Canada's Carney pushes'new alliance' with EU despite Trump threats Canadian Prime Minister Mark Carney greets European Commission President Ursula von der Leyen after delivering a speech at the European Parliament in Strasbourg, France, on Thursday. Brussels - Canadian Prime Minister Mark Carney shrugged off a threat of U.S. retaliation Thursday as he pitched a "new alliance" with Europe as a bulwark against outside pressures, in a closely watched speech to the European Parliament. His speech came after EU chief Ursula von der Leyen suggested Canada could become the 27-nation bloc's first "associate member" in her annual State of the Union address on Wednesday. "Europe and Canada are stronger together," Carney said, in a speech that drew a standing ovation, saying his country welcomed the EU proposal and wanted deeper cooperation on issues from artificial intelligence to defense and energy. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Canada welcomes EU proposal to become'associate member' Canadian Prime Minister Mark Carney has welcomed the European Union's ambition for Canada to become its first associate member, calling for closer cooperation on defence, critical minerals and energy security. Carney told the European Parliament in Strasbourg on Thursday that Canada and Europe were stronger together in the face of geopolitical rupture. His comments came a day after European Commission President Ursula von der Leyen proposed opening the door to Canada's associate membership, a status that does not currently exist. US President Donald Trump earlier called the idea laughable and threatened very serious tariffs on Europe if he considered it a hostile act. Canada and the US are locked in an escalating trade dispute, with each side imposing tit-for-tat tariffs after bilateral talks collapsed last month.

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Many data-driven decision problems are formulated using a nominal distribution estimated from historical data, while performance is ultimately determined by a deployment distribution that may be shifted, context-dependent, partially observed, or stress-induced. This tutorial presents modern generative models, particularly flow- and score-based methods, as mathematical tools for constructing decision-relevant distributions. From an operations research perspective, their primary value lies not in unconstrained sample synthesis but in representing and transforming distributions through transport maps, velocity fields, score fields, and guided stochastic dynamics. We present a unified framework based on pushforward maps, continuity, Fokker-Planck equations, Wasserstein geometry, and optimization in probability space. Within this framework, generative models can be used to learn nominal uncertainty, construct stressed or least-favorable distributions for robustness, and produce conditional or posterior distributions under side information and partial observation. We also highlight representative theoretical guarantees, including forward-reverse convergence for iterative flow models, first-order minimax analysis in transport-map space, and error-transfer bounds for posterior sampling with generative priors. The tutorial provides a principled introduction to using generative models for scenario generation, robust decision-making, uncertainty quantification, and related problems under distributional shift.

As regression is a widely studied problem, many methods have been proposed to solve it, each of them often requiring setting different hyper-parameters. Therefore, selecting the proper method for a given application may be very difficult and relies on comparing their performances. Performance is usually measured using various metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), or R-squared (R${}^2$). These metrics provide a numerical summary of predictive accuracy by quantifying the difference between predicted and actual values. However, while these metrics are widely used in the literature for summarizing model performance and useful to distinguish between models performing poorly and well, they often aggregate too much information. This article addresses these limitations by introducing a novel visualization approach that highlights key aspects of regression model performance. The proposed method builds upon three main contributions: (1) considering the residuals in a 2D space, which allows for simultaneous evaluation of errors from two models, (2) leveraging the Mahalanobis distance to account for correlations and differences in scale within the data, and (3) employing a colormap to visualize the percentile-based distribution of errors, making it easier to identify dense regions and outliers. By graphically representing the distribution of errors and their correlations, this approach provides a more detailed and comprehensive view of model performance, enabling users to uncover patterns that traditional aggregate metrics may obscure. The proposed visualization method facilitates a deeper understanding of regression model performance differences and error distributions, enhancing the evaluation and comparison process.

A London doctor controlled robotic surgical arms in Gibraltar to perform prostate cancer telesurgery in near real time from 1,500 miles away.

We also study the more general case of an additional entropy regularizer.

Inference via knowledge compilation has also been used for many applications in neuro-symbolic AI,suchasconstrained generation [2,54]andneural logic programming [34,28].

Adapting to dynamic data distributions is a practical yet challenging task.