1 . Expressive arts therapy is a form of therapy that uses dance, drama, music, poetry, and art to enhance one's overall well－being. The arts are used to let go, _____ and to release.

People have been using the arts as tools for _____for many centuries. In the early 1940's expressive arts therapy became formally _____ and has since provided meaningful therapeutic _____ for people of all ages in a variety of treatment settings. There is no right or wrong way in the arts and clients are encouraged to be _____ with self－expression. Expressive arts therapy focuses on the _____ of making art and exploring _____ the piece means to the individual, _____ the final product.

Through the arts, people can _____ ideas and feelings that may be hard to put into words, such as negative memories and stress. It has been _____ that use of the arts can help people develop social skills, _____ anxiety, manage behavior, and increase self－confidence. A free form of expression makes clients _____ of their own work by reducing the need to compare themselves to others, an unfortunate problem seen today.

Expressive arts therapists are professionally _____. Anyone can be an expressive art therapist provided that they get the appropriate training. Knowledge in psychology is _____ to all training, but from there training _____ includes getting a master's degree in counseling with specialized instruction in implementing (实施) the art, music, poetry, and dance/movement therapeutically. Some professionals extend their _____ and obtain a PhD in expressive therapy.

_____ expressive arts therapy is unique, the clinical goal is _____ to other mental health professions. This _____ is to facilitate clients' growth and _____ change.

3 . If We Are Not Just Animals, What Are We?

Philosophers and theologians (神学家) in the Christian tradition have long regarded human beings as separate from the other animals by the presence of the divine spark (神圣的火花) that is believed to exist within them. This inner source of illumination, the soul, is something that can never be grasped from without, and, as such, must be something that is detached in some fundamental manner from the natural order of things such that the soul continues to exist even after the death of the body, perhaps taking wing for some supernatural place following its demise (死亡).

Recent advances in genetics, neuroscience and evolutionary psychology have all but killed off this idea.     1     For quite clearly, although we are animals, bound in the web of causality (因果关系) that joins us to the zoosphere, we are not just animals.

This fundamental question is as relevant to the philosophical inquiry of today as it had been for the ancient Greeks. In a thousand different ways, we have drawn and continue to draw distinctions between ourselves and the rest of nature.     2     We believe that people have rights, that they have sovereignty (完全独立的) over their own lives, and that those who live by enslaving (使成为奴隶) or abusing others are denying their own humanity.

Evolutionary psychologists tell another story. Morality, they argue, is an adaptation. If organisms (生物体) compete for resources, a strategy of cooperation will be more successful in the long run than a strategy of pure selfishness. Cooperative features of an organism will therefore be selected over time. And all that is special in the human condition can be understood in this way — as the outcome of a long process of adaptation that has given us the unbeatable advantage of morality, whereby we can resolve our conflicts without fighting and adjust to the demands that upset us from every side.

The astonishing moral equipment of the human being — including rights and duties, personal obligations, justice, resentment (憎恨), judgment, forgiveness — is the deposit (沉积物) left by millenniums of conflict.     3     It is an evolved mechanism whereby the human organism proceeds through life sustained on every side by bonds of mutual interest.

I am fairly confident that the picture painted by the evolutionary psychologists is true, but I am also convinced that this is not the whole truth.     4     We human beings do not see one another as animals see one another, as fellow members of a species. We relate to one another not as objects but as subjects, as creatures who address one another “I” to “you.”

By speaking in the first person, we can make statements about ourselves, answer questions, and engage in reasoning and advice in ways that avoid all the normal methods of discovery. As a result, we can participate in dialogues founded on the assurance that, when you and I both speak sincerely, what we say is trustworthy: We are “speaking our minds.” This is the heart of the I-You encounter. Hence as persons we live in a life-world that is not reducible (可简化的) to the world of nature, any more than the life in a painting is reducible to the lines and colors from which it is composed.

4 . ‘Small Data’ Are Also Crucial for Machine Learning

Many people relate “artificial intelligence” with “big data.” There’s a reason for that: some of the most prominent AI breakthroughs in the past decade have relied on enormous data sets. Image _________ made great progress in the 2010s thanks to the development of ImageNet, a data set containing millions of images hand sorted into thousands of categories. More recently, GPT-3, a language model, was trained on _________ online texts to produce humanlike text in Jan, 2021. So it is not surprising to see AI being tightly connected with “big data” in the _________ imagination. But AI is not only about large data sets, and research in “small data” approaches has grown extensively over the past decade. The so-called transfer learning serves as an especially _________ example.

Also known as “fine-tuning,” transfer learning is helpful in settings where you have _________ data on the task of interest but abundant data on a related problem. You need to first train a model using a big data set and then retrain slightly using a smaller one related to your _________ problem. A research team working on German-language speech recognition, _________, showed that they could improve their results by starting with an English-language speech model trained on a larger data set. Then, they used transfer learning to _________ that model for a smaller data set of German-language audio.

Small data approaches such as transfer learning are more _________ than more data-intensive methods. They can promote progress in areas where little or no data exist, such as in forecasting natural hazards that occur relatively __________. In this context, small data approaches will become increasingly important as more organizations look to diversify AI application areas and invest in previously __________ fields.

Despite the progress in research, transfer learning has received relatively little __________. While many machine learning experts are likely familiar with it at this point, the existence of techniques such as transfer learning does not seem to have reached the awareness of the broader space of policymakers in positions of making important decisions about AI funding and __________.

As long as the success of small data technique like transfer learning is __________, resources can be allocated to support their widespread use. In that case, we can help correct the popular __________ regarding the role of data in AI and foster innovation in new directions.

6 . People with a rare genetic disorder known as Prader-Willi syndrome never feel full, and this excess hunger can lead to life-threatening obesity (肥胖症). Scientists studying the problem have now found that the fist-shaped structure known as the cerebellum (小脑) — which had not previously been linked to hunger — is key to regulating satiation (饱食) in those with this condition.

This finding is the latest in a series of discoveries revealing that the cerebellum, long thought to be primarily involved in movement harmony, also plays a broad role in cognition, emotion and behavior. “We’ve opened up a whole field of cerebellar control of food intake,” says Albert Chen, a neuroscientist at the Scintillon Institute in California.

The project began with an accidental observation: Chen and his team noticed they could make mice stop eating by activating small pockets of neurons (神经元) in regions known as the anterior deep cerebellar nuclei (aDCN), within the cerebellum. Fascinated, the researchers gathered data using functional MRI to compare brain activity in 14 people who had Prader-Willi syndrome with activity in 14 unaffected people while each testee viewed images of food -- either immediately following a meal or after fasting (禁食) for at least four hours.

New analysis of these scans revealed that activity in the same regions Chen’s group had accurately pointed out in mice, the aDCN, appeared to be significantly disturbed in humans with Prader-Willi syndrome. In healthy individuals, the aDCN were more active in response to food images while fasting than just after a meal, but no such difference was identifiable in participants with the disorder. The result suggested that the aDCN were involved in controlling hunger. Further experiments on mice, conducted by researchers from several different institutions, demonstrated that activating the animals’ aDCN neurons dramatically reduced food intake by weakening how the brain’s pleasure center responds to food.

For years neuroscientists studying appetite focused mainly either on the hypothalamus, a brain area involved in regulating energy balance, or on reward-processing centers such as the nucleus accumbens (伏隔核). But this group has identified a new feeding center in the brain, says Elanor Hinton, a neuroscientist at the University of Bristol in England who was not involved with the study. “I’ve been working in appetite research for the past 15 years or so, and the cerebellum has just not been a target,” Hinton says. “I think this is going to be important both for Prader-Willi syndrome and, much more widely, to address obesity in the general population.”

8 . “ Humans and machine algorithms (算法) have complementary (互补的) strengths and weaknesses. Each uses different sources of information and strategies to make predictions and decisions, ” said Mark Steyvers, UCI professor of cognitive sciences. “ We show through experiments that humans can improve the predictions of AI even when human accuracy is below that of the AI, and vice versa (反之亦然). This accuracy is higher than combining predictions from two individuals or two AI algorithms. ”

To test the framework, researchers conducted an image classification experiment where human participants and computer algorithms worked separately to correctly identify disorderly pictures of animals and everyday items including chairs, bottles, bicycles and trucks. The human participants ranked their confidence in the accuracy of each image identification as low, medium or high, while the machine classifier generated a continuous score. The results showed large differences in confidence between humans and AI algorithms across images.

“ Human participants were confident that a particular picture contained a chair, for example, while the AI algorithm was confused about the image, ” said Padhraic Smyth, UCI Chancellor’s Professor of computer science. “ Similarly, the AI algorithm was able to confidently provide a label for the object shown, while human participants were unsure if the disorderly picture contained any recognizable object. ”

When predictions and confidence scores from both were combined using the researchers’ new Bayesian framework, the mixed model led to better performance than either human or machine predictions achieved alone.

“ While the past research has demonstrated the benefits of combining machine predictions or combining human predictions, this work shows a new direction in demonstrating the potential of combining human and machine predictions, pointing to new and improved approaches to human-AI cooperation, ” Smyth said.

“ The blend of cognitive science focusing on understanding how humans think and behave and computer science in which technologies are produced will provide further insight into how humans and machines can cooperate to build more accurate artificially intelligent systems, ” the researchers said.

9 . A huge crowd has gathered to watch China’s new scientific research ship enter the water for the first time. This ship, equipped with on-board labs and the latest scientific kit, will eventually explore the world’s oceans. But it is also going to help China plunge beneath the waves: it will serve as a launch-pad for submarines that can dive to the deepest parts of the ocean. “Humans know much less about the deep oceans than we know about the surface of the Moon and Mars. That’s why I want to develop the facility for ocean scientists to reach the deep seas,” says Prof. Cui Weicheng.

He is the dean of deep sea science at Shanghai Ocean University but he has also set up a private company called Rainbow Fish, which built the new research ship and is busy developing submersibles. One of its unmanned subs reached a depth of 4,000m (13,000ft) in its most recent trial. But Rainbow Fish’s ultimate goal is manned exploration and it plans to take humans to the very bottom of the ocean the Mariana Trench, in the Pacific, at a depth of nearly 11,000m (36,000ft). He shows me around a life-size model of the submarine and explains that there is room inside for a crew of three, who will be protected by a thick metal sphere.”At the moment, we are in the design stage, so we are testing several extremely high-strength materials for it.” It will have to bear immense pressures from the crushing weight of water above. If there are any weaknesses, the submarine will implode. The deepest ocean is a place few people have ever experienced first-hand. The first dive to the Mariana Trench was carried out in 1960 by US Navy Lieutenant Don Walsh and Swiss engineer Jacques Picard. Their vessel, the Bathyscaphe Trieste, creaked and groaned as it made the descent, taking nearly five hours.

The only other manned expedition was carried out by Hollywood director James Cameron, who took a solo plunge in a bright green submarine in 2012. Rainbow Fish wants its sub to be next. The team insists its venture isn’t about politics and that it is looking to collaborate with American, Russian and European scientists. It is, though, a commercial operation. The company plans to charge people to use its research ship and submarines, and is targeting three groups, says managing director Dr. Wu Xin. “The first is definitely the scientists who are interested in studying deep-sea science and technology. The second group is offshore companies and oil companies. The last one is tourists and adventurers [who] want to go down themselves to have a look at what’s going on there,” he says. This kind of entrepreneurial approach may be a new model for science in China. Deep-sea research is a difficult, high-risk activity — and much of the ocean remains unexplored. But Cui, who hopes to be the first Chinese person to reach the Mariana Trench, believes that China could be the nation to truly open up this final frontier.

10 . By the age of seven months, most children have learned that objects still exist even when they are out of sight. Put a toy under a blanket and a child that old will know it is still there, and that he can reach underneath the blanket to get it back. This understanding, of “object permanence”, is a normal developmental milestone, as well as a basic tenet of reality. It is also something that self-driving cars do not have. And that is a problem. For a self-driving car, a bicycle that is momentarily hidden by a passing van is a bicycle that has ceased to exist.

This failing is basic to the now-widespread computing discipline that has arrogated to itself the slightly misleading moniker of artificial intelligence (AI). Current AI, based on the idea of machine learning, works by building up complex statistical models of the world, but it lacks a deeper understanding of reality. Similar techniques are used to train self-driving cars to operate in traffic. Cars thus learn how to obey lane markings, avoid other vehicles, hit the brakes at a red light and so on. But they do not understand many things a human driver takes for granted—that other cars on the road have engines and four wheels, or that they obey traffic regulations (usually) and the laws of physics (always). And they do not understand object permanence.

In a recent paper in Artificial Intelligence, Mehul Bhatt of Orebro University, in Sweden, describes a different approach. He and his colleagues took some existing AI programs which are used by self-driving cars and bolted onto them a piece of software called a symbolic-reasoning engine.

Instead of approaching the world probabilistically, as machine learning does, this software was programmed to apply basic physical concepts to the output of the programs that process signals from an autonomous vehicle's sensors. This modified output was then fed to the software which drives the vehicle. The concepts involved included the ideas that discrete objects continue to exist over time, that they have spatial relationships with one another-such as “in-front-of” and “behind”—and that they can be fully or partly visible, or completely hidden by another object. The improvement was not huge, but it proved the principle. And it also yielded something else. For, unlike a machine-learning algorithm, a reasoning engine can tell you the reason why it did what it did. A machine-learning program cannot do that. Besides helping improve program design, such information will, Dr Bhatt reckons, help regulators and insurance companies. It may thus speed up public acceptance of autonomous vehicles.