Estimating causal effects from randomized experiments is central to clinical research. Reducing the statistical uncertainty in these analyses is an important objective for statisticians. Registries, prior trials, and health records constitute a growing compendium of historical data on patients under standard-of-care conditions that may be exploitable to this end. However, most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control. Here, we propose a use of historical data that exploits linear covariate adjustment to improve the efficiency of trial analyses without incurring bias. Specifically, we train a prognostic model on the historical data, then estimate the treatment effect using a linear regression while adjusting for the trial subjects' predicted outcomes (their prognostic scores). We prove that, under certain conditions, this prognostic covariate adjustment procedure attains the minimum variance possible among a large class of estimators. When those conditions are not met, prognostic covariate adjustment is still more efficient than raw covariate adjustment and the gain in efficiency is proportional to a measure of the predictive accuracy of the prognostic model. We demonstrate the approach using simulations and a reanalysis of an Alzheimer's Disease clinical trial and observe meaningful reductions in mean-squared error and the estimated variance. Lastly, we provide a simplified formula for asymptotic variance that enables power and sample size calculations that account for the gains from the prognostic model for clinical trial design.

We determine the resource scaling of machine learning-based quantum state reconstruction methods, in terms of both inference and training, for systems of up to four qubits. Further, we examine system performance in the low-count regime, likely to be encountered in the tomography of high-dimensional systems. Finally, we implement our quantum state reconstruction method on a IBM Q quantum computer and confirm our results.

Dimensionality reduction methods for count data are critical to a wide range of applications in medical informatics and other fields where model interpretability is paramount. For such data, hierarchical Poisson matrix factorization (HPF) and other sparse probabilistic non-negative matrix factorization (NMF) methods are considered to be interpretable generative models. They consist of sparse transformations for decoding their learned representations into predictions. However, sparsity in representation decoding does not necessarily imply sparsity in the encoding of representations from the original data features. HPF is often incorrectly interpreted in the literature as if it possesses encoder sparsity. The distinction between decoder sparsity and encoder sparsity is subtle but important. Due to the lack of encoder sparsity, HPF does not possess the column-clustering property of classical NMF -- the factor loading matrix does not sufficiently define how each factor is formed from the original features. We address this deficiency by self-consistently enforcing encoder sparsity, using a generalized additive model (GAM), thereby allowing one to relate each representation coordinate to a subset of the original data features. In doing so, the method also gains the ability to perform feature selection. We demonstrate our method on simulated data and give an example of how encoder sparsity is of practical use in a concrete application of representing inpatient comorbidities in Medicare patients.

Scientists in Denmark have uncovered two new species of deadly fungi that devour from the inside, bursting from the abdomen of their still-living prey. The parasites--Strongwellsea acerosa and Strongwellsea tigrinae--infect adult flies, which continue to buzz around for days with massive holes in their bodies. As they do, the fungi rain spores from these holes down onto other unsuspecting flies. Thousands of torpedo-shaped spores can shoot out like a rocket from a single fly. The corpse of a fly with two large holes in its abdomen, caused by the fungus Strongwellsea tigrinae. Researchers from the Natural History Museum of Denmark and the University of Copenhagen's Department of Plant and Environmental Sciences have reported on the two new fungi.

One of the Air Force's oldest planes became the first military aircraft to fly with artificial intelligence as its copilot on Dec. 15. A U-2 from Beale Air Force Base, Calif., flew with an AI algorithm that controlled the Dragon Lady's sensors and tactical navigation during a local training sortie. The algorithm, developed by Air Combat Command's U-2 Federal Laboratory and named ARTUµ in a reference to the droid that serves as a copilot in the Star Wars film franchise, took over tasks normally handled by the pilot, in turn letting the flier focus on the flying. "ARTUµ's groundbreaking flight culminates our three-year journey to becoming a digital force," said Will Roper, the Air Force's assistant secretary of acquisition, in a release. Failing to realize AI's full potential will mean ceding decision advantage to our adversaries." The laboratory used more than a half-million simulated training missions to build the algorithm, which took over sensors after takeoff. The training scenario focused on a simulated missile strike, with ARTUµ finding enemy missile launchers and the pilot looking for adversary aircraft--both using the U-2's radar, according to the release. "We know that in order to fight and win in a future conflict with a peer adversary, we must have a decisive digital advantage," Air Force Chief of Staff Gen. Charles Q. Brown Jr said in the release. "AI will play a critical role in achieving that edge, so I'm incredibly proud of what the team accomplished.

But there is no doubt that the pandemic has hastened the adoption of emerging digital technologies, ushered in a new era of remote and flexible working arrangements, increased organisations' reliance on digital infrastructure and exposed our tech-related strengths and weaknesses alike. Leaving 2020 in the rear-view mirror, we count down our top 10 predictions for 2021 and beyond in the domain of Digital Law in Australia. Despite an existing principles-based framework for the protection of privacy under the Privacy Act, in recent years the Federal Government has preferred to introduce parallel privacy requirements, such as the 13 Privacy Safeguards under the Consumer Data Right legislation and the privacy aspects of the upcoming Data Availability and Transparency Act for Government agencies. These nascent regimes are similar enough to the existing privacy regime to encourage complacency and different enough to give any compliance function a headache. Overlapping and often sectorial regulation adds to the increasing complexity of privacy law in Australia.

The Lunar Gateway, NASA's outpost that will orbit the moon as part of its upcoming Artemis program, will be equipped with external robotics from the Canadian Space Agency (CSA), NASA announced today. The culmination of an earlier partnership around Artemis, NASA's first major program to bring astronauts to the moon in half a century, CSA plans to build a "next-generation" robotic arm, the aptly named Canadarm3. That device will be able to reach many parts of the Gateway's exterior, giving astronauts an easy way to make repairs. Additionally, NASA says CSA will create robotic interfaces for Gateway modules, which will help with the installation of the outpost's first two scientific instruments. CSA aims to deliver the Candarm3 to the Gateway in 2026 via a commercial logistics supply flight.

Iran's supreme leader and the country's president both warned America on Wednesday that the departure of President Donald Trump does not immediately mean better relations between the two nations. The remarks come as Iran approaches the first anniversary of the U.S. drone strike that killed Revolutionary Guard Gen. Qassem Soleimani in Baghdad, an attack that nearly plunged Washington and Tehran into an open war after months of tensions. In recent weeks, a scientist who founded Iran's military nuclear program two decades ago was gunned down in an attack in a rural area outside of Tehran that The Associated Press accessed for the first time Wednesday. Supreme Leader Ayatollah Ali Khamenei spoke in Tehran at the Imam Khomeini Hosseinieh, or congregation hall, where he attended a meeting with Soleimani's family and top military leaders. They all sat some 16 feet away from the 81-year-old Khamenei, who wore a face mask due to the coronavirus pandemic still raging in Iran.

Innovative automakers, software developers and tech companies are transforming the automotive industry. Today, drivers enjoy enhanced entertainment, information options and connection with the outer world. As cars move toward more autonomous capabilities, the stakes are increasing in terms of security. As per a report by the UN, Europol and cybersecurity company Trend Micro, cyber-criminals could exploit disruptive technologies, including artificial intelligence (AI) and machine learning (ML) to conduct attacks against autonomous cars, drones and IoT-connected vehicles. The rapid increase in these technologies inevitably creates a rich target for hackers looking to get access to personal information and control the essential automotive functions and features.

It's been two weeks since Google fired Timnit Gebru, a decision that still seems incomprehensible. Gebru is one of the most highly regarded AI ethics researchers in the world, a pioneer whose work has highlighted the ways tech fails marginalized communities when it comes to facial recognition and more recently large language models. Of course, this incident didn't happen in a vacuum. Case in point: Gebru was fired the same day the National Labor Review Board (NLRB) filed a complaint against Google for illegally spying on employees and the retaliatory firing of employees interested in unionizing. Gebru's dismissal also calls into question issues of corporate influence in research, demonstrates the shortcomings of self-regulation, and highlights the poor treatment of Black people and women in tech in a year when Black Lives Matter sparked the largest protest movement in U.S. history. In an interview with VentureBeat last week, Gebru called the way she was fired disrespectful and described a companywide memo sent by CEO Sundar Pichai as "dehumanizing." To delve further into possible outcomes following Google's AI ethics meltdown, VentureBeat spoke with five experts in the field about Gebru's dismissal and the issues it raises.