Introduction To Computing Systems
Introduction To Computing Systems
3rd Edition
ISBN: 9781260150537
Author: PATT, Yale N., Patel, Sanjay J.
Publisher: Mcgraw-hill,
Expert Solution & Answer
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Chapter 2, Problem 18E

a.

Explanation of Solution

01 + 1011:

  • Express the given numbers with the same number of bits.
  • Addition of leading 0’s do not affect the result.
  • The process of addition can perform on “1” digit at a time from right to left.
  • A sum digit and a carry digit are generated at each point.
  • It the user adds “1” and “1”, it generates the sum digit “0” and carry digit “1”.
  • The given 2-bit  binary number “01” is extended to 4-bit binary number as “0001”
  • The addition of the binary numbers is as follows,

  • Therefore, the resultant binary bit pattern is “1100”...

b.

Explanation of Solution

11 + 01010101:

  • Express the given numbers with the same number of bits.
  • The process of addition can perform on “1” digit at a time from right to left.
  • A sum digit and a carry digit are generated at each point.
  • It the user adds “1” and “1”, it generates the sum digit “0” and carry digit “1”.
  • It the user adds “1” and “0”, it generates the sum digit “1” and carry digit “0”.
  • The given 2-bit  binary number “11” is extended to 8-bit binary number as “00000011”
  • The addition of the binary numbers is as follows,

  • Therefore, the resultant binary bit pattern is “01011000”...

c.

Explanation of Solution

0101 + 110:

  • Express the given numbers with the same number of bits.
  • The process of addition can perform on “1” digit at a time from right to left.
  • A sum digit and a carry digit are generated at each point.
  • It the user adds “1” and “1”, it generates the sum digit “0” and carry digit “1”.
  • It the user adds “1” and “0”, it generates the sum digit “1” and carry digit “0”.
  • The given 3-bit  binary number “110” is extended to 4-bit binary number as “0110”
  • The addition of the binary numbers is as follows,

  • Therefore, the resultant binary bit pattern is “1011”...

d.

Explanation of Solution

01 + 10:

  • The given binary numbers have the same number of bits.
  • The process of addition can perform on “1” digit at a time from right to left.
  • A sum digit and a carry digit are generated at each point.
  • It the user adds “1” and “1”, it generates the sum digit “0” and carry digit “1”.
  • It the user adds “1” and “0”, it generates the sum digit “1” and carry digit “0”.
  • The addition of the binary numbers is as follows,

  • Therefore, the resultant binary bit pattern is “11”...

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Here are two diagrams. Make them very explicit, similar to Example Diagram 3 (the Architecture of MSCTNN). graph LR subgraph Teacher_Model_B [Teacher Model (Pretrained)] Input_Teacher_B[Input C (Complete Data)] --> Teacher_Encoder_B[Transformer Encoder T] Teacher_Encoder_B --> Teacher_Prediction_B[Teacher Prediction y_T] Teacher_Encoder_B --> Teacher_Features_B[Internal Features F_T] end subgraph Student_B_Model [Student Model B (Handles Missing Labels)] Input_Student_B[Input C (Complete Data)] --> Student_B_Encoder[Transformer Encoder E_B] Student_B_Encoder --> Student_B_Prediction[Student B Prediction y_B] end subgraph Knowledge_Distillation_B [Knowledge Distillation (Student B)] Teacher_Prediction_B -- Logits Distillation Loss (L_logits_B) --> Total_Loss_B Teacher_Features_B -- Feature Alignment Loss (L_feature_B) --> Total_Loss_B Partial_Labels_B[Partial Labels y_p] -- Prediction Loss (L_pred_B) --> Total_Loss_B Total_Loss_B -- Backpropagation -->…
Please provide me with the output  image of both of them . below are the diagrams code I have two diagram : first diagram code  graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder --> Student_A_Features[Student A Features F_A] end subgraph Knowledge_Distillation_A [Knowledge Distillation (Student A)] Teacher_Prediction -- Logits Distillation Loss (L_logits_A) --> Total_Loss_A Teacher_Features -- Feature Alignment Loss (L_feature_A) --> Total_Loss_A Ground_Truth_A[Ground Truth y_gt] -- Prediction Loss (L_pred_A)…
I'm reposting my question again please make sure to avoid any copy paste from the previous answer because those answer did not satisfy or responded to the need that's why I'm asking again  The knowledge distillation part is not very clear in the diagram. Please create two new diagrams by separating the two student models: First Diagram (Student A - Missing Values): Clearly illustrate the student training process. Show how knowledge distillation happens between the teacher and Student A. Explain what the teacher teaches Student A (e.g., handling missing values) and how this teaching occurs (e.g., through logits, features, or attention). Second Diagram (Student B - Missing Labels): Similarly, detail the training process for Student B. Clarify how knowledge distillation works between the teacher and Student B. Specify what the teacher teaches Student B (e.g., dealing with missing labels) and how the knowledge is transferred. Since these are two distinct challenges…
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