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PhD Defense - Franziska Meier
Tue, May 03, 2016 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Towards a Probabilistic Motor Skill Learning Framework
Location: RTH 406
Time: 10am - noon, May 3rd, 2016
PhD Candidate: Franziska Meier
Committee members:
Prof. Stefan Schaal (Chair)
Prof. Gaurav Sukhatme
Prof. Yan Liu
Prof. James Finley
Abstract:
While learning in robotics has always been seen as one of the
hallmarks to accomplish autonomous behaviors, so far, there is no coherent and robust approach to robot learning. For instance, realizing complex behaviors, such as manipulation skills, often involves a mixing and matching of planning, control, and learning modules, dominated by the insights of the robotics researcher, but not by a coherent design and/or algorithmic principle. Thus, most robot learning approaches have largely remained a proof-of-concept rather then a general research approach towards robot learning.
In this thesis we aim to move towards a motor skill learning framework coherently routed in probabilistic representations. The use of probabilistc graphical models for different learning modules can foster a principled combination of these modules to form an integrated approach to skill representations. Towards this goal I will present
contributions from two directions: Creating computationally efficient approximations of probabilistic graphical models and developing probabilistic solutions to problems in motor skill learning.
In the first part of my talk I will present our work on scaling up learning in graphical models such that the use of a complex graphical model -- as would be required for complex motor skill representations with perceptual coupling -- is feasible.
In the second part of the talk, I will then present probabilistic approaches to two subproblems of motor skill learning. First I will introduce a probabilistic version of dynamic movement primitives. With the help of this formulation we can implement online movement recognition and perform segmentation of complex skill sequences into movement primitives.
Finally, I will tackle the problem of learning internal models, exemplified by inverse dynamics learning. Having a good inverse dynamics model ensures that we can execute trajectories in an accurate yet compliant manner. I will present a real-time capable drifting Gaussian process approach to learning a local approximation of the
inverse dynamics model on the fly.
Location: Ronald Tutor Hall of Engineering (RTH) - 406
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
USC Stem Cell Seminar: Stacey Finley, USC
Tue, May 03, 2016 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Stacey Finley, USC
Talk Title: Applying computational systems biology to predict tumor angiogenesis signaling
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Abstract: Angiogenesis, the formation of new blood vessels, enables tumors to obtain oxygen and nutrients from the surrounding microenvironment, promoting tumor growth. Both promoters and inhibitors of angiogenesis contribute to the response to anti-angiogenic cancer therapies; however, there is no quantitative framework that enables investigation of angiogenic factors together in tumor tissue. This presentation will discuss our ongoing work to construct predictive models of key factors involved in regulating tumor angiogenesis. The models provide a tool with which to study the response to anti-angiogenic therapies for a range of tumor types.
Host: Megan McCain
More Info: https://calendar.usc.edu/event/speaker_stacey_finley_usc?utm_campaign=widget&utm_medium=widget&utm_source=USC+Event+Calendar%3A+Beta#.VvGX33DFl04
Webcast: http://keckmedia.usc.edu/stem-cell-seminarWebCast Link: http://keckmedia.usc.edu/stem-cell-seminar
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Study Day Spring 2016
Tue, May 03, 2016 @ 11:00 AM - 04:00 PM
Viterbi School of Engineering Student Affairs
Student Activity
Get a jump-start on studying for finals with review sessions for select engineering classes and open study time. Come with your books, your friends, and your questions!
Stop by the sign-in table in the RTH to get your breakfast burrito.
To register, click here https://myviterbi.usc.edu/vasa/?PostingID=1234567992.Location: 105, 109, 115
Audiences: Undergrad
Contact: Christine Franks
Event Link: https://myviterbi.usc.edu/vasa/?PostingID=1234567992
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Inaugural USC Day of SCupport
Wed, May 04, 2016
Aerospace and Mechanical Engineering, Astronautical Engineering, Aviation Safety and Security Program, Alfred E. Mann Department of Biomedical Engineering, John Brooks Slaughter Center, Thomas Lord Department of Computer Science, Daniel J. Epstein Department of Industrial and Systems Engineering, DEN@Viterbi, Information Sciences Institute, Technology & Applied Computing Program (TAC), Ming Hsieh Department of Electrical and Computer Engineering, Mork Family Department of Chemical Engineering and Materials Science, Sonny Astani Department of Civil and Environmental Engineering, USC Viterbi School of Engineering, Viterbi School of Engineering Alumni
Receptions & Special Events
On May 4, 2016, the USC community will unite to demonstrate the loyalty and SCupport of the Trojan Family. The goal: Rally as many people as possible to create the single largest day of giving participants in USC history.
Let us show the world that the collective power of the Trojan Family is unmatched anywhere. Every donor counts and all gifts, no matter the size, make a difference.
Connect with the USC community by using #DayofSCupport on Facebook, Twitter, LinkedIn, Instagram, and other social media. Your pride in USC can best be shown by our joining together to spread the word about USC Day of SCupport.
Thank you for your SCupport, and Fight On!
Visit DayofSCupport.usc.edu to plant your sword and show your support!
#DayofSCupportWebCast Link: dayofscupport.usc.edu
Audiences: Everyone Is Invited
Contact: James Morse
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Communications, Networks & Systems (CommNetS) Seminar
Wed, May 04, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Wotao Yin, UCLA
Talk Title: Theory and Applications of Asynchronous Parallel Computing
Series: CommNetS
Abstract: Single-core performance stopped improving around 2005. However, 64 CPU-cores workstations, thousand-core GPUs, and even eight-core cellphones are sold at affordable prices. To take advantages of multiple cores, we must parallelize our algorithms. In order for iterative algorithms to have strong parallel performance, it is important to reduce or even remove synchronizations so that the cores can keep running with the information they have, even when the latest one has not arrived. This talk explains why such async-parallel computing is both theoretically sound and practically attractive. In particular, we study fixed-point iterations of a nonexpansive operator and show that randomized async-parallel iterations will almost surely converge to a fixed point, as long as the operator has a fixed point and the step size is properly chosen. Roughly speaking, the convergence speed scales linearly with the number of cores when the number of cores is no more than the square root of the number of variables. As special cases, novel algorithms for linear equation systems, machine learning, distributed and decentralized optimization are introduced. On sparse logistic regression and others, new async-parallel algorithms run order-of-magnitude faster than the traditional sync-parallel algorithms. This is joint work with Zhimin Peng (UCLA), Yangyang Xu (IMA), and Ming Yan (Michigan State).
Biography: Wotao Yin is a professor in the Department of Mathematics of UCLA. His research interests lie in computational optimization and its applications in image processing, machine learning, and other inverse problems. He received his B.S. in mathematics from Nanjing University in 2001, and then M.S. and Ph.D. in operations research from Columbia University in 2003 and 2006, respectively. During 2006 - 2013, he was with Rice University. He won NSF CAREER award in 2008 and Alfred P. Sloan Research Fellowship in 2009. His recent work has been in optimization algorithms for large-scale and distributed signal processing and machine learning problems.
Host: Dr. Mahdi Soltanolkotabi
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
AI Seminar-Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL
Thu, May 05, 2016 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Marc Smith, Social Media Research Foundation
Talk Title: Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL
Series: Artificial Intelligence Seminar
Abstract: Networks are a data structure commonly found in any social media service that allows populations to author collections of connections. The Social Media Research Foundation's NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. With NodeXL, network charts become as easy to create as pie charts. Recent research created by applying the tool to a range of social media networks has already revealed the variations in network structures present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, Facebook and email networks will be presented.
Description: We now live in a sea of tweets, posts, blogs, and updates coming from a significant fraction of the people in the connected world. Our personal and professional relationships are now made up as much of texts, emails, phone calls, photos, videos, documents, slides, and game play as by face-to-face interactions. Social media can be a bewildering stream of comments, a daunting fire hose of content. With better tools and a few key concepts from the social sciences, the social media swarm of favorites, comments, tags, likes, ratings, updates and links can be brought into clearer focus to reveal key people, topics and sub-communities. As more social interactions move through machine-readable data sets new insights and illustrations of human relationships and organizations become possible. But new forms of data require new tools to collect, analyze, and communicate insights.
The Social Media Research Foundation (http://www.smrfoundation.org), formed in 2010 to develop open tools and open data sets, and to foster open scholarship related to social media. The Foundation's current focus is on creating and publishing tools that enable social media network analysis and visualization from widely used services like email, Twitter, Facebook, flickr, YouTube and the WWW. The Foundation has released the NodeXL project (http://nodexl.codeplex.com/), a spreadsheet add-in that supports "network overview discovery and exploration". The tool fits inside your existing copy of Excel in Office 2007, 2010 and 2013 and makes creating a social network map similar to the process of making a pie chart.
Using NodeXL, users can easily make a map of public social media conversations around topics that matter to them. Maps of the connections among the people who recently said the name of a product, brand or event can reveal key positions and clusters in the crowd. Some people who talk about a topic are more in the "center" of the graph, they may be key influential members in the population. NodeXL makes it a simple task to sort people in a population by their network location to find key people in core or bridge positions. NodeXL supports the exploration of social media with import features that pull data from personal email indexes on the desktop, Twitter, Flickr, YouTube, Facebook, Wikis, blogs and WWW hyper-links. The tool allows non-programmers to quickly generate useful network statistics and metrics and create visualizations of network graphs.
A book Analyzing Social Media Networks with NodeXL: Insights from a connected world is available from Morgan-Kaufmann. The book provides an introduction to the history and core concepts of social network analysis along with a series of step-by-step instructions that illustrate the use of the key features of NodeXL. The second half of the book is dedicated to chapters by a number of leading social media researchers that each focus on a single social media service and the networks it contains. Chapters on Twitter, email, YouTube, flickr, Facebook, Wikis, and the World Wide Web illustrate the network data structures that are common to all social media services.
A recent report co-authored with the Pew Research Center's Internet Project documents the discovery of the six basic forms of social media network structures present in social media platforms like Twitter. The report, "Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters" provides a step by step guide to analyzing social media networks.
Biography: Marc Smith is a sociologist specializing in the social organization of online communities and computer mediated interaction. Smith leads the Connected Action consulting group and lives and works in Silicon Valley, California. Smith co-founded and directs the Social Media Research Foundation (http://www.smrfoundation.org/), a non-profit devoted to open tools, data, and scholarship related to social media research.
Smith is the co-editor with Peter Kollock of Communities in Cyberspace (Routledge), a collection of essays exploring the ways identity; interaction and social order develop in online groups. Along with Derek Hansen and Ben Shneiderman, he is the co-author and editor of Analyzing Social Media Networks with NodeXL: Insights from a connected world, from Morgan-Kaufmann which is a guide to mapping connections created through computer-mediated interactions.
Smith's research focuses on computer-mediated collective action: the ways group dynamics change when they take place in and through social cyberspaces. Many "groups" in cyberspace produce public goods and organize themselves in the form of a commons (for related papers see: http://www.connectedaction.net/marc-smith/). Smith's goal is to visualize these social cyberspaces, mapping and measuring their structure, dynamics and life cycles. While at Microsoft Research, he founded the Community Technologies Group and led the development of the "Netscan" web application and data mining engine that allowed researchers studying Usenet newsgroups and related repositories of threaded conversations to get reports on the rates of posting, posters, crossposting, thread length and frequency distributions of activity. He contributes to the NodeXL project (http://nodexl.codeplex.com/) that adds social network analysis features to the familiar Excel spreadsheet. NodeXL enables social network analysis of email, Twitter, Flickr, WWW, Facebook and other network data sets.
The Connected Action consulting group (http://www.connectedaction.net) applies social science methods in general and social network analysis techniques in particular to enterprise and internet social media usage. SNA analysis of data from message boards, blogs, wikis, friend networks, and shared file systems can reveal insights into organizations and processes. Community managers can gain actionable insights into the volumes of community content created in their social media repositories. Mobile social software applications can visualize patterns of association that are otherwise invisible.
Smith received a B.S. in International Area Studies from Drexel University in Philadelphia in 1988, an M.Phil. in social theory from Cambridge University in 1990, and a Ph.D. in Sociology from UCLA in 2001. He is an adjunct lecturer at the College of Information Studies at the University of Maryland. Smith is also a Distinguished Visiting Scholar at the Media-X Program at Stanford University.
Host: Emilio Ferrara
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=2abc878bdcdd43c1bbba80cdc09562c41dLocation: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=2abc878bdcdd43c1bbba80cdc09562c41d
Audiences: Everyone Is Invited
Contact: Peter Zamar
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Fog: A new architecture for network distributed computation, communication, control and storage
Thu, May 05, 2016 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mung Chiang, Princeton University
Talk Title: Fog: A new architecture for network distributed computation, communication, control and storage
Abstract: Fog architecture distributes computation, communication, control and storage closer to end users along the cloud-to-things continuum, promising the potential benefits in cognition, efficiency, agility and latency, and possibly enabling applications in 5G, IoT and big data. This talk overviews the opportunities and challenges in this research area and discusses the emergent industry momentum in fog.
Biography: Mung Chiang is the Arthur LeGrand Doty Professor of Electrical Engineering at Princeton University. His research on networking received the 2013 Alan T. Waterman Award, the highest honor to US young scientists and engineers. His textbook "Networks: Friends, Money and Bytes" and online course reached 250,000 students since 2012. He founded the Princeton EDGE Lab in 2009, which bridges the theory-practice gap in edge networking research by spanning from proofs to prototypes. He co-founded a few startups in mobile, IoT and big data areas and co-founded the Open Fog Consortium. Chiang is the Director of Keller Center for Innovations in Engineering Education at Princeton University and the inaugural Chairman of Princeton Entrepreneurship Council.
Host: Urbashi Mitra, ubli@usc.edu, EEB 536, x04667
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 539
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
PhD Defense - Guan Pang
Thu, May 05, 2016 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: 3D Object Detection in Industrial Site Point Clouds
Location: SAL 322
Time: 12:00pm - 2:00pm, May 5th, 2016
PhD Candidate: Guan Pang
Committee members:
Prof. Ulrich Neumann (Chair)
Prof. Aiichiro Nakano
Prof. C.-C. Jay Kuo (Outside Member)
Abstract:
Detection of three dimensional (3D) objects in point clouds is a challenging problem. Existing methods either focus on a specific type of object or scene, or require prior segmentation, both of which are usually inapplicable on real-world industrial applications.
This thesis describe three methods to tackle the problem, with gradually improving performance and efficiency. The first is a general purpose 3D object detection method that combines Adaboost with 3D local features, without requirement for prior object segmentation. Experiments demonstrated competitive accuracy and robustness to occlusion, but this method suffers from limited rotation invariance. As an improvement, another method is presented with a multi-view detection approach that projects the 3D point clouds into several 2D depth images from multiple viewpoints, transforming the 3D problem into a series of 2D problems, which reduces complexity, stabilizes performance, and achieves rotation invariance. The problem is the huge amount of projected views and rotations that need to be individually detected, limiting the complexity and performance of 2D algorithm choice. Thus the third method is proposed to solve this with the introduction of convolutional neural network, because it can handle all viewpoints and rotations for the same class of object together, as well as predicting multiple classes of objects with the same network, without the need for individual detector for each object class. The detection efficiency is further improved by concatenating two extra levels of early rejection networks with binary outputs before the multi-class detection network.
3D object detection in point clouds is crucial for 3D industrial point cloud modeling. Prior efforts focus on primitive geometry, street structures or indoor objects, but industrial data has rarely been pursued. We integrate several algorithm components into an automatic 3D modeling system for industrial site point clouds, including modules for pipe modeling, plane classification and object detection, and solves the technology gaps revealed during the integration. The integrated system is able to produce classified models of large and complex industrial scenes with a quality that outperforms leading commercial software and comparable to professional hand-made models.
This thesis also describes an earlier work in multi-modal image matching which inspires later research in 3D object detection by 2D projections. Most existing 2D descriptors only work well on images of a single modality with similar texture. This proposal presents a novel basic descriptor unit called a Gixel, which uses an additive scoring method to sample surrounding edge information. Several Gixels in a circular array create the Gixel Array Descriptor, excelling in multi-modal image matching with dominant line features.
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
USC Viterbi V.I.P. Event in San Diego: Water Sustainability In Coastal Regions
Thu, May 05, 2016 @ 05:30 PM - 07:30 PM
Sonny Astani Department of Civil and Environmental Engineering, USC Viterbi School of Engineering, Viterbi School of Engineering Alumni
Receptions & Special Events
Join us on Thursday, May 5 for a presentation on Water Sustainability in Coastal Regions: Integrated Systems of Wastewater Re-use and Desalination by Amy Childress, PhD, Professor in the the Sonny Astani Department of Civil and Environmental Engineering Department.
As you may know, California is facing a historic drought and challenging issues related to water use. How does the research at USC Viterbi School of Engineering impact these issues and lead to breakthroughs and advances? How are we preparing to provide water to our growing communities?
Thursday, May 5, 2016 at 5:30 PM
Hosted by Debra Reed, BS '78, Chairman and CEO, Sempra Energy
Location: Sempra Energy, 488 8th Avenue, San Diego, CA 92101
Admission for USC alumni, USC parents, and guests is $10 per person and features a reception. All are welcome, but space is limited.
If you have any questions or would like to order over the phone please contact Maita Schuster at mrschust@usc.edu or 213.740.4880.
WHAT IS VITERBI INNOVATION PARTNERS (V.I.P.)?
V.I.P. is a yearly giving, engagement, and recognition program where alumni, parents and
friends come together in support of our engineering students and the advancement of
engineering here at USC Viterbi. For more information, please visit: http://viterbigiving.usc.edu/vip/Audiences: USC Alumni
Contact: James Morse
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
CS Colloquium: John Lafferty (University of Chicago) - Statistical Learning Under Communication and Shape Constraints
Fri, May 06, 2016 @ 11:00 AM - 12:15 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: John Lafferty, University of Chicago
Talk Title: Statistical Learning Under Communication and Shape Constraints
Series: Yahoo! Labs Machine Learning Seminar Series
Abstract: Imagine that I estimate a statistical model from data, and then want to share my model with you. But we are communicating over a resource constrained channel. By sending lots of bits, I can communicate my model accurately, with little loss in statistical risk. Sending a small number of bits will incur some excess risk. What can we say about the tradeoff between statistical risk and the communication constraints? This is a type of rate distortion and constrained minimax problem, for which we provide a sharp analysis in certain nonparametric settings. We also consider the problem of estimating a high dimensional convex function, and develop a screening procedure to identify irrelevant variables. The approach adopts on a two-stage quadratic programming algorithm that estimates a sum of one-dimensional convex functions, beating the curse of dimensionality that holds under smoothness constraints. Joint work with Yuancheng Zhu and Min Xu.
Host: Yan Liu
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
PhD Defense - Shay Deutsch
Fri, May 06, 2016 @ 02:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Learning the Geometric Structure of High Dimensional Data using the Tensor Voting Graph
Location: SAL 322
Time: 2:30pm - 4:30pm, May 6th, 2016
PhD Candidate: Shay Deutsch
Committee members:
Prof. Gerard Medioni (Chair)
Prof. Aiichiro Nakano
Prof. Antonio Ortega (Outside Member)
Abstract:
This study addresses a range of fundamental problems in unsupervised manifold learning. Given a set of noisy points in a high dimensional space that lie near one or more possibly intersecting smooth manifolds, different challenges include learning the local geometric structure at each point, geodesic distance estimation, and clustering. These problems are ubiquitous in unsupervised manifold learning, and many applications in computer vision as well as other scientific applications would benefit from a principled approach to these problems.
In the first part of this thesis we present a hybrid local-global method that leverages the algorithmic capabilities of the Tensor Voting framework. However, unlike Tensor Voting, which can learn complex structures reliably only locally, our method is capable of reliably inferring the global structure of complex manifolds using a unique graph construction called the Tensor Voting Graph (TVG). This graph provides an efficient tool to perform the desired global manifold learning tasks such as geodesic distance estimation and clustering on complex manifolds, thus overcoming one of one of the main limitations of Tensor Voting as a strictly local approach. Moreover, we propose to explicitly and directly resolve the ambiguities near the intersections with a novel algorithm, which uses the TVG and the positions of the points near the manifold intersections.
In the second part of this thesis we propose a new framework for manifold denoising based processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian. The suggested approach, called MFD, uses the Spectral Graph Wavelet transform in order to perform non-iterative denoising directly in the graph frequency domain. To the best of our knowledge, MFD is the first attempt to use graph signal processing tools for manifold denoising on unstructured domains. We provide theoretical justification for our Manifold Frequency Denoising approach on unstructured graphs and demonstrate that for smooth manifolds the coordinate signals also exhibit smoothness. This is first demonstrated in the case of noiseless observations, by proving that manifolds with smoother characteristics creates more energy in the lower frequencies. Moreover, it is shown that higher frequency wavelet coefficients decay in a way that depends on the smoothness properties of the manifold, which is explicitly tied to the curvature properties. We then provide an analysis for the case of noisy points and a noisy graph, establishing results which tie the noisy graph Laplacian to the noiseless graph Laplacian characteristics, induced by the smoothness manifold properties and the graph construction properties.
Finally, the last part of this research merges the Manifold Frequency Denoising and the Tensor Voting Graph methods into a uniform framework, which allows us to denoise and analyze a general class of noisy manifolds with singularities also in the presence of outliers. We demonstrate that the limitation of the Spectral Graph Wavelets in its flexibility to analyze certain classes of graph signals can be overcome for manifolds with singularities using certain graph construction and regularization methods. This allows us to take into account global smoothness characteristics without over-smoothing in the manifold discontinuations (which correspond to high frequency bands of the Spectral Graph Wavelets), and moreover is robust to a large amount of outliers.
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
NL Seminar-THE TECHKNACQ PROJECT: BUILDING PEDAGOGICALLY TUNED READING LISTS FROM TECHNICAL CORPORA
Fri, May 06, 2016 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Gully Burns, USC/ISI
Talk Title: THE TECHKNACQ PROJECT: BUILDING PEDAGOGICALLY TUNED READING LISTS FROM TECHNICAL CORPORA
Series: Natural Language Seminar
Abstract: This work is geared towards developing pedagogically-tuned information retrieval systems to help learners select the most informative documents as a reading list for a given query over a given technical corpus. This work will enable learners to understand complex subjects more quickly. I will discuss our overall methodology, our efforts to study dependency between topics within a technical corpus and improvements to evaluating topic quality. I will describe ongoing efforts to study a document's pedagogical value to the end user and future directions for this enterprise.
Biography: Gully Burns' focus is to develop pragmatic knowledge engineering systems for scientists in collaboration with experts from the field of AI. He was originally trained as a physicist at Imperial College in London before switching to do a Ph.D. in neuroscience at Oxford. He came to work at USC in 1997, developing the 'NeuroScholar' project in Larry Swanson's lab before joining the Information Sciences Institute in 2006. He is as Research Lead at ISI.
Host: Xing Shi and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Sonny Astani Department Seminar
Fri, May 06, 2016 @ 03:00 PM - 03:30 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Meida Chan, Civil Engineering PhD student, USC
Talk Title: Development of a data acquisition-planning framework for hybrid data collection techniques to achieve blind-spots free 3D point cloud
Abstract: As-is 3D building models are valuable in many ways such as urban planning, historical building information storage, building renovation, facility management, building energy simulation and so on. Data acquisition for complete and accurate as-is 3D building reconstruction is a time consuming and labor intensive process. Establishing a data acquisition plan before or during the data acquisition process is necessary. As such, there has been extensive research on developing/advancing data acquisition planning algorithms with a single data acquisition technique. However, for buildings that have complex building structure and architectural elements, data collection process with a single data acquisition technique is not sufficient neither effective. The hypothesis behind this research study is that image-based technique (photogrammetry) and range-based technique (laser scanning) are complementary to each other and that the combination of the two techniques can improve the quality of the derived as-is 3D point cloud in terms of completeness and accuracy. As such, this research study will develop a framework that will provide an improved data acquisition process to support the creation of complete and accurate 3D models of existing buildings, while reducing the total cost of data acquisition by eliminating the need for site revisits and reworking of the data collection process.
Host: Lucio Soibelman
Location: Seeley G. Mudd Building (SGM) - 101
Audiences: Everyone Is Invited
Contact: Kaela Berry
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Sonny Astani Department Seminar
Fri, May 06, 2016 @ 03:30 PM - 04:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Xin Wang, Research Institute of Disaster Science, Tohoku University, Japan
Talk Title: 1D Wave Propagation Analysis and Shear-Wave Velocity Extraction of Super High-Rise Buildings Based on Ambient Vibrations Measurement
Abstract: Shaking modes of super high-rise buildings are very complex. In the first part of this study, three 2-D frame models of super high-rise buildings including bending and shear deflections in each member are used to simulate shear-wave propagation within the building. Different shaking modes at the lower stories of the three models are designed, each with a different mass-and-rigidity distributions, such as: (i) all stories shaking in a shear-bending mode, (ii) the lower eight stories shaking in pure bending mode, and (iii) the fourth to eighth stories shaking in pure bending mode. The wave reflections at the boundaries of stories with different shaking modes are examined from the response waves and the impulse responses with respect to the response of the top. Because of the wave interference, it is difficult to observe the travel path directly from the response waves. However, the travel path and the reflected waves can be observed clearly from impulse responses. For the stories shaking in pure bending mode, similar to the models (ii) and (iii), because there is no inter-story shear deformation, the apparent shear-rigidity of these stories seems infinite, which leads to zero shear-wave travel time and shear-wave velocities cannot be extracted successfully. In the second part of this study, 1D vertical shear-wave propagation in two super high-rise buildings are identified using ambient vibration response recorded by a portable array. The identified shear-wave propagation from the impulse response, including the boundary conditions, is compared with the simulated ones. Attempt is made to identify the shear-wave velocities for the individual stories.
Biography: Dr. Xin Wang is an Assistant Professor of the Research Institute of Disaster Science of Tohoku University in Japan. Her research combines knowledge of seismology and civil engineering and aims to disaster prevention from earthquakes. Her research topics include building damage detection and building damage causes examination due to ground shaking during big earthquake disasters, e.g. the 2008 Wenchuan Earthquake in China, the 2011 Great East Japan Earthquake, and the 2014 Ludian Earthquake in China. She is currently studying damage from the recent 2016 Kumamoto Earthquake in Japan. Her main recent research topics are Structural Health Monitoring of super high-rise buildings, and earthquake response recording systems using smart devices. Native of China, Dr. Wang received her B.S. and M.S. degrees in Civil Engineering from the Dalian Jiaotong University and Southeast University, respectively, after which she moved to Japan and earned her Ph.D. degree from the Aichi Institute of Technology.
Host: Maria Todorovska
Location: Seeley G. Mudd Building (SGM) - 101
Audiences: Everyone Is Invited
Contact: Kaela Berry
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Viterbi Pre-College Robotics Invitational
Sat, May 07, 2016 @ 07:30 AM - 02:00 PM
Viterbi School of Engineering K-12 STEM Center
Receptions & Special Events
A middle school and high school competition where student teams design, build and program robots that emulate a solution to a societal issue such as health care and medical technology, clean water, green energy solutions, etc.
More Information: Robotic Invitational Flyer.pdf
Location: Ronald Tutor Hall of Engineering (RTH) -
Audiences: Everyone Is Invited
Contact: Darin Gray/Viterbi Pre-College
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
