Java: An Introduction to Problem Solving and Programming (8th Edition)
8th Edition
ISBN: 9780134462035
Author: Walter Savitch
Publisher: PEARSON
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Chapter 12.1, Problem 12STQ
Program Plan Intro
Garbage value: It is the unused values which are present in memory during declaration of variables.
Default value: The variables are assigned by some default value based on their variable type. For example, default value for variable of type “int” is “0”.
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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 12 Solutions
Java: An Introduction to Problem Solving and Programming (8th Edition)
Ch. 12.1 - Suppose aList is an object of the class...Ch. 12.1 - Prob. 2STQCh. 12.1 - Prob. 3STQCh. 12.1 - Prob. 4STQCh. 12.1 - Can you use the method add to insert an element at...Ch. 12.1 - Prob. 6STQCh. 12.1 - Prob. 7STQCh. 12.1 - If you create a list using the statement...Ch. 12.1 - Prob. 9STQCh. 12.1 - Prob. 11STQ
Ch. 12.1 - Prob. 12STQCh. 12.2 - Prob. 13STQCh. 12.2 - Prob. 14STQCh. 12.2 - Prob. 15STQCh. 12.2 - Prob. 16STQCh. 12.3 - Prob. 17STQCh. 12.3 - Prob. 18STQCh. 12.3 - Prob. 19STQCh. 12.3 - Write a definition of a method isEmpty for the...Ch. 12.3 - Prob. 21STQCh. 12.3 - Prob. 22STQCh. 12.3 - Prob. 23STQCh. 12.3 - Prob. 24STQCh. 12.3 - Redefine the method getDataAtCurrent in...Ch. 12.3 - Repeat Question 25 for the method...Ch. 12.3 - Repeat Question 25 for the method...Ch. 12.3 - Repeat Question 25 for the method...Ch. 12.4 - Revise the definition of the class ListNode in...Ch. 12.4 - Prob. 30STQCh. 12.5 - What is the purpose of the FXML file?Ch. 12.5 - Prob. 32STQCh. 12 - Repeat Exercise 2 in Chapter 7, but use an...Ch. 12 - Prob. 2ECh. 12 - Prob. 3ECh. 12 - Repeat Exercises 6 and 7 in Chapter 7, but use an...Ch. 12 - Write a static method removeDuplicates...Ch. 12 - Write a static method...Ch. 12 - Write a program that will read sentences from a...Ch. 12 - Repeat Exercise 12 in Chapter 7, but use an...Ch. 12 - Write a program that will read a text file that...Ch. 12 - Revise the class StringLinkedList in Listing 12.5...Ch. 12 - Prob. 12ECh. 12 - Write some code that will use an iterator to...Ch. 12 - Prob. 14ECh. 12 - Write some code that will use an iterator to...Ch. 12 - Prob. 17ECh. 12 - Revise the method selectionSort within the class...Ch. 12 - Repeat the previous practice program, but instead...Ch. 12 - Repeat Practice Program 1, but instead write a...Ch. 12 - Write a program that allows the user to enter an...Ch. 12 - Write a program that uses a HashMap to compute a...Ch. 12 - Write a program that creates Pet objects from data...Ch. 12 - Repeat the previous programming project, but sort...Ch. 12 - Repeat the previous programming project, but read...Ch. 12 - Prob. 9PPCh. 12 - Prob. 10PPCh. 12 - Prob. 11PPCh. 12 - Prob. 12PPCh. 12 - Prob. 13PPCh. 12 - Prob. 14PPCh. 12 - Prob. 15PP
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- 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…arrow_forwardThe 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 (missing values vs. missing labels), they should not be combined in the same diagram. Instead, create two separate diagrams for clarity. For reference, I will attach a second image…arrow_forwardNote : please avoid using AI answer the question by carefully reading it and provide a clear and concise solutionHere 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…arrow_forward
- 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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