resistors do resistor stuff.

resistors in series

Their resistance add!

\begin{equation} R_{eq} = R_1 + R_2 + \dots \end{equation}

CURRENT remains the same through the resistors.

resistors in parallel

Their resistance add by inverse fraction!

\begin{equation} \frac{1}{R_{eq}} = \frac{1}{R_1} + \frac{1}{R_2} + \dots \end{equation}

VOLTAGE remains the same through the resistors.

in C/C++, its a keyword:

f(restrict char *a,
  restrict char *b)

means that a and b are unique pointers to the objects to which they refer.

A reticle is a photomask/template for a lithography system (like a negative). KLA was the first company to automatically inspect wafers and reticles.

feed both accepted and rejected into your model, and get two scalars out \(r_{\text{rejected}}\), and \(r_{\text{chosen}}\):

\begin{equation} \mathcal{L}_{RM} = \log \qty(1 + e^{r_{\text{rejected}}-r_{\text{chosen}}}) \end{equation}

1. train only for one epoch
2. you should be getting low accuracy scores
3. you may need to ensemble, margin loss

• ppo gets the best model

1. Starting with random residue noise: coordinates + backbones
2. Diffusion happens: train like diffusion, with the goal of increasing binding affinities
3. Eventually resolves to valid protein structures given the binding environments

Basically, start with only the desired substraight, and the diffuse the sequence around that small sequence with the goal of higher affinity binding: i.e. allow only the binding site to stay and regenerating the rest.

RFDiffusion is available starting THIS WEEK!