湖南省师范大学附属中学2021-2022学年高二下学期期中考试英语试题

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2021-2022-2 师大附中高二下期中考试

英语

时量：120 分钟满分：150 分

第一部分听力（共两节，满分 30 分）略

第二部分阅读（共两节，满分 50 分）

第一节（共 15 小题；每小题 2.5 分，满分 37.5 分）

阅读下列短文，从每题所给的 A、B、C、D四个选项中选出最佳选项。

Women scientists around the world have made significant contributions to Stem (science, technology,

engineering, and maths). Here are four who pushed boundaries and changed the world.

Wu Chienshiung (1912—1997)

Born in Taicang, Jiangsu province, Wu took part in the Manhattan Project which helped create the world’s first

nuclear weapon. Her famous Wu experiment overturned the theory of parity in physics. This breakthrough led to a

Nobel Prize that was awarded to her male colleagues, but Wu’s critical role in the work was overlooked.

Hedy Lamarr (1914—2000)

Austria-born Lamarr starred in a lot of Hollywood films and made great success. She was also super smart and

a self-taught inventor. During the second world war, Lamarr together with George Antheil, a composer, developed a

radio guidance system. The principle of their work is part of the basis of Bluetooth and wireless technology.

Katherine Johns (1918—2020)

African American NASA mathematician Johnson’s calculations were critical in getting the first US astronauts

to space and back safely. During her 33-year career at NASA, Katherine earned a reputation for mastering complex

calculations and was referred to as a “human computer”.

Tu Youyou (1930—)

Born in Ningbo, Zhejiang province, Tu shared the 2015 Nobel Prize in Physiology or Medicine with two other

foreign scientists, for her work in discovering artemisinin, a drug used to treat malaria. Her work has saved millions

of lives all over the world. Tu is the first Chinese Nobel winner in physiology or medicine and the first female

citizen of the PRC to win a Nobel Prize.

1. Besides being an inventor, Hedy Lamarr is also a(n) ________.

A. composer B. producer C. actress D. mathematician

2. What is Tu Youyou’s greatest contribution to the world?

A. She treated people for cancer. B. She is the first Chinese Nobel winner.

C. Her discovery helped save millions. D. Her research project is one of a kind.

3. What do the four female scientists have in common according to the text?

A. They shared the same interest. B. They each constructed a theory.

C. They were all awarded the Nobel Prize. D. They all made a difference to the world.

It was near New Year 2014, and David Fajgenbaum, who was battling Castleman disease, was rushed to a CT

scan in an Arkansas hospital. Tears streamed down his face. He thought about the first patient who’d died under his

care and how her brain had bled in a similar way from a stroke(中风).

But he survived. Also, this experience led David to complete “the undertaking” surprising his doctors. “You

learn a lot by almost dying,” they said.

It was in 2010 that David got very sick and was hospitalized for five months. He was learning to become a

doctor at the University of Pennsylvania in memory of his mother who died of cancer. The examination showed it

was idiopathic multi-centric Castleman disease (MCD). First described in 1954, Castleman presents partly like an

autoimmune(自身免疫的) condition and partly like cancer. It doesn’t happen very often — there’re only around

7,000 new cases each year in the United States, so a very limited number of experts are on it.

In his hospital bed, David felt weak. His organs were failing, and he noticed curious red spots on his skin. He

asked doctors what the “blood moles” meant. But they, focused on saving his life, weren’t interested in them.

Castleman disease struck David four more times over the next three years. Despite it all, he managed to graduate

and later founded the Castleman Disease Collaborative Network (CDCN) devoted to fighting Castleman disease.

In late 2013, Castleman struck again, landing David in that Arkansas hospital. It marked his closest brush with

death yet. After this time, David set out to try to save his own life. Examining his medical charts, he zeroed in on an

idea that researchers hadn’t explored. With considerable effort, he found a drug called Sirolimus might be

beneficial to his discase. After consulting with his doctors, he picked up the drug. So far, it’s working. David is in

recovery and devoting his medical career to saving other patients like him. “I hope my story offers lessons far

beyond medicine about what people can do when they’re up against a brick wall,” says David.

4. How did David feel when having the CT scan?

Grateful. B. Awkward. C. Hopeless. D. Unsure.

5. What does “the undertaking” in paragraph 2 refer to?

A. David founded the CDCN. B. David recovered from Castleman.

C. David survived the CT scan. D. David found a way to treat his disease.

6. What do we know about Castleman disease?

A. It is a rare disease. B. It killed David’s mother.

C. It is a kind of cancer. D. It interested many experts.

Which of the following can best describe David?

A. Selfless and optimistic. B. Determined and observant.

C. Ambitious and confident. D. Warm-hearted and hardworking.

No one can say whether human-like robots will have a sweet dream, but they will almost certainly need

periods of rest that offer benefits like what sleep provides to living brains, according to new research from Los

Alamos National Laboratory (LANL).

“We study spiking neural networks (尖峰神经网络), which are systems that learn much as living brains do,”

said Yijing Watkins, a computer scientist from LANL. “We trained a neuromorphic (神经形态的) processor in a

way how humans and other biological systems learn from their environment during childhood development.”

Watkins and her research team found that the network simulations (模拟) became unstable after continuous periods

of unattended learning. When they exposed the networks to states that are analogous to what living brains

experience during sleep, stability was recovered. “It was as though we were giving the neural networks a good

night’s rest,” said Watkins.

The discovery came about as the research team worked to develop neural networks that are as close as how

humans and other biological systems learn to see. The group initially struggled with stabilizing simulated neural

networks undergoing unattended dictionary training, which involves classifying objects without providing

examples to compare them to. The researchers expose the networks to an artificial simulation of sleep as nearly a

final effort to stabilize them. They experimented with various types of noise. The best results came when they used

waves of so-called Gaussian noise. These waves can make sure that the neural networks keep stable.

The group’s next goal is to apply their algorithm (算法) to Intel’s Loihi, a product which uses spiking neural

networks to work. They hope allowing Loihi to sleep from time to time will enable it to stably process information

from a camera in real time. If it can confirm the need for sleep in artificial brains, we can probably expect the same

to be true of human-like robots and other intelligent machines that may come about in the future.

8. What did Watkins and her research team find?

A. Neural networks are far from stable.

B. Artificial brains may need break as well.

C. Neural networks are very sensitive to noise.

D. Human-like robots need equal rest to humans.

9. What does the underlined word “analogous” in paragraph 2 mean?

A. Contrary. B. Beneficial. C. Similar. D. Related.

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