EXPERIENCING MIS >CUSTOM<
7th Edition
ISBN: 9781323518731
Author: KROENKE
Publisher: PEARSON C
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Chapter 3.6, Problem 4SW
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Influence of proliferation of driverless car in automotive insurance industry:
- Proliferation of driverless car can influence the automotive insurance industry intensively.
- Consider a driverless car is capable of avoiding accidents completely and does not require any coverage policy.
- It will not cover any insurance costs for the driving habits and records of th...
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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 3 Solutions
EXPERIENCING MIS >CUSTOM<
Ch. 3.6 - Prob. 1SWCh. 3.6 - Prob. 2SWCh. 3.6 - Prob. 3SWCh. 3.6 - Prob. 4SWCh. 3.6 - Prob. 5SWCh. 3 - Prob. 1EGDQCh. 3 - Prob. 2EGDQCh. 3 - Prob. 3EGDQCh. 3 - Prob. 4EGDQCh. 3 - Prob. 5EGDQ
Ch. 3 - Prob. 6EGDQCh. 3 - Prob. 7EGDQCh. 3 - Prob. 8EGDQCh. 3 - Prob. 1GDQCh. 3 - Prob. 2GDQCh. 3 - Prob. 3GDQCh. 3 - Prob. 4GDQCh. 3 - Prob. 1ARQCh. 3 - Prob. 2ARQCh. 3 - Prob. 3ARQCh. 3 - Prob. 4ARQCh. 3 - Prob. 5ARQCh. 3 - Prob. 6ARQCh. 3 - Prob. 1UYKCh. 3 - Prob. 2UYKCh. 3 - Prob. 3UYKCh. 3 - Prob. 4CECh. 3 - Prob. 5CECh. 3 - Prob. 6CECh. 3 - Prob. 7CECh. 3 - Prob. 8CSCh. 3 - Prob. 9CSCh. 3 - Prob. 10CSCh. 3 - Prob. 11CSCh. 3 - Prob. 12CSCh. 3 - Prob. 13CSCh. 3 - Prob. 14CSCh. 3 - Prob. 15CSCh. 3 - Prob. 16CSCh. 3 - Prob. 17MMLCh. 3 - Prob. 18MML
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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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- CPS 2231 - Computer Programming – Spring 2025 City Report Application - Due Date: Concepts: Classes and Objects, Reading from a file and generating report Point value: 40 points. The purpose of this project is to give students exposure to object-oriented design and programming using classes in a realistic application that involves arrays of objects and generating reports. Assignment Instructions: You are tasked with developing a program to use city data from an online database and generate a city details report. 1) Create a new Project in Eclipse called "HW7”. 2) Create a class "City.java" in the project and implement the UML diagram shown below and add comments to your program. 3) The logic for the method "getCityCategory" of City Class is below: a. If the population of a city is greater than 10000000, then the method returns "MEGA" b. If the population of a city is greater than 1000000 and less than 10000000, then the method returns "LARGE" c. If the population of a city is greater…arrow_forwardPlease calculate the average best-case IPC attainable on this code with a 2-wide, in-order, superscalar machine: ADD X1, X2, X3 SUB X3, X1, 0x100 ORR X9, X10, X11 ADD X11, X3, X2 SUB X9, X1, X3 ADD X1, X2, X3 AND X3, X1, X9 ORR X1, X11, X9 SUB X13, X14, X15 ADD X16, X13, X14arrow_forwardOutline the overall steps for configuring and securing Linux servers Consider and describe how a mixed Operating System environment will affect what you have to do to protect the company assets Describe at least three technologies that will help to protect CIA of data on Linux systemsarrow_forward
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