diff --git a/pages/CSE559A/CSE559A_L21.md b/pages/CSE559A/CSE559A_L21.md
new file mode 100644
index 0000000..52405d8
--- /dev/null
+++ b/pages/CSE559A/CSE559A_L21.md
@@ -0,0 +1,215 @@
+# CSE559A Lecture 21
+
+## Continue on optical flow
+
+### The brightness constancy constraint
+
+$$
+I_x u(x,y) + I_y v(x,y) + I_t = 0
+$$
+Given the gradients $I_x, I_y$ and $I_t$, can we uniquely recover the motion $(u,v)$?
+
+- Suppose $(u, v)$ satisfies the constraint: $\nabla I \cdot (u,v) + I_t = 0$
+- Then $\nabla I \cdot (u+u', v+v') + I_t = 0$ for any $(u', v')$ s.t. $\nabla I \cdot (u', v') = 0$
+- Interpretation: the component of the flow perpendicular to the gradient (i.e., parallel to the edge) cannot be recovered!
+
+#### Aperture problem
+
+- The brightness constancy constraint is only valid for a small patch in the image
+- For a large motion, the patch may look very different
+
+Consider the barber pole illusion
+
+### Estimating optical flow (Lucas-Kanade method)
+
+- Consider a small patch in the image
+- Assume the motion is constant within the patch
+- Then we can solve for the motion $(u, v)$ by minimizing the error:
+
+$$
+I_x u(x,y) + I_y v(x,y) + I_t = 0
+$$
+
+How to get more equations for a pixel?
+Spatial coherence constraint:  assume the pixel’s neighbors have the same (𝑢,𝑣)
+If we have 𝑛 pixels in the neighborhood, then we can set up a linear least squares system:
+
+$$
+\begin{bmatrix}
+I_x(x_1, y_1) & I_y(x_1, y_1) \\
+\vdots & \vdots \\
+I_x(x_n, y_n) & I_y(x_n, y_n)
+\end{bmatrix}
+\begin{bmatrix}
+u \\ v
+\end{bmatrix} = -\begin{bmatrix}
+I_t(x_1, y_1) \\ \vdots \\ I_t(x_n, y_n)
+\end{bmatrix}
+$$
+
+#### Lucas-Kanade flow
+
+Let $A=
+\begin{bmatrix}
+I_x(x_1, y_1) & I_y(x_1, y_1) \\
+\vdots & \vdots \\
+I_x(x_n, y_n) & I_y(x_n, y_n)
+\end{bmatrix}$
+
+$b = \begin{bmatrix}
+I_t(x_1, y_1) \\ \vdots \\ I_t(x_n, y_n)
+\end{bmatrix}$
+
+$d = \begin{bmatrix}
+u \\ v
+\end{bmatrix}$
+
+The solution is $d=(A^T A)^{-1} A^T b$
+
+Lucas-Kanade flow: 
+
+- Find $(u,v)$ minimizing $\sum_{i} (I(x_i+u,y_i+v,t)-I(x_i,y_i,t-1))^2$
+- use Taylor approximation of $I(x_i+u,y_i+v,t)$ for small shifts $(u,v)$ to obtain closed-form solution
+
+### Refinement for Lucas-Kanade
+
+In some cases, the Lucas-Kanade method may not work well:
+- The motion is large (larger than a pixel)
+- A point does not move like its neighbors
+- Brightness constancy does not hold
+
+#### Iterative refinement (for large motion)
+
+Iterative Lukas-Kanade Algorithm
+
+1. Estimate velocity at each pixel by solving Lucas-Kanade equations
+2. Warp It towards It+1 using the estimated flow field
+   - use image warping techniques
+3. Repeat until convergence
+
+Iterative refinement is limited due to Aliasing
+
+#### Coarse-to-fine refinement (for large motion)
+
+- Estimate flow at a coarse level
+- Refine the flow at a finer level
+- Use the refined flow to warp the image
+- Repeat until convergence
+
+![Lucas Kanade coarse-to-fine refinement](https://notenextra.trance-0.com/CSE559A/Lucas_Kanade_coarse-to-fine_refinement.png)
+
+#### Representing moving images with layers (for a point may not move like its neighbors)
+
+- The image can be decomposed into a moving layer and a stationary layer
+- The moving layer is the layer that moves
+- The stationary layer is the layer that does not move
+
+![Lucas Kanade refinement with layers](https://notenextra.trance-0.com/CSE559A/Lucas_Kanade_refinement_with_layers.png)
+
+### SOTA models
+
+#### 2009
+
+Start with something similar to Lucas-Kanade
+
+- gradient constancy
+- energy minimization with smoothing term
+- region matching
+- keypoint matching (long-range)
+
+#### 2015
+
+Deep neural networks
+
+- Use a deep neural network to represent the flow field
+- Use synthetic data to train the network (floating chairs)
+
+#### 2023
+
+GMFlow
+
+use Transformer to model the flow field
+
+## Robust Fitting of parametric models
+
+Challenges:
+
+- Noise in the measured feature locations
+- Extraneous data: clutter (outliers), multiple lines
+- Missing data: occlusions
+
+### Least squares fitting
+
+Normal least squares fitting
+
+$y=mx+b$ is not a good model for the data since there might be vertical lines
+
+Instead we use total least squares
+
+Line parametrization: $ax+by=d$
+
+$(a,b)$ is the unit normal to the line (i.e., $a^2+b^2=1$)
+$d$ is the distance between the line and the origin
+Perpendicular distance between point $(x_i, y_i)$ and line $ax+by=d$ (assuming $a^2+b^2=1$):
+
+$$
+|ax_i + by_i - d|
+$$
+
+Objective function:
+
+$$
+E = \sum_{i=1}^n (ax_i + by_i - d)^2
+$$
+
+Solve for $d$ first: $d =a\bar{x}+b\bar{y}$
+Plugging back in:
+
+$$
+E = \sum_{i=1}^n (a(x_i-\bar{x})+b(y_i-\bar{y}))^2 = \left\|\begin{bmatrix}x_1-\bar{x}&y_1-\bar{y}\\\vdots&\vdots\\x_n-\bar{x}&y_n-\bar{y}\end{bmatrix}\begin{pmatrix}a\\b\end{pmatrix}\right\|^2
+$$
+
+We want to find $N$ that minimizes $\|UN\|^2$ subject to $\|N\|^2= 1$
+Solution is given by the eigenvector of $U^T U$ associated with the smallest eigenvalue
+
+Drawbacks:
+
+- Sensitive to outliers
+
+### Robust fitting
+
+General approach: find model parameters 𝜃 that minimize
+
+$$
+\sum_{i} \rho_{\sigma}(r(x_i;\theta))
+$$
+
+$r(x_i;\theta)$: residual of $x_i$ w.r.t. model parameters $\theta$
+$\rho_{\sigma}$: robust function with scale parameter $\sigma$, e.g., $\rho_{\sigma}(u)=\frac{u^2}{\sigma^2+u^2}$
+
+Nonlinear optimization problem that must be solved iteratively
+
+- Least squares solution can be used for initialization
+- Scale of robust function should be chosen carefully
+
+Drawbacks:
+
+- Need to manually choose the robust function and scale parameter
+
+### RANSAC
+
+Voting schemes
+
+Random sample consensus: very general framework for model fitting in the presence of outliers
+
+Outline:
+
+- Randomly choose a small initial subset of points
+- Fit a model to that subset
+- Find all inlier points that are "close" to the model and reject the rest as outliers
+- Do this many times and choose the model with the most inliers
+
+### Hough transform
+
+
+
diff --git a/pages/CSE559A/_meta.js b/pages/CSE559A/_meta.js
index f222b11..9643c4e 100644
--- a/pages/CSE559A/_meta.js
+++ b/pages/CSE559A/_meta.js
@@ -23,4 +23,5 @@ export default {
     CSE559A_L18: "Computer Vision (Lecture 18)",
     CSE559A_L19: "Computer Vision (Lecture 19)",
     CSE559A_L20: "Computer Vision (Lecture 20)",
+    CSE559A_L21: "Computer Vision (Lecture 21)",
 }
diff --git a/pages/Math416/Math416_L22.md b/pages/Math416/Math416_L22.md
new file mode 100644
index 0000000..8b7216d
--- /dev/null
+++ b/pages/Math416/Math416_L22.md
@@ -0,0 +1,120 @@
+# Math416 Lecture 22
+
+## Chapter 9: Generalized Cauchy Theorem
+
+### Winding numbers
+
+Definition:
+
+Let $\gamma:[a,b]\to\mathbb{C}$ be a closed curve. The **winding number** of $\gamma$ around $z\in\mathbb{C}$ is defined as
+
+$$
+\frac{1}{2\pi i}\Delta(arg(z-z_0),\gamma)
+$$
+
+where $\Delta(arg(z-z_0),\gamma)$ is the change in the argument of $z-z_0$ along $\gamma$.
+
+#### Interior of curve
+
+The interior of $\gamma$ is the set of points $z\in\mathbb{C}$ such that the winding number of $\gamma$ around $z$ is non-zero.
+
+$$
+int_\gamma(z)=\{z\in\mathbb{C}|\frac{1}{2\pi i}\Delta(arg(z-z_0),\gamma)\neq 0\}
+$$
+
+#### Contour
+
+The winding number of a contour $\Gamma$ around $z$ is the sum of the winding numbers of the contours $\gamma_j$ around $z$.
+
+$$
+ind_\Gamma(z)=\sum_{j=1}^nn_j ind_{\gamma_j}(z)
+$$
+
+A contour is simple if $ind_\gamma(z)=\{0,1\}$ for all $z\in\mathbb{C}\setminus\gamma([a,b])$.
+
+#### Separation lemma
+
+Let $\Omega\subseteq \mathbb{C}$ be open, let $K\subset \Omega$ be compact, then $\exists$ a simple contour $\Gamma\subset \Omega\setminus K$ such that
+
+$$
+K\subset int_\Gamma(\Gamma)\subset \Omega
+$$
+
+Proof:
+
+First we show that $\exists$ a simple contour $\Gamma\subset \Omega\setminus K$
+
+Let $0<\delta<dist(K,\partial\Omega)$.
+
+We draw a grid fo horizontal ad vertical lines each separated from each other by $\delta$.
+
+Let $S_1,S_2,\dots,S_n$ be the squares that intersect $K$.
+
+Let $\sigma_j$ be the boundary of $S_j$ traversed in counterclockwise direction.
+
+Let $\varepsilon$ be the set of edges with exactly one $s_j$ for $j=1,2,\dots,q$.
+
+Note that $\varepsilon\subseteq \Omega\setminus K$.
+
+We claim that $\varepsilon$ forms a contour.
+
+Proof of Claim:
+
+Say a sequence of edges $E_1,E_2,\dots,E_p$, $E_i\in \varepsilon$. from a chain if terminal points of $E_k$ is the initial point of $E_{k+1}$ for $1\leq k\leq p-1$.
+
+Say it forms a cycle if inaddition the terminal points of $E_p$ is the initial point of $E_1$.
+
+Any cycle is a piecewise continuous closed curve.
+
+We want to show that $\varepsilon$ is a disjoint union of cycles.
+
+We can prove that every terminal point of an edge in $\varepsilon$ is an initial point of an edge in $\varepsilon$. By case analysis for the state of the four square around the terminal point.
+
+Let $\gamma=(E_1,E_2,\dots,E_p)$ be a maximal cycle in $\varepsilon$. (Maximal means that we cannot add another edge to it while still having a cycle.)
+
+Then $\gamma$ is a cycle.
+
+Look at the terminal point of $E_p$, This is initial point for some edge $E'$, where $E'$ is one of the edges of $\gamma$. 
+
+If $E'$ is not $E_1$, then we can add $E'$ to $\gamma$ to form a larger cycle. Contradiction. (You can do this by case analysis. If there is three edges, then there must be four.)
+
+Thus $E'=E_1$.
+
+Thus $\gamma$ is a cycle.
+
+We can now remove $\gamma$ from $\varepsilon$ to form a new set $\varepsilon'$.
+
+We can repeat this process to form a disjoint union of cycles using induction.
+
+Second, we show that $int_\Gamma(\Gamma)\subset \Omega$.
+
+Let $z_0\in int(S_j)$ for some $1\leq j\leq q$.
+
+$ind_{S_k}(z_0)=\begin{cases}
+1 & k=j\\
+0 & k\neq j
+\end{cases}$
+
+Thus 
+
+$$
+\begin{aligned}
+\sum_{k=1}^q ind_{S_k}(z_0)&=\sum_{k=1}^q \frac{1}{2\pi i}\int_{\partial S_k}\frac{1}{z-z_0}dz\\
+&=1\\
+&=ind_\Gamma(z_0)
+\end{aligned}
+$$
+
+So if $z_0\in int(\bigcup_{j=1}^q S_j)$, then $ind_\Gamma(z_0)=1$.
+
+And $\bigcup_{j=1}^q S_j\supset K$, so $z_0\in int_\Gamma(K)$.
+
+Let $z_1\in\mathbb{C}\setminus\left(\bigcup_{j=1}^q S_j\cup\Gamma\right)$.
+
+Then $ind_{S_k}(z_1)=0$ for all $1\leq k\leq q$.
+
+Thus $\sum_{k=1}^q ind_{S_k}(z_1)=0=ind_\Gamma(z_1)$.
+
+QED
+
+Continue on Generalized Cauchy Theorem next time!!
diff --git a/pages/Math416/_meta.js b/pages/Math416/_meta.js
index eb59322..dfff81c 100644
--- a/pages/Math416/_meta.js
+++ b/pages/Math416/_meta.js
@@ -25,4 +25,5 @@ export default {
     Math416_L19: "Complex Variables (Lecture 19)",
     Math416_L20: "Complex Variables (Lecture 20)",
     Math416_L21: "Complex Variables (Lecture 21)",
+    Math416_L22: "Complex Variables (Lecture 22)",
 }