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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221018T100000
DTEND;TZID=America/New_York:20221018T113000
DTSTAMP:20260405T155616
CREATED:20220830T154411Z
LAST-MODIFIED:20220830T154411Z
UID:10007237-1666087200-1666092600@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Exergy-based Methods as a Promising Modern Thermodynamic Evaluation and Optimization Tool"
DESCRIPTION:Exergy-based methods are powerful tools for developing\, evaluating\, understanding\, and improving energy conversion systems. In addition to conventional methods\, advanced exergy-based analyses consider (a) the interactions among components of the overall system\, and (b) the real potential for improving each important system component. The main role of an advanced analysis is to provide energy conversion system designers and operators with information useful for improving the design and operation of such systems. This presentation will include the advanced exergy-based evaluations and optimization methods as well. Advanced exergy-based method means splitting the exergy destruction\, the capital investment cost\, and the component-related environmental impact associated with each single component of an energy conversion system into endogenous/exogenous and avoidable/unavoidable parts and using a further splitting of the exogenous exergy destruction improves (a) our understanding of the processes that take place\, and (b) the quality of the conclusions for improvement obtained from the analysis. It will be discussed the main features and some recent developments in the area of advanced exergy-based methods. Application of the method to different energy-conversion systems will be demonstrated.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-exergy-based-methods-as-a-promising-modern-thermodynamic-evaluation-and-optimization-tool/
LOCATION:Zoom – Email MEAM for Link\, peterlit@seas.upenn.edu
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221018T120000
DTEND;TZID=America/New_York:20221018T130000
DTSTAMP:20260405T155616
CREATED:20220901T140716Z
LAST-MODIFIED:20220901T140716Z
UID:10007242-1666094400-1666098000@seasevents.nmsdev7.com
SUMMARY:Joint PSOC/Center for Soft & Living Matter Seminar: “Feeling for cell function” (Jochen Guck\, Max Planck Institute)
DESCRIPTION:Fall 2022 Hybrid-Seminar Series  \nSpecial joint seminar on Tuesday October 18th at 12:00 Noon \nTowne 225 / Raisler Lounge   \nFor Zoom link\, please contact <manu@seas.upenn.edu
URL:https://seasevents.nmsdev7.com/event/joint-psoc-center-for-soft-living-matter-seminar-feeling-for-cell-function-jochen-guck-max-planck-institute/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="PSOC":MAILTO:manu@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221018T153000
DTEND;TZID=America/New_York:20221018T163000
DTSTAMP:20260405T155616
CREATED:20221005T151746Z
LAST-MODIFIED:20221005T151746Z
UID:10007319-1666107000-1666110600@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Equilibrium Complexity and Deep Learning"
DESCRIPTION:Deep Learning has recently made significant progress in learning challenges such as speech and image recognition\, automatic translation\, and text generation\, much of that progress being fueled by the success of gradient descent-based optimization methods in computing local optima of non-convex objectives. From robustifying machine learning models against adversarial attacks to causal inference\, training generative models\, multi-robot interactions\, and learning in strategic environments\, many outstanding challenges in Machine Learning lie at its interface with Game Theory. On this front\, however\, Deep Learning has been less successful. Here\, the role of single-objective optimization is played by equilibrium computation\, but gradient-descent based methods fail to find equilibria\, and even computing local equilibria — the analog of computing local optima in single-agent settings — has remained elusive. \n \nWe shed light on these challenges through a combination of learning-theoretic\, complexity-theoretic\, and game-theoretic techniques\, presenting obstacles and opportunities for Machine Learning and Game Theory going forward\, including recent progress on multi-agent reinforcement learning.\n \n(I will assume no deep learning\, game theory\, or complexity theory background for this talk and present results from joint works with Noah Golowich\, Stratis Skoulakis\, Manolis Zampetakis\, and Kaiqing Zhang.)
URL:https://seasevents.nmsdev7.com/event/cis-seminar-equilibrium-complexity-and-deep-learning/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221019T120000
DTEND;TZID=America/New_York:20221019T133000
DTSTAMP:20260405T155616
CREATED:20220909T133002Z
LAST-MODIFIED:20220909T133002Z
UID:10007262-1666180800-1666186200@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: New approaches to detecting and adapting to domain shifts in machine learning\, Zico Kolter\, Ph.D. (Carnegie Mellon University)
DESCRIPTION:ABSTRACT: \nMachine learning systems\, in virtually every deployed system\, encounter data from a qualitatively different distribution than what they were trained upon.  Effectively dealing with this problem\, known as domain shift\, is thus perhaps the key challenge in deploying machine learning methods in practice.  In this talk\, I will motivate some of these challenges in domain shift\, and highlight some of our recent work on two topics.  First\, I will present our work on determining if we can even just evaluate the performance of machine learning models under distribution shift\, without access to labelled data.  And second\, I will present work on how we can better adapt our classifiers to new data distributions\, again assuming access only to unlabelled data in the new domain.
URL:https://seasevents.nmsdev7.com/event/asset-seminar-tba-zico-kolter-carnegie-mellon-university/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221019T150000
DTEND;TZID=America/New_York:20221019T160000
DTSTAMP:20260405T155616
CREATED:20221012T173334Z
LAST-MODIFIED:20221012T173334Z
UID:10007330-1666191600-1666195200@seasevents.nmsdev7.com
SUMMARY:Fall 2022 GRASP SFI: Srinath Sridhar\, Brown University\, “Learning to Generate\, Edit\, and Arrange 3D Shapes"
DESCRIPTION:This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom. \nABSTRACT\nIn computer vision and robotics\, we often need to deal with 3D objects. For instance\, we may want to generate instances of 3D chairs\, edit the generated chairs using natural language instructions\, or arrange them in a canonical orientation. In this talk\, I will present some of our work on addressing these problems. First\, I will talk about ShapeCrafter\, a model for recursively generating and modifying 3D shapes using natural language descriptions. ShapeCrafter generates a 3D shape distribution that gradually evolves as more phrases are added resulting in shapes closer to text instructions. In addition\, I will introduce the notions of invariance\, equivariance\, and ‘canonicalization’\, and discuss their importance in 3D understanding. I will describe ConDor\, a self-supervised method for canonicalizing the orientation of full and partial 3D shapes. Finally\, I will identify future directions including opportunities for expanding 3D understanding to neural fields\, articulating objects\, and object collections.
URL:https://seasevents.nmsdev7.com/event/fall-2022-grasp-sfi-srinath-sridhar-brown-university-tba/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221020T153000
DTEND;TZID=America/New_York:20221020T163000
DTSTAMP:20260405T155616
CREATED:20221005T153839Z
LAST-MODIFIED:20221005T153839Z
UID:10007320-1666279800-1666283400@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Rater Equivalence: An Interpretable Measure of Classifier Accuracy Against Human Labels"
DESCRIPTION:In many classification tasks\, the ground truth is either noisy or subjective. Examples of noisy ground truth include: does this radiology image show a cancerous growth? does this radar data portend an imminent tornado? Examples of subjective ground truth include: which of two alternative paper titles is better? is this comment toxic? what is the political leaning of this news article? We refer to tasks where human labels are the only indication of ground truth available at the time that decisions must be made as survey settings. In these settings\, measures of classifier accuracy against human labels\, such as precision\, recall\, and cross-entropy\, confound the quality of the classifier with the level of agreement among human raters. Thus\, they have no meaningful interpretation on their own. We describe a procedure that\, given a dataset with predictions from a classifier and K labels per item\, rescales any underlying accuracy measure into one that has an intuitive interpretation. The K raters are divided into a source panel and a target panel. The source panel’s labels for an item are combined to produce a predicted label for another rater. Both the source panel predictions and classifier predictions are scored against the same target panel’s labels. The rater equivalence of any classifier is the minimum number of source raters needed to produce the same expected score as that found for the classifier. We explore the stability of the rater equivalence measure as the target panel size varies and find one underlying measure\, determinant mutual information\, for which it is invariant.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-rater-equivalence-an-interpretable-measure-of-classifier-accuracy-against-human-labels/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221020T223000
DTEND;TZID=America/New_York:20221020T233000
DTSTAMP:20260405T155616
CREATED:20221006T171538Z
LAST-MODIFIED:20221006T171538Z
UID:10007326-1666305000-1666308600@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: “What Governs Grain Boundary Migration?"
DESCRIPTION:Curvature is the common driving force for grain boundary motion in all polycrystals. However\, models and simulations derived from curvature-based motion cannot predict irregular\, albeit commonly observed\, grain growth behavior. To build better predictive models\, we need to employ new tools to understand what governs grain growth. First\, I will demonstrate how high energy x-ray diffraction microscopy (HEDM) can be used to observe grain growth in real 3D polycrystalline systems. In a grain growth study employing HEDM of strontium titanate\, we find that curvature is a poor predictor of grain boundary migration. Instead\, anisotropic grain boundary properties are hypothesized to override the contribution of curvature. Second\, I will describe how reinforcement learning\, a machine learning tool\, can capture the underlying behavior of an evolving Markov decision process and “teach” it to maximize the “rewards” regarding the agreement between prediction and simulation. To demonstrate the feasibility of this approach\, we built a deep reinforcement model that emulates grain growth by training on Monte Carlo Potts grain growth simulations. The developed reinforcement model was validated on different microstructural architectures to ensure that it captures the underlying physics. The accuracy of our short and long-term predictions will be evaluated. Then\, I will discuss how HEDM and our machine learning model can be combined to understand how anisotropic grain boundaries migrate in 3D polycrystals.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-what-governs-grain-boundary-migration/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221021T103000
DTEND;TZID=America/New_York:20221021T114500
DTSTAMP:20260405T155616
CREATED:20220926T165944Z
LAST-MODIFIED:20220926T165944Z
UID:10007306-1666348200-1666352700@seasevents.nmsdev7.com
SUMMARY:Fall 2022 GRASP on Robotics: Jim Ostrowski\, Blue River Technology\, "Robotics and Deep Learning in Production Agriculture"
DESCRIPTION:This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance via Zoom. \n  \nABSTRACT\nWith a growing population to feed\, a heightened awareness of the environmental impact of agriculture\, and continued challenges of labor availability\, the need is greater than ever for advanced technologies applied to automation and autonomy in agriculture.  In this talk\, I will describe two projects that we have been developing\, and discuss the role of robotics\, computer vision\, and machine learning in delivering commercially viable products.  The products include a smart spraying solution called See & Spray Ultimate and an autonomous tractor for tillage.  I will explore some of the key focus areas\, technological development\, and common themes that have allowed us to move these products to production.
URL:https://seasevents.nmsdev7.com/event/fall-2022-grasp-on-robotics-jim-ostrowski-blue-river-technology/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221021T120000
DTEND;TZID=America/New_York:20221021T130000
DTSTAMP:20260405T155616
CREATED:20221017T141305Z
LAST-MODIFIED:20221017T141305Z
UID:10007332-1666353600-1666357200@seasevents.nmsdev7.com
SUMMARY:PRECISE Center/xLab presents: Routing with Privacy for drone package delivery systems\, Max Z. Li\, Ph.D. (University of Michigan)
DESCRIPTION:ABSTRACT: \nUncrewed aerial vehicles (UAVs)\, or drones\, are increasingly being used to deliver goods from vendors to customers. To safely conduct these operations at scale\, drones are required to broadcast position information as codified in remote identification (remote ID) regulations. However\, location broadcast of package delivery drones introduces a privacy risk for customers using these delivery services: Third-party observers may leverage broadcast drone trajectories to link customers with their purchases\, potentially resulting in a wide range of privacy risks. \nWe propose a probabilistic definition of privacy risk based on the likelihood of associating a customer to a vendor given a package delivery route. Next\, we quantify these risks\, enabling drone operators to assess privacy risks when planning delivery routes. We then evaluate the impacts of various factors (e.g.\, drone capacity) on privacy and consider the trade-offs between privacy and delivery wait times. Finally\, we propose heuristics for generating routes with privacy guarantees to avoid exhaustive enumeration of all possible routes and evaluate their performance on several realistic delivery scenarios.
URL:https://seasevents.nmsdev7.com/event/precise-center-xlab-presents-routing-with-privacy-for-drone-package-delivery-systems-max-z-li-ph-d-university-of-michigan/
LOCATION:Levine Hall 279\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221021T140000
DTEND;TZID=America/New_York:20221021T150000
DTSTAMP:20260405T155616
CREATED:20221010T134351Z
LAST-MODIFIED:20221010T134351Z
UID:10007329-1666360800-1666364400@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: "Computation of Flow-Induced Sound at Low Mach Numbers"
DESCRIPTION:Abstract: Flow-induced noise is a significant problem for air\, road and marine vehicles as well as many other engineering applications.  At low Mach numbers\, large disparities in energy levels and length scales between the flow and the concomitant sound present unique challenges for acoustic predictions.  This talk will start with a brief overview of computational methods for low-Mach-number flow noise in the framework of Lighthill’s aeroacoustic theory in combination with high-fidelity flow simulations\, followed by a discussion of recent investigations of several aeroacoustic problems involving airframe noise and propeller noise.  A study of rotor interaction with an axisymmetric turbulent boundary layer (TBL) at the tail-end of a body of revolution (BOR) will be highlighted.  The TBL on the nose and midsection of the BOR is computed using wall-modeled large-eddy simulation whereas that in the acoustically important tail-cone section is wall resolved.  This approach is shown to predict the correct turbulence statistics of rotor inflow and sound-pressure spectra compared with experimental data.  Correlation and spectral analyses demonstrate rapidly growing turbulence structures in the decelerating tail-cone TBL whose interaction with successive rotor blades generates spectral peaks\, known as haystacking peaks\, in the broadband sound pressure spectra.  The spatial and frequency characteristics of blade acoustic dipole sources will be discussed in relation to the turbulence properties of the boundary layer.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-computation-of-flow-induced-sound-at-low-mach-numbers/
LOCATION:PICS Conference Room 534 – A Wing \, 5th Floor\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
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