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TOEIC・英語 大学生・専門学校生・社会人

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15 語数: 398 語 出題校 法政大 5 We are already aware that our every move online is tracked and analyzed. But you 2-53 couldn't have known how much Facebook can learn about you from the smallest of social interactions - a 'like'*. (1) Researchers from the University of Cambridge designed (2) a simple machine-learning 2-54 system to predict Facebook users' personal information based solely on which pages they had liked. E "We were completely surprised by the accuracy of the predictions," says Michael 2-55 Kosinski, lead researcher of the project. Kosinski and colleagues built the system by scanning likes for a sample of 58,000 volunteers, and matching them up with other 10 profile details such as age, gender, and relationship status. They also matched up those likes with the results of personality and intelligence tests the volunteers had taken. The team then used their model to make predictions about other volunteers, based solely on their likes. The system can distinguish between the profiles of black and white Facebook users, 15 getting it right 95 percent of the time. It was also 90 percent accurate in separating males and females, Democrats and Republicans. Personality traits like openness and intelligence were also estimated based on likes, and were as accurate in some areas as a standard personality test designed for the task. Mixing what a user likes with many kinds of other data from their real-life activities could improve these predictions even more. 20 Voting records, utility bills and marriage records are already being added to Facebook's database, where they are easier to analyze. Facebook recently partnered with offline data companies, which all collect this kind of information. This move will allow even deeper insights into the behavior of the web users. 25 30 (3) - Sarah Downey, a lawyer and analyst with a privacy technology company, foresees insurers using the information gained by Facebook to help them identify risky customers, and perhaps charge them with higher fees. But there are potential benefits for users, too. Kosinski suggests that Facebook could end up as an online locker for your personal information, releasing your profiles at your command to help you with career planning. Downey says the research is the first solid example of the kinds of insights that can be made through Facebook. "This study is a great example of how the little things you do online show so much about you,” she says. "You might not remember liking things, " but Facebook remembers and (4) it all adds up.", * a 'like': フェイスブック上で個人の好みを表示する機能。 日本語版のフェイスブックでは「いいね!」 と表記される。 2-56 2-57 2-58 36

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物理 大学生・専門学校生・社会人

なぜこのように変換されるのか説明してもらいたいです!

には、惑星は楕円軌道を描いて運動している。 万有引力を受けて運動する このような惑星の運動を考えるには, 2次元極座標を用いるのが便利であ る。そこで,2次元極座標を用いると,質点の速度と加速度がどのように 表され、運動方程式がどんな形に表されるのかを、考えてみよう。 r-y 直交座標系で位置 (x,y)において速度v=(ひょ,ひy)=(エン)をも って運動している質点P を考える。 図 8.2に示 すように, 2次元極座標系での速度成分 (Ur, Up) ~ と -y 直交座標系での速度成分 (vs, vy) の間に は,第6章で考えた回転座標系の場合と同様に, Ur= vxCOS+vy sin y ひ y HP (8.5) r v=vxsin +vy cosp I の関係が成り立つ。 図8.2 速度の極座標表示 質点Pの位置は,(x,y)=(rcos, rsin) と書けるが,Pが運動し の関数であるから, 合成関数の微分により速 は時刻 ているとき 度成分 (x, y) は, v=i=icosp-rsin (8.6) vy=y=isinp+rocos p と書ける。これを (85) 式へ代入して、速度の極座標表示 10r=j (8.7) V₁ = 14 を得る。 この結果は、上のような計算をせずに理解す ることができる。 図 8.3のように, 速度vの動 成分は,動径の増加する割合であり, vr =と書ける。 次に v は,動径に垂直な速度 成分であり, 原点を中心とした一定の半径r の円周に沿った速さである。 したがって, ve は半径r, 中心角の扇形の弧の長さの 増加する割合であり,v=at d ro 図8.3 極座標での速度成分 (x)=r(rは一定)と書ける。また、 は円運動の角速度であるから,v=r=rw は,円運動している質点 118

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