Implementing parallel loops using nested parallelism
Spawn is a subroutine that executes or run at the same time as that of parent. So by the
Spawning both parent and child work simultaneously. The main motive behind for evolving the concept of spawning is to achieve parallelism, which results increase the overall performance of computer.
Work is defined as total time required for completing the entire multithreaded computation on a single processor.
Span is defined as the maximum time required for completing the strands along any path in the directed acyclic graph (DAG). It is an expensive path which contains maximum number of strands.
Work is the running time of a computation on a single processor. Consider it as.
Now suppose there are unlimited numbers of processors. Then the span is denoted by
The ratio of and, that is gives the parallelism of the multithreaded computation.
a.
Implementing parallel loop of SUM-ARRAY using nested parallelism (spawn and sync):
Consider the 2 arrays of size say and.
Suppose is the third array which stores the sum of the given two arrays. The implementation of NESTED-SUM-ARRAYS algorithm is similar to MAT-VEC-MAIN-LOOP. The NESTED-SUM-ARRAYS algorithm has three arrays(A,B,C) and the range of arrays(i,j) as a parameter. In the below algorithm i and j specify the range up to which addition of two array perform. The implementation of SUM-ARRAYS using nested Parallelism is as follow:
NESTED-SUM-ARRAYS
if
else
Spawn NESTED-SUM-ARRAYS
NESTED-SUM-ARRAYS
sync
Analyzing the above modified algorithm:
Here three methods which are work, span and parallelism are used for analyzing the algorithm which is as follow:
Work:
Consider the size of the array as, calculate the sum by calling procedure having argument as follow,
NESTED-SUM-ARRAYS
Work, can be given by below recurrence
Applying Master theorem (In the analysis of the algorithm, master theorem gives the solution in asymptotic term for the recurrence relation),
Hence work of NESTED-SUM-ARRAYS algorithm is.Span:
Span of the above procedure can be given by below recurrences,
Applying master theorem (In the analysis of the algorithm, master theorem gives the solution in asymptotic term for the recurrence relation),
Hence span of NESTED-SUM-ARRAYS algorithm is
Parallelism:
Parallelism is the ratio of work by span that is ratio of by.
Therefore,
Parallelism=
=
Hence span of NESTED-SUM-ARRAYS algorithm is
b.
Consider.
Since,
, therefore
Since varies from 0 to (for loop line number 4 of the algorithm SUM-ARRAYS’ on 27-1 of the textbook), therefore maximum value of will be.
Now, (line number 5 of the algorithm SUM-ARRAYS’)
will have value always less than or equal to n
Therefore,
Now in each call of ADD-SUBARRAY procedure (problem 27-1 of the textbook), it has for loop runs for 1 time and contributes a single value to therefore it will take time.
Now since
SUM_ARRAYS’runs times and each time it takes time to execute.
Therefore total work, will be given by,
… … (1)
Span of the SUM_ARRAYS’will be equal to span of the single call to ADD-SUBARRAY plus constant time work done in first 3 lines plus in order to account the overhead of spawning calls to ADD-SUBARRAY.
Therefore,
In this case
Therefore,
That is,
Parallelism=
=
=
Hence the parallelism of SUM_ARRAYS’() algorithm is when grain-size is 1.c.
For a given each iteration of for loop of procedure SUM-ARRAYS’ will result in times iteration of the ADD-SUBARRAY procedure accept for the last iteration. Last iteration it will run for times.
Therefore span of ADD-SUBARRAY will be,
=
=
Parallelism=
Since parallelism
Therefore, to achieve a maximum parallelism, span should be minimum.
.
That is,
should be minimum.
Since,
Therefore will be minimum when,
Therefore,
That is,
Saving temporary space in matrix multiplication
Spawn is a subroutine that executes or run at the same time as that of parent. So by the
Spawning both parent and child work simultaneously. The main motive behind for evolving the concept of spawning is to achieve parallelism, which results increase the overall performance of computer.
Work is defined as total time required for completing the entire multithreaded computation on a single processor.
Span is defined as the maximum time required for completing the strands along any path in the directed acyclic graph (DAG). It is an expensive path which contains maximum number of strands.
Work is the running time of a computation on a single processor. Consider it as.
Now suppose there are unlimited numbers of processors. Then the span is denoted by
The ratio of and, that is gives the parallelism of the multithreaded computation.
a.
A recursive multithreaded algorithm that removes the essence for the ephemeral matrix say at the cost of increasing the span to
It can be done by computing and inserting sync at a particular location as mentioned in the following algorithm,
P-MATRIX-MULTIPLY
//Find number of rows in X array
//Use nested loop to initialize each element of Z matrix with 0 and then multiply
//X and Y matrix by calling another function
parallel for to
parallel for to
//Call function to multiply both matrix and store it simultaneously into
//another matrix
P-MATRIX-MULTIPLY-RECURSIVE
P-MATRIX-MULTIPLY-RECURSIVE
//Find number of rows in X array
//Check number of rows is 1 or not.
if
//multiply X and Y matrix and then store in Z matrix
else partition , and into sub-matrices
Spawn P-MATRIX-MULTIPLY-RECURSIVE
spawn P-MATRIX-MULTIPLY-RECURSIVE
spawn P-MATRIX-MULTIPLY-RECURSIVE
P-MATRIX-MULTIPLY-RECURSIVE
sync
spawn P-MATRIX-MULTIPLY-RECURSIVE
spawn P-MATRIX-MULTIPLY-RECURSIVE
spawn P-MATRIX-MULTIPLY-RECURSIVE
P-MATRIX-MULTIPLY-RECURSIVE
sync
b.
Work:
The work of P-MATRIX-MULTIPLY procedure can be calculated by serializing the parallel for loop and calculating its running time and comparing it with procedure P-MATRIX-MULTIPLY-RECURSIVE.
Since both of these two procedures are executing one by one therefore the procedure having maximum work will be the total work done.
Work performed by doubly nested parallel for loop of P-MATRIX-MULTIPLY algorithm is … … (1)
Now there is another procedure P-MATRIX-MULTIPLY-RECURSIVE. Its work can be defined by recurrence given below (consider 8 recursive function),
… … (2)
Applying master theorem (In the analysis of the algorithm, master theorem gives the solution in asymptotic term for the recurrence relation),
Therefore from (1) and (2) total work will be,
… … (3)
Hence work of P-MATRIX-MULTIPLY algorithm is.
Span:
Span of the doubly nested parallel for loop of P-MATRIX-MULTIPLY algorithm is … … (4)
P-MATRIX-MULTIPLY-RECURSIVE, there are two groups of spawned recursive calls. Therefore, the span of P-MATRIX-MULTIPLY-RECURSIVE will be by the following recurrence,
Applying master theorem (In the analysis of the algorithm, master theorem gives the solution in asymptotic term for the recurrence relation),
… … (5)
Therefore total span of the above algorithm will be as following,
… … (6)
Hence work of P-MATRIX-MULTIPLY algorithm is.c.
Parallelism:
Parallelism is the ratio of work by span that is ratio of by.
Therefore,
Parallelism=
=
=
Parallelism for matrices will be,
=
Parallelism of P-MATRIX-MULTIPLY-RECURSIVE is.
Therefore the present algorithm that is P-MATRIX-MULTIPLY has 10 times less parallelism compared to P-MATRIX-MULTIPLY-RECURSIVE algorithm.
Multithreaded matrix algorithm
Spawn is a subroutine that executes or run at the same time as that of parent. So by the
Spawning both parent and child work simultaneously. The main motive behind for evolving the concept of spawning is to achieve parallelism, which results increase the overall performance of computer.
Work is defined as total time required for completing the entire multithreaded computation on a single processor.
Span is defined as the maximum time required for completing the strands along any path in the directed acyclic graph (DAG). It is an expensive path which contains maximum number of strands.
Work is the running time of a computation on a single processor. Consider it as.
Now suppose there are unlimited numbers of processors. Then the span is denoted by
The ratio of and, that is gives the parallelism of the multithreaded computation.
a.
In order to make LU-DECOMPOSITION Algorithm parallel, replace ordinary ‘for’ loop with the Parallel ‘for’ loop so that all iteration of parallel ‘for’ loop run simultaneously. The Parallel LU-DECOMPOSITION Algorithm is as follow:
LU-DECOMPOSITION+
Letand be new matrices
Initializewith below the diagonal
Initializewith on the diagonal and above the diagonal
//Use parallel loop to initialise elements of A matrix with elements of U
//matrix that is with 0
parallel for to
//initialize ukk element with the akk element
//use loop to traverse to all row.
parallel for to
//Divide A matrix element with the diagonal element of U matrix
//use loop to traverse to all row and all column.
parallel for to
parallel for to
//Subtract L, U matrix product from A matrix.
//return L and U matrix
return and
Analyzing the above modified algorithm:
Here three methods which are work, span and parallelism are used for analyzing the algorithm which is as follow:
Work:
Work of above algorithm can calculate by replacing parallel for loop with ordinary for loop and thus calculating its running time.
Since there are 3 nested for loop each running for times therefore, work will be:
Span:
Span of the parallel for loop running for will be.
will be,
Parallelism:
Parallelism is the ratio of work by span that is ratio of by.
Therefore,
Parallelism=
=
=b.
In order to make LUP-DECOMPOSITION Algorithm parallel replace ordinary for loop with the Parallel for loop so that all iteration of parallel for loop run simultaneously.
The Parallel LUP-DECOMPOSITION Algorithm is as follow:
LUP-DECOMPOSITION
//find number of rows in A matrix
Let be a new array
//initialize elements of array with 1 to n numbers
parallel for to
//use loop to initialize elements of P matrix with 0.
parallel for to
parallel for to
//Check particular ith row and kth column element is
//greater than P matrix element or not
if
//store particular ith row and kth column of A matrix in
// P matrix
//check P is equal to 0 or not.
if
error “singular matrix”
//Exchange kth and k’th element of array
exchangewith
//use loop exchange to exchange kth and k’th row of A matrix.
parallel for to
exchangewith
//use nested loop to traverse to all row and all column.
parallel for to
//Divide ith row and kth column element of A matrix
//with the ith row and kth column element of A matrix
parallel for to
Analyzing the above modified algorithm:
Here three methods which are work, span and parallelism are used for analyzing the algorithm which is as follow:
Work:
Work of above algorithm can calculate by replacing parallel for loop with ordinary for loop and thus calculating its running time.
Since there are 3 nested for loop each running for times therefore, work will be:
Span:
Span of the parallel for loop running for will be .
will be:
Parallelism:
Parallelism is the ratio of work by span that is ratio of by.
Therefore,
Parallelism=
c.
In order to make LUP-SOLVE Algorithm parallel replace ordinary for loop with the Parallel for loop so that all iteration of parallel for loop runs simultaneously. The Parallel LUP-SOLVE Algorithm is as follow:
LUP-SOLVE
//Find number of row in L matrix
letbe a new vector of length
//use loop to solve yusing forward substitution
parallel for to
//use loop to solve xusing backward substitution
parallel for down to
return x
Analyzing the above modified algorithm:
Here three methods which are work, span and parallelism are used for analyzing the algorithm which is as follow:
Work:
Work of above algorithm can calculate by replacing parallel for loop with ordinary for loop and thus calculating its running time.
Since there are 3 nested for loop each running for times therefore, work will be:
Span:
Two parallel for loop used in the above LUP-SOLVEalgorithm hence Span of the above algorithm is
Parallelism:
Parallelism is the ratio of work by span that is ratio of by.
Therefore,
Parallelism=
d.
Consider the equation (28.11), (28.12) and (28.13) on section 28.2 of the textbook,
Consider the procedure and recurrence relation on section 28.2 of the textbook,
Since
Work and Span:
So works measure as taking every inverse matrix expression in series manner means with-out applying any parallel concept. This concept used in span measurement,
These values come when multiplication not taking a constant time but addition does.
Inverse of a matrix takes time of polynomial of power two in work but in span it takes as a logarithm of power two and in last multiplication process increase the power from 2 to 4.
So,Work becomes
Span becomes:
Parallelism:
Parallelism is the ratio of work by span that is ratio of by.
Therefore,
Parallelism=
Saving temporary space in matrix multiplication
Spawn is a subroutine that executes or run at the same time as that of parent. So by the
Spawning both parent and child work simultaneously. The main motive behind for evolving the concept of spawning is to achieve parallelism, which results increase the overall performance of computer.
Work is defined as total time required for completing the entire multithreaded computation on a single processor.
Span is defined as the maximum time required for completing the strands along any path in the directed acyclic graph (DAG). It is an expensive path which contains maximum number of strands.
Work is the running time of a computation on a single processor. Consider it as.
Now suppose there are unlimited numbers of processors. Then the span is denoted by
The ratio of and, that is gives the parallelism of the multithreaded computation.
a.
A recursive multithreaded algorithm that removes the essence for the ephemeral matrix say at the cost of increasing the span to
It can be done by computing and inserting sync at a location as mentioned in the following algorithm,
P-MATRIX-MULTIPLY
//Find number of rows in X array
//Use nested loop to initialize each element of Z matrix with 0 and then multiply
//X and Y matrix by calling another function
parallel for to
parallel for to
//Call function to multiply both matrix and store it simultaneously into
//another matrix
P-MATRIX-MULTIPLY-RECURSIVE
P-MATRIX-MULTIPLY-RECURSIVE
//Find number of rows in X array
//Check number of rows is 1 or not.
if
//multiply X and Y matrix and then store in Z matrix
else partition , and into sub-matrices
Spawn P-MATRIX-MULTIPLY-RECURSIVE
spawn P-MATRIX-MULTIPLY-RECURSIVE
spawn P-MATRIX-MULTIPLY-RECURSIVE
P-MATRIX-MULTIPLY-RECURSIVE
sync
spawn P-MATRIX-MULTIPLY-RECURSIVE
spawn P-MATRIX-MULTIPLY-RECURSIVE
spawn P-MATRIX-MULTIPLY-RECURSIVE
P-MATRIX-MULTIPLY-RECURSIVE
sync
The algorithm takes time by the following recurrence relation:
and
The span is
b.
Work:
The work of P-MATRIX-MULTIPLY procedure can be calculated by serializing the parallel for loop and calculating its running time and comparing it with procedure P-MATRIX-MULTIPLY-RECURSIVE.
Since both of these two procedures are executing one by one therefore the procedure having maximum work will be the total work done.
Work performed by doubly nested parallel for loop of P-MATRIX-MULTIPLY algorithm is … … (1)
Now there is another procedure P-MATRIX-MULTIPLY-RECURSIVE. Its work can be defined by recurrence given below (consider 8 recursive function),
… … (2)
Therefore from (1) and (2) total work will be,
… … (3)
Hence work of P-MATRIX-MULTIPLY algorithm is.
Span:
Span of the doubly nested parallel for loop of P-MATRIX-MULTIPLY algorithm is … … (4)
P-MATRIX-MULTIPLY-RECURSIVE, there are two groups of spawned recursive calls. Therefore, the span of P-MATRIX-MULTIPLY-RECURSIVE will be by the following recurrence,
Applying master theorem (In the analysis of the algorithm, master theorem gives the solution in asymptotic term for the recurrence relation),
… … (5)
Therefore, total span of the above algorithm will be as following,
… … (6)
Hence work of P-MATRIX-MULTIPLY algorithm is.
c.
Parallelism:
The correctness can be proved by performing induction on the recursive calls that are made to P-SCAN-2-AUX. On a single call, n is set to 1 and the algorithm sets y [1] = x [1] which is correct. The first half of the array has elements which are computed accurately because fewer recursive calls are required. The y[i] can be computed as follows:
The above term is computed with P-SCAN-2-AUX and the time taken is T(n) = . The span is and the parallelism is
d.
Fill the line 8 of P-SCAN-UP by and lines 5 and 6 of P-SCAN-DOWN by v and
e.
The P-SCAN-UP satisfies the recurrence = . The running time of P-SCAN-DOWN is also . The P-SCAN-3 also satisfies T(n) = .
The span of P-SCAN-UP and P-SCAN-DOWN is .
Thus, the span of P-SCAN-3 is . The parallelism is
Multithreading a simple stencil calculation
Spawn is a subroutine that executes or run at the same time as that of parent. So by the
Spawning both parent and child work simultaneously. The main motive behind for evolving the concept of spawning is to achieve parallelism, which results increase the overall performance of computer.
Work is defined as total time required for completing the entire multithreaded computation on a single processor.
Span is defined as the maximum time required for completing the strands along any path in the directed acyclic graph (DAG). It is an expensive path which contains maximum number of strands.
Work is the running time of a computation on a single processor. Consider it as.
Now suppose there are unlimited numbers of processors. Then the span is denoted by
The ratio of and, that is gives the parallelism of the multithreaded computation.
a.
For simplicity assume that is an exact power of 2.
Dividematrix into four sub matrices.
Assuming that BASE-CASE is given to us,
SIMPLE-STENCIL
//check length of array is 1.
if
=BASE-CASE
else
// Calculate sub-matrix
SIMPLE-STENCIL
// Calculate sub-matrices A12and A21in parallel.
Spawn SIMPLE-STENCIL
SIMPLE-STENCIL
sync
// Calculate sub-matrix
SIMPLE-STENCIL
Analyzing the algorithm:
Here three methods which are work, span and parallelism are used for analyzing the algorithm which is as follow:
Work:
Work of SIMPLE-STENCIL can be calculating by computing the running time of its serialization.
That can be given by following recurrences,
Applying master theorem (In the analysis of the algorithm, master theorem gives the solution in asymptotic term for the recurrence relation),
Span:
Since one of the SIMPLE-STENCIL calls is spawned therefore, Span can be given by following recurrences,
Applying master theorem (In the analysis of the algorithm, master theorem gives the solution in asymptotic term for the recurrence relation),
Parallelism:
Parallelism is the ratio of work by span that is ratio of by.
Therefore,
Parallelism=
=b.
Modification in above algorithm,
P-STENCIL-MOD
//check length of array is 1.
if
BASE-CASE
else
//Group1: compute sub-matrix
P-STENCIL-MOD
//Group2:compute sub-matrices and in parallel
Spawn P-STENCIL-MOD
P-STENCIL-MOD
sync
//Group3:compute sub-matrices , , and
Spawn P-STENCIL-MOD
Spawn P-STENCIL-MOD
P-STENCIL-MOD
sync
//Group4:compute sub-matrices and
Spawn P-STENCIL-MOD
P-STENCIL-MOD
sync
//Group5:compute sub-matrix
P-STENCIL-MOD
It is quite easy to see that matrices have been divided into nine matrices.
In above figure same group specifies that they can be computed parallel.
Analyzing the above modified algorithm:
Here three methods which are work, span and parallelism are used for analyzing the algorithm which is as follow:
Work:
Work of the above algorithm can be calculated by computing the running time of its serialization.
That can be given by following recurrences,
Applying master theorem (In the analysis of the algorithm, master theorem gives the solution in asymptotic term for the recurrence relation),
Span:
It is quite obvious that span can be given by follow recurrences,
Parallelism:
Parallelism is the ratio of work by span that is ratio of by.
Therefore,
Parallelism=
=
=c.
As per question divide matrices in , matrices.
Generalizing the X we can get the following matrices for above configuration,
Hence the sub-problems can be solved into 2b-1 groups.
Analyzing the above algorithm:
Here three methods which are work, span and parallelism are used for analyzing the algorithm which is as follow:
Work:
Work can be given by following recurrences,
Span:
From above span can be given by following recurrences,
Parallelism:
Parallelism is the ratio of work by span that is ratio of by.
Therefore,
Parallelism=
=
=
=
To prove that parallelism is for any choice of it is necessary to prove that exponent of in the parallelism is strictly less than 1 for any choice of.
,
Therefore,
Hence,
Therefore,
=
Where,
Hence proved
d .
Pseudocode for a multithreaded algorithm,
Parallel-STENCIL
//find number of rows in A array.
//Calculate all entries on the anti-diagonal and above it
for to
parallel for to
=BASE-CASE
//Calculate all entries below the anti-diagonal
for to
parallel for to
=BASE-CASE
Analyzing the above modified algorithm:
Here three methods which are work, span and parallelism are used for analyzing the algorithm which is as follow:
Work:
Work of the above algorithm can calculate by replacing the parallel for loop with ordinary for loop and thencomputing its running time.
Since both nested for loop are in series therefore total running time will be some of those,
Since both of the nested for loop runs for,
Therefore work of above algorithm will be as follow,
Span:
Span of a parallel for loop having iteration is given by.
Hence span, of above algorithm having iterations as,
will be given by
Parallelism:
Parallelism is the ratio of work by span that is ratio of by.
Therefore,
Parallelism=
Randomized multithreaded algorithm:
Spawn:
• It is a subroutine that executes or runs at the same time as that of its parent.
• By spawning, both parent and child work simultaneously.
• The main motive behind spawning is to achieve parallelism, which results in increasing the overall performance of the computer.
Work:
• It is defined as the total time required for completing the entire multithreaded computation on a single processor.
Span:
• It is defined as the maximum time required for completing the strands along any path in the directed acyclic graph (DAG).
• It is an expensive path which contains maximum number of strands.
• Work is the running time of a computation on a single processor. Consider it as.
• Suppose there are unlimited numbers of processors. Then the span is denoted by
• The ratio of and, that is, gives the parallelism of the multithreaded computation.
a.
is an expectation value associated with a randomized algorithm.
It is defined as , whereis a random number generated by randomized algorithm and is its probability of getting generated.
Since is the probability associated with th randomized outcome, its summation will be 1.
That is,
Work law can be defined as .
Span law can be defined as.
Greedy scheduler bound can be defined as.
b.
Consider the following data:
Randomized multithreaded algorithm for which 1% of the time is,
Randomized multithreaded algorithm for which 99% of the time is,
Now,
Speed up:
Speed up:
It is obvious from (1) and (2) that speedup of multithreaded algorithm should be defined as ¸ rather than.
Parallelism of a randomized multithreaded algorithm should be defined as the ratio.
is defined as
Here, is the work of the th outcome and is probability of that outcome.
Thus, is an expected value of the work for a long range of outcomes. In other words, it is the average work expected.
is defined as
Here, is the SPAN of the th outcome and is probability of that outcome.
Thus, is an expected value of the span for a long range of outcomes. In other words, it is the average span expected.
Since parallelism of an algorithm should be approximately equal to an average case scenario, therefore, the parallelism should be the average case of all outcomes.
Hence, parallelism should be defined as.
d.
Use nested parallelism to make Randomized-Quick-sort algorithm on section 7.3 of the textbook as a multithreaded algorithm.
In following algorithm, p and r represent the range of the array.
P-RANDOMIZED-QUICKSORT
//Check range of array is valid or not.
if
//call function to divide the array into two halves
RANDOMIZED-PARTITION
spawn P-RANDOMIZED-QUICKSORT
P-RANDOMIZED-QUICKSORT
//Function to divide the array
RANDOMIZED-PARTITION
// Function to choose a random value between and including boundaries.
if RANDOM
exchangewith
// partitioning the array (PARTITION) on the section 7.1 of the textbook
return PARTITION
e.
In above algorithm, at each level of recursion, the partition is done by RANDOMIZED-PARTITION which places a constant fraction of the elements on one side of the partition.
The recursion tree has depth of and work done at each level is.
Therefore, total work.
The recursion tree has depth of and parallel time at taken each level is .
Therefore, total span is given as shown below:
Parallelism is the ratio of work by span, that is, ratio of by.
Therefore, parallelism


This sample shows how to extract some data from Banana document and load it into the worksheet using the Excel JavaScript API.
This sample shows how to extract some data from Banana document and load it into the worksheet using the Excel JavaScript API.


{#{% load staticfiles %}#}
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Inspinia we app framework base on Bootstrap 3 © 2014


Planets go around the sun at different rates. Calculate your age on any of the eight planets in our solar system and see how many years you might have left on them!
Back to my portfolio.


Open Your Eyes(Abre Los Ojos) ***
rated R in Spanish with English subtitles an Artisan Films release starring Eduardo Noriega, Penelope Cruz, Chete Lera, Fele Martinez, Gerard Barray, Pepe Navarro, Najwa Nimri written by Alejandro Amenabar and Mateo Gil directed by Alejandro Amenabar
As anyone who has seen The Matrix and eXistenZ knows, this year we have has an abundance of exceptional science-fiction films. I am not a very big sci-fi fan, but as the year 2000 approaches, these films have begun to fascinate me immensely.
The latest entry into the genre, Alejandro Amenabar's psychological thriller Open Your Eyes, suffers from bad timing. While the film is indeed very good, I have seen it shortly after two very similar films(look in the first paragraph), and the originality doesn't strike me nearly as much as it could have.
 Infact, the first scene of Open Your Eyes, a dream sequence, shows Cesar waking up, going outside, and realizing that there is no one on the streets). As he is interrogated by a prison psychologist(Chete Lera), we, through the eyes of Cesar, try to figure out exactly what happened.
Cesar decides to throw a party at his house, and his best friend Pelayo(Fele Martinez) brings with him Sofia(Penelope Cruz), a beautiful woman whom he met in the library. Cesar falls instantly in love, and he ends up taking her back to her home after the party.
After a night of chatting, they agree to meet again the next day. But as Cesar walks outside, his lusty mistress, Nuria(Najwa Nimri), drives by in her car, and begs him to join her for one last rendezvous. He gets in the car, and she ends up driving off the side of the road and hitting a tree, killing herself and completely wrecking Cesar's face.
>From then on, the film goes through many, many twists as Cesar tries to figure out what the hell is going on. There is a line in this year's extraordinary The Matrix in which Morpheus(Laurence Fishburne) asks: "Have you ever had a dream that you're so sure was real? What if you were unable to wake from that dream? How would you tell the difference between the dream world, and the real world?"
Open Your Eyes is essentially about our dreams, and the tricks that our subconscious plays on us. While the film is clever and psychological, it is not as fun or stylish as The Matrix and eXistenZ. Open Your Eyes is definitely worthy of an audience, as long as they're not expecting the flash of the others. I'm sure I would have enjoyed this film more had I seen it earlier, though.
Regardless, Alejandro Amenabar is a smashing new talent, and this film was a hit in it's native Spain. Penelope Cruz, who made her American debut in last year's embarrasing The Hi-Lo Country, shows depth as well as beauty, and hopefully her appeal will cross over to America. Open Your Eyes is an incredible movie, and if this is not one of the new sci/fi genre's best, then the genre's gonna thrive for a long while. Once again, congrats to Artisan Films, quickly becoming one of best distributors in film today.
a review by Akiva Gottlieb, The Teenage Movie Critic<EMAIL_ADDRESS>http://www.angelfire.com/mo/film watch me on TBS' "Dinner And A Movie" May 21, 8:05pm EST
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tailscale
A private WireGuard network service.
Some subcommands such as `tailscale up` have their own usage documentation.
More information: home page
- Connect to Tailscale:
sudo tailscale up
- Disconnect from Tailscale:
sudo tailscale down
- Display the current Tailscale IP addresses:
tailscale ip
- Ping a peer node at the Tailscale layer and display which route it took for each response:
tailscale ping ip|hostname
- Analyze the local network conditions and display the result:
tailscale netcheck
- Start a web server for controlling Tailscale:
tailscale web
- Display a shareable identifier to help diagnose issues:
tailscale bugreport
- Display help for a subcommand:
tailscale subcommand --help


{# This form is rendered manually to accommodate passing the samples that the user selected to views.py #}
{# https://simpleisbetterthancomplex.com/article/2017/08/19/how-to-render-django-form-manually.html #}
{% endblock %}
{% block extra_javascript %}
{% endblock %}


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These results/analyses do not necessarily reflect the views of the University of Exeter and it is not an official position of the University. The analyses here reflect the personal views of the aforementioned researchers. The reader should also be aware that, although we have used all tools necessary to ensure the correctness of our analyses, this work has not been independently peer-reviewed. We welcome your feedback on our analyses and methodology.
Executive summary
Our aim is to assess the effects of the COVID-19 restriction measures on the mobility patterns of people in the UK. These measures are strong public health policies which came into place as a consequence of the COVID-19 pandemic and its potential impact on the British population and on the NHS. To do so, we analyse changes in the average levels of mobility of anonymous mobile phone users across the country at different time periods, which include the periods when the restriction measures are in place and enforced by authorities.
Summary of main initial findings
In early March, before restriction measures were enforced, mobility levels decreased by about 10% compared to their normal levels before the pandemic.
In the middle of March, after people were encouraged to work from home and reduce their travelling, mobility levels dropped by about 50% compared to before the pandemic.
From March 24th onwards the UK entered a state of lockdown, with only essential travelling allowed. This led to a reduction of about 70% in the mobility levels.
Mobility levels have dropped consistently in all areas across the UK after the lockdown measurements.
These results present our initial analysis of the restriction measures and their effect on mobility across the UK. This might be of interest to epidemiologist who can use this to estimate contact matrices, and to public health policy makers who have to assess the impact of their policies on the British population.
Timeline of COVID-19 development in the UK
In January 2020, cases of the novel coronavirus started being reported outside of China. These cases were typically from people who had recently travelled to the Chinese Hubei province, where the disease started spreading. Events related to COVID-19 in the UK started unfolding at the end of January, with the first reported cases in the country. A brief timeline for the UK is the following:
31st January 2020: two Chinese nationals, one of whom studied at the University of York, were the first confirmed cases in the UK.
6th February 2020: a man in Brighton became the third confirmed case in the UK. He had previously travelled to Singapore and then visited a ski resort in France.
10th February 2020: the number of confirmed cases in the UK increases to 10, due to infections linked to the man in Brighton.
27th February 2020: the total number of confirmed cases is 16, with the first sign of infections spreading from the outbreak in Northern Italy due to people travelling back from the affected regions.
1st March 2020: the official number of cases is 36, and there are signs of infections amongst people with no history of recent travel abroad.
11th March 2020: the confirmed cases in the UK are now 456. The World Health Organisation declares the outbreak a pandemic.
12th March 2020: the UK Chief Medical Office raises the risk from moderate to high. Anyone with a new continuous cough or a fever is recommended to self-isolate for seven days, schools cancel trips abroad and people over 70 are asked to avoid cruises.
13th March 2020: many sporting events are postponed, as well as the 2020 UK local elections.
16th March 2020: the number of cases is over 1,500, and the number of deaths is 55. The Prime Minister advises against non essential travelling, and encourages people to avoid pubs, clubs and theatres. Working from home is encouraged.
23rd March 2020: the Prime Minister announces stricter measures for the UK coming into place from the following day (March 24th). People are asked to stay home except for essential food shopping, essential work travel and one form of exercise per day only with members of their household.
26th March 2020: the death toll is 422, and the number of people that tested positive is 11,568.
27th March 2020: both the Prime Minister and the Health Secretary announce testing positive for the virus.
Note that this timeline is not meant to be complete, but is only an indication of how major events related to COVID-19 unfolded in the UK over time, and which restrictions were put into place at what point in time. For a more complete report on the development of COVID-19 in the UK, you can read more here.
The Analysis
Baseline February 2020
A key indicator to evaluate the effectiveness of these restrictions and assess the compliance of the population is the reduction in mobility. Here, we will use a large, national-scale data set on human mobility, provided by the collaboration with Cuebiq, to evaluate the adherence to these measures by the general population.
These reports are based on similar analysis carried out on Italian data
Pepe E. et al. 2020 and US data
Klein B. et al. 2020. We aim to provide and assess the changes in commuting and mobility at local authority level across UK during the COVID-19 health crisis.
In order to assess whether movement of people has reduced in the UK during the restriction measures, we first have to establish what the regular level of movement across country is. We refer to this as our baseline. We use the radius of gyration as our measure of mobility. The radius of gyration provides an indication of the characteristic distance travelled by a person during a given time period.
We estimate our baseline by considering the average mobility during the period between 01 and 04 of February 2020. This period is before restriction measures were put into place by the British government, and does not contain significant events, such as bank holidays or festivities, that may affect our baseline estimates. For our baseline, we use the median radius of gyration labeled as Degree of Mobility. Figure 1 (A) depicts the results of our baseline estimates at local authority level.
As an example of a comparison between the baseline and another day, we selected the 22nd of February and showed the comparison result in Figure 1 (B). In the figure, the Mobility Activity represents the percentage of user mobility with respect to the baseline; a value of 100% indicates that the mobility is the same as the baseline, whereas lower values of mobility activity represent a reduction in the mobility of people in that area. We do not report values higher than 100% as they are not of interest for this analysis.
As the Figure shows, on an average day only few areas within the UK present a lower radius of gyration when compared to the baseline. This supports the validity of our baseline estimate, since on an average day we would not expect the radius of gyration to be very different from the baseline (note that there will always be a few areas with higher or lower mobility levels).
Changes in mobility
We want to assess the changes in the levels of mobility in the UK during the COVID-19 pandemic. We compare the weekly levels of mobility to our baseline, and we present the results as a percentage of the baseline. We refer to this as the mobility activity. For instance, if the mobility activity has a value of 50, this corresponds to a mobility level which is 50% lower when compared to our baseline. In the animation below, we show how the mobility has been changing since the beginning of February. We can see that, until early March, the mobility activity was remaining roughly constant, but it starts to drop sharply soon after the first restriction measures have been put into place.
17th March 2020
By March 17th 2020, the confirmed number of COVID-19 cases in the UK had increased to about five hundred and the Chief Medical Officer advised schools to cancel school trips and recommended people with coronavirus symptoms (fever of dry continuous cough) to self isolate for seven days. Figure 2 depicts the comparison between this day and the baseline. Visual inspection clearly reveals several areas which have a decreased radius of gyration, as compared to Figure 1 (B). However, the majority of locations still presents no decrease in the average mobility.
26 March 2020
On March 23rd 2020, the Prime Minister asked the British people to stop all non-essential travelling, effectively imposing a state of lockdown in the UK starting on March 24th. We anticipate that this restriction measure will lead to a big reduction in the levels of mobility in the upcoming days. We analyse the mobility activity two days after the lockdown started to investigate the impact of this measure. Figure 3 presents the changes in the radius of gyration between March 26th and our baseline. We clearly see a reduction in the mobility of people across the majority of the UK. We also note that the reduction is broadly similar across the whole country.
01 April 2020
We anticipate that the restriction measures on people's mobility imposed by the British Government will take a few days to show their full effect. Here, we compare the median radius of gyration on April 1st with our baseline ( Figure 4). We find a reduction in mobility across the majority of the United Kingdom.
The Trend of UK
Here, we consider the overall trend over time across the UK. We find a clear drop in mobility activity roughly starting on the first weekend of March, a few days before the first restriction measures were put into place. We find that the reduction is consistent across England, Wales, Scotland, and Northern Ireland.
Comparing mobility activity before and after the restriction measures
Our goal is to assess whether restriction measures are having the desired effect in reducing the mobility of British people. Here, we directly compare the degree of mobility in different time periods across the four countries in the United Kingdom. After March 23rd, when an effective lockdown was imposed on the country, we see a great reduction in the levels of mobility across all of the UK.
Detailed mobility trend in England
Detailed mobility trend in Scotland
Detailed mobility trend in Norther Ireland
Detailed mobility trend in Wales
Validation of Data
We want to assess the validity of our data in terms of how representative our sample is. Here, we compare the percentage of the population in each area against the percentage of the number of users. We find a high correlation in our dataset with an R2 of 0.92. Please note that the two points in the top right correspond to areas in London.
Data & Methods
This research has been performed using data from anonymized users who have opted-in to provide access to their location data anonymously, through a GDPR-compliant framework. The single users were not identifiable at any research steps. The residential areas were inferred at an aggregated local authority level. The analysis has been performed on a sample 250K users across the UK. We performed the radius of gyration analysis using the definition of (Gonzalez, M. et al Nature 2008). Moreover, each mobility value of a given region is the median value of the distribution of the radius of gyration of the users within a temporal window of 8 days centred around a given day.
Cuebiq Inc. is a consumer insights company that analyzes visitation patterns based on aggregated and privacy-enhanced mobility data, to provide measurement, support academic research and humanitarian initiatives.
Contact us
If you have any questions, comments or would like to know more about our analysis, you can contact us at the following email addresses:
Riccardo Di Clemente, Exeter University
Brennan Lake, Cuebiq Inc.
Acknowledgments
This preliminary analysis is a collaboration between the Department of Computer Science of University of Exeter and Cuebiq Inc. In response to the COVID-19 crisis, Cuebiq is providing insights to academic and humanitarian groups through a multi-stakeholder data collaborative for timely and ethical analysis of aggregate human mobility patterns. We thank Pietro Gravino for the useful discussions.


SUBROUTINE CTPRFS( UPLO, TRANS, DIAG, N, NRHS, AP, B, LDB, X, LDX, $ FERR, BERR, WORK, RWORK, INFO ) * * -- LAPACK routine (version 3.1) -- * Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. * November 2006 * * Modified to call CLACN2 in place of CLACON, 10 Feb 03, SJH. * * .. Scalar Arguments .. CHARACTER DIAG, TRANS, UPLO INTEGER INFO, LDB, LDX, N, NRHS * .. * .. Array Arguments .. REAL BERR( * ), FERR( * ), RWORK( * ) COMPLEX AP( * ), B( LDB, * ), WORK( * ), X( LDX, * ) * .. * * Purpose * ======= * * CTPRFS provides error bounds and backward error estimates for the * solution to a system of linear equations with a triangular packed * coefficient matrix. * * The solution matrix X must be computed by CTPTRS or some other * means before entering this routine. CTPRFS does not do iterative * refinement because doing so cannot improve the backward error. * * Arguments * ========= * * UPLO (input) CHARACTER*1 * = 'U': A is upper triangular; * = 'L': A is lower triangular. * * TRANS (input) CHARACTER*1 * Specifies the form of the system of equations: * = 'N': A * X = B (No transpose) * = 'T': A**T * X = B (Transpose) * = 'C': A**H * X = B (Conjugate transpose) * * DIAG (input) CHARACTER*1 * = 'N': A is non-unit triangular; * = 'U': A is unit triangular. * * N (input) INTEGER * 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 matrices B and X. NRHS >= 0. * * AP (input) COMPLEX array, dimension (N*(N+1)/2) * The upper or lower triangular matrix A, packed columnwise in * a linear array. The j-th column of A is stored in the array * AP as follows: * if UPLO = 'U', AP(i + (j-1)*j/2) = A(i,j) for 1<=i<=j; * if UPLO = 'L', AP(i + (j-1)*(2n-j)/2) = A(i,j) for j<=i<=n. * If DIAG = 'U', the diagonal elements of A are not referenced * and are assumed to be 1. * * B (input) COMPLEX array, dimension (LDB,NRHS) * The right hand side matrix B. * * LDB (input) INTEGER * The leading dimension of the array B. LDB >= max(1,N). * * X (input) COMPLEX array, dimension (LDX,NRHS) * The solution matrix X. * * LDX (input) INTEGER * The leading dimension of the array X. LDX >= max(1,N). * * FERR (output) REAL array, dimension (NRHS) * The estimated forward error bound for each solution vector * X(j) (the j-th column of the solution matrix X). * If XTRUE is the true solution corresponding to X(j), FERR(j) * is an estimated upper bound for the magnitude of the largest * element in (X(j) - XTRUE) divided by the magnitude of the * largest element in X(j). The estimate is as reliable as * the estimate for RCOND, and is almost always a slight * overestimate of the true error. * * BERR (output) REAL array, dimension (NRHS) * The componentwise relative backward error of each solution * vector X(j) (i.e., the smallest relative change in * any element of A or B that makes X(j) an exact solution). * * WORK (workspace) COMPLEX array, dimension (2*N) * * RWORK (workspace) REAL array, dimension (N) * * INFO (output) INTEGER * = 0: successful exit * < 0: if INFO = -i, the i-th argument had an illegal value * * ===================================================================== * * .. Parameters .. REAL ZERO PARAMETER ( ZERO = 0.0E+0 ) COMPLEX ONE PARAMETER ( ONE = ( 1.0E+0, 0.0E+0 ) ) * .. * .. Local Scalars .. LOGICAL NOTRAN, NOUNIT, UPPER CHARACTER TRANSN, TRANST INTEGER I, J, K, KASE, KC, NZ REAL EPS, LSTRES, S, SAFE1, SAFE2, SAFMIN, XK COMPLEX ZDUM * .. * .. Local Arrays .. INTEGER ISAVE( 3 ) * .. * .. External Subroutines .. EXTERNAL CAXPY, CCOPY, CLACN2, CTPMV, CTPSV, XERBLA * .. * .. Intrinsic Functions .. INTRINSIC ABS, AIMAG, MAX, REAL * .. * .. External Functions .. LOGICAL LSAME REAL SLAMCH EXTERNAL LSAME, SLAMCH * .. * .. Statement Functions .. REAL CABS1 * .. * .. Statement Function definitions .. CABS1( ZDUM ) = ABS( REAL( ZDUM ) ) + ABS( AIMAG( ZDUM ) ) * .. * .. Executable Statements .. * * Test the input parameters. * INFO = 0 UPPER = LSAME( UPLO, 'U' ) NOTRAN = LSAME( TRANS, 'N' ) NOUNIT = LSAME( DIAG, 'N' ) * IF( .NOT.UPPER .AND. .NOT.LSAME( UPLO, 'L' ) ) THEN INFO = -1 ELSE IF( .NOT.NOTRAN .AND. .NOT.LSAME( TRANS, 'T' ) .AND. .NOT. $ LSAME( TRANS, 'C' ) ) THEN INFO = -2 ELSE IF( .NOT.NOUNIT .AND. .NOT.LSAME( DIAG, 'U' ) ) THEN INFO = -3 ELSE IF( N.LT.0 ) THEN INFO = -4 ELSE IF( NRHS.LT.0 ) THEN INFO = -5 ELSE IF( LDB.LT.MAX( 1, N ) ) THEN INFO = -8 ELSE IF( LDX.LT.MAX( 1, N ) ) THEN INFO = -10 END IF IF( INFO.NE.0 ) THEN CALL XERBLA( 'CTPRFS', -INFO ) RETURN END IF * * Quick return if possible * IF( N.EQ.0 .OR. NRHS.EQ.0 ) THEN DO 10 J = 1, NRHS FERR( J ) = ZERO BERR( J ) = ZERO 10 CONTINUE RETURN END IF * IF( NOTRAN ) THEN TRANSN = 'N' TRANST = 'C' ELSE TRANSN = 'C' TRANST = 'N' END IF * * NZ = maximum number of nonzero elements in each row of A, plus 1 * NZ = N + 1 EPS = SLAMCH( 'Epsilon' ) SAFMIN = SLAMCH( 'Safe minimum' ) SAFE1 = NZ*SAFMIN SAFE2 = SAFE1 / EPS * * Do for each right hand side * DO 250 J = 1, NRHS * * Compute residual R = B - op(A) * X, * where op(A) = A, A**T, or A**H, depending on TRANS. * CALL CCOPY( N, X( 1, J ), 1, WORK, 1 ) CALL CTPMV( UPLO, TRANS, DIAG, N, AP, WORK, 1 ) CALL CAXPY( N, -ONE, B( 1, J ), 1, WORK, 1 ) * * Compute componentwise relative backward error from formula * * max(i) ( abs(R(i)) / ( abs(op(A))*abs(X) + abs(B) )(i) ) * * where abs(Z) is the componentwise absolute value of the matrix * or vector Z. If the i-th component of the denominator is less * than SAFE2, then SAFE1 is added to the i-th components of the * numerator and denominator before dividing. * DO 20 I = 1, N RWORK( I ) = CABS1( B( I, J ) ) 20 CONTINUE * IF( NOTRAN ) THEN * * Compute abs(A)*abs(X) + abs(B). * IF( UPPER ) THEN KC = 1 IF( NOUNIT ) THEN DO 40 K = 1, N XK = CABS1( X( K, J ) ) DO 30 I = 1, K RWORK( I ) = RWORK( I ) + $ CABS1( AP( KC+I-1 ) )*XK 30 CONTINUE KC = KC + K 40 CONTINUE ELSE DO 60 K = 1, N XK = CABS1( X( K, J ) ) DO 50 I = 1, K - 1 RWORK( I ) = RWORK( I ) + $ CABS1( AP( KC+I-1 ) )*XK 50 CONTINUE RWORK( K ) = RWORK( K ) + XK KC = KC + K 60 CONTINUE END IF ELSE KC = 1 IF( NOUNIT ) THEN DO 80 K = 1, N XK = CABS1( X( K, J ) ) DO 70 I = K, N RWORK( I ) = RWORK( I ) + $ CABS1( AP( KC+I-K ) )*XK 70 CONTINUE KC = KC + N - K + 1 80 CONTINUE ELSE DO 100 K = 1, N XK = CABS1( X( K, J ) ) DO 90 I = K + 1, N RWORK( I ) = RWORK( I ) + $ CABS1( AP( KC+I-K ) )*XK 90 CONTINUE RWORK( K ) = RWORK( K ) + XK KC = KC + N - K + 1 100 CONTINUE END IF END IF ELSE * * Compute abs(A**H)*abs(X) + abs(B). * IF( UPPER ) THEN KC = 1 IF( NOUNIT ) THEN DO 120 K = 1, N S = ZERO DO 110 I = 1, K S = S + CABS1( AP( KC+I-1 ) )*CABS1( X( I, J ) ) 110 CONTINUE RWORK( K ) = RWORK( K ) + S KC = KC + K 120 CONTINUE ELSE DO 140 K = 1, N S = CABS1( X( K, J ) ) DO 130 I = 1, K - 1 S = S + CABS1( AP( KC+I-1 ) )*CABS1( X( I, J ) ) 130 CONTINUE RWORK( K ) = RWORK( K ) + S KC = KC + K 140 CONTINUE END IF ELSE KC = 1 IF( NOUNIT ) THEN DO 160 K = 1, N S = ZERO DO 150 I = K, N S = S + CABS1( AP( KC+I-K ) )*CABS1( X( I, J ) ) 150 CONTINUE RWORK( K ) = RWORK( K ) + S KC = KC + N - K + 1 160 CONTINUE ELSE DO 180 K = 1, N S = CABS1( X( K, J ) ) DO 170 I = K + 1, N S = S + CABS1( AP( KC+I-K ) )*CABS1( X( I, J ) ) 170 CONTINUE RWORK( K ) = RWORK( K ) + S KC = KC + N - K + 1 180 CONTINUE END IF END IF END IF S = ZERO DO 190 I = 1, N IF( RWORK( I ).GT.SAFE2 ) THEN S = MAX( S, CABS1( WORK( I ) ) / RWORK( I ) ) ELSE S = MAX( S, ( CABS1( WORK( I ) )+SAFE1 ) / $ ( RWORK( I )+SAFE1 ) ) END IF 190 CONTINUE BERR( J ) = S * * Bound error from formula * * norm(X - XTRUE) / norm(X) .le. FERR = * norm( abs(inv(op(A)))* * ( abs(R) + NZ*EPS*( abs(op(A))*abs(X)+abs(B) ))) / norm(X) * * where * norm(Z) is the magnitude of the largest component of Z * inv(op(A)) is the inverse of op(A) * abs(Z) is the componentwise absolute value of the matrix or * vector Z * NZ is the maximum number of nonzeros in any row of A, plus 1 * EPS is machine epsilon * * The i-th component of abs(R)+NZ*EPS*(abs(op(A))*abs(X)+abs(B)) * is incremented by SAFE1 if the i-th component of * abs(op(A))*abs(X) + abs(B) is less than SAFE2. * * Use CLACN2 to estimate the infinity-norm of the matrix * inv(op(A)) * diag(W), * where W = abs(R) + NZ*EPS*( abs(op(A))*abs(X)+abs(B) ))) * DO 200 I = 1, N IF( RWORK( I ).GT.SAFE2 ) THEN RWORK( I ) = CABS1( WORK( I ) ) + NZ*EPS*RWORK( I ) ELSE RWORK( I ) = CABS1( WORK( I ) ) + NZ*EPS*RWORK( I ) + $ SAFE1 END IF 200 CONTINUE * KASE = 0 210 CONTINUE CALL CLACN2( N, WORK( N+1 ), WORK, FERR( J ), KASE, ISAVE ) IF( KASE.NE.0 ) THEN IF( KASE.EQ.1 ) THEN * * Multiply by diag(W)*inv(op(A)**H). * CALL CTPSV( UPLO, TRANST, DIAG, N, AP, WORK, 1 ) DO 220 I = 1, N WORK( I ) = RWORK( I )*WORK( I ) 220 CONTINUE ELSE * * Multiply by inv(op(A))*diag(W). * DO 230 I = 1, N WORK( I ) = RWORK( I )*WORK( I ) 230 CONTINUE CALL CTPSV( UPLO, TRANSN, DIAG, N, AP, WORK, 1 ) END IF GO TO 210 END IF * * Normalize error. * LSTRES = ZERO DO 240 I = 1, N LSTRES = MAX( LSTRES, CABS1( X( I, J ) ) ) 240 CONTINUE IF( LSTRES.NE.ZERO ) $ FERR( J ) = FERR( J ) / LSTRES * 250 CONTINUE * RETURN * * End of CTPRFS * END


{% extends 'projects/base_projects.html' %} {% block actual_content %}
One and a half years ago I officially joined SharpScholar as a technical co-founder. It has been an epic journey from 1 researcher to 16 professors and 9 Universities, from my basement to the Silicon Valley. Along the way, there naturally things I have done well and things I wish to improve on. The following series of posts is an attempt at connecting the dots backwards:
My role at SharpScholar
Defining SharpScholar (a product perspective)
What we are doing well
What we are doing poorly
How I would do things differently


Maze AI v0.5.0 MazeServer.MazeAi.AStar View Source
this module define A* rules.
in this algorithm, frontier is same as BFS with a tiny difference!
frontier will sort by ascending path_cost
order after every push.
Link to this section Summary
Functions
this expander is same as MazeAi.expander
but it will check path_cost for expand a node!
frontier pop rule for A*.
frontier push rule for A*.
it will sort frontier by ascending path_cost
order.
it will calculates path cost of a node with path cost of its parent node plus one!
heuristic calculation with diagonal distance calculator.
A* search. it will search same as BFS.
Link to this section Functions
this expander is same as MazeAi.expander
but it will check path_cost for expand a node!
frontier pop rule for A*.
frontier push rule for A*.
it will sort frontier by ascending path_cost
order.
it will calculates path cost of a node with path cost of its parent node plus one!
heuristic calculation with diagonal distance calculator.
Examples
iex> MazeServer.MazeAi.AStar.h({1, 2}, {4, 5})
6
and this is true: 2 * max((4-1), (5-2)) = 6
A* search. it will search same as BFS.


"Why?" I thought, repulsed.
"Don't rely on your own perspective," You said —
but that made no sense at all.
"Wherever you go, realize I'm there, working," You said.
"What have you ever done for me?" I retorted.
"I'll tell you where to go," You said.
And I thought "I'm sure you will!"
There was silence for a few seconds, as You quizzically raised an eyebrow to say "Really? Not even you believe that!"
*sigh*You were right, as usual.
And then You leaned over, gave me a hug, and said "Come on, let's go this way…"
(Proverbs 3:5,6; Malachi1:2a)


andOffset - Journal of Nuclear Medicine
corresponding to each coordinate is first added to an offset voltage level, then amplified by a . Theoretically the gain control could be made continuously . Page778,under Reference(5), the author's name is Gupta, RG. 1022. THE JOURNAL .
http://jnm.snmjournals.org/content/25/9/1019.full.pdf
Treatment for Herniated or Bulging Discs
Portmann_AT.ppt - Stanford Synchrotron Radiation Lightsource
names and how to connect to the computer control system). Easy access to important data (offsets, gains, rolls, max/min, etc.) Integrate simulation and online .
http://www-ssrl.slac.stanford.edu/lcls/epics/talks/wedam/portmann_at.ppt
PIDTuningClassical - ControlsWiki
b is the set point value of the signal, also known as bias or offset. . P or PI control can be used with low controller gain. . For each, name the procedure and explain how the given measured information is used to pick the parameters of the .
http://controls.engin.umich.edu/wiki/index.php/PIDTuningClassical
offset gain controls names Herniated or Bulging Discs
Modeling of analog part for DDS3 signal generator. Part 1 - Zeroing ...
Jun 28, 2012 . If we look at DDS2 signal generator offset and gain control circuit we can see that there is a problem in it. We regulate offset before amplitude .
http://www.radiolocman.com/shem/schematics.html?di=134073
Gray Balance Control in Offset Printing with the ECI /bvdm Gray ...
Standard process offset printing states that the correct dot gain for each different . The identification line below the patches contains the name of the control .
http://www.eci.org/_media/downloads/eci_bvdm_gray_control_strip_old_versions/eci_bvdm_graycon_doc_eng.pdf
Europe offsets Johnson Controls' Asia gains- MSN Money
Nov 2, 2012 . Europe offsets Johnson Controls' Asia gains . The company posted growth in Asia and North America, but this was more than offset by a decline in Europe. Earnings of the company . NAME, LAST, CHANGE, % CHANGE .
http://money.msn.com/top-stocks/post.aspx?post=f1a65a81-8b08-4546-a963-edb55b30f50a
Official Impairment Listing for Spinal Disorders
Gr-baz - SpenchWiki
Nov 30, 2012 . The module name is baz, so in Python one would write: . LNA gain control (the gain range depends on the tuner); for the E4000: . Specifically I have fixed it to manual gain control and also disabled the DC offset loop as on .
http://wiki.spench.net/wiki/Gr-baz
- vca animal hospitals in n c (narrowing of the spine)
PID Tuning Tutorial
What Is PID - Tutorial Information about PID control, including a tutorial. . Manufacturers of PID controllers use different names to identify the three modes. . the controller a large gain at low frequencies that results in eliminating offset and .
http://www.expertune.com/tutor.htmlMapping - XLR8
. gain when on speakerphone call (original setting: 01, recommended setting: 03) (master control at offset 160: 08) offset 178 bluetooth voice name/voice record .
http://xlr8.us/hofo/map.txt
Chapter 5: Testing Converters - Analog Devices
are used in traditional industrial control, measurement, or instrumentation applications where monotonicity, DNL, INL, gain, and offset are important. End- Point .
http://www.analog.com/library/analogDialogue/archives/39-06/Chapter%205%20Testing%20Converters%20F.pdf
Hawaiian Kingdom - Political History
These individuals were organized under the name "Honolulu Rifles. . The insurgents used the alien vote to offset the majority vote of the aboriginal Hawaiian . in order to gain control of the Legislative Assembly, while the so- called 1887 .
http://www.hawaiiankingdom.org/political-history.shtml
Medical-Vocational Allowance for Herniated Disc
Itau Gains on Expectation Cost Control to Offset Revenue Decline ...
Itau Gains on Expectation Cost Control to Offset Revenue Decline. By Denyse Godoy - 2012-10-24T20:40:58Z. Itau Unibanco Holding SA (ITUB4), Latin .
http://www.bloomberg.com/news/2012-10-24/itau-gains-on-expectation-cost-control-to-offset-revenue-decline.html
Ch. 3 - The PID Algorithmn
ProportionalOffset. Offset can be reduced by increasing gain. Proportional control with low gain. Proportional control with higher gain .
http://www.jashaw.com/pid/tutorial/pid3.html
Appealing a Denial of Benefits
Verilog-A Sample Library - Advanced Functional Electrical Models
Dec 11, 1998 . Module Name V_ctrl_pwl_limited_amp.va, V_ctrl_pwl_limited_amp. Terminals, in : input terminal : input voltage ctrl : gain control terminal .
http://www.eda.org/verilog-ams/htmlpages/sample_lib.afem.html
Pitfalls In Seismic Amplitude Vs. Offset Analysis: Case Histories
This new technology has been called by many names (seismic lithology, . The first is the automatic gain control (AGC) applied to older (pre 1975) data.
http://www.onepetro.org/mslib/servlet/onepetropreview?id=OTC-5279-MS


A 22 years old machine learning enthusiast. passionate about solving real-world problems using AI and ML. I'm currently working as an intern at MakerGram. When am not working you can find me watching netflix 🍿 reading stories 📚 & eating snacks 🍔 and the one and only weakness is sleeping!!
I sometimes post stuff on Medium
Favourite quote
"You’re gonna die"


BORDELLO OF BLOOD A film review by Paul-Michael Agapow Copyright 1997 Paul-Michael Agapow
The second outing from the "Tales From the Crypt" crew: the prim Katherine (Eleniak) works for moneyed evangelist Reverend Current (Sarandon). However her brother (Feldman) has gone missing and she turns to sleazy two-bit detective Guttman (Miller). He in turn is lead to a funeral home which is a front for a brothel run by arch-vampire Lillith (Everhart) ...
There is a big problem at the heart of the "Tales from the Crypt" formula , TV and movies, which is difficult to put a finger on. But the epicenter is this: for the writers (4 on "Bordello of Blood") and producers (11!) this is not so much an attempt to create a new story as it is to slavishly imitate a genre. Or perhaps to slavishly imitate their idea of a genre. Thirty years ago, cheap and nasty four-colour comics titillated 15 year-olds. Now those 15 year-olds have translated the comics to the big screen, with much sentiment and little creativity.
It's not scary. It's not meant to be scary, you say. It's not very dramatic. It's not supposed to be dramatic, you reply. It's not especially funny. It's not really a comedy, you explain. So what is it?
In fact "Bordello of Blood" is not much of anything. Even at the very concise runtime of 87 minutes (around 80 if you cut out the superfluous shrieking bookends by that muppet with Tourettes, the Cryptkeeper) it has barely enough to do. The creators are simply trading on the resonances of the movie, wanting you to say "This is just like those old schlocky horror films and comics! Great!" But as the current revival of old TV favourites (Brady Bunch, Flintstones, the Saint etc.) demonstrates, bringing a small idea to the big screen with big production values does not necessarily make that idea big. More the converse - a movie length production demands movie size ideas and story, not just money. Those are things that "Bordello" ain't got.
Occasionally "Bordello" does spark, never to the extent that its predecessor "Demon Knight" did, largely when some meta-joke occurs. Confronted with the vampiric bordello, the detective Guttman starts theorising "duchovnian riffs". Teenybopper heartthrob Corey Feldman shows up just long enough to be slaughtered mercilessly. The cast is competent if unspectacular, Eleniak and Everhart being present purely for their looks. Sarandon alone shows some life, perhaps being not a great actor but a good one.
About the worst thing you could say about "Bordello of Blood" is that it's self-indulgent and largely harmless. Perhaps the "Tales" producers will retire the series or at least think more before hauling it out next time. [*/misfire] and velvet-covered furniture on the Sid and Nancy scale.
"Tales from the Crypt Presents: Bordello of Blood" Directed by Gilbert Adler. Starring Dennis Miller, Erika Eleniak, Angie Everhart, Chris Sarandon, Corey Feldman. Released 1996.
------ paul-michael agapow (agapow@latcs1.oz.au), La Trobe Uni, Infocalypse [archived at http://www.cs.latrobe.edu.au/~agapow/Postviews/]
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belongs to the author.
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Related links: index of all rec.arts.movies.reviews reviews


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OSU basketball: Montreal Pena signs with Cowboys, joins AAU teammates
by Jacob UnruhSTILLWATER — Oklahoma State has a trio of Titans.
The Cowboys made one final splash in the early signing period, landing three-star power forward Montreal Pena, an AAU teammate of other signees Cade Cunningham and Rondel Walker.
All three played over the summer for the Texas Titans, which announced Pena’s decision on Thursday. His National Letter of Intent was signed before the deadline.
The Arlington Martin (Texas) product is 6 feet 9 and 200 pounds. Pena chose the Cowboys over offers from Baylor, TCU, Wake Forest, North Texas, Oral Roberts, Texas-Arlington and Texas-El Paso.
Related Photos


First technical post. Yes, this is my first post where I actually take my instructor’s advice, which is to blog about the things in Ruby and Rails I am least certain about in an effort to better understand them.
One thing that I’m pretty sure is going to come in handy but don’t feel comfortable with is the Ruby Module. Let’s start simple and then extract. Module - what is it?
“Same, same, Class but different”
Just like a class it is a:
And maybe other stuff, but I said I’d keep it simple for your sake of course (cough cough and mine).
Ok so let’s just write some code:
1 2 3 4 5 6 7 8 9 10 11 |
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Ok so say you’re at 7/11 but in SINGAPORE or something and you want to find out if they sell Thai Redbull - very common situation. So you want to mixin your Singlish module into your English class so that you can call on some instance methods which basically translate your English to Singlish for you. We mixin the Singlish module by using the Ruby “include” keyword:
Can, la!
1 2 3 4 5 |
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Now you can do this:
1 2 |
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Ok, easy right? But what if we want our module to have a little more functionality?
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 |
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Now within our Singlish module, we have both class and instance methods that we can mixin to our English class. But how do we do that, you ask? We continue to use the “include” keyword to include the module’s instance methods in the English class. To mixin the Singlish modules’s class methods, however, we must use the “extend” keyword.
1 2 3 4 5 6 7 |
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Now, look what we can (and can’t) do!
1 2 3 4 5 6 7 8 9 10 11 12 |
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It doesn’t technically matter what you named the nested modules within the Singlish module. In my example, I name them (reasonably) “InstanceMethods” and “ClassMethods” but I could have named them “LeatherJacket” and “Tomato”. As long as I “include LeatherJacket” and “extend Tomato,” the class wil inherit LeatherJacket’s methods as instance methods and Tomato’s methods as class methods for the English class to use.


{% extends "base.html" %} {% block title %}Site is Temporarily Down{% endblock %} {% block content %}
We have been notified of the problem and will try our best to make sure it does not happen again


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--- title: "COVID-19 and R Part VI" author: "Drew Tyre" date: "2020-03-18" output: word_document: default html_document: code_folding: hide draft: no slug: COVID-19_and_R_VI tags_include: - R - OPD - COVID19 categories: Research og_image: /post/covid-19_in_r_vi_files/figure-html/featured_image-1.png ---
Tuesday’s number of new cases in the USA are inside the prediction interval, but are still below expectations. Italy’s numbers continue showing clear signs of a slowdown relative to exponential growth. Nonetheless, Italy continues to add more severe cases per day than their ICU bed capacity. Unless things change drastically, I predict the USA will start to exceed ICU bed capacity by March 23, maybe sooner in some locations. Certainly by the end of March.
That outcome is not set in stone. The time to act is now. Even a one day delay in reducing transmission rate dramatically increases the peak number of sick people. If you’re lucky to be in a low risk group, you can still spread the virus. Although your symptoms are mild, the person you hand the virus off to could experience much worse symptoms. You don’t have to hermetically seal your house. I like this graphic:
DISCLAIMER: These unofficial results are not peer reviewed, and should be treated as such. My goal is to learn about forecasting in real time, how simple models compare with more complex models, and even how to compare different models.
Another way to put the above: you get the research you pay for!
The figure below has changed quite a bit, and so I’ll highlight some key points here. The y axis is now cases / 100,000 people to make it easier to compare Italy and the USA. As before, the BLUE LINES are exponential growth models that assume the rate of increase is constant. The dashed lines are predictions from that model. The estimated model and predictions for both countries are on both panels. The lines and intervals for the other country are faded out. The RED LINES are 20 x the number of ICU beds per 100,000 people in each country.
Much as yesterday, Tuesday’s new cases in the USA are inside the prediction intervals but below expectations for the exponential model1. Italy’s numbers continue to fall outside of the prediction interval for the exponential model. Cases are growing faster in the USA than Italy during the first couple weeks because the lines are converging.
The horizontal red lines are the 20 x number of ICU beds per 100,000 people. I multiply by 20 assuming that only 5% of cases need ICU beds. It’s easy to see why things are going so poorly in Italy even though the growth in the number of new cases has leveled off. Every day they are adding more severe cases per capita than the number of ICU beds. And that assumes all the beds are available – they weren’t to begin with and certainly not now. In addition the cases are not evenly distributed geographically; some parts of Italy have a much higher number of cases per capita than others. This is also true in the USA. California, Washington state, and New York state will overwhelm their ICU capacity sooner than the rest of the country.
Unless things change radically, I expect USA to be in a similar pickle as Italy after March 23, certainly by the end of March.
Could be sooner in some places. There is a lot of uncertainty around the red line that isn’t accounted for. After I wrote that prediction (honestly), I read this CNBC report in which Governor Cuomo suggests that 110,000 beds will be needed in mid-May. Looking closely at the numbers in that article, I think there is a lot of confusion about the difference between the cumulative number of cases and the peak daily number of cases. Even many of the really sick people recover! It is clear that someone is giving Governor Cuomo projections, but he obviously doesn’t understand them. More important than the number of hospital beds, or even ICU beds, is the number of ventilators. 3000 in New York state.
Yesterday I made a back-of-the-envelope calculation about the number of true cases in Nebraska, assuming that only 20% of cases get reported. This morning I found this paper estimating that only 14% of cases in China were documented prior to Jan 23 travel restrictions. So my guess might not have been far wrong! It could be worse here, because the frequency of testing is very low. I heard an ancedote of someone being hospitalized with COVID-19 like symptoms, but not tested. In the USA only 31878 people have been tested to date, compared with 42976 in Canada! That comparison gets worse if you recall that Canada has a population ~10x smaller than the USA.
I’m going to call this one good. Keep your distance everyone, wash your hands, and stay positive. This too will pass.


My first artical in this site This is the content of my first blog post in this site. 这应该是一篇草稿。 print "hello world"


About Me
Hi, my name’s Brett and I’m a senior network system administrator. As well as administrating systems I also have experience making software and web apps. I have worked at a number of companies gaining teamwork and leadership skills.
I am most skilled in: AWS and Eating Pizza
Projects
Super awesome project
github.com/sproogenI started this project as a way if learning React and it has since grown into a fully fledged app. I have learned many skills through this and been I’m very proud of having this in my portfolio. If you don’t have a project as awesome as this I would advise you make one.
Experience
Solving 21st century problems by diging holes and making game changing products like the not a flamethrower
Every company needs its networks properly administered and The Boring Compay is no exception. Digging holes is hard and I play my part making sure the whole company stays connected. I lead a team of 5 people and enjoy driving the company to try new technologies.
Education
Harvard University
BSc Computer Science
2010 - 2013
Established in 1636, Harvard is the oldest higher education institution in the United States, and is widely regarded in terms of its influence, reputation, and academic pedigree as a leading university in not just the US but also the world.
During my time at Harvard I learnt most of my key skills that have I have taken through my career such as teamwork and working to tight deadlines. I thouroughly enjoyed my time as university and learnt a lot about a healthy work life balance.
I spent a lot of my free time as a committee member of the Harvard Mountaineering Club taking on roles such as Trip Secretary and Vice-President.
A Little More About Me
Alongside my interests in networks and software engineering some of my other interests and hobbies are:
- Rock climbing
- Gaming
- Knitting
- Becoming a ninja


Content
Content
Content
Content
Thank you for purchasing this item. We provide support for all of our Items at http://templines.com/, but please make sure you read this documentation first. Thank you so much!
If you are not familiar with HTML and CSS, I highly recommend that you to hire a specialist. It will save your time and you will get only positive emotions while using our product.
Let's look at the structure of Landing Page. It includes its own sections with content. On request you can add or remove sections of the site. Please, be careful when removing unwanted sections. It can cause errors in theme.js. All tags have to be closed properly.
It effect makes waiting process while page loading less boring for the user. Please just use this code .
js/contact_me.js
Email Settingsmail/contact_me.php
css/master.css
Global Theme Stylescss/theme.css
Main Slidercss/main-slider.css
Responsive Stylescss/responsive.css
Theme jsjs/theme.js
Icons Fonts fonts/
Changelogs file changelog.txt


Introduction
Today We’d like to introduce you to Kandan an Open Source alternative to HipChat.
What is Kandan? Kandan is a private chat service for your company or team. You can invite colleagues to share ideas and files in a persistent group chat room or rooms.
Unlike HipChat or other alternatives, Kandan is completely Open Source and can be hosted internally or externally by your organization or by a third party.
Background
Kandan was originally developed by Cloudfuji and they started development on it back on March 6th of 2012. Development went pretty fast and furious until about June 4th of 2012. After that there wasn’t another commit to the repository until some pull requests came in during the month of January of 2013.
In talking with the team at Cloudfuji both James Gifford and Tony Guntharp agreed to take over maintenance of the project.
Features
These are features that work out of the box on any provider:
- Easy deploy to CloudFoundry, Heroku, dotCloud, etc.
- Collaborative team chat
- Unlimited channels
- Embed formats for images and youtube videos with requests for others (twitter, facebook, g+, etc.)
- Synchronized sound player - play any audio-tag compatible url for the whole channel (Pending :P)
- /me command!
- Highly extensible plugin format
Get Involved
Since Kandan is a fully open-source app, we would appreciate if you dive in and start adding features, fixing bugs (what bugs?), and cleaning up the code.
Make sure you check out these resources as well.
- GitHub Issues Tracker
- Twitter @kandanapp
- Kandan Demo (Yes we eat our own DogFood)
You can also email us at<EMAIL_ADDRESS>Conclusion
We’re targetting a 1.0 release in just about two weeks time which will primarily consist of bug fixes. After that release we plan on doing regular minor releases that add new features so if there’s anything you want to see in newer versions please create an Issue.


---
title: Publications
---
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Publications for Outreach
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WITNESS LIST Juvenile Justice & Family Issues Committee February 20, 2003-10:30A Agency Overview For: Stansbury, Tom (Texas Family Law Foundation) On: Bryant, Cynthia (Child Support/Office of Attorney General) Chapmond, Thomas (Department of Protective and Regulatory Services) Fabelo, Tony (Criminal Justice Policy Council) Robinson, Steve (Texas Youth Commission) Spriggs, Vicki (Texas Juvenile Probation Commission)


Murder charge laid in relation to Invercargill death
by New Zealand Police"Murder charge laid in relation to Invercargill death "
A 31-year-old man has been charged with murder in relation to the death of a woman on Sunday 17 November.
The man will be appearing in Invercargill District Court this afternoon.
As the matter is now before the Court, no further comment can be made at this time.
ENDS


About Anteneh Tsegaye
My name is Anteneh . I am cool, funny and friendly
My Classes this Quarter are :
- CS 401 Moderen PRogramming Practice
- CS 472 Web Application Programming
- CS 431 Algorithms
My fevorite movies are:
- NoteBook (IMDB)
- Genere Love
- Length 120 min
- The Passion of Christ(IMDB)
- Genere Love
- Length 120 min
- Love Donot cost a thing (IMDB)
- Genere Love
- Length 120 min
My Moods
Sad:
Happy
Fun Facts about My Neighbors
- Bini is an Ethiopian four eyed freindly person
- Asir an Egyptian freind who likes asking


2 Samuel 3:16
And her husband went with her along weeping behind her to Bahurim. Then said Abner unto him, Go, return. And he returned.


Delivering AI Applications with Open Source Platforms at Scale
- Pantelis Monogioudis, Gurudutt Hosanghadi, Lorant Farkas, Chris White
- Augmented Intelligence & Devices Research Lab
- Bell-Labs, Murray Hill, NJ, USA
About me
20 years of experience in leading Research & Development projects. I started in wireless communications (PHY/MAC) in the 90s and in 2000’s started diversifying into Machine Learning initially for wireless networks and later for all sorts of applications. Currently leading ~15 researchers in Murray Hill NJ and Budapest in Applied Machine Learning working on real-time multisensory machine learning applications. 1 (failed) startup, 15 journal publications and 50 patents issued/pending. As of spring 2019 I am also with the Computer Science department of NJIT. I live in Randolph, NJ with my wife, my two boys Alex & Nicholas and my dog Bleu.
We are hiring ML researchers / engineers in Paris-Saclay region - please get in touch with me via linkedin / twitter if you are interested.
A nautical analogy on where we are today with AI
DARPA's AI development in waves
Symbolic GOFAI
> 1980
Connectionism
> 2010
AGI (kind of)
2020s
Meaning and resoning in NLP, Inference and Representation of Causality, Uncertainty Representations, Long-term Goal Planning, World Models
AI in Health
What we need from AI Architecture
-
Composability
pointing to microservices and atomic operations. -
Scalability & Resilience
pointing to cloud-native (Kubernetes and FaaS) architectures. -
Portability
across workstation, training rigs, dedicated edge devices and public clouds.
AI Architecture Building Blocks - Deep Learning Foundation
www.acumos.org - the need for AI marketplaces
What other opportunities are there ?
Video AI opportunities in surveillance
Deep Learning in 2017
After spending 3200W, 2-socket CPU, 8 x GTX1080Ti, 16 x 720p cameras, algorithms are unable to pass the mirror test.
2 year old
She figures it out in less than 1 min.


SPARQL for domain [_domain_] _versionInfo_
Click
List
to go Back to [_domain_].
Execute SPARQL
SPARQL Tutorial
Name of query to
save
on execute:
or
delete
Pick a saved query to execute:
_saved_
_item_
_/saved_
NaN
Query Builder Helper:
[Make your selection and watch the query modified]
orderBy
resource
DESC()
Show used-by info _predicates_
Use
_predicate_
orderBy
DESC() _/predicates_
_prefixes_
Select
DISTINCT
REDUCED
_select_
Where
_where_
Order By
_orderBy_
OFFSET
LIMIT
Execute SPARQL
Results for resources of type [_resourceType_]:
_report_
_error_
#
_headers_
_th_
_/headers_
_rows_
_num_
_values_
_td_
_/values_
_/rows_


EBD - Easton Bible DIctionary
Amalek
AMALEK
"AMALEK "dweller in a valley, the son of Eliphaz and grandson of Esau(
Genesis 36:12
;
1 Chronicles 1:36
); the chief of an Idumean tribe (
Genesis 36:16
). His mother was a Horite, a tribe whose territory the descendants of Esau had seized.
"AMALEK "dweller in a valley, the son of Eliphaz and grandson of Esau(Genesis 36:12; 1 Chronicles 1:36); the chief of an Idumean tribe (Genesis 36:16). His mother was a Horite, a tribe whose territory the descendants of Esau had seized.


Table: My favorite records.
album
artist
released
Rubber Soul
The Beatles
1965
Brown Eyed Girl
Van Morrison
1967
Mellon Collie and the Infinite Sadness
The Smashing Pumpkins
1995
album
artist
released


A common way of showing part-to-whole data - but be aware that it's difficult to accurately compare the size of the segments.
Non-responsive chart
Responsive image set


About Me
At the age of 33 I can say I have already changed the direction of my career three times. With a background in education and most recently financial services, I have decided to move toward the direction of coding.
What ignited my interest in coding is my love for video games. A badly coded game can completely ruin your immersion. Bugs can completely destroy your experience whether it be unresponsive combat or constantly getting stuck in corners when exploring an open sandbox environment.
I love the idea of creating something out of nothing. Being able to turn an idea into something tangible. Coding is the future and I want to be a part of it. My ultimate dream is to someday program video games and help bring joy and entertainment to millions through flawlessly coded games.


I worked at AT&T in Holmdel, NJ and Menlo Park, CA, from 1995 until 2000.
In Holmdel, NJ, I shared an office with a coworker. When I moved to Menlo Park, CA, I shared a small room with two other people before finally getting my own office. It had a door that I could close (but not lock). Here is a picture of me in that office in Menlo Park, CA.
In Holmdel, NJ, none of the offices had windows. In Menlo Park, CA, pretty much all the offices had a window, except for the small room I occupied upon my arrival. This was the view from my window in Menlo Park, CA. It opened on the courtyard in the middle of the building which had a water fountain in the center.
In New Jersey, I had to drive to work. There was a lot of parking space around the building.


| shape-package | Functions for plotting graphical shapes, colors |
| A4 | opens A4-sized window |
| Arrowhead | adds arrowheads to a plot |
| Arrows | adds arrows with improved arrowhead to a plot |
| colorlegend | adds a color legend to a plot. |
| cylindersegment | adds part of a cylinder to a plot |
| drapecol | draping colors over a persp plot |
| emptyplot | open a plot without axes, labels,... |
| femmecol | red-green-blue color palette |
| filledcircle | adds colored circle to a plot |
| filledcylinder | adds a colored and rotated cylinder to a plot |
| filledellipse | adds a colored and rotated ellipse to a plot |
| filledmultigonal | adds a colored and rotated multigonal shape to a plot |
| filledrectangle | adds a colored and rotated rectangle to a plot |
| filledshape | adds a colored shape to a plot |
| getellipse | x-y coordinates of ellipse |
| graycol | white-black color palette |
| greycol | white-black color palette |
| intpalette | color palettes |
| plotcircle | adds part of a colored circle to a plot |
| plotellipse | adds part of a colored and rotated ellipse to a plot |
| rotatexy | rotates 2-column matrix around a midpoint |
| roundrect | adds a rounded rectangular box to a plot |
| shadepalette | color palette inbetween two extremes |
| shape | Functions for plotting graphical shapes, colors |
| textflag | adds a filled rounded rectangular box with a text to a plot |
| writelabel | adds a label next to a plot |