Deploying simple, proven methods for making our roads safer could save more than 1 million lives worldwide each year. Every year, traffic crashes kill more than one million people, and they're the leading cause of death for children and young adults. In the US, 100 people a day lose their lives this way, in many cases not on highways but on rural and local streets. "Traffic deaths are a leading cause of preventable death, yet we treat it like it's inevitable," says Leah Shahum, founder and director of the Vision Zero Network, a nonprofit focused on making streets less deadly. The causes of a crash can be muddy. Drivers make mistakes like speeding or getting distracted, but infrastructure fails them too: Perhaps they were on a road built for speed and not safety, in a screen-filled vehicle, or at an intersection where pedestrians, cyclists, and other cars aren't visible.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Trump and Xi summit may just be a'horse and pony show', experts say Share Trump and Xi summit may just be a'horse and pony show', experts say on social media As United States President Donald Trump rolled out the red carpet for Chinese President Xi Jinping with a rare personal welcome at the airport, to be followed by a state banquet and a military flyover, experts warn to temper expectations as the summit is more focused on "re-establishing trust" rather than having any meaningful deals announced at the end of it. The US and China have been locked in a trade war since Trump's first term as president when he introduced levies on imports of certain industrial goods, and Beijing retaliated with tariffs on a range of US exports including soya beans, pork and electric vehicles. Some of those policies continued under Trump's successor, President Joe Biden, who also introduced other measures, including curbs on chip exports. While both sides have since stepped back from that cliff after the two leaders met in late October in Busan on the sidelines of the APEC summit in South Korea, US tariffs are still at a hefty average of 36.5 percent, while China's tariffs on US goods average out to 31 percent. "This is not a bargaining summit," Reza Hasmath, academic faculty adviser at The China Institute at the University of Alberta, told Al Jazeera.

You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Hundreds of families thought they were buying their slice of the American dream... but now they claim surprise'eyesore' has cost them THOUSANDS of dollars and ruined their lives: 'We're trapped' Southern elementary school erupts as parents bitterly divided over new transgender teacher... with furious families clashing over claims children are'too young' for a'biological male in a dress' Meghan wears Princess Diana's diamond and sapphire earrings as she steps out with Prince Harry to support music mogul friend David Foster WNBA players' union turns on Sophie Cunningham with bombshell statement on'hate, abuse, and demonization' of trans people SARAH VINE: Why, after ten years of Harry and Meghan, these TWO little-known incidents stay with me. But for all her mistakes, she's not solely responsible for the shambles that followed Hollywood angst over Justin and Hailey Bieber's marriage is ...

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Environment Animals Wildlife Bears Beluga whale live cam launches. July 15 is a big day for Arctic animals. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Baby belugas are born with gray, bluish, or brownish-gray skin. Breakthroughs, discoveries, and DIY tips sent six days a week.

Predictions in the form of sets of probability distributions, so-called credal sets, provide a suitable means to represent a learner's epistemic uncertainty. In this paper, we propose a theoretically grounded approach to credal prediction based on the statistical notion of relative likelihood: The target of prediction is the set of all (conditional) probability distributions produced by the collection of plausible models, namely those models whose relative likelihood exceeds a specified threshold. This threshold has an intuitive interpretation and allows for controlling the trade-off between correctness and precision of credal predictions. We tackle the problem of approximating credal sets defined in this way by means of suitably modified ensemble learning techniques. To validate our approach, we illustrate its effectiveness by experiments on benchmark datasets demonstrating superior uncertainty representation without compromising predictive performance. We also compare our method against several state-of-the-art baselines in credal prediction.

This paper investigates the computational complexity of reinforcement learning within a novel linear function approximation regime, termed partial qπ-realizability. In this framework, the objective is to learn an ϵ-optimal policy with respect to a predefined policy set Π, under the assumption that all value functions corresponding to policies in Π are linearly realizable. This framework adopts assumptions that are weaker than those in the qπ-realizability setting yet stronger than those in the q -realizability setup. As a result, it provides a more practical model for reinforcement learning scenarios where function approximation naturally arise. We prove that learning an ϵ-optimal policy in this newly defined setting is computationally hard. More specifically, we establish NP-hardness under a parameterized greedy policy set (i.e., argmax) and, further, show that--unless NP = RP--an exponential lower bound (exponential in feature vector dimension) holds when the policy set contains softmax policies, under the Randomized Exponential Time Hypothesis. Our hardness results mirror those obtained in the q -realizability settings, and suggest that computational difficulty persists even when the policy class Πis expanded beyond the optimal policy, reinforcing the unbreakable nature of the computational hardness result regarding partial qπ-realizability under two important policy sets. To establish our negative result, our primary technical contribution is a reduction from two complexity problems, δ-MAX-3SAT and δ-MAX-3SAT(b), to instances of our problem settings: GLINEAR-κ-RL (under the greedy policy set) and SLINEAR-κ-RL (under the softmax policy set), respectively. Our findings indicate that positive computational results are generally unattainable in the context of partial qπ-realizability, in sharp contrast to the qπ-realizability setting under a generative access model.