Computer Science: An Overview (13th Edition) (What's New in Computer Science)
13th Edition
ISBN: 9780134875460
Author: Glenn Brookshear, Dennis Brylow
Publisher: PEARSON
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Chapter 1.9, Problem 5QE
Program Plan Intro
A human eye is more sensitive to the change in brightness than to the changes in color.
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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…
Chapter 1 Solutions
Computer Science: An Overview (13th Edition) (What's New in Computer Science)
Ch. 1.1 - What input bit patterns will cause the following...Ch. 1.1 - In the text, we claimed that placing a 1 on the...Ch. 1.1 - Assuming that both inputs to the flip-flop in...Ch. 1.1 - a. If the output of an AND gate is passed through...Ch. 1.1 - Prob. 5QECh. 1.1 - Prob. 6QECh. 1.2 - If the memory cell whose address is 5 contains the...Ch. 1.2 - Prob. 2QECh. 1.2 - Design a sequence of steps that correctly...Ch. 1.2 - How many bits would be in the memory of a computer...
Ch. 1.3 - Prob. 1QECh. 1.3 - Prob. 2QECh. 1.3 - Prob. 3QECh. 1.3 - Prob. 4QECh. 1.3 - Prob. 5QECh. 1.3 - Prob. 6QECh. 1.4 - Here is a message encoded in ASCII using 8 bits...Ch. 1.4 - In the ASCII code, what is the relationship...Ch. 1.4 - Prob. 3QECh. 1.4 - Prob. 4QECh. 1.4 - Convert each of the following binary...Ch. 1.4 - Prob. 6QECh. 1.4 - What is the largest numeric value that could be...Ch. 1.4 - An alternative to hexadecimal notation for...Ch. 1.4 - What is an advantage of representing images via...Ch. 1.4 - Prob. 10QECh. 1.5 - Convert each of the following binary...Ch. 1.5 - Convert each of the following base ten...Ch. 1.5 - Convert each of the following binary...Ch. 1.5 - Express the following values in binary notation:...Ch. 1.5 - Perform the following additions in binary...Ch. 1.6 - Convert each of the following twos complement...Ch. 1.6 - Prob. 2QECh. 1.6 - Suppose the following bit patterns represent...Ch. 1.6 - Suppose a machine stores numbers in twos...Ch. 1.6 - In the following problems, each bit pattern...Ch. 1.6 - Prob. 6QECh. 1.6 - Prob. 7QECh. 1.6 - Prob. 8QECh. 1.6 - Prob. 9QECh. 1.6 - Prob. 10QECh. 1.6 - Prob. 11QECh. 1.7 - Prob. 1QECh. 1.7 - Prob. 3QECh. 1.7 - Prob. 4QECh. 1.8 - What makes Python an interpreted programming...Ch. 1.8 - Write Python statements that print the following:...Ch. 1.8 - Write Python statements to make the following...Ch. 1.8 - Write a Python statement that given an existing...Ch. 1.9 - Prob. 1QECh. 1.9 - Prob. 2QECh. 1.9 - Prob. 3QECh. 1.9 - Prob. 4QECh. 1.9 - Prob. 5QECh. 1.9 - Prob. 6QECh. 1.9 - Prob. 7QECh. 1.10 - Prob. 1QECh. 1.10 - Could errors have occurred in a byte from Question...Ch. 1.10 - Prob. 3QECh. 1.10 - Prob. 4QECh. 1.10 - Prob. 5QECh. 1.10 - Prob. 6QECh. 1 - Determine the output of each of the following...Ch. 1 - a. What Boolean operation does the circuit...Ch. 1 - a. If we were to purchase a flip-flop circuit from...Ch. 1 - Assume that both of the inputs in the following...Ch. 1 - The following table represents the addresses and...Ch. 1 - How many cells can be in a computers main memory...Ch. 1 - Prob. 7CRPCh. 1 - Prob. 8CRPCh. 1 - Prob. 9CRPCh. 1 - Prob. 10CRPCh. 1 - Suppose a picture is represented on a display...Ch. 1 - Prob. 12CRPCh. 1 - Prob. 13CRPCh. 1 - If each sector on a magnetic disk contains 1024...Ch. 1 - How many bytes of storage space would be required...Ch. 1 - Prob. 16CRPCh. 1 - Prob. 17CRPCh. 1 - Suppose a typist could type 60 words per minute...Ch. 1 - Prob. 19CRPCh. 1 - Prob. 20CRPCh. 1 - Prob. 21CRPCh. 1 - Prob. 22CRPCh. 1 - Prob. 23CRPCh. 1 - Prob. 24CRPCh. 1 - Prob. 25CRPCh. 1 - Prob. 26CRPCh. 1 - Prob. 27CRPCh. 1 - Prob. 28CRPCh. 1 - Prob. 29CRPCh. 1 - Prob. 30CRPCh. 1 - Prob. 31CRPCh. 1 - Prob. 32CRPCh. 1 - Prob. 33CRPCh. 1 - Prob. 34CRPCh. 1 - Prob. 35CRPCh. 1 - Prob. 36CRPCh. 1 - Prob. 37CRPCh. 1 - Prob. 38CRPCh. 1 - Prob. 39CRPCh. 1 - Prob. 40CRPCh. 1 - Prob. 41CRPCh. 1 - Prob. 42CRPCh. 1 - Prob. 43CRPCh. 1 - Prob. 44CRPCh. 1 - Prob. 45CRPCh. 1 - What would be the hexadecimal representation of...Ch. 1 - Prob. 47CRPCh. 1 - Prob. 48CRPCh. 1 - Prob. 49CRPCh. 1 - Prob. 50CRPCh. 1 - Prob. 51CRPCh. 1 - Prob. 52CRPCh. 1 - Prob. 53CRPCh. 1 - Prob. 54CRPCh. 1 - Prob. 55CRPCh. 1 - Prob. 56CRPCh. 1 - Prob. 57CRPCh. 1 - Prob. 58CRPCh. 1 - Write and test a Python script that, given a...Ch. 1 - Prob. 61CRPCh. 1 - Prob. 2SICh. 1 - Prob. 3SICh. 1 - Prob. 4SICh. 1 - Prob. 5SICh. 1 - Prob. 6SICh. 1 - Prob. 7SI
Knowledge Booster
Similar questions
- 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 (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_forwardHere 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_forward
- here 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_forwardWe are doing a custom JSTL custom tag to make display page to access a tag handler. Write two custom tags: 1) A single tag which prints a number (from 0-99) as words. Ex: <abc:numAsWords val="32"/> --> produces: thirty-two 2) A paired tag which puts the body in a DIV with our team colors. Ex: <abc:teamColors school="gophers" reverse="true"> <p>Big game today</p> <p>Bring your lucky hat</p> <-- these will be green text on blue background </abc:teamColors> Details: The attribute for numAsWords will be just val, from 0 to 99 - spelling, etc... isn't important here. Print "twenty-six" or "Twenty six" ... . Attributes for teamColors are: school, a "required" string, and reversed, a non-required boolean. - pick any four schools. I picked gophers, cyclones, hawkeyes and cornhuskers - each school has two colors. Pick whatever seems best. For oine I picked "cyclones" and red text on a gold body - if…arrow_forward
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