SUBROUTINE ZTRSNA( JOB, HOWMNY, SELECT, N, T, LDT, VL, LDVL, VR, $ LDVR, S, SEP, MM, M, WORK, LDWORK, RWORK, $ INFO ) * * -- LAPACK routine (version 3.1) -- * Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. * November 2006 * * Modified to call ZLACN2 in place of ZLACON, 10 Feb 03, SJH. * * .. Scalar Arguments .. CHARACTER HOWMNY, JOB INTEGER INFO, LDT, LDVL, LDVR, LDWORK, M, MM, N * .. * .. Array Arguments .. LOGICAL SELECT( * ) DOUBLE PRECISION RWORK( * ), S( * ), SEP( * ) COMPLEX*16 T( LDT, * ), VL( LDVL, * ), VR( LDVR, * ), $ WORK( LDWORK, * ) * .. * * Purpose * ======= * * ZTRSNA estimates reciprocal condition numbers for specified * eigenvalues and/or right eigenvectors of a complex upper triangular * matrix T (or of any matrix Q*T*Q**H with Q unitary). * * Arguments * ========= * * JOB (input) CHARACTER*1 * Specifies whether condition numbers are required for * eigenvalues (S) or eigenvectors (SEP): * = 'E': for eigenvalues only (S); * = 'V': for eigenvectors only (SEP); * = 'B': for both eigenvalues and eigenvectors (S and SEP). * * HOWMNY (input) CHARACTER*1 * = 'A': compute condition numbers for all eigenpairs; * = 'S': compute condition numbers for selected eigenpairs * specified by the array SELECT. * * SELECT (input) LOGICAL array, dimension (N) * If HOWMNY = 'S', SELECT specifies the eigenpairs for which * condition numbers are required. To select condition numbers * for the j-th eigenpair, SELECT(j) must be set to .TRUE.. * If HOWMNY = 'A', SELECT is not referenced. * * N (input) INTEGER * The order of the matrix T. N >= 0. * * T (input) COMPLEX*16 array, dimension (LDT,N) * The upper triangular matrix T. * * LDT (input) INTEGER * The leading dimension of the array T. LDT >= max(1,N). * * VL (input) COMPLEX*16 array, dimension (LDVL,M) * If JOB = 'E' or 'B', VL must contain left eigenvectors of T * (or of any Q*T*Q**H with Q unitary), corresponding to the * eigenpairs specified by HOWMNY and SELECT. The eigenvectors * must be stored in consecutive columns of VL, as returned by * ZHSEIN or ZTREVC. * If JOB = 'V', VL is not referenced. * * LDVL (input) INTEGER * The leading dimension of the array VL. * LDVL >= 1; and if JOB = 'E' or 'B', LDVL >= N. * * VR (input) COMPLEX*16 array, dimension (LDVR,M) * If JOB = 'E' or 'B', VR must contain right eigenvectors of T * (or of any Q*T*Q**H with Q unitary), corresponding to the * eigenpairs specified by HOWMNY and SELECT. The eigenvectors * must be stored in consecutive columns of VR, as returned by * ZHSEIN or ZTREVC. * If JOB = 'V', VR is not referenced. * * LDVR (input) INTEGER * The leading dimension of the array VR. * LDVR >= 1; and if JOB = 'E' or 'B', LDVR >= N. * * S (output) DOUBLE PRECISION array, dimension (MM) * If JOB = 'E' or 'B', the reciprocal condition numbers of the * selected eigenvalues, stored in consecutive elements of the * array. Thus S(j), SEP(j), and the j-th columns of VL and VR * all correspond to the same eigenpair (but not in general the * j-th eigenpair, unless all eigenpairs are selected). * If JOB = 'V', S is not referenced. * * SEP (output) DOUBLE PRECISION array, dimension (MM) * If JOB = 'V' or 'B', the estimated reciprocal condition * numbers of the selected eigenvectors, stored in consecutive * elements of the array. * If JOB = 'E', SEP is not referenced. * * MM (input) INTEGER * The number of elements in the arrays S (if JOB = 'E' or 'B') * and/or SEP (if JOB = 'V' or 'B'). MM >= M. * * M (output) INTEGER * The number of elements of the arrays S and/or SEP actually * used to store the estimated condition numbers. * If HOWMNY = 'A', M is set to N. * * WORK (workspace) COMPLEX*16 array, dimension (LDWORK,N+6) * If JOB = 'E', WORK is not referenced. * * LDWORK (input) INTEGER * The leading dimension of the array WORK. * LDWORK >= 1; and if JOB = 'V' or 'B', LDWORK >= N. * * RWORK (workspace) DOUBLE PRECISION array, dimension (N) * If JOB = 'E', RWORK is not referenced. * * INFO (output) INTEGER * = 0: successful exit * < 0: if INFO = -i, the i-th argument had an illegal value * * Further Details * =============== * * The reciprocal of the condition number of an eigenvalue lambda is * defined as * * S(lambda) = |v'*u| / (norm(u)*norm(v)) * * where u and v are the right and left eigenvectors of T corresponding * to lambda; v' denotes the conjugate transpose of v, and norm(u) * denotes the Euclidean norm. These reciprocal condition numbers always * lie between zero (very badly conditioned) and one (very well * conditioned). If n = 1, S(lambda) is defined to be 1. * * An approximate error bound for a computed eigenvalue W(i) is given by * * EPS * norm(T) / S(i) * * where EPS is the machine precision. * * The reciprocal of the condition number of the right eigenvector u * corresponding to lambda is defined as follows. Suppose * * T = ( lambda c ) * ( 0 T22 ) * * Then the reciprocal condition number is * * SEP( lambda, T22 ) = sigma-min( T22 - lambda*I ) * * where sigma-min denotes the smallest singular value. We approximate * the smallest singular value by the reciprocal of an estimate of the * one-norm of the inverse of T22 - lambda*I. If n = 1, SEP(1) is * defined to be abs(T(1,1)). * * An approximate error bound for a computed right eigenvector VR(i) * is given by * * EPS * norm(T) / SEP(i) * * ===================================================================== * * .. Parameters .. DOUBLE PRECISION ZERO, ONE PARAMETER ( ZERO = 0.0D+0, ONE = 1.0D0+0 ) * .. * .. Local Scalars .. LOGICAL SOMCON, WANTBH, WANTS, WANTSP CHARACTER NORMIN INTEGER I, IERR, IX, J, K, KASE, KS DOUBLE PRECISION BIGNUM, EPS, EST, LNRM, RNRM, SCALE, SMLNUM, $ XNORM COMPLEX*16 CDUM, PROD * .. * .. Local Arrays .. INTEGER ISAVE( 3 ) COMPLEX*16 DUMMY( 1 ) * .. * .. External Functions .. LOGICAL LSAME INTEGER IZAMAX DOUBLE PRECISION DLAMCH, DZNRM2 COMPLEX*16 ZDOTC EXTERNAL LSAME, IZAMAX, DLAMCH, DZNRM2, ZDOTC * .. * .. External Subroutines .. EXTERNAL XERBLA, ZDRSCL, ZLACN2, ZLACPY, ZLATRS, ZTREXC * .. * .. Intrinsic Functions .. INTRINSIC ABS, DBLE, DIMAG, MAX * .. * .. Statement Functions .. DOUBLE PRECISION CABS1 * .. * .. Statement Function definitions .. CABS1( CDUM ) = ABS( DBLE( CDUM ) ) + ABS( DIMAG( CDUM ) ) * .. * .. Executable Statements .. * * Decode and test the input parameters * WANTBH = LSAME( JOB, 'B' ) WANTS = LSAME( JOB, 'E' ) .OR. WANTBH WANTSP = LSAME( JOB, 'V' ) .OR. WANTBH * SOMCON = LSAME( HOWMNY, 'S' ) * * Set M to the number of eigenpairs for which condition numbers are * to be computed. * IF( SOMCON ) THEN M = 0 DO 10 J = 1, N IF( SELECT( J ) ) $ M = M + 1 10 CONTINUE ELSE M = N END IF * INFO = 0 IF( .NOT.WANTS .AND. .NOT.WANTSP ) THEN INFO = -1 ELSE IF( .NOT.LSAME( HOWMNY, 'A' ) .AND. .NOT.SOMCON ) THEN INFO = -2 ELSE IF( N.LT.0 ) THEN INFO = -4 ELSE IF( LDT.LT.MAX( 1, N ) ) THEN INFO = -6 ELSE IF( LDVL.LT.1 .OR. ( WANTS .AND. LDVL.LT.N ) ) THEN INFO = -8 ELSE IF( LDVR.LT.1 .OR. ( WANTS .AND. LDVR.LT.N ) ) THEN INFO = -10 ELSE IF( MM.LT.M ) THEN INFO = -13 ELSE IF( LDWORK.LT.1 .OR. ( WANTSP .AND. LDWORK.LT.N ) ) THEN INFO = -16 END IF IF( INFO.NE.0 ) THEN CALL XERBLA( 'ZTRSNA', -INFO ) RETURN END IF * * Quick return if possible * IF( N.EQ.0 ) $ RETURN * IF( N.EQ.1 ) THEN IF( SOMCON ) THEN IF( .NOT.SELECT( 1 ) ) $ RETURN END IF IF( WANTS ) $ S( 1 ) = ONE IF( WANTSP ) $ SEP( 1 ) = ABS( T( 1, 1 ) ) RETURN END IF * * Get machine constants * EPS = DLAMCH( 'P' ) SMLNUM = DLAMCH( 'S' ) / EPS BIGNUM = ONE / SMLNUM CALL DLABAD( SMLNUM, BIGNUM ) * KS = 1 DO 50 K = 1, N * IF( SOMCON ) THEN IF( .NOT.SELECT( K ) ) $ GO TO 50 END IF * IF( WANTS ) THEN * * Compute the reciprocal condition number of the k-th * eigenvalue. * PROD = ZDOTC( N, VR( 1, KS ), 1, VL( 1, KS ), 1 ) RNRM = DZNRM2( N, VR( 1, KS ), 1 ) LNRM = DZNRM2( N, VL( 1, KS ), 1 ) S( KS ) = ABS( PROD ) / ( RNRM*LNRM ) * END IF * IF( WANTSP ) THEN * * Estimate the reciprocal condition number of the k-th * eigenvector. * * Copy the matrix T to the array WORK and swap the k-th * diagonal element to the (1,1) position. * CALL ZLACPY( 'Full', N, N, T, LDT, WORK, LDWORK ) CALL ZTREXC( 'No Q', N, WORK, LDWORK, DUMMY, 1, K, 1, IERR ) * * Form C = T22 - lambda*I in WORK(2:N,2:N). * DO 20 I = 2, N WORK( I, I ) = WORK( I, I ) - WORK( 1, 1 ) 20 CONTINUE * * Estimate a lower bound for the 1-norm of inv(C'). The 1st * and (N+1)th columns of WORK are used to store work vectors. * SEP( KS ) = ZERO EST = ZERO KASE = 0 NORMIN = 'N' 30 CONTINUE CALL ZLACN2( N-1, WORK( 1, N+1 ), WORK, EST, KASE, ISAVE ) * IF( KASE.NE.0 ) THEN IF( KASE.EQ.1 ) THEN * * Solve C'*x = scale*b * CALL ZLATRS( 'Upper', 'Conjugate transpose', $ 'Nonunit', NORMIN, N-1, WORK( 2, 2 ), $ LDWORK, WORK, SCALE, RWORK, IERR ) ELSE * * Solve C*x = scale*b * CALL ZLATRS( 'Upper', 'No transpose', 'Nonunit', $ NORMIN, N-1, WORK( 2, 2 ), LDWORK, WORK, $ SCALE, RWORK, IERR ) END IF NORMIN = 'Y' IF( SCALE.NE.ONE ) THEN * * Multiply by 1/SCALE if doing so will not cause * overflow. * IX = IZAMAX( N-1, WORK, 1 ) XNORM = CABS1( WORK( IX, 1 ) ) IF( SCALE.LT.XNORM*SMLNUM .OR. SCALE.EQ.ZERO ) $ GO TO 40 CALL ZDRSCL( N, SCALE, WORK, 1 ) END IF GO TO 30 END IF * SEP( KS ) = ONE / MAX( EST, SMLNUM ) END IF * 40 CONTINUE KS = KS + 1 50 CONTINUE RETURN * * End of ZTRSNA * END


I am currently the owner of Web Only Solutions (Pty) LTD. A company specialising in internet services like, Web Hosting, Domain Registrations, Website Development as welll as custom App Development.
I am a Full-Stack developer and have developed several applications on my own as well as participating regularly in open-source projects on Git-Hub.
View ResumeThis was one of my first projects! It's an online hosted Invoicing System that allows you to do Quotes, Invoices, capture payments and track projects. Available for free to the public.
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Do you need to get in contact for any development?
Email Me


Instructions: If you’re reading this online, press A on the micro:bit below to display a happy face, and B to show a sad face.
If you need to print this project, please use the Printer friendly version.
title: Club leader notes —
In this project, children will learn how to use input events to execute sequences of instructions, by making an interactive badge. Pressing the buttons on the micro:bit will change the image that’s displayed.
For this project, the MakeCode (PXT) microbit editor should be used.
You can find a completed version of this project at makecode.microbit.org/#pub:90418-17495-16581-63753, and the compiled .hex file can be downloaded by clicking the ‘Download Project Materials’ link for this project, which contains:
on button pressed
);This project covers elements from the following strands of the Raspberry Pi Digital Making Curriculum:
on button pressed
event;— /collapse —
title: Project materials —
— /collapse —


See: Description
| Interface | Description |
|---|---|
| Conversation |
Allows the application to manage the conversation context by marking
the current conversation as transient or long-running, specifying a conversation identifier, or setting the conversation
timeout.
|
| Class | Description |
|---|---|
| ApplicationScoped.Literal |
Supports inline instantiation of the
ApplicationScoped annotation. |
| BeforeDestroyed.Literal |
Supports inline instantiation of the
BeforeDestroyed qualifier. |
| ConversationScoped.Literal |
Supports inline instantiation of the
ConversationScoped annotation. |
| Dependent.Literal |
Supports inline instantiation of the
Dependent annotation. |
| Destroyed.Literal |
Supports inline instantiation of the
Destroyed qualifier. |
| Initialized.Literal |
Supports inline instantiation of the
Initialized qualifier. |
| RequestScoped.Literal |
Supports inline instantiation of the
RequestScoped annotation. |
| SessionScoped.Literal |
Supports inline instantiation of the
SessionScoped annotation. |
| Exception | Description |
|---|---|
| BusyConversationException |
Indicates that the container has rejected a request because a concurrent request is associated with the same conversation
context.
|
| ContextException |
Indicates a problem relating to context management.
|
| ContextNotActiveException |
Indicates that a context is not active.
|
| NonexistentConversationException |
Indicates that the conversation context could not be restored.
|
| Annotation Type | Description |
|---|---|
| ApplicationScoped |
Specifies that a bean is application scoped.
|
| BeforeDestroyed |
An event with this qualifier is fired when a context is about to be destroyed, i.e.
|
| ConversationScoped |
Specifies that a bean is conversation scoped.
|
| Dependent |
Specifies that a bean belongs to the dependent pseudo-scope.
|
| Destroyed |
An event with this qualifier is fired when a context is destroyed, i.e.
|
| Initialized |
An event with this qualifier is fired when a context is initialized, i.e.
|
| NormalScope |
Specifies that an annotation type is a normal scope type.
|
| RequestScoped |
Specifies that a bean is request scoped.
|
| SessionScoped |
Specifies that a bean is session scoped.
|
Annotations and interfaces relating to scopes and contexts.
A scope type is a Java annotation annotated
@Scope
or
@NormalScope
.
The scope of a bean determines the lifecycle and visibility of
its instances. In particular, the scope determines:
The following built-in scopes are provided:
@Dependent
,
@RequestScoped
,
@ConversationScoped
,
@SessionScoped
,
@ApplicationScoped
,
@Singleton
.
The container provides an implementation of the Context
interface for each of the built-in scopes. The built-in request,
session, and application contexts support servlet, web service
and EJB invocations. The built-in conversation context supports
JSF requests.
For other kinds of invocations, a portable extension may define a custom context object for any or all of the built-in scopes. For example, a third-party web application framework might provide a conversation context object for the built-in conversation scope.
The context associated with a built-in scope propagates across local, synchronous Java method calls, including invocation of EJB local business methods. The context does not propagate across remote method invocations or to asynchronous processes such as JMS message listeners or EJB timer service timeouts.
Most scopes are normal scopes. Normal scopes are declared
using @NormalScope
.
If a bean has a normal scope, every client executing in a certain
thread sees the same contextual instance of the bean. This instance is
called the current instance of the bean. The operation
Context.get(Contextual)
of the
context object for a normal scope type always returns the current
instance of the given bean.
Any scope that is not a normal scope is called a pseudo-scope.
Pseudo-scopes are declared using @Scope
.
The concept of a current instance is not well-defined in the case of
a pseudo-scope. Different clients executing in the same thread may
see different instances of the bean. In the extreme case of the
@Dependent
pseudo-scope,
every client has its own private instance of the bean.
All built-in scopes are normal scopes, except for the
@Dependent
and
@Singleton
pseudo-scopes.
A reference to a bean obtained from the container via programmatic lookup is called a contextual reference. A contextual reference for a bean with a normal scope refers to the current instance of the bean. A contextual reference for a bean are valid only for a certain period of time. The application should not invoke a method of an invalid reference.
The validity of a contextual reference for a bean depends upon whether the scope of the bean is a normal scope or a pseudo-scope:
ContextNotActiveException
is thrown
by the container.A reference to a bean obtained from the container via dependency injection is a special kind of contextual reference, called an injected reference. Additional restrictions apply to the validity of an injected reference:
jakarta.enterprise.inject


My Strenth and Limitaions
Strengths:
- My commitment to work and study. I always put extra effort to achieve my career/study goals.
- I am well organised. I review my study plan everyday and make sure myself complete the tasks on time.
- My desire of learning new things.
- Team work, I respect people and I am willing to give support and share my knowledges when is needed.
Limitations:
- I have no programming background which I might need more time to understand the concepts and materials.
- I am too humble and shy, i concern other's feeling very much and sometimes I might not speak up my thoughts and ideas.
- Sometimes I feel stress and anxious when I get stuck.
- English as a second language.
How will I manage my workload in this program
During the self-driven course, I will go over the structure of the sprint and set up a weekly study plan for myself. I will review my study plan each day to make sure I am on the right track. My goal is to work at least 8 hours per day and may be having some stretch to 10 to 12 hours if I feel left behind or If I want to explore more. I will take a 5 mins regular break every hour to stretch my body, and have some fresh air, these help me to refresh my brain, so that I can stay focus through out the day. I will also attend the weekly workshop and update my faciliator with my progress. For the bootcamp, I am commited that i will follow facilitator's instructions to complete the daily tasks. I will be fully commited to the projects, giving support and learn from my cohorts.
How and Where will I seek help in timely way?
When I get stuck, I will try to review the problem myself. If I still can not resolve it, I will search it on google or different forums, as this is the usually way when we working in the development industry. If I still have no hints, then I will seek help from my cohorts and facilitors by using slack or emailing them.
What do I expect from the facilitation team?
I would expect the facilitation team to give support when help is needed, as well as give me advices or share their experience on learning software development.


JSON ABC
Sorts JSON alphabetically
Buy me a coffee
Options:
Spare plain Arrays
Prioritize based on type (primitives, then arrays, then objects)
Treat input as javascript object
Treat input as javascript interface
Use backticks for strings
Twitter
Github
Blog


I'm a founder and teacher in "Web Educator" school for Web Design.
School started in 2009. with aim to help young people getting to know coding, designing and developing (themself).
During last year, school attended more than 200 students.
Beside teaching, my responsibilities are: Prepare teaching program and materials, Administering Online courses, SEO and marketing
School website is first on Google for keyword "course for web design" ("kurs web dizajna" - Serbian)


HTML Acceptance Level Test Suite- f4playertesthtml.hqx or f4playertesthtml.zip-This test suite is designed to ensure that all the basic features of Flash are functioning correctly with your Flash Player. This test suite was designed with web browser testing in mind and the suite is navigated through HTML. If you are not testing with HTML capabilities then you will need to use the SWF only Acceptance Level Test Suite.
SWF only Acceptance Level Test Suite -f4playertestswf.hqx or f4playertestswf.zip -This test suite is designed to work with a Ported Flash Player without HTML capability. The test suite is designed to ensure that all basic features of Flash Player are functioning correctly with your Ported Flash Player.
Inside each hqx or zip file you will find:
Problems with the test suite or questions
about functionality can be directed to:
<EMAIL_ADDRESS>last updated:7 Feburary 2000
flash_source@macromedia.com


With a formal Bachelor in Engineering Degree, I identify myself a hobbyist. I like to build high impact micro services and scalable distributed systems, a bit of UI magic, a pinch of data analytics and lots of line of code. I like to teach too :P
I love to ride motorcycles, play guitar, sing and write songs. I make terrible sketches too :P. I am a physics enthusiast who wonders about Life, the Universe and Everything. I have authored two patents and competed in the prestigious ACM-ICPC regionals. DIY is the way to go!!!
View ProjectsOnline Social Networks (OSNs) have become an integral part of daily life in recent years. They have been used as a means for a rich variety of activities, such as seeking service providers or recommendations. In these activities, trust is one of the most important factors for participants’ decision-making process. Therefore, it is necessary and significant to predict the trust between two participants who have no direct interactions.
Hone your Real Time Stock Trading skills with real time stock prices, virtual money, portfolio building, price analysis, personal recommendations and leaderboard. Taste the flavor of spring based micro services with react JS UI, postgresSQL and RabbitMQ Stack.
Simple Java based REST/CRUD Micro service with React UI for basic college management. [4hrs Challenge code time]


|
Groovy Documentation | |||||||
| FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | METHOD | DETAIL: FIELD | METHOD |
org.vertx.groovy.core.streams.WriteStream org.vertx.groovy.core.streams.ExceptionSupport
@groovy.transform.CompileStatic interface WriteStream extends ExceptionSupport
Represents a stream of data that can be written to.
Any class that implements this interface can be used by a Pump to pump data from a ReadStream to it.
| Method Summary | |
|---|---|
java.lang.Object
|
drainHandler(groovy.lang.Closure handler)
Set a drain handler on the stream. |
boolean
|
isWriteQueueFull()
This will return true if there are more bytes in the write queue than the value set using setWriteQueueMaxSize |
java.lang.Object
|
setWriteQueueMaxSize(int maxSize)
Set the maximum size of the write queue to maxSize. |
java.lang.Object
|
write(Buffer data)
Write some data to the stream. |
| Methods inherited from interface ExceptionSupport | |
|---|---|
| exceptionHandler |
| Method Detail |
|---|
java.lang.Object drainHandler(groovy.lang.Closure handler)
boolean isWriteQueueFull()
java.lang.Object setWriteQueueMaxSize(int maxSize)
java.lang.Object write(Buffer data)
Groovy Documentation


Welcome to the home of Ebbsfleet United Reds. We are currently playing in the Selkent Youth League at Under 10's.
Below you can see our latest results, upcoming fixtures, as well as a gallery of images from recent games!
| Comp | Home Team | Home Score | - | Away Score | Away Team | Date | Address | Goals | Man of the Match |
|---|---|---|---|---|---|---|---|---|---|
| Friendly | Maidstone United Black | 3 | - | 3 | Ebbsfleet United Red | 03/09/2022 | Gallagher Stadium, ME14 1LQ | Luke (2), Albie | Mason |
| Friendly | Dartford FC Black | 4 | - | 1 | Ebbsfleet United Red | 13/09/2022 | Princes Park Stadium, DA1 1RT | Max | - |
| League | Ebbsfleet United Red | 7 | - | 3 | South Darenth | 18/09/2022 | St George's School, DA11 7LS | Luke (4), Albie, Presley, Tolu | Albie |
| League | Danson Sports White | 4 | - | 4 | Ebbsfleet United Red | 25/09/2022 | Mayplace Sports Ground, DA1 4RB | Albie (2), Luke, Tise | Oliver |
| League | Ebbsfleet United Red | 4 | - | 2 | Ebbsfleet United White | 02/10/2022 | St George's School, DA11 7LS | Luke (2), Albie, Tolu | Tolu |
| League | Dartford FC Blue | 2 | - | 2 | Ebbsfleet United Red | 09/10/2022 | Princes Park Stadium, DA1 1RT | Luke (2) | Tise |
| Selkent Cup | Ebbsfleet United Red | 1 | - | 4 | Erith & Belvedere FC | 16/10/2022 | Marathon Football Grounds, SE3 8DW | Luke | - |
| Selkent Cup | Ebbsfleet United Red | 2 | - | 3 | Hayden Youth Sharks | 16/10/2022 | Marathon Football Grounds, SE3 8DW | Luke, Tolu | - |
| League | A.I.M Football Club | P | - | P | Ebbsfleet United Red | 30/10/2022 | Bexley Business Academy, DA18 4DW | - | - |
| League | Cray Wanderers Piranhas | 0 | - | 4 | Ebbsfleet United Red | 06/11/2022 | Coldharbour Leisure Centre, SE9 3LX | Luke (2), Albie, Tolu | Oliver |
| League | A.I.M Football Club | 1 | - | 6 | Ebbsfleet United Red | 13/11/2022 | Rose Cottage Playing Fields, SE18 2JD | Luke (2), Max (2), Presley, Tolu | Presley |
| Friendly | Foots Cray Lions | 4 | - | 7 | Ebbsfleet United Red | 20/11/2022 | Cray Youth & Community Centre, DA14 5ED | Max (2), Tise (2), Henry, Luke, Tolu | Tolu |
| League | Crayford Arrows Black | 1 | - | 3 | Ebbsfleet United Red | 27/11/2022 | Crayford Arrows FC, DA1 4RL | Albie, Luke, Oliver | Tolu |
| League | Hayden Youth Vipers | P | - | P | Ebbsfleet United Red | 04/12/2022 | Leyton Cross Road, DA2 7AN | - | - |
| Friendly | Long Lane | P | - | P | Ebbsfleet United Red | 11/12/2022 | Kidbrooke Playing Fields, SE3 8PB | - | - |
| League | Ebbsfleet United Red | 2 | - | 3 | Dartford FC Blue | 08/01/2023 | Riverview School, DA12 4SD | Luke, Tolu | Tise |
| League | Ebbsfleet United Red | 1 | - | 3 | Dartford FC Black | 15/01/2023 | Riverview School, DA12 4SD | Luke | Oliver |
| League | Ebbsfleet United Red | P | - | P | Samuel Montagu FC Blue | 22/01/2023 | Riverview School, DA12 4SD | - | - |
| Friendly | Ebbsfleet United Reds | 2 | - | 0 | Maidstone | 28/01/2023 | Cascades Leisure Centre, DA12 4LG | Albie (2) | - |
| League | Welling United Reds | 2 | - | 1 | Ebbsfleet United Red | 29/01/2023 | Baldon Sports, SE9 5LU | Max | Max |
| League | Ebbsfleet United Red | 0 | - | 4 | Erith & Belvedere | 05/02/2023 | Riverview School, DA12 4SD | - | Ben |
| Friendly | Metrogas | 1 | - | 1 | Ebbsfleet United Red | 12/02/2023 | John Roan Playing Fields, SE3 9NF | Luke | Henry |
| Selkent Vase | Kent Football United | 0 | - | 2 | Ebbsfleet United Red | 19/02/2023 | Marathon Football Grounds, SE3 8DW | Henry, Luke | - |
| Selkent Vase | Lewisham Project FC | 1 | - | 0 | Ebbsfleet United Red | 19/02/2023 | Marathon Football Grounds, SE3 8DW | - | - |
| League | Samuel Montagu FC Blue | 2 | - | 1 | Ebbsfleet United Red | 26/02/2023 | Samuel Montagu Youth Centre, SE3 8ND | Max | Ben |
| Friendly | Ebbsfleet United Reds | 4 | - | 1 | Maidstone | 04/03/2023 | Cascades Leisure Centre, DA12 4LG | Luke (3), Max | - |
| League | Lewisham Project FC | 0 | - | 2 | Ebbsfleet United Red | 05/03/2023 | Blackheath Common, SE3 0TZ | Luke (2) | Albie |
| League | Foots Cray Lions | 1 | - | 5 | Ebbsfleet United Red | 12/03/2023 | Cray Youth & Community Centre, DA14 5ED | Tise (3), Luke, Max | Tise |
| League | Ebbsfleet United Red | 2 | - | 2 | Metrogas FC Lions | 19/03/2023 | Riverview School, DA12 4SD | Luke, Tise | Mason |
| League | Young Stars Lions | P | - | P | Ebbsfleet United Red | 26/03/2023 | TBC | - | - |
| Friendly | Valley View | 1 | - | 3 | Ebbsfleet United Red | 02/04/2023 | Greenacre Academy, ME5 0LP | Albie, Henry, Max | Albie |
| League | Ebbsfleet United Red | 0 | - | 3 | Russellers Blues | 16/04/2023 | Riverview School, DA12 4SD | - | Tolu |
| League | Ebbsfleet United Red | 2 | - | 0 | Dynamo Youth | 23/04/2023 | Riverview School, DA12 4SD | Luke, Max | Tolu |
| Friendly | Farnborough OBG Colts | 0 | - | 12 | Ebbsfleet United Red | 30/04/2023 | Farnborough Sports Club, BR6 7BA | Albie (3), Luke (3), Max (3), Tise (2), Tolu | Henry |
| League | Ebbsfleet United Red | 1 | - | 1 | Welling United Wings | 07/05/2023 | Riverview School, DA12 4SD | Albie | Luke |
| League | Young Stars Lions | 4 | - | 5 | Ebbsfleet United Red | 14/05/2023 | Marathon Football Grounds, SE3 8DW | Luke (4), Henry | Henry |
| Friendly | New Ash Green | - | - | - | Ebbsfleet United Red | 21/05/2023 | TBC | - | - |
| Comp | Home Team | Home Score | - | Away Score | Away Team | Date | Address | Goals | Man of the Match |
|---|---|---|---|---|---|---|---|---|---|
| League | Ebbsfleet United Red | - | - | - | TBC | TBC | St George's School, DA11 7LS | - | - |
| Name | Appearances | Goals | Man of the Match |
|---|---|---|---|
| Mason | 20 | 0 | 1 |
| Tise | 23 | 5 | 3 |
| Ben | 22 | 0 | 2 |
| Presley | 23 | 2 | 1 |
| Oliver | 20 | 1 | 3 |
| Henry | 23 | 2 | 1 |
| Luke | 23 | 28 | 1 |
| Tolu | 23 | 6 | 5 |
| Albie | 23 | 7 | 2 |
| Max | 17 | 6 | 1 |
| Season | Played | Wins | Draws | Losses | Goals Scored | Goals Conceded | Goal Difference |
|---|---|---|---|---|---|---|---|
| 2022/2023 | 23 | 10 | 4 | 9 | 57 | 46 | +11 |
#1
Goalkeeper
#5
Defence
#4
Defence
#3
Defence / Midfield
#2
Defence / Midfield
#8
Midfield
#6
Midfield
#9
Midfield
#10
Attack
#7
Attack
Click on the link below to go to the Selkent website, containing information relating to the team.
If you need any additional information, contact the management team.


We have to face a situation when people hopes you more than you are capable or willling to do something work done.  You may have enter the circumstances by anyway.
But you like to change this all but don't get anyway to get out of it. When you don't have any idea to get out of it you would get stucked within for remaining time of your life.
Read more...


| practical5_agentframework_xy | index c:\users\liz\documents\python\geog5995\practicals\abm\restart\practicals---final\submission\practical5_agentframework_xy.py |
Created on Mon Sep 30 17:24:49 2019
@author: Liz Osbourn
This file is covered by the LICENSING file in the root of this project.
Creates Agent class
Defines move() behaviour of Agent
agent's y and x move randomly +-1, torus solution to deal with boundary effects
Protects y and x coordinates using _y and _x
Sets getter, setter and del of _y and _x with property()
| Modules | ||||||
|
| Classes | ||||||||
|
|


Huge thanks to the Vine team for making such an awesome app!
VineGif'R uses the following open-source libraries: ANGif / GifPro, by Alex Nichol, Hpple.


About Me
Hi my name is Min! I like cooking, basketball, and movies! I am originally from the Bay Area, and I moved to Seattle in March of 2020.
My Education
- I graduated in 2019 from UC Santa Barbara
- I graduated from Heritage High School in 2015
My Work Experience
After college, I worked as a researcher at Seattle Children's Research Institute. My last research project focused on infant brain development, specifically looking for biomarkers for potential developmental disorders.
My Goals
My goal is to ultimately receive my certificate for advanced software development in python, and then break into the tech field as a software developer. I did a bit of coding here and there at my old job and I realized just how much I really enjoyed it. I was always curious about entering tech and the timing lined up perfectly for me to join Code Fellows.
My Top 10 Favorite Movies
- Paris, Texas
- Magnolia
- Lady Bird
- Parasite
- Solaris
- Akira
- Mulholland Drive
- There Will Be Blood
- Lost in Translation
- Yi Yi


{{ template "tmp_head.html" }}
{{ template "tmp_navi.html" . }}
Chart
Procedures
Pull images list in Chart
Retag images
Sync images to worker nodes
Install Chart archive
Set Values
Set and overwrite
values.yaml
during
procedures, values can be separated with commas:
a=b,c.d=e
Registry
Will be used by both
retag
and
sync
procedures
Timeout
Supports units are
"ms", "s", "m", "h"
Verbose mode
Fire in the hole
{{ template "tmp_script.html" . }}


Javascript Playground
Select Code Editor
Monaco
Monaco
- The editor that powers Visual Studio Code.
- The most advanced open-source browser-based editor.
- Recomended for advanced experiments.
- Large (1.4mb)
Ace
Ace
- Used by Cloud9, Khan Academy, Plunker, and more.
- Strong feature set, including syntax highlighting and code folding.
- Recomended for basic experiments.
- Lightweight-ish (120kb)
shift+enter to run code.


· Owner: Dr. E.C. Prall
· Age: 1875
· Brick Pattern: English Garden Wall
· Architectural Style: Greek Revival characteristics. This small, nearly square home has the door on the right side of the house with a window which is almost the height of the door, a trend which appears later in the 19th Century. Above the door and window with shutters, the woodwork with a Parthenon pediment motif forms the lintels.


Join us in creating a MORE HUMANE Society
OHS has hundreds of animals available for adoption. Find yours!
The lifesaving work of OHS is possible because of donors like you.
Become a volunteer, attend an event, or find other ways to support OHS.
The OHS Training and Behavior Department offers training classes, private consultations, and workshops. Sign up today!
The monthly e-newsletter from OHS: news, events, pets & more.
Total adoptions made: With your help, we’ll find homes for 11,000 pets this year.
Daniel Loves The Oregon Humane Society who are responsible for rescues, heals and adopts more than 11,000 pets each year. They never place a time limit on how long cats, dogs and other pets stay at our shelter. OHS relies on donations to support our adoption, education, and animal rescue programs.


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...an overview on our distributed agile approach...
Bogdan Roman | Software Engineer | 2015
I've never been a great fan
A necessary evil...
Mostly because of the way organizations apply it
Individuals and interactions over processes and tools
Working software over comprehensive documentation
Customer collaboration over contract negotiation
Responding to change over following a plan
That is, while there is value in the items on the right, we value the items on the left more.
The Endava Adaptive Model
A framework for delivering software projects
Both Agile / Planned Iterative
Built internally based on our experiences
Abstract Model
Abstract model -> requires implementation
Mobile Banking and Payments platform
Built from scratch
Scrum
12 development teams
> 150 people involved
5 Countries / > 7 Locations
> 1 year
Thank you for listening
Email me @ Bogdan Roman


JAR Inventory is a business tracking management application that allows any company to track product.
Languages and Technologies: HTML, CSS, Javascript, jQuery, Bootstrap, Node.JS, and NPM Install (mysql, sequelize, express, passport, eslint).
The Date Night application is designed to help individuals plan the perfect dinner and a movie date. The deployed link is the one our team worked on. The updated link has local storage and clear button.
Languages and Technologies: HTML, CSS, Javascript, jQuery, W3.CSS Framework, TMDB API, and Yelp API.
The Work Day Scheduler Application is a simple work day calendar from 9AM to 7PM. The block is color coded to indicate whether it occurs in the past (gray), present (red) or future green.
Languages and Technologies: HTML, CSS, Javascript, jQuery, Bootstrap, and Moment.js.
There's definitely more room to improve in these projects. I'm grateful to have the opportuntiy to learn! Check out my Github for other projects.


Layout utilities
There are different layout utilities, that allow you to change everything that relates to the layout and position of elements.
Layout container
It is recommended not to have full-width containers with content, but instead to give them a maximum width and to center them. You can look at the header and this content area to see the effect.
To do so, you need to add the .container
class. By default, the width of it is 72rem, however you can override that in your "_child.scss".
Element float
You can make elements float to the left or to the right, by giving them the classes .has-float-left
or .has-float-right
, respectively. You can remove the float from an element with .has-float-none
and clear the float around an element with .has-float-clear
.
Flexbox grid
Co-Design provides a flexible and powerful CSS flexbox grid. It must be in a .grid
container and each item (cell) must have the class .grid--cell
.
Then you can add any of the classes .is-X
with X between one and twelve (or any other number if you override it in the "_child.scss"). Furthermore we have responsive grid classes with .is-X-sm
, .is-X-md
and .is-X-lg
, being effective only, when the viewport size is under 32rem, between 32rem and 64rem or above 64rem, respectively.
By default, the elements of a grid are put on the next line, when there isn't enough space (wrap). However, sometimes that isn't wanted. For these cases, it's possible to add the class .is-nowrap
to the grid.
Example


Comments
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This is Host Event #1's description. Hopefully it stays centers like it is.
This is the event's description.
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No comments yet. So why not post the first comment?
User: {{comment.username}} {{comment.created | date}}


Step 9 Data Protection Act
Submission
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The Department for Communities & Local Government (DCLG, for ERDF) and the Department for Work and Pensions (DWP, for ESF) undertake to use its best endeavours to hold confidential any information provided in any application form submitted, subject to our contracting obligations under law, including the Freedom of Information Act 2000. If you consider that any of the information submitted in the application form should not be disclosed because of its sensitivity then this should be stated with the reason for considering it sensitive. DCLG and/or DWP will then consult with you in considering any request received under the Freedom of Information Act 2000 before replying to such a request.
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Sometimes you will be required to supply personal information relating to third parties, ie delivery partners. In these circumstances you must ensure that you inform the delivery partner how you use their information and that this information will be passed to us.
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If you believe that any information that we hold about you is inaccurate or incorrect, please tell us and we will correct it.


* [Bayesian Deep Nets and Deep Gaussian Processes](#bayesian-deep-nets-and-deep-gaussian-processes)
* [Inference for Deep Gaussian Processes](#inference-for-deep-gaussian-processes)
* [Convolutional Nets and Gaussian Processes](#convolutional-nets-and-gaussian-processes)
* [Bayesian Convolutional Nets](#bayesian-convolutional-nets)
* [Calibration of Bayesian Convolutional Nets](#calibration-of-bayesian-convolutional-nets)
* [Random Feature Expansions for Shallow and Deep Gaussian Processes](#random-feature-expansions-for-shallow-and-deep-gaussian-processes)
* [Variational Inference](#variational-inference)
* [Variational Inference for Gaussian Process Models](#variational-inference-for-gaussian-process-models)
* [Unsupervised learning with Deep Gaussian Processes](#unsupervised-learning-with-deep-gaussian-processes)
* [Multi-task Learning with Gaussian Processes](#multi-task-learning-with-gaussian-processes)
* [Bayesian Optimization](#bayesian-optimization)
* [Other GP and DGP Models](#other-gp-and-dgp-models)
### Bayesian Deep Nets and Deep Gaussian Processes
-
A. G. de G. Matthews, J. Hron, M. Rowland, R. E. Turner, and Z. Ghahramani.
Gaussian process behaviour in wide deep neural networks.
In International Conference on Learning Representations, 2018.
-
K. Cutajar, E. V. Bonilla, P. Michiardi, and M. Filippone.
Random feature expansions for deep Gaussian processes.
In D. Precup and Y. W. Teh, editors, Proceedings of the 34th
International Conference on Machine Learning, volume 70 of Proceedings
of Machine Learning Research, pages 884--893, International Convention
Centre, Sydney, Australia, Aug. 2017. PMLR.
-
Y. Gal and Z. Ghahramani.
Dropout As a Bayesian Approximation: Representing Model Uncertainty
in Deep Learning.
In Proceedings of the 33rd International Conference on
International Conference on Machine Learning - Volume 48, ICML'16, pages
1050--1059. JMLR.org, 2016.
-
D. K. Duvenaud, O. Rippel, R. P. Adams, and Z. Ghahramani.
Avoiding pathologies in very deep networks.
In Proceedings of the Seventeenth International Conference on
Artificial Intelligence and Statistics, AISTATS 2014, Reykjavik, Iceland,
April 22-25, 2014, volume 33 of JMLR Workshop and Conference
Proceedings, pages 202--210. JMLR.org, 2014.
-
R. M. Neal.
Bayesian Learning for Neural Networks (Lecture Notes in
Statistics).
Springer, 1 edition, Aug. 1996.
### Inference for Deep Gaussian Processes
-
H. Salimbeni and M. Deisenroth.
Doubly Stochastic Variational Inference for Deep Gaussian
Processes.
In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus,
S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information
Processing Systems 30, pages 4588--4599. Curran Associates, Inc., 2017.
-
M. D. Hoffman.
Learning deep latent Gaussian models with Markov chain Monte
Carlo.
In D. Precup and Y. W. Teh, editors, Proceedings of the 34th
International Conference on Machine Learning, volume 70 of Proceedings
of Machine Learning Research, pages 1510--1519, International Convention
Centre, Sydney, Australia, Aug. 2017. PMLR.
-
M. Havasi, J. M. Hernández-Lobato, and J. J. Murillo-Fuentes.
Inference in Deep Gaussian Processes using Stochastic Gradient
Hamiltonian Monte Carlo.
In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi,
and R. Garnett, editors, Advances in Neural Information Processing
Systems 31, pages 7506--7516. Curran Associates, Inc., 2018.
-
K. Cutajar, E. V. Bonilla, P. Michiardi, and M. Filippone.
Random feature expansions for deep Gaussian processes.
In D. Precup and Y. W. Teh, editors, Proceedings of the 34th
International Conference on Machine Learning, volume 70 of Proceedings
of Machine Learning Research, pages 884--893, International Convention
Centre, Sydney, Australia, Aug. 2017. PMLR.
-
T. D. Bui, D. Hernández-Lobato, J. M. Hernández-Lobato, Y. Li, and
R. E. Turner.
Deep Gaussian Processes for Regression using Approximate Expectation
Propagation.
In M.-F. Balcan and K. Q. Weinberger, editors, Proceedings of
the 33nd International Conference on Machine Learning, ICML 2016, New York
City, NY, USA, June 19-24, 2016, volume 48, pages 1472--1481. JMLR.org,
2016.
-
A. C. Damianou and N. D. Lawrence.
Deep Gaussian Processes.
In Proceedings of the Sixteenth International Conference on
Artificial Intelligence and Statistics, AISTATS 2013, Scottsdale, AZ, USA,
April 29 - May 1, 2013, volume 31 of JMLR Proceedings, pages
207--215. JMLR.org, 2013.
-
J. Hensman and N. D. Lawrence.
Nested Variational Compression in Deep Gaussian Processes, Dec.
2014.
arxiv:1412.1370.
### Convolutional Nets and Gaussian Processes
-
V. Kumar, V. Singh, P. K. Srijith, and A. Damianou.
Deep Gaussian Processes with Convolutional Kernels, June 2018.
arXiv:1806.01655.
-
M. van der Wilk, C. E. Rasmussen, and J. Hensman.
Convolutional Gaussian Processes.
In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus,
S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information
Processing Systems 30, pages 2849--2858. Curran Associates, Inc., 2017.
-
J. Bradshaw, Alexander, and Z. Ghahramani.
Adversarial Examples, Uncertainty, and Transfer Testing Robustness
in Gaussian Process Hybrid Deep Networks, July 2017.
arXiv:1707.02476.
-
R. Calandra, J. Peters, C. E. Rasmussen, and M. P. Deisenroth.
Manifold Gaussian Processes for regression.
In 2016 International Joint Conference on Neural Networks,
IJCNN 2016, Vancouver, BC, Canada, July 24-29, 2016, pages 3338--3345,
2016.
-
A. G. Wilson, Z. Hu, R. R. Salakhutdinov, and E. P. Xing.
Stochastic Variational Deep Kernel Learning.
In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett,
editors, Advances in Neural Information Processing Systems 29, pages
2586--2594. Curran Associates, Inc., 2016.
### Bayesian Convolutional Nets
-
G.-L. Tran, E. V. Bonilla, J. Cunningham, P. Michiardi, and M. Filippone.
Calibrating Deep Convolutional Gaussian Processes.
In K. Chaudhuri and M. Sugiyama, editors, Proceedings of Machine
Learning Research, volume 89 of Proceedings of Machine Learning
Research, pages 1554--1563. PMLR, 16--18 Apr 2019.
-
F. Laumann, K. Shridhar, and A. L. Maurin.
Bayesian Convolutional Neural Networks, June 2018.
arXiv:1806.05978.
-
A. Garriga-Alonso, C. E. Rasmussen, and L. Aitchison.
Deep Convolutional Networks as shallow Gaussian Processes.
In International Conference on Learning Representations, 2019.
-
Y. Gal and Z. Ghahramani.
Bayesian convolutional neural networks with Bernoulli approximate
variational inference.
In 4th International Conference on Learning Representations
(ICLR) workshop track, 2016.
### Calibration of Bayesian Convolutional Nets
-
G.-L. Tran, E. V. Bonilla, J. Cunningham, P. Michiardi, and M. Filippone.
Calibrating Deep Convolutional Gaussian Processes.
In K. Chaudhuri and M. Sugiyama, editors, Proceedings of Machine
Learning Research, volume 89 of Proceedings of Machine Learning
Research, pages 1554--1563. PMLR, 16--18 Apr 2019.
-
B. Lakshminarayanan, A. Pritzel, and C. Blundell.
Simple and Scalable Predictive Uncertainty Estimation using Deep
Ensembles.
In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus,
S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information
Processing Systems 30, pages 6402--6413. Curran Associates, Inc., 2017.
-
A. Niculescu-Mizil and R. Caruana.
Predicting Good Probabilities with Supervised Learning.
In Proceedings of the 22Nd International Conference on Machine
Learning, ICML '05, pages 625--632, New York, NY, USA, 2005. ACM.
-
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger.
On Calibration of Modern Neural Networks.
In D. Precup and Y. W. Teh, editors, Proceedings of the 34th
International Conference on Machine Learning, volume 70 of Proceedings
of Machine Learning Research, pages 1321--1330, International Convention
Centre, Sydney, Australia, Aug. 2017. PMLR.
### Random Feature Expansions for Shallow and Deep Gaussian Processes
-
K. Cutajar, E. V. Bonilla, P. Michiardi, and M. Filippone.
Random feature expansions for deep Gaussian processes.
In D. Precup and Y. W. Teh, editors, Proceedings of the 34th
International Conference on Machine Learning, volume 70 of Proceedings
of Machine Learning Research, pages 884--893, International Convention
Centre, Sydney, Australia, Aug. 2017. PMLR.
-
F. X. Yu, A. T. Suresh, K. M. Choromanski, D. N. Holtmann-Rice, and S. Kumar.
Orthogonal Random Features.
In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett,
editors, Advances in Neural Information Processing Systems 29, pages
1975--1983. Curran Associates, Inc., 2016.
-
Q. Le, T. Sarlos, and A. Smola.
Fastfood - Approximating Kernel Expansions in Loglinear Time.
In 30th International Conference on Machine Learning (ICML),
2013.
-
Y. Gal and Z. Ghahramani.
Dropout As a Bayesian Approximation: Representing Model Uncertainty
in Deep Learning.
In Proceedings of the 33rd International Conference on
International Conference on Machine Learning - Volume 48, ICML'16, pages
1050--1059. JMLR.org, 2016.
-
A. Rahimi and B. Recht.
Random Features for Large-Scale Kernel Machines.
In J. C. Platt, D. Koller, Y. Singer, and S. T. Roweis, editors,
Advances in Neural Information Processing Systems 20, pages 1177--1184.
Curran Associates, Inc., 2008.
### Variational Inference
-
D. P. Kingma and M. Welling.
Auto-Encoding Variational Bayes.
In Proceedings of the Second International Conference on
Learning Representations (ICLR 2014), Apr. 2014.
-
A. Graves.
Practical Variational Inference for Neural Networks.
In J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and
K. Q. Weinberger, editors, Advances in Neural Information Processing
Systems 24, pages 2348--2356. Curran Associates, Inc., 2011.
### Variational Inference for Gaussian Process Models
-
T. V. Nguyen and E. V. Bonilla.
Automated variational inference for Gaussian process models.
In Advances in Neural Information Processing Systems. 2014.
-
J. Hensman, N. Fusi, and N. D. Lawrence.
Gaussian processes for big data.
In Uncertainty in Artificial Intelligence, 2013.
-
A. Dezfouli and E. V. Bonilla.
Scalable inference for Gaussian process models with black-box
likelihoods.
In Advances in Neural Information Processing Systems. 2015.
-
E. V. Bonilla, K. Krauth, and A. Dezfouli.
Generic Inference in Latent Gaussian Process Models.
arXiv e-prints, page arXiv:1609.00577, Sep 2016.
-
M. Titsias.
Variational learning of inducing variables in sparse Gaussian
processes.
In Artificial Intelligence and Statistics, 2009.
-
K. Krauth, E. V. Bonilla, K. Cutajar, and M. Filippone.
AutoGP: Exploring the capabilities and limitations of Gaussian
process models.
In Uncertainty in Artificial Intelligence, 2017.
### Unsupervised learning with Deep Gaussian Processes
-
R. Domingues, P. Michiardi, J. Zouaoui, and M. Filippone.
Deep Gaussian process autoencoders for novelty detection.
Machine Learning, 107(8-10):1363--1383, 2018.
-
Z. Dai, A. Damianou, J. González, and N. Lawrence.
Variational Auto-encoded Deep Gaussian Processes, Feb. 2016.
-
N. Lawrence.
Probabilistic non-linear principal component analysis with Gaussian
process latent variable models.
Journal of Machine Learning Research, 6:1783--1816, 2005.
-
K. Grochow, S. L. Martin, A. Hertzmann, and Z. Popović.
Style-based inverse kinematics.
In ACM SIGGRAPH 2004 Papers, SIGGRAPH '04, pages 522--531, New
York, NY, USA, 2004. ACM.
### Multi-task Learning with Gaussian Processes
-
K. M. A. Chai, C. K. Williams, S. Klanke, and S. Vijayakumar.
Multi-task Gaussian process learning of robot inverse dynamics.
In Advances in Neural Processing Systems, pages 265--272, 2008.
-
E. V. Bonilla, K. M. A. Chai, and C. K. I. Williams.
Multi-task Gaussian process prediction.
In Advances in Neural Processing Systems, 2008.
-
M. A. Álvarez and N. D. Lawrence.
Computationally efficient convolved multiple output Gaussian
processes.
Journal of Machine Learning Research, 12(5):1459--1500, 2011.
-
A. G. Wilson, D. A. Knowles, and Z. Ghahramani.
Gaussian process regression networks.
In International Conference on Machine Learning, 2012.
-
P. Boyle.
Gaussian Processes for Regression and Optimisation.
PhD thesis, Victoria University of Wellington, 2007.
### Bayesian Optimization
-
D. R. Jones.
A Taxonomy of Global Optimization Methods Based on Response
Surfaces.
Journal of Global Optimization, 21(4):345--383, 2001.
-
J. Snoek, H. Larochelle, and R. P. Adams.
Practical Bayesian optimization of machine learning algorithms.
In Advances in neural information processing systems, pages
2951--2959, 2012.
-
W. Chu and Z. Ghahramani.
Preference learning with Gaussian processes.
In Proceedings of the 22nd international conference on Machine
learning, pages 137--144. ACM, 2005.
-
K. Swersky, J. Snoek, and R. P. Adams.
Multi-task Bayesian optimization.
In Advances in neural information processing systems, pages
2004--2012, 2013.
### Other GP and DGP Models
-
R. Domingues, P. Michiardi, J. Zouaoui, and M. Filippone.
Deep Gaussian process autoencoders for novelty detection.
Machine Learning, 107(8-10):1363--1383, 2018.
-
M. van der Wilk, C. E. Rasmussen, and J. Hensman.
Convolutional Gaussian Processes.
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S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information
Processing Systems 30, pages 2849--2858. Curran Associates, Inc., 2017.
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This chapter explains how to effectively use the services offered by SEAM by means of a real world language layer. The presentation is focused on the design aspects and does not include full code. The full source code can be downloaded as part of the Alice Source tree from here.
Alice is a functional programming language based on Standard ML, extended with support for concurrent, distributed, and constraint programming. Alice extends Standard ML with several new features:
For more detailed information, see the Alice Homepage.
In order to build the Alice language layer, that is the alice.dll, on top of SEAM, several steps are required:
Given this, the Alice VM becomes seam.exe alice.dll args, with args some alice component.
Standard ML runtimes usually are implemented using full type-erasure, that is, nothing of the ML static type information remains visible at runtime level. The same applies to the Alice language layer, conventiently obliviating the need to know about ML types on store data level.
On runtime level, we have to model the following scalar types:
On runtime level, we have to model the following composed types:
// Define labels of Alice data types class Alice { public: static const BlockLabel Array = MIN_DATA_LABEL; static const BlockLabel Cell = (BlockLabel) (MIN_DATA_LABEL + 1); static const BlockLabel ConVal = (BlockLabel) (MIN_DATA_LABEL + 2); static const BlockLabel Record = (BlockLabel) (MIN_DATA_LABEL + 3); static const BlockLabel Vector = (BlockLabel) (MIN_DATA_LABEL + 4); static const BlockLabel BIG_TAG = (BlockLabel) (MIN_DATA_LABEL + 5); static const BlockLabel MIN_TAG = (BlockLabel) (MIN_DATA_LABEL + 6); static const BlockLabel MAX_TAG = MAX_DATA_LABEL; ... }; // Define the data types class AliceDll Array : private Block { ... }; class AliceDll Cell : private Block { ... }; class AliceDll Constructor : private ConcreteRepresentation { ... }; class AliceDll ConVal : private Block { ... }; class AliceDll TagBal : private Block { ... }; class AliceDll BigTagVal : private Block { ...}; class AliceDll UniqueConstructor : public Constructor { ... }; class AliceDll Vector : private Block { ... }; class AliceDll Word8Array : private String { ... }; class AliceDll Word8Vector : private string { ... }; class AliceDll Record : private Block { ... }; class AliceDll BigInt : private Chunk { ... };
Alice algebraic datatypes are represented as store blocks that are given an appropriate alternative number. By default, the tag field of the store is used that severly limits the number of possible alternatives (for example 128 on 32-bit implementations). To circumvent this an additional implementation is provided that uses an extra field to store the alternative number. Static type information allows for proper disambiguation of the implementation used. The types are called TagTal and BigTagVal, respectively.
For documentary purposes, the full implementation of the array datatype is shown below:
class AliceDll Array : private Block { private: enum { LENGTH_POS, BASE_SIZE }; public: static const u_int maxLen = MAX_BIGBLOCKSIZE - BASE_SIZE; using Block::ToWord; static Array *New(u_int length) { Block *b = Store::AllocBlock(Alice::Array, BASE_SIZE + length); b->InitArg(LENGTH_POS, length); return STATIC_CAST(Array *, b); } static Array *FromWord(word x) { Block *b = Store::WordToBlock(x); Assert(b == INVALID_POINTER || b->GetLabel() == Alice::Array); return STATIC_CAST(Array *, b); } static Array *FromWordDirect(word x) { Block *b = Store::DirectWordToBlock(x); Assert(b->GetLabel() == Alice::Array); return STATIC_CAST(Array *, b); } u_int GetLength() { return Store::DirectWordToInt(GetArg(LENGTH_POS)); } void Init(u_int index, word value) { InitArg(BASE_SIZE + index, value); } void Update(u_int index, word value) { ReplaceArg(BASE_SIZE + index, value); } word Sub(u_int index) { return GetArg(BASE_SIZE + index); } };
The basic procedure is simple: select a unique label for the data type, derive from the appropriate base class, provide your own constructor and tag conversion and finally provide the requires accessors. In case you need to provide internal and external representation, it is required to derive from the ConcreteRepresentation base class. More details will be explained later.
Remember that each store block is guaranteed only to be at least of the size requested. It might be larger as well. Therefore, it is required (as for example for arrays and vectors) to additionally store the requested size in case this is needed because the default GetSize() will return the effective block size. However, for a statically typed language like Alice most datatypes do not need to store their exact size as their size is statically known.
You might have noted the AliceDll flag. This is to provide the appropriate DLL import/exports declarations on Windows and defaults to the empty string on Unix-like operating systems.
This section highlights the design ideas of alice code and how its execution is implemented on top of SEAM.
Alice is a functional language. Apart from the goals of genericity and simplicity, six central design decisions were made for its code representation:
Alice code is represented as a directed acyclic graph that is well suited for the needs of runtime transformation, in particular runtime compilation to native code. The DAG representation is defined to be the external representation of the code. The current implementation supports two internal representations of Alice code:
In support for efficient runtime compilation, the code representation is extended with an annotations field that carries statically pre-computed information to speed up runtime processing. In particar, this includes approximated identifier liveness information suitable as input to linear-scan register allocation.
Note that storing code as a DAG in the first place together with explicit control flow information removes the need for clumsily reconstructing this information from plain byte code at runtime.
Implementing (Alice) code and its execution requires several steps to be done:
Below the code interfaces for the interpreted concrete representation are shown:
class AliceDll AliceConcreteCode : private ConcreteCode { private: enum { ABSTRACT_CODE_POS, TRANSFORM_POS, SIZE }; public: static word New(TagVal *abstractCode); TagVal *GetAbstractCode(); Transform *GetAbstractRepresentation(); void Disassemble(std::FILE *file); using Block::ToWord; static AliceConcreteCode *FromWord(word x); static AliceConcreteCode *FromWordDirect(word x); }; class AbstractCodeFrame : public StackFrame { protected: enum { PC_POS, CLOSURE_POS, LOCAL_ENV_POS, FORMAL_ARGS_POS, SIZE }; public: class Environment : private Array { public: using Array::ToWord; void Add(word id, word value); word Lookup(word id); void Kill(word id, TagVal *pc, Closure *globalEnv); static Environment *New(u_int size); static Environment *FromWordDirect(word x); }; // AbstractCodeFrame Accessors u_int GetSize(); bool IsHandlerFrame(); TagVal *GetPC(); void SetPC(TagVal *pc); Closure *GetClosure(); Environment *GetLocalEnv(); Vector *GetFormalArgs(); void SetFormalArgs(word formalArgs); // AbstractCodeFrame Constructor static AbstractCodeFrame *New(Interpreter *interpreter, word pc, Closure *closure, Environment *env, word formalArgs); }; class AbstractCodeInterpreter : public Interpreter { private: AbstractCodeInterpreter(): Interpreter() {} public: static AbstractCodeInterpreter *self; static void Init(); // See Interpreter interface for details ... virtual Result Run(StackFrame *sFrame); ... };
So far, only interfaces have been shown. Now we will show some of the method bodies:
word AliceConcreteCode::New(TagVal *abstractCode) { abstractCode->AssertWidth(AbstractCode::functionWidth); ConcreteCode *concreteCode = ConcreteCode::New(AbstractCodeInterpreter::self, SIZE); Chunk *name = Store::DirectWordToChunk(AliceLanguageLayer::TransformNames::function); Transform *transform = Transform::New(name, abstractCode->ToWord()); concreteCode->Init(ABSTRACT_CODE_POS, abstractCode->ToWord()); concreteCode->Init(TRANSFORM_POS, transform->ToWord()); return concreteCode->ToWord(); } static AbstractCodeFrame *New(Interpreter *interpreter, word pc, Closure *closure, Environment *env, word formalArgs) { NEW_STACK_FRAME(frame, interpreter, SIZE); frame->InitArg(PC_POS, pc); frame->InitArg(CLOSURE_POS, closure->ToWord()); frame->InitArg(LOCAL_ENV_POS, env->ToWord()); frame->InitArg(FORMAL_ARGS_POS, formalArgs); return STATIC_CAST(AbstractCodeFrame *, frame); } // This is shown to give the reader an impression on how computation on SEAM // works. It will not be explained in detail. Worker::Result AbstractCodeInterpreter::Run(StackFrame *sFrame) { AbstractCodeFrame *frame = STATIC_CAST(AbstractCodeFrame *, sFrame); Assert(sFrame->GetWorker() == this); TagVal *pc = frame->GetPC(); Closure *globalEnv = frame->GetClosure(); AbstractCodeFrame::Environment *localEnv = frame->GetLocalEnv(); Vector *formalArgs = frame->GetFormalArgs(); ... while (true) { loop: switch (AbstractCode::GetInstr(pc)) { ... case AbstractCode::PutVar: // of id * idRef * instr { localEnv->Add(pc->Sel(0), GetIdRefKill(pc->Sel(1), pc, globalEnv, localEnv)); pc = TagVal::FromWordDirect(pc->Sel(2)); } break; ... case AbstractCode::Raise: // of idRef { word requestWord = GetIdRef(pc->Sel(0), globalEnv, localEnv); Transient *transient = Store::WordToTransient(requestWord); if (transient != INVALID_POINTER) REQUEST(transient->ToWord()); KillIdRef(pc->Sel(0), pc, globalEnv, localEnv); Scheduler::SetCurrentData(requestWord); Scheduler::SetCurrentBacktrace(Backtrace::New(frame->Clone())); return Worker::RAISE; } break; ... default: Error("AbstractCodeInterpreter::Run: unknown instr tag"); } } }
In case of the native code, things are analog except for one complication. The native concrete code at first consists of a so-called byneed value that encapulates the actual runtime compilation. Once the byneed is requested, the runtime compilation takes place and finally the byneed is bound to the resulting real native concrete code. Remember that reference chains of bound transients are eventually cut by SEAM's garbage collector.
The main task of the runtime compiler consists of detecting special cases related to function application, in particular the elimination of unnecessary invokations of Scheduler::PushCall. Native functions are invoked directly and important primitives are inlined once enough context information is available to the runtime compiler.
Alice pickling is built on top of SEAM pickling by defining the following transforms:
Setting up a transform is easy, for example Alice.function:
... static word AliceFunctionHandler(word x) { TagVal *abstractCode = TagVal::FromWordDirect(x); return AliceLanguageLayer::concreteCodeConstructor(abstractCode); } ... String *aliceFunction = String::New("Alice.function"); TransformNames::function = aliceFunction->ToWord(); RootSet::Add(TransformNames::function); Unpickler::RegisterHandler(aliceFunction, AliceFunctionHandler); ...
In the example above, the AliceLanguageLayer::concreteCodeConstructor of course has to do the main work of creating the internal representation. The point here is that registering your own handlers is all you need to do to "teach" SEAM pickling your language specific representation transformations. In the current implementation, the concrete code constructor either points to AliceConcreteCode::New or NativeConcreteCode::New, depending on the users selection.
Alice language components are implemented as a pair consisting of an interface (called signature) and a structure implementing this interface. The Alice VM now needs a initial way of obtaining Alice components at runtime, which is performed by the Alice BootLinker. Its interface is shown below.
struct AliceDll NativeComponent { const char *name; word (*init)(); }; class AliceDll Component: private Block { private: static const u_int ENTRY_LABEL = MIN_DATA_LABEL; enum { SIGN_POS, STR_POS, SIZE }; public: word GetSign(); word GetStr(); static Component *New(word sign, word str); static Component *FromWordDirect(word entry); using Block::ToWord; }; class AliceDll BootLinker { private: static word componentTable; static word keyQueue; static u_int numberOfEntries; static ChunkMap *GetComponentTable(); public: static void Init(NativeComponent *nativeComponents); static Queue *GetKeyQueue(); static u_int GetNumberOfEntries(); static void EnterComponent(String *key, word sign, word str); static Component *LookupComponent(String *key); static void Link(String *url); static void Link(Thread *thread, String *url); };
The boot linker is initialized with a set of native components that will be evaluated and entered in the global component table.
Enters an evaluated component into the global components table. It will be accessible under key key and the components signature and structure are denoted by sign and str, respectively.
Performs a component lookup using key key. Indicates failure by returning INVALID_POINTER.
Tries to link the component denoted by url url. In case linking succeeds, the resulting component will be entered into the component table. The latter alternative tries to link currently within the thread denoted by thread.
The Alice boot linker is fully implemented on top of SEAM, however, a bit lengthy because of a bunch of side conditions to be tested for. Since it requires a lot of understanding of the Alice component model, only the boot linker interface is shown for explanatory purposes.
The Alice FFI corresponds to the SEAM FFI except that Alice defines its own component interface. It is best understood by looking first at an example, the AliceTimer component:
#include "Alice.hh" static double startTime; DEFINE0(Timer_start) { // Time::GetElapsedMicroseconds is part of the public SEAM interface startTime = Time::GetElapsedMicroseconds(); RETURN_UNIT; } END DEFINE0(Timer_check) { double curTime = Time::GetElapsedMicroseconds(); double result = (curTime - startTime) / 1000.0; RETURN(Store::IntToWord(static_cast(result))); } END word InitComponent() { Record *record = Record::New(2); INIT_STRUCTURE_N(record, "AliceTimer", "start", Timer_start, 0, 1); INIT_STRUCTURE_N(record, "AliceTimer", "check", Timer_check, 0, 1); RETURN_STRUCTURE("AliceTimer$", record); }
Similar to the SEAM FFI, components need to include Alice.hh to get started. Defining functions is unchanged except for some more pre-defined data types.
Alice component interfaces are records to allow for runtime pruning and merging of fields which takes place as part of type coercions. You need to allocate a record whith a sufficient number of slots, equaling the number of entities you wish to export. No ordering applies to the entries since access is performed by name via hashing.Alice native components are compiled using alicetool which is a wrapper for seamtool that takes care of adding the appropriate include pathes.
Argument declarations are transient-aware, that is, will request their arguments in order to obtain the untagged representation.
Declares that the variable var denotes an alice array, whose value is bound to array.
Declares that the variable var denotes a bolean, whose value is bound to boolVal.
Declares that the variable var denotes a reference cell, whose value is bound to cell.
Declares that the variable var denotes a constructed value, whose value is bound to conVal.
Declares that the variable var denotes a real, whose value is bound to realVal.
Declares that the variable var denotes a alice record, whose value is bound to record.
Declares that the variable var denotes a (big) tag value, whose value is bound to tagVal. Note that you need to know what type of tag value is expected as they are not interchangable.
Declares that the variable var denotes a SEAM thread, whose value is bound to thread.
Declares that the variable var denotes a vector, whose value is bound to vector.
Declares that the variable var denotes a word 8 vector, whose value is bound to vector.
Declares that the variable var denotes a list of length length, whose head is bound to tagVal. Does not work for infinite lists.
In addition to the standard RETURN(value) from SEAM, the following convenience macros are defined:
Returns a boolean value.
Returns a real value.
Returns unit.
Populates a slot in the record denoted by record, with componentName.fieldName the resulting path of the entry. inArity denotes the number of arguments of the function denoted by function, and outArity the number of return values.
Corresponds to INIT_STRUCTURE_N(record, componentName, fieldName, function, inArity, 1).
Returns the structure denoted by record under label label. Note that it is policy to provide a non-Alice print name, such as name$, with name the component name.
Primitives are native functions or values that are part of the abstract Alice platform. They differ from regular FFI functions only in that they provide an abstract representation. Alice primitives are managed in a global PrimitiveTable. This table is used by the corresponding Alice.primitive.function and Alice.primitive.value transform to obtain the internal representation. The code snipped shown below shows how these primitives are registered.
class AliceDll PrimitiveTable { private: static word valueTable; static word functionTable; static void Register(const char *name, word value); static void Register(const char *name, Interpreter::function value, u_int inArity, u_int outArity = 1); ... void RegisterFuture(); ... static word LookupValue(Chunk *name); static word LookupFunction(Chunk *name); }; void PrimitiveTable::Register(const char *name, Interpreter::function value, u_int inArity, u_int outArity) { word transformName = AliceLanguageLayer::TransformNames::primitiveFunction; Transform *abstract = Transform::New(Store::DirectWordToChunk(transformName), String::New(name)->ToWord()); word function = Primitive::MakeFunction(name, value, inArity, outArity, abstract); word closure = Closure::New(function, 0)->ToWord(); Register(name, closure); ChunkMap::FromWordDirect(functionTable)-> Put(String::New(name)->ToWord(), function); } // Defined in appropriate primitive module (here the Future module) DEFINE1(Future_await) { if (Store::WordToTransient(x0) != INVALID_POINTER) { REQUEST(x0); } else { RETURN(x0); } } END void PrimitiveTable::RegisterFuture() { ... Register("Future.await", Future_await, 1); ... } // Defined in language layer main file static word AlicePrimitiveValueHandler(word x) { return PrimitiveTable::LookupValue(Chunk::FromWordDirect(x)); } static word AlicePrimitiveFunctionHandler(word x) { return PrimitiveTable::LookupFunction(Chunk::FromWordDirect(x)); }


No matching services found.
| Service | Version | Slice | Installed | Health Checks | Instances | Deployment Log | Installation |
|---|---|---|---|---|---|---|---|
|
|
{{ service.Version }} | {{ service.Slice }} |
In Progress
|
|
{{ service.InstancesCount.Present }} {{ service.InstancesCount.Present === 1 ? 'instance' : 'instances' }} | Show Deployment |
|


| Interface | Description |
|---|---|
| GUIDGenerator |
Global unique ID generator interface User: Kuba Date: 14.5.2014 Time: 11:37
|
| StartComputationStrategy |
Strategy design pattern, used in computation graph - when all inputs are
filled, the execute method is triggered User: Kuba Date: 10.5.2014 Time:
14:12
|
| Class | Description |
|---|---|
| ComputationGraph |
Describes batch holding sets of nodes representing each part of the graph Is
observable User: Kuba Date: 11.5.2014 Time: 21:27
|
| ComputationNode |
Computation node stores inputs and outputs for some task in computation graph
(recommend, search, compute].
|
| DataProcessingComputationNode |
Computation node for data pro and pre processing User: Kuba Date: 16.8.2014
Time: 14:00
|
| ExperimentGraph |
The type Experiment graph.
|
| ExperimentItem | |
| IncrementalGUIDGenerator |
Incremental guid generator User: Kuba Date: 14.5.2014 Time: 11:39
|
| ModelComputationNode |
Computation node for executing ML experiments User: Kuba Date: 18.5.2014
Time: 15:52
|
| RecommenderComputationNode |
Computation node for recommending User: Kuba Date: 18.5.2014 Time: 15:27
|
| SearchComputationNode |
Computation node for searching User: Kuba Date: 18.5.2014 Time: 15:34
|
| Enum | Description |
|---|---|
| ExperimentItem.ProblemStatus |


 Same as setting the display to "none"
Now stop.
Imagine you're discovering a new world. A world of wonders, of delight, of misteries.
A world you couldn't even imagine in the past.

This paragraph is TOGGLE paragraph. Am it here, am I gone?


How to play.
Use the arrow keys to move.
Dodge or shoot the oncoming traffic on your bike.
Hitting the cars will increase your score.
If you get hit you will lose power when the power reaches 0 its game over. Your score will be displayed at the end.
How to play.
Use the arrow keys to move.
Dodge or shoot the oncoming traffic on your bike.
Hitting the cars will increase your score.
If you get hit you will lose power when the power reaches 0 its game over. Your score will be displayed at the end.


{% extends "base.html" %} {% block content %} Thanks! Your vote for {{ name }} has been registered. {% endblock content %}


Real-world problems related to the Traveling Salesman Problem often introduce considerations that make the situation far more difficult. For example, it is easy to imagine that price considerations may have to be balanced against salesman morale, customer availability, and market conditions if a real salesman were at issue. But even staying with simple cost functions, symmetry and the triangle inequality may not hold.
What carries over are:
The desire to estimate bounds (i.e. what is the optimal conceivable solution), because that will tell you whether it is worth struggling for more improvements.
Heuristic problem solving techniques.
As an illustration, consider a problem that can be formulated similarly to the Traveling Salesman Problem but requires new analysis.
Given a set of words, a wordsnake is a string containing all the words in the set. The optimal wordsnake is the one of shortest length. For example, given the words “super” and “perfect,” two possible wordsnakes are “superfect” or “perfectsuper.” The first one is shorter, so is better. Your problem is to find an optimal (shortest) wordsnake covering a set of words, given that you are allowed to rearrange the words.
Here are the words:
subway
dentist
wayward
highway
terrible
english
less
blessed
warden
rib
stash
shunt
hunter
What does this have to do with Traveling Salesman? Well, let’s look at edges between pairs of words, for example, “warden” → “english.” We will consider the cost of this edge to be the number of letters of the target word (“english” in this case) that will be needed in the wordsnake that combines these two in this order (i.e., beyond those letters also included in “warden”). For this edge, the wordsnake would have “wardenglish,” so the cost of this edge is the length of “glish” or 5. Note that the edge “english” → “warden” has a cost of 6 (the full length of “warden”). So symmetry need not hold (though the triangle inequality does).
What would be a good lower bound on the length of the optimal wordsnake? Please pause to consider this before you read on.
I would vote for the following: For each word, consider all edges having that word as target. Find the edge having the smallest cost and add up all such smallest costs. For example, consider “shunt.” The lowest cost edge having “shunt” as a target is “stash” → “shunt” at a cost of 3. So associate 3 with “shunt.” Now just add all the associated costs with each word and you have an approximate lower bound to the cost. (It’s approximate because it’s conceivable that a word could be found in a partly constructed wordsnake. For example, if the words were “shunt,” “stash,” “till,” and “until,” then the wordsnake stashuntill would contain “until” even though the lowest cost edge from any single word to “until” has a cost of two.)
How close can you come to this approximate optimal for these words?
How close can you come to this approximate optimal for these words?
Order the words as follows (according to their best edges) and see that you come very close to the lower bound.
subway
wayward
warden
english
shunt
hunter
terrible
rib
blessed
less
dentist
stash
highway
Here is the corresponding wordsnake:
subwaywardenglishunterriblessedentistashighway


Airtel Money
MobileMoney
This is an initiative to collect stories of how people interact with money in different parts of the world so as designers and developers we may do better to understand the difficulties people face with its access and use, and how to make experiences that fit Bitcoin as a global money system in their daily lives.
There is currently a push for Rwandans to go cashless — especially given the COVID19 pandemic, and the risks of people making exchanges in close proximity.
PERSONA D9C7
Time is money, but stay frugal.
PERSONA DBEA
Cash is king, but times are changing.
PERSONA 23C0
Money is my favorite topic to talk about.
PERSONA 0x00
More Comming Soon...
MobileMoney
MobileMoney
Mobile App
Remittance
Remittance
The Eastern Caribbean Central Bank has launched a pilot program for a digital version of the Eastern Caribbean Digital Dollar (XCD) in 5 island nations; Antigua and Barbuda, Grenada, Saint Lucia and St. Kitts.
Mobile App
Mobile App
Mobile App
Remittance
Remittance


[172] The White House, , https://www.whitehouse.gov/briefing- room/statements-releases/2022/08/24/fact-sheet-president-biden-announces-student- loan-relief-for-borrowers-who-need-it-most/, August 2022.


The Problem
- Scarborough food can only say so much alone. Chefs and owners have story to tell, but who is listening?
- Downtown and uptown Toronto dining get all the attention, including the togotoronto.com website
- Scarborough food is often lower margin and paying delivery services 30% is not sustainable, especially during the pandemic


Inception wrote:And what are your settings, to make the game even more awesome?
wasakakero wrote:Best tool ever, someone knows how to config to get max fps without visuals things that i dont need?
PostProcess.DynamicAOEnable 0
Render.DrawFps 1
Render.DrawScreenInfo 0
Render.PerfOverlayEnable 1
Render.PerfOverlayVisible 0
RenderDevice.Dx11Enable 1
RenderDevice.ForceRenderAheadLimit 0
RenderDevice.TripleBufferingEnable 0
Screenshot.Format png
UI.DrawEnable 1
WorldRender.DxDeferredCsPathEnable 0
WorldRender.MotionBlurEnable 0
WorldRender.SpotLightShadowmapResolution 256
WorldRender.TransparencyShadowmapsEnable 0
xXJumpyXx wrote:Use this with FXAA injector and Game Booster to make the game more awesome :)Inception wrote:And what are your settings, to make the game even more awesome?
elementofprg wrote:wasakakero wrote:Best tool ever, someone knows how to config to get max fps without visuals things that i dont need?
To help push the max out of BF3 on my slightly aging system. I went into my Program Files(x86)/Origin Games/Battlefield 3/ folder and made a text file and name it user.cfg
In that text file I have:
GameTime.MaxVariableFps 200.000000
PostProcess.DynamicAOEnable 0
Render.DrawFps 1
Render.DrawScreenInfo 0
Render.PerfOverlayEnable 1
Render.PerfOverlayVisible 0
RenderDevice.Dx11Enable 1
RenderDevice.ForceRenderAheadLimit 0
RenderDevice.TripleBufferingEnable 0
Screenshot.Format png
UI.DrawEnable 1
WorldRender.DxDeferredCsPathEnable 0
WorldRender.MotionBlurEnable 0
WorldRender.SpotLightShadowmapResolution 256
WorldRender.TransparencyShadowmapsEnable 0
wasakakero wrote:elementofprg wrote:wasakakero wrote:Best tool ever, someone knows how to config to get max fps without visuals things that i dont need?
To help push the max out of BF3 on my slightly aging system. I went into my Program Files(x86)/Origin Games/Battlefield 3/ folder and made a text file and name it user.cfg
In that text file I have:
GameTime.MaxVariableFps 200.000000
PostProcess.DynamicAOEnable 0
Render.DrawFps 1
Render.DrawScreenInfo 0
Render.PerfOverlayEnable 1
Render.PerfOverlayVisible 0
RenderDevice.Dx11Enable 1
RenderDevice.ForceRenderAheadLimit 0
RenderDevice.TripleBufferingEnable 0
Screenshot.Format png
UI.DrawEnable 1
WorldRender.DxDeferredCsPathEnable 0
WorldRender.MotionBlurEnable 0
WorldRender.SpotLightShadowmapResolution 256
WorldRender.TransparencyShadowmapsEnable 0
i was looking around for the shadowmap option, and i found that if i put the values under than 256 i would get some FPS, im correct?
P.S: does the gamebooster really works? i had been using that a long time ago, and it didnt help me at all to get a "bost"
Dendari wrote:It works for me, but do not expect a great FPS boost.
About those advanced tweaks with the console commands, I've a problem with them, every time I start the game it doesn't care what value I've chosen.
Realmware wrote:Warning! Unlike all of our other tools, BF3 Colour Tweaker could potentially conflict with PunkBuster. Use it at your own risk. See thehttp://bf3.realmware.co.uk/colour-tweaker/faq for more information.


--- layout: post title: A hidden new feature in Service Pack 2 ---
Just noticed while doing some clean-up work (as my machine seems to have gotten into some kind of neurotic reboot sequence…) that there is something new in the Add/Remove control panel…
You can now click on “show updates” and it will show you the patches that were previously hidden. For example, XP Service pack 2 is put under “Windows XP – Software Updates”. How cool is that?
Now my question is, how do you get the installer to know about that?


SLEEPY HOLLOW A film review by Mark R. Leeper
CAPSULE: This is much more a tribute to than an adaptation of the Washington Irving story that has become a children's Halloween classic. SLEEPY HOLLOW is an exquisitely sepulchral horror adventure involving witchcraft and a head-hunting Hessian headless horseman from hell. This poetic and beautifully realized film surpasses all of director Tim Burton's earlier work but THE NIGHTMARE BEFORE CHRISTMAS. Rating: 8 (0 to 10), high +2 (-4 to +4) Spoiler warning: the review is followed by some comments on the film intended for people who have already seen the film, one of which could be a serious spoiler.
One of the greatest horror films ever made is Edgar Ulmer's 1934 THE BLACK CAT. It is a delightful black comedy, an exercise in mordant horror starring both Boris Karloff and Bela Lugosi in their first film together. It claims to be based on the classic story by Edgar Allan Poe, but one can search for hours through the film and not find anything that Poe would recognize. One could hardly call it a retelling of the Poe. It is a little easier to find elements of Washington Irving's "Legend of Sleepy Hollow" in Tim Burton's new SLEEPY HOLLOW, but it is amazing to see how many people are calling this a retelling of the Irving. It most certainly is not. Instead the story has been disassembled and many of the pieces have been used in telling the story of what might be a supernatural killer bringing sleepless nights to the little New York State village of Sleepy Hollow in 1799.
Ichabod Crane (played by Johnny Depp) is an enlightened police detective and reformer from New York City. He is excited about the coming of the new century. (He calls it a new "millennium," indicating that there must have been a bunch of people working on this film who did not realize that new centuries and new millennia do not always come at the same time. So not everything scary about this film is intentional.) Though Crane himself is afraid of the sight of blood-- and seemingly even his own shadow--he believes that new scientific methods will revolutionize police detective work. He even has invented for examining evidence a set of instruments that would do David Cronenberg proud. However, his methods are not welcomed by the local constable and burgomaster (cameos by Alun Armstrong and Christopher Lee). Crane is sent by carriage upstate to investigate some recent mysterious killings in Sleepy Hollow. Three decapitation murders have taken place in the little village and Crane is to report to prominent citizen Baltus Van Tassel (Michael Gambon) and begin his investigation. And if scientific methods can be employed, so much the better.
Physically, Depp is all wrong to play the gangly Ichabod Crane. However the queasy detective shows great internal dissonance when the strength of his enlightened ideas push against his physical and psychological weaknesses. And he tries to hide his distress, not very successfully, with his cold-fish demeanor. The strain of this police case eventually is really too much for him and he finds he is cracking under the strain. It is a delightful change to have a vulnerable hero who has self-doubts and weaknesses. Perfectly matching the spirit of the film is a terrific Michael Gambon. He must have spent hours in front of a mirror achieving just the perfect facial expression for each of his scenes, frequently conveying at the same time disdain, disgust, and fear. His daughter, Katrina Van Tassel, is Christina Ricci who in spite of her buxom appearance still looks a lot like Little Wednesday from THE ADDAMS FAMILY. Perhaps that is a mold she will take a long time breaking. Miranda Richardson quietly plays Van Tassel's wife, but is clearly the source of most of Van Tassel's strength. When the Hessian is given his head he is played by Christopher Walken who appears to be having a grand time of it. Rumor has it that Tim Burton is so introverted that he wants to deal with as few new actors per film as possible. As a result many of his actors get used over and over. Familiar faces include Depp, of course, but also Jeffrey Jones and Lisa Marie, all from Ed Woods's entourage. There are more familiar faces including Michael Gough, Ian McDiarmid, and Casper Van Dien.
Tim Burton is as much a visual stylist as a director. His particular visual style is recognizable and goes back to his amateur film FRANKENWEENIE. In this film he uses that style a little less, but it is still recognizable. The visual aspects of this film are, in fact, its greatest attraction. This is a nice film to look at. Frequently large mattes are used to create scenes, but in a departure, they are not intended to be realistic. Instead they are either obvious paintings of idealized nature or of smoke-filled scenes of villages setting a tone for the film. Sleepy Hollow is an area of mists and darkness. The few sunny scenes are memories of the past and again at the end of the film when normality returns. Burton intentionally lifts images from classic horror films. The film opens with an image of dripping blood spattering borrowed from the Hammer film (THE HORROR OF) DRACULA. Later an image is borrowed from THE PIT AND THE PENDULUM.
Washington Irving might not have thought much of SLEEPY HOLLOW. But for a horror film, it is poetic and surprisingly pleasurable. As it rarely does when I am watching a film it occurred to me at about the halfway point that I was really enjoying the film. Even with all its faults, this film is fun. I give it 8 on the 0 to 10 scale and a high +2 on the -4 to +4 scale.
SPOILER WARNING... SPOILER WARNING... SPOILER WARNING...
There are several points I would fine-tune with this film including: 1) The birdcage on the disk is always filmed as if it is right side up including at the end when it is filmed from both front and back. Seen from the front the cage and bird should be upside-down if the user sees it right side up. 2) The ending of the film is completely given away much too soon by the voice of the witch in the woods. Anyone who saw the very good TV-movie MERLIN will have heard the same false voice used extensively. 3) Toward the end of the film a clue is presented and interpreted. Crane says that a dead body will not bleed. And starting a short time after the death of the body that is true. But if the filmmakers were aware of that fact, why did they have the earlier pregnant victim's body bleed when it was dug up? They should have known it could not do that.
Mark R. Leeper<EMAIL_ADDRESS>Copyright 1999 Mark R. Leeper
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Array of search objects (submitted to data source for record filtering).
Array, default = []
When user executes a search the grid will generate the array of search objects. User can execute a search in a variety of ways:
by typing in global search field, selecting a field and typing search text, or clicking the "Search..." button and submitting a
multi-field search. Regardless of the way, the grid will generate the search array.
As a developer you can define initial searches during object creation:
Even though you created initial search, the user can clear or overwrite it. You can defined some search fields as hidden if you want
to prevent user from messing up your search, or you can add search data into .postData
object which will get submitted to the server and will not allow user to clear it.
The structure of the search object is:
You can use .search() method to submit searches.


Metaplasticity is a term describing the likelihood that a given stimulus will induce neuronal plasticity, based on the previous activity experienced by that neuron. Several studies provide evidence that neurons receiving "priming activity" (such as neurotransmitters, paracrine signals, or hormones) minutes to days prior will show a lower threshold for induction of long term potentiation. Click Here for more info.


standardized-audio-context Demo
This is a little demo page to show how to use standardized-audio-context.
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