This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

Fast, stealthy, and cheap--autonomous, semisubmersible drone boats carrying tons of cocaine could be international law enforcement's nightmare scenario. A big one just came ashore. Colombian military officials intercepted this 40-foot-long uncrewed fiberglass "narco sub" in the ocean just off Tayrona National Park. On a bright morning last April, a surveillance plane operated by the Colombian military spotted a 40-foot-long shark-like silhouette idling in the ocean just off Tayrona National Park. It was, unmistakably, a "narco sub," a stealthy fiberglass vessel that sails with its hull almost entirely underwater, used by drug cartels to move cocaine north. The plane's crew radioed it in, and eventually nearby coast guard boats got the order, routine but urgent: Intercept. In Cartagena, about 150 miles from the action, Captain Jaime González Zamudio, commander of the regional coast guard group, sat down at his desk to watch what happened next.

Hyperbolic models can reproduce the heavy-tailed degree distribution, high clustering, and hierarchical structure of empirical networks. Current algorithms for finding the hyperbolic coordinates of networks, however, do not quantify uncertainty in the inferred coordinates. We present BIGUE, a Markov chain Monte Carlo (MCMC) algorithm that samples the posterior distribution of a Bayesian hyperbolic random graph model. We show that combining random walk and random cluster transformations significantly improves mixing compared to the commonly used and state-of-the-art dynamic Hamiltonian Monte Carlo algorithm. Using this algorithm, we also provide evidence that the posterior distribution cannot be approximated by a multivariate normal distribution, thereby justifying the use of MCMC to quantify the uncertainty of the inferred parameters.

Even if you believe, as some do, that the world has moved from Peak TV to Trough TV, there are still more shows released in any given year than any one person could consume (trust us, we tried). Between major networks, cable television channels, and streaming services, there's just too much to watch. You're bound to miss your new favorite binge-watch. Below are our picks for the best TV shows you might have missed in 2023. If you buy something using links in our stories, we may earn a commission. This helps support our journalism.

Simulations are gaining increasingly significance in the field of autonomous driving due to the demand for rapid prototyping and extensive testing. Employing physics-based simulation brings several benefits at an affordable cost, while mitigating potential risks to prototypes, drivers, and vulnerable road users. However, there exit two primary limitations. Firstly, the reality gap which refers to the disparity between reality and simulation and prevents the simulated autonomous driving systems from having the same performance in the real world. Secondly, the lack of empirical understanding regarding the behavior of real agents, such as backup drivers or passengers, as well as other road users such as vehicles, pedestrians, or cyclists. Agent simulation is commonly implemented through deterministic or randomized probabilistic pre-programmed models, or generated from real-world data; but it fails to accurately represent the behaviors adopted by real agents while interacting within a specific simulated scenario. This paper extends the description of our proposed framework to enable real-time interaction between real agents and simulated environments, by means immersive virtual reality and human motion capture systems within the CARLA simulator for autonomous driving. We have designed a set of usability examples that allow the analysis of the interactions between real pedestrians and simulated autonomous vehicles and we provide a first measure of the user's sensation of presence in the virtual environment.

Machine learning applications can be found in virtually every aspect of our day-to-day lives. Our product recommendations, social media feeds, email spam filters, traffic predictions, virtual personal assistants, and more, are all driven by machine learning. Companies are increasingly on the hunt for talented machine learning practitioners, so there’s no time like the present to gain those highly sought-after skills!

When Gilbert Serrano opened the door of his potential dream house, a modern, two-bedroom rental, he was surprised to be greeted not by a real estate agent, but by a robot. An iPad mounted on the machine displayed an agent's smiling face. These robots, rolled out last summer in the Bay Area by high-tech property management startup Zenplace, are intended to take the hassle out of coordinating showing times between agents and prospective renters. They're just one piece of the new wave of technology that's changing the way houses are bought, sold and rented, as platforms such as Zillow, Redfin and a host of smaller startups have eroded the real estate agent's importance. These days, clients can use artificial intelligence to comb property data without a human real estate agent, take virtual tours of promising houses from their couches, and even apply for their favorite apartments online.