A Guide to SQL
9th Edition
ISBN: 9781111527273
Author: Philip J. Pratt
Publisher: Course Technology Ptr
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Chapter 2, Problem 3SCG
Program Plan Intro
Functional dependency:
- A column “X” is said to be functionally dependent on another column “Y”, when at any point of time, a value for “X” determines a single value for “Y”.
- In other words, a column “X” is said to be functionally dependent on another column “Y”, when two tuples agree on attribute “X” values then they should also agree on attribute “Y” values.
- It is usually represented as “XY”, where the terms “X” and “Y” are composite.
- It can be said as either “Y” is functionally dependent on “X” or as “X” functionally determines “Y”.
Normalization:
- Normalization refers to the method of representing a
database in the terms of relations, which are in the standard normal forms. - It is organized in a table manner to reduce data redundancy and data dependency.
Third Normal Form (3NF):
A table or a relation is said to be in third normal form if and only if it is in second normal form and if the candidate keys are its only determinants.
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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 2 Solutions
A Guide to SQL
Ch. 2 - Prob. 1RQCh. 2 - What is an attribute?Ch. 2 - What is a relationship? What is a one-to-many...Ch. 2 - Prob. 4RQCh. 2 - What is a relation?Ch. 2 - Prob. 6RQCh. 2 - Prob. 7RQCh. 2 - How do you qualify the name of a field, and when...Ch. 2 - Prob. 9RQCh. 2 - What is a primary key? What is the primary key for...
Ch. 2 - Prob. 11RQCh. 2 - Prob. 12RQCh. 2 - Define second normal form. What types of problems...Ch. 2 - Define third normal form. What types of problems...Ch. 2 - Prob. 15RQCh. 2 - Prob. 16RQCh. 2 - List the changes you would need to make to your...Ch. 2 - Prob. 1TDCh. 2 - Prob. 2TDCh. 2 - Prob. 3TDCh. 2 - Prob. 4TDCh. 2 - Prob. 5TDCh. 2 - Prob. 1CATCh. 2 - Identify the functional dependencies in the...Ch. 2 - Prob. 3CATCh. 2 - Determine the functional dependencies that exist...Ch. 2 - Prob. 2SCGCh. 2 - Prob. 3SCG
Knowledge Booster
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- Here is a clear background and explanation of the full method, including what each part is doing and why. Background & Motivation Missing values: Some input features (sensor channels) are missing for some samples due to sensor failure or corruption. Missing labels: Not all samples have a ground-truth RUL value. For example, data collected during normal operation is often unlabeled. Most traditional deep learning models require complete data and full labels. But in our case, both are incomplete. If we try to train a model directly, it will either fail to learn properly or discard valuable data. What We Are Doing: Overview We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models: Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing…arrow_forwardhere is a diagram code : graph LR subgraph Inputs [Inputs] A[Input C (Complete Data)] --> TeacherModel B[Input M (Missing Data)] --> StudentA A --> StudentB end subgraph TeacherModel [Teacher Model (Pretrained)] C[Transformer Encoder T] --> D{Teacher Prediction y_t} C --> E[Internal Features f_t] end subgraph StudentA [Student Model A (Trainable - Handles Missing Input)] F[Transformer Encoder S_A] --> G{Student A Prediction y_s^A} B --> F end subgraph StudentB [Student Model B (Trainable - Handles Missing Labels)] H[Transformer Encoder S_B] --> I{Student B Prediction y_s^B} A --> H end subgraph GroundTruth [Ground Truth RUL (Partial Labels)] J[RUL Labels] end subgraph KnowledgeDistillationA [Knowledge Distillation Block for Student A] K[Prediction Distillation Loss (y_s^A vs y_t)] L[Feature Alignment Loss (f_s^A vs f_t)] D -- Prediction Guidance --> K E -- Feature Guidance --> L G --> K F --> L J -- Supervised Guidance (if available) --> G K…arrow_forwarddetails explanation and background We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models: Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing for some samples). We use knowledge distillation to guide both students, even when labels are missing. Why We Use Two Students Student A handles Missing Input Features: It receives input with some features masked out. Since it cannot see the full input, we help it by transferring internal features (feature distillation) and predictions from the teacher. Student B handles Missing RUL Labels: It receives full input but does not always have a ground-truth RUL label. We guide it using the predictions of the teacher model (prediction distillation). Using two students allows each to specialize in…arrow_forward
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