Select a calendar:
Filter December Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for December 08, 2014
-
Meet USC: Admission Presentation, Campus Tour, & Engineering TalkMon, Dec 08, 2014
Viterbi School of Engineering Undergraduate Admission
Receptions & Special Events
This half day program is designed for prospective freshmen and family members. Meet USC includes an information session on the University and the Admission process; a student led walking tour of campus and a meeting with us in the Viterbi School. Meet USC is designed to answer all of your questions about USC, the application process and financial aid. Reservations are required for Meet USC. This program occurs twice, once at 8:30 a.m. and again at 12:30 p.m. Please visit http://www.usc.edu/admission/undergraduate/firstyear/prospective/meetusc_sw.html to check availability and make an appointment. Be sure to list an Engineering major as your "intended major" on the webform!
Location: Ronald Tutor Campus Center (TCC) - USC Admission Office
Audiences: Everyone Is Invited
Contact: Viterbi Admission
-
USC Graduate Engineering Information Session
Mon, Dec 08, 2014 @ 06:00 AM - 07:00 AM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
The University of Southern California Viterbi School of Engineering, a top ranked graduate engineering program by U.S News and World Report, is located Los Angeles and offers Master's and Doctoral programs in a variety of engineering disciplines. Join us for an information session and Q&A to learn about the academic programs available, application criteria, and scholarships.
Register to attendLocation: ONLINE EVENT
Audiences: Students with a background in engineering, math or science are welcome to attend.
Contact: Laura Hartman
-
Penalized Maximum-likelihood PET Image Reconstruction for Lesion Detection
Mon, Dec 08, 2014 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Li Yang, University of California-Davis
Talk Title: Penalized Maximum-likelihood PET Image Reconstruction for Lesion Detection
Abstract: Detecting cancerous lesions is a major clinical application in emission tomography. Statistical reconstruction methods based on the penalized maximum-likelihood (PML) principle have been developed to improve image quality. A number of metrics have been used to evaluate the quality of the reconstructed PET images, such as spatial resolution, noise variance, contrast-to-noise ratio, etc. Work has been done to optimize PML reconstruction to achieve uniform resolution and to maximize the contrast-to-noise ratio. However, these technical metrics do not necessarily reflect the performance of a clinical task. Here we focus on lesion detection and use a task-specific metric to evaluate the image quality. A multiview channelized Hotelling observer (mvCHO) is used to assess the lesion detectability in 3D images to mimic the condition where a human observer examines three orthogonal views of a 3D image for lesion detection. We derive simplified theoretical expressions that allow fast prediction of the detectability of a 3D lesion. We apply the theoretical results to guide the design of a shift-variant quadratic penalty function in PML reconstruction to maximize detectability of lesions at unknown locations in fully 3D PET. The proposed method is evaluated using computer-based Monte Carlo simulations as well as real patient data with a superimposed lesion.
Furthermore, we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the conventional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric images are generated by reconstructing a sequence of dynamic PET images first and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram data by incorporating the Patlak model into the image reconstruction procedure. The PML reconstruction is used in both the indirect and direct reconstruction methods. Simplified expressions for evaluating the lesion detectability on Patlak parametric images have been derived and applied to the selection of the regularization parameter value to maximize the lesion detectability. Good agreements between the theoretical predictions and the Monte Carlo results are observed. The theoretical formula also shows the benefit of the direct method in dynamic PET reconstruction for lesion detection.
Biography: Li Yang received his B.S. degree in precision instrumentation from Tianjin University (China) in 2009. Currently, he is pursuing his Ph.D. degree in biomedical engineering at University of California-Davis under the supervision of Prof. Jinyi Qi. His research interests are image quality evaluation and statistical image reconstruction for emission tomography
Host: Prof. Richard Leahy
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 25
Audiences: Everyone Is Invited
Contact: Talyia Veal
-
USC Viterbi Code Dojo
Mon, Dec 08, 2014 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Workshops & Infosessions
Drop-in Q&A/help sessions, coordinated by VAST and CS@SC in preparation for the Hour of Code, sponsored by code.org. More details available at: http://hourofcode.com/us , specifics coming soon.
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
PhD Defense - Dan Ingold
Mon, Dec 08, 2014 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Dissertation Title
A model for estimating schedule acceleration in agile software development projects
PhD Candidate
Dan Ingold
Committee
Barry Boehm (chair), Leana Golubchik, William GJ Halfond, Behrokh Khoshnevis (outside member)
Time and Place
Monday, 8 Dec 2014, 1pm
SAL 322 Conference Room
Abstract
This research assesses the effect of product, project, process, people and risk factors on schedule for software development projects that employ agile methods. Prior research identified these factors as being significant within lean/agile organizations with a history of rapid-response to new product development needs. This work integrates these factors into CORADMO, the Constructive Rapid Application Development Model, an offshoot of the COCOMO family of effort and schedule estimation models.
CORADMO is based on a systems dynamics model of the agile development process, which simulates the flow of development tasks and change items through the process. The five major factors are elaborated into twelve sub-factors, most having a second-, third- or higher-order effect on schedule. Each of the factors and sub-factors is rated along a six-element Likert scale, which determines a set of weighing multipliers derived from COCOMO, COSYSMO, and other models. These multipliers are applied to the systems dynamics model elements that affect task production, change rates, defect insertion, refactoring, and other processes, and the schedule effects assessed.
The results of this modeling show very good ability to predict the schedule outcomes of agile projects. The research evaluates the dynamic model against twelve commercial projects, which show from 2% schedule overrun to 56% underrun, and that implement a variety of product types using diverse languages. The twelve factors were rated for each project based on information the projects provided, and the simulated schedule results compared with the actual schedules realized. Although wide-range validation is limited due to the availability of test data, the CORADMO model is able to predict accurately the actual schedule outcomes of these commercial projects.
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
-
Speculative Dynamical Systems: How Technical Trading Rules Determine Price Dynamics
Mon, Dec 08, 2014 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Li-Xin Wang, Ph.D., Xian Jiaotong University, Department of Automation Science and Technology
Talk Title: Speculative Dynamical Systems: How Technical Trading Rules Determine Price Dynamics
Abstract: In this talk, I will first show how to use fuzzy systems theory to convert the following technical trading rules commonly used by stock practitioners into price dynamical equations: moving average rules, support and resistance rules, trend line rules, big buyer and big seller rules, manipulator rules, band and stop rules, and volume and relative strength rules. Then, I will analyze the price dynamical model with the moving average rules in detail, showing: (1) there exist an infinite number of price equilibriums, but all these equilibriums are unstable; (2) volatility is a deterministic function of the model parameters; (3) short-term prediction is possible with the âprediction horizonâ characterized by the Lyapunov exponent; and (4) how return correlations move from sub-diffusion to norm-diffusion and then to super-diffusion as the model parameters change. Finally, I will apply the big buyer/seller model to Hong Kong stocks and show how to detect big buyers in the market and follow them up to make money. Specifically, I will develop two trading strategies, namely Follow-the-Big-Buyer and Ride-the-Mood, and apply them to the top 20 banking and real estate stocks listed in the Hong Kong Stock Exchange for the seven-year period from July 3, 2007 to July 2, 2014; the results show that the net profits would increase 67% or 120% on average if an investor switched from the benchmark Buy-and-Hold strategy to the Follow-the-Big-Buyer or Ride-the-Mood strategies during this period, respectively. This talk is based on the paper: http://ssrn.com/abstract=2508276.
Biography: Li-Xin Wang received the Ph.D. degree from the Department of Electrical Engineering, University of Southern California, in 1992. From 1992 to 1993, he was a Postdoctoral Fellow with the Department of Electrical Engineering and Computer Science, University of California at Berkeley. From 1993 to 2007, he was on the faculty of the Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology (HKUST). In 2007, he resigned from his tenured position at HKUST to become an independent researcher and investor in the stock and real estate markets in Hong Kong and China. In Fall 2013, he returned to academic and joined the faculty of the Department of Automation Science and Technology, Xian Jiaotong University, Xian, China, after a fruitful hunting journey across the wild land of investment to achieve financial freedom. His research interests are dynamical models of asset prices, market microstructure, trading strategies, fuzzy systems, and adaptive nonlinear control. Dr. Wang received USCâs Phi Kappa Phi Student Recognition Award in 1992.
Host: Professor Jerry Mendel
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
