Manitoba
Canadian premier wants to ban social media and AI chatbots for kids in Manitoba
The province's premier, Wab Kinew, proposed the ban during a fundraiser, but didn't elaborate on key details. Manitoba could be the first province in Canada to establish a social media ban for kids, but the proposal's details aren't very clear yet. The province's premier, Wab Kinew, announced during a fundraiser event on Saturday and on X that Manitoba would put in place a ban for social media and AI chatbots for its youth. They're doing these very awful things to kids all in the name of a few likes, all in the name of more engagement, and all in the name of money, Kinew said at the event. Our kids will never be for sale and their attention and their childhoods should never be profited from.
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- Information Technology > Communications > Social Media (1.00)
- Information Technology > Artificial Intelligence > Natural Language > Chatbot (1.00)
- Information Technology > Communications > Mobile (0.99)
Water flow in prairie watersheds is increasingly unpredictable -- but AI could help
In recent years, the Prairies have seen bigger swings in climate conditions -- very wet years followed by very dry ones. That makes an already unpredictable landscape even harder to forecast, with real consequences for flood preparedness and water quality. The challenge is the landscape itself. Much of the Canadian Prairies sit within the Prairie Pothole Region, a landscape dotted with millions of shallow wetlands and depressions. Water doesn't simply run downhill into a stream, it is stored first.
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- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.48)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Regression (0.46)
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- Asia > Middle East > Jordan (0.04)
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- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.98)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.46)
The Doomsday Glacier Is Getting Closer and Closer to Irreversible Collapse
An analysis of the expansion of cracks in the Thwaites Glacier over the past 20 years suggests that a total collapse could be only a matter of time. Known as the "Doomsday Glacier," the Thwaites Glacier in Antarctica is one of the most rapidly changing glaciers on Earth, and its future evolution is one of the biggest unknowns when it comes to predicting global sea level rise. The eastern ice shelf of the Thwaites Glacier is supported at its northern end by a ridge of the ocean floor. However, over the past two decades, cracks in the upper reaches of the glacier have increased rapidly, weakening its structural stability. A new study by the International Thwaites Glacier Collaboration (ITGC) presents a detailed record of this gradual collapse process.
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Rare polar bear adoption could save cub's life
Rare polar bear adoption could save cub's life The cubs were born into a well-studied'celebration' of polar bears in Canada. Breakthroughs, discoveries, and DIY tips sent every weekday. Scientists in Churchill, Manitoba, Canada (aka the polar bear capital of the world) have confirmed that a wild female polar bear has adopted a cub that is not her own. This rare behavior was captured on cameras during the polar bear's annual migration along Western Hudson Bay . Researchers from Environment and Climate Change Canada and Polar Bears International spotted the mother bear (designated as bear X33991) during spring 2025, when she came out of her maternity den.
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Communication-Efficient Learning for Satellite Constellations
Tudose, Ruxandra-Stefania, Grüss, Moritz H. W., Kim, Grace Ra, Johansson, Karl H., Bastianello, Nicola
Satellite constellations in low-Earth orbit are now widespread, enabling positioning, Earth imaging, and communications. In this paper we address the solution of learning problems using these satellite constellations. In particular, we focus on a federated approach, where satellites collect and locally process data, with the ground station aggregating local models. We focus on designing a novel, communication-efficient algorithm that still yields accurate trained models. To this end, we employ several mechanisms to reduce the number of communications with the ground station (local training) and their size (compression). We then propose an error feedback mechanism that enhances accuracy, which yields, as a byproduct, an algorithm-agnostic error feedback scheme that can be more broadly applied. We analyze the convergence of the resulting algorithm, and compare it with the state of the art through simulations in a realistic space scenario, showcasing superior performance.
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$\text{R}^2\text{R}$: A Route-to-Rerank Post-Training Framework for Multi-Domain Decoder-Only Rerankers
Wang, Xinyu, Wu, Hanwei, Hu, Qingchen, Tai, Zhenghan, Tian, Jingrui, Ding, Lei, Chi, Jijun, He, Hailin, Kwok, Tung Sum Thomas, Cui, Yufei, Lyu, Sicheng, Li, Muzhi, Li, Mingze, Yu, Xinyue, Zhou, Ling, Lu, Peng
Decoder-only rerankers are central to Retrieval-Augmented Generation (RAG). However, generalist models miss domain-specific nuances in high-stakes fields like finance and law, and naive fine-tuning causes surface-form overfitting and catastrophic forgetting. To address this challenge, we introduce R2R, a domain-aware framework that combines dynamic expert routing with a two-stage training strategy, Entity Abstraction for Generalization (EAG). EAG introduces a counter-shortcut mechanism by masking the most predictive surface cues, forcing the reranker to learn domain-invariant relevance patterns rather than memorizing dataset-specific entities. To efficiently activate domain experts, R2R employs a lightweight Latent Semantic Router that probes internal representations from the frozen backbone decoder to select the optimal LoRA expert per query. Extensive experiments across different reranker backbones and diverse domains (legal, medical, and financial) demonstrate that R2R consistently surpasses generalist and single-domain fine-tuned baselines. Our results confirm that R2R is a model-agnostic and modular approach to domain specialization with strong cross-domain robustness.
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