Sakura crosshair| #VALORANT code: 0;p;0;P;c;8;u;FF8D89FF;o;0.157;d;1;b;1;a;0.484;m;1;0t;4;0l;2;0o;2;0a;1;0e;0.39;1o;3;1a;1;1m;0;1e;0.177

skye crosshair| #VALORANT code: 0;s;1;P;c;8;u;7FFF0AFF;h;0;d;1;b;1;m;1;0t;6;0l;1;0o;1;0a;1;0f;0;1o;2;1a;0.61;1m;0;1e;0.34

Piplup crosshair | #VALORANT code: 0;s;1;P;c;4;o;1;d;1;z;6;a;0;f;0;m;1;0t;10;0l;10;0v;0;0g;1;0o;10;0a;0;0f;0;1t;6;1l;0;1v;3;1g;1;1o;0;1a;1;1s;0.053;1e;0.206

このクロスヘア最強！ 0;p;0;c;1;s;1;P;c;8;u;458550FF;o;1;b;1;0t;3;0l;9;0v;9;0o;4;0a;1;0f;0;1b;0;A;c;7;h;0;d;1;z;3;0l;17;0v;17;0o;17;0e;1.0;1t;5;1l;9;1v;9;1a;0.862;1s;1.0;1e;1.0;S;c;0;s;2.852;o;1 #VALORANT

このクロスヘアバフってるから使ってみて!! 0;c;1;s;1;P;u;0000FFFF;o;0.2;f;0;m;1;0t;1;0l;3;0v;2;0g;1;0o;2;0a;1;0f;0;1b;0;S;d;0 #VALORANT #おすすめのクロスヘア教えて

Premier初ACE！ Meiy君がアイボで6k ACEした時の1321クロスヘア良いぞ！ 0;p;0;s;1;P;o;0;f;0;0l;3;0o;1;0a;1;0f;0;1b;0;A;o;1;d;1;0t;0;0l;0;0o;0;0a;0;0f;0;1l;0;1a;1;S;s;0

for(float i,z,d,f;i++<1e2;o+=vec4(4,6,8.+z,0)/f-min(dFdx(z)*r.y+z,0.)/exp(d*d/.1)){vec3 p=z*(FC.rgb*2.-r.xyy)/r.y,c=p;p.z+=8.;c.z*=3.;for(f=1.;f++<9.;c+=sin(c.yzx*f+z+t*.5)/f);z+=min(f=.1+abs(.2*c.y+abs(p.y+.8)),d=max(length(p)-3.,.9-length(p-vec3(-1,1,3))))/7.;}o=tanh(o/2e3);

Hail, hail Mother Mahalakshmi! H a i l M... o t h e r L a k s h m i, b y w h o s e g r a c e h a p p i n e s s, p r o s p e r i t y a n d u n b r o k e n g o o d f o r t u n e s h o w e r u p o n t h e h o m e a n d c o u r t y a r d O Mother Lakshmi,seated on🙌 Grokshow more

Hail, hail Mother Mahalakshmi! H a i l M... o t h e r L a k s h m i, b y w h o s e g r a c e h a p p i n e s s, p r o s p e r i t y a n d u n b r o k e n g o o d f o r t u n e s h o w e r u p o n t h e h o m e a n d c o u r t y a r d O Grokshow more

Hail, hail maa Mahalakshmi! H a i l M... o t h e r L a k s h m i, b y w h o s e g r a c e h a p p i n e s s, p r o s p e r i t y a n d u n b r o k e n g o o d f o r t u n e s h o w e r u p o n t h e h o m e a n d c o u r t y a r d O Maa Lakshmi, seated on the lotus, Grokshow more

Hail,hail Maa Mahalakshmi! H a i l M o... t h e r L a k s h m i, b y w h o s e g r a c e h a p p i n e s s, p r o s p e r i t y a n d u n b r o k e n g o o d f o r t u n e s h o w e r u p o n t h e h o m e a n d c o u r t y a r d O Mother Lakshmi,seated on the lotus Grokshow more

Orchard vec3 p,v=normalize(FC.rgb*2.-r.xyx),c=v/v.y;c.z+=.5*t;for(float z,i,b,g,m;i++<5e1;z+=.8*max(b=length((p.y-m)/1e2/(abs(sin(c.xz/.1))-.05/v.y)),min(4.-m,g=length(sin(p.xz)+1.-.1*(1.+sin(p.y-p.zx*.5))*m))-b),o.rgb+=(.7-v)/(g+b))p=z*v+1.,p.z-=t,m=abs(++p.y);o=tanh(o/5e2);

"Waveform" for(float i,d,z,f;i++<9e1;o+=(cos(z*.5+t+vec4(0,2,4,3))+1.3)/d/z){vec3 p=z*normalize(FC.rgb*2.-r.xyy)+1.;f=max(-p.y,0.);p.y+=f+f;for(d=1.;d<3e1;d+=d)p.y+=cos(p*d+2.*t*cos(d)+z).x/d;z+=d=(.1*f+abs(p.y-1.)/(++f*f)+max(d=p.z+3.,-d*.1))/8.;}o=tanh(o/9e2);

E K D A N T B H A... L S U R A T,M U S H A K D A M A N R A T🚩🙏 JAY SHRI GANESHAY NAMAH 🙏 J A Y S H R I G A N E S H A Y N A M A H 🙏 MANGAL MURTI MORAYA ✨ M A N G A L M U R T I M O R A Y A ✨ GANPATI BAPPA MORAYA ❤️ G A N P A T I B A P P A M O R A Y A ❤️ C H A N D R A S U R Y A K O C H H O D I D E E P A K J A L A T 🙌 Grokshow more

"Storm" in 238 bytes: for(float i,z,d,s;i++<1e2;o+=vec4(.2/d,9,2,1)/d){vec3 p=z*normalize(FC.rgb*2.-r.xyy),a=p;p.z+=7.;s=length(p); for(d=0.;d++<9.;a+=sin(a*d-t).yzx/d); z+=d=length(vec4(sin(s+s-t/.1+a*sin(a.yzx/3.+t))+.9,min(s-=4.,-s/.1)*.4))*.1;} o=tanh(o/8e3);

a=(m,d=mag(k=2*cos(i*342),e=sin(i*271)*2)/1.6)=>point(k*(p=5+2*sin(d*8-t*3+m))+9/d*sin(k*2)+89*sin(c=d*d/9-t/8+m)+200,79*sin(c*2)+9/d*sin(e*2)+e*p+200) t=0,draw=$=>{t||createCanvas(w=400,w);background(9).stroke(w,116);for(t+=PI/60,i=1e4;i--;)a(i%4*5)}//#つぶやきProcessing

Following many two-dimensional turbulence animations, it is time to... share computer code! Implementation of a vorticity-streamfunction method with a pseudo-spectral discretization + third-order Runge-Kutta for time integration (our CFD class semester project). The MATLAB code: M = 256; % number of points N = M; Lx = 2*pi; Ly = 2*pi; nu = 5e-4; % kinematic viscosity Sc = 0.7; % Schmidt number beta = 0; % meridional gradient of Coriolis parameter ar = 0.02; %random number amplitude b = 1; % mean scalar gradient CFLmax = 0.8; tend = 200; % end time x=linspace(0,Lx,M+1); x(end)=[]; dx = Lx/M; kx=[0:M/2 -M/2+1:-1]*2*pi/Lx; y=linspace(0,Ly,N+1); y(end)=[]; ky=[0:N/2 -N/2+1:-1]*2*pi/Ly; dy = Ly/N; time = 0; index_kmax = ceil(M/3); kmax = kx(index_kmax); filter = ones(M,N); filter(index_kmax+1:2*index_kmax+3,index_kmax+1:2*index_kmax+3)=0; rng(64); [u, v, omega, psi, ddx, ddy, idel2, kk, k2]=deal(zeros(M,N)); for j=1:N ddx(:,j)=1i*kx; end for i=1:M ddy(i,:)=1i*ky; end for i=1:M for j=1:N idel2(i,j)=-kx(i)^2-ky(j)^2; end end idel2=1./idel2; idel2(1,1)=0; for i=1:M for j=1:N kk(i,j)=kx(i)^2+ky(j)^2; k2(i,j)=kx(i)^2+ky(j)^2; if kk(i,j) >= 6^2 && kk(i,j) <= 7^2 % forcing kk(i,j) = -kk(i,j); end if kk(i,j) <= 2^2 kk(i,j) = 8*kk(i,j); % large-scale dissipation end end end for i=1:M for j=1:N u(i,j) = cos(2*x(i))*sin(2*y(j))+ar*rand; v(i,j) = -sin(2*x(i))*cos(2*y(j))+ar*rand; end end uhat = fft2(u); vhat = fft2(v); omegahat = ddx.*vhat - ddy.*uhat; % make vorticity phi = rand(size(u)); phihat = fft2(phi); ncid = netcdf.create(' 'CLOBBER'); dimid_x = netcdf.defDim(ncid, 'x', M); dimid_y = netcdf.defDim(ncid, 'y', N); dimid_time = netcdf.defDim(ncid, 'time', netcdf.getConstant('NC_UNLIMITED')); varid_x = netcdf.defVar(ncid, 'x', 'NC_FLOAT', [dimid_x]); varid_y = netcdf.defVar(ncid, 'y', 'NC_FLOAT', [dimid_y]); varid_u = netcdf.defVar(ncid, 'u', 'NC_FLOAT', [dimid_x, dimid_y, dimid_time]); varid_v = netcdf.defVar(ncid, 'v', 'NC_FLOAT', [dimid_x, dimid_y, dimid_time]); varid_omega = netcdf.defVar(ncid, 'vorticity', 'NC_FLOAT', [dimid_x, dimid_y, dimid_time]); varid_phi = netcdf.defVar(ncid, 'scalar', 'NC_FLOAT', [dimid_x, dimid_y, dimid_time]); varid_dissipation = netcdf.defVar(ncid, 'dissipation', 'NC_FLOAT', [dimid_x, dimid_y, dimid_time]); varid_time = netcdf.defVar(ncid, 'time', 'NC_FLOAT', [dimid_time]); netcdf.endDef(ncid); netcdf.putVar(ncid, varid_time, 0, 1, time); netcdf.putVar(ncid, varid_x, 0, M, x); netcdf.putVar(ncid, varid_y, 0, N, y); netcdf.putVar(ncid, varid_phi, [0, 0, 0], [M, N, 1], phi); netcdf.putVar(ncid, varid_u, [0, 0, 0], [M, N, 1], u); netcdf.putVar(ncid, varid_v, [0, 0, 0], [M, N, 1], v); netcdf.putVar(ncid, varid_omega, [0, 0, 0], [M, N, 1], omega); netcdf.putVar(ncid, varid_dissipation, [0, 0, 0], [M, N, 1], 0*omega); dt = 0.5*min([dx dy]); nstep = 1; while time < tend psihat = -idel2.*omegahat; uhat = ddy.*psihat; vhat = -ddx.*psihat; u = real(ifft2(uhat)); v = real(ifft2(vhat)); omegadx = real(ifft2(ddx.*omegahat)); omegady = real(ifft2(ddy.*omegahat)); facto = exp(-nu*8/15*dt*kk); factp = exp(-nu/Sc*8/15*dt*k2); r0o = -fft2(u.*omegadx+v.*omegady)+beta*vhat; r0p = -fft2(u.*real(ifft2(ddx.*phihat))+v.*real(ifft2(ddy.*phihat)))+b*vhat; omegahat = facto.*(omegahat + dt*8/15*r0o); % update omega phihat = factp.*(phihat + dt*8/15*r0p); % update phi %%%% Substep 2 psihat = -idel2.*omegahat; uhat = ddy.*psihat; vhat = -ddx.*psihat; u = real(ifft2(uhat)); v = real(ifft2(vhat)); omegadx = real(ifft2(ddx.*omegahat)); omegady = real(ifft2(ddy.*omegahat)); r1o = -fft2(u.*omegadx+v.*omegady)+beta*vhat; r1p = -fft2(u.*real(ifft2(ddx.*phihat))+v.*real(ifft2(ddy.*phihat)))+b*vhat; omegahat = omegahat + dt*(-17/60*facto.*r0o + 5/12*r1o); phihat = phihat + dt*(-17/60*factp.*r0p + 5/12*r1p); facto = exp(-nu*(-17/60+5/12)*dt*kk); factp = exp(-nu/Sc*(-17/60+5/12)*dt*k2); omegahat = omegahat.*facto; phihat = phihat.*factp; %%%% Substep 3 psihat = -idel2.*omegahat; uhat = ddy.*psihat; vhat = -ddx.*psihat; % max(max(abs(real(ifft2(1i*ddx.*uhat+1i*ddy.*vhat))))) % divergence u = real(ifft2(uhat)); v = real(ifft2(vhat)); omegadx = real(ifft2(ddx.*omegahat)); omegady = real(ifft2(ddy.*omegahat)); r2o = -fft2(u.*omegadx+v.*omegady)+beta*vhat; r2p = -fft2(u.*real(ifft2(ddx.*phihat))+v.*real(ifft2(ddy.*phihat)))+b*vhat; omegahat = omegahat + dt*(-5/12*facto.*r1o + 3/4*r2o); phihat = phihat + dt*(-5/12*factp.*r1p + 3/4*r2p); facto = exp(-nu*(-5/12+3/4)*dt*kk); factp = exp(-nu/Sc*(-5/12+3/4)*dt*kk); omegahat = omegahat.*facto; phihat = phihat.*factp; phihat = filter.*phihat; omegahat = filter.*omegahat; time = time + dt; nstep = nstep + 1; CFL = max(max(abs(u)))/dx*dt+max(max(abs(v)))/dy*dt; if mod(nstep,20)==0 phi = real(ifft2(phihat)); omega = real(ifft2(omegahat)); dissipation = 2*nu*(real(ifft2(ddx.*uhat)).^2 + real(ifft2(ddy.*uhat)).^2 + real(ifft2(ddx.*vhat)).^2 + real(ifft2(ddy.*vhat)).^2); eta = (nu^3/mean(dissipation,'all'))^0.25; subplot(221); pcolor(x,y,omega'); title('Vorticity'); shading flat; axis equal tight; colorbar; drawnow subplot(222); pcolor(x,y,phi'); title('Scalar'); shading flat; axis equal tight; colorbar; drawnow subplot(223); pcolor(x,y,dissipation'); title('Dissipation'); shading flat; axis equal tight; colorbar; drawnow subplot(224); pcolor(x,y,u); title('u-velocity'); shading flat; axis equal tight; colorbar; drawnow fprintf(1,'step = %d time = %g dt = %g CFL = %g kmax*eta = %g %g\n', nstep, time, dt, CFL, eta.*kmax, eta.*kmax/sqrt(Sc)); [~, dim_time_len] = netcdf.inqDim(ncid,dimid_time); netcdf.putVar(ncid, varid_time, [dim_time_len], [1], time); netcdf.putVar(ncid, varid_phi, [0, 0, dim_time_len], [M, N, 1], phi); netcdf.putVar(ncid, varid_u, [0, 0, dim_time_len], [M, N, 1], u); netcdf.putVar(ncid, varid_v, [0, 0, dim_time_len], [M, N, 1], v); netcdf.putVar(ncid, varid_omega, [0, 0, dim_time_len], [M, N, 1], omega); netcdf.putVar(ncid, varid_dissipation, [0, 0, dim_time_len], [M, N, 1], dissipation); netcdf.sync(ncid); end dt = CFLmax/CFL*dt; %0.0005; end netcdf.close(ncid);show more

[Graph Convolutional Network] by hand ✍️ Graph Convolutional Networks... (GCNs), introduced by Thomas Kipf and Max Welling in 2017, have emerged as a powerful tool in the analysis and interpretation of data structured as graphs. This exercise demonstrates how GCN works in a simple application: binary classification. -- Goal -- Predict if a node in a graph is X. -- Architecture -- 🟪 Graph Convolutional Network (GCN) 1. GCN1(4,3) 2. GCN2(3,3) 🟦 Fully Connected Network (FCN) 1. Linear1(3,5) 2. ReLU 3. Linear2(5,1) 4. Sigmoid Simplications: • Adjacent matrices are not normalized. • ReLU is applied to messages directly. -- Walkthrough -- [1] Given ↳ A graph with five nodes A, B, C, D, E [2] 🟩 Adjacency Matrix: Neighbors ↳ Add 1 for each edge to neighbors ↳ Repeat in both directions (e.g., A->C, C->A) ↳ Repeat for both GCN layers [3] 🟩 Adjacency Matrix: Self ↳ Add 1's for each self loop ↳ Equivalent to adding the identity matrix ↳ Repeat for both GCN layers [4] 🟪 GCN1: Messages ↳ Multiply the node embeddings 🟨 with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [5] 🟪 GCN1: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The purpose is the pool messages from each node's neighbors as well as from the node itself. ↳ The result is a new feature per node [6] 🟪 GCN1: Visualize ↳ For node 1, visualize how messages are pooled to obtain a new feature for better understanding ↳ [3,0,1] + [1,0,0] = [4,0,1] [7] 🟪 GCN2: Messages ↳ Multiply the node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [8] 🟪 GCN2: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The result is a new feature per node [9] 🟪 GCN2: Visualize ↳ For node 3, visualize how messages are pooled to obtain a new feature for better understanding ↳ [1,2,4] + [1,3,5] + [0,0,1] = [2,5,10] [10] 🟦 FCN: Linear 1 + ReLU ↳ Multiply node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is a new feature per node ↳ Unlike in GCN layers, no messages from other nodes are included. [11] 🟦 FCN: Linear 2 ↳ Multiply node features with weights and biases [12] 🟦 FCN: Sigmoid ↳ Apply the Sigmoid activation function ↳ The purpose is to obtain a probability value for each node ↳ One way to calculate Sigmoid by hand ✍️ is to use the approximation below: • >= 3 → 1 • 0 → 0.5 • <= -3 → 0 -- Outputs -- A: 0 (Very unlikely) B: 1 (Very likely) C: 1 (Very likely) D: 1 (Very likely) E: 0.5 (Neutral)show more

[LSTM] by Hand ✍️ LSTMs have been the most... effective architecture to process long sequences of data, until our world was taken over by the Transformers. LSTMs belong to the broader family of recurrent neural network (RNNs) that process data sequentially in a recurrent manner. Transformers, on the other hand, abandon recurrence and use self-attention instead to process data concurrently in parallel. Recently, there is renewed interest in recurrence as people realized self-attention doesn’t scale to extremely long sequences, like hundreds of thousands of tokens. Mamba is a good example to bring back recurrence. All of a sudden, it is cool to study LSTMs. How do LSTMs work? [1] Given ↳ 🟨 Input sequence X1, X2, X3 (d = 3) ↳ 🟩 Hidden state h (d = 2) ↳ 🟦 Memory C (d = 2) ↳ Weight matrices Wf, Wc, Wi, Wo Process t = 1 [2] Initialize ↳ Randomly set the previous hidden state h0 to [1, 1] and memory cells C0 to [0.3, -0.5] [3] Linear Transform ↳ Multiply the four weight matrices with the concatenation of current input (X1) and the previous hidden state (h0). ↳ The results are feature values, each is a linear combination of the current input and hidden state. [4] Non-linear Transform ↳ Apply sigmoid σ to obtain gate values (between 0 and 1). • Forget gate (f1): [-4, -6] → [0, 0] • Input gate (i1): [6, 4] → [1, 1] • Output gate (o1): [4, -5] → [1, 0] ↳ Apply tanh to obtain candidate memory values (between -1 and 1) • Candidate memory (C’1): [1, -6] → [0.8, -1] [5] Update Memory ↳ Forget (C0 .* f1): Element-wise multiply the current memory with forget gate values. ↳ Input (C’1 .* o1): Element-wise multiply the “candidate” memory with input gate values. ↳ Update the memory to C1 by adding the two terms above: C0 .* f1 + C’1 .* o1 = C1 [6] Candiate Output ↳ Apply tanh to the new memory C1 to obtain candidate output o’1. [0.8, -1] → [0.7, -0.8] [7] Update Hidden State ↳ Output (o’1 .* o1 → h1): Element-wise multiply the candidate output with the output gate. ↳ The result is updated hidden state h1 ↳ Also, it is the first output. Process t = 2 [8] Initialize ↳ Copy previous hidden state h1 and memory C1 [9] Linear Transform ↳ Repeat [3] [10] Update Memory (C2) ↳ Repeat [4] and [5] [11] Update Hidden State (h2) ↳ Repeat [6] and [7] Process t = 3 [12] Initialize ↳ Copy previous hidden state h2 and memory C2 [13] Linear Transform ↳ Repeat [3] [14] Update Memory (C3) ↳ Repeat [4] and [5] [15] Update Hidden State (h3) ↳ Repeat [6] and [7]show more

For bankers A – APY (Atal Pension Yojana) B... – Bank Assurance (Bancassurance) C – CKYC D – Deposit (Savings / Current / Term Deposit) E – e-KYC / Aadhaar Enrollment F – FD (Fixed Deposit) G – GST (Goods & Services Tax) H – Home Loan I – Insurance / IMPS (Immediate Payment Service)/Interest J – Jan Dhan Account (PMJDY) K – KYC (Know Your Customer) L – Loan (Personal / Housing / MSME / Agri) M – Mobile Banking/MDP N – NEFT (National Electronic Funds Transfer) O – Overdraft (OD) P – PMJJBY / PMSBY (Insurance Schemes) Q – QR Code Payments (UPI QR) R – Re-KYC/Recovery / Recurring Deposit /RD/RAM S – Saving Account T – Term Deposit (TD) U – UPI (Unified Payments Interface) V – Vigilance / Video KYC W – Working Capital/Withdrawal X – Xpress Loans / XBRL Reporting Y – YONO / Youth Savings Account Z – Zero Balance Account/ZO/ZMDPshow more