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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
Macro-PK Experiments: Level of Control - Repeatability - Distance Effect - Confinement Effect
In order to better qualify these effects, this paper presents ongoing research that was conducted since 2021 in different directions: the ability or not for the putative PK-agent to control the movement, the level of repeatability of the experiments, the impact of the distance of the PK-agent from the target, and the impact of the confinement of the target. [...]the following points can be mentioned: * The methodology used with PK-agent experimenting from home gives the capability of multiple experiments. Since November 2022, we are on the basis of one report a week (outside the vacation period). [...]the PK-agent doesn't do other experiments outside the one reported that avoids the file drawer effect. * The repeatability obtained with detailed documentation (videos, report) seems to be in contradiction with the elusiveness principle often attached to these phenomena. * Although some precedent experiments done in our lab seems to present a decline effect with the distance, confirming the attenuation effect proposed by G. W. Roll, these new experiments with this PK-agent didn't show an impact of the distance on the rotational speed of the target.
Journal Article
Risk Shocks
We augment a standard monetary dynamic general equilibrium model to include a Bernanke-Gertler-Gilchrist financial accelerator mechanism. We fit the model to US data, allowing the volatility of cross-sectional idiosyncratic uncertainty to fluctuate over time. We refer to this measure of volatility as risk. We find that fluctuations in risk are the most important shock driving the business cycle.
Journal Article
An AI-Application-Oriented In-Class Teaching Evaluation Model by Using Statistical Modeling and Ensemble Learning
In-class teaching evaluation, which is utilized to assess the process and effect of both teachers’ teaching and students’ learning in a classroom environment, plays an increasingly crucial role in supervising and promoting education quality. With the rapid development of artificial intelligence (AI) technology, the concept of smart education has been constantly improved and gradually penetrated into all aspects of education application. Considering the dominant position of classroom teaching in elementary and undergraduate education, the introduction of AI technology into in-class teaching evaluation has become a research hotspot. In this paper, we propose a statistical modeling and ensemble learning-based comprehensive model, which is oriented towards in-class teaching evaluation by using AI technologies such as computer vision (CV) and intelligent speech recognition (ISR). Firstly, we present an index system including a set of teaching evaluation indicators combining traditional assessment scales with new values derived from CV and ISR-based AI analysis. Next, we design a comprehensive in-class teaching evaluation model by using both the analytic hierarchy process-entropy weight (AHP-EW) and AdaBoost-based ensemble learning (AdaBoost-EL) methods. Experiments not only demonstrate that the two modules in the model are respectively applicable to the calculation of indicators with different characteristics, but also verify the performance of the proposed model for AI-based in-class teaching evaluation. In this comprehensive in-class evaluation model, for students’ concentration and participation, ensemble learning module is chosen with less root mean square error (RMSE) of 8.318 and 9.375. In addition, teachers’ media usage and teachers’ type evaluated by statistical modeling module approach higher accuracy with 0.905 and 0.815. Instead, the ensemble learning approaches the accuracy of 0.73 in evaluating teachers’ style, which performs better than the statistical modeling module with the accuracy of 0.69.
Journal Article