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aslan jackley family pic

The good news is that divorce rates in America are going down

The bad news is that Americans tend to work insane, relationship-damaging hours. 

According to a Gallup poll released earlier this year, the average American worker logs 46.7 hours a week.

A full 39% of people report working over 50 hours a week, enough to qualify them as "workaholics" — people who, by the way, have double the divorce rate of the rest of the population

Which presents the bind that hard-charging careerists are in: Is it possible to put in the hours to make it to the top professionally while also nurturing a mutually life-affirming relationship? 

Can we, in other words, have it all

Some people do. Consider the case of Jessica Jackley and Reza Aslan, parents of twin three-year-old boys and a new baby due in January. You may recognize their names. Jackley cofounded the microfinance site Kiva and the crowdfunding platform Profounder and has her first book coming out next year. Aslan teaches religious studies at the University of Southern California and authored "Zealot," a… Continue reading

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KentuckyFC writes Statisticians have long thought it impossible to tell cause and effect apart using observational data. The problem is to take two sets of measurements that are correlated, say X and Y, and to find out if X caused Y or Y caused X. That’s straightforward with a controlled experiment in which one variable can be held constant to see how this influences the other. Take for example, a correlation between wind speed and the rotation speed of a wind turbine. Observational data gives no clue about cause and effect but an experiment that holds the wind speed constant while measuring the speed of the turbine, and vice versa, would soon give an answer. But in the last couple of years, statisticians have developed a technique that can tease apart cause and effect from the observational data alone. It is based on the idea that any set of measurements always contain noise. However, the noise in the cause variable can influence the effect but not the other way round. So the noise in the effect dataset is always more complex than the noise in the cause dataset. The new statistical test, known as the additive noise model, is designed to find this asymmetry. Now statisticians have tested the model on 88 sets of cause-and-effect data, ranging from altitude and temperature measurements at German weather stations to the correlation between rent and apartment size in student accommodation.The results suggest that the additive noise model can tease apart cause and effect correctly in up to 80 per cent of the cases (provided there are no confounding factors or selection effects). That’s a useful new trick in a statistician’s armoury, particularly in areas of science where controlled experiments are expensive, unethical or practically impossible.

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An anonymous reader writes Australia is starting a public consultation process for new legislation that further restricts the publication and export of technology on national security grounds. The public consultation starts now (a few days before Christmas) and it is due by Jan 30th while a lot of Australians are on holidays. I don’t have the legal expertise to dissect the proposed legislation, but I’d like some more public scrutiny on it. I find particularly disturbing the phrase "The Bill includes defences that reverse the onus of proof which limit the right to be presumed innocent until proven guilty" contained in this document, also available on the consultation web site.

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