A typical way in which network data is recorded is to measure all interactions involving a specified set of core nodes, which produces a graph containing this core together with a potentially larger set of fringe nodes that link to the core. Interactions between nodes in the fringe, however, are not present in the resulting graph data. For example, a phone service provider may only record calls in which at least one of the participants is a customer; this can include calls between a customer and a non-customer, but not between pairs of non-customers. Knowledge of which nodes belong to the core is crucial for interpreting the dataset, but this metadata is unavailable in many cases, either because it has been lost due to difficulties in data provenance, or because the network consists of found data obtained in settings such as counter-surveillance. This leads to an algorithmic problem of recovering the core set. Since the core is a vertex cover, we essentially have a planted vertex cover problem, but with an arbitrary underlying graph. We develop a framework for analyzing this planted vertex cover problem, based on the theory of fixed-parameter tractability, together with algorithms for recovering the core. Our algorithms are fast, simple to implement, and out-perform several baselines based on core-periphery structure on various real-world datasets.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Michigan is joining an effort to curb deceptive uses of artificial intelligence and manipulated media through state-level policies as Congress and the Federal Elections Commission continue to debate more sweeping regulations ahead of the 2024 elections. Campaigns on the state and federal level will be required to clearly say which political advertisements airing in Michigan were created using artificial intelligence under legislation expected to be signed in the coming days by Gov. Gretchen Whitmer, a Democrat. It also would prohibit use of AI-generated deepfakes within 90 days of an election without a separate disclosure identifying the media as manipulated.

President Joe Biden on Wednesday signed an executive order that will narrowly prohibit certain United States investments in sensitive technology in China and require government notification of funding in other tech sectors. Biden said in a letter to Congress he was declaring a national emergency to deal with the threat of advancement by countries like China "in sensitive technologies and products critical to the military, intelligence, surveillance, or cyber-enabled capabilities". The long-awaited order authorises the US treasury secretary to prohibit or restrict certain US investments in Chinese entities in three sectors: semiconductors and microelectronics, quantum information technologies, and certain artificial intelligence systems. Senior administration officials said that the effort stemmed from national security goals, rather than economic interests and that the categories it covered were narrow in scope. The order seeks to blunt China's ability to use US investments in its technology companies to upgrade its military while also preserving broader levels of trade that are vital for both nations' economies.

Technologies such as artificial intelligence (AI) and virtual reality (VR) are rapidly expanding opportunities for teaching and learning, and they are giving college administrators new and different ways to track student outcomes. To learn more about the impact of these technologies, we attended a handful of panels on the topic led by higher education and technology leaders at Educause's annual conference in Denver this week. From teaching with VR to tracking student success with AI, we explore how colleges and universities are using new technologies to conduct research, teach students and create smarter campuses. Virtual and augmented reality tools can provide students with experiences that would be otherwise too expensive or even impossible to replicate in the real world, from exploring the inside of a cell to traversing faraway planets, said D. Christopher Brooks, director of research at the Educause Center for Analysis and Research. At Hamilton College, for example, these tools are changing the way the 1,850-student liberal arts institution teaches human anatomy.

Most examples of artificial intelligence in the enterprise today are just on the fringe of what's possible with the technology in the future, according to a group of 10 industry thought leaders who met in New York City on Monday as part of the latest edition of Think Tank by Adobe. Kathryn Hume, VP of product and strategy at Integrate.ai, offered some early perspective by saying that AI is not a new concept. In fact, the exploration of AI has been going on for well over 50 years. "We are swimming in data," Hume said. "There's a tremendous amount of it, and we are seeing an increase in the importance of the machine-learning algorithm. However, we're still in the early days of a true AI revolution, with many questions yet to be answered around what the tech is and is not, according to Susan Etlinger, an industry analyst at Altimeter Group, who also moderated the panel. AI Today AI's impact in the enterprise today is twofold. The first is AI-powered analytics systems built using data and algorithms, and whose decision-making gets better over time. These systems enable organizations to collect, store, and process data at scale, which, in turn, helps them make informed decisions for the future, Hume said. AI also is helping organizations become more efficient with its ability to "sense, think, and act to achieve a set of objectives," said Anand Rao, partner and global AI and innovation lead at PwC. He pointed to natural language processing, the ability to identify and classify objects, reasoning, problem solving, planning, and even simulating as examples of capabilities. "We as humanity are having this awakening," added Chris Duffey, head of AI innovation and strategy at Adobe (CMO.com's "There is this technology out there that accelerates and augments our thinking.

Implementing deep learning in enterprise settings requires a lot more than just downloading some open source algorithms, but with talent scarce, businesses are finding it takes creativity and an open-minded approach to achieve results. "Established industries are largely missing out on the benefits of AI," said Ryan Kottenstette, co-founder and CEO of Silicon Valley geospatial data company Cape Analytics LLC. "If you're not in the tech sector, you might be waiting a bit longer for the benefits of AI to be realized." In recent years, deep learning has taken huge strides. Algorithmic processes like neural networks, which historically lived more in the realm of mathematical theory, have moved into some enterprise use cases, like computer vision and process automation. But adoption has been uneven.

At Daniel Boulud's eponymous restaurant in Manhattan, a brigade of chefs works in unison every night to cook Dover sole with green asparagus or roasted veal tenderloin with a gorgonzola emulsion. The artfully crafted dishes call to mind the old Julia Child quote: "It's so beautifully arranged on the plate, you know someone's fingers have been all over it." This quote does not apply to Boulud's new venture in Boston, Spyce. Four MIT robotics students approached the legendary chef with an idea of creating a restaurant in which robots--not chefs--make the food. Impressed with the technology the MIT guys showed him, Boulud became an investor and culinary director.

Photos of space are everywhere online. Their beauty is dazzling, showing a universe awash in color and light. But if you're a skeptic, you've likely wondered whether it all truly looks like that in real life. Michael Benson tries his best to show you in his exhibition Otherworlds: Visions of Our Solar System. The artist took data from NASA and ESA missions to make 77 images of everything from Pluto to Europa that approximate true color as much as humanly possible. The work spans five decades of space exploration, and presents a realistic, flyby tour of the universe.