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In this work, we present Unity Point-Cloud Interactive Core, a novel interactive point cloud rendering pipeline for the Unity Development Platform. The goal of the proposed pipeline is to expedite the development process for point cloud applications by encapsulating the rendering process as a standalone component, while maintaining flexibility through an implementable interface. The proposed pipeline allows for rendering arbitrarily large point clouds with improved performance and visual quality. First, a novel dynamic batching scheme is proposed to address the adaptive point sizing problem for level-of-detail (LOD) point cloud structures. Then, an approximate rendering algorithm is proposed to reduce overdraw by minimizing the overall number of fragment operations through an intermediate occlusion culling pass. For the purpose of analysis, the visual quality of renderings is quantified and measured by comparing against a high-quality baseline. In the experiments, the proposed pipeline maintains above 90 FPS for a 20 million point budget while achieving greater than 90% visual quality during interaction when rendering a point-cloud with more than 20 billion points. 

The goal of the proposed project is to transform a large transportation hub into a smart and accessible hub (SAT-Hub), with minimal infrastructure change. The societal need is significant, especially impactful for people in great need, such as those who are blind and visually impaired (BVI) or with Autism Spectrum Disorder (ASD), as well as those unfamiliar with metropolitan areas. With our inter-disciplinary background in urban systems, sensing, AI and data analytics, accessibility, and paratransit and assistive services, our solution is a hu-man-centric system approach that integrates facility modeling, mobile navigation, and user interface designs. We leverage several transportation facili-ties in the heart of New York City and throughout the State of New Jersey as testbeds for ensuring the relevance of the research and a smooth transition to real world applications. 

TV advertising makes up more than one third of total ad spending, and is transacted based on forecast ratings and viewership. Over time, forecast accuracy has decreased due to fragmentation of consumer behavior. Through a comprehensive study we find that an assortment of models combined with an ensemble method leads to better accuracy than any single method. This results in an 11% improvement over a naive baseline method, across 100 of the largest networks. 

We are a year into the development of a software tool for modeling and simulation (M&S) of 1D and 2D kinematics consistent with Newton's laws of motion. Our goal has been to introduce modeling and computational thinking into learning high-school physics. There are two main contributions from an M&S perspective: (1) the use of conceptual modeling, and (2) the application of Finite State Machines (FSMs) to model physical behavior. Both of these techniques have been used by the M&S community to model high-level "soft systems" and discrete events. However, they have not been used to teach physics and represent ways in which M&S can improve physics education. We introduce the NSF-sponsored STEPP project along with its hypothesis and goals. We also describe the development of the three STEPP modules, the server architecture, the assessment plan, and the expected outcomes.