type, it is enough to specify its values for constructors of dimension less than or equal to n + 1. For example, if X is a set, to define a function Quotient A R -> X, it is enough to specify its value for each a : A and prove that this definition respects the equivalence relation (this proof is the value corresponding to the second constructor). This works for the definition of quotients we gave above and also for quotients defined as a higher inductive type.
If P x is a proposition, to define a function \Pi (x : Quotient A R) -> P x, it is enough to specify it for the constructor in~. This becomes even more useful when P depends on several elements of the quotient. For example, if we have 3 elements x, y, z : Quotient A R (which is defined as a higher inductive type with 3 constructors), then to prove some property about them, we need to consider only one case (since its the only 0-dimensional one) instead of nine.
Class System
Arend has records and classes. Classes are just records with additional functionality, which makes them into a haskell-style type classes.
Inheritance
Records are just Sigma types with named fields. For example, we can define the type of monoids as follows:
\record Monoid (E : \Set)
| ide : E
| \infixl 7 * : E -> E -> E
| ide-left (x : E) : ide * x = x
| ide-right (x : E) : x * ide = x
| *-assoc (x y z : E) : (x * y) * z = x * (y * z)
Records can be inherited. If a record extends some base record, then it will have the same fields as the base one together with its own fields. For example, we can define a group as a monoid together with three additional fields:
\record Group \extends Monoid
| inverse : E -> E
| inverse-left (x : E) : inverse x * x = ide
| inverse-right (x : E) : x * inverse x = ide
Extensions also can implement some of the fields of the base record. Such a record is equivalent to the Sigma type which has all not implemented fields. For example, the type of commutative monoids can be defined as follows:
\record CMonoid \extends Monoid
| *-comm (x y : E) : x * y = y * x
| ide-right x => *-comm x ide *> ide-left x
For commutative monoids, it is enough to prove that ide is the left identity since the fact that it is the right identity follows from the commutativity. We can prove this in the definition of commutative monoids by implementing the corresponding field.
Records also support multiple inheritance. For example, we can define the type of commutative groups simply by extending the types of groups and commutative monoids:
\record CGroup \extends Group, CMonoid
Anonymous Extensions
Parameters of records are actually their fields. So, the following two definitions of pointed sets are actually equivalent:
\record Pointed (E : \Set)
| ide : E
\record Pointed'
| E' : \Set
| ide' : E'
The reason why Arend does not have record parameters is because it has anonymous extensions instead. An anonymous extension of a record R is an expression of the form R { | f_1 => e_1 … | f_n => e_n }, where f_1, … f_n are fields of R and e_1, … e_n are expressions of the corresponding types. Such an expression is the same as a records which extends R and implements specified fields, but we do not have to define this record separately. The anonymous extension described above can also be written as R e_1 … e_n. In this case, fields are implemented in the same order they were defined in the record.
Let us consider some examples:
- The type Monoid is the type of monoids.
- The type Monoid Nat is the type of monoid structures on the set Nat.
- The type Monoid Nat 1 (Nat.*) is the type of proofs that 1 and Nat.* determine the structure of a monoid on Nat.
Note that the latter two examples look as if the type of monoids was defined with 1 and 3 parameters, respectively. Thus, we do not have to worry which fields should be defined as parameters and which should be left as fields. We can use any record as if it was defined in all of these styles at the same time.
Classes
Let us say we have the definition of monoids as described above. Then we want to write expressions of the form (x * y) * ide * z, but we cannot do this. The reason is that * and ide are fields. This means that they require one additional argument, an instance of Monoid. Instances of records usually cannot be inferred automatically. For this reason, it is better to define Monoid as a class. A class is defined in the same way as a record but with the keyword \class instead of \record.
Instances of classes are inferred automatically. This inference algorithm looks for instances in two places. First, it looks for parameters of a definition in which the field call is located. If there is an appropriate parameter, then it will be used as a class instance. For example, instances in the function test below will be inferred as shown in test2.
\func test {M : Monoid} (x y z : M) => (x * y) * ide * z
\func test2 {M : Monoid} (x y z : M) => (x M.* y) M.* M.ide M.* z
If there is no appropriate local instance, the algorithm will search the set of global instances. A global instance is just a function defined using the keyword \instance instead of \func. For example, we can prove that Nat is an instance of the class Monoid and use this instance as shown below:
\instance NatMonoid : Monoid Nat
| ide => 1
| * => Nat.*
| ide-left x => {?} -- the proofs are omitted
| ide-right x => {?}
| *-assoc x y z => {?}
\func test (x y z : Nat) => (x * y) * 1 * (z * ide)
Universe Levels
Arend has a hieararchy of universes parameterized by two natural number. The first parameter corresponds to the usual (predicative) level of the universe. The second parameter corresponds to the homotopy level of types in this universe. The universe of level (p,h) is written as \h-Type p. The universe \0-Type p can be abbreviated as \Set p. We can also write \Type p h instead of \h-Type p.
Levels cannot be added with each other, but we have the successor function \suc and the maximum function \max. There is also the largest homotopy level \oo, which corresponds to untruncated types.
The universe \h-Type p belongs to the universe \(h+1)-Type (p+1). Universes are cummulative: if A : \h-Type p, then A : \h’-Type p’ for all h’ and p’ greater than or equal to h and p, respectively.
Homotopy Levels
If a type A belongs to a universe \(h+1)-Type p, the type of paths a = {A} a’ belongs to the universe \h-Type p. This means that universes of h-types behave in the same way as the usual ones (defined as subtypes of untruncated universes), but we do not have to carry around proofs that some type belongs to some homotopy level.
The Universe of Propositions
The smallest universe is \Prop, the universe of propositions. This universe is impredicative, that is it does not have the predicative level. Thus, if A is a set of any predicative level, then a = {A} a’ belongs to \Prop. Also, if B a belongs to \Prop, then this is also true for \Pi (a : A) -> B a regardless of levels of A.
Universe Polymorphism
To define a polymorphic function (or any other kind of definition), we do not have to specify levels of types explicitly. The correct level of universes usually can be inferred automatically. For example, we can define the following function:
\func test : \Type => \Type
The inferred levels of the universe in the type of the functions are greater than the levels of the universe in the body by one.
Every definition has two implicit level parameters \lp and \lh for the predicative and homotopy levels, respectively. These levels also do not have to be specified explicitly since they also can be inferred in the same way as universe levels. Consider the following examples:
\func id {A : \Type} (a : A) => a
\class Pointed (E : \Type)
| point : E
\func test => id Pointed
The levels of the universe of the field E are inferred to (\lp, \lh). This implies that Pointed belongs to \Type (\suc \lp) (\suc \lh). Thus, the first implicit argument of the function id in the definition test is inferred to \Type (\suc \lp) (\suc \lh). This implies that the level arguments are inferred to (\suc (\suc \lp), \suc (\suc \lh)).
Language Extensions
A language extension is a Java class which is invoked during type-checking. This can be used to implement custom operations on the abstract syntax tree which are not supported by the language. They can also be used to implement various decision procedures for proof automation.
The analogical feature is macro
in Agda, elaboration reflection in Idris, and Ltac
in Coq.
To do this, you’ll need Arend API (you can also download its sources).


|
0.8.2 API documentation
|
Image, representation for a single texture level. More...
Public Member Functions | |
| void | clear () |
| Clear the entire image storage_linear with zeros. | |
| template<typename genType > | |
| void | clear (genType const &Texel) |
| Clear the entire image storage_linear with Texel which type must match the image storage_linear format block size If the type of genType doesn't match the type of the image format, no conversion is performed and the data will be reinterpreted as if is was of the image format. More... | |
| void * | data () |
| Return a pointer to the beginning of the image instance data. | |
| void const * | data () const |
| Return a pointer to the beginning of the image instance data. | |
| template<typename genType > | |
| genType * | data () |
| Return a pointer of type genType which size must match the image format block size. | |
| template<typename genType > | |
| genType const * | data () const |
| Return a pointer of type genType which size must match the image format block size. | |
| bool | empty () const |
| Return whether the image instance is empty, no storage_linear or description have been assigned to the instance. | |
| extent_type | extent () const |
| Return the dimensions of an image instance: width, height and depth. | |
| format_type | format () const |
| Return the image instance format. | |
| image () | |
| Create an empty image instance. | |
| image (format_type Format, extent_type const &Extent) | |
| Create an image object and allocate an image storoge for it. | |
| image (image const &Image, format_type Format) | |
| Create an image object by sharing an existing image storage_linear from another image instance. More... | |
| template<typename genType > | |
| genType | load (extent_type const &TexelCoord) |
| Load the texel located at TexelCoord coordinates. More... | |
| size_type | size () const |
| Return the memory size of an image instance storage_linear in bytes. | |
| template<typename genType > | |
| size_type | size () const |
| Return the number of blocks contained in an image instance storage_linear. More... | |
| template<typename genType > | |
| void | store (extent_type const &TexelCoord, genType const &Data) |
| Store the texel located at TexelCoord coordinates. More... | |
|
explicit |
Create an image object by sharing an existing image storage_linear from another image instance.
This image object is effectively an image view where format can be reinterpreted with a different compatible image format. For formats to be compatible, the block size of source and destination must match.
| void clear | ( | genType const & | Texel | ) |
Clear the entire image storage_linear with Texel which type must match the image storage_linear format block size If the type of genType doesn't match the type of the image format, no conversion is performed and the data will be reinterpreted as if is was of the image format.
| genType load | ( | extent_type const & | TexelCoord | ) |
Load the texel located at TexelCoord coordinates.
It's an error to call this function if the format is compressed. It's an error if TexelCoord values aren't between [0, dimensions].
| size_type size | ( | ) | const |
Return the number of blocks contained in an image instance storage_linear.
genType size must match the block size conresponding to the image format.
| void store | ( | extent_type const & | TexelCoord, |
| genType const & | Data | ||
| ) |
Store the texel located at TexelCoord coordinates.
It's an error to call this function if the format is compressed. It's an error if TexelCoord values aren't between [0, dimensions].


Trump Blames California Fires on Governor's 'Terrible Job' of Forest Management
Earlier this week, the Californian Department of Forestry and Fire Protection said that most of the fires in the state were contained.
US President Donald Trump harshly criticised California Governor Gavin Newsom’s handling of forest management on Sunday, blaming him for the fires currently engulfing the western state.
Thousands of people had to leave their homes in California in October amid raging wildfires. Massive power shutoffs have been introduced. On Saturday, the California Department of Forestry and Fire Protection said that the majority of fires in the state were over 70 per cent contained.


Teen deaths from London’s ‘knife epidemic’ now at 12-year high
The number of teenagers stabbed to death in the capital is now at its highest level for more than a decade.
This year alone some 23 teens have been fatally attacked and grieving relatives have called the statistics ‘seriously alarming.’
The grim toll is one more than the total for 2008 and on the way to surpassing the 26 young lives who were lost in 2007.
This year’s youngest victim was just 14 years old.
Jaden Moodie was the first teenager to be stabbed to death in the capital this year.
The 14-year-old was knocked off a moped and stabbed on January 8 in Leyton.
Mohammed Usman Mirza, 19, became the latest after he was killed in Ilford on Tuesday night.
A total of 25 teenagers have died in 2019, 23 of whom suffered fatal knife wounds.
Jodie Chesney is this year’s only female knife victim after she was attacked in a park in Romford in March.
Advertisement Advertisement
Her father Peter told the BBC: ‘Over 20 teenagers have been fatally stabbed this year and that is shocking to me.

Her Uncle Terry called the situation ‘alarming’ and added: ‘It is proof that this knife epidemic is spiralling out of control.’
The Metropolitan Police told Metro.co.uk that they have launched 124 murder inquiries this year.
British Transport Police are dealing with a further three, meaning the total is close to surpassing that of last year.
In 2018 some 135 people were murdered or unlawfully killed and the murder rate overtook that of New York for the first time.
Commissioner Cressida Dick has previously said that detectives were operating in a ‘very challenging’ environment and had been met with a ‘wall of silence’ in some cases.
Campaigners have said knife crime is a symptom of a wider problem within London based around poverty and attraction to gang culture.
Last month the chief executive of charity Barnado’s, Javed Khan, said: ‘It’s totally unacceptable that the knife crime crisis continues to claim so many young lives, with offences at a record high.
‘Knife crime is a symptom of a much wider, complex problem. Too many young people are suffering a “poverty of hope” and facing a future with no qualifications, no job prospects and no role models, making them vulnerable to criminal gangs who force them to deliver drugs and carry knives.’
London's 2019 teen murder victims
1. January 8th: Jaden Moodie, 14, stabbed to death after being knocked off a moped in Leyton, east London
2. January 29th: Nedim Bilgin, 17, repeatedly stabbed in Islington, north London
3. February 5th: Lejean Richards, 19, stabbed to death in Battersea, south London
4. February 23rd: Kamali Gabbidon-Lynck, 19, died in hospital hours after being chased into a hairdressing salon in Wood Green, north London, where he was shot and stabbed
5. March 1st: Sixth form student Jodie Chesney, 17, stabbed to death in park in Harold Hill, east London
6. March 7th: Ayub Hassan, 17, fatally stabbed in the chest in the middle of the afternoon near West Kensington tube station in west London
7. March 22nd: Abdirashid Mohamoud, 17, stabbed in Isleworth, west London
8. May 1st: Tashaun Aird, 15, stabbed to death in Dalston, east London
9. May 5th: McCaulay Junior Urugbezi-Edwards, 18, chased and stabbed in Elephant and Castle, south London
10. June 14th: Cheyon Evans, 18, fatally stabbed in Tooting in broad daylight
11. June 14th: Eniola Aluko, 19, shot dead in broad daylight in Plumstead
12. June 26th: Yusuf Mohamed, 18, stabbed to death outside a greengrocers in Shepherd’s Bush
13. June 30th: Amara Toure, 18, of Manor Park, died in hospital after being stabbed in Walworth
14. July 11th: Kye Manning, 19, stabbed to death and another two teens slashed during a running brawl through the centre of Purley
15. August 12th: Alex Smith, 16, stabbed to death in Islington
16. August 15th: Solomon Small, 18, stabbed to death in Brixton
17. August 20th: Amrou Greenidge, 18, died in hospital from head injuries two days after being deliberately knocked off a bicycle near his home in Fulham and then being assaulted.
18. August 27th: Santino Dymiter, 18, stabbed to death in Plaistow
19. Sept 3rd: Michael Irving, 15, from Woolwich, stabbed to death during fight in Stratford.
20. Sept 5th: Perry Jordan Brammer, 15, died in hospital six days after being repeatedly stabbed near the Broadwater Farm Estate in Tottenham.
21. September 10th: Josiph Beker, 17, known to his friends and family as Yousef, stabbed to death in Edgware Road.
22. October 10th: Baptista Adjei, 15, fatally stabbed while wearing his school uniform in Stratford.
23. October 10th: Clinton Evbota, 18, repeatedly stabbed on the Brandon Estate in Camberwell where he used to live
24. Nov 7th: Hakim Sillah, 18, fatally stabbed while attending knife awareness course at Hillingdon Civic Centre in Uxbridge.
25. Nov 19th: Mohammed Usman Mirza, 19, stabbed to death in Ilford.


Arshneet Simulator
Welcome to the GitHub Project Page for the Arshneet Simulator. This game was originally created by a peer of ours. Here's a link to his version.
Features
- You get to play as Arshneet (obviously)
- Randomizers deciding if you make it through certain events
- Deaths that make almost no sense at all
- Personal attacks toward several characters based off of real people!
- Open-source: contribute to your favourite game or make it your own!
- Unplayable if you tick that option on the dialogue box that says "Disable alerts from this webpage"!
Embed
If you want this game on your site, use the following HTML:

This version isn't heavily styled allowing customization of the embedded element.
You can check the `embed.css` file to see how it's currently styled. For your convenience, here's the HTML after it's constructed with the JavaScript:

<link rel="stylesheet" type="text/css" href="https://retraction.github.io/arsh-sim/embed.css">

Play Arshneet Simulator

Source

Creator: Brahmjot Grewal
Main Writer: Damanjit Gill
If you're here but not from IELC 2015-2016, you might miss some inside jokes.
P.S. This is both mine (Ryan Nguyen) and Damanjit Gill's's first large project, so if we're doing something wrong, feel free to help us out.


As of April 2021:
The intake of retired racers has slowed to a trickle, while there has been a huge number of interested adopters. We are making every effort to bring more greyhounds into our program, and will use this pause to focus on fulfilling those applications already on our waiting list. This organization continues to provide support and community to our fellow greyhound owners. Updates will be published as the situation changes.
An update will be published as soon as reasonable.
If you have any concerns/changes to your application, please contact: adopt@galtx-centex.org


| HONK(3) | Library Functions Manual | HONK(3) |
honk
— API
access
In addition to the standard web interface, some functionality is
available via the honk
HTTP API.
With the exception of login, all requests should contain the following form values.
The API URL for all actions other than login and logout is /api.
Send a POST request to /dologin with the following form values.
This will return a token to be used for future requests. The token is valid for one year.
Send a request to /logout with the token to be expired.
The action value should be “honk”. Content type should be multipart/form-data if an attachment is included. The following values are recognized:
Upon success, the honk action will return the URL for the created honk.
Upload just an attachment using donk and donkdesc. Content type must be multipart/form-data. Will return the XID.
The “gethonks” action can be used to query for honks. The following parameters are used.
The result will be returned as json.
The “zonkit” action began life as a delete function, but has since evolved some other powers as specified by the wherefore parameter. The target of the action is specified by the what parameter and is generally the XID of a honk.
Wherefore must be one of the following.
Send anything. No limits, no error checking.
Refer to the sample code in the toys directory.
| December 10, 2020 | OpenBSD 6.7 |


Tutorials
You can learn SCALE-Sim by following one our our tutorials that we provide here, including videos, code examples and easy-to-use Jupyter notebooks with examples!
ISCA 2021
Learnings:- How to start
- The first simple experiment
- Extending SCALE-Sim
- Krittika: Distributed GEMM simulator
- Integrating SCALE-Sim in your simulator
ASPLOS 2021
Learnings:- How to start
- The first simple experiment
- Extending SCALE-Sim
- Integrating SCALE-Sim in your simulator


Total Available Cash: $20
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Package Reference¶
Networks¶
-
class
noesis.networks.dual_encoder.
Base
(vocab_size, max_len, hidden_size, input_dropout_p, dropout_p, n_layers, rnn_cell)[source]¶ Applies a multi-layer RNN to an input sequence.
Warning
Do not use this class directly, use one of the sub classes.
Parameters: - vocab_size (int) – size of the vocabulary
- max_len (int) – maximum allowed length for the sequence to be processed
- hidden_size (int) – number of features in the hidden state h
- input_dropout_p (float) – dropout probability for the input sequence
- dropout_p (float) – dropout probability for the output sequence
- n_layers (int) – number of recurrent layers
- rnn_cell (str) – type of RNN cell (Eg. ‘LSTM’ , ‘GRU’)
- Inputs:
*args
,**kwargs
*args
: variable length argument list.**kwargs
: arbitrary keyword arguments.
-
forward
(*args, **kwargs)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
class
noesis.networks.dual_encoder.
DualEncoder
(context, response)[source]¶ ” Applies dual encoder architecture.
Parameters: - context (noesis.networks.dual_encoder.Encoder) – encoder RNN for context information
- response (noesis.networks.dual_encoder.Encoder) – encoder RNN for responses information
- Inputs: context_var, responses_var
- context_var : a tensor containing context information
- responses_var : a tensor containing responses per context information
- Outputs: output
- output (batch, num_responses): tensor containing scaled probabilities of responses
- Examples::
>>> dual_encoder = DualEncoder(ctx_encoder, resp_encoder) >>> output = dual_encoder(ctx_variable, resp_variable)
-
forward
(context_var, responses_var)[source]¶ Applies a multi-layer RNN to an input sequence.
Parameters: - context_var (batch, seq_len) – tensor containing the features of the context sequence.
- responses_var (batch, num_responses, seq_len) – tensor containing the features of the responses sequence.
- Returns: output
- output (batch, num_responses): variable containing the scaled probabilities over responses
-
class
noesis.networks.dual_encoder.
Encoder
(vocab_size, max_len, hidden_size, input_dropout_p=0, dropout_p=0, n_layers=1, bidirectional=False, rnn_cell='gru', variable_lengths=False, embedding=None, update_embedding=True)[source]¶ Applies a multi-layer RNN to an input sequence.
Parameters: - vocab_size (int) – size of the vocabulary
- max_len (int) – a maximum allowed length for the sequence to be processed
- hidden_size (int) – the number of features in the hidden state h
- input_dropout_p (float, optional) – dropout probability for the input sequence (default: 0)
- dropout_p (float, optional) – dropout probability for the output sequence (default: 0)
- n_layers (int, optional) – number of recurrent layers (default: 1)
- bidirectional (bool, optional) – if True, becomes a bidirectional encodr (defulat False)
- rnn_cell (str, optional) – type of RNN cell (default: gru)
- variable_lengths (bool, optional) – if use variable length RNN (default: False)
- embedding (torch.Tensor, optional) – Pre-trained embedding. The size of the tensor has to match the size of the embedding parameter: (vocab_size, hidden_size). The embedding layer would be initialized with the tensor if provided (default: None).
- update_embedding (bool, optional) – If the embedding should be updated during training (default: False).
- Inputs: inputs, input_lengths
- inputs: list of sequences, whose length is the batch size and within which each sequence is a list of token IDs.
- input_lengths (list of int, optional): list that contains the lengths of sequences in the mini-batch, it must be provided when using variable length RNN (default: None)
- Outputs: output, hidden
- output (batch, seq_len, hidden_size): tensor containing the encoded features of the input sequence
- hidden (num_layers * num_directions, batch, hidden_size): tensor containing the features in the hidden state h
- Examples::
>>> encoder = EncoderRNN(input_vocab, max_seq_length, hidden_size) >>> output, hidden = encoder(input)
-
forward
(input_var, input_lengths=None)[source]¶ Applies a multi-layer RNN to an input sequence.
Parameters: - input_var (batch, seq_len) – tensor containing the features of the input sequence.
- input_lengths (list of int, optional) – A list that contains the lengths of sequences in the mini-batch
- Returns: output, hidden
- output (batch, seq_len, hidden_size): variable containing the encoded features of the input sequence
- hidden (num_layers * num_directions, batch, hidden_size): variable containing the features in the hidden state h
Dataset¶
-
class
noesis.dataset.dataset.
Dataset
[source]¶ A class that encapsulates a dataset.
Warning
Do not use this constructor directly, use one of the class methods to initialize.
Note
Source or target sequences that are longer than the respective max length will be filtered.
Parameters: max_len (int) – maximum source sequence length -
classmethod
from_file
(path, vocab=None, max_vocab=50000)[source]¶ Initialize a dataset from the file at given path. The file must contains a list of TAB-separated pairs of sequences.
Note
Source or target sequences that are longer than the respective max length will be filtered. As specified by maximum vocabulary size, source and target vocabularies will be sorted in descending token frequency and cutoff. Tokens that are in the dataset but not retained in the vocabulary will be dropped in the sequences.
Parameters: - path (str) – path to the dataset file
- vocab (Vocabulary) – pre-populated Vocabulary object or a path of a file containing words for the source language, default None. If a pre-populated Vocabulary object, src_max_vocab wouldn’t be used.
- max_vocab (int) – maximum source vocabulary size
-
make_batches
(batch_size)[source]¶ Create a generator that generates batches in batch_size over data.
Parameters: batch_size (int) – number of pairs in a mini-batch Yields: (list (str), list (str)) – next pair of source and target variable in a batch
-
classmethod
Trainers¶
-
class
noesis.trainers.supervised_trainer.
SupervisedTrainer
(expt_dir='experiment', loss_func=CrossEntropyLoss(), batch_size=64, random_seed=None, checkpoint_every=100, print_every=100)[source]¶ The SupervisedTrainer class helps in setting up a training framework in a supervised setting.
Parameters: - expt_dir (optional, str) – experiment Directory to store details of the experiment, by default it makes a folder in the current directory to store the details (default: experiment).
- loss_func (torch.nn.CrossEntropyLoss, optional) – loss for training, (default: torch.nn.CrossEntropyLoss)
- batch_size (int, optional) – batch size for experiment, (default: 64)
- checkpoint_every (int, optional) – number of batches to checkpoint after, (default: 100)
-
train
(model, data, num_epochs=5, resume=False, dev_data=None, optimizer=None)[source]¶ Run training for a given model.
Parameters: - model (models.networks) – model to run training on, if resume=True, it would be overwritten by the model loaded from the latest checkpoint.
- data (models.dataset.dataset.Dataset) – dataset object to train on
- num_epochs (int, optional) – number of epochs to run (default 5)
- resume (bool, optional) – resume training with the latest checkpoint, (default False)
- dev_data (models.dataset.dataset.Dataset, optional) – dev Dataset (default None)
- optimizer (pytorch.optim, optional) – optimizer for training (default: Optimizer(pytorch.optim.Adam, max_grad_norm=5))
Evaluator¶
-
class
noesis.evaluator.evaluator.
Evaluator
(loss_func=CrossEntropyLoss(), batch_size=64)[source]¶ Class to evaluate models with given datasets.
Parameters: - loss (torch.NN.CrossEntropyLoss, optional) – loss for evaluator (default: torch.NN.CrossEntropyLoss)
- batch_size (int, optional) – batch size for evaluator (default: 64)
-
evaluate
(model, data)[source]¶ Evaluate a model on given dataset and return performance.
Parameters: - model (models.networks) – model to evaluate
- data (dataset.dataset.Dataset) – dataset to evaluate against
Returns: loss of the given model on the given dataset
Return type: loss (float)
Vocabulary¶
-
class
noesis.dataset.vocabulary.
Vocabulary
(size)[source]¶ A Vocabulary stores a set of words belonging to a particular language. Words in the source vocabulary are mapped to unique integer IDs during encoding. Words in the target vocabulary are recovered from the model’s output during decoding. In addition to the words in the actual language, a Vocabulary includes three reserved tokens (and IDs) for the start-of-sentence and end-of-sentence markers, and for a special ‘mask’ marker used to handle rare/unknown words. The Vocabulary is sorted in descending order based on frequency. If the number of words seen is greater than the maximum size of the Vocabulary, the remaining least-frequent words are ignored.
Parameters: size (int) – maximum number of words allowed in this vocabulary -
add_sequence
(sequence)[source]¶ Adds a sequence of words to the vocabulary.
Parameters: sequence (list(str)) – list of words, e.g. representing a sentence.
-
add_token
(token)[source]¶ Adds an occurrence of a token to the vocabulary, incrementing its observed frequency if the word already exists.
Parameters: token (int) – word to add
-
indices_from_sequence
(sequence)[source]¶ Maps a list of words to their token IDs, or else the ‘mask’ token if the word is rare/unknown.
Parameters: sequence (list(str)) – list of words to map Returns: list of mapped IDs Return type: list(int)
-
classmethod
load
(file_name)[source]¶ Loads a Vocabulary from a pickle file on disk.
Parameters: file_name (str) – path to the pickle file Returns: loaded Vocabulary Return type: Vocabulary
-
save
(file_name)[source]¶ Writes this Vocabulary to disk in a pickle file.
Parameters: file_name (str) – path to the target pickle file
-
Utilities¶
-
noesis.dataset.utils.
prepare_data
(path, tokenize_func=<function space_tokenize>, format='JSON')[source]¶ Reads a tab-separated data file where each line contains a source sentence and a target sentence. Pairs containing a sentence that exceeds the maximum length allowed for its language are not added. :param path: path to the data file :type path: str :param tokenize_func: function for splitting words in a sentence (default is single-space-delimited) :type tokenize_func: func :param format: data format for input file. Default is JSON. :type format: str
Returns: list of ((context, list of candidates), target) string pairs Return type: list((str, list(str)), str)
-
noesis.dataset.utils.
read_vocabulary
(path, max_num_vocab=50000)[source]¶ Helper function to read a vocabulary file. :param path: filepath to raw vocabulary file :type path: str :param max_num_vocab: maximum number of words to read from vocabulary file :type max_num_vocab: int
Returns: read words from vocabulary file Return type: set
Checkpoint¶
-
class
noesis.util.checkpoint.
Checkpoint
(model, optimizer, epoch, step, vocab, path=None)[source]¶ The Checkpoint class manages the saving and loading of a model during training. It allows training to be suspended and resumed at a later time (e.g. when running on a cluster using sequential jobs).
To make a checkpoint, initialize a Checkpoint object with the following args; then call that object’s save() method to write parameters to disk.
Parameters: - model (seq2seq) – seq2seq model being trained
- optimizer (Optimizer) – stores the state of the optimizer
- epoch (int) – current epoch (an epoch is a loop through the full training data)
- step (int) – number of examples seen within the current epoch
- vocab (Vocabulary) – vocabulary for the input language
-
CHECKPOINT_DIR_NAME
¶ str – name of the checkpoint directory
-
TRAINER_STATE_NAME
¶ str – name of the file storing trainer states
-
MODEL_NAME
¶ str – name of the file storing model
-
VOCAB_FILE
¶ str – name of the input vocab file
-
classmethod
get_latest_checkpoint
(experiment_path)[source]¶ Given the path to an experiment directory, returns the path to the last saved checkpoint’s subdirectory.
Precondition: at least one checkpoint has been made (i.e., latest checkpoint subdirectory exists).
Parameters: experiment_path (str) – path to the experiment directory Returns: path to the last saved checkpoint’s subdirectory Return type: str
-
classmethod
load
(path)[source]¶ Loads a Checkpoint object that was previously saved to disk.
Parameters: path (str) – path to the checkpoint subdirectory Returns: checkpoint object with fields copied from those stored on disk Return type: checkpoint (Checkpoint)
-
save
(experiment_dir)[source]¶ Saves the current model and related training parameters into a subdirectory of the checkpoint directory. The name of the subdirectory is the current local time in Y_M_D_H_M_S format.
Parameters: experiment_dir (str) – path to the experiment root directory Returns: path to the saved checkpoint subdirectory Return type: str


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__construct(string $class)
Construct our reflection class.
Construct our reflection class.
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— Rachel Appel (@RachelAppel) September 3, 2019
This is a non-programming post in the series I call “re-stating the obvious”. This series talks about topics that have been discussed at length, I’m just making my own observations about them. You can find all posts in this series here.
Disclaimer: I’m not a designer, so please take the opinions in this post with a pinch of salt.
UI and UX
A good user interface is a user acquisition tool, while a good user experience is a user retention tool.
To put it differently, a good user interface attracts users to your system, while a good user experience gets users to keep using your system. A couple of important observations about this:
- Neither is a subset of the other; a good UI does not necessarily translate to good UX and it is possible to have a good UX while the UI is poor.
- Both of these are important; their relative importance depends on the nature of the system (more on this when we discuss some examples in a minute).
Good UI, not so good UX
Let’s look at some examples where UI is good, but UX not so:
- Automatic soap dispensers: I got one for myself because it worked great when I tried it (It had a great UI). However during everyday use, I realized that it dispenses too much soap and that causes a mess near the sink. It has a poor UX.
- Xcode: All those “poof” animations looks so sleek (amazing UI) but they completely and utterly destroy the overall IDE experience (unacceptably poor UX). So many actions rely on gimmicky gestures and are completely undiscoverable (how do you remove a breakpoint that you set in XCode? Clue: It is not “right click and remove” like every other sane IDE on the planet)
- In-car controls: Multi-function controls on the steering wheels give them such a futuristic look. Those buttons on the infotainment console looks straight out of a spaceship. Amazing UI. But, how many times do you need to click on the steering wheel button to achieve a certain task? How reliably can you turn on or off the defogger using the center console? Can you do all of this without taking your eyes off the road?
There was a reason I chose that last example. Imagine an ill-thought out UX that causes a driver to take their eyes off the road for even a couple of seconds at three-digit speeds. The consequences can be disastrous, even death. This is compounded by the fact that the problems with the UX are often not discoverable until it is too late.
This goes back to the relative importance part. The In-car controls example above is an instance where UX ought to be given a lot more importance than just UI.
Good UX with mediocre UI
Now, some examples of systems where the UX is good but UI not so:
- Elevators: The elevator in my apartment is old-fashioned and … how do I say this … boring. It is not really welcoming in appearance. However, it just works. All the buttons are intuitive, it is disabled-friendly with braille buttons and you just cannot go wrong using it. The UX is near-perfect even though the UI does leave a lot to be desired.
- Older cars: My first car was very low on features, but now that I look back I have to admit the UX for the driver was fantastic. It had barely any features but whatever little it did, was a breeze to operate. The only control on the steering wheel was the horn, the stalks on the steering wheel column only had the light, indicators and wiper controls. The Air conditioner and defogger were trivial to operate without taking my eyes off the road, even in the dark. Fantastic UX, not an inviting UI at all.
- Git: (Uh oh .. I failed in my resolve to not include any programming related examples). Git is an indispensable tool for programmers and makes our lives so much easier. The whole experience of cheap branching, not requiring a network to work and more make it orders of magnitude better than its predecessors like CVS. However, the commands are named rather weirdly. Worse, some commands do very different things compared to what they did in previous source control systems like SVN. Incredibly good UX, a UI that leaves a lot to be desired.
This category probably has a lot more examples than the first. Think of all the things around you that just work even though they are not particularly attractive.
Conclusion
UI ≠ UX. In my limited experience, UI is given more importance than UX. In my opinion this is counter-productive in the long run, both for businesses and users.


Casey McArdle
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Stars flock to funeral of legendary Cork actor and comic Niall Toibin in Dublin
President Higgins and actor Aiden Gillen were among the mourners
by Edel HughesStars flocked to the funeral of legendary Cork actor and comic Niall Toibin in Dublin today.
The Ballykissangel star was laid to rest in Mount Jerome Cemetery following his death at the age of 89.
Mr Toibin passed away after a long illness in Dublin last Wednesday.
The Cork native is survived by his children Sean, Muireann, Aisling, Sighle and Fiana.
His wife Judy died in 2002.
Mr Toibin's funeral was held on Monday afternoon in the Church of St Paul of the Cross, Mount Argus.
Among those paying their respects were President Michael D Higgins, actors Aiden Gillen, Geraldine Plunkett and Stephen Rea and Jon Kenny of the D'Unbelievables.
Niall Tóibín was born in Cork city on 21 November 1929.
He is best known for playing Father MacAnally in the hit BBC series which ran from 1996 until 2001, starring opposite Stephen Tompkinson and Dervla Kirwan.
Another high profile role was starring opposite Nicole Kidman and Tom Cruise in 1992 movie Far and Away.
Another high profile role was starring opposite Nicole Kidman and Tom Cruise in 1992 movie Far and Away.
Tóibín started his career in the 1950s by appearing in a series of radio dramas before graduating to film and TV as well as the stage.
In 1970, he appeared in the film Ryan's Daughter with Robert Mitcheum, and went on to land a role in Irish TV drama Bracken with Gabriel Byrne in 1978.
He appeared in the TV adaptation of Brideshead Revisited with Jeremy Irons in 1981, and with Kidman and Cruise in 1992 Ron Howard drama Far and Away.
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In 2003, he scored another Hollywood role by playing Judge Ballaugh in Cate Blanchett's thriller Veronica Guerin.
He continued working up until last year, with his final project being a short film called Remains.
Tóibín was handed a number of honours for his acting work over the years, picking up a lifetime achievement award from the Irish Film and Television Academy 2011 and being awarded the freedom of Cork in 2015.


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Ford Fiesta driver taken to hospital as car flips off road near Burton
He ended up getting trapped between a tree and a telegraph pole
by Helen Kreft, Jenny MoodyA driver had to be rescued by firefighters after an accident saw him trapped in his car in a South Derbyshire country lane.
The vehicle rolled on the lanes in Drakelow, at the junction of Cadley Lane and Caldwell Road, and ended up trapped between a tree and a telegraph pole.
A spokesman for Derbyshire police said they were called at 9.35am today, November 22, by East Midlands Ambulance Service as one man was trapped in the vehicle.
A spokesman for East Midlands Ambulance Service said: "We received a call at 9.23am on November 22 to Caldwell Road in Drakelow.
"The caller reported that there had been a road traffic collision.
"We sent a paramedic in a car and a crewed ambulance.”
The spokesman said the patient had been taken to Queen's Medical Centre, Nottingham.
One motorist, who did not wish to be named but uses that road every day, said: "That bend in the lanes is probably one of the tightest bends around here.
"And considering it is on a road with a 60mph limit, I think there should be warnings about how tight it is.
"I think a lot of people could misjudge it if they are new to the area, so there should be signs warning people that it is a tighter bend than they might think.
"I don't know if that had anything to do with what happened here - I just hope the driver is Okay."


The researcher for this study is Rebecca Ramnauth, who can be reached at
<EMAIL_ADDRESS>if you have questions, concerns, or compliants, or if you wish to withdraw from the study.
This study has received ethics approval from the Institutional Review Board at Yale University. They can be reached by phone at<PHONE_NUMBER>, or by email at<EMAIL_ADDRESS>if your questions, concerns, or complaints are not being answered by the research team, if you want to talk to someone besides the research team, or if you have questions about your rights as a research participant.


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Our founder, Karen Shirk is alive today because of a service dog, Ben. Karen was home after open-heart surgery when a deadly mix of meds left her barely conscious and fighting for life. The phone rang and rang. Ben finally picked it up and placed it beside her, then began to bark. It was her dad, and he rushed over and got her the help she needed (click here to download help pdf).
We had humble beginnings with two dogs in a one-bedroom apartment in October of 1998. Initially Karen contracted trainers to do the actual service dog work. The first few years were slow and Karen worked with a small number of people needing dogs within her own community.
Within a few years it became evident to her that no one seemed to be training skilled service dogs with public access for young children. Most agencies would not work with kids at all or would give them companion dogs for use in the home. A few would place the dogs who flunked out of their adult programs with kids for use in the home. Karen felt a calling to work with these kids and with the help of the Internet and a newly formed website, she began to let people know she would help regardless of age. It was at that time that business began to boom and 4 Paws was growing at as much as 300% in a year’s time.
Make your donation to Therapy Pet Co.!
If you are donating to a child’s fundraising, you must include the name of that child in the “Notes” section of the form.
If you are donating to a specific project, (for example, sponsoring a placement class, an item on the wish list, etc.), please enter it in the “Notes” section as well.
Founder
Marketing & Public Relations
Adoption Specialist
Therapy Pet Co. is a nonprofit, 501(c)(3) organization whose mission is to place quality service dogs with children with disabilities and veterans who have lost use of limbs or hearing; help with animal rescue, and educate the public regarding use of service dogs in public places.


| I l@ve RuBoard |
Where to Look for UpdatesAs before, updates, corrections, and supplements for this book will be maintained at the author's web site, http://www.rmi.net/~lutz. Look for the second edition's link on that page for all supplemental information related to this version of the book. As for the first edition, I will also be maintaining a log on this web site of Python changes over time, which you should consider a supplement to this text. Beginning with this edition, I am making available a user-driven book errata reporting system on the World Wide Web, at this site: There, you'll find forms for submitting book problem reports and comments, as well as viewing the report database by various sort keys. Reports are stored in a publicly browsable database by default, but an option lets you email them privately instead. The PyErrata system also happens to be written in Python, and is an example presented and shipped with this book; see Chapter 14. Figure P-4 shows what the root page of PyErrata looks like. Figure P-4. The PyErrata book updates siteIf any of these addresses stop working over time, these pages might also be accessible from O'Reilly's web site, http://www.oreilly.com.[5] I'm still happy to receive direct emails from readers, of course, but the PyErrata site will hopefully streamline the report-posting process.
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