I need to get vaxxed for overseas travel. Which one is the safest? Should I just wait for Novavax?

Is it true that many physicists think they're qualified to talk about topics unrelated to physics?

PREMISE: The more you learn, the more you can learn. The more you learn, the more ideas, concepts, reference points, raw information, and collated data you will have at your command. As a consequence, constant, active learning throughout one's life allows one to view situations from new perspectives, to use new frameworks for thinking, and to apply oneself in a number of different ways. Simply put, knowledge, or "crystallized intelligence" as it is known in psychology, is massively, woefully underrated. He with the most knowledge wields the most advantage over the comparatively ignorant, especially in today's age of ubiquitous Internet access and deluge of output (for good or for ill) from academia. In the modern era, replete with information overload via the Internet, ignorance, as with weakness, is a choice. "Project: Fantastic" is my answer in the negative.

GOALS: To expand my knowledge to the point where I could hold a meaningful conversation with Mr. Fantastic, Tony Stark, or other fictional super-geniuses. To command, at barest minimum, a basic working knowledge of scientific fields and practical skills as varied and as deeply as possible.

Please tell me youve gone to see Shang chi and the legend of the ten rings

Ok so, diffusion models have been used from generating NEW images. What I'm trying to achieve is modify an already existing image using this process.

Diffusion models are about going from the extremely noised version of an image (litterally just gaussian noise) to the clear denoised version of it, following a markov chain like so:
T -> T-1 -> ... -> t -> t-1 -> ... -> 0
Each step, to go from t to t-1 we use a trained model to extract the "added" noise and remove it from the image.

This is a deterministric process, so given the same starting noise, using the same trained model, we obtain the same final image result.

The problem that I have is with the other way around, going from a clear image to a noise version of it. This process is stochastic if I just generate a new Gaussian noise and add it.

Should I just train a new model (Maybe an autoencoder) to generate a noisy version an input image that the other model can reconstruct?

I'm way too tired for this...

okay so my company is building a small device that needs to accept disposable co2 cartridges.

so far so good.

but what the hell is the damn name of the port you screw the gas canisters into to release the gas?!?!?!?

>be me
>senior in university for electrical engineering
>my entire time in college I got good grades
>yet this entire time I feel like I haven’t learned anything
>never really done any projects before
>had two internships but again feel like I haven’t learned anything from them
>covid’s online learning didn’t help
What do bros? I just feel like I coasted through college without learning anything, and that all I did was just memorize formulas and that’s it. Don’t know anything about actual engineering and now I’m about to graduate. I feel like I don’t deserve it and will fail

Plot twist: the universe is actually actively creating new laws of physics whenever you discover them. The universe hasn't even decided what caused the matter-to-antimatter ratio yet, and the laws of the universe are literally incomplete until you design an experiment that forces the universe to make a decision about itself and its own laws. Humanity has actively molded the laws of the universe with our research of it.

SpaceX is a lie. This is how the view of that altitude should look like. They made the Cupola a black outer ring to look its in +10000 km heights.

So we've reached the great filter. Humanity as we know it will be toast between 2050-2100, or possibly earlier. Due to climate change and a general economic collapse.
What did we get so wrong? And what could we have done to avoid it? Discuss.