intuition | Quantitative trading kit , for hackers

Some Background (feel free to skip):

I'm very new to Rust, I come from a Haskell background (just in case that gives you an idea of any misconceptions I might have).

I am trying to write a program which, given a bunch of inputs from a database, can create customisable reports. To do this I wanted to create a Field datatype which is composable in a sort of DSL style. In Haskell my intuition would be to make Field an instance of Functor and Applicative so that writing things like this would be possible:

I am trying to understand the constructor of a std::thread but fail to understand how parameter types are represented/handled. Judging from cppreference, a simplified constructor could be sketched as follows:

Suppose you have a neural network with 2 layers A and B. A gets the network input. A and B are consecutive (A's output is fed into B as input). Both A and B output predictions (prediction1 and prediction2) Picture of the described architecture You calculate a loss (loss1) directly after the first layer (A) with a target (target1). You also calculate a loss after the second layer (loss2) with its own target (target2).

Does it make sense to use the sum of loss1 and loss2 as the error function and back propagate this loss through the entire network? If so, why is it "allowed" to back propagate loss1 through B even though it has nothing to do with it?

This question is related to this question https://datascience.stackexchange.com/questions/37022/intuition-importance-of-intermediate-supervision-in-deep-learning but it does not answer my question sufficiently. In my case, A and B are unrelated modules. In the aforementioned question, A and B would be identical. The targets would be the same, too.

(Additional information) The reason why I'm asking is that I'm trying to understand LCNN (https://github.com/zhou13/lcnn) from this paper. LCNN is made up of an Hourglass backbone, which then gets fed into MultiTask Learner (creates loss1), which in turn gets fed into a LineVectorizer Module (loss2). Both loss1 and loss2 are then summed up here and then back propagated through the entire network here.

Even though I've visited several deep learning lectures, I didn't know this was "allowed" or makes sense to do. I would have expected to use two loss.backward(), one for each loss. Or is the pytorch computational graph doing something magical here? LCNN converges and outperforms other neural networks which try to solve the same task.

In the TypeScript code snippet below, I need to assign from one object to another where both of them are Partial. Here, my intuition says that typescript should be able to understand what is going on because at line (B), the type of key is typeof InjectMap. So, it should be able to assign values from input to output correctly.

If we have a double-precision value in C++ and do a static_cast on it, will the returned value always be smaller in absolute value? My intuition behind this says yes for the following reasons.

• The set of possible single precision exponents is strictly a subset of double precision exponents
• In converting the double precision mantissa to single precision, bits are probably truncated off then end to fit the double's mantissa into the float's mantissa. However, it's not impossible that rounding up is sometimes done to the next highest floating point value if it's more accurate. Perhaps this is system-dependent, or defined in some standard.

I have experimented some numerically with this in the following program. It appears that sometimes, rounding up happens, and other times, round down.

Where can I find more info about how I can expect this rounding to behave? Does it always round to the nearest float?

I'm trying to index a column for each row in a matrix.

Suppose I have a numpy array A with the shape (n,m).
I also have a numpy array B with the shape (n,) containing integers between 0 and m, so they can be used as indices for the 2nd axis of A.

I want to get a numpy array C with shape (n,) with C[i] = A[i,B[i]], so every row of A yields one value based on the index in B.

Surely I could use this last expression in a for loop or list comprehension, but how would I do it using the efficiency of numpy?

My first intuition was C = A[:,B] but this clearly yields something else. (shape (n,n))

I am creating a Smart Contract (BEP20 token) based on the BEP20Token template (https://github.com/binance-chain/bsc-genesis-contract/blob/master/contracts/bep20_template/BEP20Token.template). The public contructor was modified to add some token details. However all of the standard functions are giving compile time issues like Overriding function is missing.

** here is the source code **

I am working on my first Flutter app, and I am struggling to find a good solution to this problem. My app has multiple pages and different routes that the user can take. Currently, I am redefining the app bar for every one of these pages which has not been a huge deal since all have been placing on the app bar is the title for every page. I would like to place a button on the app bar for each page that will return the user to the home page, no matter where they are in the app. I also still need to be able to change the title on the app bar on the different pages. I could just redefine this navigation functionality for each page, but intuition tells me that there has to be a better solution, as this would violate the DRY principle. Thanks in advance!

I'm using a pre-trained word2vec model (word2vec-google-news-300) to get the embeddings for a given list of words. Please note that this is NOT a list of words that we get after tokenizing a sentence, it is just a list of words that describe a given image.

Now I'd like to get a single vector representation for the entire list. Does adding all the individual word embeddings make sense? Or should I consider averaging? Also, I would like the vector to be of a constant size so concatenating the embeddings is not an option.

It would be really helpful if someone can explain the intuition behind considering either one of the above approaches.