We propose a novel simulation model that is able to predict the per-level churn and pass rates of Angry Birds Dream Blast, a popular mobile free-to-play game. Our primary contribution is to combine AI gameplay using Deep Reinforcement Learning (DRL) with a simulation of how the player population evolves over the levels. The AI players predict level difficulty, which is used to drive a player population model with simulated skill, persistence, and boredom. This allows us to model, e.g., how less persistent and skilled players are more sensitive to high difficulty, and how such players churn early, which makes the player population and the relation between difficulty and churn evolve level by level. Our work demonstrates that player behavior predictions produced by DRL gameplay can be significantly improved by even a very simple population-level simulation of individual player differences, without requiring costly retraining of agents or collecting new DRL gameplay data for each simulated player.

Neuralink's mission statement includes the words: "If you can't beat them, join them". Neuralink has been operational since 2016 when Elon Musk founded the company along with a group of neuroscientists. The team ranges in expertise from brain-machine interfaces to electrical engineering and overcoming obstacles associated with wearable devices. Elon Musk said on Twitter earlier this week that Neuralink would be hosting a live webcast to demonstrate a working Brain-Machine Interface (BMI) device. Here's what you need to know.

The 1994 action movie Double Dragon, based on the 1987 beat-em-up arcade game, is a surprisingly enjoyable low-budget romp. Science fiction author Zach Chapman says it reminds him of old favorites such as Surf Ninjas and Teenage Mutant Ninja Turtles. "It should be a cult classic," Chapman says in Episode 429 of the Geek's Guide to the Galaxy podcast. It seems like people enjoyed making the movie. Geek's Guide to the Galaxy host David Barr Kirtley also enjoyed Double Dragon, particularly the performance from Party of Five star Scott Wolf.

The graph represents a network of 6,145 Twitter users whose tweets in the requested range contained "data visualization", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 27 August 2020 at 06:39 UTC. The requested start date was Thursday, 27 August 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 8-day, 17-hour, 22-minute period from Thursday, 13 August 2020 at 00:10 UTC to Friday, 21 August 2020 at 17:32 UTC.

A robot camera has been used in UK seas for the first time to monitor the behaviour of basking sharks. SharkCam was deployed off the west coast of Scotland where the sharks gather to breed after migrating from waters off west Africa. Basking sharks, an endangered species, are the world's second largest fish after whale sharks, sometimes growing to more than 10m (33ft) long. SharkCam followed three sharks off the coasts of Coll and Tiree. The robot monitored the animals from a distance and recorded behaviour that suggested they arrive in Scottish waters to breed rather than feed.

In 2003, the SARS outbreak took the world by surprise. "For me, the SARS outbreak was an eye-opening event," says Dr. Kamran Khan, infectious disease physician, professor of medicine and public health at the University of Toronto, and founder and CEO of BlueDot. "I recognized that we'd never seen anything like it before, but there would be more outbreaks like this again in the future." Khan spent the next 10 years studying infectious disease spread, looking for a way to better detect and respond to threats like SARS and the ones that followed. By 2013, machine learning technology had advanced to the point where he was able to put his vision of a digital global warning system into action -- and BlueDot was born.

Online display advertising is growing rapidly in recent years thanks to the automation of the ad buying process. Real-time bidding (RTB) allows the automated trading of ad impressions between advertisers and publishers through real-time auctions. In order to increase the effectiveness of their campaigns, advertisers should deliver ads to the users who are highly likely to be converted (i.e., purchase, registration, website visit, etc.) in the near future. In this study, we introduce and examine different models for estimating the probability of a user converting, given their history of visited URLs. Inspired by natural language processing, we introduce three URL embedding models to compute semantically meaningful URL representations. To demonstrate the effectiveness of the different proposed representation and conversion prediction models, we have conducted experiments on real logged events collected from an advertising platform.

This paper studies the statistical and computational limits of high-order clustering with planted structures. We focus on two clustering models, constant high-order clustering (CHC) and rank-one higher-order clustering (ROHC), and study the methods and theory for testing whether a cluster exists (detection) and identifying the support of cluster (recovery). Specifically, we identify the sharp boundaries of signal-to-noise ratio for which CHC and ROHC detection/recovery are statistically possible. We also develop the tight computational thresholds: when the signal-to-noise ratio is below these thresholds, we prove that polynomial-time algorithms cannot solve these problems under the computational hardness conjectures of hypergraphic planted clique (HPC) detection and hypergraphic planted dense subgraph (HPDS) recovery. We also propose polynomial-time tensor algorithms that achieve reliable detection and recovery when the signal-to-noise ratio is above these thresholds. Both sparsity and tensor structures yield the computational barriers in high-order tensor clustering. The interplay between them results in significant differences between high-order tensor clustering and matrix clustering in literature in aspects of statistical and computational phase transition diagrams, algorithmic approaches, hardness conjecture, and proof techniques. To our best knowledge, we are the first to give a thorough characterization of the statistical and computational trade-off for such a double computational-barrier problem. Finally, we provide evidence for the computational hardness conjectures of HPC detection and HPDS recovery.

Sunshine has given way to wind and rain, as the motorboat chugs through a fjord in the Faroe Islands. "Its a bit windy here," says Olavur Gregarsen. "We'll see how far we can get to the harvesting boat." We soon reach a sheltered spot where steep mountains are looking down on hundreds of buoys bobbing in the sea. "They are holding up a horizontal line," explains Mr Gregarsen, the managing director of Ocean Rainforest, a seaweed producer.

The 75th anniversary of Japan formally surrendering to the U.S. aboard the battleship USS Missouri on Sept. 2, 1945, arrives at a moment when the question of how the war is remembered feels more necessary than ever. Veterans' stories, books, movies and TV shows have kept memories of the war alive for the last 75 years, but how will those stories be told when there are fewer people around who lived through those era-defining years? Recently, some people in younger generations have turned to a perhaps surprising source for World War II stories: video games. Games have become more realistic not only in terms of technological advancements, but also in terms of featuring real people and, at least in terms of blockbuster games like Medal of Honor and Call of Duty, getting input from real experts on military history. For example, the upcoming virtual-reality game Medal of Honor: Above and Beyond will feature documentary shorts, and creators interviewed WWII veterans about their wartime experiences to inform the set, which includes missions across Europe and in Tunisia.