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Events for April 21, 2010
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Meet USC: Admission Presentation, Campus Tour, & Engineering Talk
Wed, Apr 21, 2010
Viterbi School of Engineering Undergraduate Admission
University Calendar
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 9:00 a.m. and again at 1:00 p.m. Please visit http://www.usc.edu/admission/undergraduate/visit/meet_usc.html to check availability and make an appointment. Be sure to list an Engineering major as your "intended major" on the webform!
Location: USC Admission Center
Audiences: Prospective Freshmen and Family Members - RESERVATIONS REQUIRED
Contact: Admission Intern
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CS Colloq: Barak Fishbain
Wed, Apr 21, 2010 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Talk Title: Network flow algorithms for sensor networks and visual data analysisSpeaker: Barak FishbainHost: Prof. Cyrus ShahabiAbstract:As digital environments become increasingly complex, and the tools for managing information become increasingly advanced, it is essential to assist users in selecting their short term and long term attentional focus. In this talk a novel graph-cut based approaches for multi-dimensional data analysis are presented. These methods are highly robust and most efficient which allows for the analysis of significantly large data sets. Air quality control and video segmentation are presented as representative applications.
Air quality control is addressed by the use of sensors network, where each sensor is mounted on a moving vehicle, for the purpose of detecting various threats. An example scenario is that of multiple taxi cabs each carrying a detector. The detectors' positions are continuously reported from GPS data. The level of detected risk is then reported from each detector at each position. The problem is to delineate the presence of a potentially dangerous source and its approximate location by identifying a small area that has an elevated concentration of reported risk. This problem of using spatially deployed mobile
detector networks to identify and locate risks is modeled and formulated. Then it is shown to be solvable in polynomial time and with a combinatorial network flow algorithm. The efficiency of the algorithm enables its use in real time, and in areas containing a large number of deployed detectors.
In video segmentation a typical goal is to group together similar objects, or pixels in the case of image processing. At the same time another goal is to have each group distinctly dissimilar from the rest and possibly to have the group size fairly large. These goals are often combined as a ratio optimization problem. State-of-the-art methods address these ratio problems by employing nonlinear continuous approaches, such as spectral techniques.
These spectral techniques deliver solutions in real numbers which are not feasible to the discrete partitioning problem. Furthermore, these continuous approaches are relatively computationally expensive. In this talk a novel graph-cut based approaches for optimally solving a set of segmentation ratio problems are presented. These algorithms guarantee optimal solution to the respective problem and consistent output between different runs.
These methods are most efficient which allows for the segmentation of significantly large video data sets.
The work was done with Prof. Dorit S. Hochbaum, University of California at Berkeley.Bio:Barak Fishbain received his Ph.D in EE from Tel-Aviv University, Israel in 2008. His research interests are Computer Vision, Image Processing, Video Surveillance and Medical Imaging. Currently he is a postdoctoral fellow in the Dept. of Industrial Engineering and Operations Research in the University of California at Berkeley, USA
Location: Charles Lee Powell Hall (PHE) - 333
Audiences: Everyone Is Invited
Contact: CS Front Desk
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Architectural and Circuit-Level Design Techniques for Power and Temperature Optimizations in On-Chip
Wed, Apr 21, 2010 @ 01:00 PM - 02:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Hosted by Prof Timothy M. PinkstonSpeaker: Houman Homayoun, University of California, IrvineAbstract:
In order to reduce register file's peak temperature in an embedded processor, we propose RELOCATE: an architectural solution which redistributes the access pattern to physical registers through a novel register allocation mechanism. The goal is to keep some partitions unused (idle) and cooling down. The temperature of idle partitions is further reduced by power gating them into destructive sleep mode to reduce their leakage power. The redistribution mechanism changes the active region periodically to modulate the activity within the register file and prevent the active region from heating up excessively. Our approach resulted in an average reduction of 8.3°C in the register file's peak temperature for standard benchmarks.Also, recent studies have shown that peripheral circuits, including decoders, wordline drivers, input and output drivers, contribute a large fraction of the overall cache leakage. In addition, as technology migrates to smaller geometries, leakage contribution to total power consumption increases faster than dynamic power, indicating that leakage will be a major contributor to overall power consumption. This work also proposes a combination of circuit and architectural techniques to maximize leakage power reduction in embedded processor's on-chip caches by targeting leakage in cache peripheral circuits. Experimental results indicate that the proposed techniques can keep the L1 cache peripherals in one of the low-power modes for more than 85% of total execution time, on average. This translates to an average leakage power reduction of 50% for 65nm technology. The DL1 cache energy-delay product is reduced, on average, by 20%. The overall processor power is reduced by up to 8.7% (an average of 5.3%). Biography:
Houman Homayoun is a PhD student in the department of computer science at the University of California, Irvine. His research is on power-temperature and reliability-aware memory and processor design optimizations and spans the areas of computer architecture and circuit design. From 2006 to 2007 he was working in Novelics, a leading provider of system-on-chip (SoC) embedded memory, where he was the principle architect of a parametrizable BIST microprocessor. The chip was successfully taped-out and delivered in 130, 90 and 65nm. Homayoun received his BS degree in electrical engineering in 2003 from Sharif University of Technology, Tehran, Iran. He received his MS degree in computer engineering in 2005 from University of Victoria, Canada.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - -248
Audiences: Everyone Is Invited
Contact: Janice Thompson
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Multidisciplinary Performance-Based Design Processes
Wed, Apr 21, 2010 @ 01:00 PM - 02:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: John Haymaker, AIA, PhD, LEED ap, Assistant Professor, Civil and Environmental Engineering, Stanford UniversityAbstract:Modern design challenges require multidisciplinary stakeholders and designers to systematically generate and analyze large spaces of alternatives for multiple criteria. This is a complex social and technical process that requires clear and rapid communication. I present case studies that illustrate how and why current practice is unable to do this efficiently and effectively. A fundamental social and technical shift to new performance-based design methods is needed. I describe an industrial scale platform of methods that my research team is developing to address this need: • The Process Integration Platform (PIP) helps teams communicate and manage multidisciplinary processes;
Design Scenarios helps them transform requirements into parametric design spaces; • Process Integration and Design Optimization (PIDO) automates the analysis of these spaces for daylight, energy, structure, cost and other criteria; • Multi-Attribute, Collaborative Design, Analysis, and Decision Integration (MACDADI), facilitates fast, formal, collaborative decision making;• The Design Exploration Assessment Method (DEAM) measures design challenges, strategies, and explorations to assist in new process development and selection.
These methods enable design teams to more efficiently and effectively execute multidisciplinary performance-based design processes.Location: Kaprielian Hall (KAP) - 209 (Avaiable on Webex upon request)
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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On the Limiting Behavior of Regularizations of the Euler Equations with Vortex Sheet Initial Data
Wed, Apr 21, 2010 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Monika Nitsche Associate Professor Department of Mathematics University of New Mexico, Albuquerque ABSTRACT:
The vortex sheet is a mathematical model for a shear layer in which the layer is approximated by a surface. Vortex sheet evolution has been shown to approximate the motion of shear layers well, both in the case of free layers and of separated flows at sharp edges. Generally, the evolving sheets develop singularities in finite time. To approximate the fluid past this time, the motion is regularized and the sheet defined as the limit of zero regularization. However, besides weak existence results in special cases, very little is known about this limit. In particular, it is not known whether the limit is unique or whether it depends on the regularization. I will discuss several regularizing mechanisms, including physical ones such as fluid viscosity, and purely numerical ones such as the vortex blob and the Euler-alpha methods. I will show results for a model problem and discuss some of the unanswered questions of interest. --------------------------------------------------------------------------------Professor Nitsche received her PhD degree in 1992 from University of Michigan Ann Arbor under the guidance of Prof. Robert Krasny. She held various postdoctoral position (UC Boulder, IMA, OSU, Tufts) until she joined the faculty of the department of Mathematics at the University of New Mexico, Albuquerque in 1999. Her research interests lie in the numerical study of vortex flows and the development of numerical methods for such flows. She has also done some work on internal waves in density stratified flows.
Location: Seaver Science Library, Rm 150
Audiences: Everyone Is Invited
Contact: April Mundy
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CS Colloq: Chun-Nan Hsu - CANCELLED
Wed, Apr 21, 2010 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
CANCELLEDTalk Title: Accelerating Machine Learning by Aggressive ExtrapolationSpeaker: Chun-Nan HsuHost: Prof. Dennis McLeodAbstract:This talk presents how to accelerate statistical machine learning algorithms for large scale applications by aggressive extrapolation. Extrapolation methods, such as Aitken's acceleration, have the advantage that they can achieve quadratic convergence with an overhead linear to the dimension of the training data. However, they can be numerically unstable and their convergence is only locally guaranteed. We show that this can be fixed by a double extrapolation method. There are two options for the extrapolation, global or component-wise. Previously, it was not clear which option is more effective. We show a general condition to determine which option will be more effective and show how to apply the condition to the training of Bayesian networks and conditional random fields (CRF). Then we show that extrapolation can accelerate on-line learning with a method called Periodic Step-size Adaptation (PSA). We show that PSA is an approximation of a theoretic "single-pass" on-line learning method, which can converge to an empirical optimum in a single pass through the training examples. With a single-pass on-line learning method, disk I/O can be minimized when a training set is too large to fit in memory. Experimental results for a wide variety of models, including CRF, linear SVM, and convolutional neural networks, show that single-pass performance of PSA is always very close to empirical optimum. Finally, an application to gene mention tagging for biological text mining will be presented, which achieved the top score in BioCreative 2 challenge.Bio:Dr. Chun-Nan Hsu is a computer scientist at Information Sciences Institute (ISI). Prior to joining ISI, he is Research Fellow and Leader of the Adaptive Internet Intelligent Agents (AIIA) Lab at the Institute of Information Science, Academia Sinica, Taipei, Taiwan. His research interests include machine learning, data mining, databases and bioinformatics. He earned his M.S. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles, CA, in 1992 and 1996, respectively. In 1996, before he passed his doctoral oral exam, he had been offered a position as Assistant Professor at the Department of Computer Science and Engineering, Arizona State University, Tempe, AZ. He taught there for two years before he returned to Taiwan in 1998. Since 2005, he has been the principal investigator of the Advanced Bioinformatics Core, National Research Program in Genomic Medicine, Taiwan, and leading one of the largest research efforts in computerized drug design and discovery in Taiwan. In 2006, the first drug candidate due to the use of the software his team developed was commercialized. In 2007, his teams achieved the best scores in the BioCreative 2 text mining challenge. Dr. Hsu has published 78 scientific articles since 1993. Some of the articles have been cited more than 300 times. Currently, Dr. Hsu has been working on applying artificial intelligence to computational biology and bioinformatics.
Location: Mark Taper Hall Of Humanities (THH) - 114
Audiences: Everyone Is Invited
Contact: CS Front Desk
