3D Scene Annotation for Efficient Rendering on Mobile Devices

This paper presents a new approach for efficient 3D rendering on mobile devices, where selective rendering can be achieved with the help of 3D scene annotation. By taking advantage of first person environments in most 3D applications, we are able to annotate the flooring details of the 3D space. This allows 3D environments to be interfaced using a higher level view of objects. With the higher level of scene understanding, it is possible to determine which 3D objects are not required for loading or rendering based on the viewer’s location and its surrounding constraints

An analysis of China in eight fourth-grade geography textbooks

Thesis (Ed.M.)--Boston Universit

Implementation of a land use and spatial interaction model based on random utility choices and social accounting matrices

Random utility modelling has been established as one of the main paradigms for the implementation of land use and transport interaction (LUTI) models. Despite widespread application of such models, the respective literature provides relatively little detail on the theoretical consistency of the overall formal framework of the random utility based LUTI models. To address this gap, we present a detailed formal description of a generic land use and spatial interaction model that adheres to the random utility paradigm through the explicit distinction between utility and cost across all processes that imply behaviour of agents. The model is rooted in an extended input-output table, with the workforce and households accounts being disaggregated by socio-economic type. Similarly, the land account is broken down by domestic and non-domestic land use types. The model is developed around two processes. Firstly, the generation of demand for inputs required by established production; the estimation of the level of demand between sectors, households and land use types is supported by social accounting techniques. When appropriate the implicit production functions are assumed depended on costs of inputs, which gives rise to price-elastic demands. Secondly, the spatial assignment of demanded inputs (industrial activity, workforce, land) to locations of production; here sequences of decisions are used to distribute demand (both spatially and, when necessary, a-spatially) and to propagate costs and utilities of production and consumption that emerge from imbalances between supply and demand. The implementation of this generic model is discussed in relation to the case of the Greater South East region of the UK, including London, the South East and the East of England. We present the calibration process, data requirements, necessary assumptions and resulting implications. We discuss outputs under various land use strategies and economic scenarios, such as regulated versus competing land uses, constrained versus unconstrained densities, and high versus low economic and population growth rates. By adjusting the design constraints of the spatial planning and infrastructure supply strategies we aim to improve their sustainability.

Data management study, volume 5. Appendix B - Contractor data package Planetary Quarantine /PQ/ Final report

Contractor data management package for Voyager spacecraft sterilization projec

Improving Learning Outcomes of Students in Junior High School Number 1 Bengkulu City with Online Learning Model through Cloud X Application

The research aims to determine how to implement an online learning model to increase learning outcomes of learners. This type of research is class action research (PTK). The subject in this study was a student of class IX.7 Junior High School Number 1 Bengkulu City school year 2020/2021 amounting to 33 learners. The data was collected by using the final cycle concept comprehension test form using the google form feature. The results showed that the application of online learning models during the Covid-19 pandemic through the Cloud X application and using power point media and learning videos could improve student learning outcomes. The increase in student learning outcomes can be seen from the average scores of students from cycle I to cycle II is: 76.90; 81.12 and classical learning completeness of students is 36.36%; 78.79%

An estimate of the impact that mega-resort casino-hotels have on existing Las Vegas strip casino/hotels

The objective of this research paper is to estimate the impact that new mega-resorts have on existing Las Vegas strip hotel/casinos; A multiple linear regression equation will be created to determine the impact that new Las Vegas mega-resort properties may have on the gross gaming revenue of existing Las Vegas strip casino/hotels; The results of this study found two primary impacts: (1) new mega-resorts did not change the growth rate of gross gaming revenue over an extended period of time and (2) new mega-resorts took a share of the gross gaming revenue resulting in a negative impact on existing Las Vegas strip casino/hotels

Does environmental enrichment while studying improve recall?

Previous research suggests that studying audio and visual stimuli in two different rooms increases verbal recall, as compared to studying twice in only one room (Smith, Glenberg, & Bjork, 1978). The present study utilized this paradigm, and also separated the room and modality factors as sources of environmental enrichment. In Experiment 1, subjects learned a list of 40 common English words twice, in either one or two different rooms, and were tested in a third room (N = 60). In Experiment 2, subjects learned the same word lists, using either one or two modalities (audition and vision), and again were tested in a third environment (N = 59). As predicted from the theory of Smith and Vela (2001), the usual improvement in memory from either room or modality enrichment did not occur when short time intervals were used between learning and recall, and the mean recall scores were essentially identical. The enrichment effect is interpreted as involving the development of categorized memory information over time, thus enabling retrieval strategies to operate, rather than an increase in the strength of initial learning

Foundation Models for Semantic Novelty in Reinforcement Learning

Effectively exploring the environment is a key challenge in reinforcement learning (RL). We address this challenge by defining a novel intrinsic reward based on a foundation model, such as contrastive language image pretraining (CLIP), which can encode a wealth of domain-independent semantic visual-language knowledge about the world. Specifically, our intrinsic reward is defined based on pre-trained CLIP embeddings without any fine-tuning or learning on the target RL task. We demonstrate that CLIP-based intrinsic rewards can drive exploration towards semantically meaningful states and outperform state-of-the-art methods in challenging sparse-reward procedurally-generated environments.Comment: Foundation Models for Decision Making Workshop at Neural Information Processing Systems, 202