Q, K, and V vectors are multiplied together, then the results are concatenated with each other, and are finally applied to the matrix of these quantities in the "multi-head" form of a certain mechanism. These quantities are uniformly sampled from the range negative to positive inverse square root of input number in a technique unusually named for its developer's first name, Xavier initialization. These quantities are updated during runtime in the "attention" mechanism that is central to (*) transformer models. These quantities are the coefficients in a sum that is fed into a function like softmax or ReLU (“rel-you”) The (-5[1])biases or, more commonly, these quantities are updated by performing gradient descent on the loss function through backpropagation. For 10 points, name these quantities in a neural network that represent the connection strength between neurons. (10[1])■END■