Why do you think that the drop probability P of a RED gateway does not simply increase linearly from P = 0 at MinThresh to P = 1 at MaxThresh?

A company has a dedicated link of 100 Mbps. Each employee, when online, transmits continuously at 12 Mbps when active, but each user is active only 20 percent of the time. Assume packet switching is used. (a) What is the probability that more than 2 users are transmitting at the same time?

A file of F = 3 x 105bits is distributed to N  The server has an upload rate of us = 1 x 105 bps. Each peer has a download rate of di = 1 x 105 bps and an upload rate of u = 30,000 bps. For N = 1 to 10, and for both client-server and P2P architectures, plot a single graph with minimum distribution time on the y-axis and N on the x-axis. Use a table to show all the values for N = 1 to 10 and use the values to plot the graph. Clearly show the calculation only for N = 1, 2, and 3.

I am trying to execute the backward pass for a convolution network with layers (2 layer conv, relu, max pooling and linear, then softmax loss calculation). I have a hard time to retrieve x, y and probs at backward pass, the code does not recognize them as attributes. Please fix my code and show codes and output. Thank you   from .softmax_ce import SoftmaxCrossEntropyfrom .relu import ReLUfrom .max_pool import MaxPoolingfrom .convolution import Conv2Dfrom .linear import Linear class ConvNet:   """   Max Pooling of input   """   def __init__(self, modules, criterion):       self.modules = []       for m in modules:           if m['type'] == 'Conv2D':               self.modules.append(                   Conv2D(m['in_channels'],                          m['out_channels'],                          m['kernel_size'],                          m['stride'],                          m['padding'])               )           elif m['type'] == 'ReLU':               self.modules.append(…