5.04-3. Bellman Ford Algorithm - a change in DV (1, part 3). Consider the network below, and suppose that at t=0, the link between nodes g and h goes down. And so at t=0, nodes g and h recompute their DVs. Following this recomputation, to which nodes will h send its new distance vector? (Note: to answer this question, you'll need to know some of the DV entries at g and h at t=0, but hopefully they'll be obvious by inspection). a 1 1 at t=0 the link (with a cost of 6) between nodes g and h goes down b. compute ∞ g all nodes 8 1 6 1 e 1 compute h 1 1 1 1 node i only nodes i and e only O node h does not send out its distance vector, since none of the least costs have changed to any destination. O nodes i and e and g only node e only

5.04-3. Bellman Ford Algorithm - a change in DV (1, part 3). Consider the network below, and suppose that at t=0, the link between nodes g and h goes down. And so at t=0, nodes g and h recompute their DVs. Following this recomputation, to which nodes will h send its new distance vector? (Note: to answer this question, you'll need to know some of the DV entries at g and h at t=0, but hopefully they'll be obvious by inspection). a 1 1 at t=0 the link (with a cost of 6) between nodes g and h goes down b. compute ∞ g all nodes 8 1 6 1 e 1 compute h 1 1 1 1 node i only nodes i and e only O node h does not send out its distance vector, since none of the least costs have changed to any destination. O nodes i and e and g only node e only

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Computer Science A way to avoid overfitting in Deep Neural Networks is to add an additional term R to the loss function L (for example L can be the cross entropy loss) as follows: L(w) + λR(w). (1) You know that one choice for R is the L2 norm, i.e. R(w) = ||w||2 2 . One friend of yours from the School of Maths told you however that there’s no need to use squares (i.e. powers of two) and that you can achieve the same effect by using absolute values, i.e. the L1 norm: R(w) = ||w||1. Would you agree with him? i.e. is the use of the L2 norm equivalent to using the L1 norm for regularization purposes? Justify your answer
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5.04-3. Bellman Ford Algorithm - a change in DV (1, part 3). Consider the network below, and suppose that at t=0, the link between nodes g and h goes down. And so at t=0, nodes g and h recompute their DVs. Following this recomputation, to which nodes will h send its new distance vector? (Note: to answer this question, you’ll need to know some of the DV entries at g and h at t=0, but hopefully they’ll be obvious by inspection).
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