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This Quarantine, I explored a lot of dating apps available on Playstore just to make the right decision on which one I should stick—sharing my experiences, only to help someone like me to make a better decision. The online trend of dating in India is increasing every day. Especially during these times when the whole world is locked inside their homes, working online, studying online, meeting friends online then why not dating online? Staying alone for a long time, especially when you are not allowed to go out and meet people, can affect your mental health. Symptoms like panic attacks and depression are seen among the people since they are inside their houses for a long time. So what can we do to cheer up ourselves? Yes, we are doing that. Long lost school friends, college friends, gully cricket friends, hostel roommates, called everyone. Daily called parents, thrice, just to let them know that we are absolutely doing fine and are safe.
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A first meeting at Community-Driven Research Day could lead to the perfect marriage of academia and, you guessed it, community. Community-Driven Research Day, held on Wednesday at the University of Pennsylvania , is an annual event at which community groups pitch their causes to academic institutions with the goal of making connections that could lead to future research collaborations. The goal is to match similarly minded organizations to solve public health issues in Philadelphia. At the event, organizations pitched solutions to a variety issues, including fighting depression, helping the hungry and combating distracted driving. In a keynote, a city official said partnerships with community groups have been vital to some major public health initiatives in Philadelphia. They help us to bridge cultural divides. Research Day posters. Photo by Adam Bender. Each organization is expected to award at least one grant. Only attendees will be eligible.
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In current practice, when dating the root of a Bayesian language phylogeny the researcher is required to supply some of the information beforehand, including a distribution of root ages and dates for some nodes serving as calibration points. In addition to the potential subjectivity that this leaves room for, the problem arises that for many of the language families of the world there are no available internal calibration points. Here we address the following questions: Can a new Bayesian framework which overcomes these problems be introduced and how well does it perform? The new framework that we present is generalized in the sense that no family-specific priors or calibration points are needed. We moreover introduce a way to overcome another potential source of subjectivity in Bayesian tree inference as commonly practiced, namely that of manual cognate identification; instead, we apply an automated approach. Dates are obtained by fitting a Gamma regression model to tree lengths and known time depths for 30 phylogenetically independent calibration points. This model is used to predict the time depths of both the root and the internal nodes for language families, producing a total of 1, dates for families and subgroups. It turns out that results are similar to those of published Bayesian studies of individual language families. The performance of the method is compared to automated glottochronology, which is an update of the classical method of Swadesh drawing upon automated cognate recognition and a new formula for deriving a time depth from percentages of shared cognates. In terms of errors and correlations with known dates, ASJP works better than the new method and both work better than automated glottochronology.