Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Last week's White House report reiterated President Biden's employer mandate that businesses with 100 or more employees require every worker to be fully vaccinated for COVID-19 or tested weekly. Jeffrey Zients, the White House COVID-19 response coordinator, summarized in last week's press briefing that, "We are on track to quadruple the supply of rapid, at-home tests available to Americans by December to more than 200 million a month and to increase the number of places Americans can access free testing in the United States to 30,000 community-based locations." He emphasized the president's staunch commitment in adding $1 billion of extra funding already to the recent $2 billion investment to increase supply.

Photographs and computer vision techniques using artificial intelligence are able to detect the presence of bats on bridges automatically with over 90% accuracy, according to our new study. More than 40 species of bats are found in the U.S., and many of them are endangered or threatened. Bats often nest by the hundreds or thousands underneath bridges, so transportation departments are required to survey for them before conducting repair or replacement projects. I conducted the recently published study with colleagues at the University of Virginia's MOB Lab in collaboration with the Virginia Transportation Research Council. Bridge surveys are important for protecting threatened and endangered bat species. Guano, or excrement, droppings and stains are common signs that bats are present.

It's Ada Lovelace Day and once again we're delighted to introduce you to "50 women in robotics you need to know about"! From the Afghanistan Girls Robotics Team to K.G.Engelhardt who in 1989 founded, and was the first Director of, the Center for Human Service Robotics at Carnegie Mellon, these women showcase a wide range of roles in robotics. We hope these short bios will provide a world of inspiration, in our ninth Women in Robotics list! They are researchers, industry leaders, and artists. Some women are at the start of their careers, while others have literally written the book, the program or the standards.

China is dominating the U.S. by leaps and bounds in the field of artificial intelligence, and the American military's foot dragging on evolving its technology with the times is endangering the future of the country, warned departing Pentagon software chief Nicolas Chaillan. "We have no competing fighting chance against China in 15 to 20 years. Right now, it's already a done deal; it is already over in my opinion," Chaillan told the Financial Times. "Whether it takes a war or not is kind of anecdotal." Calling U.S. government and military cyber defenses akin to "kindergarten level," he added that Americans have "good reason to be angry".

In the early days of the Covid-19 pandemic, the Centers for Disease Control and Prevention produced a simple chart to illustrate how measures like mask wearing and social distancing could "flatten the curve" and reduce the peak of infections. The chart was amplified by news sites and shared on social media platforms, but it often lacked a corresponding text description to make it accessible for blind individuals who use a screen reader to navigate the web, shutting out many of the 253 million people worldwide who have visual disabilities. This alternative text is often missing from online charts, and even when it is included, it is frequently uninformative or even incorrect, according to qualitative data gathered by scientists at MIT. These researchers conducted a study with blind and sighted readers to determine which text is useful to include in a chart description, which text is not, and why. Ultimately, they found that captions for blind readers should focus on the overall trends and statistics in the chart, not its design elements or higher-level insights.

In the early days of the Covid-19 pandemic, the Centers for Disease Control and Prevention produced a simple chart to illustrate how measures like mask wearing and social distancing could "flatten the curve" and reduce the peak of infections. The chart was amplified by news sites and shared on social media platforms, but it often lacked a corresponding text description to make it accessible for blind individuals who use a screen reader to navigate the web, shutting out many of the 253 million people worldwide who have visual disabilities. This alternative text is often missing from online charts, and even when it is included, it is frequently uninformative or even incorrect, according to qualitative data gathered by scientists at MIT. These researchers conducted a study with blind and sighted readers to determine which text is useful to include in a chart description, which text is not, and why. Ultimately, they found that captions for blind readers should focus on the overall trends and statistics in the chart, not its design elements or higher-level insights.

On Wednesday, October 13 at 8:30am CT, William Shatner, who starred as Captain Kirk in Star Trek: The Original Series, will be going where no Hollywood star has gone before -- a suborbital sojourn that will take him 66 miles to the edge of space where he will be able to marvel at the curvature of earth and enjoy zero gravity weightlessness with his Blue Origin crew members. The entire experience is expected to last about 10 minutes and will be similar to the ride that Blue Origin and Amazon founder Jeff Bezos took this past summer with his brother and crew. Shatner might be the oldest person to rocket to space at 90 years old, but he's not the first actor to go. Feature filmmakers from Russia landed on the International Space Station last week beating Tom Cruise to bragging rights. The actor has been working on a $200 million Universal Studios film with SpaceX founder Elon Musk which NASA tweeted last year is expected to be shot on the space station.

The key to personalized news recommendation is to match the user's interests with the candidate news precisely and efficiently. Most existing approaches embed user interests into a representation vector then recommend by comparing it with the candidate news vector. In such a workflow, fine-grained matching signals may be lost. Recent studies try to cover that by modeling fine-grained interactions between the candidate news and each browsed news article of the user. Despite the effectiveness improvement, these models suffer from much higher computation costs online. Consequently, it remains a tough issue to take advantage of effective interactions in an efficient way. To address this problem, we proposed an end-to-end Selective Fine-grained Interaction framework (SFI) with a learning-to-select mechanism. Instead of feeding all historical news into interaction, SFI can quickly select informative historical news w.r.t. the candidate and exclude others from following computations. We empower the selection to be both sparse and automatic, which guarantees efficiency and effectiveness respectively. Extensive experiments on the publicly available dataset MIND validates the superiority of SFI over the state-of-the-art methods: with only five historical news selected, it can significantly improve the AUC by 2.17% over the state-of-the-art interaction-based models; at the same time, it is four times faster.

The knowledge that an intelligent and autonomous mobile robot has and is able to acquire of itself and the environment, namely the situation, limits its reasoning, decision-making, and execution skills to efficiently and safely perform complex missions. Situational awareness is a basic capability of humans that has been deeply studied in fields like Psychology, Military, Aerospace, Education, etc., but it has barely been considered in robotics, which has focused on ideas such as sensing, perception, sensor fusion, state estimation, localization and mapping, spatial AI, etc. In our research, we connected the broad multidisciplinary existing knowledge on situational awareness with its counterpart in mobile robotics. In this paper, we survey the state-of-the-art robotics algorithms, we analyze the situational awareness aspects that have been covered by them, and we discuss their missing points. We found out that the existing robotics algorithms are still missing manifold important aspects of situational awareness. As a consequence, we conclude that these missing features are limiting the performance of robotic situational awareness, and further research is needed to overcome this challenge. We see this as an opportunity, and provide our vision for future research on robotic situational awareness.

Quantification is the research field that studies methods for counting the number of data points that belong to each class in an unlabeled sample. Traditionally, researchers in this field assume the availability of labelled observations for all classes to induce a quantification model. However, we often face situations where the number of classes is large or even unknown, or we have reliable data for a single class. When inducing a multi-class quantifier is infeasible, we are often concerned with estimates for a specific class of interest. In this context, we have proposed a novel setting known as One-class Quantification (OCQ). In contrast, Positive and Unlabeled Learning (PUL), another branch of Machine Learning, has offered solutions to OCQ, despite quantification not being the focal point of PUL. This article closes the gap between PUL and OCQ and brings both areas together under a unified view. We compare our method, Passive Aggressive Threshold (PAT), against PUL methods and show that PAT generally is the fastest and most accurate algorithm. PAT induces quantification models that can be reused to quantify different samples of data. We additionally introduce Exhaustive TIcE (ExTIcE), an improved version of the PUL algorithm Tree Induction for c Estimation (TIcE). We show that ExTIcE quantifies more accurately than PAT and the other assessed algorithms in scenarios where several negative observations are identical to the positive ones.