Esports have become major international sports with hundreds of millions of spectators. Esports games generate massive amounts of telemetry data. Using these to predict the outcome of esports matches has received considerable attention, but micro-predictions, which seek to predict events inside a match, is as yet unknown territory. Micro-predictions are however of perennial interest across esports commentators and audience, because they provide the ability to observe events that might otherwise be missed: esports games are highly complex with fast-moving action where the balance of a game can change in the span of seconds, and where events can happen in multiple areas of the playing field at the same time. Such events can happen rapidly, and it is easy for commentators and viewers alike to miss an event and only observe the following impact of events. In Dota 2, a player hero being killed by the opposing team is a key event of interest to commentators and audience. We present a deep learning network with shared weights which provides accurate death predictions within a five-second window. The network is trained on a vast selection of Dota 2 gameplay features and professional/semi-professional level match dataset. Even though death events are rare within a game (1% of the data), the model achieves 0.377 precision with 0.725 recall on test data when prompted to predict which of any of the 10 players of either team will die within 5 seconds. An example of the system applied to a Dota 2 match is presented. This model enables realtime micro-predictions of kills in Dota 2, one of the most played esports titles in the world, giving commentators and viewers time to move their attention to these key events.
In computer graphics and virtual environment development, a large portion of time is spent creating assets -one of these being the terrain environment, which usually forms the basis of many large graphical worlds. The texturing of height maps is usually performed as a post-processing step -with software requiring access to the height and gradient of the terrain in order to generate a set of conditions for colouring slopes, flats, mountains etc. With further additions such as biomes specifying which predominant texturing the region should exhibit such as grass, snow, dirt etc. much like the real-world. These methods combined with a height map generation algorithm can create impressive terrain renders which look visually stunning -however can appear somewhat repetitive. Previous work has explored the use of variants of Generative Adversarial Networks for the learning of elevation data through real-world data sets of world height data. In this paper, a method is proposed for learning not only the height map values but also the corresponding satellite image of a specific region. This data is trained through a non-spatially dependant generative adversarial network, which can produce an endless amount of variants of a specific region. The
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.