The document provides a humorous list and critique of 150 ill-conceived vehicles from 1888 to modern times. It begins with the Serpollet Dampfdreirad coal-powered carriage and ends with critiques of various modern cars. Throughout, it ridicules ugly, poorly designed, impractical, or bizarre vehicles in a sarcastic tone, pointing out questionable design choices and mocking the intended audiences of many of the featured vehicles.
The document provides a list and brief descriptions of 150 ill-conceived vehicles. It begins by describing the Serpollet Dampfdreirad from 1888 that ran on coal and had the passenger sitting directly in front of the driver. It then discusses various absurd, ugly, poorly designed, and impractical vehicles over time from a variety of manufacturers, poking fun at their flaws and questioning the judgment of their designers.
Danny Thompson is preparing to set a new land speed record at Bonneville Salt Flats in his father Mickey Thompson's streamliner Challenger II. Danny aims to achieve over 500 mph, which would make him the fastest piston-engined vehicle. He has rebuilt the 1968 vehicle over several decades to modern safety standards while retaining the original design. In the final week before the record attempt, Danny and his small team meticulously prepare the 2000hp nitrous-fueled HEMI engines and vehicle systems. Danny hopes to honor his father's legacy and achieve the fastest speed for a piston engine car.
The document contains 20 poems by Jemima Rivas published on Poemhunter.com in 2016. The poems cover a variety of themes including nature, relationships, personal reflections and more. They range from 3 to 6 stanzas in length and explore topics like love, heartbreak, family and everyday observations through vivid imagery and metaphor.
This document provides summaries of several protest poems and songs. It begins with Bob Dylan's "The Times They Are A-Changin'" which calls for social and political change. It also includes lyrics from "A Hard Rain's A-Gonna Fall" by Bob Dylan and "Ohio" by Neil Young about hardships and violence. The document concludes with poems addressing various political and environmental issues such as "Imagine the Angels of Bread" by Martin Espada and "Give Peace a Chance" written by John Lennon.
The document provides a humorous list and critique of 150 ill-conceived vehicles from 1888 to modern times. It begins with the Serpollet Dampfdreirad coal-powered carriage and ends with critiques of various modern cars. Throughout, it ridicules ugly, poorly designed, impractical, or bizarre vehicles in a sarcastic tone, pointing out questionable design choices and mocking the intended audiences of many of the featured vehicles.
The document provides a list and brief descriptions of 150 ill-conceived vehicles. It begins by describing the Serpollet Dampfdreirad from 1888 that ran on coal and had the passenger sitting directly in front of the driver. It then discusses various absurd, ugly, poorly designed, and impractical vehicles over time from a variety of manufacturers, poking fun at their flaws and questioning the judgment of their designers.
Danny Thompson is preparing to set a new land speed record at Bonneville Salt Flats in his father Mickey Thompson's streamliner Challenger II. Danny aims to achieve over 500 mph, which would make him the fastest piston-engined vehicle. He has rebuilt the 1968 vehicle over several decades to modern safety standards while retaining the original design. In the final week before the record attempt, Danny and his small team meticulously prepare the 2000hp nitrous-fueled HEMI engines and vehicle systems. Danny hopes to honor his father's legacy and achieve the fastest speed for a piston engine car.
The document contains 20 poems by Jemima Rivas published on Poemhunter.com in 2016. The poems cover a variety of themes including nature, relationships, personal reflections and more. They range from 3 to 6 stanzas in length and explore topics like love, heartbreak, family and everyday observations through vivid imagery and metaphor.
This document provides summaries of several protest poems and songs. It begins with Bob Dylan's "The Times They Are A-Changin'" which calls for social and political change. It also includes lyrics from "A Hard Rain's A-Gonna Fall" by Bob Dylan and "Ohio" by Neil Young about hardships and violence. The document concludes with poems addressing various political and environmental issues such as "Imagine the Angels of Bread" by Martin Espada and "Give Peace a Chance" written by John Lennon.
This document provides an overview of various punctuation marks:
1. It discusses quotations, parentheses, capitalization, commas, apostrophes, periods, colons, semicolons, dashes, hyphens, ellipses, question marks, exclamation points, brackets, slashes, and spaces.
2. For each punctuation mark, it provides one or more rules for proper usage, along with examples to illustrate those rules.
3. The document is intended as a guide for understanding and applying different punctuation in written English. It covers many of the most common punctuation marks and when to use them correctly.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
This document provides an overview of various punctuation marks:
1. It discusses quotations, parentheses, capitalization, commas, apostrophes, periods, colons, semicolons, dashes, hyphens, ellipses, question marks, exclamation points, brackets, slashes, and spaces.
2. For each punctuation mark, it provides one or more rules for proper usage, along with examples to illustrate those rules.
3. The document is intended as a guide for understanding and applying different punctuation in written English. It covers many of the most common punctuation marks and when to use them correctly.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
8. Pg. 94
3.What potential dangers does
Adams’s mother warn him about?
Adams’s mother warns him against
falling asleep, speeding, and
hitchhikers (lines 61– 68).
Answer
The
Questions
9. Pg. 94
4. What is Adams’s destination?
Where and when did he begin his
journey? (lines 47–48)
Answer
The
Questions
10. Pg. 94
5. Why is Adams’s mother crying?
(lines 61–62)
Answer
The
Questions
11. Pg. 94
6. In what kind of mood does
Adams begin his trip? What
happens to change how he feels?
(lines 80–82)
Answer
The
Questions
13. Pg. 95
7. What happens on the Brooklyn
Bridge?
Answer
The
Questions
14. Pg. 95
7. What happens on the Brooklyn
Bridge?
A hitchhiker steps off the path, and
Adams has to swerve to avoid him. His
car skids as a result.
Answer
The
Questions
15. Pg. 95
8. In what ways is Adams’s encounter
with the hitchhiker both similar to and
different from his previous ones?
Answer
The
Questions
16. Pg. 95
8. In what ways is Adams’s encounter with the
hitchhiker both similar to and different from his
previous ones?
The hitchhiker looks exactly the same. On the
turnpike, however, he hails Adams. This
behavior is different from what he has done
before. Seeing him a third time suggests that the
hitchhiker will reappear frequently throughout
Adams’s journey
Answer
The
Questions
17. Pg.95
9. What happens the first time Adams
sees the hitchhiker? (lines 84–91)
Answer
The
Questions
18. Pg.95
10. What is Adams’s theory about how
the hitchhiker beat him to the Skyway?
(lines 99–102)
Answer
The
Questions
19. Pg. 95
11. Why does Adams start to get
nervous when he sees the hitchhiker a
third time? (lines 115–119)
Answer
The
Questions
20. Pg. 95
12. Discuss the examples of
foreshadowing.
The mechanic says that they have not had
“a drop of rain all week” (line 130).
The mechanic says that a hitchhiker would
be a “sight for sore eyes” (lines 141–142).
Answer
The
Questions
22. Pg. 96
13. What might the presence of the
hitchhiker at a detour indicate for
Ronald Adams?
Answer
The
Questions
23. Pg. 96
13. What might the presence of the
hitchhiker at a detour indicate for
Ronald Adams?
The hitchhiker might be indicating that
Adams’s life is about to take an
unexpected direction.
Answer
The
Questions
24. Pg. 96
14. Adams says he stops to get a cup
of coffee. What is the real reason that
he stops at the roadside stand?
He thinks he sees the hitchhiker there.
He is desperate to talk to someone
and get reassurance that he is not just
seeing things
Answer
The
Questions
25. Pg. 96
14. Adams says he stops to get a cup
of coffee. What is the real reason that
he stops at the roadside stand?
He thinks he sees the hitchhiker there.
He is desperate to talk to someone
and get reassurance that he is not just
seeing things
Answer
The
Questions
26. Pg. 96
15. How has Adams changed since
leaving Brooklyn?
He is now nervous and uncertain.
Traveling this long distance alone no
longer seems like the fun adventure he
expected it to be.
Answer
The
Questions
27. Pg. 96
15. How has Adams changed since
leaving Brooklyn?
He is now nervous and uncertain.
Traveling this long distance alone no
longer seems like the fun adventure he
expected it to be.
Answer
The
Questions
29. Pg. 97
16. Discuss In lines 283–288, the
hitchhiker has spots of rain on his
shoulders even though it is a baking
hot day in Oklahoma. What might this
be proof of?
The hitchhiker is not real; he is a
supernatural creature.
Answer
The
Questions
30. Pg. 97
16. Discuss In lines 283–288, the
hitchhiker has spots of rain on his
shoulders even though it is a baking
hot day in Oklahoma. What might this
be proof of?
The hitchhiker is not real; he is a
supernatural creature.
Answer
The
Questions
32. Pg. 98
17. What does this scene suggest
about the chances of Adams’s arriving
in California?
He will most likely die before reaching
California. The hitchhiker seems to be
trying to kill him, and Adams is
becoming tired and desperate.
Answer
The
Questions
33. Pg. 98
17. What does this scene suggest
about the chances of Adams’s arriving
in California?
He will most likely die before reaching
California. The hitchhiker seems to be
trying to kill him, and Adams is
becoming tired and desperate.
Answer
The
Questions
34. Pg. 98
18. What is the girl’s first impression of
Adams? How does it change?
She thinks she is quite lucky to have
been picked up by a good-looking guy
who has a nice car. Then he sees a
“phantom” and tries to “run him
down” (lines 359–374). She no longer
feels safe with him.
Answer
The
Questions
35. Pg. 98
18. What is the girl’s first impression of
Adams? How does it change?
She thinks she is quite lucky to have
been picked up by a good-looking guy
who has a nice car. Then he sees a
“phantom” and tries to “run him
down” (lines 359–374). She no longer
feels safe with him.
Answer
The
Questions
36. Pg. 98
19. Is Adams’s reason for wanting to run
over the hitchhiker rational? Explain.
He wants to run him over to prove he
exists. However, if the hitchhiker is real,
Adams could kill him.
Answer
The
Questions
37. Pg. 98
19. Is Adams’s reason for wanting to run
over the hitchhiker rational? Explain.
He wants to run him over to prove he
exists. However, if the hitchhiker is real,
Adams could kill him.
Answer
The
Questions
39. Pg. 99
20. When is the last time that
Adams had a good night’s sleep?
In lines 166–167, he mentions
having had a good night’s sleep in
Pittsburgh, but he does not appear
to have slept since then.
Answer
The
Questions
40. Pg. 99
20. When is the last time that
Adams had a good night’s sleep?
In lines 166–167, he mentions
having had a good night’s sleep in
Pittsburgh, but he does not appear
to have slept since then.
Answer
The
Questions
41. Pg. 99
21. What might sleep symbolize? What
does this mean for Adams?
Sleep can refer to death. Adams’s
death might be foreshadowed by the
mention of sleep in this passage and
throughout the play.
Answer
The
Questions
42. Pg. 99
21. What might sleep symbolize? What
does this mean for Adams?
Sleep can refer to death. Adams’s
death might be foreshadowed by the
mention of sleep in this passage and
throughout the play.
Answer
The
Questions
43. Pg. 99
22. Why does Adams crash the car into
the fence? (lines 371–372)
Answer
The
Questions
44. Pg. 99
23. What is the girl’s reaction?
(lines 388–391)
Answer
The
Questions
45. Pg. 99
24. Where does Adams offer to drive
her? Why?
(lines 395–409)
Answer
The
Questions
46. Pg. 99
25. What does he do when she gets
out of the car? (lines 414–419)
Answer
The
Questions
48. Pg. 100
26. What do the images in this passage
suggest about Adams’s future?
All of the images in this passage are
cold, empty, and lifeless, suggesting a
future without hope or even life
Answer
The
Questions
49. Pg. 100
26. What do the images in this passage
suggest about Adams’s future?
All of the images in this passage are
cold, empty, and lifeless, suggesting a
future without hope or even life
Answer
The
Questions
51. Pg. 101
27. What does Adams learn when he calls
home? Was this outcome foreshadowed?
Adams swerves to avoid a hitchhiker on the
Brooklyn Bridge, and there is a terrible sound of
skidding (lines 90 – 92)
Adams learns that he died in a car accident on
the Brooklyn Bridge (lines 540 – 542)
Answer
The
Questions
52. Pg. 101
27. What does Adams learn when he calls
home? Was this outcome foreshadowed?
Adams swerves to avoid a hitchhiker on the
Brooklyn Bridge, and there is a terrible sound of
skidding (lines 90 – 92)
Adams learns that he died in a car accident on
the Brooklyn Bridge (lines 540 – 542)
Answer
The
Questions
73. 1. Do you think The Hitchhiker is a good horror story?
2. Where is Ronald Adams when the radio play begins?
3. Why does the repeated sight of the hitchhiker give Adams "the willies?"
4. What does Adams learn at the end of the play?
5. What do you think has really happened to Adams?
6. Who do you think the hitchhiker really is?
7. What do you think would have happened if Adams had stopped and pick up
the hitchhiker the first time?
8. Why do you think horror stories are so popular?
Answer
The
Questions
83. What is a junction?
A.
A lapse in judgment
B.
A place of connection
C.
A decision of importance
D.
An obstacle to independence
Answer
The
Questions
84. Foreshadowing means
•A.
•The author gives you a hint of future action in the story
•B.
•The author is speaking in 1st person point of view
•C.
•The author is going back in time
•D.
•The author is writing about a memory that he has had
Answer
The
Questions
85. Monotony is a
•A.
•Lack of variation
•B.
•Feeling of aloneness
•C.
•Sense of control
•D.
•Battle of wits
Answer
The
Questions
86. Something that is sinister is
•A.
•Eerie or fascinating
•B.
•Evil or menacing
•C.
•Invisible or blinding
•D.
•Defeated or unstable
Answer
The
Questions
87. A lark is
•A.
•A distorted image
•B.
•A carefree adventure
•C.
•A cherished memory
•D.
•An unlikely situation
Answer
The
Questions
88. What is an assurance?
•A.
•An attraction to someone
•B.
•An emphasis on actual events
•C.
•A guarantee of something
•D.
•A belief in an unpopular idea
Answer
The
Questions
89. At the end of the radio play, Adams realizes that six days earlier he
•A.
•Lost his mother to a fatal disease
•B.
•Killed the hitchhiker on the train tracks
•C.
•Died in an automobile accident in Brooklyn
•D.
•Went insane and abandoned his responsibilities
Answer
The
Questions