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Events for April 03, 2014
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PhD Defense - Rong Yang
Thu, Apr 03, 2014 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Rong Yang
Title: Addressing Human Decision Making in Security Games: Models and Algorithms
Committee:
Milind Tambe (chair)
Fernando Ordornez
Rajiv Maheswaran
Johnathan Gratch
Richard John (outside member)
Vincent Conitzer (Duke)
Abstract:
Security is a world-wide concern in a diverse set of settings, such as protecting ports, airport and other critical infrastructures, interdicting the illegal flow of drugs, weapons and money, preventing illegal poaching/hunting of endangered species and fish, suppressing crime in urban areas and securing cyberspace. Unfortunately, with limited security resources, not all the potential targets can be protected at all times. Game-theoretic approaches â in the form of ”security games” â have recently gained significant interest from researchers as a tool for analyzing real-world security resource allocation problems leading to multiple deployed systems in day-to-day use to enhance security of US ports, airports and transportation infrastructure. One of the key challenges that remains open in enhancing current security game applications and enabling new ones originates from the perfect rationality assumption of the adversaries â an assumption may not hold in the real world due to the bounded rationality of human adversaries and hence could potentially reduce the effectiveness of solutions offered.
My thesis focuses on addressing the human decision-making in security games. It seeks to bridge the gap between two important sub-fields in game theory: algorithmic game theory and behavioral game theory. The former focuses on efficient computation of equilibrium solution concepts, and the latter develops models to predict the behaviors of human players in various game settings. More specifically, I provide: (i) the answer to the question of which of the existing models best represents the salient features of the security problems, by empirically exploring different human behavioral models from the literature; (ii) algorithms to efficiently compute the resource allocation strategies for the security agencies considering these new models of the adversaries; (iii) real-world deployed systems that range from security of ports to wildlife security.
Location: Henry Salvatori Computer Science Center (SAL) - 126
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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CS Student Colloquium: Zhenzhen Gao - City-Scale Aerial LiDAR Point Cloud Visualization
Thu, Apr 03, 2014 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Zhenzhen Gao, USC
Talk Title: City-Scale Aerial LiDAR Point Cloud Visualization
Series: Student Seminar Series
Abstract: Aerial LiDAR (Light Detection and Ranging) is cost-effective in acquiring terrain and urban information by mounting a downward-scanning laser on a low-flying aircraft. It produces huge volumes of unconnected 3D points. This thesis focuses on the interactive visualization of aerial LiDAR point clouds of cities, which is applicable to a number of areas including virtual tourism, security, land management and urban planning.
A framework needs to address several challenges in order to deliver useful visualizations of aerial LiDAR cities. Firstly, the data is 2.5D, in that the sensor is only able to capture dense details of the surfaces facing it, leaving few samples on vertical building walls. Secondly, the data often suffers from noise and under-sampling. Finally, the large size of the data can easily exceed the memory capacity of a computer system.
This thesis first introduces a visually-complete rendering framework for aerial LiDAR cities. By inferring classification information, building walls and occluded ground areas under tree canopies are completed either through pre-processing point cloud augmentation or through online procedural geometry generation. A multi-resolution out-of-core strategy and GPU-accelerated rendering enable interactive visualization of virtually unlimited size data. With adding only a slight overhead to existing point-based approaches, the framework provides comparable quality to visualizations of off-line pre-computation of 3D polygonal models.
The thesis then presents a scalable out-of-core algorithm for mapping colors from aerial oblique imagery to city-scale aerial LiDAR points. Without intensive processing of points, colors are mapped via a modified visibility pass of GPU splatting, and a weighting scheme leveraging image resolution and surface orientation.
To alleviate visual artifacts caused by noise and under-sampling, the thesis shows an off-line point cloud refinement algorithm. By explicitly regularizing building boundary points, the algorithm can effectively remove noise, fill gaps, and preserve and enhance both normal and position discontinuous features for piece-wise smoothing buildings with arbitrary shape and complexity.
Finally, the thesis introduces a new multi-resolution rendering framework that supports real-time refinement of aerial LiDAR cities. Without complex computation and without user interference, simply based on curvature analysis of points of uniform sized spatial partitions, hierarchical hybrid structures are constructed indicating whether to represent a partition as point or polygon. With the help of such structures, both rendering and refinement are dynamically adaptive to views and curvatures. Compared to visually-complete rendering, the new framework is able to deliver comparable visual quality with less than 8\% increase in pre-processing time and 2-5 times higher rendering frame-rates. Experiments on several cities show that the refinement improves rendering quality for large magnification under real-time constraint.
Host: CS PHD Committee
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
