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　　​​How Big Data Can Help Save the World

　　Our ability to collect data far outpaces our ability to fully utilize it—yet those data may hold the key to solving some of the biggest global challenges facing us today.

　　Take, for instance, the frequent outbreaks of waterborne illnesses as a consequence of war or natural disasters. The most recent example can be found in Yemen, where roughly 10,000 new suspected cases of cholera are reported each week—and history is riddled with similar stories. What if we could better understand the environmental factors that contributed to the disease, predict which communities are at higher risk, and put in place protective measures to stem the spread?

　　Answers to these questions and others like them could potentially help us avert catastrophe.

　　We already collect data related to virtually everything, from birth and death rates to crop yields and traffic flows. IBM estimates that each day, 2.5 quintillion bytes of data are generated. To put that in perspective: that’s the equivalent of all the data in the Library of Congress being produced more than 166,000 times per 24-hour period. Yet we don’t really harness the power of all this information. It’s time that changed—and thanks to recent advances in data analytics and computational services, we finally have the tools to do it.

　　As a data scientist for Los Alamos National Laboratory, I study data from wide-ranging, public sources to identify patterns in hopes of being able to predict trends that could be a threat to global security. Multiple data streams are critical because the ground-truth data (such as surveys) that we collect is often delayed, biased, sparse, incorrect or, sometimes, nonexistent.

　　For example, knowing mosquito incidence in communities would help us predict the risk of mosquito-transmitted disease such as dengue, the leading cause of illness and death in the tropics. However, mosquito data at a global (and even national) scale are not available.

　　To address this gap, we’re using other sources such as satellite imagery, climate data and demographic information to estimate dengue risk. Specifically, we had success predicting the spread of dengue in Brazil at the regional, state and municipality level using these data streams as well as clinical surveillance data and Google search queries that used terms related to the disease. While our predictions aren’t perfect, they show promise. Our goal is to combine information from each data stream to further refine our models and improve their predictive power.

　　Similarly, to forecast the flu season, we have found that Wikipedia and Google searches can complement clinical data. Because the rate of people searching the internet for flu symptoms often increases during their onset, we can predict a spike in cases where clinical data lags.

　　We’re using these same concepts to expand our research beyond disease prediction to better understand public sentiment. In partnership with the University of California, we’re conducting a three-year study using disparate data streams to understand whether opinions expressed on social media map to opinions expressed in surveys.

　　For example, in Colombia, we are conducting a study to see whether social media posts about the peace process between the government and FARC, the socialist guerilla movement, can be ground-truthed with survey data. A University of California, Berkeley researcher is conducting on-the-ground surveys throughout Colombia—including in isolated rural areas—to poll citizens about the peace process. Meanwhile, at Los Alamos, we’re analyzing social media data and news sources from the same areas to determine if they align with the survey data.

　　If we can demonstrate that social media accurately captures a population’s sentiment, it could be a more affordable, accessible and timely alternative to what are otherwise expensive and logistically challenging surveys. In the case of disease forecasting, if social media posts did indeed serve as a predictive tool for outbreaks, those data could be used in educational campaigns to inform citizens of the risk of an outbreak (due to vaccine exemptions, for example) and ultimately reduce that risk by promoting protective behaviors (such as washing hands, wearing masks, remaining indoors, etc.).

　　All of this illustrates the potential for big data to solve big problems. Los Alamos and other national laboratories that are home to some of the world’s largest supercomputers have the computational power augmented by machine learning and data analysis to take this information and shape it into a story that tells us not only about one state or even nation, but the world as a whole. The information is there; now it’s time to use it.

　　来源：Scientific American​​​​

2

　　​​Why your desk job is so damn exhausting

　　THIS is the greatest mystery of my adult life: How can I spend all day typing at a computer and go home feeling exhausted? How could merely activating the small muscles of my fingers leave me craving the couch at the end of the day?

　　This question actually lies very close to one of the more hotly contested issues in psychology: What causes mental fatigue? Why is desk work so depleting?

　　"It is kind of a mystery, to be honest," says Michael Inzlicht, a University of Toronto psychologist who studies self-control, motivation, and fatigue.

　　But scientists do have some clues. There are two main hypotheses for why we get so tired from work when we're not physically active. Let's dive in.

　　Hypothesis 1: we get so tired because we deplete an internal store of energy

　　One hypothesis is resource depletion. That is, throughout the day, we draw on a limited store of mental energy.

　　Some people call this willpower or self-control: the forceful use of mental energy to get at a goal.

　　When our willpower stores get used up, we get tired. The analogy here is like a tank of gas; when it's empty, the tank sputters out.

　　This hypothesis is called ego depletion, and it makes intuitive sense.

　　But the problem is, increasingly, psychologists aren't sure it's real. The basic ego depletion effect is that drawing on self-control to complete hard task drains us, but that wasn't found in a recent 23-lab replication effort. Also, critics of the hypothesis argue, it doesn't make much physiological sense.

　　One study estimated that a hardworking brain struggling with self-control may barely draw on the energy equivalent of a fraction of a single Tic-Tac compared to a brain at rest. Most of our caloric energy expenditure, as Vox's Julia Belluz explains, goes into the background work of keeping our hearts, brains, and other organs running.

　　"'Does your brain's energy expenditure go up when you're doing a hard math problem compared to when you're zoning out watching TV? And everyone who has measured that has said 'no'," Kevin Hall, an obesity researcher at the National Institutes of Health, recently told her.

　　Overall, we have a bad intuition about how our brains and bodies use energy. And it doesn't look like ego depletion is the answer to this vexing question.

　　Hypothesis 2: we get so tired because our motivation runs out

　　So if the resource depletion model of fatigue - gas running out - doesn't make much sense, what does?

　　The other hypothesis from psychology involves motivation. That as we work on a task, we struggle to focus on it or eventually lose interest in it. We become less motivated to do the task. We become drawn to the things we want to do (scrolling Instagram or reading music blogs, for instance), rather than the things we have to do. And this tension possibly causes fatigue.

　　In August, researchers in the UK published new evidence that finds some indirect evidence for the motivational model.

　　This study tracked 100 nurses in the UK over two 12-hour shifts. Throughout the shifts, the nurses reported how fatigued they felt at regular intervals. They also wore devices that monitored and tracked the amount of physical activity they were engaged in. On average, the more hours the nurses worked, the more fatigued they felt. But when the researchers investigated what could possibly explain the fatigue, they found some interesting patterns.

　　Here's the topline result: There was no correlation between the amount of physical work the nurses did and their feelings of fatigue. "In some people, physical activity is fatiguing," Derek Johnston, the Aberdeen University psychologist who led the study, says. "But in other people, it is energising." The study also found that the nurses' subjective sense of how demanding their job was of them was not correlated with fatigue either.

　　Instead, they found this small correlation: The nurses who were least likely to feel fatigued from their work also felt the most in control of their work, and the most rewarded for it. These feelings may have boosted their motivation, which may have boosted their perception of having energy.

　　Mr Inzlicht has also found evidence for the motivational model in his work. A few years ago, he and Carleton University psychologist Marina Milyavskaya monitored 159 students at McGill University in Canada for a week. Throughout the week, the participants were peppered with text message questions about what temptations, desires, and effortful self-control they were engaging in at the moment, and whether they felt drained.

　　"What was surprising to us was the biggest predictor was not whether they had exerted self-control ," Mr Inzlicht says. Instead, the predictor was the number of temptations they felt.

　　"If you're typing at work, and if you're anything like me, you got a few browsers open, you got Twitter open. These lead us down these rabbit holes that lead to temptations," he says. Temptations make us less motivated to do our work, which, in turn, may make us tired.

　　And there may be an evolutionary reason for why our brains would do this.

　　"As an organism, we need to meet multiple goals to survive," Mr Inzlicht explains. We're not solely focused on finding food or finding mates, sleeping, or pursuing our passions in life. We need to do all these things to be a healthy, thriving species. "Because these multiple goals compete with one another , we need a mechanism in place that signals, 'Hey, stop doing that thing and do something else'." That mechanism, he suggests, could be fatigue.

　　In this light, boosting our motivation to stay on a task could lead us to feel less fatigued. One study found that just paying people some money when they're depleted can keep them on task. A similar thing is found in studies on physical endurance: People can be easily pushed to work beyond what they think is their physical limit.

　　Why we need to figure out fatigue

　　As mentioned, psychologists don't have this all sorted out. For one, it's hard to track people, their motivations, desires, and fatigue throughout the day. Smartphone technology and Fitbit-like activity trackers are making it easier to track people in a dense, data-driven way. But there would need to be more research with larger samples to show, for sure, that fatigue is a motivation problem.

　　Learning about fatigue matters. When we're fatigued, we're prone to careless - perhaps dangerous - errors. Work is less enjoyable. And overall, fatigue is just not a nice feeling. The more we learn about fatigue, the better we can design safe, fulfilling work environments. This is helpful for bosses too: How can they best set up situations to make workers feel energised, motivated, and productive throughout the day?

　　To avoid becoming fatigued, I'm going to stop typing now.

　　来源：VOX​​​​

笔译完形填空题源

　　​​Sleep is part of a person's daily activity cycle. There are several different stages of sleep, and they too occur in cycles.

　　睡眠是人每天日常活动循环的一部分。人的睡眠分几个阶段，而这些阶段也是循环发生的。

　　If you are an average sleeper, your sleep cycle is as follows. When you first drift off into slumber, your eyes will roll about a bit, your temperature will drop slightly, your muscles will relax, and your breathing well slow and become quite regular. Your brain waves slow down a bit too, with the alpha rhythm of rather fast waves predominating for the first few minutes. This is called stage 1 sleep. For the next half hour or so, as you relax more and more, you will drift down through stage 2 and stage 3 sleep. The lower your stage of sleep, the slower your brain waves will be. Then about 40 to 60 minutes after you lose consciousness you will have reached the deepest sleep of all. Your brain waves will show the large slow waves that are known as the delta rhythm. This is stage 4 sleep.

　　如果你是一个正常的睡眠者，你的睡眠循环会这样进行。在你开始昏昏入睡时，你的眼睛会滚动几下，体温略有下降，肌肉放松，呼吸变得缓慢而有节奏。除了开始几分钟比较快的α节奏外，脑电波也稍有减缓。这被称为第一阶段睡眠。在随后约半小时内，你进一步放松，进入第二和第三阶段睡眠。睡眠越深入，脑电波就越缓慢。大约在开始睡眠后的40到60分钟，你将进入沉睡状态。这时的脑电波表现为巨大的缓波，被称为δ节奏。这就是第四阶段睡眠。

　　You do not remain at this deep fourth stage all night long, but instead about 80 minutes after you fall into slumber, your brain activity level will increase again slightly. The delta rhythm will disappear, to be replaced by the activity pattern of brain waves. Your eyes will begin to dart around under your closed eyelids as if you were looking at something occurring in front of you. This period of rapid eye movement lasts for some 8 to 15 minutes and is called REM sleep. It is during REM sleep period, your body will soon relax again, your breathing will grow slow and regular once more, and you will slip gently back from stage 1 to stage 4 sleep - only to rise once again to the surface of near consciousness some 80 minutes later.

　　但你并不是整夜都保持这种沉睡状态。入睡后约80分钟左右，你的大脑运动水平会再度略有提高。δ节奏消失，并被脑电波的运动图形取代。你的眼睛会在闭着的眼睑下迅速转动，就好象你在看着眼前发生的什么事情。这种迅速的眼球运动持续约8-15分钟，这一阶段睡眠被称之为快速眼动(REM)睡眠。在REM睡眠阶段，你的肢体会很快再度放松，呼吸也再次放慢并变得有节奏，你会轻松地从第一阶段滑入第四阶段睡眠——直到大约80分钟后重新接近清醒状态。

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