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DTSTART;TZID=America/New_York:20191202T120000
DTEND;TZID=America/New_York:20191202T130000
DTSTAMP:20260408T051836
CREATED:20190919T185009Z
LAST-MODIFIED:20190919T185009Z
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SUMMARY:PSOC Seminar : 'Deconstructing tumor architecture'
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/psoc-seminar-deconstructing-tumor-architecture/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191203T103000
DTEND;TZID=America/New_York:20191203T120000
DTSTAMP:20260408T051836
CREATED:20191108T144015Z
LAST-MODIFIED:20191108T144015Z
UID:10006330-1575369000-1575374400@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Physics-Informed Neural Networks (PINNs) for Physical Problems & Biological Problems"
DESCRIPTION:We will present a new approach to develop a data-driven\, learning-based framework for predicting outcomes of physical and biological systems and for discovering hidden physics from noisy data. We will introduce a deep learning approach based on neural networks (NNs) and generative adversarial networks (GANs). We also introduce new NNs that learn functionals and nonlinear operators from functions and corresponding responses for system identification. Unlike other approaches that rely on big data\, here we “learn” from small data by exploiting the information provided by the physical conservation laws\, which are used to obtain informative priors or regularize the neural networks. We will also make connections between Gauss Process Regression and NNs and discuss the new powerful concept of meta-learning. We will demonstrate the power of PINNs for several inverse problems in fluid mechanics\, solid mechanics and biomedicine including wake flows\, shock tube problems\, material characterization\, brain aneurysms\, etc\, where traditional methods fail due to lack of boundary and initial conditions or material properties.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-physics-informed-neural-networks-pinns-for-physical-problems-biological-problems/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191203T110000
DTEND;TZID=America/New_York:20191203T120000
DTSTAMP:20260408T051836
CREATED:20190828T155001Z
LAST-MODIFIED:20190828T155001Z
UID:10006276-1575370800-1575374400@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "Beyond Supervised Learning for Biomedical Imaging"
DESCRIPTION:Abstract: Today\, many biomedical imaging tasks\, such as 3D reconstruction\, denoising\, detection\, registration\, and segmentation\, are solved with machine learning techniques. In this talk\, I will present a flexible learning-based framework that has allowed us to derive efficient solutions for a variety of such problems\, without relying on heavy supervision. I will primarily employ image registration as a concrete application and present the details of VoxelMorph\, our unsupervised learning-based image registration tool. I will show empirical results obtained by co-registering thousands of brain MRI scans where VoxelMorph has yielded state-of-the-art accuracy with runtimes that are orders of magnitude faster than conventional tools. Finally\, I will present some recent results where we used VoxelMorph to learn conditional deformable templates that can reveal population variation as a function of factors of interest\, such as aging or genetics. Our code is freely available at https://github.com/voxelmorph/voxelmorph.
URL:https://seasevents.nmsdev7.com/event/ese-seminar-mert-sabuncu/
LOCATION:Smilow Center Auditorium\, 3400 Civic Center Blvd\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191203T120000
DTEND;TZID=America/New_York:20191203T130000
DTSTAMP:20260408T051836
CREATED:20191125T193432Z
LAST-MODIFIED:20191125T193432Z
UID:10006340-1575374400-1575378000@seasevents.nmsdev7.com
SUMMARY:MEAM Special Seminar: "Differences in Material Properties of Trabecular Bone Tissue from Modeling- and Remodeling-Based Bone Formation in Rats"
DESCRIPTION:Bone undergoes continuous changes during life through processes of bone modeling and remodeling. Modeling-based bone formation (MBF) i.e. bone formation without prior activation of osteoclastic resorption\, only occurs during growth\, healing\, and in response to external mechanical loading. Remodeling-based bone formation (RBF)\, which is tightly coupled with bone resorption by osteoclasts\, plays a predominant role maintaining skeletal health. Recent studies identified the activation of MBF as an important mechanism by which anabolic agents\, such as intermittent parathyroid hormone (PTH)\, rapidly elicit new bone formation. Due to the challenge of differentiating between MBF and RBF on a thick bone specimen\, the quality of the bone tissue generated through these two distinct cellular mechanisms is unknown. Therefore\, the goal of this study is to (1) develop an imaging method that can be coupled with a mechanical testing platform for reliable identification and examination of material properties of MBF and RBF on thick bone sections\, and (2) define the differences in material properties of trabecular bone tissue from MBF and RBF.
URL:https://seasevents.nmsdev7.com/event/meam-special-seminar-differences-in-material-properties-of-trabecular-bone-tissue-from-modeling-and-remodeling-based-bone-formation-in-rats/
LOCATION:CRB Auditorium\, 415 Curie Boulevard\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Student
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191205T104500
DTEND;TZID=America/New_York:20191205T114500
DTSTAMP:20260408T051836
CREATED:20191115T171649Z
LAST-MODIFIED:20191115T171649Z
UID:10006337-1575542700-1575546300@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "In-Situ Resistance Degradation & Switching of Bulk Fe-doped SrTiO3 and Yttria-Stabilized Zirconia"
DESCRIPTION:Perovskite-type titanates are the standard dielectric used in multilayer ceramic capacitors (MLCCs)\, and yttria-stabilized (YSZ) is the prototypical fast oxygen conductor used in solid oxide fuel cells (SOFCs) and oxygen sensors. Under a DC electric field\, the resistance of YSZ and Fe-doped strontium titanate (Fe-STO) degrades in an indistinguishable way. This is surprising because\, unlike YSZ\, Fe-STO is a p-type semiconductor\, so they cannot possibly degrade by the same mechanism. For decades\, it is known that the degradation of perovskite titanates is caused by the migration of oxygen vacancies towards the cathode\, where they are blocked\, and thus pile up\, which changes the region to n-type. In contrast\, through highly accelerated in situ lifetime tests\, we determined\, for the first time\, that the DC voltage induces in YSZ a metal-insulator transition that propagates from the cathode to the anode\, which lowers the resistance by orders of magnitude. In situ studies have further provided direct observations and mechanisms of resistance switching of both YSZ and Fe-STO. This is also of great importance as they relate to another technologically relevant device: resistance random access memory (ReRAM)\, which is currently explored for neuromorphic computing.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-in-situ-resistance-degradation-switching-of-bulk-fe-doped-srtio3-and-yttria-stabilized-zirconia/
LOCATION:Auditorium\, LRSM Building\, 3231 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191206T150000
DTEND;TZID=America/New_York:20191206T163000
DTSTAMP:20260408T051836
CREATED:20191204T163136Z
LAST-MODIFIED:20191204T163136Z
UID:10006341-1575644400-1575649800@seasevents.nmsdev7.com
SUMMARY:ESE PhD Thesis Defense: "Affordances and Control of a Spine Morphology for Robotic Quadrupedal Locomotion"
DESCRIPTION:Abstract: How does a robot’s body affect what it can do? This talk explores this question with respect to a morphology common to biology but rare in robotics: the presence of a bendable back. Using the Canid and Inu quadrupedal robots\, I describe and quantify several advantages afforded by this morphological design choice for legged machines that can be evaluated against the spine’s added weight and complexity. Additionally\, I discuss control strategies related to quadrupedal running with a spine.
URL:https://seasevents.nmsdev7.com/event/ese-phd-thesis-defense-jeff-duperret/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191210T150000
DTEND;TZID=America/New_York:20191210T160000
DTSTAMP:20260408T051836
CREATED:20191205T222025Z
LAST-MODIFIED:20191205T222025Z
UID:10006343-1575990000-1575993600@seasevents.nmsdev7.com
SUMMARY:MEAM Doctoral Dissertation Defense: "Estimation\, Mapping and Navigation with Micro Aerial Vehicles for Infrastructure Inspection"
DESCRIPTION:Multi-rotor Micro Aerial Vehicles (MAV) have become popular robotic platforms in the last decade due to their manufacturability\, agility and diverse payload options. Amongst the most promising applications areas of MAVs are inspection\, air delivery\, surveillance\, search and rescue\, real estate\, entertainment and photography to name a few. While GPS offers an easy solution for outdoor autonomy\, using onboard sensors is the only solution for autonomy in constrained indoor environments. In this work\, we study onboard state estimation\, mapping and navigation of a small MAV equipped with a minimal set of sensors inside GPS-denied axisymmetric tunnel-like environments such as penstocks. We primarily focus on state estimators formulated for different sensor suits which include 2D/3D lidars\, cameras and Inertial Measurement Units (IMU). Penstocks are pitch dark environments and offer very weak visual texture even with onboard illumination\, hence our estimators primarily rely on lidars and IMU. The point cloud data returned by the lidar consists of either elliptical contours or indiscriminate partial cylindrical patches making localization along the tunnel axis theoretically impossible. Cameras track features on the walls using the onboard illumination to estimate the velocity along the tunnel axis unobservable to range sensors. Information from all sensors are then fused in a central Kalman Filter for 6 Degrees-of-Freedom (DOF) state estimation. These approaches are validated through onsite experiments conducted in four different dams demonstrating state estimation\, environment mapping\, autonomous and shared control.
URL:https://seasevents.nmsdev7.com/event/meam-doctoral-dissertation-defense-estimation-mapping-and-navigation-with-micro-aerial-vehicles-for-infrastructure-inspection/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191217T130000
DTEND;TZID=America/New_York:20191217T150000
DTSTAMP:20260408T051836
CREATED:20191216T182744Z
LAST-MODIFIED:20191216T182744Z
UID:10006346-1576587600-1576594800@seasevents.nmsdev7.com
SUMMARY:CBE Dissertation Defense: "Controllable\, Synthetic Membrane-Less Organelles from Recombinant Proteins"
DESCRIPTION:Committee: Dr. Daniel A. Hammer\, Advisor; Drs. Kathleen J. Stebe\, Daeyeon Lee and Elizabeth Rhoades
URL:https://seasevents.nmsdev7.com/event/cbe-dissertation-defense-controllable-synthetic-membrane-less-organelles-from-recombinant-proteins/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Student
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
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