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Events for April

  • USC Viterbi Robotics Open House

    Fri, Apr 07, 2017 @ 09:00 AM - 05:00 PM

    USC Viterbi School of Engineering

    University Calendar


    The annual Robotics Open House will be on Friday, April 7, 2017, from 9 a.m. - 5 p.m., with all the robotics research labs open for school groups, families, and individuals to view interactive demonstrations to learn about how the next generation of robots will help society in health, manufacturing, education, environmental protection, communication, and homeland security. Demonstrations include research on how swarms of drones communicate to one another autonomously, how underwater robots help protect the oceans, how brain circuitry gives researchers insights into building and programming robots, and how difficult but useful it is to make robots walk instead of roll. There will also be a premiere of the short film, When Dinosaurs Ruled the Earth, about how a child with autism breaks out of his imaginary worlds thanks to a friendship with a robot.
    https://viterbipk12.usc.edu/research/robotics-openhouse

    More Information: USC Robotics Open House 2017.pdf

    Location: Ronald Tutor Hall of Engineering (RTH) - Check in for maps at courtyard between RTH & EEB (3710 McClintock Ave.)

    Audiences: free event, pre-registration required!

    Posted By: Katie Mills

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  • W.V.T. Rusch Engineering Honors Program Colloquium

    Fri, Apr 07, 2017 @ 01:00 PM - 01:50 PM

    USC Viterbi School of Engineering

    University Calendar


    Join us for a presentation by Dr. Daniel Oppenheimer, Professor of Marketing and Psychology at the UCLA Anderson School of Management, titled "A Dozen (or More) Studies on the Psychology of Decision Making."

    Location: Henry Salvatori Computer Science Center (SAL) - 101

    Audiences: Everyone Is Invited

    Posted By: Ramon Borunda/Academic Services

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  • W.V.T. Rusch Engineering Honors Program Colloquium

    Fri, Apr 14, 2017 @ 01:00 AM - 01:50 PM

    USC Viterbi School of Engineering

    University Calendar


    Join us for a presentation by , Prof. Malancha Gupta, Associate Professor, Mork Family Department of Chemical Engineering and Materials Science at the University of Southern California, titled "Functional Polymer Films."

    Location: Henry Salvatori Computer Science Center (SAL) - 101

    Audiences: Everyone Is Invited

    Posted By: Ramon Borunda/Academic Services

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  • PhD Defense - Rongqi Qiu

    Tue, Apr 18, 2017 @ 10:00 AM - 12:00 PM

    Computer Science

    University Calendar


    PhD Candidate: Rongqi Qiu

    Committee: Ulrich Neumann (CS, chair), Panayiotis Georgiou (EE), Aiichiro Nakano (CS)

    Title: Geometric Modeling and Shape Analysis of 3D Point Clouds

    Time: April 18 (Tuesday) 10-12pm

    Room: SAL 322

    Abstract:

    Automatic reconstruction of large-scale scenes from 3D point clouds has been a complex problem. It can be decomposed into two sub-problems, namely, primitives and parts. While primitives are regular geometric shapes, parts are relatively irregular and isolated objects.

    In primitive reconstruction, two systems under different scenarios are presented. The first one reconstructs pipe-runs from industrial site point clouds. The key idea is that by adopting statistical analysis over point normals, global similarities are discovered from raw data to guide primitive fitting, thus increasing robustness. The second system extracts pole-like objects from urban point clouds and posed multi-view images. The presented method takes advantage of the complementary information from 3D point clouds and 2D posed images to recover these objects.

    In part reconstruction, a modeling-by-recognition strategy is followed. Instead of directly meshing on a noisy scan, a similar object is retrieved from a pre-defined CAD model library. Then, geometric analysis is applied on the query and template point cloud to accomplish two tasks. The first one is to compute dense correspondences between query and template objects, thus making it possible to transfer real-world color to template models. The method segments both point clouds into parts consistently and then computes part-level correspondences. The dense mapping allows color or other parameter transfers. The second task is to segment an object into functional parts using a small set of pre-segmented template objects as examples. The main idea is to seek partial matches and transfer segmentation labels from examples to the input object. The resulting segmentation is a key step towards shape understanding.

    Location: Henry Salvatori Computer Science Center (SAL) - 322

    Audiences: Everyone Is Invited

    Posted By: Lizsl De Leon

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  • PhD Defense - Xinran He

    Tue, Apr 18, 2017 @ 01:00 PM - 03:00 PM

    Computer Science

    University Calendar


    Phd Candidate: Xinran He

    Committee:
    Yan Liu (chair)
    David Kempe
    Kristina Lerman
    Thomas Valente

    Date/Time: April 18th 1-3pm

    Room: PHE 223

    Title Understanding Diffusion Processes: Inference and Theory

    Abstract:

    Nowadays online social networks have become a ubiquitous tool for people's social communications. Analyzing these social networks offers great potential to shed light on the human social structure, and create better channels to enable social communications and collaborations. While most social analysis tasks begin with extracting or learning the social network and the associated parameters, it remains a very challenging task due to the amorphous nature of social ties and the noise and incompleteness in the observations. As a result, the inferred social network is likely to be of low accuracy and high level of noise which impacts the performance of analysis and applications depending on the inferred parameters.

    In this thesis, we study the following important questions with a special focus on analyzing diffusion behaviors in social networks to achieve real practicality: (1) How to utilize special properties of social networks to improve the accuracy of the extracted network under noisy and missing data? (2) How to characterize the impact of noise in the inferred network and carry out robust analysis and optimization?

    To address the first challenge towards accurate network inference, we tackle the issue of mitigating the impact of incomplete observations with a focus on learning influence function from incomplete observations. To address the challenge of data scarcity in inferring diffusion networks, we propose a hierarchical graphical model to jointly infer multiple diffusion networks accurately. To utilize the rich content information in cascades, we propose the HawkesTopic model to analyze text-based cascades by combining temporal and content information.

    To address the second challenge towards designing robust Influence Maximization algorithms, we first propose a framework to measure the stability of Influence Maximization with the Perturbation Interval Model to characterize the noise in the inferred diffusion network. We then design an efficient algorithm for Robust Influence Maximization to find influential users robust in multiple diffusion settings.

    Location: Charles Lee Powell Hall (PHE) - 223

    Audiences: Everyone Is Invited

    Posted By: Lizsl De Leon

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  • PhD Defense- Koki Nagano

    Wed, Apr 19, 2017 @ 10:30 AM - 12:30 PM

    Computer Science

    University Calendar


    PhD Candidate: Koki Nagano

    Committee: Paul Debevec (CS, chair), Hao Li (CS), Jernej Barbic (CS), Aiichiro Nakano (CS), Michelle Povinelli (EE)

    Title: Multi-scale Dynamic Capture for High Quality Digital Humans

    Time: April 19 (Wednesday) 10:30-12:30pm

    Room: KAP 164

    Abstract:

    Digitally creating a virtual human indistinguishable from a real human has been one of the central goals of Computer Graphics, Human-Computer Interaction, and Artificial Intelligence. Such digital characters are not only the primary creative vessel for immersive storytellers and filmmakers, but also a key technology to understand the process of how humans think, see, and communicate in the social environment. In order for digital character creation techniques to be valuable in simulating and understanding humans, the hardest challenge is for them to appear believably realistic from any point of view in any environment, and to behave and interact in a convincing manner.

    Creating a photorealistic rendering of a digital avatar is increasingly more accessible due to rapid advancement in sensing technologies and rendering techniques. However, generating realistic movement and dynamic details that are compatible with such a photorealistic appearance still relies on manual work from experts, which hinders the potential impact of digital avatar technologies in real world applications. Generating dynamic details is especially important for facial animation, as humans are extremely tuned to sense people's intentions from facial expressions.

    In this dissertation, we propose systems and approaches for capturing the appearance and motion to reproduce high fidelity digital avatars that are rich in subtle motion and appearance details. We aim for a framework which can generate consistent dynamic detail and motion at the resolution of skin pores and fine wrinkles, and can provide extremely high resolution microstructure deformation for use in cinematic storytelling or immersive virtual reality environments.

    This thesis presents three principal techniques for achieving multi-scale dynamic capture for digital humans. The first is a multi-view capture system and a stereo reconstruction technique which directly produces a complete high-fidelity head model with consistent facial mesh topology. Our method jointly solves for stereo constraints and consistent mesh parameterization from static scans or a dynamic performance, producing dense correspondences on an artist quality template. Additionally, we propose a technique to add dynamic per-frame high and middle frequency details from the flat-lit performance video. Second, we propose a technique to estimate high fidelity 3D scene flow from multiview video. The motion estimation fully respects high quality data from multiview input, and can be incorporated to any facial performance capture pipeline to improve the fidelity of the facial motion. Since the motion can be estimated without relying on any domain-specific priors or regularization, our method scales well to modern systems with many high-resolution cameras. Third, we present a technique to synthesize dynamic skin microstructure details to produce convincing facial animation. We measure and quantify how skin microstructure deformation contributes to dynamic skin appearance, and present an efficient way to simulate dynamic skin microstructure. When combined with the state-of-the art performance capture and face scanning techniques, it can significantly improve the realism of animated faces for virtual reality, video games, and visual effects.

    Location: Kaprielian Hall (KAP) - 164

    Audiences: Everyone Is Invited

    Posted By: Lizsl De Leon

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  • PhD Defense- Hongyi Xu

    Thu, Apr 20, 2017 @ 12:00 PM - 02:00 PM

    Computer Science

    University Calendar


    PhD Candidate: Hongyi Xu

    Title: Interactive Material and Damping Design.

    Date: 04/20/17
    Time: 12-2pm
    Location: SAL 213

    Committee:

    Jernej Barbic (Chair)
    Hao Li
    Yong Chen (Outside)

    Abstract:

    Finite Element Method (FEM) has been widely used for simulations of three-dimensional deformable objects. To produce compelling and artist-controllable FEM dynamics, the choices of material elasticity and damping properties are critically important. This thesis presents an intuitive and interactive design method to explore the high-dimensional space of material and damping for use in FEM simulations in computer graphics, animation and related fields.

    This thesis first demonstrates how to intuitively explore the space of isotropic and anisotropic nonlinear materials, for design of FEM animations. Previous applications of nonlinear solid elasticity employed materials from a few standard families such as linear corotational, nonlinear St.Venant-Kirchhoff and Neo-Hookean material. However, the spaces of all nonlinear isotropic and anisotropic materials are infinite-dimensional and much broader than these standard materials. We simplify this infinite-dimensional material space with the Valansis-Landel hypothesis and demonstrate how to easily design arbitrary isotropic and anisotropic nonlinear elasticity with local control, using a spline interface. Our materials accelerate simulation design and enable visual effects that are difficult or impossible to achieve with standard nonlinear materials.

    Material properties may vary across the volume of the object, producing heterogeneous deformable behaviors. My thesis presents an interactive inverse method to design heterogeneous material distributions, which conform to prescribed displacements and internal elastic forces at a few selected positions. However, this optimization problem is high-dimensional and solving it in the full space is not practical for interactive design. We demonstrate scalability to complex examples using a novel model reduction of the material space, which accelerates the optimization by two orders of magnitude and makes the convergence much more robust.

    FEM dynamics is largely affected also by the damping properties, in addition to elasticity. This thesis gives a damping design method and interface whereby the user can set the damping properties so that motion aligned with each of a few chosen example deformations is damped by an independent user-prescribed amount, achieving anisotropic damping effects. Similar to our spline-based elasticity, we also achieve nonlinear damping that depends on the example deformation magnitudes, by editing a single spline curve for each example. The nonlinear damping curves can also be automatically inferred from high-level user inputs, such as the amount of amplitude loss in one oscillation cycle. Our method enables an artist-directable and intuitive approach to controlling nonlinear and anisotropic damping, which can generate effects not possible with previous methods and better capture real-world damping dynamics


    Location: Henry Salvatori Computer Science Center (SAL) - 213

    Audiences: Everyone Is Invited

    Posted By: Lizsl De Leon

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  • PhD Defense - Chien-Chun Hung

    Thu, Apr 27, 2017 @ 02:15 AM - 04:15 PM

    Computer Science

    University Calendar


    PhD Candidate:
    Chien-Chun Hung

    Title:
    Resource Scheduling in Geo-distributed Computing

    Date & Time:
    April 27th, Thursday; 2:15-4:15pm

    Room:
    SAL 322

    Committee:
    Professor Leana Golubchik (advisor)
    Professor Bhaskar Krishnamachari (external member)
    Professor Wyatt Lloyd
    Professor Minlan Yu
    Doctor Ganesh Ananthanarayanan (Microsoft Research)

    Abstract:
    Due to the growing needs in computing and the increasing volume of data, cloud service providers deploy multiple datacenters around the world in order to provide fast computing response. Many applications utilizing such geo-distributed deployment include web search, user behavior analysis, machine learning applications and live camera feeds processing. Depending on the characteristics of the applications, their data may be generated, stored, and processed across the geo-distributed sites. Hence, how to efficiently process the data across the geo-distributed sites has become critical for the applications' performance.

    Existing solutions first aggregate all the required data to one location and execute the computation within the site. Such solutions incur a large amount of data transfer across the WAN and lead to prolonged response time for the applications due to the significant network delay. An emerging trend is to instead distribute the computation across the sites based on data distribution, and aggregate only the results afterward. Recent works have shown such new approach results in an improvement of 3-19X in response time, or 250X in the reduction of WAN bandwidth usage.

    Despite the preliminary gains, the performance of the geo-distributed jobs highly depends on how the resources are scheduled, which raises new challenges as the trivial extensions of state-of-the-art scheduling solutions lead to sub-optimal performance.

    In this thesis, we first take an initiative step for improving the performance of geo-distributed jobs from the perspective of computation resource. We provide the insights into how conventional Shortest Remaining Processing Time (SRPT) falls short due to the lack of scheduling coordination among the sites, and propose a light-weight heuristic that significantly improves the jobs' response time. We also design a new job scheduling heuristic that coordinates the workload demands and the resource availability among the sites, and greedily schedule for the job that can quickly finish.
    The trace-driven simulation studies show that our proposed scheduling heuristics effectively reduce the response time for the geo-distributed jobs by up to 50%.

    Next, we take a step further by addressing the geo-distributed jobs' performance from the perspectives of both the computation and the network resources. Specifically, we address the scheduling challenge of the heterogeneity of the resources availability across the sites and the mismatch of the data distribution across the geo-distributed sites. We formulate the task placement decisions into Linear Programming optimization, and allocate the resources to the job that can finish quickly. In addition to the response time, our design can also nicely incorporate other performance goals, e.g., fairness and WAN usage, with simple control knobs. The EC2-based deployment of our prototype and the large-scale trace-driven simulations showed that our solutions can improve the response time of the baseline in-place scheduling approach by up to 77%, and improve the state-of-the-art geo-distributed analytics solution by up to 55%.

    Finally, we expand to a more general setting in which each job has multiple configuration options, and its quality depends on the configuration it utilizes. We motivate this problem by the scenario of processing live camera feeds across hierarchical clusters. In this setting, we focus on the scheduling problem of jointly deciding job configuration and placement for concurrent jobs, and design efficient heuristic to maximize the overall quality with available resources across the geo-distributed sites. Our evaluation based on the Azure deployment of our prototype showed that the proposed solution outperforms the stat-of-the-art video analytics scheduler by up to $2.3X$, and outperforms the widely deployed Fair Scheduler by up to $15.7X$, in terms of the average quality of the concurrent jobs.


    Location: Henry Salvatori Computer Science Center (SAL) - 322

    Audiences: Everyone Is Invited

    Posted By: Lizsl De Leon

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