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Support for a public health approach to cannabis policy as an alternative to prohibition and criminalization is gaining momentum. Recent drug policy changes in the United States suggest growing political feasibility for legal regulation of cannabis in other North American jurisdictions. This commentary discusses the outcomes of an interdisciplinary policy meeting with Canadian experts and knowledge users in the area of substance use interventions. The meeting explored possibilities for applying cross-substance learning on policy interventions for alcohol, tobacco and cannabis, towards the goal of advancing a public health framework for reducing harms associated with substance use in Canada. The meeting also explored how the shift in approach to cannabis policy can provide an opportunity to explore potential changes in substance use policy more generally, especially in relation to tobacco and alcohol as legally regulated substances associated with a heavy burden of illness. Drawing from the contributions and debates arising from the policy meeting, this commentary identifies underlying principles and opportunities for learning from policy interventions across tobacco, alcohol and cannabis, as well as research gaps that need to be addressed before a public health framework can be effectively pursued across these substances.

Giesbrecht, N., Wettlaufer, A., Simpson, S., April, N., Asbridge, M., Cukier, S., Mann, R., McAllister, J., Murie, A., Pauley, C., Plamondon, L., Stockwell, T., Thomas, G., Thompson, K., & Vallance, K. (2016). Strategies to reduce alcohol-related harms and costs in Canada: A comparison of provincial policies. The International Journal Of Alcohol And Drug Research, 5(2), 33-45. doi:http://dx.doi.org/10.7895/ijadr.v5i2.221Aims: To compare Canadian provinces across 10 research-based alcohol policy and program dimensions.Design and Measures: The 10 Canadian provinces were assessed on the following 10 policy dimensions: alcohol pricing; alcohol control system; physical availability; drinking and driving; marketing and advertising; legal drinking age; screening, brief intervention, and referrals; server training, challenge, and refusal programs; provincial alcohol strategy; warning labels and signs. Data were collected from official documents, including provincial legislation, regulations, and policy, and strategy documents. Three international experts on alcohol policy contributed to refining the protocol. Provincial scores were independently determined by two team members along a 10-point scale for each dimension, and the scores were expressed as a percentage of the ideal. Weighting of dimensions according to scope of impact and effectiveness was applied to obtain the final scores. National and provincial scores were calculated for each dimension and consolidated into overall averages.Findings: Overall, the consolidated national mean is 47.2% of the ideal, with Ontario scoring highest at 55.9%, and Québec lowest at 36.2%. Across dimensions, Legal Drinking Age and Challenge and Refusal Programs scored highest at 75% and 61%, respectively, while Warning Labels and Signs scored lowest at 18% of the ideal. Pricing, rated third highest among dimensions at 57%, should nevertheless remain a priority for improvement, given it is weighted highest in terms of effectiveness and scope.Conclusions and Implications: Policy dimension scores vary among the provinces, with substantial room for improvement in all. Since spring 2013, several provinces have taken steps to implement specific alcohol policies. Concerted action involving multiple stakeholders and alcohol policies is required to reduce the burden of alcohol problems across Canada.

International audience ; We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Among others, manifolds come equipped with families of Riemannian metrics, with associated exponential and logarithmic maps, geodesics and parallel transport. Statistics and learning algorithms provide methods for estimation, clustering and dimension reduction on manifolds. All associated operations are vectorized for batch computation and provide support for different execution backends, namely NumPy, PyTorch and TensorFlow, enabling GPU acceleration. This paper presents the package, compares it with related libraries and provides relevant code examples. We show that Geomstats provides reliable building blocks to foster research in differential geometry and statistics, and to democratize the use of Riemannian geometry in machine learning applications. The source code is freely available under the MIT license at http://geomstats.ai.

International audience ; We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Among others, manifolds come equipped with families of Riemannian metrics, with associated exponential and logarithmic maps, geodesics and parallel transport. Statistics and learning algorithms provide methods for estimation, clustering and dimension reduction on manifolds. All associated operations are vectorized for batch computation and provide support for different execution backends, namely NumPy, PyTorch and TensorFlow, enabling GPU acceleration. This paper presents the package, compares it with related libraries and provides relevant code examples. We show that Geomstats provides reliable building blocks to foster research in differential geometry and statistics, and to democratize the use of Riemannian geometry in machine learning applications. The source code is freely available under the MIT license at http://geomstats.ai.

International audience ; We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Among others, manifolds come equipped with families of Riemannian metrics, with associated exponential and logarithmic maps, geodesics and parallel transport. Statistics and learning algorithms provide methods for estimation, clustering and dimension reduction on manifolds. All associated operations are vectorized for batch computation and provide support for different execution backends, namely NumPy, PyTorch and TensorFlow, enabling GPU acceleration. This paper presents the package, compares it with related libraries and provides relevant code examples. We show that Geomstats provides reliable building blocks to foster research in differential geometry and statistics, and to democratize the use of Riemannian geometry in machine learning applications. The source code is freely available under the MIT license at http://geomstats.ai.

International audience ; We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Among others, manifolds come equipped with families of Riemannian metrics, with associated exponential and logarithmic maps, geodesics and parallel transport. Statistics and learning algorithms provide methods for estimation, clustering and dimension reduction on manifolds. All associated operations are vectorized for batch computation and provide support for different execution backends, namely NumPy, PyTorch and TensorFlow, enabling GPU acceleration. This paper presents the package, compares it with related libraries and provides relevant code examples. We show that Geomstats provides reliable building blocks to foster research in differential geometry and statistics, and to democratize the use of Riemannian geometry in machine learning applications. The source code is freely available under the MIT license at http://geomstats.ai.

International audience ; We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Among others, manifolds come equipped with families of Riemannian metrics, with associated exponential and logarithmic maps, geodesics and parallel transport. Statistics and learning algorithms provide methods for estimation, clustering and dimension reduction on manifolds. All associated operations are vectorized for batch computation and provide support for different execution backends, namely NumPy, PyTorch and TensorFlow, enabling GPU acceleration. This paper presents the package, compares it with related libraries and provides relevant code examples. We show that Geomstats provides reliable building blocks to foster research in differential geometry and statistics, and to democratize the use of Riemannian geometry in machine learning applications. The source code is freely available under the MIT license at http://geomstats.ai.

International audience ; We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Among others, manifolds come equipped with families of Riemannian metrics, with associated exponential and logarithmic maps, geodesics and parallel transport. Statistics and learning algorithms provide methods for estimation, clustering and dimension reduction on manifolds. All associated operations are vectorized for batch computation and provide support for different execution backends, namely NumPy, PyTorch and TensorFlow, enabling GPU acceleration. This paper presents the package, compares it with related libraries and provides relevant code examples. We show that Geomstats provides reliable building blocks to foster research in differential geometry and statistics, and to democratize the use of Riemannian geometry in machine learning applications. The source code is freely available under the MIT license at http://geomstats.ai.

International audience ; We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Among others, manifolds come equipped with families of Riemannian metrics, with associated exponential and logarithmic maps, geodesics and parallel transport. Statistics and learning algorithms provide methods for estimation, clustering and dimension reduction on manifolds. All associated operations are vectorized for batch computation and provide support for different execution backends, namely NumPy, PyTorch and TensorFlow, enabling GPU acceleration. This paper presents the package, compares it with related libraries and provides relevant code examples. We show that Geomstats provides reliable building blocks to foster research in differential geometry and statistics, and to democratize the use of Riemannian geometry in machine learning applications. The source code is freely available under the MIT license at http://geomstats.ai.