Block matrix Puns

https://www.matrixresurrections.net/images/legal/billingblock.png as found on whatisthematrix.com

The cast list reads in the following order:

• Keanu Reeves
• Carrie-Anne Moss
• Yahya Abdul-Mateen II
• Jessica Henwick
• Jonathan Groff
• Neil Patrick Harris
• Priyanka Chopra Jonas
• and Jada Pinkett Smith

Note this is not the entire cast list, nor does the placement of each name (necessarily) indicate the size of each role.

But I do feel it's going to be kinda indicative. To give an example, Priyanka Chopra Jonas, who I believe is playing Sati, probably has a smaller role than someone like Yahya Abdul-Mateen II's Morpheus and Jessica Henwick's Bugs, who seem to be directly involved with the awakening and guidance of Neo, per the trailer.

Neil Patrick Harris seems like a villain (since he is associated with the color blue and his naration in the pre-trailer clips was in stark contrast with that of Yahya's Morpheus) and villains usually have less screen time than the heroes. Jonathan Groff could go either way.

Jada Pinkett Smith probably has a smaller but significant role considering she was a part of the previous films (she will likely be the one that explains what has happened in all those years or who is this new Morpheus), and combined with the fact she is somewhat well known, she receives "last billing" ("and <name here>").

Finally, I'm sure that indeed Keanu has the most screen time in the film, but Carrie's might be a little less than her billing suggests, since the trailer doesn't show so much of her.

Timestamp: https://imgur.com/a/EWrUgg2

Otter, earl, and PAWS hellcap sold.

Gothcaps cyber goth hellcap - $85

HWS popsi - $120

Unknown shark - $20

Cat caps walrus - $20

Prime keys klacken s - $40

Glenn blank - $20

Also looking to purchase a pair of matrix 2.0 add rear weight blocks - open to most colors but very much want the raw brass color. These are shown in yellow here for reference: https://i.imgur.com/eisjSdC.jpg

Cuz I'd Schur like to complement you ;)

I have something important to confess. I have no idea what the Schur complement is since I "learned" about it in my last semester of college, as it was transitioning online, after I already secured a job offer. All I remember from Optimization Theory is juuust enough to make this glorious pun. Totally worth

Anyway now that I've scared away all but the most dedicated of pun enthusiasts, hit me up with your most original pun! Or complain to me about how contrived mine is. Hell, tell me anything, it's really hot right now and I don't want to turn on my AC so I'm trying not to melt into a puddle.

For more about me check out my profile but don't read anything that has less than 5 upvotes cuz that probably means it totally sucks. Thanks for coming to my Ted talk.

I know that the determinant has the property that a block diagonal matrix A with blocks A_1 ... A_n has the determinant det(A) = prod(det(A_i)). I think the same should hold for the matrix permanent, and some quick numerical tests seem to suggest that's true, but I have been unable to find a statement of this anywhere, let alone a proof. If anyone can confirm it / point me in the right direction I would much appreciate it. The reason for asking is that I'm doing some simulations where I need to take the permanent of block diagonal matrices and being able to fractorize it would be a big help.

I am hoping someone can help me begin to address my deficiencies in understanding this problem. On Math Stack Exchange, the OP is asking how to find eigenvalues and eigenvectors of block Toeplitz matrices. I have a similar problem I wish to solve, but I would like to understand their work first, so my question is this:

How did the OP go from, T, a 2N-by-2N matrix, represented as the sum of tensor products (the Pauli matrix step) to T as the sum of (2-by-2) matrices after plugging in the eigenvalues of each (N-by-N) sub-block of T?

I understand that the eigenvalues of the tridiagonal Toeplitz matrices fill each of the elements of the 2-by-2 representation of T, and also that the eigenvectors of tridiagonal N-by-N Toeplitz matrices (e.g., A, B, etc) are all the same, and thus simultaneously diagonalize all blocks of T(2N-by-2N).

Thanks for the help!

Journal of the American Chemical SocietyDOI: 10.1021/jacs.0c12404

Bhavin V. Pipaliya, Daria N. Trofimova, Rebecca L. Grange, Madhu Aeluri, Xu Deng, Kavan Shah, Andrew W. Craig, John S. Allingham, and P. Andrew Evans

https://ift.tt/3xEFvhz

I have searched the internet, but have not found a solution to my problem.

i have a matrix

f = [1 1 2 2;
     1 1 2 2;
     3 3 4 4;
     3 3 4 4]

which i want to turn in a vector (row or collumn doesnt matter),

such as A1 = [1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4]

then i could use the funciton reshape to get 4 blocks of 2x2-matrices that look like this:

[1 1; 
 1 1],
 
[2 2; 
 2 2],
 
[3 3; 
 3 3],
 
[4 4; 
 4 4]

what i DON'T want:

When i use f(:)' or f(:) ,or even reshape to convert f into a row/collumn i get:

f = [1 1 3 3 1 1 3 3 2 2 4 4 2 2 4 4]

% now when i try to create the matrix, i get the wrong one:

reshape(f, [2 2 4])

ans(:,:,1) =

     1     3
     1     3


ans(:,:,2) =

     1     3
     1     3


ans(:,:,3) =

     2     4
     2     4


ans(:,:,4) =

     2     4
     2     4

And i don't want to use mat2cell. Also, the matrix can be much larger, but always so that it can be split up in many 2x2-blocks. The elements can be random, like random 1 or 0 filling up the matrix, i just used 1 - 4 to better demonstrate my problem.

Any ideas how this can be done efficiently? Thank you, and sorry for my english .

No matter how I look at it, I see a conceptual similarity between the covariance matrix-P(t) in Recursive Least Squares (RLS) and the self-attention block in Transformers.P(t) in RLS helps to calculate Kalman gain (K) which puts more emphasis on the relevant features. Self-attention block also allows attending to more relevant features. So apart from the differences in the learning method, are there any conceptual differences between the self-attention block and the covariance matrix in RLS?

Thanks in advance

Edit: After thinking a bit more about it. I guess, the covariance matrix in RLS is mainly there to decorrelate the input features. However, something still tells me that RLS can become a powerful beast if it is mutated into a Transformer-like architecture by adding multiple layers + nonlinearity.

https://preview.redd.it/c5mftbtmve741.png?width=2912&format=png&auto=webp&s=487c5a435116f5c60b6a4d8eb15c99c5a0aa32d3

In this post, I’m going to discuss the efficiency of block sparse matrix-vector multiplication on GPU. To show some real-live application results, I develop a Matrix Structural Analysis application, which is used to simulate the Golden Gate bridge structure.

Do we have the technology to block out the sun completely like they did in Matrix. I know that mega volcano can kick up enough dust to block out the sun so I'm just wondering like a old cartoon villain can we do it man-made.

Thanks in advance.

I have a file with text like.
abc
def
ghi
And I want the matrix ['a','b','c'; 'd','e','f';'g','h','i'] How do I do this? Thanks

How do I find the eigenvalues of a 6x6 matrix using the block matrix idea:

In the Matrix, humans blocked out the Sun which was the source of power for the Machines. My question is, what did the Machines use for their source of power inbetween the time the Sun was blocked out, to when they started using humans as their source of power?

Obviously D.Va is very strong and has probably never been off-meta. I don't think she is OP but I think a few nerfs could be justified, she has a 95% pick rate in pro play. I play D.Va a lot but I still feel sometimes she doesn't really have any weaknesses and can be run in almost any comp or map.

I'm paging a thread I saw on /r/dvamains : https://www.reddit.com/r/DvaMains/comments/a8qjo7/does_matrix_not_work_vs_sniper_shots_how_does/

The below are my perspective (that resulted from what I've got from that thread), which I do not guarantee to be fully accurate. So please read that thread first.

Defense matrix has a conic hexagonal shape that works in one direction. And the further the cone is from dva, the bigger the surface it has, making it quasi omnipotent facing characters that dva has in her line of sight. However, because defense matrix is not a 2d plane shape but has a certain thickness to it, it becomes possible as discussed by the commenters in that thread to block incoming sniper shots even if you are not facing the snipers themselves, but by simply launching defense matrix inbetween your ally's hurtbox and the adversary's shots.

However, what sparkled that discussion is that the behaviour is inconsistent (some times it works, so n times it doesn't), or that there is a lack of video demonstration. Perhaps the dm was too close to the ally, thus the cone at the time too small to cover the head's hurtbox, making it vulnerable (and making dm useless at that range) to the adversary's shot. Had D.Va been further from her ally, would they have survived?

It's just speculation at this point, and I have nothing to show for it.

I feel that this question needs more testing in a controlled environment to fully dissect it. But in order to run this test you would need at least 3 people on the plan. Which I do not have.

It would be a great public service if someone have the means to do it, do it.

Tl; dr: Calling every educational youtubers/streamers! (Karmawhores are also welcome!) This is your chance for some new material (and time to rake up some new subs /s)

Edit: grammar

Judging by the PTR notes, does this mean DVA is going to be able to block/absorb Roadhog hooks?

D.Va

Defense Matrix Projectiles (like Roadhog’s Chain Hook or Tracer’s Pulse Bomb) no longer need to travel a minimum distance before they can be blocked

Developer Comments: Previously, there was a minimum distance a projectile had to travel before it could be destroyed by Defense Matrix. This made it nearly worthless in situations where an enemy was right next to your teammate, such as when Roadhog hooks your ally. This change removes that restriction so Defense Matrix should now reliably destroy projectiles regardless of how far they have traveled.

Hi, I'm a linear algebra teacher. I just gave a fun and tricky lecture on block multiplication. I went into a bit more depth than the textbook, or anything else I've seen, because I wanted to show how all the different versions of matrix multiplication are examples of conformal partitions and block multiplication, and give students the confidence to make up their own weird formulas. (the versions I used: definition of matrix times a vector, columns of AB are linear combos of columns of A using columns of B as weights, rows of AB are linear combos of rows of B using rows of A as weights, the row-column rule, the column-row expansion)

I also introduced some terminology that I made up and I want to know if there's an official term for it. When A is m x n and B is n x p, and you partition the n columns of A along with the n rows of B, these partitions must match up exactly. These correspond to addition of the block products. I call these "inner partitions" because the n is on the inside of (m x n)(n x p).

On the other hand, when you partition the m rows of A and/or the p columns of B, they don't have to match up in any way at all. These correspond to blocks that just sit in different parts of the result matrix. I call these "outer partitions" because the m and p are on the outside of (m x n)(n x p).

Because it is a confusing topic, the students were a bit confused. I would like to make it a little simpler by renaming the different partitions "L and anti-L partitions" instead of "inner and outer partitions." To write an anti-L you just draw a backwards L. The idea is that the vertical line in L is on the left, when a vertical line partitioning the columns of A is on the left, and the horizontal line partitioning rows of B is on the right in AB. So it actually looks kinda like an L with an inner partition, and kinda like a backwards L with an outer partition.

So yeah, anybody have a better name for these?

Hi all, I need to implement tiling multiplication using row-major format for class and I don't get my program to work.

I have 3 Matrices MatA[m][k], MatB[k][n] and MatC[m][n], and 2 variables alpha and beta.

The multiplication was defined as MatC = beta*MatC + alpha*MatA*MatB. All of them are previously initialized so I don't have to make values of MatC = 0.0

Our professor gave us a function that compares our results with the algorithm made by him, and I got to have the naive algorithm working with normal matrices and transposing MatB having 0% error, but I don't know why this is giving me some percentage of error.

Can someone help me and tell me if you see any error that it has? Thank you.

void alumno_dgemm(int m, int n, int k, double alpha, double *MatA, int lda, double *MatB, int ldb, double beta, double *MatC, int ldc){

  const int block_size = 363; //sqrt( (3072*1024)/(8*3) ) = 362'0387, the professor gave us this formula)

   int i0,j0,r0,i1,j1,r1;
   double e;
   for(i0=0; i0<m; i0+=block_size){
     //int imax = block_size > m-i0 ? m-i0 : block_size;
     int imax = m>i0+block_size ? i0+block_size : m;

     for(j0=0; j0<n; j0+=block_size){
       //int jmax = block_size > n-j0 ? n-j0 : block_size;
       int jmax = n>j0+block_size ? j0+block_size : n;

       for(r0=0; r0<k; r0+=block_size){
         //int rmax = block_size > k-r0 ? k-r0 : block_size;
         int rmax = k>r0+block_size ? r0+block_size : k;

         for(i1=i0; i1<imax; i1++){
           for(j1=j0; j1<jmax; j1++){
             e=0.0;
             for(r1=r0; r1<rmax; r1++){
                e = MatA[i1*lda+r1]*MatB[r1*ldb+j1];
             }
             MatC[i1*ldc+j1] = beta*MatC[i1*ldc+j1] + alpha*e;
           }
         }
       }
     }
   }
}

Example:

A|B|C

1|2|3

!|@|#

In which A1!, B2@, C3#, ABC, 123, and !@# each stand on their own as a short story.

For those interested in a better challenge: Could you make it reversible? Could you find a way to include the diagonals as well? Could it make sense if any operation were performed on the matrix?

If this sparks your interest then what other topological morphology could you use to write an arrayed story?

https://imgur.com/r7HrhmX

This is a block matrix formed by matrices A and B, which are from a dynamical system x'=Ax+Bu

N is an integer.

Does anyone knows what this block matrix is called? Or if it even has a name?

It somehow resembles Vandermonded matrix but with block matrix and the powers are in decreasing order stopping until the diagonal line.