The study, from the University of South Australia, found that there are differences between the way marijuana users and non-users walk. Specifically, marijuana users have stiffer shoulders, more flexible elbows and quicker knees, which move faster than those of non-users, while walking. While differences in their movements were detected, there were no significant differences between the balancing abilities and neurological functions of users and non-users. The study's authors are calling for more research that can determine exactly how marijuana affects people's movements, as the drug continues to be legalized in the US. Medical marijuana is legal in 30 US states and Washington, DC.
Anti-abortion groups will no longer be able to use women's post-abortion mental health as an argument to support their stance against abortion as a result of a study indicating there was no significant difference in the mental health of women who had experienced abortions. The study, published in JAMA Psychiatry on Wednesday, tracked about 1,000 women who had abortions within a five-year period and found those who underwent the procedure did not experience more anxiety, depression, low self-esteem or dissatisfaction with life than women who were denied abortions. In contrast, researchers found women who had been denied abortion access because they were too far along in their pregnancies actually had more psychological symptoms following the denial. However, after about six months, their mental health started to improve and became similar to the mental health of women who were able to have an abortion. Although there have been studies comparing the psychological differences between women who had abortions and women who decided to keep their babies, the new research, named the Turnaway Study by University of California, San Francisco's Advancing New Standards in Reproductive Health program, is the first to focus solely on the mental health of women close to or beyond the limit of when a clinic is legally able to perform an abortion.
The war on drugs has failed – and certain illegal substances should now be decriminalized, according to some of the world's leading health experts. Instead, the war on drugs and zero-tolerance policies have harmed public health and abused human rights, according to a report from the Johns Hopkins-Lancet Commission on Public Health and International Drug Policy. Drug laws that were intended to protect the public have contributed to disease transmission, lethal violence, discrimination, forced displacement and undermined people's right to health. And so, experts are calling for reforms to drug policy, so that non-violent minor drug offences should be decriminalized, with a greater emphasis on treatment rather than punishment. Global health experts revealed the'war on drugs' has harmed public health and abused human rights.
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at https://physics.bu.edu/~pankajm/MLnotebooks.html )