【推荐1】Conventional wisdom says that hardship can make us before our time. In fact, a new study suggests that violence not only leaves long-term scars on children's bodies, but also changes their DNA, causing changes that equal to seven to ten years of premature aging (提前衰老).

“Scientists measured this by studying the ends of children' s chromosomes(染色体), called telomeres.” says Idan Shalev, lead author of a study published in Molecular Psychiatry.

Telomeres are special DNA sequences (序列)which prevent the DNA in chromosomes from separating. They get shorter each time a cell divides, until a cell cannot divide any more and dies.

Several factors have been found to shorten telomeres including smoking, radiation (辐射) and psychological stress(心理压力), such as being treated badly when young, and taking care of someone who is permanently ill.

In the study, researchers examined whether exposure to(暴露于) violence could make children' s telomeres shorten faster than normal. They interviewed the mothers of 236 children at the ages of 5, 7 and 10, asking whether the children had been exposed to violence between the mother and her partner, having been treated badly in a physical way by an adult, or bullying. Researchers measured the children's telomeres— in cells from their cheeks—at the ages of 5 and 10.

Telomeres shortened more quickly in children exposed to two or more types of violence, says Shalev. Unless such a pattern changes, the study suggests these children could be expected to develop diseases related to aging, such as heart attacks or memory loss, seven to ten years earlier than children of the same age.

Shalev says there is hope for these children. His study found that in some cases, telomeres can lengthen."Better exercise and stress reduction are three things that may be able to lengthen telomeres,” he says

1. The new study found that ________.

A．hardship has a long-term effect on a child's mind	B．violence leaves scars on a child's mind
C．violence can speed up a child's aging	D．hardship can change a child's DNA

2. According to the passage, telomeres ________.

A．are at the ends of people's chromosomes	B．can help prevent DNA from separating
C．can make a cell die quickly	D．become shorter before they die

3. What can we learn from the passage?

A．Violence can cause quick cell division in children’s body.

B．Children at the ages of 5 to 10 are more likely to be exposed to violence.

C．Being treated badly will make a child's telomeres shorten faster.

D．Children who have shorter telomeres may have a heart attack earlier.

4. What is the best title for the passage?

A．Violence Aging Children’s DNA.

B．Children’s Changing DNA Patterns

C．Violence and Telomeres

D．The Function of Telomeres

【推荐3】During the rosy years of elementary school(小学), I enjoyed sharing my dolls and jokes, which allowed me to keep my high social status. I was the queen of the playground. Then came my tweens and teens, and mean girls and cool kids. They rose in the ranks not by being friendly but by smoking cigarettes, breaking rules and playing jokes on others, among whom I soon found myself.

Popularity is a well-explored subject in social psychology. Mitch Prinstein, a professor of clinical psychology sorts the popular into two categories: the likable and the status seekers. The likables’ plays-well-with-others qualities strengthen schoolyard friendships, jump-start interpersonal skills and, when tapped early, are employed ever after in life and work. Then there’s the kind of popularity that appears in adolescence: status born of power and even dishonorable behavior.

Enviable as the cool kids may have seemed, Dr. Prinstein’s studies show unpleasant consequences. Those who were highest in status in high school, as well as those least liked in elementary school, are “most likely to engage(从事) in dangerous and risky behavior.”

In one study, Dr. Prinstein examined the two types of popularity in 235 adolescents, scoring the least liked, the most liked and the highest in status based on student surveys(调查研究). “We found that the least well-liked teens had become more aggressive over time toward their classmates. But so had those who were high in status. It clearly showed that while likability can lead to healthy adjustment, high status                                        has just the opposite effect on us.”

Dr. Prinstein has also found that the qualities that made the neighbors want you on a play date—sharing, kindness, openness—carry over to later years and make you better able to relate and connect with others.

In analyzing his and other research, Dr. Prinstein came to another conclusion: Not only is likability related to positive life outcomes, but it is also responsible for those outcomes, too. “Being liked creates opportunities for learning and for new kinds of life experiences that help somebody gain an advantage, ” he said.

1. What sort of girl was the author in her early years of elementary school?

A．Unkind.

B．Lonely.

C．Generous.

D．Cool.

2. What is the second paragraph mainly about?

A．The classification of the popular.

B．The characteristics of adolescents.

C．The importance of interpersonal skills.

D．The causes of dishonorable behavior.

3. What did Dr. Prinstein’s study find about the most liked kids?

A．They appeared to be aggressive.

B．They tended to be more adaptable.

C．They enjoyed the highest status.

D．They performed well academically.

4. What is the best title for the text?

A．Be Nice—You Won’t Finish Last

B．The Higher the Status, the Better

C．Be the Best—You Can Make It

D．More Self-Control, Less Aggressiveness

【推荐3】Many early approaches to language technology—and particularly translation—got stuck in a conceptual dead end: the rules-based approach. In translation, this meant trying to write rules to analyze the text of a sentence in the language of origin, breaking it down into a sort of abstract “interlanguage” and rebuilding it according to the rules of the target language. These approaches showed early promise. But language is full of ambiguities (歧义) and exceptions, so such systems were hugely complicated and easily broke down when tested on sentences beyond the simple set they had been designed for.

Nearly all language technologies began to get a lot better with the application of statistical methods, often called a “brute force” approach. This relies on software scanning vast amounts of data, looking for patterns and learning from precedent. For example, in parsing language (breaking it down into its grammatical components), the software learns from large bodies of text that have already been parsed by humans. It uses what it has learned to make its best guess about a previously unseen text. In machine translation, the software scans millions of words already translated by humans, again looking for patterns. In speech recognition, the software learns from a body of recordings and the transcriptions made by humans.

Thanks to the growing power of processors, falling prices for data storage and, most crucially, the explosion in available data, this approach eventually bore fruit. Mathematical techniques that had been known for decades came into their own, and big companies with access to enormous amounts of data were ready to benefit.

The final advance, which began only about five years ago, came with the arrival of deep learning through digital neural networks (DNNs). These are often claimed to have qualities similar to those of the human brain: “neurons” are connected in software, and connections can become stronger or weaker in the process of learning. But Nils Lenke, head of research for a language technology company, explains matter-of-factly that “DNNs are just another kind of mathematical model,” the basis of which had been well understood for decades. What changed was the hardware being used.

Almost by chance, DNN researchers discovered that the graphical processing units (GPUs) used to produce graphics in applications like video games were also brilliant at handling neural networks. The technique has already produced big leaps in quality for all kinds of deep learning, including decoding handwriting, recognizing faces and classifying images. Now they are helping to improve all manner of language technologies, often bringing enhancements of up to 30%. That has shifted language technology from usable at a pinch to really rather good. But so far no one has quite worked out what will move it on from merely good to reliably great.

1. According to the passage, which of the following is a problem of the rules-based approach?

A．Rules are difficult to identify in some languages.

B．It is impossible to find all the rules in a language.

C．The rules are often misinterpreted by researchers.

D．There are always rules too abstract to understand.

2. ________ has a part to play in enabling the “brute force” approach to gradually bear fruit?

A．The increasing amount of data

B．The falling prices of scanning software

C．The availability of recordings and transcripts

D．The wider knowledge people have of language

3. Which of the following is Nils Lenke most likely to agree with about DNNs?

A．They are a big breakthrough.	B．They depend more on hardware.
C．They are not innovative in nature.	D．They haven’t been scientifically proved.

4. What does the writer imply about GPUs?

A．They are inferior to DNNs in their quality of deep learning.

B．They have made language technology reliably greater than ever.

C．They have achieved limited success regarding language technology.

D．They help enhance language technologies by means of image classification.