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Events for March 04, 2010
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Munushian Lecture Series: Dr. Ken Gabriel - Deputy Director, DARPA
Thu, Mar 04, 2010 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Munushian Lecture Series
Dr. Ken Gabriel
Deputy Director, Defense Advanced Research Projects Agency(DARPA)Title of talk: "Breaking Rules, Making Rules"
Location: Mudd Hall Room 106
Audiences: Everyone Is Invited
Contact: USC Ming Hsieh Department
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PhD Defense: Characterization of visual cells using generic models and natural stimuli.
Thu, Mar 04, 2010 @ 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Receptions & Special Events
Title: Characterization of visual cells using generic models and natural stimuli.Committee:
Dr. Norberto M. Grzywacz (co-chair)
Dr. Jerry M. Mendel (co-chair)
Dr. Vassilis Z. Marmarelis
Dr. Judith A. Hirsch
Thesis:http://confucius.usc.edu/~rapela/thesis/thesis.pdfAbstract:Traditionally visual cells have been characterized using their responses to artificial stimuli by simple parametric models. However, recent investigations show that visual cells adapt to the statistical properties of the stimuli used to probe them. Thus, to characterize visual cells in their natural operating conditions, it is important to use naturalistic stimuli. Simple parametric models are designed for specific classes of cells, making assumptions about their response properties. But, if these assumptions do not match the cell response properties, the interpretation of the estimated model parameters isquestionable. An alternative is to use generic non-parametric models that can characterize a broad range of cell classes. This thesis contains technical and scientific contributions. Technically, we develop methods to estimate generic non-parametric models of visual cells from their responses to arbitrary, including natural, stimuli. In the first part of this thesis, we introduce the Volterra Relevant Space Technique (VRST), that allows the estimation of spatial Volterra models of visual cells from their responses to natural stimuli. Disregarding temporal properties of the response generation mechanism for the estimation of spatial Volterra models is a good first approximation. However, in most conditions responses of visual cells are not spatial, but spatio temporal. So, in the second part of this dissertation we build the extended Projection Pursuit Regression (ePPR) algorithm, that estimates a very general model for the characterization of visual cells in
space and time. The generality of the ePPR model reveals differences in response properties of cortical cells to natural and random stimuli that had not been observed with existing models. Thus, scientifically this thesis shows that using natural stimuli for the characterization of visual cells is relevant
to understand natural vision.
Location: Hedco Neurosciences Building (HNB) - 107
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Printed Assembly of Micro/Nanomaterials with Silicon and Gallium Arsenide Based Compound Semiconduct
Thu, Mar 04, 2010 @ 12:45 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Printed Assembly of Micro/Nanomaterials with Silicon and Gallium Arsenide Based Compound Semiconductors for High Performance Photovoltaics and OptoelectronicsDr. Jongseung Yoon
Beckman Institute for Advanced Science and Technology
UIUCAbstract
In the first part of my talk, I will present our recent work that explores techniques to exploit silicon for unusual photovoltaic module designs. Silicon, in amorphous or various crystalline forms, is used in >90% of all installed photovoltaic (PV) capacity. The high natural abundance of silicon, with the excellent reliability and good efficiency of solar cells made with it, suggest its continued use, on massive scales, for the foreseeable future. As a result, although there is significant promise for organics, nanocrystals, nanowires and other new materials for photovoltaics, many opportunities continue to exist for research into unconventional means for using silicon in advanced PV systems. We developed new approaches to exploit printed arrays of ultrathin, monocrystalline Si solar microcells for unconventional photovoltaic modules. The resulting devices can offer many useful features, including high degrees of mechanical flexibility, user-definable levels of transparency, ultra-thin form factor micro-optic concentrator designs, together with the potential for high efficiency and low cost.In the second part of my presentation, I will discuss about releasable epitaxial multilayer assemblies of gallium arsenide (GaAs) based compound semiconductors for high performance photovoltaics and optoelectronics. Compound semiconductors such as GaAs provide unmatched performance in photovoltaic and optoelectronic devices. Current methods for growing and fabricating these materials are incompatible with the most important modes of use, particularly in photovoltaics, where large quantities of material must be distributed over large areas on low cost, amorphous foreign substrates. We developed new methods that address many of these challenges, through cost effective production of bulk quantities of high quality functional films of GaAs from thick, epitaxial assemblies formed in a single deposition sequence on a growth wafer. Specialized designs enabled separation, release and assembly of individual active layers in these stacks to create devices on substrates ranging from glass, to silicon and plastic, in quantities and over areas that exceed possibilities with conventional approaches.
Location: Hedco Pertroleum and Chemical Engineering Building (HED) - 116
Audiences: Everyone Is Invited
Contact: Petra Pearce Sapir
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CS Colloq: David Sontag
Thu, Mar 04, 2010 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Talk Title: Approximate Inference in Graphical Models using LP Relaxations
Speaker: David Sontag
Host: Prof. Craig KnoblockAbstract:
Graphical models such as Markov random fields have been successfully applied to a wide variety of fields, from computer vision and natural language processing, to computational biology. Exact probabilistic inference is generally intractable in complex models having many dependencies between the variables.In this talk, I will discuss recent work on using linear programming relaxations to perform approximate inference. By solving the LP relaxations in the dual, we obtain efficient message-passing algorithms that, when the relaxations are tight, can provably find the most likely (MAP) configuration.Our algorithms succeed at finding the MAP configuration in protein side-chain placement, protein design, and stereo vision problems. More broadly, this talk will highlight emerging connections between machine learning, polyhedral combinatorics, and combinatorial optimization.Bio:
David is a Ph.D. candidate in Computer Science at MIT. He received his Bachelor's degree in Computer Science from the University of California, Berkeley in 2005. His research focuses on theory and practical algorithms for learning and probabilistic inference in very large statistical models. His work has been awarded with an outstanding student paper award at NIPS in 2007 and a best paper award at UAI in 2008. He currently has the Google Fellowship in Machine Learning.
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: CS Front Desk
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Raytheon Information Session
Thu, Mar 04, 2010 @ 05:30 PM - 07:30 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Join representatives of this company as they share general company information and available opportunities.
Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 106
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Services
