Due to the rapid innovation of technology and the desire to find and employ biomarkers for neurodegenerative disease, high-dimensional data classification problems are routinely encountered in neuroimaging studies. To avoid over-fitting and to explore relationships between disease and potential biomarkers, feature learning and selection plays an important role in classifier construction and is an important area in machine learning. In this article, we review several important feature learning and selection techniques including lasso-based methods, PCA, the two-sample t-test, and stacked auto-encoders. We compare these approaches using a numerical study involving the prediction of Alzheimer's disease from Magnetic Resonance Imaging.

The Order of Canada marks its 50th anniversary this year with 99 new appointments on its Canada Day honours list, including renowned figures from the fields of law, government, entertainment and sport, as well as Canadians whose contributions are less widely known. The list includes soccer star Christine Sinclair, television host Alex Trebek, actor Catherine O'Hara and Globe and Mail editorial cartoonist Brian Gable. Three people were named to the highest rank, Companion of the Order of Canada: former Supreme Court Justice Marshall Rothstein, National Arts Centre president Peter Herrndorf and The Prince of Wales. Nineeteen people were named Officers of the Order of Canada, including former spymaster Richard Fadden, hockey player Mark Messier and actor Michael Myers. There were 77 people named as members of the Order, including opera singer Tracy Dahl, historian Bill Waiser, public health nurse Cathy Crowe and Indigenous leader Terrance Paul.

"The connectivity that is the heart of globalisation can be exploited by states with hostile intent to further their aims.[…] The risks at stake are profound and represent a fundamental threat to our sovereignty." "It's not MI6's job to warn of internal threats. It was a very strange speech. Was it one branch of the intelligence services sending a shot across the bows of another? Or was it pointed at Theresa May's government? Does she know something she's not telling us?" Senior intelligence analyst, April 2017 In June 2013, a young American postgraduate called Sophie was passing through London when she called up the boss of a firm where she'd previously interned. The company, SCL Elections, went on to be bought by Robert Mercer, a secretive hedge fund billionaire, renamed Cambridge Analytica, and achieved a certain notoriety as the data analytics firm that played a role in both Trump and Brexit campaigns. But all of this was still to come. London in 2013 was still basking in the afterglow of the Olympics. Britain had not yet Brexited. The world had not yet turned. "That was before we became this dark, dystopian data company that gave the world Trump," a former Cambridge Analytica employee who I'll call Paul tells me. "It was back when we were still just a psychological warfare firm." Was that really what you called it, I ask him. Psychological operations – the same methods the military use to effect mass sentiment change.

Yet despite the promise of a revolution in how we interact with services and companies online, progress has been utterly miserable - the vast majority of chatbots are gimmicky, pointless or just flat out broken. But this week I was given great cause for optimism, in the form of Alec Jones, a 14-year-old from Victoria, Canada. For the past six months, Alec been working on Christopher Bot, a chatbot that helps students keep track of homework they've been given over the course of a week. To set things up, a student shares his or her schedule with Christopher Bot, and from then on it will send a quick message at the end of each lesson asking if any homework had been set. "Do you have homework for maths?" it asked 30-year-old me pretending to be a child for the sake of this piece. "Your teacher needs to chill out on the homework," came the auto-response, adding, "what homework do you have?"

Using analogical model formulation with sketches to solve Bennett Mechanical Comprehension Test problems.

A hitchhiking robot has completed a 3,700-mile journey across Canada Sunday, capping off a research project that explores the relationship between robots and humans. A team of researchers from a group of Canadian universities created hitchBOT, a talking robot made out of a bucket, garden gloves and rain boots that set out on its coast-to-coast Canadian trip in Nova Scotia on July 26. It finished the journey in Victoria, British Columbia. "Usually, we are concerned with whether we can trust robots," said Dr. Frauke Zeller, Assistant Professor in the School of Professional Communication at Ryerson University. "This project asks: can robots trust human beings?"

He or she -- it's hard to tell -- is short and friendly, if a little fashion-challenged. The robot employs artificial intelligence, speech recognition, social media and other tools to bum rides from motorists. Deposited last Monday on Highway 102 outside Halifax, hitchBot by Friday had journeyed to just west of Toronto. Its travels are being documented on Twitter, on Instagram and on the robot's website, which charts its progress on a map. The gender-neutral robot was conceived by university researchers David Harris Smith and Frauke Zeller, who view its quest as part performance art, part social experiment.

Video Friday is your weekly selection of awesome robotics videos, collected by your Chemputer -savvy Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Here are highlights from the RoboCup 2016 finals, with Tech United up against Water (in pink). Don't worry about keeping score, because it goes to penalty kicks at the end.

Toronto resident Simon Treadwell wheeled a garbage bin onto a snow-bound lot next to his property one evening this past winter. Inside the bin was a smelly mixture of wet and dry cat food, sardines, and fried chicken. Treadwell sprinkled some of the mix on and around the bin, made sure his three motion-activated night vision cameras were on, and went back into his house. Treadwell was testing a new lid latch he had devised in response to the city of Toronto's request for proposals: The city needed help keeping raccoons out of people's garbage. For over a decade, residents had been asked to place organic compostable materials such as vegetables, meat, bones, and even paper towels into green bins.

Not all NP-complete problems share the same practical hardness with respect to exact computation. Whereas some NP-complete problems are amenable to efficient computational methods, others are yet to show any such sign. It becomes a major challenge to develop a theoretical framework that is more fine-grained than the theory of NP-completeness, and that can explain the distinction between the exact complexities of various NP-complete problems. This distinction is highly relevant for constraint satisfaction problems under natural restrictions, where various shades of hardness can be observed in practice. Acknowledging the NP-hardness of such problems, one has to look beyond polynomial time computation. The theory of subexponential-time complexity provides such a framework, and has been enjoying increasing popularity in complexity theory. An instance of the constraint satisfaction problem with n variables over a domain of d values can be solved by brute-force in dn steps (omitting a polynomial factor). In this paper we study the existence of subexponential-time algorithms, that is, algorithms running in do(n) steps, for various natural restrictions of the constraint satisfaction problem. We consider both the constraint satisfaction problem in which all the constraints are given extensionally as tables, and that in which all the constraints are given intensionally in the form of global constraints. We provide tight characterizations of the subexponential-time complexity of the aforementioned problems with respect to several natural structural parameters, which allows us to draw a detailed landscape of the subexponential-time complexity of the constraint satisfaction problem. Our analysis provides fundamental results indicating whether and when one can significantly improve on the brute-force search approach for solving the constraint satisfaction problem.