Аннотирование научного текста.
Исходный текст:
What is a neural network and how does its operation differ from that of a digital computer?
By Mohamad Hassoun
Artificial neural networks are parallel computational models, comprising densely interconnected adaptive processing units. These networks are composed of many but simple processors (relative, say, to a PC, which generally has a single, powerful processor) acting in parallel to model nonlinear static or dynamic systems, where a complex relationship exists between an input and its corresponding output.
A very important feature of these networks is their adaptive nature, in which «learning by example» replaces «programming» in solving problems. Here, «learning» refers to the automatic adjustment of the system's parameters so that the system can generate the correct output for a given input; this adaptation process is reminiscent of the way learning occurs in the brain via changes in the synaptic efficacies of neurons. This feature makes these models very appealing in application domains where one has little or an incomplete understanding of the problem to be solved, but where training data is available.
One example would be to teach a neural network to convert printed text to speech. Here, one could pick several articles from a newspaper and generate hundreds of training pairs – an input and its associated «desired» output sound – as follows: the input to the neural network would be a string of three consecutive letters from a given word in the text. The desired output that the network should generate could then be the sound of the second letter of the input string. The training phase would then consist of cycling through the training examples and adjusting the network parameters – essentially, learning – so that any error in output sound would be gradually minimized for all input examples. After training, the network could then be tested on new articles. The idea is that the neural network would «generalize» by being able to properly convert new text to speech.
Another key feature is the intrinsic parallel architecture, which allows for fast computation of solutions when these networks are implemented on parallel digital computers or, ultimately, when implemented in customized hardware. In many applications, however, they are implemented as programs that run on a PC or computer workstation.
Artificial neural networks are viable models for a wide variety of problems, including pattern classification, speech synthesis and recognition, adaptive interfaces between humans and complex physical systems, function approximation, image compression, forecasting and prediction, and nonlinear system modeling.
These networks are «neural» in the sense that they may have been inspired by the brain and neuroscience, but not necessarily because they are faithful models of biological, neural or cognitive phenomena. In fact, many artificial neural networks are more closely related to traditional mathematical and / or statistical models, such as nonparametric pattern classifiers, clustering algorithms, nonlinear filters and statistical regression models, than they are to neurobiological models.
(«Scientific American», May, 2007)
Аннотация:
In the given work comparison of nervous system and the digital computer is resulted. The example is resulted as the brain solves problems studying as examples, and the computer by means of programming. It is described as viable model of an artificial neural network.
Реферирование научного текста.
Исходный текст:
Mars sand dunes shift and change annually, images show
By Jason Palmer
Vast sand dunes near the northern pole of Mars are not frozen relics of a distant past, but shift and change every Martian year, data have shown. A hi-tech camera aboard Nasa's Mars Reconnaissance Orbiter has spotted UK-sized dune fields that are among the most dynamic on the Red Planet. Causes, says a report in Science, include carbon dioxide gas that freezes solid onto the dunes each winter. As it thaws in spring, the gas released destabilises, causing sand avalanches. The dune fields at high northern latitudes of Mars were first spotted by the Mariner 9 mission, launched in 1971. But only with the benefit of the High-Resolution Imaging Science Experiment (Hirise) orbiting Mars has the dynamic nature of the dunes finally been revealed. «Hirise has been monitoring seasonal processes for several years now and we've seen for a long time these strange spots and streaks that form, particularly on the sand dunes when they're defrosting», said Alfred McEwen, a planetary geologist at the University of Arizona who lead the Hirise team. A series of images taken of the dune fields over two Martian years – nearly four years on Earth – after the departure of the annual ice clearly show a changing picture of the Martian surface.
«What we've noticed more recently though is in looking at these sand dunes from year to year there are new gullies, new channels that form on the dunes, and we're seeing gullies only a year-old that have been repaired again – so there's a lot of activity we weren't aware of», Professor McEwen told BBC News. There's lots of debate about whether features we see on Mars could be produced in the current Mars climate or whether they require different conditions.
These findings lead to understanding where and when sand is moving, what that implies for both the weather and surface properties on Mars, and tweaking and calibrating various models that can be used to understand Mars in the past as well as today.
(«Science and technology report», BBC News)
Реферированный текст:
Ежегодное изменение дюн Марса.
Обширные дюны около северного полюса Марса не замерзающие реликвии отдалённого прошлого, но по полученным данным они перемещаются каждый марсианский год. Причины этому углекислый газ, который замораживает дюны каждую зиму. Так как каждой весной все оттаивает, то выпущенный газ дестабилизирует поверхность, вызывая лавины из песка. В 1971 году миссия «Mariner 9» определила области дюн в высоких северных широтах Марса.
Миссия «Hirise» показала динамический характер дюн Марса. «Hirise» наблюдала сезонные процессы в течение нескольких лет. Поученные изображения областей дюн после размораживания ежегодного оледенения показали изменяющуюся картину Марсианской поверхности.
Эти результаты помогают понять, где и когда песок перемещается, какую погоду это подразумевает, поверхностные свойства Марса, а так же создание и корректировка различных моделей, которые могут использоваться для понятия Марса как в прошлом так и в настоящем.
3. Активизация и закрепление лексики по темам «Science», «Scientific discoveries», «Inventors and inventions».
1. It’s important to maintain proper operation of the reactor.
2. Radioactive substances are harmful to health.
3. Nuclear weapons continue to pose a danger.
4. The rise in sea levels has been predicted as a consequence of global warming.
5. The 1987 hurricane was the worst natural catastrophe to hit England for decades.
6. Britain is committed to a 30 per cent reduction in carbon dioxide production by 2005.
7. Mrs. Thatcher began to sell into private hands many publicly-owned production and service firms.
8 The President knew that some congressmen would support him.
9. Industrial and nuclear waste spreads in water rapidly.
10. Chemicals and pesticides pollute the environment.
11. We started to live in a small town but now we live in London.
12. He was justified because he didn’t break the law.
13. A doctor must respect the wishes of patients.
14. The summer was very dry and there was a threat of fires in the forest.
15. He studied nuclear physics at the university.
16. International Children’s Fund was formed to improve the living conditions of children.
17. A polyglot is a person who has mastered some languages.
18. They used instruments in road building.
19. This scientist won the Nobel Prize for his discovery in Physics.
20. Alfred Nobel tried to avoid publicity.
21. Alfred Nobel often thought about the meaning of his life.
22. Michael Faraday is an English scientist who was born in a poor labouring family.
23. Teach your children how to care for their pets.
24. What hopes you leave the town so early?
25. They used explosives to cut the tunnel through the mountain.
26. The hardest work in mines is now performed by robots.
27. His ability to work day and night was known to his colleagues.
28. I don’t know this word. Do you know meaning of this word?
29. She will too be here today. She promised to come.
30. You shouldn’t kill spiders just because you are afraid of them.
31. The car accident took place in the street and many people were injured.
32. He realized that without the experiment his work would be useless.
33. I will finish my work while you are playing chess.
34. If you learn by your own mistakes you will be able to avoid problems in future.
35. Economists expect the economy to grow by 5 % next year.
36. This student deserves an excellent mark. He knows so much.
37. Atomic ice-breaker works on nuclear energy.
38. You must choose the correct answer.
39. Alfred Nobel’s wish was form a fund.
40. A Nobel did much for the strengthening of permanent armies.
Список используемой литературы:
1. Английский язык для инженеров / Т. Ю. Полякова, Е. В. Синявская, О. И. Тынкова, Э. С. Улановская. – М.: Высшая школа, 2006.
2. Методические указания к практическим занятиям по дисциплине «Английский язык» АННОТИРОВАНИЕ И РЕФЕРИРОВАНИЕ для студентов вторых курсов технических специальностей / сост. Л. М. Митрофаненко, И. Н. Морозова. – Ставрополь: СевКавГТУ, 2011.
3. ГОСТ 7.9-95. Реферат и аннотация. Общие требования. – М.: Юрист, 1998.
