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DTSTART;TZID=America/New_York:20190205T104500
DTEND;TZID=America/New_York:20190205T114500
DTSTAMP:20260409T042547
CREATED:20190117T132940Z
LAST-MODIFIED:20190117T132940Z
UID:10006139-1549363500-1549367100@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Hairy Hydrodynamics"
DESCRIPTION:Flexible slender structures in flow are everywhere. While a great deal is known about individual flexible fibers interacting with fluids\, considerably less work has been done on fiber ensembles — such as fur or hair — in flow. These hairy surfaces are abundant in nature and perform multiple functions from thermal regulation to water harvesting to sensing. Motivated by these biological systems\, we consider several examples of hairy surfaces interacting with flow including drinking bats and diving sea otters. In the first example we consider viscous dipping\, a feeding method utilized by many nectar drinking animals. This mechanism is reminiscent of Landau-Levich-Derjaguin (LLD) dip coating\, and has been analyzed through the LLD framework in previous studies. However\, many viscous dippers have hairy structures on their tongues that enhance fluid uptake. In this study\, we investigate the impact of mesoscale hairy structures on feeding efficiency. In the second example\, we take inspiration from semi-aquatic mammals (such as fur seals\, otters\, and beavers) which have specially adapted fur that serves as an effective insulator both above and below water. Many of these animals have evolved pelts that naturally entrap air when they dive. Here we investigate diving conditions and fur properties which amplify air entrainment.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-hairy-hydrodynamics/
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
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190205T104500
DTEND;TZID=America/New_York:20190205T114500
DTSTAMP:20260409T042547
CREATED:20190124T124157Z
LAST-MODIFIED:20190124T124157Z
UID:10006142-1549363500-1549367100@seasevents.nmsdev7.com
SUMMARY:MSE Faculty Candidate Seminar: "Disorder and Superconductivity in 2D TMD Heterostructures"
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/mse-faculty-candidate-seminar-disorder-and-superconductivity-in-2d-tmd-heterostructures/
LOCATION:Glandt Forum\, Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Faculty
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190206T150000
DTEND;TZID=America/New_York:20190206T160000
DTSTAMP:20260409T042547
CREATED:20190110T195801Z
LAST-MODIFIED:20190110T195801Z
UID:10006123-1549465200-1549468800@seasevents.nmsdev7.com
SUMMARY:CBE Faculty Candidate Seminar: "Novel Electrolyte Design to Control Electrochemistry in Energy Storage Systems"
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-novel-electrolyte-design-to-control-electrochemistry-in-energy-storage-systems/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190207T104500
DTEND;TZID=America/New_York:20190207T114500
DTSTAMP:20260409T042547
CREATED:20190201T160414Z
LAST-MODIFIED:20190201T160414Z
UID:10006144-1549536300-1549539900@seasevents.nmsdev7.com
SUMMARY:MSE Faculty Candidate Seminar: "Energy storage and neuromorphic computing using electrochemical ion insertion"
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/mse-faculty-candidate-seminar-energy-storage-and-neuromorphic-computing-using-electrochemical-ion-insertion/
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:20190207T110000
DTEND;TZID=America/New_York:20190207T120000
DTSTAMP:20260409T042547
CREATED:20190110T200124Z
LAST-MODIFIED:20190110T200124Z
UID:10006124-1549537200-1549540800@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "Liquid Silicon: A New Computing Paradigm Enabled by Monolithic 3D Cross-Point Memory"
DESCRIPTION:Almost every subfield of electrical engineering and computer science are undergoing disruptive times. With Moore’s Law coming to an end\, an expanded roadmap for semiconductors beyond traditional CMOS scaling becomes unclear. At the other end\, traditional application software development is being replaced by emerging machine learning techniques whose success will\, in turn\, rely on the availability of powerful\, efficient and flexible computer systems. Due to these emerging applications\, architecture is transitioning from mainstream CPU to heterogeneous and diverse options such as GPU\, TPU\, etc. The confluence of these key trends has created a wide efficiency gap\, due to the mismatch between emerging application requirements and the relatively slow evolutionary improvements in existing CMOS-based computer hardware. \nTo close the gap\, in this talk\, I will present a reconfigurable memory-oriented computing fabric\, namely Liquid Silicon (L-Si) by leveraging the monolithic 3D stacking capability of RRAM. L-Si addresses several key fundamental limitations of state-of-the-art reconfigurable architectures including FPGA\, etc. in supporting emerging data-/search-intensive applications (e.g.\, machine learning and neural networks) through a series of innovations. It\, for the first time\, extends the configuration capabilities of existing reconfigurable architectures (FPGA\, CGRA) from computation to the whole spectrum\, from full memory to full computation\, or intermediate states in between (partial memory and partial computation). Thus\, it allows users more flexibility in customizing hardware to better match an application’s characteristics\, for higher performance and energy efficiency. The talk will consist of four parts\, technology\, architecture\, compiler tool\, and algorithm\, with a combined EE and CS flavor.
URL:https://seasevents.nmsdev7.com/event/ese-seminar-liquid-silicon-a-new-computing-paradigm-enabled-by-monolithic-3d-cross-point-memory/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190208T110000
DTEND;TZID=America/New_York:20190208T120000
DTSTAMP:20260409T042547
CREATED:20190204T145129Z
LAST-MODIFIED:20190204T145129Z
UID:10006148-1549623600-1549627200@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "Local Geometric Spectral Data Analysis"
DESCRIPTION:Modern technological developments have enabled the acquisition and storage of increasingly large-scale\, high-resolution\, and high-dimensional data in many fields. Yet in domains such as biomedical data\, the complexity of these datasets and the unavailability of ground truth pose significant challenges for data analysis and modeling. In this talk\, I present new unsupervised geometric approaches for extracting structure from large-scale high-dimensional data. By looking deep within the spectrum of the graph-Laplacian\, we define a new robust measure\, the Spectral Embedding Norm\, to separate clusters from background\, and demonstrate its application to both outlier detection and data visualization. This measure further motivates a new greedy clustering approach based on Local Spectral Viewpoints for identifying high-dimensional overlapping clusters while disregarding noisy clutter. We demonstrate our approach on two-photon calcium imaging data\, successfully extracting hundreds of individual cells. Finally\, to address the computational complexity of applying spectral approaches to large-scale data\, we present a new randomized near-neighbor graph construction. Compared to the traditional k-nearest neighbor graph\, using our near-neighbor graph for spectral clustering on datasets of a few million points is two orders of magnitude faster\, while achieving similar clustering accuracy. \nJoint work with Ronald Coifman\, Jackie Schiller\, Maria Lavzin\, Xiuyuan Cheng\, George Linderman\, Ariel Jaffe\, Yuval Kluger and Stefan Steinerberger.
URL:https://seasevents.nmsdev7.com/event/ese-seminar-local-geometric-spectral-data-analysis-2/
LOCATION:Berger Auditorium (Room 13)\, Skirkanich Hall\, 210 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
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