manthideaal

Just a reflection about this: Could a modification of this backscattering technique be used so that hundred of this devices use the same channel to transmit a set?, I am thinking about collecting the information of hundred of devices each sending a single bit in the same channel. This must be something like dividing a channel in subchannels but I am thinking about using the same frequency in the final output. Yes, it should be better if I read something about transmission of information.

I am thinking about an API in which you assing a number (index of device) to each device and you receive a set of bits ( for example danger/no danger) without having to make any modification to wifi signals or having to select, create or buy new channels and without danger of interfering with other signals because use a lot of bandwidth.

In the paper there are some bounds about the number of states in the automata as a function of the length of the input. So one could limit the length of the input when using back references to bound the complexity of the algorithm. They have used their algorithm for snort (network intrusion detection) using asic. The author could contact the authors of the paper and ask for (or pay for) an implementation.

By the way, good work ripgrep and rust.

See theses two answers:

https://stackoverflow.com/questions/39396491/is-there-a-way-...

https://stackoverflow.com/questions/39255471/what-is-the-poi...

if you call a function in the form module:function-name then erlang detect and use the latest compiled version of the module, if you use function-name without the module then the old version is used. Is advised to use the module:function-name in tail call position.

In erlang the code is compiled to an intermmediate representation for a virtual machine, BEAM (erlang virtual machine), usually the functions you define are very short and in this case the BEAM code is sent over the network, so the universal server receives the compiled or transpiled code and executed it using the BEAM. You can compare it to a lisp server in which you send the program as a list, in lisp code is data.

You can find more information about erlang in this Ph.D Thesis (1), on page 86 is an example of a universal server, explained with detail.

https://erlang.org/download/armstrong_thesis_2003.pdf

Perhaps to protect against ReDoS the client should use an extended finite automata (1).

https://www.arl.wustl.edu/~pcrowley/a25-becchi.pdf

(1) Extending Finite Automata to Efficiently Match Perl-Compatible Regular Expressions.

I agree with you, the phrase should be preceded with: Caution, this is a math joke, don't lose sleep trying to grok it.

I wonder what's the point of using such a phrase, it doesn't help you to grok the concept of monad. It can help you to know that someone has given a new definition to sound cute, shame on them. By the way I admire MacLane as a math person, but people seem to use category theory to sell snake oil. Category theory is a tool to give names to some diagrams and properties that are used frequently to avoid repeating the same argument in proofs, is a dry (don't repeat yourself, as in the ruby motto). If you are an expert in category theory you can give short proofs of known facts. Category theory is like pipes in unix, you pipe diagrams to show properties. Grep, sed, awk analoges are functors, categories and natural transformations. The input is a collection of diagrams and the output is a new diagram that has a universal property and it receives a name and a collection of properties that are supposed to be useful to proof new theorems.

The definition of a monoid here is not the usual definition, is a new definition for the special case of a strict category as defined MacLane's Book Categories for the Working Mathematicean. Since you maybe thinking about composition of endofunctors and unit endofunctors you get a confused picture. The monad as a monoid in the category of endofunctors is a way to show that you can confuse people using two different definition of an usual concept and both give different results. I got this from (1), look for "main confusion": monoid in the category of endofunctors is defined in a new way, and is not the expected monoid in the set of all endofunctors.

The definition of monoid in monomial categories is on page 166 and Monads in a category are on page 133. As a math person I know what is a monoid (usual term) but I did not know what is a monoid in a monomial category (well I know now because is on page 166 of the book).

(1) https://stackoverflow.com/questions/3870088/a-monad-is-just-...

I am not able to wait for a video to explain something, I prefer to read books. I don't know what is good about this teacher. Linear Algebra Done Right is a good book. I am not able to appreciate Gilbert Strang style. But algebra is easy to grasp if you are motivated and prepared for hard work. I recall he stressed SVD. Once you are up on statistics SVD is a very useful tool, but I find his style overly pragmatic, if you are able to read a book like Linear Algebra Done Right you will appreciate what a non pragmatic style mean here. Anyway this can be sour grapes, I would like my students to enjoy my lessons. Algebra Done Right goal is that you can demonstrate and learn for facts and proofs. GS book is about using the tools of linear algebra.

It seems you learned from the decline of Perl, so you have the experience of a warrior. But Rhombus represents the hope of a better future for Racket, so another step in this fight should be to embrace the future and learn from the past, I don't know how.

Thanks, nice. bytes.fromhex is really reading bytes from a hex string, so perhaps a better name should be bytes.fromhexstring, but since python names are short I understand the trade-off. Also, my first impression is that hex should be called bytes.tohexstring, but for those using python daily I understand the need of shorter names.

Why?, not a native English speaker, I looked up doge and it has no meaning, any tips for other meaning?

python, decoding and encoding the message

a="53414e544120414e442048495320574f524b53484f50"

"".join([chr(int(a[i:(i+2)],16)) for i in range(0,len(a),2)])

=> 'SANTA AND HIS WORKSHOP'

a = 'SANTA AND HIS WORKSHOP'

"".join([hex(ord(c)) for c in a]).replace('0x','')

=> '53414e544120414e442048495320574f524b53484f50'

About the work you cite, I think that double descent is simply because the extra number of parameters (low bias) used produces a large variance when the input data is small, but as more data is introduced the extra parameters don't play any role, that is they are prunned. So the high bias is relative to the quantity of availabe information (training data). So the second descent starts when the extra parameters are prunned, in practice their coefficients tends to zero, the system learns that those coefficient don't play any role.

The critique is about the importance of priors in BNN. In my humble understanding of Bayesian reasoning the argument to defend any prior is that with enought data the learning method converges to the real distribution, so if the result of any learning method depends heavily of any prior assumption then that assumption is crucial and in no way can it be taken randomly. On the other hand, it is well known that deep learning can learn any random model, so in the end I think all of this is about the bias-variance trade off. If your prior has an infinite number of adjustable parameters (zero bias) then the variance becomes infinite ( your result will depend and becomes equal to the training set).

So in practice one should choose the prior with the minimum number of parameters (bias) that shows a good learning performance on the available training set.

Anyway, trying to measure how a prior generalizes or not in BNN seems to be another way of thinking about bias-variance, if there is more than this, I would like to know.

The example of misuse of the CLT illustrates that a mixture of normals is not a normal distribution. For example the height of people is a mixture of two normals, one for men and the other for women, this mixture has two modes and so it is not bell shaped and is not a normal distribution.