A Guide to SQL
A Guide to SQL
9th Edition
ISBN: 9781111527273
Author: Philip J. Pratt
Publisher: Course Technology Ptr
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Chapter 4, Problem 10SCG
Program Plan Intro

“SELECT” command:

The “SELECT” command is used to retrieve data in a database.

Syntax for selecting values from the table is as follows:

SELECT STUDENT_ID FROM STUDENT;

  • The given query is used to display each student ID from “STUDENT” table.

Naming the computed column in SQL:

  • User can assign a name to the computation by using below ways:
    • Name the computation with the word “AS”.
    • User desired name.

Example:

The example for naming the computed column in SQL is shown below:

SELECT STUDENT_ID, STUDENT_NAME, MAXIMUM_LIMIT – SCORE AS STUDENT_CREDIT FROM STUDENT;

The above query is used to display student id, student name and student credit from “STUDENT” table.

STUDENT_IDSTUDENT_NAMESTUDENT_CREDIT
123Merry90
208John86
408Rose98
564Joseph78

Explanation:

  • User can compute the student credit using the expression “MAXIMUM_LIMIT – SCORE”.
  • From the given query, the computation is calculated by using “-” operator that is from “MAXIMUM_LIMIT – SCORE”.
  • Here the computed result is name as “STUDENT_CREDIT” by using the word “AS”.

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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)…

Chapter 4 Solutions

A Guide to SQL

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