Deep neural networks (DNNs) have been successfully applied to many real-world problems, but a complete understanding of their dynamical and computational principles is still lacking. Conventional theoretical frameworks for analysing DNNs often assume random networks with coupling weights obeying Gaussian statistics. However, non-Gaussian, heavy-tailed coupling is a ubiquitous phenomenon in DNNs. Here, by weaving together theories of heavy-tailed random matrices and non-equilibrium statistical physics, we develop a new type of mean field theory for DNNs which predicts that heavy-tailed weights enable the emergence of an extended critical regime without fine-tuning parameters. In this extended critical regime, DNNs exhibit rich and complex propagation dynamics across layers. We further elucidate that the extended criticality endows DNNs with profound computational advantages: balancing the contraction as well as expansion of internal neural representations and speeding up training processes, hence providing a theoretical guide for the design of efficient neural architectures.

We discuss an approach to probabilistic forecasting based on two chained machine-learning steps: a dimensional reduction step that learns a reduction map of predictor information to a low-dimensional space in a manner designed to preserve information about forecast quantities; and a density estimation step that uses the probabilistic machine learning technique of normalizing flows to compute the joint probability density of reduced predictors and forecast quantities. This joint density is then renormalized to produce the conditional forecast distribution. In this method, probabilistic calibration testing plays the role of a regularization procedure, preventing overfitting in the second step, while effective dimensional reduction from the first step is the source of forecast sharpness. We verify the method using a 22-year 1-hour cadence time series of Weather Research and Forecasting (WRF) simulation data of surface wind on a grid.

Investigators from Cedars-Sinai have created an artificial intelligence-enabled tool that may make it easier to predict if a person will have a heart attack. The tool, described in The Lancet Digital Health, accurately predicted which patients would experience a heart attack in five years based on the amount and composition of plaque in arteries that supply blood to the heart. Plaque buildup can cause arteries to narrow, which makes it difficult for blood to get to the heart, increasing the likelihood of a heart attack. A medical test called a coronary computed tomography angiography (CTA) takes 3-D images of the heart and arteries and can give doctors an estimate of how much a patient's arteries have narrowed. Until now, however, there has not been a simple, automated and rapid way to measure the plaque visible in the CTA images.

Developed by a team at Cedars-Sinai, the novel AI technology can accurately forecast early signs of a heart attack, predicting which patients will experience a heart attack in five years based on the level and composition of plaque in arteries that supply the heart with blood. The findings of the team's research, which was funded by the National Heart, Lung, and Blood Institute, are published in The Lancet Digital Health. When plaque builds up, it can result in a narrowing of the arteries, making it more challenging for blood to be transported to the heart, which increases the chances of a heart attack. Traditionally, health professionals employ a medical test called coronary computed tomography angiography (CTA) to capture 3D images of the heart and arteries, which gives the doctors an estimate of how much a patient's arteries have narrowed. However, until now, there has not been an efficient, automated, and rapid method for measuring the plaque shown in the CTA images.

A team of researchers at the University of South Australia has reverse engineered the visual systems of hoverflies to detect drones from nearly four kilometers away. The autonomous systems experts at the university worked alongside others at Flinders University and defense company Midspar Systems.

The Douglass Society founder Rob Smith tells'The Next Revolution' the administration has been a'failure on every single level.' The Violence Against Women Act, which President Biden reauthorized last week, should lead to significant strides in combating online abuse including cyberstalking and the nonconsensual distribution of sexually explicit images, but leaders should explore additional opportunities to reduce the odds that online relationship abuse occurs in the first place through preventative education, rather than just providing redress for victims, according to One Love. "While it's a crime to pull your pants down in the streets -- a Class B misdemeanor, punishable by a fine or jail time -- there was nothing stopping anyone from exposing themselves in your DMs, texts, or even via AirDrop, a disturbing trend," Bumble said. In 2018, a representative study of its users found one in three women reported receiving unsolicited lewd photos, otherwise known as cyberflashing, with 96% not happy to receive them, per their website. President Joe Biden responds to a question about Russian President Vladimir Putin in the East Room of the White House, Wednesday, March 16, 2022, in Washington.

Estimated ship dimensions: 0.56 inches length x 6.14 inches width x 9.21 inches height Estimated ship weight: 1.08 pounds We regret that this item cannot be shipped to PO Boxes. This item cannot be shipped to the following locations: United States Minor Outlying Islands, American Samoa (see also separate entry under AS), Puerto Rico (see also separate entry under PR), Northern Mariana Islands, Virgin Islands, U.S., APO/FPO, Guam (see also separate entry under GU)

Titouan Bernicot was 16 when he had an epiphany: Everything he loved about living on an island in the South Pacific was tied to coral reefs. Bernicot, 23, started Coral Gardeners in April 2017 from his bedroom. Now, he and his team are setting their sights on an audacious goal: to replant 1 million corals across the world by 2025. If they have any hope of hitting that number, they'll have to dramatically increase their planting, an effort that is being aided by a new artificial intelligence system. "Our goals for the next couple of years are to become the largest and most advanced coral restoration program on the planet," he said in an interview outside his office on the island.

The COVID-19 pandemic has supercharged the scope of the issues the global healthcare industry was already grappling with. When the pandemic arrived, healthcare organisations often struggled to find the basic information they needed to respond -- whether it was disease and death rates or the availability of hospital beds and critical supplies. Among other problems, the pandemic highlighted the desperate need for collaborative data analytics in healthcare. As McKinsey observed, healthcare's digital barriers are often decidedly non-technological. The technology is out there (or rapidly evolving) -- in October 2020, Pfizer and IBM researchers announced that they have developed a machine learning technique that can predict Alzheimer's disease years before symptoms develop.

The Ukraine defense ministry announced that it is now using facial recognition technology from an American startup to combat misinformation, identify the dead, and expose Russian assailants. The technology, which is like a search engine for faces that aggregates data from millions of social media users across the open web, had previously stirred controversy due to privacy complaints. After the war broke out, the American-based artificial intelligence company Clearview reached out to Ukraine's government, offering its services free of charge. This week, the collaboration became official and Clearview's facial recognition tech is now claimed to be used for security purposes, such as vetting people of interest at checkpoints. Clearview claims that it has amassed a database of over 10 billion photos posted publically on the internet from sites like Facebook, Instagram, Flickr, and Getty Images.