Concepts Of Programming Languages
12th Edition
ISBN: 9780134997186
Author: Sebesta, Robert W.
Publisher: Pearson,
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Chapter 4, Problem 3PS
Explanation of Solution
Trace of the recursive descent parser for the string:
STEP-1:
Next token is: 11 Next lexeme is a
Enter
Enter
Enter <factor>
STEP-2:
Next token is: 21 Next lexeme is +
Exit
Exit
STEP-3:
Next token is: 11 Next lexeme is b
Enter
Enter
STEP-4:
Next token is: 23 Next lexeme is x
Exit
STEP-5:
Next token is: 11 Next lexeme is c
Enter
STEP-6:
Next token is -1: Next lexeme is EOF
Exit
Exit
Exit
Extended Backus-Naur Form (EBNF):
BNF is a natural notation for describing syntax described by John Backus and Peter Naur. By extending it, we had EBNF which only enhances the readability and writability of BNF
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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…
details 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…
We 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…
Chapter 4 Solutions
Concepts Of Programming Languages
Ch. 4 - Prob. 1RQCh. 4 - Prob. 2RQCh. 4 - Prob. 3RQCh. 4 - Prob. 4RQCh. 4 - Prob. 5RQCh. 4 - Prob. 6RQCh. 4 - Prob. 7RQCh. 4 - Prob. 8RQCh. 4 - Prob. 9RQCh. 4 - Prob. 10RQ
Ch. 4 - Prob. 11RQCh. 4 - Prob. 12RQCh. 4 - Prob. 13RQCh. 4 - Prob. 14RQCh. 4 - Prob. 15RQCh. 4 - Prob. 16RQCh. 4 - Prob. 17RQCh. 4 - Prob. 18RQCh. 4 - Prob. 19RQCh. 4 - Prob. 20RQCh. 4 - Prob. 21RQCh. 4 - Prob. 22RQCh. 4 - Prob. 23RQCh. 4 - What was Knuths insight in developing the LR...Ch. 4 - Prob. 25RQCh. 4 - Prob. 26RQCh. 4 - Is left recursion a problem for LR parsers?Ch. 4 - Prob. 1PSCh. 4 - Prob. 2PSCh. 4 - Prob. 3PSCh. 4 - Prob. 4PSCh. 4 - Prob. 7PSCh. 4 - Prob. 8PS
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