Libbum

I predicted this back in 2004 as an angsty teenager: https://evilschemes.livejournal.com/3007.html

It is surprisingly good! Not clear if you can go without a manual step where you need to change the likes of SPEAKER_00 to Bob and SPEAKER_01 to Sarah, but I've not had it mess up on me at all transcribing 2 hour long conversations between 6 people.

A set of clay tablets that depict the Sumerian/Babylonian epic of creation.

https://whispers.neophilus.net/

You may have been reading a different version of the documentation. Documenter.jl outputs are dependent on the code actually running (and it's the package DifferentialEquations.jl uses to build its docs).

An additional thing to understand here is that PFAS is not a new boundary at all in the context of the planetary boundaries framework - they fall under the already defined category of Novel Entities.

Since that's a catch all category for many things (nuclear waste, other synthetic chemicals, there's even debate with the original authors of the framework if Artificial Intelligence should be considered), the boundary value itself is not currently defined.

One school of thought is that the boundary value for this category should be zero - as any synthetic substance is more than nature generates.

Regardless, papers like this one are helpful to piece together all of the novel entities research amd get a better picture of how this boundary interacts with the rest of the Earth System.

https://audiobookstore.com/ has a pretty extensive range of DRM-free titles.

That's interesting! Care to share your fail2ban config for this?

Yeah, this is the crux. Here's a comment from one of the devs when I asked about the polynomial vs NN basis:

The answer is quite simple really. Classical basis functions suffer from the curse of dimensionality because if you tensor product polynomial basis functions or things like Fourier basis, with N basis functions in each direction, then you have N^d parameters that are required in order to handle every combination `sin(x) + sin(2x) + ... + sin(y) + sin(2y) + ... + sin(x)sin(y) + sin(2x)sin(y) + ....`

Neural networks only grow polynomially with dimensional, so at around 8 dimensional objects it becomes more efficient. In fact, this is why we have https://diffeqflux.sciml.ai/dev/layers/BasisLayers/

I certainly agree with the NNs are used as hammers point. Until coming across the UODE concept I was of the opinion they were more parlour trick than anything useful. Here though, I could see some validity.

These comments are appreciated - I think a discussion like this is lacking in the SciML docs (or at least not visible enough). Will have a chat with some of the devs and see if there's something we can add.

I'm not entirely confident in answering that directly, so perhaps you can check my reasoning here.

If F is completely unknown, perhaps you start training with a 10 dimensional polynomial basis. What is the (computational) cost of obtaining your solution? Once you have it, will this polynomial accurately represent your system in any real world manner? Perhaps higher order parameters are needed to approximate trigonometric functions - are you able to easily add such functions to your training basis? If not - then your basis could be too restrictive to provide you with a minimal implementation of your control variable.

It looks like you work with this stuff far more than I have, so perhaps that's not an adequate answer.

Another way to look at this though: If you only wanted to characterise your system with polynomials, UODEs + SINDy can do this for you - the NN is simply the optimisation method that's in place of any other optimisation algorithm.

Since the network only acts on a small portion of the entire system, we can constrain it in such a way that dramatically simple NNs work just fine.

`FastChain(FastDense(3,32,tanh), FastDense(32,32,tanh), FastDense(32,2))` (from [0]) would take three inputs from your basis, run it through one hidden layer and provide you with two trained parameters.

This [1] example uses two hidden layers, its one of the more complex solutions I've seen so far. To move to this complexity from a simpler chain, we first make sure our solution is not in a local minima [2], then proceed to increase the parameter count if the NN fails to converge.

[0] https://diffeqflux.sciml.ai/dev/FastChain/ [1] https://github.com/ChrisRackauckas/universal_differential_eq... [2] https://diffeqflux.sciml.ai/dev/examples/local_minima/

To respond to both the parent question, and this comment: indeed, this is black-box optimal control in essence.

However, this method is just one small aspect of the SciML [0] ecosystem now. The article is a little outdated in that sense.

Once obtaining your NN control parameter, it's now possible to use Sparse Identification of Nonlinear Dynamics (SINDy) on that parameter to recover equations of motion governing it [1].

The real promise of these methods is to use the universal approximator power of NNs to get around the 'curse of dimensionality' & uncover presently unknown representations of motion within any system. Take a look at [2] for a more detailed description.

[0] https://sciml.ai/ [1]: https://datadriven.sciml.ai/dev/sparse_identification/sindy/ [2]: https://arxiv.org/abs/2001.04385

I mostly read scientific papers. Quite a good number of them are two column. Haven't come across k2pdfopt before - I'll check it out, thanks!

Version 1. But as I say, the hardware is good. Version 2 will have even better hardware specs, but the operating system I suspect will be the same.

I wouldn't recommend this. PDF support is horrible, particularly if you have a two column layout file. You cannot correctly zoom in far enough in most cases - in the sense that zoom is possible, just absolutely impractical. Without that, you generally need a magnifying glass to read the text on the display.

Very nice hardware, just awful software.

The British Museum are working on digitising their collection with 3D scans and high resolution imaging. The catalogue is slowly coming online, an example: https://research.britishmuseum.org/research/collection_onlin...

There are efforts to do exactly that. The Cuneiform Digital Library Initiative [0] has some interesting projects you may wish to browse [1] as an example.

[0] https://cdli.ucla.edu/

[1] https://github.com/cdli-gh

For android I use "Listen Audiobook Player". Automatic progress tracking for multiple books, and generally all round great for daily audiobook usage.

I would strongly suggest serving this landing page via http/2.0 - your 504 requests over http/1.1 are currently taking 3.5 minutes for me to load 2MB of data.

https://ibb.co/5BWrqbx

My biggest project, it's about 6k loc. So maybe that's not what you meant by 'big', but many of the others here have listed a lot of closed source apps.

https://github.com/Libbum/Odyssey/tree/master/src

Fair enough. It's taken me a while to understand how he works, and certainly I can see your point. His passion for the project helps the community immensely in many ways and can be spotty in other aspects as a consequence. That's a price I'm willing to pay for using his vision, others may not see it that way though. It's a unique community in that sense for sure.

An ellie app to play around with: https://ellie-app.com/6YQNRR4MmmZa1

There is a lack of leadership and community building in this project. ... All issues on Github, all posts on Elm Discourse get no response.

This is completely the opposite in my experience. There is a massively strong hierarchy of leadership, core development and active users. All willing to help at all times. There are quirks, sure - but it is by far the most accepting language community I've been a part of. The Discourse has been a fountain of information: have never had an unanswered query before, and don't see many of them around at all.

There's an Andriod client for that: https://github.com/zeapo/Android-Password-Store#readme

Plenty of other extensions, managers here: https://www.passwordstore.org/#other

Finished my PhD in quantum computing three years ago now.

My first post-doc was in laser-matter interactions. We had an experimental team that were focusing PWs of power on to ultra-thin films and separating the substance into its constituent parts (electrons and protons I mean). The plan was to increase the proton acceleration yields & make a consistent, tight bunch (in the energy spectra) so that we could use it for next generation cancer treatments. That's a long way off and no-one really has a good idea what else we can do with this system.

That annoyed me. There's not enough application or relevance there. So I've moved to Earth System science. I do a lot of global climate-economy coupled models & attempt to implement climate models with human decision making as a part of the system rather than some form of external forcing.

Rather than using reveal.js, perhaps consider the GUI version at slides.com?

Beamer is a great tool as well, there are some really nice themes for it. My favourite is Metropolis: https://github.com/matze/mtheme The markup is Latex, so you'll need a latex toolchain for that.

This is really cool! Simple and very effective. Well done.

My blog is at https://axiomatic.neophilus.net/ where I write mostly about software/math/physics. A static site built using Zola https://www.getzola.org/

I also have a travel/photoblog which has some really cool features. It's written in Elm https://odyssey.neophilus.net/

That's good to know. Thanks. Would be interesting to know the extent of the definitions here. If I am an academic in a non-related field, but am interested in the sociology of a terrorist group purely intellectually, I wonder if 3 b ii is extended to me in that situation.

I'm not in complete disagreement to your sentiment. I think this will be the case most of the time. There are plenty of singular cases where wording rather than spirit have been used to make examples though - mostly to the detriment of personal freedoms. Markus Meechan for example.