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Racing the Beam
The Atari Video Computer System dominated the home video game market so completely that \"Atari\" became the generic term for a video game console. The Atari VCS was affordable and offered the flexibility of changeable cartridges. Nearly a thousand of these were created, the most significant of which established new techniques, mechanics, and even entire genres. This book offers a detailed and accessible study of this influential video game console from both computational and cultural perspectives. Studies of digital media have rarely investigated platforms--the systems underlying computing. This book (the first in a series of Platform Studies) does so, developing a critical approach that examines the relationship between platforms and creative expression. Nick Montfort and Ian Bogost discuss the Atari VCS itself and examine in detail six game cartridges: Combat, Adventure, Pac-Man, Yars' Revenge, Pitfall!, and Star Wars: The Empire Strikes Back. They describe the technical constraints and affordances of the system and track developments in programming, gameplay, interface, and aesthetics. Adventure, for example, was the first game to represent a virtual space larger than the screen (anticipating the boundless virtual spaces of such later games as World of Warcraft and Grand Theft Auto), by allowing the player to walk off one side into another space; and Star Wars: The Empire Strikes Back was an early instance of interaction between media properties and video games. Montfort and Bogost show that the Atari VCS--often considered merely a retro fetish object--is an essential part of the history of video games.
eBook
The game console : a photographic history from Xbox to Atari.
\"A photographic history of video game consoles and the hardware inside them\"-- Provided by publisher.
Book
Hydrogen Storage Performance of Mg/MgHsub.2 and Its Improvement Measures: Research Progress and Trends
Due to its high hydrogen storage efficiency and safety, Mg/MgH[sub.2] stands out from many solid hydrogen storage materials and is considered as one of the most promising solid hydrogen storage materials. However, thermodynamic/kinetic deficiencies of the performance of Mg/MgH[sub.2] limit its practical applications for which a series of improvements have been carried out by scholars. This paper summarizes, analyzes and organizes the current research status of the hydrogen storage performance of Mg/MgH[sub.2] and its improvement measures, discusses in detail the hot studies on improving the hydrogen storage performance of Mg/MgH[sub.2] (improvement measures, such as alloying treatment, nano-treatment and catalyst doping), and focuses on the discussion and in-depth analysis of the catalytic effects and mechanisms of various metal-based catalysts on the kinetic and cyclic performance of Mg/MgH[sub.2]. Finally, the challenges and opportunities faced by Mg/MgH[sub.2] are discussed, and strategies to improve its hydrogen storage performance are proposed to provide ideas and help for the next research in Mg/MgH[sub.2] and the whole field of hydrogen storage.
Journal Article
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
Journal Article