2018 年 06 月大学英语六级考试真题（第 2 套）

Did Sarah Josepha Hale write "Mary 's Little Lamb," the eternal nursery rhyme (儿歌) about a girl named Mary with a stubborn lamb? This is still dispute, but it's clear that the woman __26__ for writing it was one of America 's most fascinating __27__. In honor of the poem 's publication on May 24,1830, here's more about the __28__ author 's life.

Hale wasn't just a writer, she was also a __29__ social advocate, and she was particularly __30__ with an ideal New England, which she associated with abundant Thanksgiving meals that she claimed had "a deep moral influence." She began a nationwide __31__ to have a national holiday declared that would bring families together while celebrating the __32__ festivals. In 1863, after 17 years of advocacy including letters to five presidents, Hale got it. President Abraham Lincoln, during the Civil War, issued a __33__ setting aside the last Thursday in November for the holiday.

The true authorship of "Mary's Little Lamb" is disputed.. According to New England Historical Society, Hale wrote only one part of the poem, but claimed authorship. Regardless of the author, it seems that the poem was __34__ by a real event. When young Mary Sawyer was followed to school by a lamb in 1816, it caused some problems. A bystander named John Roulstone wrote a poem about the event, then, at some point, Hale herself seems to have helped write it. However, if a 1916 piece by her great-niece is to be trusted, Hale claimed for the __35__ of her life that "Some other people pretended that someone else wrote the poem".

Human memory is notoriously unreliable. Even people with the sharpest facial-recognition skills can only remember so much.

It's tough to quantify how good a person is at remembering. No one really knows how many different faces someone can recall, for example, but various estimates tend to hover in the thousands-based on the number of acquaintances a person might have.

Machines aren't limited this way. Give the right computer a massive database of faces, and it can process what it sees-then recognize a face it's told to find-with remarkable speed and precision. This skill is what supports the enormous promise of facial-recognition software in the 21st century. It's also what makes contemporary surveillance systems so scary.

The thing is, machines still have limitations when it comes to facial recognition. And scientists are only just beginning to understand what those constraints are. To begin to figure out how computers are struggling, researchers at the University of Washington created a massive database of faces- they call it MegaFace- and tested a variety of facial-recognition algorithms (算法) as they scaled up in complexity. The idea was to test the machines on a database that included up to 1 million different images of nearly 700,000 different people-and not just a large database featuring a relatively small number of different faces, more consistent with what's been used in other research.

As the databases grew, machine accuracy dipped across the board. Algorithms that were right 95% of the time when they were dealing with a 13, 000-image database, for example, were accurate about 70% of the time when confronted with 1 million images. That's still pretty good, says one of the researchers, Ira Kemelmacher-Shlizerman."Much better than we expected, "she said.

Machines also had difficulty adjusting for people who look a lot alike-either doppelgangers(长相极相似的人), whom the machine would have trouble identifying as two separate people, or the same person who appeared in different photos at different ages or in different lighting, whom the machine would incorrectly view as separate people.

"Once we scale up, algorithms must be sensitive to tiny changes in identities and at the same time invariant to lighting, pose, age, "Kemelmacher-Shlizerman said.

The trouble is, for many of the researchers who'd like to design systems to address these challenges, massive datasets for experimentation just don't exist--at least, not in formats that are accessible to academic researchers. Training sets like the ones Google and Facebook have are private. There are no public databases that contain millions of faces. MegaFace's creators say it's the largest publicly available facial-recognition dataset out there.

"An ultimate face recognition algorithm should perform with billions of people in a dataset, "the researchers wrote.

There're currently 21.5 million students in America, and many will be funding their college on borrowed money. Given that there's now over $1.3 trillion in student loans on the books, it's pretty clear that many students are far from sensible. The average student's debt upon graduation now approaches $40, 000, and as college becomes ever more expensive, calls to make it "free" are multiplying. Even Hillary Clinton says that when it comes to college, "Costs won't be a barrier. "

But the only way college could be free is if the faculty and staff donated their time, the buildings required no maintenance, and campuses required no utilities. As long as it's impossible to produce something from nothing, costs are absolutely a barrier.

The actual question we debate is who should pay for people to go to college. If taxpayers are to bear the cost of forgiving student loans, shouldn't they have a say in how their money is used?

At least taxpayers should be able to decide what students will study on the public dime. If we're going to force taxpayers to foot the bill for college degrees, students should only study those subjects that're of greatest benefit to taxpayers. After all, students making their own choices in this respect is what caused the problem in the first place. We simply don't need more poetry, gender studies, or sociology majors. How do we know which subjects benefit society? Easy.

Average starting salaries give a clear indication of what type of training society needs its new workers to have. Certainly, there're benefits to a college major beyond the job a student can perform. But if we're talking about the benefits to society, the only thing that matters is what the major enables the student to produce for society. And the value of what the student can produce is reflected in the wage employers are willing to pay the student to produce it.

A low wage for elementary school teachers, however, doesn't mean elementary education isn't important. It simply means there're too many elementary school teachers already.

Meanwhile, there're few who're willing and able to perform jobs requiring a petroleum engineering major, so the value of one more of those people is very high.

So we can have taxpayers pick up students' tuition in exchange for dictating what those students will study. Or we can allow students both to choose their majors and pay for their education themselves. But in the end, one of two things is true:

Either a college major is worth its cost or it isn't. If yes, taxpayer financing isn't needed If not taxpayer financing isn't desirable. Either way, taxpayers have no business paying for students' college education.