muxamilian

With L4 you mean the transport layer? I’m a bit confused because in your examples you show layer 3 (IP).

This eBPF-based package dropper using machine learning (decision tree) might be of interest to you: https://github.com/CN-TU/machine-learning-in-ebpf

I have had a "Netatmo" device for some years and it seems to work well. Whenever I use my gas stove the CO2 reading increases.

Decreasing network latency by tweaking routers/developing new software:

https://www.bufferbloat.net

Which is basically unusably buggy.

For example, tf.sort only sorts up to 16 values and overwrites the rest with -0. Apparently not fixed for over one year: https://developer.apple.com/forums/thread/689299

Also, tf.random always returns the same random numbers: https://developer.apple.com/forums/thread/69705

Although I guess these bugs are not the fault of Tensorflow's plugin architecture but rather Apple's implementation.

There's also self selection: You're only going to submit to a top conference if you think there is a slight chance of your work being accepted. Thus, one could argue that papers submitted to top conferences are already better than average. This means that the acceptance rate is way lower in fact.

Interesting, I guess you could just take a fireplace video (one hour or more) and train duralava and see what happens. I guess you won’t get perfect results but could be fun!

Edit: It could be easier than with a lava lamp because for the fire there is no complex physical model. For the lava lamp you have to take care that everything that goes up at some point goes down again etc.

You show the neural net many video snippets of a real lava lamp. It tries to output videos that are supposed to imitate the behavior of a real lava lamp. So it actually outputs videos of lava lamps but to do that convincingly, it has to understand the physical behavior of a lava lamp at least a little bit.

Cloudflare does that: https://www.cloudflare.com/en-gb/learning/ssl/lava-lamp-encr...

That’s an interesting aspect, which I haven’t thought of. I think real world lava lamps have very chaotic behavior. I think the neural network, however, just learns the most common behaviors of a lava lamp but so far cannot learn every aspect of a lava lamp. Training a bigger neural network could work though…

Yeah I know several people who tell me the same like you... Again not proper science but it seems that caffeine tolerance and metabolism is quite different from person to person.

I drank coffee for several years (around 4 cups a day, not later than 4pm) and suffered from insomnia and didn’t know the origin. Since I stopped caffeine intake, my insomnia is basically gone and I feel a lot more functional because of better sleep.

So my impression (yeah I have to admit it’s a study of size 1) is rather that caffeine is more dangerous than I thought.

The article repeatedly talks about “Eurocrats”. This is a combat term used by Eurosceptics but certainly not a term anyone would use who wants to write a neutral article.

Whenever someone writes “Eurocrats” it’s a good idea to stop reading if you want unbiased information.