Task 2. Read some more useful phrases and word combinations for rendering the article and distribute them into 4 items of the plan for rendering the article.

1) This is a survey article on what tells us …

2) The author shows that …

3) The author examines / presents …

4) The author studies (изучает) the question / the problem of …

5) In the cited (упомянутый) article the author investigates / makes some observations (наблюдения) …

6) The author’s article contains some information on …

7) In this paper, the author describes his approach (подход) to …

8) The author’s main point of this article is that …

9) This article is based on …

10) The value (ценность) of this article is …

11) The author proves the fact that …

12) The author is concerned with the problem for …

13) The author carries out this goal clearly, illustrating it with examples of …

14) In his characteristic style the author describes the work done on …

15) This article gives an interesting account (сообщение, доклад, отчёт) of …

16) The author carries out an (interesting, old/new, simple) argument when …

Data Storage: Memory That Does It All

ScienceDaily (Aug. 2, 2012) — Using the correct annealing temperature is key to making fast, non- volatile computer memory.

Computers often do not run as fast as they should because they are constantly transferring information between two kinds of memory: a fast, volatile memory connected to the CPU, and a slow, non-volatile memory that remembers data even when switched off. A universal memory that is fast, power-efficient and non-volatile would allow new designs that avoid this bottleneck. Hao Meng and co-workers at the A*STAR Data Storage Institute have now shed new light on how to manufacture such a memory.

The researchers explored a special class of universal memory called spin-transfer torque magnetic random access memory (MRAM). A spin-transfer torque MRAM typically comprises two magnetic films that are separated by an insulating layer. The resistance between the two films is low if the magnetization direction in each film is parallel, and high if it is anti-parallel. Information is stored in the relative magnetization between the two films, and read out by measuring resistance. The magnetization directions can be switched by applying spin torque to the films' magnetic domains (using a spin polarized electric current).

High-temperature annealing is a key step in the manufacture of an MRAM cell. Annealing alters the crystal structure of the cell materials, which in turn changes the degree of magnetization and how the cell functions. In particular, the greater change in resistance between parallel and anti-parallel magnetizations, the better the memory will function. Previous studies have shown that this resistance change increases as the annealing temperature increases, but drops if the annealing temperature rises too much.

Meng and co-workers extended this analysis to other critical MRAM characteristics. They focused on a cell made with CoFeB magnetic films, which has a natural magnetization direction outside of the plane of the film. They found that the annealing temperature that yielded maximum resistance variation exceeded the temperature necessary for maximum thermal stability. This is critical information for design engineers, who must balance these two metrics against each other.

Meng and co-workers also found that the minimum current density necessary to change the film magnetization increased with annealing temperature. A lower current is desirable for practical cell operation. The current density could be lowered by reducing the thickness of the magnetic films. However, lower thicknesses also produced an undesirable reduction in resistance variation. By explicitly demonstrating the trade-offs necessary in the design of spin torque MRAMs, the data is expected to help engineers design the next generation of these promising devices. /2300/

annealing – отжиг

volatile – переменный, непостоянный, временный

torque - момент вращения, закручивающий момент

Who’s the Most Influential in a Social Graph? New Software Recognizes Key Influencers Faster Than Ever

ScienceDaily (Sep. 7, 2012) — At an airport, many people are essential for planes to take off. Gate staffs, refueling crews, flight attendants and pilots are in constant communication with each other as they perform required tasks. But it's the air traffic controller who talks with every plane, coordinating departures and runways. Communication must run through her in order for an airport to run smoothly and safely.

In computational terms, the air traffic controller is the " betweenness centrality," the most connected person in the system. In this example, finding the key influencer is easy because each departure process is nearly the same.

Determining the most influential person on a social media network (or, in computer terms, a graph) is more complex. Thousands of users are interacting about a single subject at the same time. New people (known computationally as edges) are constantly joining the streaming conversation.

Georgia Tech has developed a new algorithm that quickly determines betweenness centrality for streaming graphs. The algorithm can identify influencers as information changes within a network. The first-of-its-kind streaming tool was presented this week by Computational Science and Engineering Ph.D. candidate Oded Green at the Social Computing Conference in Amsterdam.

"Unlike existing algorithms, our system doesn't restart the computational process from scratch each time a new edge is inserted into a graph," said College of Computing Professor David Bader, the project's leader. "Rather than starting over, our algorithm stores the graph's prior centrality data and only does the bare minimal computations affected by the inserted edges."

In some cases, betweenness centrality can be computed more than 100 times faster using the Georgia Tech software. The open source software will soon be available to businesses.

Bader, the Institute's executive director for high performance computing, says the technology has wide-ranging applications. For instance, advertisers could use the software to identify which celebrities are most influential on Twitter or Facebook, or both, during product launches.

"Despite a fragmented social media landscape, data analysts would be able to use the algorithm to look at each social media network and mark inferences about a single influencer across these different platforms," said Bader.

As another example, the algorithm could be used for traffic patterns during a wreck or traffic jam. Transportation officials could quickly determine the best new routes based on gradual side-street congestion.

The accepted paper was co-authored by Electrical and Computer Engineering Ph.D. candidate Rob McColl.

betweenness - отношение промежуточности, промежуточность

centrality - центральное положение

influencer – источник влияния

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