True/False Questions. Write your asnwers as: Q1-1: correct answer, Q1-2: correct answer, etc. 1. T/F-Disjoint union assuming that variants are chosen from type S or type T = (#S)^ (#T) 2. T/F-Cardinality of mapping S→T is: (#S)x(#T) →(#T)#5 3. T/F- Smalltalk and Lisp are dynamically typed PLs whereas Prolog and Perl are statically typed. 4. T/F- If you can print a piece of code, or assign it to a variable, it's an expression. If you can't, it's a statement! 5. T/F-memcpy() copies specific number of bytes from source to destination in RAM, where as strcpy() copies a constant / string into another string. 6. T/F-The semantics of a PL describe the sequence of symbols that make up valid programs 7. T/F-Enumeration variables are typically implemented using a subrange of the integers. 8. T/F-Ambiguity is a desirable syntactic attribute. 9. T/F-The execution of programs developed in a compiled language is typically more efficient than the interpreted ones. 10. T/F- One common approach to storage for a variant record type is to allocate adequate storage for the larger variant for all records of that type.

True/False Questions. Write your asnwers as: Q1-1: correct answer, Q1-2: correct answer, etc. 1. T/F-Disjoint union assuming that variants are chosen from type S or type T = (#S)^ (#T) 2. T/F-Cardinality of mapping S→T is: (#S)x(#T) →(#T)#5 3. T/F- Smalltalk and Lisp are dynamically typed PLs whereas Prolog and Perl are statically typed. 4. T/F- If you can print a piece of code, or assign it to a variable, it's an expression. If you can't, it's a statement! 5. T/F-memcpy() copies specific number of bytes from source to destination in RAM, where as strcpy() copies a constant / string into another string. 6. T/F-The semantics of a PL describe the sequence of symbols that make up valid programs 7. T/F-Enumeration variables are typically implemented using a subrange of the integers. 8. T/F-Ambiguity is a desirable syntactic attribute. 9. T/F-The execution of programs developed in a compiled language is typically more efficient than the interpreted ones. 10. T/F- One common approach to storage for a variant record type is to allocate adequate storage for the larger variant for all records of that type.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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∑ = {C,A,G,T}, L = { w : w = CAjGnTmC, m = j + n }. For example, CAGTTC ∈ L; CTAGTC ∉ L because the symbols are not in the order specified by the characteristic function; CAGTT ∉ L because it does not end with C; and CAGGTTC ∉ L because the number of T's do not equal the number of A's plus the number of G's. Prove that L ∉ RLs using the RL pumping theorem.
Let the domain of discourse be all courses ever offered at CSUMB. Translate "no upper-division class is a prereq for a lower-division class" using this translation key: Lx x is a lower-division course Ux x is an upper-division course Pxy x is a pre-requisite for y a CST329 b CST238 C CST334 Use A and E for the quantifier symbols, just like we do with the proof checker. Your answer should be the formula and nothing else.
Step 1. Intersection over Union # def intersection_over_union(dt_bbox, gt_bbox): ---> return iou Step 2. Evaluate Sample We now have to evaluate the predictions of the model. To do this, we will write a function that will do the following: Take model predictions and ground truth bounding boxes and labels as inputs. For each bounding box from the prediction, find the closest bounding box among the answers. For each found pair of bounding boxes, check whether the IoU is greater than a certain threshold iou_threshold. If the IoU exceeds the threshold, then we consider this answer as True Positive. Remove a matched bounding box from the evaluation. For each predicted bounding box, return the detection score and whether we were able to match it or not. def evaluate_sample(target_pred, target_true, iou_threshold=0.5): # ground truth gt_bboxes = target_true['boxes'].numpy() gt_labels = target_true['labels'].numpy() # predictions dt_bboxes =…
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