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 2, Problem 2SI
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Planned obsolescence:
The technique, which is used to get attraction and create sensation among users to purchase a newer version or model of the accessory or a commodity, is known as planned obsolescence.
This technique is basically to create deliberate sensation of need among users to get a newer version even though the newer version is not much advanced and do not come with great advancements.
The plot to sell the products is a clever strategy to generate profitable revenue, but also to create an increase in the size of consumer groups.
The identification of technology that holds significant value for the customer is carried out. After the complete analysis of the technology discovered, the technology is implemented in the production line...
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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
Computer Science: An Overview (13th Edition) (What's New in Computer Science)
Ch. 2.1 - What sequence of events do you think would be...Ch. 2.1 - What information must the CPU supply to the main...Ch. 2.1 - Prob. 3QECh. 2.2 - Prob. 1QECh. 2.2 - In the text, JUMP instructions were expressed by...Ch. 2.2 - Is the instruction If 0 equals 0, then jump to...Ch. 2.2 - Write the example program in Figure 2.7 in actual...Ch. 2.2 - The following are instructions written in Vole...Ch. 2.2 - What is the difference between the instructions...Ch. 2.2 - Here are some instructions in English. Translate...
Ch. 2.3 - Prob. 1QECh. 2.3 - Suppose the Vole memory cells at addresses 0xB0 to...Ch. 2.3 - Suppose the Vole memory cells at addresses 0xA4 to...Ch. 2.3 - Suppose the Vole memory cells at addresses 0xF0 to...Ch. 2.4 - Prob. 1QECh. 2.4 - Prob. 2QECh. 2.4 - Prob. 3QECh. 2.4 - a. Suppose you XOR the first 2 bits of a string of...Ch. 2.4 - Prob. 5QECh. 2.4 - Prob. 6QECh. 2.4 - Prob. 7QECh. 2.4 - Prob. 8QECh. 2.4 - Prob. 9QECh. 2.4 - Prob. 10QECh. 2.4 - Using Vole machine language (Appendix C), write a...Ch. 2.4 - Prob. 12QECh. 2.5 - Prob. 1QECh. 2.5 - Prob. 2QECh. 2.5 - Prob. 3QECh. 2.6 - The hypotenuse example script truncates the sides...Ch. 2.6 - Prob. 2QECh. 2.6 - The Python built-in function str () will convert a...Ch. 2.6 - Use the Python built-in bin () to write a script...Ch. 2.6 - Prob. 6QECh. 2.7 - Referring back to Questions 3 of Section 2.3, if...Ch. 2.7 - Prob. 2QECh. 2.7 - Suppose there were two central processing units...Ch. 2 - a. In what way are general-purpose registers and...Ch. 2 - Answer the following questions in Vole machine...Ch. 2 - Prob. 3CRPCh. 2 - What is the value of the program counter in the...Ch. 2 - Prob. 5CRPCh. 2 - Prob. 6CRPCh. 2 - Prob. 7CRPCh. 2 - Suppose a machine language is designed with an...Ch. 2 - Translate the following instructions from English...Ch. 2 - Rewrite the program in Figure 2.7 assuming that...Ch. 2 - Classify each of the following Vole instructions...Ch. 2 - Prob. 12CRPCh. 2 - Prob. 13CRPCh. 2 - Suppose the memory cells at addresses 0x00 through...Ch. 2 - Suppose the memory cells at addresses 0x00 through...Ch. 2 - Suppose the memory cells at addresses 0x00 through...Ch. 2 - Suppose the memory cells at addresses 0x00 through...Ch. 2 - Prob. 18CRPCh. 2 - If the Vole executes an instruction every...Ch. 2 - Prob. 20CRPCh. 2 - Prob. 21CRPCh. 2 - Prob. 22CRPCh. 2 - Prob. 23CRPCh. 2 - Write a program in Vole to compute the sum of...Ch. 2 - Prob. 26CRPCh. 2 - Prob. 27CRPCh. 2 - Suppose the following program, written in Vole, is...Ch. 2 - Summarize the steps involved when the Vole...Ch. 2 - Summarize the steps involved when the Vole...Ch. 2 - Summarize the steps involved when the Vole...Ch. 2 - Suppose the registers 0x4 and 0x5 in the Vole...Ch. 2 - Prob. 33CRPCh. 2 - Prob. 34CRPCh. 2 - Prob. 35CRPCh. 2 - Prob. 36CRPCh. 2 - Prob. 37CRPCh. 2 - Prob. 38CRPCh. 2 - Prob. 39CRPCh. 2 - Prob. 40CRPCh. 2 - Prob. 41CRPCh. 2 - Prob. 42CRPCh. 2 - a. What single instruction in the Vole machine...Ch. 2 - Write a Vole program that reverses the contents of...Ch. 2 - Write a Vole program that subtracts the value...Ch. 2 - Prob. 46CRPCh. 2 - Suppose a person is typing forty words per minute...Ch. 2 - Prob. 48CRPCh. 2 - Suppose the Vole communicates with a printer using...Ch. 2 - Write a Vole program that places 0s in all the...Ch. 2 - Prob. 51CRPCh. 2 - Prob. 52CRPCh. 2 - Suppose you are given 32 processors, each capable...Ch. 2 - Prob. 54CRPCh. 2 - Prob. 55CRPCh. 2 - Describe how the average of a collection of...Ch. 2 - Write and test a Python script that reads in a...Ch. 2 - Write and test a Python script that reads in a...Ch. 2 - Prob. 59CRPCh. 2 - Suppose a computer manufacturer develops a new...Ch. 2 - Prob. 2SICh. 2 - Prob. 3SICh. 2 - Prob. 4SICh. 2 - Suppose a manufacturer produces a computer chip...Ch. 2 - Prob. 6SICh. 2 - Prob. 7SICh. 2 - Prob. 8SI
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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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