Former GOP operative Scott Leiendecker just bought Dominion Voting Systems, giving him ownership of voting systems used in 27 states. The news last week that Dominion Voting Systems was purchased by the founder and CEO of Knowink, a Missouri-based maker of electronic poll books, has left election integrity activists confused over what, if anything, this could mean for voters and the integrity of US elections. The company, acquired by Scott Leiendecker, a former Republican Party operative and election director in Missouri before founding Knowink, said in a press release that he was rebranding Dominion, which has headquarters in Canada and the United States, under the name Liberty Vote "in a bold and historic move to transform and improve election integrity in America" and to distance the company from false allegations made previously by President Donald Trump and his supporters that the company had rigged the 2020 presidential election to give the win to President Joe Biden. The Liberty release said that the rebranded company will be 100 percent American owned, that it will have a "paper ballot focus" that leverages hand-marked paper ballots, will "prioritize facilitating third-party auditing," and is "committed to domestic staffing and software development." The press release provided no details, however, to explain what this means in practice.

Biologically Inspired Design (BID), or Biomimicry, is a problem-solving methodology that applies analogies from nature to solve engineering challenges. For example, Speedo engineers designed swimsuits based on shark skin. Finding relevant biological solutions for real-world problems poses significant challenges, both due to the limited biological knowledge engineers and designers typically possess and to the limited BID resources. Existing BID datasets are hand-curated and small, and scaling them up requires costly human annotations. In this paper, we introduce BARcode (Biological Analogy Retriever), a search engine for automatically mining bio-inspirations from the web at scale. Using advances in natural language understanding and data programming, BARcode identifies potential inspirations for engineering challenges. Our experiments demonstrate that BARcode can retrieve inspirations that are valuable to engineers and designers tackling real-world problems, as well as recover famous historical BID examples. We release data and code; we view BARcode as a step towards addressing the challenges that have historically hindered the practical application of BID to engineering innovation.

Remarkable footage shows how a revolutionary technique is letting scientists peer into cells at the genetic level -- both imaging the cell and sequencing its DNA. Unlike traditional microscopes, which use light, the new approach uses DNA'bar codes' to label each molecule in the cell. From readings of the complex interactions of these labels with the molecules and each other, a computer algorithm can work backward to reveal an image of the cell. DNA microscopy could find myriad applications -- including helping scientists study immune cells and tumours to develop new treatments to fight cancer. The unorthodox imaging technique was developed by biophysicist Joshua Weinstein and colleagues at the Broad Institute in Cambridge, Massachusetts.

Persistence landscapes map persistence diagrams into a function space, which may often be taken to be a Banach space or even a Hilbert space. In the latter case, it is a feature map and there is an associated kernel. The main advantage of this summary is that it allows one to apply tools from statistics and machine learning. Furthermore, the mapping from persistence diagrams to persistence landscapes is stable and invertible. We introduce a weighted version of the persistence landscape and define a one-parameter family of Poisson-weighted persistence landscape kernels that may be useful for learning. We also demonstrate some additional properties of the persistence landscape. First, the persistence landscape may be viewed as a tropical rational function. Second, in many cases it is possible to exactly reconstruct all of the component persistence diagrams from an average persistence landscape. It follows that the persistence landscape kernel is characteristic for certain generic empirical measures. Finally, the persistence landscape distance may be arbitrarily small compared to the interleaving distance.

The food industry has been taking heat from consumers and critics who are demanding healthier ingredients, transparency about where their meals come from and better treatment of animals. There is also a growing awareness of the harmful effect that food production can have on the environment. Now big food companies and entrepreneurs are taking advantage of advances in robotics and data science to meet those challenges--and the trend will likely continue as technology improves, and natural ingredients become easier to cultivate. It also helps that venture capitalists are flocking to the companies cooking up these innovations. This year is on pace to set a record for this decade for venture investment in food technology, according to the PitchBook Platform data provider.

Pattern matching and predicting an exigent need in hospitals is a difficult task for skilled medical staffs, but not for AI and machine learning. Medical staffs do not have the luxury of observing each of their patients on a full-time basis. Although incredibly good at identifying the immediate needs of patients in obvious circumstances, nurses and medical staffs do not possess the capabilities of discerning the future from a complex array of patient symptoms exhibited over a reasonable period. Machine learning has the luxury of not only observing and analyzing patient data 24/7, but also combining information collected from multiple sources, i.e. historical records, daily evaluations by medical staff, and real-time measurements of vitals such as heart rate, oxygen usage and blood pressure. The application of AI in the assessment and prediction of imminent heart attacks, falls, strokes, sepsis and complications is currently underway all over the world.

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. While we don't have any formal research to back this up, we're pretty confident that if you ask anyone with an Amazon Echo smart speaker where they keep it, most will tell you theirs is in the kitchen. After all, it's a central location in the home that gets a lot of traffic--not to mention that Alexa can be quite helpful when you're cooking! You probably already know that Alexa can help with converting measurements, setting timers, and even displaying recipes if you have an Echo Show or Echo Spot.

Sitting at the desk in his lower-campus office at Cold Spring Harbor Laboratory, the neuroscientist Tony Zador turned his computer monitor toward me to show off a complicated matrix-style graph. Imagine something that looks like a spreadsheet but instead of numbers it's filled with colors of varying hues and gradations. Casually, he said: "When I tell people I figured out the connectivity of tens of thousands of neurons and show them this, they just go'huh?' But when I show this to people …" He clicked a button onscreen and a transparent 3-D model of the brain popped up, spinning on its axis, filled with nodes and lines too numerous to count.

Mobile devices have become incredibly popular for their ability to weave modern conveniences such as Internet access and social networking into the fabric of daily life. For people with disabilities, however, these devices have the potential to unlock unprecedented new possibilities for communication, navigation and independence. This emergence of mobile "assistive" technologies, influenced heavily by the passage of the Americans with Disabilities Act (ADA) 25 years ago, marks a major step forward for people with disabilities. The U.S. Congress passed the ADA in July 1990 as a civil rights law to protect people with disabilities from discrimination. The act requires that businesses, schools and government agencies must follow certain requirements to ensure people have equal access to their services and facilities.

Determining the position of a robot in an environment, termed localization, is one of the challenges facing roboticist. Localization is essential to solving more complex problems such as locomotion, path planning and environmental learning. Our lab is developing a multi-agent system to use multiple small robots to accomplish tasks normally completed by larger robots. However, because of the reduced size of these robots, methods previously used to determine the position of the robot, such as GPS, cannot be employed. The problem we are facing is that we need to be able to determine the position of each of the robots in this multi-agent system simultaneously. We have developed a system to help track and identify robots using an overhead camera and LEDs, mounted on the robots, to efficiently solve the localization problem.