1 . Men’s sport has for decades benefited from what investors call a “flywheel effect”. Big broadcast and sponsorship deals throw money into the game readily.    1    . That, in turn, generates even more money the next time around.

Women’s sport has long suffered the opposite phenomenon.    2    With few games available, viewers cannot tune in, and few viewers means little money.

The flywheel may, at last, be starting to turn round.    3    The first quarter of 2022 saw the highest viewership, up by around 50% on 2019, the last full year before the pandemic. Women’s sport is resisting a general trend for people to watch less television. Streaming (流媒体), meanwhile, offers a way to show games without expensive deals with broadcasters, helping to build fanbases.

    4    Viewership of the Women’s National Basketball Association (WNBA) in America rose by 50% in 2021 compared with 2020. In February, the WNBA felt confident, raising $75 million and valuing it at $1 billion. The prize pool for this year’s Women’s Championship is $16 million, double the value in 2017.

One big change is that several big rights-holders, including FIFA and World Rugby, now sell, sponsorship rights for women’s events, separately, rather than bonded with the men’s tournaments.    5    

A．Start with viewing figures.

B．Money is starting to follow eyeballs.

C．Many associations fund the women’s sports.

D．Some think sponsors get a better deal with women’s sports.

E．Broadcasters have unwillingly shown it without being sure that people will watch.

F．That attracts more players and raises the level of play, which helps attract more viewers.

G．That forces buyers and sellers to think about exactly how much such rights might be worth.

2 . Like any doctor, Jacques Fellay wants to give his patients the best care. Hidden inside our bodies are genetic (基因的) markers that can tell him which patients could probably have diseases such as AIDS and provide early treatments. However, there are worries for Fellay: they contain sensitive details that could lead to embarrassment, discrimination or even worse.

A new kind of encryption, fully homomorphic encryption (FHE) (全同态加密), is making it possible for data users to run multiple operations on genetic data without seeing the contents. This can help end big data’s privacy problem, and Fellay’s patients can be some of the first to benefit.

In 1978, the concept of homomorphic encryption (HE) was firstly proposed. According to it, one could encrypt and share data with others, who could analyze and perform calculations on the data with no idea what it means. After getting the data back, the data user could simply arrive at the result using the secret key and it will make total sense.

In 2009, Craig Gentry firstly provided a workable FHE program. Like HE, it rests on a mathematical idea called a homomorphism, which mostly relies on using algebra (代数) to map data from one form to another without changing its underlying structure. However, it supports multiple operations on encrypted data, rather than only one calculation in HE.

Later, Gentry went on to work at IBM, which now has complicated FHE tools to run encrypted data. In the medical field, for example, it detected signs of COVID-19 infection by applying FHE to analyze the encrypted CT scans of more than 1, 500 people’s lungs. In a paper published in October 2021, the team used FHE to collect data from multiple sources and predicted the effect of cancer treatments or the process of HIV infection, as in Fellay’s case.

Workable, but still slow: calculations on the FHE-encrypted data could take millions of times longer than those on raw data. But, as Goldwasser says, “If you believe that security is not a plus, but it’s a must,” she says, “then in some sense there is no overhead.”

1. What is Fellay concerned about?

A．Serious diseases.

B．Sensitive patients.

C．The safety of personal data.

D．The lack of genetic information.

2. In what way is FHE different from HE?

A．It speeds up the analyses.

B．It allows more calculations.

C．It prevents direct access to data.

D．It depends on the use of algebra.

3. What is Goldwasser’s attitude towards FHE?

A．Contradictory.

B．Unclear.

C．Favorable.

D．Intolerant.

4. What is the main idea of the text?

A．FHE protects privacy in the big data era.

B．FHE opens a new world for medical workers.

C．Gentry proposed a new encryption technology.

D．Homomorphic encryption developed over time.

3 . Recent snowstorms in Southern California left several people dead and others trapped in their homes in the San Bernardino Mountains.

During the severe weather, members of the community _________ to help a 79-year-old man get to his wife, who was experiencing a medical _________ miles away. The man, Robert Rice, was confident he could _________ the storms with the supplies he had stocked.

But things took an unexpected turn when he learned that his wife, Ann Rice, was struggling to _________ . She had a(n)_________ of chronic (慢性的) lung disease and had been on a ventilator for 3 years. The nursing facility where she lived was 27 miles from Robert, and doctors believed she could die as carbon dioxide _________in her lungs.

Robert was _________ that he would be unable to be by her side because of the storm, which had covered most of the area with snow and left many families _________. His daughter _________ the fact that he wasn’t going to be there, and was trying to prepare him for that, but he was in__________. He kept thinking he’d figure out a way to get himself down there.

Doctors were able to __________ her more time, but her worsening condition urged him to do all he could to get to her. He tried to clear some of the driveway himself, but could not __________ the falling snow.

That’s when he posted a plea (请求) on Facebook. Soon，a lot of neighbors __________ helped to shovel his driveway. With their joint effort, Robert eventually __________ Ann and spent time with her in the hospital.

“I’m so grateful for their __________,” he told the Times. “I believe there were other people who probably needed the help more than me.”

1.

A．united

B．expected

C．failed

D．promised

2.

A．treatment

B．research

C．emergency

D．training

3.

A．predict

B．survive

C．avoid

D．measure

4.

A．move

B．concentrate

C．breathe

D．communicate

5.

A．condition

B．memory

C．experience

D．history

6.

A．accumulated

B．formed

C．circulated

D．melted

7.

A．angry

B．afraid

C．shocked

D．guilty

8.

A．speechless

B．aimless

C．powerless

D．jobless

9.

A．proved

B．doubted

C．ignored

D．accepted

10.

A．denial

B．favour

C．need

D．trouble

11.

A．lend

B．buy

C．owe

D．assign

12.

A．make use of

B．keep track of

C．put up with

D．keep up with

13.

A．casually

B．hesitantly

C．voluntarily

D．accidentally

14.

A．followed

B．reached

C．contacted

D．protected

15.

A．kindness

B．acceptance

C．encouragement

D．tolerance

4 . As well as competitions, there are events such as poetry salons ________ you can share poems with others.

A．which

B．when

C．where

D．what

5 . Check out what’s coming soon, and what’s in development. Playbill will update these listings when new information is made available.

THE COLLABORATION at Samuel J. Friedman Theatre

The play tells a true story in New York. Fifty-six-year-old Warhol’s star is falling. Jean is the new wonder-kid taking the art world by storm. When Jean agrees to work together with Warhol on a new exhibition, it soon becomes the talk of the city. The two artists set foot on a shared journey, both artistic and deeply personal, which redraws both their worlds.

ALMOST FAMOUS at Bernard B. Jacobs Theatre

Based on the 2,000 film of the same name, the musical features Pulitzer winner Kitt and a book by Crowe, who earned an Oscar award for penning the original film based on his own teenage experience. The coming-of-age story tracks a 15-year-old music fan named William who follows the emerging band Stillwater on tour.

THE PIANO LESSON at Ethel Barrymnore Theatre

The play is the fourth in Wilson’s Century Cycle, which digs into the Black experience in every decade of the 20th century. Set in Pittsburgh’s Hill District in 1936, it centers around a brother and a sister involved in a battle over a piano carved with the faces of their ancestors.

KIMBERLY AKIMBO at Booth Theatre

Kim is a bright and funny high school girl, who happens to look like a 72-year-old lady. And yet her aging disease may be the least of her problems. Forced to deal with family secrets, and possible crime charges, Kim is determined to explore happiness in a world where not even time is on her side.

1. What kind of play is THE COLLABORATION?

A．A romance.

B．A sci-fi story.

C．A fairy tale.

D．A life story.

2. Which theatre should you visit if you want to learn about racial issues?

A．Samuel J. Friedman Theatre.

B．Bernard B. Jacobs Theatre.

C．Ethel Barrymore Theatre.

D．Booth Theatre.

3. What can you see in both ALMOST FAMOUS and KIMBERLY AKIMBO?

A．Struggles against crime charges.

B．Cutting-edge film technology.

C．Award-winning performances.

D．Teenagers’ growth experiences.

6 . When Elinor Lobel was 16, a “smart” insulin (胰岛素) pump was attached to her body. Powered by AI, it tracks her glucose levels and administers the right dose of insulin at the right time to keep her healthy. It is one of the new ways that data and AI can help improve lives.

Books that criticize the dark side of data are plentiful. They generally suggest there is much more to fear than fete in the algorithmic(算法的)age.

But the intellectual tide may be turning. One of the most persuasive supporters of a more balanced view is Elinor Lobel’s mother, Orly, a law professor. In The Equality Machine she acknowledges AI’s capacity to produce harmful results. But she shows how, in the right hands, it can also be used to fight inequality and discrimination.

A principle of privacy rules is “minimization”: collect and keep as little information as possible, especially in areas such as race and gender. Ms Lobel flips the script, showing how in hiring, pay and the legal system, knowing such characteristics leads to fairer outcomes.

Ms Lobel’s call to use more, not less, personal information challenges data-privacy orthodoxy(正统观念). But she insists that “tracking differences is key to detecting unfairness.” She advocates g loosening of privacy rules to provide more transparency(透明)over algorithmic decisions.

The problems with algorithmic formulae(公式) are tackled in depth in Escape from Model Land by Erica Thompson of the School of Economics. These statistical models are the backbone of big data and AL. Yet a perfect model will always be beyond reach. “All models are wrong,” runs a wise saying. “Some are useful.”

Ms Thompson focuses on a challenge she calls the Hawkmoth Effect. In the better known Butterfly Effect, a serviceable model, Vin the prediction of climate change, becomes less reliable over time because of the complexity of what it is simulating(模拟), or because of inaccuracies in the original data. In the Hawkmoth Effect, by contrast, the model itself is flawed; it might fail to take full account of the interplay between humidity, wind and temperature.

The author calls on data geeks to improve their solutions to real-world issues, not merely refine their formulae—in other words, to escape from model land. “We do not need to have the best possible answer,” she writes, “only a reasonable one.”

Both these books exhibit a healthy realism about data, algorithms and their limitations. Both recognize that making progress involves accepting limitations, whether in law or coding. As Ms Lobel puts it: “It’s always better to light a candle than to curse the darkness.”

1. Ms Lobel intends to convey that

A．minimisation is a good privacy rule to go by

B．algorithms are currently challenged by data privacy

C．employing more personal data should be encouraged

D．identifying algorithms’ problems leads to better outcomes

2. What can we learn about “Hawkmoth Effect”?

A．It develops from Butterfly Effect.

B．It emphasizes accuracy of original data.

C．It enjoys popularity in climate research field.

D．It is mentioned to show the model can be faulty.

3. Which of the following does the writer probably agree?

A．Using algorithms to detect differences is hard.

B．The application of data and algorithms is limited.

C．The reliability of data should be attached importance to.

D．Improving algorithms involves accepting its imperfection.

4. Which would be the best title for this passage?

A．The Algorithm’s Prospect	B．The Algorithm’s Mercy
C．The Algorithm’s Complexity	D．The Algorithm’s Recognition

8 . —Do you know a new shopping mall opened in our town yesterday?
—Yes. Why don’t we pay a visit ______ we have time?

A．since

B．though

C．until

D．unless

9 . 阅读下面短文，在空白处填入1个适当的单词或括号内单词的正确形式。

Falling around December 22nd, the winter solstice (冬至) is one of the traditional Chinese festivals     1     also one of the 24 solar terms (节气).

The winter solstice festival originated from the Han Dynasty (202 BC-220 AD) and     2     (develop) well in the Tang and Song dynasties (618-1279). People in the old times attached great     3     (important) to the festival. In the Han Dynasty, the festival was regarded as the Winter Festival and people had one day off for the holiday. Officials organized the ceremonies     4     (celebrate) the day and people visited each other with delicious food.

Traditions and customs for the day vary     5     different areas of China. In the northern part of China, people usually eat dumplings, and mutton to celebrate the day. In the southern part, tang yuan and noodles are the festival food. The dumpling is the     6     (essence) food for people in northern China. After the winter solstice, it will become colder and colder. By eating dumplings, people think they can avoid     7     (they) ears from being frozen as the dumplings look like people’s ear.

Tangyuan,     8     is popular in southern China, is a kind of glutinous rice (糯米) ball with different fillings. It can     9     (boil) or fried. People think after     10     (eat) it, they will age one. People also use it to present to relatives and friends.

10 . Life is full of ups and downs. As we grow, we come to understand the fact that sometimes ________hard we try, things may not turn out as expected.

A．although

B．however

C．as

D．though