SUBROUTINE DSGESV( N, NRHS, A, LDA, IPIV, B, LDB, X, LDX, WORK, + SWORK, ITER, INFO) * * -- LAPACK PROTOTYPE driver routine (version 3.1.1) -- * Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. * February 2007 * * .. * .. WARNING: PROTOTYPE .. * This is an LAPACK PROTOTYPE routine which means that the * interface of this routine is likely to be changed in the future * based on community feedback. * * .. * .. Scalar Arguments .. INTEGER INFO,ITER,LDA,LDB,LDX,N,NRHS * .. * .. Array Arguments .. INTEGER IPIV(*) REAL SWORK(*) DOUBLE PRECISION A(LDA,*),B(LDB,*),WORK(N,*),X(LDX,*) * .. * * Purpose * ======= * * DSGESV computes the solution to a real system of linear equations * A * X = B, * where A is an N-by-N matrix and X and B are N-by-NRHS matrices. * * DSGESV first attempts to factorize the matrix in SINGLE PRECISION * and use this factorization within an iterative refinement procedure to * produce a solution with DOUBLE PRECISION normwise backward error * quality (see below). If the approach fails the method switches to a * DOUBLE PRECISION factorization and solve. * * The iterative refinement is not going to be a winning strategy if * the ratio SINGLE PRECISION performance over DOUBLE PRECISION performance * is too small. A reasonable strategy should take the number of right-hand * sides and the size of the matrix into account. This might be done with a * call to ILAENV in the future. Up to now, we always try iterative refinement. * * The iterative refinement process is stopped if * ITER > ITERMAX * or for all the RHS we have: * RNRM < SQRT(N)*XNRM*ANRM*EPS*BWDMAX * where * o ITER is the number of the current iteration in the iterative * refinement process * o RNRM is the infinity-norm of the residual * o XNRM is the infinity-norm of the solution * o ANRM is the infinity-operator-norm of the matrix A * o EPS is the machine epsilon returned by DLAMCH('Epsilon') * The value ITERMAX and BWDMAX are fixed to 30 and 1.0D+00 respectively. * * Arguments * ========= * * N (input) INTEGER * The number of linear equations, i.e., the order of the * matrix A. N >= 0. * * NRHS (input) INTEGER * The number of right hand sides, i.e., the number of columns * of the matrix B. NRHS >= 0. * * A (input or input/ouptut) DOUBLE PRECISION array, * dimension (LDA,N) * On entry, the N-by-N coefficient matrix A. * On exit, if iterative refinement has been successfully used * (INFO.EQ.0 and ITER.GE.0, see description below), then A is * unchanged, if double precision factorization has been used * (INFO.EQ.0 and ITER.LT.0, see description below), then the * array A contains the factors L and U from the factorization * A = P*L*U; the unit diagonal elements of L are not stored. * * LDA (input) INTEGER * The leading dimension of the array A. LDA >= max(1,N). * * IPIV (output) INTEGER array, dimension (N) * The pivot indices that define the permutation matrix P; * row i of the matrix was interchanged with row IPIV(i). * Corresponds either to the single precision factorization * (if INFO.EQ.0 and ITER.GE.0) or the double precision * factorization (if INFO.EQ.0 and ITER.LT.0). * * B (input) DOUBLE PRECISION array, dimension (LDB,NRHS) * The N-by-NRHS matrix of right hand side matrix B. * * LDB (input) INTEGER * The leading dimension of the array B. LDB >= max(1,N). * * X (output) DOUBLE PRECISION array, dimension (LDX,NRHS) * If INFO = 0, the N-by-NRHS solution matrix X. * * LDX (input) INTEGER * The leading dimension of the array X. LDX >= max(1,N). * * WORK (workspace) DOUBLE PRECISION array, dimension (N*NRHS) * This array is used to hold the residual vectors. * * SWORK (workspace) REAL array, dimension (N*(N+NRHS)) * This array is used to use the single precision matrix and the * right-hand sides or solutions in single precision. * * ITER (output) INTEGER * < 0: iterative refinement has failed, double precision * factorization has been performed * -1 : taking into account machine parameters, N, NRHS, it * is a priori not worth working in SINGLE PRECISION * -2 : overflow of an entry when moving from double to * SINGLE PRECISION * -3 : failure of SGETRF * -31: stop the iterative refinement after the 30th * iterations * > 0: iterative refinement has been sucessfully used. * Returns the number of iterations * * INFO (output) INTEGER * = 0: successful exit * < 0: if INFO = -i, the i-th argument had an illegal value * > 0: if INFO = i, U(i,i) computed in DOUBLE PRECISION is * exactly zero. The factorization has been completed, * but the factor U is exactly singular, so the solution * could not be computed. * * ========= * * .. Parameters .. DOUBLE PRECISION NEGONE,ONE PARAMETER (NEGONE=-1.0D+0,ONE=1.0D+0) * * .. Local Scalars .. LOGICAL DOITREF INTEGER I,IITER,ITERMAX,OK,PTSA,PTSX DOUBLE PRECISION ANRM,BWDMAX,CTE,EPS,RNRM,XNRM * * .. External Subroutines .. EXTERNAL DAXPY,DGEMM,DLACPY,DLAG2S,SLAG2D, + SGETRF,SGETRS,XERBLA * .. * .. External Functions .. INTEGER IDAMAX DOUBLE PRECISION DLAMCH,DLANGE EXTERNAL IDAMAX,DLAMCH,DLANGE * .. * .. Intrinsic Functions .. INTRINSIC ABS,DBLE,MAX,SQRT * .. * .. Executable Statements .. * ITERMAX = 30 BWDMAX = 1.0E+00 DOITREF = .TRUE. * OK = 0 INFO = 0 ITER = 0 * * Test the input parameters. * IF (N.LT.0) THEN INFO = -1 ELSE IF (NRHS.LT.0) THEN INFO = -2 ELSE IF (LDA.LT.MAX(1,N)) THEN INFO = -4 ELSE IF (LDB.LT.MAX(1,N)) THEN INFO = -7 ELSE IF (LDX.LT.MAX(1,N)) THEN INFO = -9 END IF IF (INFO.NE.0) THEN CALL XERBLA('DSGESV',-INFO) RETURN END IF * * Quick return if (N.EQ.0). * IF (N.EQ.0) RETURN * * Skip single precision iterative refinement if a priori slower * than double precision factorization. * IF (.NOT.DOITREF) THEN ITER = -1 GO TO 40 END IF * * Compute some constants. * ANRM = DLANGE('I',N,N,A,LDA,WORK) EPS = DLAMCH('Epsilon') CTE = ANRM*EPS*SQRT(DBLE(N))*BWDMAX * * Set the pointers PTSA, PTSX for referencing SA and SX in SWORK. * PTSA = 1 PTSX = PTSA + N*N * * Convert B from double precision to single precision and store the * result in SX. * CALL DLAG2S(N,NRHS,B,LDB,SWORK(PTSX),N,INFO) * IF (INFO.NE.0) THEN ITER = -2 GO TO 40 END IF * * Convert A from double precision to single precision and store the * result in SA. * CALL DLAG2S(N,N,A,LDA,SWORK(PTSA),N,INFO) * IF (INFO.NE.0) THEN ITER = -2 GO TO 40 END IF * * Compute the LU factorization of SA. * CALL SGETRF(N,N,SWORK(PTSA),N,IPIV,INFO) * IF (INFO.NE.0) THEN ITER = -3 GO TO 40 END IF * * Solve the system SA*SX = SB. * CALL SGETRS('No transpose',N,NRHS,SWORK(PTSA),N,IPIV, + SWORK(PTSX),N,INFO) * * Convert SX back to double precision * CALL SLAG2D(N,NRHS,SWORK(PTSX),N,X,LDX,INFO) * * Compute R = B - AX (R is WORK). * CALL DLACPY('All',N,NRHS,B,LDB,WORK,N) * CALL DGEMM('No Transpose','No Transpose',N,NRHS,N,NEGONE,A,LDA,X, + LDX,ONE,WORK,N) * * Check whether the NRHS normwised backward errors satisfy the * stopping criterion. If yes, set ITER=0 and return. * DO I = 1,NRHS XNRM = ABS(X(IDAMAX(N,X(1,I),1),I)) RNRM = ABS(WORK(IDAMAX(N,WORK(1,I),1),I)) IF (RNRM.GT.XNRM*CTE) GOTO 10 END DO * * If we are here, the NRHS normwised backward errors satisfy the * stopping criterion. We are good to exit. * ITER = 0 RETURN * 10 CONTINUE * DO 30 IITER = 1,ITERMAX * * Convert R (in WORK) from double precision to single precision * and store the result in SX. * CALL DLAG2S(N,NRHS,WORK,N,SWORK(PTSX),N,INFO) * IF (INFO.NE.0) THEN ITER = -2 GO TO 40 END IF * * Solve the system SA*SX = SR. * CALL SGETRS('No transpose',N,NRHS,SWORK(PTSA),N,IPIV, + SWORK(PTSX),N,INFO) * * Convert SX back to double precision and update the current * iterate. * CALL SLAG2D(N,NRHS,SWORK(PTSX),N,WORK,N,INFO) * CALL DAXPY(N*NRHS,ONE,WORK,1,X,1) * * Compute R = B - AX (R is WORK). * CALL DLACPY('All',N,NRHS,B,LDB,WORK,N) * CALL DGEMM('No Transpose','No Transpose',N,NRHS,N,NEGONE,A, + LDA,X,LDX,ONE,WORK,N) * * Check whether the NRHS normwised backward errors satisfy the * stopping criterion. If yes, set ITER=IITER>0 and return. * DO I = 1,NRHS XNRM = ABS(X(IDAMAX(N,X(1,I),1),I)) RNRM = ABS(WORK(IDAMAX(N,WORK(1,I),1),I)) IF (RNRM.GT.XNRM*CTE) GOTO 20 END DO * * If we are here, the NRHS normwised backward errors satisfy the * stopping criterion, we are good to exit. * ITER = IITER * RETURN * 20 CONTINUE * 30 CONTINUE * * If we are at this place of the code, this is because we have * performed ITER=ITERMAX iterations and never satisified the stopping * criterion, set up the ITER flag accordingly and follow up on double * precision routine. * ITER = -ITERMAX - 1 * 40 CONTINUE * * Single-precision iterative refinement failed to converge to a * satisfactory solution, so we resort to double precision. * CALL DGETRF(N,N,A,LDA,IPIV,INFO) * CALL DLACPY('All',N,NRHS,B,LDB,X,LDX) * IF (INFO.EQ.0) THEN CALL DGETRS('No transpose',N,NRHS,A,LDA,IPIV,X,LDX,INFO) END IF * RETURN * * End of DSGESV. * END


"JABNEEL "built by God. (1.) A town in the north boundary of Judah(Joshua 15:11), called afterwards by the Greeks Jamnia, the modern Yebna, 11 miles south of Jaffa. After the fall of Jerusalem (A.D. 70), it became one of the most populous cities of Judea, and the seat of a celebrated school. (2.) A town on the border of Naphtali (Joshua 19:33). Its later name was Kefr Yemmah, “the village by the sea,” on the south shore of Lake Merom.
"JABNEEL "built by God. (1.) A town in the north boundary of Judah(Joshua 15:11), called afterwards by the Greeks Jamnia, the modern Yebna, 11 miles south of Jaffa. After the fall of Jerusalem (A.D. 70), it became one of the most populous cities of Judea, and the seat of a celebrated school.
(2.) A town on the border of Naphtali (Joshua 19:33). Its later name was Kefr Yemmah, “the village by the sea,” on the south shore of Lake Merom.


Dictionary Class Reference
A simple dictionary class.
More...
#include <scowl.hpp>
List of all members.
Public Member Functions |
| | Dictionary (void) |
| | Initialize as empty dictionary.
|
| void | init (const char *fn) |
| | Perform actual initialization.
|
| int | len (void) const |
| | Return maximal length of a word.
|
| int | words (void) const |
| | Return total number of words.
|
| int | words (int l) const |
| | Return number of words with length l.
|
| const char * | word (int l, int i) const |
| | Return word number i with length l.
|
| template<class Char , class Traits > |
std::basic_ostream< Char,
Traits > & | print (std::basic_ostream< Char, Traits > &os) const |
| | Print statistics summary.
|
| | ~Dictionary (void) |
| | Destructor.
|
Protected Attributes |
| unsigned int | max_len |
| | Actual maximal length in dictionary.
|
| int | n_all_words |
| | Total number of words.
|
| int | n_words [limit_len] |
| | Number of words of some length.
|
| char * | s_words [limit_len] |
| | Beginning of words of some length.
|
| char * | chunk |
| | One big memory chunk for storing words.
|
Static Protected Attributes |
| static const unsigned int | limit_len = 64 |
| | Maximal word length support.
|
Detailed Description
A simple dictionary class.
Definition at line 53 of file scowl.hpp.
Constructor & Destructor Documentation
| Dictionary::Dictionary |
( |
void |
|
) |
[inline] |
Initialize as empty dictionary.
Definition at line 13478 of file scowl.hpp.
| Dictionary::~Dictionary |
( |
void |
|
) |
[inline] |
Member Function Documentation
| void Dictionary::init |
( |
const char * |
fn |
) |
[inline] |
Perform actual initialization.
Reads words from file with name fn. If fn is NULL, the predefined dictionary is used.
Definition at line 13486 of file scowl.hpp.
| int Dictionary::len |
( |
void |
|
) |
const [inline] |
Return maximal length of a word.
Definition at line 13595 of file scowl.hpp.
| int Dictionary::words |
( |
void |
|
) |
const [inline] |
Return total number of words.
Definition at line 13599 of file scowl.hpp.
| int Dictionary::words |
( |
int |
l |
) |
const [inline] |
Return number of words with length l.
Definition at line 13603 of file scowl.hpp.
| const char * Dictionary::word |
( |
int |
l, |
|
|
int |
i | |
|
) |
| | const [inline] |
Return word number i with length l.
Definition at line 13607 of file scowl.hpp.
template<class Char , class Traits >
| std::basic_ostream< Char, Traits > & Dictionary::print |
( |
std::basic_ostream< Char, Traits > & |
os |
) |
const [inline] |
Member Data Documentation
Maximal word length support.
Definition at line 56 of file scowl.hpp.
Actual maximal length in dictionary.
Definition at line 58 of file scowl.hpp.
Total number of words.
Definition at line 60 of file scowl.hpp.
Number of words of some length.
Definition at line 62 of file scowl.hpp.
Beginning of words of some length.
Definition at line 64 of file scowl.hpp.
One big memory chunk for storing words.
Definition at line 66 of file scowl.hpp.
The documentation for this class was generated from the following file:


Note: To complete these procedures, you must be assigned a role having the necessary permissions. To determine your role, follow the directions in Participant roles. For a basic understanding of roles and permissions, see Permissions and roles: Overview.
To download grades from the Assignments tool to a spreadsheet:
Assignments
.
Grade Report
.
Note: If the grades for an assignment have not been
released, they will not be displayed in the Grade Report, and they
will not be downloaded to the spreadsheet. To release the grades for
an assignment, from the Assignment List, click Grade
under
the appropriate assignment. Near the top of the Submissions
page,
click Release Grades
. Click Grade Report
to return
to the Grade Report
page.
Download Spreadsheet
.
Note: The downloaded file will be in Microsoft Excel
(i.e., .xls
) format.
The spreadsheet contains students' usernames and full names in the first two columns. Each subsequent column lists a different assignment, and student grades are located in the corresponding cells within each column. "No Submission" indicates the assignment has not been submitted and no grade is present; if it has been submitted but not graded, the cell is blank. The spreadsheet does not tally assignment grades.


Math facts - Multiplication Timed Quiz Game
MATH Facts Home Page
Start Multiplication Quiz
End Quiz
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Wrong
Time
Missed
Date


SOCQI SOCQI SOCQI OVERVIEW language Website: SOCQI.com Address 173 ½ East Franklin Street Chapel Hill, NC 27514 Summary SOCQI is a peer-to-peer lending platform that allows indidviduals to invest directly in renewable energy projects.


Last Revised May 16, 2018. Effective May 25, 2018.
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The HTML for step by step lists that appear in sidebars is slightly different to step by step lists that appear in normal content.
As space in the sidebar is limited we have reduced the size of the numbering, the amount of space around each number and the font size of text within each dropdown area.


function [y, dfce] = bayescls( X, model )
% BAYESCLS Bayesian classifier with reject option.
%
% Synopsis:
% [y, dfce] = bayescls(X,model)
%
% Description:
% This function implements the classifier minimizing the Bayesian risk
% with 0/1-loss function. It corresponds to the minimization of
% probability of misclassification. The input vectors X are classified
% into classes with the highest a posterior probabilities computed from
% given model.
%
% The model contains parameters of conditional class probabilities
% in model.Pclass [cell 1 x num_classes] and a priory probabilities
% in model.Prior [1 x num_classes].
%
% The function
% p = feval(model.Pclass{i}.fun, X, model.pclass{i})
% is called to evaluate the i-the class conditional probability of X.
%
% It returns class labels y [1 x num_data] for each input vector
% and matrix dfce [num_class x num_data] of unnormalized a posterior
% probabilities
% dfce(y,i) = Conditional_probability(X(:,i)|y)*Prior(y).
%
% If the field model.eps exists then the Bayesian classifier
% with the reject option is used. The eps is penalty for the
% decision "don't know" which is indicated by label y = 0.
%
% Input:
% X [dim x num_data] Vectors to be classified.
%
% model [struct] Describes probabilistic model:
% .Pclass [cell 1 x num_classes] Class conditional probabilities.
% .Prior [1 x num_classes] A priory probabilities.
%
% .eps [1x1] (optional) Penalty of decision "don't know".
%
% Output:
% y [1 x num_data] Labels (1 to num_classes); 0 for "don't know".
% dfce [num_classes x num_data] Unnormalized a posterior
% probabilities (see above).
%
% Example:
% trn = load('riply_trn');
% tst = load('riply_tst');
% inx1 = find(trn.y==1);
% inx2 = find(trn.y==2);
% model.Pclass{1} = mlcgmm(trn.X(:,inx1));
% model.Pclass{2} = mlcgmm(trn.X(:,inx2));
% model.Prior = [length(inx1) length(inx2)]/(length(inx1)+length(inx2));
% ypred = bayescls(tst.X,model);
% cerror(ypred,tst.y)
%
% See also
% BAYESDF, BAYESERR.
%
% About: Statistical Pattern Recognition Toolbox
% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac
% Czech Technical University Prague
% Faculty of Electrical Engineering
% Center for Machine Perception
% Modifications:
% 09-jun-2004, VF
% 01-may-2004, VF
% 11-mar-2004, VF, "don't" know decision added.
% 19-sep-2003, VF
[dim,num_data]=size(X);
num_classes = length( model.Pclass );
dfce=zeros(num_classes,num_data);
for i=1:num_classes,
dfce(i,:) = model.Prior(i)*feval(model.Pclass{i}.fun,X,model.Pclass{i});
end
[tmp,y] = max(dfce);
if isfield(model, 'eps'),
perror = 1-tmp./sum(dfce,1);
inx = find( perror > model.eps);
y(inx) = 0;
end
return;


| Created: | 6/26/2020 3:16:14 PM |
| Modified: | 6/26/2020 3:16:14 PM |
| Project: | |
| Advanced: |
| Attribute |
|
Public bigint summary_program_type_id |
|
Details:
|
|
Public varchar(255) summary_program_type_description |
|
Details:
|
|
Public varchar(255) created_by |
|
Details:
|
|
Public timestamp with time zone(0) created_at |
|
Details:
|
|
Public timestamp with time zone(0) updated_at |
|
Details:
|
|
Public varchar(255) updated_by |
|
Details:
|
|
Public timestamp with time zone(0) start_date |
|
Details:
|
|
Public timestamp with time zone(0) end_date |
|
Details:
|
|
Public bigserial id |
|
Details:
|
|
Public jsonb ods_meta |
|
Details:
|
|
Public integer ods_version |
|
Details:
|
|
Public varchar(255) ods_created_by |
|
Details:
|
|
Public timestamp with time zone(0) ods_created_at |
|
Details:
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| Operation | ||
|
Public summary_program_types_pkey( id: bigserial, |
||
|
Details:
<<PK>>
|
| Tag | Value |
| Owner | public |
|
Details:
|
| Property | Value |
| isFinalSpecialization: | 0 |
| Object | Type | Connection | Direction | Notes |
| «view» current_summary_program_types_view | View | Dependency | From | |
| «view» current_summary_program_types_view | View | Dependency | From |


--- layout: post tags: [Sync] date: 2009-05-21 18:54:01 UTC title: "SyncOrchestrator.Synchronize() Throws COMException When Unit Testing" --- {% include JB/setup %}
This post describes a particular problem I ran into when working with the Microsoft Sync Framework. Since I found a solution, I'm sharing it here to help others. If you are not having this particular problem, it's quite safe to skip reading the rest of the post :)
While developing a SyncProvider, I wanted to create and execute a series of Integration Tests to drive my development effort. In order to do that, I wrote a simple test that simply created a SyncOrchestrator instance and invoked its Synchronize method.
Running this test gave me this error message:
“Microsoft.Synchronization.SyncException: Retrieving the COM class factory for component with CLSID {A7B3B4EE-925C-4D6C-B007-A4A6A0B09143} failed due to the following error: 80040154. ---> System.Runtime.InteropServices.COMException: Retrieving the COM class factory for component with CLSID {A7B3B4EE-925C-4D6C-B007-A4A6A0B09143} failed due to the following error: 80040154.”
It's not often I see a COMException these days, so I was initially baffled. Since the Sync Framework also has an unmanaged API, this is really not surprising, but that didn't help me solve my problem.
What was even weirder was that when I tried running the same code in my application, this exception was not being thrown.
It took me a couple of hours to figure out what the problem was.
Here's a little hint: I'm running Windows Vista x64.
No: The issue is not that I'm running Vista :)
Even on x64, Visual Studio runs as a 32-bit process, and so does MSTest. Since my code was compiled to Any CPU, the application itself was running in a 64-bit process, whereas my unit test was running in a 32-bit process.
I tried changing my build output to x86, and now the application started throwing the same exception as the unit test did.
In other words: When running in a 64-bit process, everything worked as intended. When running in a 32-bit process, a COMException was thrown.
As it turned out, I had only installed the 64-bit version of the Sync Framework, and even though the SDK seems to contain builds for the other architectures as well, the COM Server wasn't properly registered for 32-bit use.
To resolve this issue, I downloaded and installed the x86 version of the Sync Framework as well, and the problem went away.
If you are having the same problem, I hope this post helps you resolve it.
Comments
Thank you for your message - I am happy that my post was helpful to you.
I was trying to find out why SharePoint Workspace did not work on my workstation. It was failing with the "Class not found" 80040154 error, and I found your article with a Google search on the CLSID. Installing Microsoft Sync Framework 1.0 SP1 fixed the problem, and I would never have known where to look without your article.


Much like many things, there is a generational gap present in gamers. Older gamers more commonly play more casual games usually on their phone and often not against other people. Whereas younger gamers tend to play more multiplayer games and are more focused on the action genre and represent the vast majority of those who play shooters. The biggest difference between millenials and gen Z is present in the fact that gen Z are more likely to have grown up with video games as they started having a massive market presence around the time gen Zers would be old enough to start playing them.
Alright some hardcore gamers may tell you otherwise, people who play mobile games are in fact included in those who play games. Mobile games are not often played with others but when they are we see this generational gap there as well. An increasingly popular genre of game is the party game, lots of which are made by Jackbox games. This genre of game has one central computer that runs things and then everyone else plays on their phones. They are often based on board games of a similar vein. These are popular with mostly millenials but some gen Z as well.


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| Fig. 1 |
Soil carbon decomposition models with distinct structures. a ... |
| Fig. 2 |
Violin plot of the accepted model parameter values in the th ... |
| Fig. 3 |
Difference between estimated mean soil carbon and the soil c ... |
| Fig. 4 |
Changes in global total soil carbon under RCP 8.5. 1000 para ... |
| Fig. 5 |
The relationships between soil C, model parameters, and init ... |


Farage says he will not stand for MP in election – video
by BBC | Andrew MarrNigel Farage has announced he will not make an eighth attempt to become an MP in December’s general election. ‘I have thought very hard about this: how do I serve the cause of Brexit best,’ the Brexit party leader told the BBC’s Andrew Marr. He said he would help campaign for his party’s 600 candidates in the election


Even though I have used Markov Decision Processes and I know of the bandit problem framework, suddenly I realized that I could not relate these together with reinforcement learning. That meant that there was reading and learning in order. After spending some time on the wiki page and going nowhere, I turned to this good tutorial; and I decided to post what I learnt. The author has given a few good examples, but I wanted to formulate my own so that I can paint a better picture of reinforcement learning and where MDPs and bandit problems fit in the puzzle.
Supervised Learning
Consider a child, to whom you want to teach what is a bicycle using a set of images some of which contain bicycles and some do not. You start showing the child (toddler really) the pictures and tell her whether each image contains a bicycle or not. Eventually the child will learn to identify a bicycle because of your supervision. You have just completed supervised learning, and you could try to do something similar to a computer.
Reinforcement Learning
Now suppose you want to teach the child (she is much older now) how to ride a bicycle. One brutal approach is to put her on a bicycle and let her go on her own while merely tell her that she can either turn the handle bar to the left or to the right, or to shift her body to either side to prevent falling. She may fall a few times and recognize what to do and what not to do, and eventually after a few bruises would have learnt how to ride the bicycle. This is an example of reinforcement learning. For more understanding, read the following example from the tutorial:
To provide the intuition behind reinforcement learning consider the problem of learning to ride a bicycle. The goal given to the RL system is simply to ride the bicycle without falling over. In the first trial, the RL system begins riding the bicycle and performs a series of actions that result in the bicycle being tilted 45 degrees to the right. At this point their are two actions possible: turn the handle bars left or turn them right. The RL system turns the handle bars to the left and immediately crashes to the ground, thus receiving a negative reinforcement. The RL system has just learned not to turn the handle bars left when tilted 45 degrees to the right. In the next trial the RL system performs a series of actions that again result in the bicycle being tilted 45 degrees to the right. The RL system knows not to turn the handle bars to the left, so it performs the only other possible action: turn right. It immediately crashes to the ground, again receiving a strong negative reinforcement. At this point the RL system has not only learned that turning the handle bars right or left when tilted 45 degrees to the right is bad, but that the “state” of being titled 45 degrees to the right is bad. Again, the RL system begins another trial and performs a series of actions that result in the bicycle being tilted 40 degrees to the right. Two actions are possible: turn right or turn left. The RL system turns the handle bars left which results in the bicycle being tilted 45 degrees to the right, and ultimately results in a strong negative reinforcement. The RL system has just learned not to turn the handle bars to the left when titled 40 degrees to the right. By performing enough of these trial-and-error interactions with the environment, the RL system will ultimately learn how to prevent the bicycle from ever falling over.
So as you can see here, in supervised learning, explicit input is provided whereas this is not the case with reinforcement learning and is a lot harder. Generally, a set of possible actions are specified and a notion of reward (reinforcement) is defined, and the goal is to maximize the cumulative reward by carefully choosing the actions. Falling down gets the child negative reward and probably being able to bike longer gets her positive reward. In a way, there is some input given to reinforcement learning and so it can be considered as a superset of supervised learning.
Unsupervised Learning
In case you are wondering whether learning the bicycle is an example of unsupervised learning, it is not. The goal of unsupervised learning is to find hidden patterns and structures in unlabeled data. Example: I give you four numbers {-101, -100, 100, 101}, can you find groups in this data? I am sure your first answer is to find two groups, the set of negative numbers and that of the positive ones. It was easy for you, but how does a computer do it? This is where unsupervised learning comes into pictures. Some of the tools that may be used for unsupervised learning are:
If you let the child look at the photos may be she will be able to identify that there is a particular object (the bicycle) in a few photos.
Here is an excellent lecture by Andrew Ng of Stanford on reinforcement learning:
Some of the other examples that Andew Ng states for reinforcement learning:
- Training a dog by saying
good dog
andbad dog
. - Training a computer to play chess by assigning positive reward for winning and negative for losing. After many games, the hope is to get the computer win more often.
- Training a helicopter by assigning positive reward to fly and negative reward if it crashes.
MDPs & Bandits
One way of attacking a reinforcement learning problem is to take random actions at each point of time, completely ignoring the rewards. But in most cases, that is not going to take you anywhere. So in most cases, reinforcement learning problems are formulated as Markov Decision Processes (MDPs). I will talk about it in a bit more detail in a different post.
Also, it turns out that the multi-armed bandit problem (again, it will be a separate post) is just a one state MDP where the actions correspond to the arms. The goal is to maximize the expected reward (or minimize the regret) while at each step picking one of the arms.
So there you go, I have tried to put together reinforcement learning, MDPs and bandit problems into the same context. If you find anything missing or have any other suggestions, please drop a comment!


torchfm.dataset¶
torchfm.dataset.avazu¶
-
class
torchfm.dataset.avazu.
AvazuDataset
(dataset_path=None, cache_path='.avazu', rebuild_cache=False, min_threshold=4)[source]¶ Avazu Click-Through Rate Prediction Dataset
- Dataset preparation
Remove the infrequent features (appearing in less than threshold instances) and treat them as a single feature
- Parameters
dataset_path – avazu train path
cache_path – lmdb cache path
rebuild_cache – If True, lmdb cache is refreshed
min_threshold – infrequent feature threshold
torchfm.dataset.criteo¶
-
class
torchfm.dataset.criteo.
CriteoDataset
(dataset_path=None, cache_path='.criteo', rebuild_cache=False, min_threshold=10)[source]¶ Criteo Display Advertising Challenge Dataset
- Data prepration:
Remove the infrequent features (appearing in less than threshold instances) and treat them as a single feature
Discretize numerical values by log2 transformation which is proposed by the winner of Criteo Competition
- Parameters
dataset_path – criteo train.txt path.
cache_path – lmdb cache path.
rebuild_cache – If True, lmdb cache is refreshed.
min_threshold – infrequent feature threshold.
torchfm.dataset.movielens¶
-
class
torchfm.dataset.movielens.
MovieLens1MDataset
(dataset_path)[source]¶ MovieLens 1M Dataset
- Data preparation
treat samples with a rating less than 3 as negative samples
- Parameters
dataset_path – MovieLens dataset path
- Reference:


Go to Slide 2
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Yes, any size of content can be here.
It will always be centered vertically and horizontally.
Any size of content can be added here too.
Go to Slide 3. SLIDE BOTTOM
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End the slideshow. SLIDE SQUASH
End of slideshow. SLIDE SQUASH


Malcolm Macdonald's bitter-sweet night: England note book as Three Lions book place at Euro 2020
What Newcastle United fans may have spotted during England's big night at Wembley
by Lee RyderEngland have booked their place in the finals of Euro 2020 thanks to an emphatic victory over Montenegro at Wembley.
And there were plenty of talking points as Gareth Southgate's men cruised to three points and ensure Three Lions fans have something to shout next summer.
Here's a few things we noticed on the night as the FA celebrated 1,000 international matches.
Gazza enjoys the acclaim from a packed house at Wembley
The Geordie and England legend was enjoying himself during a half-time parade as he laughed and joked with Euro 96 team-mates such as David Seaman and Tony Adams.
Gazza won the hearts and minds of the nation in Italia 90 and in the European Championships on English soil six years later.
After the game Gazza, who stood side by side with the former Arsenal shot-stopper, said in the mixed zone: "Everybody talks about my goal against Scotland at Wembley!
"People forget Dave saved the penalty and it was the best in 5,000 years at Wembley!
"The Dentist's chair celebration was planned for whoever scored next, and it was me."
Harry Kane rises past greatness but Gary Lineker makes a good point
Kane netted a treble inside 37 minutes and went past Alan Shearer, Frank Lampard, Nat Lofthouse and Tom Finney in the process to take his tally to 31.
Roy Keane commented on the difference between the quality of opposition by joking during ITV's coverage that he could possible overtake all-time leading scorer Wayne Rooney in the second half, but Lineker quickly posted to Twitter: "I would suggest, for context, that Shearer’s goals would’ve been scored against significantly stronger opposition."
Given the standard of some of the opponents for England during Kane's run, such as San Marino, Malta and Panama, people seemed to agree with Lineker after his tweet yielded over 12,500 likes.
Supermac's record of scoring five in a game survives - but he is overlooked by FA for an invite to big night
The Newcastle legend famously grabbed five goals against Cyprus at Wembley in 1975.
With Kane enjoying hat-trick during the half-time breaks Lineker tweeted that Macdonald might be feeling "twitchy" during the break about his record.
But Supermac incredible effort remained intact.
After the game a fan asked Supermac if he had been invited to the celebrations, to which he replied: "Sadly not."


Provides functions for fitting Bayesian monotonic regression models to data.
| Version: | 1.5 |
| Depends: | R (≥ 2.7.0), bootstrap, monreg, R2WinBUGS, coda (≥ 0.17.1) |
| Published: | 2018-06-22 |
| Author: | S. McKay Curtis |
| Maintainer: | S. McKay Curtis <s.mckay.curtis at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Materials: | NEWS |
| In views: | Bayesian |
| CRAN checks: | bisoreg results |
| Reference manual: | bisoreg.pdf |
| Package source: | bisoreg_1.5.tar.gz |
| Windows binaries: | r-devel: bisoreg_1.5.zip, r-release: bisoreg_1.5.zip, r-oldrel: bisoreg_1.5.zip |
| OS X binaries: | r-release: bisoreg_1.5.tgz, r-oldrel: bisoreg_1.5.tgz |
| Old sources: | bisoreg archive |
Please use the canonical form https://CRAN.R-project.org/package=bisoreg to link to this page.


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Secret Agent Collin:
Lookin' like trouble's brewing, eh? Start learnin' its smell, you know. Trouble, that is. Clear as crystals.


Notes¶
The pipeline provides two variations from Haak and colleagues [1]. In particular, this algorithm computes the fingerprints in a different way and it provides the option to use tSNE as manifold learning algorithm. The following sections describe in detail the two variations and present the results.
Fingerprinting¶
Haak and collegues [1] compute the fingerprints for each voxel in the ROI as the correlation between the time-serie represented by that voxel and the time-series obtained from a subspace of the voxels outside of the ROI. The procedure is the following: extract the OxT matrix (where O is the number of out-ROI voxels and T the length of the time-serie) from the fMRI image,reduce the out-ROI dimensionality by singular value decomposition (SVD) to obtain a new (T-1)-dimensional subspace from the O-dimensional space, compute the fingerprints as the Pearson’s correlation coefficients between each in-ROI voxel and the T-1 “voxels” from the new subspace.
The goal of Haak and collegues is to reduce the amount of memory used and build “good” fingerprints. (The fingerprints are good if the similarity measure between the fingerprints of two in-ROI voxels is high when the two voxels have the same correlations with the out-ROI voxels, low when the correlations are different). However, when they reduce the space dimensionality, the correlations between in-ROI and out-ROI voxels are lost; the goodness of the fingerprints now depends on the orientation of the subspace relative to the original space. A more natural way to extract good fingerprints is to use the SVD’s eigenvectors of the entire (in-ROI and out-ROI voxels) space. Since the right eigenvectors provide a mapping between the voxels and the desired subspace, it is possible to use the eta2 coefficients used by Haak and colleagues, this time on the mappings, to compare the voxels.
This implementation exploits this fact by the following steps: reduce the dimensionality of the N-dimensional space (where N is the number of all the voxels that represent the cortex) by SVD, consider the right eigenvectors associated with the in-ROI voxels as fingerprints. This doesn’t require computing the Pearson’s correlation coefficients making the pipeline faster.
Manifold learning¶
The user can choose to use tSNE [2] as manifold learning algorithm—instead of spectral embedding or isomap. tSNE is conceived mainly as a visualization algorithm, nevertheless it provides as output clusters of data that are well separated from each other and can be used to find well-defined connectopies. The reason why it is not used as the default is because it is sensitive to noisy data. Despite tSNE consinstently groups the voxels in the same clusters, the position of the clusters on the output dimension changes; in other words, it can visualize well-defined connectopies, but it doesn’t allow to analytically compare cross-sessions or cross-subjects results. An extension of this pipeline would be to automatically match clusters obtained from different sessions/subjects.
Results¶
The results show equal or better cross-session reproducibility and present specific differences between opposite hemispheres and subjects. The mean normalized root mean squared error (nRMSE) for the first connectopy in Haak and collegues [1] using spectral embedding is 0.061 for the left and 0.067 for the right hemisphere (95% confidence interval of [0.053, 0.070] and [0.056, 0.077] respectively); using isomap is 0.039 for the left and 0.041 for the right hemisphere (95% confidence interval of [0.035, 0.043] and [0.037, 0.046] respectively).
Spectral embedding¶
Cross-session normalized root mean squared error for the left and right hemispheres.
| Subject | nRMSE | |
|---|---|---|
| Left Hemisphere | Right Hemisphere | |
| 100307 | 0.02835 | 0.03866 |
| 103414 | 0.04054 | 0.05987 |
| 105115 | 0.02964 | 0.03264 |
Cross-subject normalized root mean squared error for session 1 and session 2 and for the left and right hemispheres.
| Subjects | Session | nRMSE | |
|---|---|---|---|
| Left Hemisphere | Right Hemisphere | ||
| 100307/103414 | 1 | 0.06109 | 0.06267 |
| 2 | 0.06161 | 0.07023 | |
| 100307/105115 | 1 | 0.06101 | 0.05632 |
| 2 | 0.07289 | 0.06792 | |
| 103414/105115 | 1 | 0.05811 | 0.06199 |
| 2 | 0.08603 | 0.10522 |
The following images show the connectopic mapping for subjects 100307, 103414 and 105115 from the Human Connectome Project. The spatial distribution of the connectopies is very similar to Haak and colleagues’ results.
Connectopies of subject 100307 from three different planes orthogonal to the X, Y, and Z axes.
| Left Hemisphire | Right Hemisphire | |
| Session 1 | ||
| Session 2 |
| Left Hemisphire | Right Hemisphire | |
| Session 1 | ||
| Session 2 |
Connectopies of subject 103414 from three different planes orthogonal to the X, Y, and Z axes.
| Left Hemisphire | Right Hemisphire | |
| Session 1 | ||
| Session 2 |
| Left Hemisphire | Right Hemisphire | |
| Session 1 | ||
| Session 2 |
Connectopies of subject 105115 from three different planes orthogonal to the X, Y, and Z axes.
| Left Hemisphire | Right Hemisphire | |
| Session 1 | ||
| Session 2 |
| Left Hemisphire | Right Hemisphire | |
| Session 1 | ||
| Session 2 |
Isomap embedding¶
Cross-session normalized root mean squared error for the left and right hemispheres.
| Subject | nRMSE | |
|---|---|---|
| Left Hemisphere | Right Hemisphere | |
| 100307 | 0.01386 | 0.01731 |
| 103414 | 0.02312 | 0.01691 |
| 105115 | 0.01359 | 0.02198 |
Cross-subject normalized root mean squared error for session 1 and session 2 and for the left and right hemispheres.
| Subjects | Session | nRMSE | |
|---|---|---|---|
| Left Hemisphere | Right Hemisphere | ||
| 100307/103414 | 1 | 0.02317 | 0.01719 |
| 2 | 0.02763 | 0.02206 | |
| 100307/105115 | 1 | 0.01925 | 0.02431 |
| 2 | 0.03177 | 0.03437 | |
| 103414/105115 | 1 | 0.02374 | 0.02408 |
| 2 | 0.03310 | 0.03264 |
The following images show the connectopic mapping for subjects 100307, 103414 and 105115 from the Human Connectome Project. The spatial distribution of the connectopies is very similar to Haak and colleagues’ results.
Connectopies of subject 100307 from three different planes orthogonal to the X, Y, and Z axes.
| Left Hemisphire | Right Hemisphire | |
| Session 1 | ||
| Session 2 |
| Left Hemisphire | Right Hemisphire | |
| Session 1 | ||
| Session 2 |
Connectopies of subject 103414 from three different planes orthogonal to the X, Y, and Z axes.
| Left Hemisphire | Right Hemisphire | |
| Session 1 | ||
| Session 2 |
| Left Hemisphire | Right Hemisphire | |
| Session 1 | ||
| Session 2 |
Connectopies of subject 105115 from three different planes orthogonal to the X, Y, and Z axes.
| Left Hemisphire | Right Hemisphire | |
| Session 1 | ||
| Session 2 |
| Left Hemisphire | Right Hemisphire | |
| Session 1 | ||
| Session 2 |
| [1] | (1, 2, 3) Haak et al., “Connectopic mapping with resting-state fMRI”, 2016 |
| [2] | Van der Maaten, Hilton, “Visualizing High-Dimensional Data Using t-SNE”, 2008 |


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4 March 2008
The Ministry of Health and Population of Egypt has announced a new human case of avian influenza A(H5N1) virus infection. The case is a 25 years old female from Sennoris District, Fayum Governorate. She developed symptoms on 24 February and was hospitalized on 27 February. Her death has now been confirmed by the Ministry of Health and Population.
Investigations into the source of her infection indicate that she had contact with sick poultry prior to becoming unwell.
Of the 45 cases confirmed to date in Egypt, 20 have been fatal.


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Press release
Lorna Fitzjohn appointed Director of Further Education and Skills
Lorna Fitzjohn HMI appointed as Director of Further Education and Skills returning to lead an area where she was previously Deputy Director.
Lorna has a strong track record in the sector. Before joining Ofsted in 2007, Lorna worked in the delivery, management and quality assurance of vocational, employment skills training and education in the post-16 sector. She has held senior management positions within primary and post-16 education, including large FE colleges.
Commenting on her appointment, Lorna said:
I am delighted to take up the role of Director for Further Education and Skills at Ofsted and will be looking to build on the work of my predecessor, Matthew Coffey HMI, in improving standards and outcomes for learners.
Further education is a vital sector which provides learners of all ages with the skills and opportunities they need to succeed. We are seeing teachers and providers make significant improvements, helping learners to get the qualifications they need to find employment in the industries they want.
However, there is still much to be done to ensure that all FE providers offer a quality learning experience. There are big changes planned for the way Ofsted inspect schools and I will be working closely with the FE sector to consider ways in which we can bring these changes into the inspection of apprenticeships, colleges and more in order to improve standards and outcomes.
As well as taking on her role as Director for FE and Skills, Lorna will continue as Regional Director for the West Midlands, which she has held since January 2013. Her predecessor, Matthew Coffey HMI, will become Chief Operating Officer at Ofsted.
Notes to editors
-
The Office for Standards in Education, Children’s Services and Skills (Ofsted) regulates and inspects to achieve excellence in the care of children and young people, and in education and skills for learners of all ages. It regulates and inspects childcare and children’s social care, and inspects the Children and Family Court Advisory Support Service (Cafcass), schools, colleges, initial teacher training, work-based learning and skills training, adult and community learning, and education and training in prisons and other secure establishments. It assesses council children’s services, and inspects services for looked after children, safeguarding and child protection.


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dlv
Debugger for the Go programming language.
More information: home page
- Compile and begin debugging the main package in the current directory (by default, with no arguments):
dlv debug
- Compile and begin debugging a specific package:
dlv debug package arguments
- Compile a test binary and begin debugging the compiled program:
dlv test
- Connect to a headless debug server:
dlv connect ip_address
- Attach to a running process and begin debugging:
div attach pid
- Compile and begin tracing a program:
dlv trace package --regexp 'regular_expression'


Congress raises Gandhi family's SPG cover withdrawal issue in Lok Sabha
A Congress leader said the previous NDA government led by Atal Bihari Vajpayee also did not remove the SPG cover of the Gandhis.
by Press Trust of IndiaCongress leader Adhir Ranjan Chowdhury on Tuesday raised the issue of withdrawal of SPG security cover to the Gandhi family, but the government said he cannot raise the matter in Zero Hour as he had not given a notice for the same.
After the notice of adjournment motion given by the party on the withdrawal of Special Protection Group (SPG) cover to Congress chief Sonia Gandhi and former party president Rahul Gandhi was rejected by Speaker Om Birla, Chowdhury raised the issue in the Zero Hour.
He said the family faces threat to life and the cover by the elite security organisation should not have been withdrawn.
The Congress leader said the previous NDA government led by Atal Bihari Vajpayee also did not remove the SPG cover of the Gandhis.
Minister of State for Parliamentary Affairs Arjun Ram Meghwal said Chowdhury cannot raise the issue in Zero Hour as he had not given notice in this regard.
"You have rejected the notice for adjournment on the issue. He did not give notice to raise the issue in Zero Hour," the minister told the Chair.
He had raised the issue on Monday also.
The notice had described the move to withdraw the cover as "arbitrary". It said the security cover was withdrawn ignoring the "existing and probable threats" to the Gandhi family.
The government has replaced the SPG security cover given to the family of former prime minister Rajiv Gandhi, who was assassinated by LTTE terrorists on May 21, 1991, by the 'Z- plus' security of the CRPF.


PorNo!
PorNo! is a porn-blocker that redirects users to "healthy" content instead of serving
"This page is blocked" to try and offer a new outlet for the user's
desires.
Powered by the open-source, URL-based porn-detection algorithm of the same name.
Platforms:
Chrome extension,
Android application
Date of birth:
March 13th (?), 2018
I wanted an app that could redirect me to my favorite music when trying to access porn. As no app of this kind existed, I remembered that I study computer science [and astronomy] and tried to build the app for myself.
––– STATUS –––
PorNo! Android 1.1: Live
PorNo! Chrome extension <IP_ADDRESS>: Live
PorNo! (original Chrome extension before takedown): Dead
Removed because the description had
"excessive/misleading wording"...?
––– Links –––
PRIVACY POLICY, on Github
Chrome extension
Android app
PorNo! algorithm, on Github
My associated thoughts, on Medium
––– KNOWN BUGS –––
Need to add better filtering for Reddit's many pornographic subreddits
Add a way to review a website after it has loaded and assess its pornographic-ness
––– RELEASE NOTES –––
PorNo! Android 1.1, March 25, 2019:
Deviantart, CNET, cda.pl should no longer be redirected from
Thank you for visiting!
Vivek Bhookya


>>
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[: caps a left branch of a fork as described in
Section II F. For example,
the verb p=: [: +/ + * -
applies the monad +/ to the result of the fork + * - .
[: signals error on any argument.
|
Caps make it possible to define a wider range of functions
as unbroken trains. For example, the maximum divided by the
product of the sum and difference would be defined by a single train,
whereas (without the use of the cap) the definition
of the maximum divided by the (monad) floor of the product
of the sum and difference would require the use of trains interrupted
by the monad. Thus:
f=: >. % + * -
g=: >. % <. @ (+ * -)
2.5 f 4
_0.410256
2.5 g 4
_0.4
The cap makes possible the use of an unbroken train as follows:
h=: >. % [: <. + * -
2.5 h 4
_0.4
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Benjamín Sánchez-Lengeling
Google Research
About Him
Benjamín earned his PhD at Harvard University under the supervision of Professor Alán Aspuru-Guzik, and currently he works at Google Brain as a Research Scientist. His research centers around using machine learning techniques to build data-driven models for prediction of molecular properties and generation of new molecules and materials via generative models. Applications include solar cells, solubility, drug-design, and smelly molecules.
For him, education has been truly a transformative experience. He dedicates part of his time to two projects of which he is a contributor and also founding member: Clubes de Ciencia México and RIIAA Conference, a summer school and ML conference in Mexico.


We buy networks!
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- scale frictionless web services
- benchmark efficient users


/* <CoffeeScript> Time = require('./Time').Time timezoneJS = require('./../lib/timezone-js.js').timezoneJS utils = require('./utils') class Timeline </CoffeeScript> */ /** * @class tzTime.Timeline * * Allows you to specify a timeline with weekend, holiday and non-work hours knocked out and timezone precision. * * ## Basic usage ## * * {TimelineIterator, Timeline, Time} = require('../') * * tl = new Timeline({ * startOn: '2011-01-03', * endBefore: '2011-01-05', * }) * * console.log(t.toString() for t in tl.getAll()) * # [ '2011-01-03', '2011-01-04' ] * * Notice how the endBefore, '2011-01-05', is excluded. Timelines are inclusive of the startOn and exclusive of the * endBefore. This allows the endBefore to be the startOn of the next with no overlap or gap. This focus on precision * pervades the design of the Time library. * * Perhaps the most common use of Timeline is to return a Timeline of ISOStrings shifted to the correct timezone. * Since ISOString comparisons give the expected chronological results and many APIs return their date/time stamps as * ISOStrings, it's convenient and surprisingly fast to do your own bucketing operations after you've gotten a Timeline * of ISOStrings. * * console.log(tl.getAll('ISOString', 'America/New_York')) * # [ '2011-01-03T05:00:00.000Z', '2011-01-04T05:00:00.000Z' ] * * ## More advanced usage ## * * Now let's poke at Timeline behavior a little more. Let's start by creating a more advanced Timeline: * * tl = new Timeline({ * startOn: '2011-01-02', * endBefore: '2011-01-07', * holidays: [ * {month: 1, day: 1}, # Notice the lack of a year specification * '2011-01-04' # Got January 4 off also in 2011. Allows ISO strings. * ] * }) * * `workDays` is already defaulted but you could have overridden it. * * console.log(tl.workDays) * # [ 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday' ] * * Another common use case is to get a Timeline to return child Timelines. You see, Timelines can be thought of as * time boxes with a startOn and an endBefore. You might have a big time box for the entire x-axis for a chart * but if you want to bucket raw data into each tick on the x-axis, you'll need to know where each sub-time box starts * and ends. * * subTimelines = tl.getAll('Timeline') * console.log((t.startOn.toString() + ' to ' + t.endBefore.toString() for t in subTimelines)) * # [ '2011-01-03 to 2011-01-05', * # '2011-01-05 to 2011-01-06', * # '2011-01-06 to 2011-01-07' ] * * Notice how the first subTimeline went all the way from 03 to 05. That's because we specified 04 as a holiday. * Timelines are contiguous without gaps or overlap. You can see that the endBefore of one subTimeline is always the startOn * of the next. * * Now, let's create a Timeline with `hour` granularity and show of the concept that Timelines also serve as time boxes by * learning about the contains() method. * * tl2 = new Timeline({ * startOn: '2011-01-02T00', * endBefore: '2011-01-07T00', * }) * * `startOn` is inclusive. * * console.log(tl2.contains('2011-01-02T00')) * # true * * But `endBefore` is exclusive * * console.log(tl2.contains('2011-01-07T00')) * # false * * But just before `endBefore` is OK * * console.log(tl2.contains('2011-01-06T23')) * # true * * All of the above comparisons assume that the `startOn`/`endBefore` boundaries are in the same timezone as the contains date. * * ## Timezone sensitive comparisions ## * * Now, let's look at how you do timezone sensitive comparisions. * * If you pass in a timezone, then it will shift the Timeline boundaries to that timezone to compare to the * date/timestamp that you pass in. This system is optimized to the pattern where you first define your boundaries without regard * to timezone. Christmas day is a holiday in any timezone. Saturday and Sunday are non work days in any timezone. The iteration * starts on July 10th; etc. THEN you have a bunch of data that you have stored in a database in GMT. Maybe you've pulled * it down from an API but the data is represented with ISOString. You then want to decide if the ISOString * is contained within the iteration as defined by a particular timezone, or is a Saturday, or is during workhours, etc. * The key concept to remember is that the timebox boundaries are shifted NOT the other way around. It says at what moment * in time July 10th starts on in a particular timezone and internally represents that in a way that can be compared to * an ISOString. * * So, when it's 3am in GMT on 2011-01-02, it's still 2011-01-01 in New York. Using the above `tl2` timeline, we say: * * console.log(tl2.contains('2011-01-02T03:00:00.000Z', 'America/New_York')) * # false * * But it's still 2011-01-06 in New York, when it's 3am in GMT on 2011-01-07 * * console.log(tl2.contains('2011-01-07T03:00:00.000Z', 'America/New_York')) * # true * * * @constructor * @param {Object} config * * @cfg {Time/ISOString} [startOn] Unless it falls on a knocked out moment, this is the first value in the resulting Timeline * If it falls on a knocked out moment, it will advance to the first appropriate moment after startOn. * You must specify 2 out of 3 of startOn, endBefore, and limit. * @cfg {Time/ISOString} [endBefore] Must match granularity of startOn. Timeline will stop before returning this value. * You must specify 2 out of 3 of startOn, endBefore, and limit. * @cfg {Number} [limit] You can specify limit and either startOn or endBefore and only get back this many. * You must specify 2 out of 3 of startOn, endBefore, and limit. * @cfg {Number} [step = 1 or -1] Use -1 to march backwards from endBefore - 1. Currently any * values other than 1 and -1 are not well tested. * @cfg {String} [granularity = granularity of startOn or endBefore] Used to determine the granularity of the ticks. * Note, this can be different from the granularity of startOn and endBefore. For example: * * { * startOn: '2012-01', # Month Granularity * endBefore: '2012-02', # Month Granularity * granularity: Time.DAY # Day granularity * } * * @cfg {String[]/String} [workDays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']] List of days of the * week that you work on. You can specify this as an Array of Strings (['Monday', 'Tuesday', ...]) or a single comma * seperated String ("Monday,Tuesday,..."). * @cfg {Array} [holidays] An optional Array of either ISOStrings or JavaScript Objects (and you can mix and match). Example: * * [{month: 12, day: 25}, {year: 2011, month: 11, day: 24}, "2012-12-24"] * * Notice how you can leave off the year if the holiday falls on the same day every year. * @cfg {Object} [workDayStartOn = {hour: 0, minute: 0}] An optional object in the form {hour: 8, minute: 15}. * If minute is zero it can be omitted. If workDayStartOn is later than workDayEndBefore, then it assumes that you * work the night shift and your work hours span midnight. * * The use of workDayStartOn and workDayEndBefore only make sense when the granularity is "hour" or finer. * * Note: If the business closes at 5:00pm, you'll want to leave workDayEndBefore to 17:00, rather * than 17:01. Think about it, you'll be open 4:59:59.999pm, but you'll be closed at 5:00pm. This also makes all of * the math work. 9am to 5pm means 17 - 9 = an 8 hour work day. * @cfg {Object} [workDayEndBefore = {hour: 24, minute: 60}] An optional object in the form {hour: 17, minute: 0}. * If minute is zero it can be omitted. */ /* <CoffeeScript> If minute is zero it can be omitted. @memoizedTicks = {} # key: stringified parameters to getAll if config.endBefore? @endBefore = config.endBefore if @endBefore != 'PAST_LAST' if utils.type(@endBefore) == 'string' @endBefore = new Time(@endBefore) @granularity = @endBefore.granularity if config.startOn? @startOn = config.startOn if @startOn != 'BEFORE_FIRST' if utils.type(@startOn) == 'string' @startOn = new Time(@startOn) @granularity = @startOn.granularity if config.granularity? @granularity = config.granularity if @startOn? @startOn = @startOn.inGranularity(@granularity) if @endBefore? @endBefore = @endBefore.inGranularity(@granularity) unless @granularity throw new Error('Cannot determine granularity for Timeline.') if @startOn == 'BEFORE_FIRST' @startOn = new Time(@startOn, @granularity) if @endBefore == 'PAST_LAST' @endBefore == new Time(@endBefore, @granularity) if !@endBefore @endBefore = new Time('PAST_LAST', @granularity) if !@startOn @startOn = new Time('BEFORE_FIRST', @granularity) @limit = if config.limit? then config.limit else utils.MAX_INT if config.workDays? @workDays = config.workDays else if config.workdays? @workDays = config.workdays else @workDays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'] if utils.type(@workDays) == 'string' @workDays = (utils.trim(s) for s in @workDays.split(',')) @holidays = if config.holidays? then config.holidays else [] for holiday, idx in @holidays if utils.type(holiday) == 'string' @holidays[idx] = new Time(holiday).getSegmentsAsObject() @workDayStartOn = if config.workDayStartOn? then config.workDayStartOn if @workDayStartOn? h = if @workDayStartOn.hour? then @workDayStartOn.hour else 0 m = if @workDayStartOn.minute? then @workDayStartOn.minute else 0 @startOnWorkMinutes = h * 60 + m if @startOnWorkMinutes < 0 @startOnWorkMinutes = 0 else @startOnWorkMinutes = 0 @workDayEndBefore = if config.workDayEndBefore? then config.workDayEndBefore if @workDayEndBefore? h = if @workDayEndBefore.hour? then @workDayEndBefore.hour else 24 m = if @workDayEndBefore.minute? then @workDayEndBefore.minute else 0 @endBeforeWorkMinutes = h * 60 + m if @endBeforeWorkMinutes > 24 * 60 @endBeforeWorkMinutes = 24 * 60 else @endBeforeWorkMinutes = 24 * 60 if config.step? @step = config.step else if config.endBefore? and @startOn?.greaterThan(@endBefore) @step = -1 else if config.endBefore? and not config.startOn? and config.limit? @step = -1 else @step = 1 utils.assert( (config.startOn? and config.endBefore?) or (config.startOn? and config.limit? and @step > 0) or (config.endBefore? and config.limit? and @step < 0), 'Must provide two out of "startOn", "endBefore", or "limit" and the sign of step must match.' ) getIterator: (tickType = 'Time', tz, childGranularity) -> </CoffeeScript> */ /** * @method getIterator * @member tzTime.Timeline * @param {String} [tickType] An optional String that specifies what type should be returned on each call to next(). * Possible values are 'Time' (default), 'Timeline', 'Date' (javascript Date Object), and 'ISOString'. * @param {String} [tz] A Sting specifying the timezone in the standard form,`America/New_York` for example. This is * required if `tickType` is 'Date' or 'ISOString'. * @param {String} [childGranularity] When tickType is 'Timeline', this is the granularity for the startOn and endBefore of the * Timeline that is returned. * @return {TimelineIterator} * * Returns a new TimelineIterator using this Timeline as the boundaries. */ /* <CoffeeScript> return new TimelineIterator(this, tickType, tz, childGranularity) getAllRaw: (tickType = 'Time', tz, childGranularity) -> </CoffeeScript> */ /** * @method getAllRaw * @member tzTime.Timeline * @param {String} [tickType] An optional String that specifies the type should be returned. Possible values are 'Time' (default), * 'Timeline', 'Date' (javascript Date Object), and 'ISOString'. * @param {String} [tz] A Sting specifying the timezone in the standard form,`America/New_York` for example. This is * required if `tickType` is 'Date' or 'ISOString'. * @param {String} [childGranularity] When tickType is 'Timeline', this is the granularity for the startOn and endBefore of the * Timeline that is returned. * @return {Time[]/Date[]/Timeline[]/String[]} * * Returns all of the points in the timeline. Note, this will come back in the order specified * by step so they could be out of chronological order. Use getAll() if they must be in chronological order. */ /* <CoffeeScript> tli = @getIterator(tickType, tz, childGranularity) temp = [] while tli.hasNext() temp.push(tli.next()) return temp getAll: (tickType = 'Time', tz, childGranularity) -> </CoffeeScript> */ /** * @method getAll * @member tzTime.Timeline * @param {String} [tickType] An optional String that specifies what should be returned. Possible values are 'Time' (default), * 'Timeline', 'Date' (javascript Date Object), and 'ISOString'. * @param {String} [tz] A Sting specifying the timezone in the standard form,`America/New_York` for example. This is * required if `tickType` is 'Date' or 'ISOString'. * @param {String} [childGranularity] When tickType is 'Timeline', this is the granularity for the startOn and endBefore of the * Timeline object that is returned. * @return {Time[]/Date[]/Timeline[]/String[]} * * Returns all of the points in the timeline in chronological order. If you want them in the order specified by `step` * then use getAllRaw(). Note, the output of this function is memoized so that subsequent calls to getAll() for the * same Timeline instance with the same parameters will return the previously calculated values. This makes it safe * to call it repeatedly within loops and means you don't need to worry about holding onto the result on the client * side. */ /* <CoffeeScript> parameterKeyObject = {tickType} if tz? parameterKeyObject.tz = tz if childGranularity? parameterKeyObject.childGranularity = childGranularity parameterKey = JSON.stringify(parameterKeyObject) ticks = @memoizedTicks[parameterKey] unless ticks? ticks = @getAllRaw(tickType, tz, childGranularity) if ticks.length > 1 if (ticks[0] instanceof Time and ticks[0].greaterThan(ticks[1])) or (utils.type(ticks[0]) is 'string' and ticks[0] > ticks[1] ) ticks.reverse() @memoizedTicks[parameterKey] = ticks return ticks ticksThatIntersect: (startOn, endBefore, tz) -> </CoffeeScript> */ /** * @method ticksThatIntersect * @member tzTime.Timeline * @param {Time/ISOString} startOn The start of the time period of interest * @param {Time/ISOString} endBefore The moment just past the end of the time period of interest * @param {String} tz The timezone you want to use for the comparison * @return {Array} * * Returns the list of ticks from this Timeline that intersect with the time period specified by the parameters * startOn and endBefore. This is a convenient way to "tag" a timebox as overlaping with particular moments on * your Timeline. A common pattern for Lumenize calculators is to use ticksThatIntersect to "tag" each snapshot * and then do groupBy operations with an OLAPCube. */ /* <CoffeeScript> utils.assert(@limit == utils.MAX_INT, 'Cannot call `ticksThatIntersect()` on Timelines specified with `limit`.') out = [] if utils.type(startOn) is 'string' utils.assert(utils.type(endBefore) is 'string', 'The type for startOn and endBefore must match.') isoDateRegExp = /\d\d\d\d-\d\d-\d\dT\d\d:\d\d:\d\d.\d\d\dZ/ utils.assert(isoDateRegExp.test(startOn), 'startOn must be in form ####-##-##T##:##:##.###Z') utils.assert(isoDateRegExp.test(endBefore), 'endBefore must be in form ####-##-##T##:##:##.###Z') utils.assert(tz?, "Must specify parameter tz when submitting ISO string boundaries.") ticks = @getAll('ISOString', tz) if ticks[0] >= endBefore or ticks[ticks.length - 1] < startOn out = [] else i = 0 ticksLength = ticks.length while i < ticksLength and ticks[i] < startOn i++ while i < ticksLength and ticks[i] < endBefore out.push(ticks[i]) i++ else if startOn instanceof Time utils.assert(endBefore instanceof Time, 'The type for startOn and endBefore must match.') startOn = startOn.inGranularity(@granularity) endBefore = endBefore.inGranularity(@granularity) if @endBefore.lessThan(@startOn) st = @endBefore en = @startOn else st = @startOn en = @endBefore if st.greaterThanOrEqual(endBefore) or en.lessThan(startOn) out = [] else ticks = @getAll() i = 0 ticksLength = ticks.length while i < ticksLength and ticks[i].lessThan(startOn) i++ while i < ticksLength and ticks[i].lessThan(endBefore) out.push(ticks[i]) i++ else throw new Error("startOn must be a String or a Time object.") return out contains: (date, tz) -> </CoffeeScript> */ /** * @method contains * @member tzTime.Timeline * @param {Time/Date/String} date can be either a JavaScript date object or an ISO-8601 formatted string * @param {String} [tz] * @return {Boolean} true if the date provided is within this Timeline. * * ## Usage: ## * * We can create a Timeline from May to just before July. * * tl = new Timeline({ * startOn: '2011-05', * endBefore: '2011-07' * }) * * console.log(tl.contains('2011-06-15T12:00:00.000Z', 'America/New_York')) * # true * */ /* <CoffeeScript> utils.assert(@limit == utils.MAX_INT, 'Cannot call `contains()` on Timelines specified with `limit`.') if date instanceof Time return date.lessThan(@endBefore) and date.greaterThanOrEqual(@startOn) utils.assert(tz? or utils.type(date) != 'date', 'Timeline.contains() requires a second parameter (timezone) when the first parameter is a Date()') switch utils.type(date) when 'string' if tz? target = timezoneJS.parseISO(date) else target = new Time(date) return target.lessThan(@endBefore) and target.greaterThanOrEqual(@startOn) when 'date' target = date.getTime() # !TODO: A - Need to use my getJSDate to be sure this behaves as expected... or test the hell out of this else throw new Error('Timeline.contains() requires that the first parameter be of type Time, String, or Date') startOn = @startOn.getJSDate(tz) endBefore = @endBefore.getJSDate(tz) return target < endBefore and target >= startOn class TimelineIterator </CoffeeScript> */ /** * @class tzTime.TimelineIterator * * In most cases you'll want to call getAll() on Timeline. TimelineIterator is for use cases where you want to get the * values in the Timeline one at a time. * * You usually get a TimelineIterator by calling getIterator() on a Timeline object. * * Iterate through days, months, years, etc. skipping weekends and holidays that you * specify. It will also iterate over hours, minutes, seconds, etc. and skip times that are not * between the specified work hours. * * ## Usage ## * * {TimelineIterator, Timeline, Time} = require('../') * * tl = new Timeline({ * startOn:new Time({granularity: 'day', year: 2009, month:1, day: 1}), * endBefore:new Time({granularity: 'day', year: 2009, month:1, day: 8}), * workDays: 'Monday, Tuesday, Wednesday, Thursday, Friday', * holidays: [ * {month: 1, day: 1}, # New Years day was a Thursday in 2009 * {year: 2009, month: 1, day: 2} # Also got Friday off in 2009 * ] * }) * * tli = tl.getIterator() * * while (tli.hasNext()) * console.log(tli.next().toString()) * * # 2009-01-05 * # 2009-01-06 * # 2009-01-07 * * Now, let's explore how Timelines and TimelineIterators are used together. * * tl3 = new Timeline({ * startOn:new Time('2011-01-06'), * endBefore:new Time('2011-01-11'), * workDayStartOn: {hour: 9, minute: 0}, * workDayEndBefore: {hour: 11, minute: 0} # Very short work day for demo purposes * }) * * You can specify that the tickType be Timelines rather than Time values. On each call to `next()`, the * iterator will give you a new Timeline with the `startOn` value set to what you would have gotten had you * requested that the tickType be Times. The `endBefore' of the returned Timeline will be set to the next value. * This is how you drill-down from one granularity into a lower granularity. * * By default, the granularity of the iterator will equal the `startOn`/`endBefore` of the original Timeline. * However, you can provide a different granularity (`hour` in the example below) for the iterator if you want * to drill-down at a lower granularity. * * tli3 = tl3.getIterator('Timeline', undefined, 'hour') * * while tli3.hasNext() * subTimeline = tli3.next() * console.log("Sub Timeline goes from #{subTimeline.startOn.toString()} to #{subTimeline.endBefore.toString()}") * subIterator = subTimeline.getIterator('Time') * while subIterator.hasNext() * console.log(' Hour: ' + subIterator.next().hour) * * # Sub Timeline goes from 2011-01-06T00 to 2011-01-07T00 * # Hour: 9 * # Hour: 10 * # Sub Timeline goes from 2011-01-07T00 to 2011-01-10T00 * # Hour: 9 * # Hour: 10 * # Sub Timeline goes from 2011-01-10T00 to 2011-01-11T00 * # Hour: 9 * # Hour: 10 * * There is a lot going on here, so let's poke at it a bit. First, notice how the second sub-Timeline goes from the 7th to the * 10th. That's because there was a weekend in there. We didn't get hours for the Saturday and Sunday. * * The above approach (`tl3`/`tli3`) is useful for some forms of hand generated analysis, but if you are using Time with * Lumenize, it's overkill because Lumenize is smart enough to do rollups based upon the segments that are returned from the * lowest granularity Time. So you can just iterate over the lower granularity and Lumenize will automatically manage * the drill up/down to day/month/year levels automatically. * * tl4 = new Timeline({ * startOn:'2011-01-06T00', # Notice how we include the hour now * endBefore:'2011-01-11T00', * workDayStartOn: {hour: 9, minute: 0}, * workDayEndBefore: {hour: 11, minute: 0} # Very short work day for demo purposes * }) * * tli4 = tl4.getIterator('ISOString', 'GMT') * * while tli4.hasNext() * console.log(tli4.next()) * * # 2011-01-06T09:00:00.000Z * # 2011-01-06T10:00:00.000Z * # 2011-01-07T09:00:00.000Z * # 2011-01-07T10:00:00.000Z * # 2011-01-10T09:00:00.000Z * # 2011-01-10T10:00:00.000Z * * `tl4`/`tli4` covers the same ground as `tl3`/`tli3` but without the explicit nesting. * * * @constructor * @param {Timeline} timeline A Timeline object * @param {String} [tickType] An optional String that specifies the type for the returned ticks. Possible values are 'Time' (default), * 'Timeline', 'Date' (javascript Date Object), and 'ISOString'. * @param {String} [childGranularity=granularity of timeline] When tickType is 'Timeline', this is the granularity for the startOn and endBefore of the * Timeline that is returned. * @param {String} [tz] A Sting specifying the timezone in the standard form,`America/New_York` for example. This is * required if `tickType` is 'Date' or 'ISOString'. */ /* <CoffeeScript> required if `tickType` is 'Date' or 'ISOString'. utils.assert(@tickType in ['Time', 'Timeline', 'Date', 'ISOString'], "tickType must be 'Time', 'Timeline', 'Date', or 'ISOString'. You provided<EMAIL_ADDRESS>utils.assert(@tickType != 'Date' or tz?, 'Must provide a tz (timezone) parameter when tickType is Date.') utils.assert(@tickType != 'ISOString' or tz?, 'Must provide a tz (timezone) parameter when returning ISOStrings.') # if timeline.granularity in ['Minute','Second', 'Millisecond'] # console.error("Warning: iterating at granularity #{timeline.granularity} can be very slow.") @tz ?= tz if timeline instanceof Timeline @timeline = timeline else @timeline = new Timeline(timeline) unless @childGranularity? @childGranularity = timeline.granularity @reset() StopIteration = if typeof(StopIteration) == 'undefined' then utils.StopIteration else StopIteration reset: () -> </CoffeeScript> */ /** * @method reset * @member tzTime.TimelineIterator * * Will go back to the where the iterator started. */ /* <CoffeeScript> if @timeline.step > 0 @current = new<EMAIL_ADDRESS>else @current = new<EMAIL_ADDRESS>@current.decrement() @count = 0 @_proceedToNextValid() _contains = (t, startOn, endBefore) -> return t.lessThan(endBefore) and t.greaterThanOrEqual(startOn) hasNext: () -> </CoffeeScript> */ /** * @method hasNext * @member tzTime.TimelineIterator * @return {Boolean} Returns true if there are still things left to iterator over. Note that if there are holidays, * weekends or non-workhours to skip, then hasNext() will take that into account. */ /* <CoffeeScript> return _contains(@current, @timeline.startOn, @timeline.endBefore) and (@count < @timeline.limit) _shouldBeExcluded: () -> if @current._isGranularityCoarserThanDay() return false # Do everything below for granularies day and lower currentInDay = @current.inGranularity('day') unless @current.dowString() in @timeline.workDays return true for holiday in @timeline.holidays if (utils.match(holiday, currentInDay)) return true if @timeline.granularity in ['hour', 'minute',' second', 'millisecond'] currentMinutes = @current.hour * 60 if @current.minute? currentMinutes += @current.minute if @timeline.startOnWorkMinutes <= @timeline.endBeforeWorkMinutes if (currentMinutes < @timeline.startOnWorkMinutes) or (currentMinutes >= @timeline.endBeforeWorkMinutes) return true else if @timeline.startOnWorkMinutes >= currentMinutes > @timeline.endBeforeWorkMinutes return true return false _proceedToNextValid: () -> while @hasNext() and @_shouldBeExcluded() if @timeline.step > 0 @current.increment() else @current.decrement() next: () -> </CoffeeScript> */ /** * @method next * @member tzTime.TimelineIterator * @return {Time/Timeline/Date/String} Returns the next value of the iterator. The start will be the first value returned unless it should * be skipped due to holiday, weekend, or workhour knockouts. */ /* <CoffeeScript> if !@hasNext() throw new StopIteration('Cannot call next() past end.') currentCopy = new Time(@current) @count++ for i in [Math.abs(@timeline.step)..1] if @timeline.step > 0 @current.increment() else @current.decrement() @_proceedToNextValid() switch @tickType when 'Time' return currentCopy when 'Date' return currentCopy.getJSDate(@tz) when 'ISOString' return currentCopy.getISOStringInTZ(@tz) when 'Timeline' config = { startOn: currentCopy.inGranularity(@childGranularity), endBefore: @current.inGranularity(@childGranularity), workDays: @timeline.workDays, holidays: @timeline.holidays, workDayStartOn: @timeline.workDayStartOn, workDayEndBefore: @timeline.workDayEndBefore } childtimeline = new Timeline(config) return childtimeline else throw new Error("You asked for tickType #{@tickType}. Only 'Time', 'Date', 'ISOString', and 'Timeline' are allowed.") exports.Timeline = Timeline exports.TimelineIterator = TimelineIterator </CoffeeScript> */


Moviereviews.org Review
Random Hearts
1 and 1/2 Stars (Out of 4)
By Mac VerStandig
<EMAIL_ADDRESS>http://www.moviereviews.org
October 11, 1999
The tagline on Random Hearts reads "In a perfect world, they never would have met." In a perfect world, I never would have seen this movie. The biggest flaw is that 20 minutes into this film, Kay Chandler (Kristin Scott Thomas) and Dutch Van Den Broeck (Harrison Ford) are the only two major characters alive; resulting in little doubt that they will end up together at some point during the laborious two-hours-and-then-some production.
Dutch is a sergeant in Internal Affairs at the District of Columbia police department. Kay is a congresswoman from New Hampshire. Although they both think they are happily married, their spouses are cheating with each other behind their backs. Dutch and Kay are soon widowed when a plane goes down carrying their partners; they subsequently discover the affair. The rest of the film is the pointless, unrealistic and often-times boring story of their researching the sexual relationship that they were blind to, and getting to know each other in the process.
Two sub plots are of little help to the already problematic film. Dutch is trying to arrest a fellow officer, a task that seems rather ordinary for Hollywood fare. All the great stereotypes get pulled, including my personal favorite of the key witness that makes bail and is murdered soon thereafter. Kay's distraction from the film's primary focus is her campaign for re-election and attempts to shield her 15 year old daughter from the truth about the father she put on a pedestal. These sub plots could have made for semi-interesting points, but the film seems afraid to explore politics or mother-daughter relations in depth, and consequently suffers.
Random Hearts' two primary characters seem to live in shells. Although both Kay and Dutch are given their own sub plots, they seem to never deal with anyone other than themselves or each other. There is plenty of opportunity, Dutch having a partner and Kay having close friends and her daughter, but the movie shies away from exploring these relationships. Veteran actor and director of this film Sydney Pollack, and critically acclaimed up-and-comer Dylan Baker both have cameo roles, but be careful not to blink too much or you will miss them both.
Other than its weak plot, it is hard to criticize Random Hearts. However, it is also hard to compliment it. Had the film taken some small risks and more fully explored its extra characters and plots it may have tasted more like a well frosted cake, rather than the dry one that equates to be.
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Anthropogenic climate change is change to the Earth's climate resulting from human causes, as opposed to climate change caused by natural causes, like volcanic eruptions, natural fluctuations in greenhouse gases, or changes to solar energy.
in-context


1. Institutional Picture of Economic Interactions
Some parts of economic discipline have different points of view on what are interactions between economic objects and describe those by different kind of economic models (Alan Kirman). Below we will base on a picture of economic interactions accepted from New Institutional Economics approach. From the point of view of institutional economics, rules of behavior in a society or “to be more formal, the limitations created by man that organize interactions between human beings” (D. North , 1997, p.17), are society institutions. Those institutions set the pattern of stimulating motives and include various limitations, created by man in order to shape up human interactions (D. North, 1997, p. 18). Following this concept, we shall differentiate between, on the one hand, the institutions that form the opportunities for society members and acting as “limitations within which people interact with each other”, on the other hand – organizations that use those opportunities and “similar to institutions, structure the interaction between people (D. North, 1997, p. 19, 23).
Certain types of activity exist in the society that raise institutions as economic reality and maintain their efficiency. In this respect, specific human activity aimed at maintaining the performance of institutions can be presented as a certain mechanism that provides for interactions between organizations and individuals. At the “entrance” to this mechanism are economy participants in a free, unorganized state. At the “exit” – the same agents but connected via a system of established short- and long-term relationships. Thus, institutions and activity that supports them transfer a set of economic agents from free, “chaotic” state into a certain ordered institutional pattern (organization consisting of the above agents and relationships between them). Here, some types of relationships and corresponding organizations exist for quite a short period of time (e.g., acts of exchange at the market), other live for much longer periods (e.g., firms and corporations). We shall call this process as the action of “institutional mechanism” meaning that within internal environment of an organization a other mechanism is working that manages organization itself.
| It is possible to depict the general institutional cross-section of economic world. At each given point in time the entire set of agents is broken down into 2 groups: 1) agents in free, “chaotic” state; 2) agents being in the bound state in the form of a set of organizations. Several types of institutional mechanisms are working in the economic system space that continuously transfer the multitude of free agents into a multitude of organizations (bound state). Due to certain reasons, relationships established between agents in the form of organizations, are regularly exhausted (they fail to correspond to objectives for the sake of which those relationships had been established). After that relationships are broken and agents resume to the free state. Thus, work cycles of the mechanisms are repeated time and again. |
Fig. 1. (see bigger image) |
Human interactions are mediated by their habitat in society; to a large extent it is established as the result of employing the technologies accumulated by the society. Maturity of technologies, particularly their information and communication constituents, is determined (in addition to institutional limitations) by one more – purely technical – type of limitations on human interactions within society. Similar as institutions set opportunities those are further used by organizations, information-communications technologies (ICT) determine the average level of information interactions (doing by information exchange) in a society and set their possible ceiling. Institutions structure interactions of the people within the framework of those technical limitations. The question is how we can describe the institution mechanism as information interactions between agents.
Next section - 2. Theoretical limits of information interactions in economic space
Previous section - Table of Contents, Introduction


---
layout: post
title: Who Am I and What Do I Do?
date: '2012-11-12T20:55:00.001-08:00'
author: headius
tags:
modified_time: '2012-11-12T20:55:43.063-08:00'
blogger_id: tag:blogger.com,1999:blog-4704664917418794835.post-3601449611286279317
blogger_orig_url: http://blog.headius.com/2012/11/who-am-i-and-what-do-i-do.html
---
"Why don't you introduce yourself..."
"My name is Charles Oliver Nutter. I am a programmer."
Is that enough?
Most of the time, it's an acceptable answer. A lot of people tend to switch off when they find out someone works in a field they they're less familiar with. Some folks that work in a related field might dig a bit: "Do you build web sites? Are you a hacker? Can you help me with my phone?" (no, yes, and no). It's pretty rare that it goes any further, and I've often felt bad about that fact. You see, I'm very proud of what I do, and I don't think it's beyond the comprehension of anyone, if properly explained. And there's what I really love to do...explain things in a way anyone can understand. So...it's time to write.


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I am a 4th year Ph.D. student in the Embedded Pervasive Lab led by Dr. Kishore Ramachandran at Georgia Tech School of Computer Science.
My interests lie in distributed systems, internet measurement, and edge computing. In my spare time, I like to draw, read, learn new languages (human ones), and wander around the city.


4764 Convoy St, San Diego, CA 92111
(858) 264-4352
Monday 12:00 PM - 11:00 PM
Tuesday 12:00 PM - 11:00 PM
Wednesday12:00 PM - 11:00 PM
Thursday 12:00 PM - 11:00 PM
Friday 12:00 PM - 12:00 AM
Saturday 12:00 PM - 12:00 AM
Sunday 12:00 PM - 12:00 AM


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Meow File is file management system for file sharing between different departments of the company.