Fox News White House correspondent Jacqui Heinrich discusses the Biden administration's failure to deliver at-home COVID tests on'Special Report.' It's been six months since President Biden said the U.S. was close to declaring "independence from COVID-19," and yet the pandemic still shows no signs of slowing after the country set a global record for the number of cases Monday due to the spread of the highly transmissible omicron variant. The U.S. reported more than 1 million new coronavirus infections on Monday, setting a global record and almost doubling the previous record set last week. Hospitalizations have also skyrocketed across the country, but deaths have held relatively steady in recent weeks. President Biden listens during a virtual meeting about reducing the costs of meat through increased competition in the meat processing industry in the South Court Auditorium at the Eisenhower Executive Office Building on Jan. 3, 2022, in Washington, D.C. (Photo by Sarah Silbiger/Getty Images) Biden gave a speech Tuesday maintaining his position that "this continues to be a pandemic of the unvaccinated," even though breakthrough cases of COVID-19 among people who are fully vaccinated continue to rise across the country as new variants emerge.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Germany's antitrust watchdog paved the way Wednesday for extra scrutiny of Google by designating it a company of "paramount significance," the first to get that label since regulators got more power to curb abusive practices by big digital companies. The Bundeskartellamt said its decision comes after rules were introduced last year that allow it "to intervene earlier and more effectively" to ban companies from using anti-competitive practices. The regulator's decision, which lasts five years, gives it extended powers to supervise Google for "abuse control."

Alexa will be the first voice assistant available beyond Earth. Amazon and Lockheed Martin have revealed NASA will carry Alexa to space aboard the Artemis I mission launching later in 2022. While that flight is uncrewed, the companies are planning a "virtual crew experience" at NASA's Johnson Space Center that will let people in Mission Control (including students and special guests) simulate conversations between the digital helper and astronauts. This is decidedly more sophisticated than the Alexa on your Echo speaker. Alexa will have access to the Orion spacecraft's telemetry data, answer "thousands" of mission-related questions and even control devices like cabin lighting.

The expedition to find the wreck of Sir Ernest Shackleton's Endurance is set to sail next month, it was announced today on the centenary of the polar explorer's death. Endurance was one of two ships used by the Imperial Trans-Antarctic expedition of 1914–1917, which hoped to make the first land crossing of the Antarctic. Carrying an expedition crew of 28 men, the 144-foot-long Endurance was a three-masted schooner barque sturdily built for operations in polar waters. Aiming to land at Vahsel Bay, the vessel became stuck in pack ice on the Weddell Sea on January 18, 1915 -- where she and her crew would remain for many months. In late October, however, a drop in temperature from 42 F to -14 F saw the ice pack begin to steadily crush the Endurance, which finally sank on November 21, 1915.

The Defense Department announced a new partnership focused on using machine learning technology to allow anyone--even those without any medical background--to treat burn wounds on the battlefield. In a sole source contract with the Beckman Laser Institute and Medical Clinic at the University of California, Irvine, the U.S. Army Institute of Surgical Research will conduct research in using machine learning algorithms to assess the severity of burn wounds in conjunction with a combination of imaging techniques, specifically Spatial Frequency Domain Imaging and Laser Speckle Imaging systems. "There is a critical unmet need for far-forward triage and monitoring tools that enhance battlefield diagnostics for burn wounds by non-burn experts," the contract announcement reads. "Whilst burns, particularly in their early stages, are dynamic and often evolving, they are influenced by secondary comorbidities." The specific technology requested by USAISR is intended to develop portable devices that can map a 3-D profile of burned skin surfaces and regions to determine burn severity "in the hands of non-expert personnel at the point of injury."

Chief information security officers' (CISOs) greatest challenge going into 2022 is countering the speed and severity of cyberattacks. The latest real-time monitoring and detection technologies improve the odds of thwarting an attack but aren't foolproof. CISOs tell VentureBeat that bad actors avoid detection with first-line monitoring systems by modifying attacks on the fly. Enterprises fail to get the most value from threat monitoring, detection, and response cybersecurity strategies because they're too focused on data collection and security monitoring alone. CISOs tell VentureBeat they're capturing more telemetry (i.e., remote) data than ever, yet are short-staffed when it comes to deciphering it, which means they're often in react mode.

Panel data models are a standard empirical tool in statistics, economics, marketing, and financial research. The conventional modeling approach is to assume that all individual heterogeneity can be summarized by an individual specific intercept, often known as the fixed effects, while assuming all covariates have a common effect among all the individuals, such that information can be pooled across individuals to gain efficiency of these common parameters. However, heterogeneous responses towards observed control variables are often better supported by empirical evidence, especially as detailed individual level data becomes more available. An increasingly popular approach to model unobserved heterogeneity in the effects of covariates on individual responses is to assume the existence of a finite number of homogeneous groups.

Electronic Intelligence (ELINT), often known as E-Intelligence, is intelligence obtained through electronic sensors. Other than personal communications, ELINT intelligence is usually obtained. The goal is usually to determine a target's capabilities, such as radar placement. Active or passive sensors can be employed to collect data. A provided signal is analyzed and contrasted to collected data for recognized signal types. The information may be stored if the signal type is detected; it can be classed as new if no match is found. ELINT collects and categorizes data. In a military setting (and others that have adopted the usage, such as a business), intelligence helps an organization make decisions that can provide them a strategic advantage over the competition. The term "intel" is frequently shortened. The two main subfields of signals intelligence (SIGINT) are ELINT and Communications Intelligence (COMINT). The US Department of Defense specifies the terminologies, and intelligence communities use the categories of data reviewed worldwide.

We perform a systematic analysis of the quality of fit of the stochastic block model (SBM) for 275 empirical networks spanning a wide range of domains and orders of size magnitude. We employ posterior predictive model checking as a criterion to assess the quality of fit, which involves comparing networks generated by the inferred model with the empirical network, according to a set of network descriptors. We observe that the SBM is capable of providing an accurate description for the majority of networks considered, but falls short of saturating all modeling requirements. In particular, networks possessing a large diameter and slow-mixing random walks tend to be badly described by the SBM. However, contrary to what is often assumed, networks with a high abundance of triangles can be well described by the SBM in many cases. We demonstrate that simple network descriptors can be used to evaluate whether or not the SBM can provide a sufficiently accurate representation, potentially pointing to possible model extensions that can systematically improve the expressiveness of this class of models.

The existence of real-world adversarial examples (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tasks such as visual perception in autonomous driving. This paper presents an extensive evaluation of the robustness of semantic segmentation models when attacked with different types of adversarial patches, including digital, simulated, and physical ones. A novel loss function is proposed to improve the capabilities of attackers in inducing a misclassification of pixels. Also, a novel attack strategy is presented to improve the Expectation Over Transformation method for placing a patch in the scene. Finally, a state-of-the-art method for detecting adversarial patch is first extended to cope with semantic segmentation models, then improved to obtain real-time performance, and eventually evaluated in real-world scenarios. Experimental results reveal that, even though the adversarial effect is visible with both digital and real-world attacks, its impact is often spatially confined to areas of the image around the patch. This opens to further questions about the spatial robustness of real-time semantic segmentation models.