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.

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?"

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?"

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.

A peloton is a group of cyclists whose individual and collective energy expenditures are reduced when cyclists ride behind others in zones of reduced air pressure; this effect is known in cycling as ‘drafting’. Through drafting cyclists couple their energy expenditures. Coupling of cyclists’ energy expenditures when drafting is the basic peloton property from which self-organized collective behaviours emerge. Here we examine peloton hysteresis, applying the definition used in the context of vehicle traffic in which a rapid deceleration to a high density state (jam) is followed by a lag in vehicle acceleration. Applying a flow analysis of volume (number of cyclists) over time, peloton hysteresis is examined in three forms: one is similar to vehicle traffic hysteresis in which rapid decelerations and increased flow (or density) are followed by extended acceleration periods and reduced flow. In cycling this is known as the accordion effect. A second kind of hysteresis results from rapid accelerations followed by periods of decreasing speeds and decreasing flow. This form of hysteresis is essentially inverse to traffic hysteresis and the accordion effect. We show this form of hysteresis using data from a mass-start bicycle points-race. A third kind of peloton hysteresis occurs when the drafting benefit is minimized on hills and weaker cyclists lose positions in the peloton, while flow/density is retained. A computer simulation shows this hysteresis among two sets of cyclist agents, each with different output capacity and models hysteresis as a peloton transitions from flat topography to a steep incline on which drafting is negligible.

Standard value function approaches to finding policies for Partially Observable Markov Decision Processes (POMDPs) are generally considered to be intractable for large models. The intractability of these algorithms is to a large extent a consequence of computing an exact, optimal policy over the entire belief space. However, in real-world POMDP problems, computing the optimal policy for the full belief space is often unnecessary for good control even for problems with complicated policy classes. The beliefs experienced by the controller often lie near a structured, low-dimensional subspace embedded in the high-dimensional belief space. Finding a good approximation to the optimal value function for only this subspace can be much easier than computing the full value function. We introduce a new method for solving large-scale POMDPs by reducing the dimensionality of the belief space. We use Exponential family Principal Components Analysis (Collins, Dasgupta & Schapire, 2002) to represent sparse, high-dimensional belief spaces using small sets of learned features of the belief state. We then plan only in terms of the low-dimensional belief features. By planning in this low-dimensional space, we can find policies for POMDP models that are orders of magnitude larger than models that can be handled by conventional techniques. We demonstrate the use of this algorithm on a synthetic problem and on mobile robot navigation tasks.

Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments an agent's ability to learn useful behaviors by making intelligent use of the knowledge implicit in behaviors demonstrated by cooperative teachers or other more experienced agents. We propose and study a formal model of implicit imitation that can accelerate reinforcement learning dramatically in certain cases. Roughly, by observing a mentor, a reinforcement-learning agent can extract information about its own capabilities in, and the relative value of, unvisited parts of the state space. We study two specific instantiations of this model, one in which the learning agent and the mentor have identical abilities, and one designed to deal with agents and mentors with different action sets. We illustrate the benefits of implicit imitation by integrating it with prioritized sweeping, and demonstrating improved performance and convergence through observation of single and multiple mentors. Though we make some stringent assumptions regarding observability and possible interactions, we briefly comment on extensions of the model that relax these restricitions.