Concepts Of Programming Languages
12th Edition
ISBN: 9780134997186
Author: Sebesta, Robert W.
Publisher: Pearson,
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Expert Solution & Answer
Chapter 4, Problem 8PS
Explanation of Solution
Complete parse for the string “(id+id)*id” using the parse table:
The complete trace of the shift reduce parser including stack contents, input string, and action is shown below.
| Stack | Input | Action |
| 0 | (id+id)*id$ | Shift 4 |
| 0(4 | id+id)*id$ | Shift 5 |
| 0(4id5 | +id)*id$ | Reduce 6 (GOTO[4, F]) |
| 0(4F3 | +id)*id$ | Reduce 4 (GOTO[4, T]) |
| 0(4T2 | +id)*id$ | Reduce 2 (GOTO[4, E]) |
| 0(4E8 | +id)*id$ | Shift 6 |
| 0(4E8+6 | id)*id$ | Shift 5 |
| 0(4E8+6id5 |
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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…
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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
Knowledge Booster
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