Increased defense spending is a boon for Germany's ailing industrial sector. Numerous companies, even those with no previous military experience, are now hoping to get in on the act. Visiting the works of Ilsenburger Grobblech GmbH is like taking a trip back in time. Way back in the 16th century, copper used to be produced at this site in the northern Harz Mountains, not far from eastern Germany' tallest peak, the Brocken. Today, slabs of steel up to 35 centimeters thick are piled up in front of the factory halls, delivered from the blast furnaces and converters of parent company Salzgitter, less than an hour's drive away. What is happening behind the factory walls, though, is part of a new hype that has gripped Germany's crisis-ridden industrial sector. A hype which many are hoping will be enough to revive it.

Apple hopes the Vision Pro will fundamentally change how we interact with our devices--that once freed from the constraints of a smartphone or tablet screen, we'll embrace "spatial computing," as the glitzy promo video shows. Gesture and eye tracking identifies where your focus is, allowing you to interact with apps without pressing buttons or a screen. That could be great for consumers. Apple explained that existing apps designed for the iPad will work on visionOS, the operating system powering the Vision Pro, without any changes. But those iPad apps will be displayed within a metaphorical window, losing much of the functionality provided by mixed reality.

We present a novel search scheme for privacy-preserving multi-agent planning, inspired by UCT search. We compare the presented approach to classical multi-agent forward search and evaluate it based on benchmarks from the CoDMAP competition.

We present a novel search scheme for privacy-preserving multi-agent planning. Inspired by UCT search, the scheme is based on growing an asynchronous search tree by running repeated trials through the tree. We describe key differences to classical multi-agent forward search, discuss theoretical properties of the presented approach, and evaluate it based on benchmarks from the CoDMAP competition. As a secondary contribution, we describe a technique that extends the regular search approach by small explorative trials which are performed subsequent to each node expansion. We show that this technique significantly increases the number of problems solved for all algorithms considered, including MAFS.

If the six projects presented at a recent TV documentary pitch session held at the Unifrance Rendez-Vous in Paris share relatively few thematic or stylistic points in common, when taken as a whole, the diverse titles relay two incontrovertible truths: While advances in filmmaking technology now offer industry creatives unprecedented freedoms, when it comes to hooking the audience, nothing beats a good story well told. Three of the six projects presented at the Rendez-Vous forum reflect the format's growing technological trends. To offer competing visions of the future, Mad Films/Camera Subjective's speculative science-fiction project "2080" will use CGI, motion capture and some of the digital production techniques pioneered by Disney's "The Mandalorian," whereas to open a window into the past, France Televisions/Program33's historical doc "The Joan of Arc Case" will use detailed digital recreations of 15th-century France. On a similar front, the four-episode edutainment project "Science in Archeology 3.0," directed by Alexandra Barbot and Ste phane Jacques, produced by Roche Productions, and handled internationally by Lucky You, looks to employ recent advances in digital mapping, photogrammetry, and scanning techniques to recreate digital models of the ancient world. At the pitch presentation, co-director Alexandra Barbot likened the digital recreations to "entering Ali Baba's cave," arguing that these new model could rekindle that same spark of discovery that lit up so many young imaginations.

Statistical-Relational Model Discovery aims to find statistically relevant patterns in relational data. For example, a relational dependency pattern may stipulate that a user's gender is associated with the gender of their friends. As with propositional (non-relational) graphical models, the major scalability bottleneck for model discovery is computing instantiation counts: the number of times a relational pattern is instantiated in a database. Previous work on propositional learning utilized pre-counting or post-counting to solve this task. This paper takes a detailed look at the memory and speed trade-offs between pre-counting and post-counting strategies for relational learning. A pre-counting approach computes and caches instantiation counts for a large set of relational patterns before model search. A post-counting approach computes an instantiation count dynamically on-demand for each candidate pattern generated during the model search. We describe a novel hybrid approach, tailored to relational data, that achieves a sweet spot with pre-counting for patterns involving positive relationships (e.g. pairs of users who are friends) and post-counting for patterns involving negative relationships (e.g. pairs of users who are not friends). Our hybrid approach scales model discovery to millions of data facts.

Many esports use a pick and ban process to define the parameters of a match before it starts. In Counter-Strike: Global Offensive (CSGO) matches, two teams first pick and ban maps, or virtual worlds, to play. Teams typically ban and pick maps based on a variety of factors, such as banning maps which they do not practice, or choosing maps based on the team's recent performance. We introduce a contextual bandit framework to tackle the problem of map selection in CSGO and to investigate teams' pick and ban decision-making. Using a data set of over 3,500 CSGO matches and over 25,000 map selection decisions, we consider different framings for the problem, different contexts, and different reward metrics. We find that teams have suboptimal map choice policies with respect to both picking and banning. We also define an approach for rewarding bans, which has not been explored in the bandit setting, and find that incorporating ban rewards improves model performance. Finally, we determine that usage of our model could improve teams' predicted map win probability by up to 11% and raise overall match win probabilities by 19.8% for evenly-matched teams.

The AI was adapted from an existing system trained to identify people most at risk from the flu, using millions of records from Maccabi going back 27 years. To make its predictions, the system draws on a range of medical data, including a person's age, BMI, health conditions such as heart disease or diabetes, and previous history of hospital admissions. The AI can trawl through a vast number of records and spot at-risk individuals who might have been missed otherwise. Maccabi also uses the AI to help determine the level of treatment the people it flags might require if they fall sick--whether they should be cared for at home, put up in a quarantine hotel, or admitted to hospital. The organization says it is now talking to major US health providers that are interested in using the AI to fast-track their own high-risk patients.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Earlier this week, the Justice Department announced that a grand jury had indicted former CIA employee Joshua Schulte for leaking classified information in 2016. While the indictment does not specify which leaks Schulte is tied to, several news organizations have reported that he provided the WikiLeaks Vault 7 documents, which comprised thousands of pages of classified material detailing the CIA's cyber operations and digital surveillance efforts. Among other revelations, the documents showed the U.S. intelligence community making widespread use of existing or repurposed techniques and computer programs to carry out its own operations. Unfortunately, the indictment offers frustratingly few clues as to how the government believes Schulte, a 29-year-old former member of the CIA's Engineering Development Group, carried out these leaks two years ago and how he was caught.

We consider the problem of parallelizing restarted backtrack search. With few notable exceptions, most commercial and academic constraint programming solvers do not learn no-goods during search. Depending on the branching heuristics used, this means that there are little to no side-effects between restarts, making them an excellent target for parallelization. We develop a simple technique for parallelizing restarted search deterministically and demonstrate experimentally that we can achieve near-linear speed-ups in practice.