Lecture | Road Postgraduate Computer Test Guide ( Second Edition | Learning library

I am making list of button that will lead to different YouTube videos on ReactJs page. The issue is that when I map the links from json file (which contains video links), all the buttons get the last link of the mapped array. I need a way that all the rendered buttons will get their respective links. My code is below, I am using react ModalVideo to show my YouTube video.

I am trying to implement the median of medians algorithm in Java. The algorithm shall determine the median of a set of numbers. I tried to implement the pseudo code on wikipedia:

https://en.wikipedia.org/wiki/Median_of_medians

I am getting a buffer overflow and don't know why. Due to the recursions it's quite difficult to keep track of the code for me.

I try to draw line COVID cases for each date. I do not have output, the lecturer gave just questions. I solved the question but my problem is the output. It looks weird. Here is the question: "For the ten countries with the highest absolute number of total deaths, draw the following line graphs to visualize whether epidemic has started to slow down and how the growth rate of new cases/deaths differs across those countries. a) Number of new cases at each date (absolute number vs per 100.000 population)"

Here is my codes:

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.

How can I initialize a ConstraintVerifier in Kotlin without using Drools and Quarkus? I already added the optaplanner-test JAR and the Maven Dependency for Optaplanner 8.6.0.Final and tried it the following way:

For a task in my Algorithms and Data Structures lecture I need to find a way to split a given 2D array into sections. Here is a example: The program is gets a Integer k>0 at the start. The arrays size is [k*k]x[k*k] For k=3 it would be something like:

I've been asked to write a rectangle method algorithm to calculate estimated area of function, however, my lecturer sent my code back and asked me to correct it, because ending condition is wrong?

I have declared a variable name within my people class which is producing an ambiguity error. I tried renaming the name variable, avoided using using namespace std, imported the required libraries, replaced character array with string type but it throws same error all the time. I referred to other posts on Stack Overflow but I could not find the solution.