Farrell calls for consideration of city bylaw to stop street harassment in Calgary – Calgary Herald
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Some other Canadian cities have rules to deal with street harassment. In London, Ont., you can be fined for using “abusive or insulting language” in a public space.
Street harassment takes many forms, from unwanted sexual comments to whistling to flashing or groping, and it’s based on someone’s perceived gender or sexual identity. It’s a point of focus for gender equity advocates, as an example of how control tactics make people feel unsafe in public spaces.
Sagesse executive director Andrea Silverstone said Monday that street harassment can’t be dismissed as one-off comments or isolated incidents.
“It’s a structured pattern of behaviour that occurs in society that makes certain people feel unsafe,” she said. “Whether they’re women or 2SLGBTQ individuals or visible minorities feeling unsafe on the street.”
Jake Stika, executive director of Next Gen Men, said street harassment is a symptom of how boys absorb the message that being a man is about power and dominance, and they start defining their interactions that way.
Street harassment, he explains, is overwhelmingly perpetuated by men, but men are also key to stopping it.
“It’s not a women’s issue. Women are impacted by it … but what we need to do as guys is take this up as our issue,” he said. “We’re the problem, but we’re also the solution.”
Stika’s organization works to redefine manhood and masculinity with youth and community programs as part of working “upstream” to stop gender-based violence and improve men’s health and relationships.
AI Solves 50-Year-Old Biology 'Grand Challenge' Decades Before Experts Predicted – ScienceAlert
A long-standing and incredibly complex scientific problem concerning the structure and behaviour of proteins has been effectively solved by a new artificial intelligence (AI) system, scientists report.
All those incremental advancements were about much more than mastering recreational diversions, however.
In the background, DeepMind’s researchers were seeking to coax their AIs towards solving much more fundamentally important scientific puzzles – such as finding new ways to fight disease by predicting infinitesimal but vitally important aspects of human biology.
Now, with the latest version of their AlphaFold AI engine, they seem to have actually achieved this very ambitious goal – or at least gotten us closer than scientists ever have before.
For about 50 years, researchers have strived to predict how proteins achieve their three-dimensional structure, and it’s not an easy problem to solve.
The astronomical number of potential configurations is so mind-bogglingly huge, in fact, that researchers postulated it would take longer than the age of the Universe to sample all the possible molecular arrangements.
Nonetheless, if we can solve this puzzle – known as the protein-folding problem – it would constitute a giant breakthrough in scientific capabilities, vastly accelerating research endeavours in things like drug discovery and modelling disease, and also leading to new applications far beyond health.
For that reason, despite the scale of the challenge, for decades researchers have been collaborating to make gains in developing solutions to the protein-folding problem.
A rigorous experiment called CASP (Critical Assessment of protein Structure Prediction) began in the 1990s, challenging scientists to devise systems capable of predicting the esoteric enigmas of protein folding.
Now, in its third decade, the CASP experiment looks to have produced its most promising solution yet – with DeepMind’s AlphaFold delivering predictions of 3D protein structures with unprecedented accuracy.
“We have been stuck on this one problem – how do proteins fold up – for nearly 50 years,” says CASP co-founder John Moult from the University of Maryland.
“To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts wondering if we’d ever get there, is a very special moment.”
In the experiment, DeepMind used a new deep learning architecture for AlphaFold that was able to interpret and compute the ‘spatial graph’ of 3D proteins, predicting the molecular structure underpinning their folded configuration.
The system, which was trained up by analysing a databank of approximately 170,000 protein structures, brought its unique skillset to this year’s CASP challenge, called CASP14, achieving a median score in its predictions of 92.4 GDT (Global Distance Test).
That’s above the ~90 GDT threshold that’s generally considered to be competitive with the same results obtained via experimental methods, and DeepMind says its predictions are only off by about 1.6 angstroms on average (about the width of an atom).
“I nearly fell off my chair when I saw these results,” says genomics researcher Ewan Birney from the European Molecular Biology Laboratory.
“I know how rigorous CASP is – it basically ensures that computational modelling must perform on the challenging task of ab initio protein folding. It was humbling to see that these models could do that so accurately. There will be many aspects to understand but this is a huge advance for science.”
It’s worth noting that the research has not yet been peer-reviewed, nor published in a scientific journal (although DeepMind’s researchers say that’s on the way).
Even so, experts who are familiar with the field are already recognising and applauding the breakthrough, even if the full report and detailed results are yet to be seen.
“This computational work represents a stunning advance on the protein-folding problem, a 50-year old grand challenge in biology,” says structural biologist Venki Ramakrishnan, president of the Royal Society.
“It has occurred decades before many people in the field would have predicted.”
The full findings are not yet published, but you can see the abstract for the research, “High Accuracy Protein Structure Prediction Using Deep Learning”, here, and find more information on CASP14 here.
A 'Beaver Full Moon' With Lunar Eclipse Happened This Morning—And Folks Took Some Stunning Photos – Good News Network
If you were up in the early hours of this morning, you may have noticed the full moon turning a shade or so darker and redder.
What you were seeing is called a penumbral lunar eclipse. Caused by the moon dipping behind the Earth’s fuzzy penumbra, or outer shadow, this subtle shading effect peaked at 4:32 am ET November 30, when—according to NASA—83% of the moon was in the shadow of our planet.
NASA has also given a list of the names November’s full moon is known by: The Algonquin tribes have long called this the Cold Moon after the long, frozen nights. Others know it as the Frost Moon, while an Old European Name is Oak Moon: perhaps because of ancient Druid traditions that involve harvesting mistletoe from oak trees for the upcoming winter solstice.
In America, the November full moon is perhaps still best known as the Beaver Moon—with Native Americans associating it with a time when the beavers are scrabbling to finish building their dens from mud and sticks and rocks in preparation for winter.
While this was the last penumbral eclipse of the year, don’t worry if you missed the occurrence due to sleep or clouds.
For those who didn’t get to witness the phenomenon in person, from San Francisco to Michigan to the Sydney Opera House, here are some stunning pictures of this year’s last partial lunar eclipse.
P.S. The next full moon will be the Cold Christmas Moon on December 29, 2020.
The full moon captured with the San Francisco skyline view at Alameda
A peaceful scene from Mackinac Island in Michigan
Surreal views from Joshua Tree
The Columbia River Gorge became a moonrise kingdom
Cool blue views were taken by this photographer in Northumberland, England
This photographer in Russia caught an image straight from a folk tale
Clouds added interest and atmosphere to these photos taken in Preston, England
A calming moment was captured on Rhode Island
The moon united photographers everywhere last night. Here’s a view from Sydney.
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