Corticosterone is to rodents as cortisol is to humans. Breakthroughs, discoveries, and DIY tips sent every weekday. It appears that rats will often indulge in a bit of cannabis if given the chance, according to new research. But just like many humans, some rodents are more prone to the recreational drug than others. As neuroscientists explain in a study recently published in the journal, stressed rats are far more likely to take a hit than their calmer relatives.

There has been a surge of recent interest in automatically learning policies to target treatment decisions based on rich individual covariates. A common approach is to train a machine learning model to predict counterfactual outcomes, and then select the policy that optimizes the predicted objective value. In addition, practitioners also want confidence that the learned policy has better performance than the incumbent policy according to downstream policy evaluation. However, due to the winner's curse-an issue where the policy optimization procedure exploits prediction errors rather than finding actual improvements-predicted performance improvements are often not substantiated by downstream policy optimization. To address this challenge, we propose a novel strategy called inference-aware policy optimization, which modifies policy optimization to account for how the policy will be evaluated downstream. Specifically, it optimizes not only for the estimated objective value, but also for the chances that the policy will be statistically significantly better than the observational policy used to collect data. We mathematically characterize the Pareto frontier of policies according to the tradeoff of these two goals. Based on our characterization, we design a policy optimization algorithm that uses machine learning to predict counterfactual outcomes, and then plugs in these predictions to estimate the Pareto frontier; then, the decision-maker can select the policy that optimizes their desired tradeoff, after which policy evaluation can be performed on the test set as usual. Finally, we perform simulations to illustrate the effectiveness of our methodology.

Single-photon Lidar imaging offers a significant advantage in 3D imaging due to its high resolution and long-range capabilities, however it is challenging to apply in noisy environments with multiple targets per pixel. To tackle these challenges, several methods have been proposed. Statistical methods demonstrate interpretability on the inferred parameters, but they are often limited in their ability to handle complex scenes. Deep learning-based methods have shown superior performance in terms of accuracy and robustness, but they lack interpretability or they are limited to a single-peak per pixel. In this paper, we propose a deep unrolling algorithm for dual-peak single-photon Lidar imaging. We introduce a hierarchical Bayesian model for multiple targets and propose a neural network that unrolls the underlying statistical method. To support multiple targets, we adopt a dual depth maps representation and exploit geometric deep learning to extract features from the point cloud. The proposed method takes advantages of statistical methods and learning-based methods in terms of accuracy and quantifying uncertainty. The experimental results on synthetic and real data demonstrate the competitive performance when compared to existing methods, while also providing uncertainty information.

GOP presidential candidate Vivek Ramaswamy discusses whether President Biden will be the 2024 Democrat nominee on "Hannity." FIRST ON FOX: Vivek Ramaswamy's Republican presidential campaign is explaining the former CEO's "awakening" on the threat China poses to the United States, following scrutiny for his former company's partnership with a Chinese Communist Party-backed company just a few years ago. Ramaswamy has repeatedly expressed his support for banning American companies from expanding into China. Just Thursday, he unveiled his plan to "decouple" from China in a speech in his home state of Ohio. "Unless you stop turning our companies into lobbying pawns, unless you actually play by the same set of rules abiding by the same standards we agreed to, then we're cutting the cord," he said.

'The Big Sunday Show' panelists discuss how artificial intelligence could turn your pet's thoughts into reality. An estimated 55 dogs were rescued from an Arizona woman's home for special needs dogs after they were discovered to be living in filthy conditions, as well as those reportedly found dead in a freezer. Police in Chandler responded to April Mclaughlin's home on Friday and found dozens of dogs living in squalor with no water. Mclaughlin had been running a shelter for special needs dogs, but the reality had spiraled into such filthy conditions that firefighters had to wear special equipment to stand breathing in the home, according to AZ Family. Officials began investigating on Sept. 8 after a vet reached out to police that some of Mclaughlin's dogs were not in healthy conditions.

When you picture a scientist, do you see a white coat-clad PhD-holder pipetting away at a lab bench? Or maybe a skygazer with a different day job who goes out on clear nights for a good view of the stars? Historically speaking, both of those examples fit the bill. German-British astronomer William Herschel was originally an amateur who observed the night sky using homemade telescopes. He discovered Uranus in 1781, working alongside his sister, Caroline Herschel, who made multiple discoveries herself.

With over 2.5 billion consumer accounts, Mastercard connects nearly every financial institution in the world and generates almost 75 billion transactions a year. As a result, the company has built over decades a data warehouse that holds "one of the best datasets about commerce really anywhere in the world," says Ed McLaughlin, president of operations and technology at Mastercard. And the company is putting that data to good use. The fastest growing part of Mastercard's business today is the services it puts around commerce, says McLaughlin. IDG's Derek Hulitzky sat down with McLaughlin and Mark Kwapiszeski, president of shared components and security solutions at Mastercard, to discuss how the company turns anonymized and aggregated data into valuable business insights and their advice for getting the best results out of machine learning models.

AI is having an impact in business, government and healthcare. But nowhere is it having more impact than for the biggest companies with the most resources. Advantages big companies have include access to lots of data and funds to buy smaller companies with the expertise to do something innovative and profitable with the data. Each company has had to decide on the best way to leverage AI for their business. "The question is how do you use AI right or use it wisely," stated Ed McLaughlin, Chief Emerging Payments Officer for Mastercard, at the recent EmTech Digital event on AI and big data, as reported in MIT Sloan Review.

As artificial intelligence continues to move into the mainstream, companies are combining AI and big data to build and design better products, react faster to changing market conditions, and protect consumers from fraud. According to experts at EmTech Digital, MIT Technology Review's annual event on artificial intelligence, big data plus AI creates a foundation for more intelligent products and services -- ones that initiate maintenance procedures before something breaks, perform more precise operations, or automatically recalibrate resources to meet changing demand and usage patterns. While AI and big data pave the way for such evolutionary use cases, the pair do not constitute a business strategy on their own accord. "The question is how do you use AI right or use it wisely," said panelist Ed McLaughlin, president of operations and technology for Mastercard. "The biggest lesson learned is how to take these powerful tools and start backward from the problem," McLaughlin said.

As artificial intelligence continues to move into the mainstream, companies are combining AI and big data to build and design better products, react faster to changing market conditions, and protect consumers from fraud. According to experts at EmTech Digital, MIT Technology Review's annual event on artificial intelligence, big data plus AI creates a foundation for more intelligent products and services -- ones that initiate maintenance procedures before something breaks, perform more precise operations, or automatically recalibrate resources to meet changing demand and usage patterns. While AI and big data pave the way for such evolutionary use cases, the pair do not constitute a business strategy on their own accord. "The question is how do you use AI right or use it wisely," said panelist Ed McLaughlin, president of operations and technology for Mastercard. "The biggest lesson learned is how to take these powerful tools and start backwards from the problem," McLaughlin said.