When musicians have chemistry, we can feel it. There's something special among them that's missing when they perform alone. Anyone who's heard a Mick Jagger solo album knows that's the case. Clearly nature wants us to jam together and take flight out of our individual selves. The reward is transcendence, our bodies tell us so. It's a question that one of the most refreshing neuroscientists who studies music has been probing lately. Refreshing because her lab is not only in academia but also on stage, where she performs as an opera singer and with chamber ensembles. Talking to Indre Viskontas is a treat because she animates her research as a scientist with her experiences as an artist.

At first glance, the Berlin startup doesn't seem so different from others: a factory floor in the rear courtyard of a building in the city's Neukölln district, stacked preserving jars filled with muesli in the kitchen, a discarded ping-pong surface repurposed as a conference table. The employees are young, relaxed and very international. The company's head and founder, Christian Kroll, is 35 years old, the same age as Mark Zuckerberg. The two men also share a quirk: To avoid wasting time in the mornings choosing an outfit, he always wears the same thing -- in his case, blank white T-shirts made from organic cotton. Zuckerberg's favorite color, by contrast, is gray.

It's 6 A.M., and the alarm clock is buzzing earlier than usual. It's not a malfunction: the smart clock scanned your schedule and adjusted because you've got that big presentation first thing in the morning. Your shower automatically turns on and warms to your preferred 103 F. The electric car is ready to go, charged by the solar panels or wind turbine on your roof. When you get home later, there's an unexpected package waiting, delivered by drone. You open it to find cold medicine.

Even in the first month of my governorship of this fine state, I began to have problems with the legislature, which belonged to the "other" political party. I had campaigned on the plan to transform the state capital, Columbville, into a Smart City, but my political party and the opposition wanted it to use different operating systems. Another problem was that we disagreed about what should be done with the three abandoned shopping centers, now that all our citizens bought their stuff online. That issue was tangled up with all the road improvements needed to keep the self-driving trucks and taxis from roaming the schoolyards, although that could have been worse if the kids were still attending classes rather than home-schooling online as most of them now did. I sent drafts of laws and budgets to the legislature, and they voted them down.

We develop a new algorithm for the turnstile heavy hitters problem in general turnstile streams, the EXPANDERSKETCH, which finds the approximate top-k items in a universe of size n using the same asymptotic O(k log n) words of memory and O(log n) update time as the COUNTMIN and COUNTSKETCH, but requiring only O(k poly(log n)) time to answer queries instead of the O(n log n) time of the other two. The notion of "approximation" is the same l2 sense as the COUNTSKETCH, which given known lower bounds is the strongest guarantee one can achieve in sublinear memory. Our main innovation is an efficient reduction from the heavy hitters problem to a clustering problem in which each heavy hitter is encoded as some form of noisy spectral cluster in a graph, and the goal is to identify every cluster. Since every heavy hitter must be found, correctness requires that every cluster be found. We thus need a "cluster-preserving clustering" algorithm that partitions the graph into pieces while finding every cluster. To do this we first apply standard spectral graph partitioning, and then we use some novel local search techniques to modify the cuts obtained so as to make sure that the original clusters are sufficiently preserved. Our clustering algorithm may be of broader interest beyond heavy hitters and streaming algorithms. Finding "frequent" or "top-k" items in a dataset is a common task in data mining. In the data streaming literature, this problem is typically referred to as the heavy hitters problem, which is as follows: a frequency vector x Rn is initialized to the zero vector, and we process a stream of updates update(i, Δ) for Δ R, with each such update causing the change xi xi Δ . The goal is to identify coordinates in x with large weight (in absolute value) while using limited memory.

Spam! That's what Lorrie Faith Cranor and Brian LaMacchia exclaimed in the title of a popular call-to-action article that appeared 20 years ago in Communications.10 And yet, despite the tremendous efforts of the research community over the last two decades to mitigate this problem, the sense of urgency remains unchanged, as emerging technologies have brought new dangerous forms of digital spam under the spotlight. Furthermore, when spam is carried out with the intent to deceive or influence at scale, it can alter the very fabric of society and our behavior. In this article, I will briefly review the history of digital spam: starting from its quintessential incarnation, spam emails, to modern-days forms of spam affecting the Web and social media, the survey will close by depicting future risks associated with spam and abuse of new technologies, including artificial intelligence (AI), for example, digital humans. After providing a taxonomy of spam, and its most popular applications emerged throughout the last two decades, I will review technological and regulatory approaches proposed in the literature, and suggest some possible solutions to tackle this ubiquitous digital epidemic moving forward. An omni-comprehensive, universally acknowledged definition of digital spam is hard to formalize. Laws and regulation attempted to define particular forms of spam, for example, email (see 2003's Controlling the Assault of Non-Solicited Pornography and Marketing Act.) However, nowadays, spam occurs in a variety of forms, and across different techno-social systems. Each domain may warrant a slight different definition that suits what spam is in that precise context: some features of spam in a domain, for example, volume in mass spam campaigns, may not apply to others, for example, carefully targeted phishing operations.

Lightweight tagging expands information and activity management practices.

Facebook and the digital virus called fake news.

Dino Distefano is a research scientist at Facebook, London, U.K., and a professor of computer science at Queen Mary University of London, U.K. Manuel Fähndrich is a software engineer at Facebook Research, Seattle, WA, USA. Francesco Logozzo is a software engineer at Facebook Research, Seattle, WA, USA. Peter W. O'Hearn is a research scientist at Facebook, London, U.K. and a professor of computer science at University College London, U.K.

Knowledge graphs are critical to many enterprises today: They provide the structured data and factual knowledge that drive many products and make them more intelligent and "magical." In general, a knowledge graph describes objects of interest and connections between them. For example, a knowledge graph may have nodes for a movie, the actors in this movie, the director, and so on. Each node may have properties such as an actor's name and age. There may be nodes for multiple movies involving a particular actor. The user can then traverse the knowledge graph to collect information on all the movies in which the actor appeared or, if applicable, directed. Many practical implementations impose constraints on the links in knowledge graphs by defining a schema or ontology. For example, a link from a movie to its director must connect an object of type Movie to an object of type Person. In some cases the links themselves might have their own properties: a link connecting an actor and a movie might have the name of the specific role the actor played. Similarly, a link connecting a politician with a specific role in government might have the time period during which the politician held that role.