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Events for October

  • Mork Family Department Fall Seminars - Thomas Burbey, Virginia Tech

    Tue, Oct 05, 2021 @ 04:00 PM - 05:20 PM

    Mork Family Department of Chemical Engineering and Materials Science

    University Calendar


    Mork Family Department Fall Seminars - Thomas Burbey, Virginia Tech
    Host: Prof. Birendra Jha

    Join Zoom Meeting
    https://usc.zoom.us/j/98225952695?pwd=d0NMenhCNkliR1ZIR1lBamRpZHh1UT09

    Meeting ID: 982 2595 2695
    Passcode: 322435

    WebCast Link: https://usc.zoom.us/j/98225952695?pwd=d0NMenhCNkliR1ZIR1lBamRpZHh1UT09

    Audiences: Everyone Is Invited

    Contact: Greta Harrison

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  • Mork Family Department Fall Seminars - Oliver Fiehn, UC, Davis

    Tue, Oct 12, 2021 @ 04:00 PM - 05:20 PM

    Mork Family Department of Chemical Engineering and Materials Science

    University Calendar


    Mork Family Department Fall Seminars - Oliver Fiehn, UC, Davis
    Host: Prof. Nick Graham


    Join Zoom Meeting
    https://usc.zoom.us/j/98225952695?pwd=d0NMenhCNkliR1ZIR1lBamRpZHh1UT09

    Meeting ID: 982 2595 2695
    Passcode: 322435

    WebCast Link: https://usc.zoom.us/j/98225952695?pwd=d0NMenhCNkliR1ZIR1lBamRpZHh1UT09

    Audiences: Everyone Is Invited

    Contact: Greta Harrison

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  • PhD Defense - Danyong Zhao

    Tue, Oct 12, 2021 @ 04:00 PM - 06:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    PhD candidate: Dangyong Zhao

    Committee:
    Jernej Barbic (Chair)
    C.-C. Jay Kuo
    Yong Chen

    Date and Time: 10/12 at 4pm


    Acquisition of Human Tissue Elasticity Properties Using Pressure Sensors


    Abstract:

    Physically based simulation of the human body in three dimensions is important in many applications in computer graphics, animation, virtual reality, virtual commerce, ergonomics and virtual medicine. Finite Element Method (FEM) is a robust and reliable approach to simulate deformable dynamics of three-dimensional elastic structures. However, for quality simulation that matches the behavior of real human tissues, FEM needs accurate material properties that correctly model real relationships between the strains and stresses in the human tissue. This thesis presents methods to capture such nonlinear materials for the human musculo-skeletal tissue (skin, fat, muscles) in vivo, through carefully designed poking experiments, force meters, lasers and ultrasound measuring devices. From our experiments, we obtain ground-truth relationships between the contact force and skin deformation. We then fit material models that best approximate the acquired real-world data.



    First, we design a measuring device that can simultaneously capture the skin contact force and the skin deformation, consisting of a force meter and a laser distance measuring device. We design sliding and pivoting joints to rigidly attach the force meter to the laser device, so that diverse human body locations (arm, hand, belly, etc.) can be measured ergonomically and reliably. We also use an ultrasound device to capture the depth of the human subcutaneous fat at different locations; enabling us to generate a 3d model of the fat layer, and optionally also the muscle layer, of the human body.



    Second, we propose a novel approach to equivalently convert 3D FEM simulations into 2D simulation, suitable for our material capture. This method permits us to greatly speed up our material optimizations, without losing any accuracy. We validated this approach by comparing the simulation result from 2D equations and the 3D traditional equations. We propose to use natural cubic splines to parameterize the three separable scalar elastic energy density functions based on the Valanis-Landel material model. Based on our novel 2D simulation method and the spline-based non-linear isotropic material, we present an efficient method to compute the gradient of the objective function for optimization and use the conjugate gradient optimization method to optimize the material.



    Lastly, we use our acquired materials to design the geometric shape of rigid supporting surfaces to maximize the ergonomics of physically based contact between the surface and a deformable human. We model the soft deformable human using a layer of FEM deformable tissue surrounding a rigid core, with measured realistic elastic material properties, and large-deformation nonlinear analysis using our material capturing and optimizing method. We define a novel cost function to measure the ergonomics of contact between the human and the supporting surface. We give a stable and computationally efficient contact model that is differentiable with respect to the supporting surface shape. This makes it possible to optimize our ergonomic cost function using gradient-based optimizers. We 3D-print the optimized shoe sole, measure contact pressure using pressure sensors, and demonstrate that the real unoptimized and optimized pressure distributions qualitatively match those predicted by our simulation.



    WebCast Link: https://usc.zoom.us/j/94230145329

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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  • Thesis Proposal - Ritesh Ahuja "Differentially Private Model Publishing for Location Services.

    Thu, Oct 14, 2021 @ 01:30 PM - 03:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    Thesis Proposal - Ritesh Ahuja

    "Differentially Private Model Publishing for Location Services."

    Time:1:30-3:00pm PST, Oct 14 (Thursday)

    Committee: Cyrus Shahabi, Aleksandra Korolova, Bhaskar Krishnamachari, Muhammad Naveed, Srivatsan Ravi

    Zoom link: https://usc.zoom.us/j/7125668882

    Abstract:
    Mobile users share their coordinates with service providers (e.g., Google Maps) in exchange for receiving services customized to their location. The service providers analyze the data and publish powerful machine learning models for location search and recommendation. Even though individual location data are not disclosed directly, the model itself retains significant amounts of specific movement details, which in turn may leak sensitive information about an individual. To preserve individual privacy, one must first sanitize location data, which is commonly done using the powerful differential privacy (DP) concept. However, existing solutions fall short of properly capturing skewness inherent to sparse location datasets, and as a result yield poor accuracy

    In this proposal, we first focus on the Spatial Range Count primitive that forms the basis for many important applications such as improving POI placement, or studying disease spread. We propose a neural histogram system (SNH) that models spatial datasets such that important density and correlation features present in the data are preserved, even when DP-compliant noise is added. SNH employs a set of neural networks that learn from diverse regions of the dataset and at varying granularities, leading to superior accuracy. We also devise a framework for effective system parameter tuning on top of public data, which helps practitioners set important system parameters while avoiding privacy leakages.

    Finally, we focus on the next-location recommendation task, which is fundamentally more challenging. Learning user-user correlations from trajectory data requires increasing the dimensionality of intermediate layers in the neural network, and in the context of privacy-preserving learning, it increases data sensitivity, and requires a large amount of noise to be introduced. We briefly show that specific model architectures and data handling processes during DP-compliant training can significantly boost learning accuracy by keeping under tight control the amount of noise required to meet the privacy constraint. We conclude by suggesting ways to learn even richer models that can accurately recommend to a user entire location sequences, as opposed to only the next location.



    WebCast Link: https://usc.zoom.us/j/7125668882

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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  • Mork Family Department Fall Seminars - William Schneider, Notre Dame

    Tue, Oct 19, 2021 @ 04:00 PM - 05:20 PM

    Mork Family Department of Chemical Engineering and Materials Science

    University Calendar


    Mork Family Department Fall Seminars - William Schneider, Notre Dame
    Host: Prof. Shaama Sharada

    Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 101

    Audiences: Everyone Is Invited

    Contact: Greta Harrison

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  • USC Day of Making w/ YouTube's Allen Pan, USC Makers, USC Robogals

    Sat, Oct 23, 2021 @ 11:00 AM - 02:15 PM

    USC Viterbi School of Engineering, Viterbi School of Engineering K-12 STEM Center

    University Calendar


    Join us for a virtual Day of Making!
    Hear from USC students and alumni about their journey in developingmaking skills that combine engineering, robotics, and creativity.
    See examples of making projects that turn imaginary objects likesuperhero shields and portal guns into reality

    11 -“ 11:50 am Allen Pan, host of YouTube channel "Sufficiently Advanced"
    https://tinyurl.com/allenpanregister


    https://tinyurl.com/dayofmakingregister
    12:15pm -“ 1:00pm
    Mini Maker Faire with thUSC Makers
    1:15pm -“ 2:15pm
    Becoming the Next Generation of Innovators with USC Robogals

    More Information: Day of Making.pdf

    Location: Register for Webinar

    Audiences: Everyone Is Invited

    Contact: Katie Mills

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  • Mork Family Department Fall Seminars - Kevin Solomon, University of Delaware

    Tue, Oct 26, 2021 @ 04:00 PM - 05:20 PM

    Mork Family Department of Chemical Engineering and Materials Science

    University Calendar


    Mork Family Department Fall Seminars - Kevin Solomon, University of Delaware
    Host: Dr. Stacey Finley

    Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 101

    Audiences: Everyone Is Invited

    Contact: Greta Harrison

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