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 18CAT
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.

“SUM” function:

  • It is the one function of aggregate function.
  • The “SUM” function is used to compute the total of the values in a column.

Example:

The example for “SUM” function is given below:

SELECT SUM(MARK_CREDIT) FROM STUDENT;

The above query is used to display the sum of all students mark credit from “STUDENT” table using “SUM” function.

“GROUP BY” Clause:

  • User can group the data using “GROUP BY” clause.
  • This clause allows the user to group data on a specific column and then computes statistics when user preferred.

Example:

The example for “GROUP BY” clause is given below:

SELECT CUSTOMER_NAME, SUM(AMOUNT) FROM CUSTOMERS GROUP BY CUSTOMER_NAME;

The above query is used to list the customer name and the sum of amount using “GROUP BY” clause.

“HAVING” Clause:

  • The “HAVING” clause is used to restrict the groups that are included.
    • This restriction does not apply to individual rows but relatively apply to groups.

Example:

The example for “HAVING” clause is given below:

SELECT STUDENT_ID, NAME, AGE, ADDRESS, TOTAL_MARK FROM STUDENTS GROUP BY AGE HAVING COUNT(AGE) >= 10;

  • The above query is used to display a record for a similar age count that would be more than or equal to “10”.

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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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