Events Calendar

 December 2024        
MondayTuesdayWednesdayThursdayFriday
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Chris Dessert, Cosmic Axiverse Background (12:30 PM - 1:30 PM)

, Astro Journal Club (2:00 PM - 3:15 PM)

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Caio Nascimento, The speed of sound in the EFTofLSS (11:00 AM - 12:15 PM)

+ Abstract:

Ravi Sheth, Back to the future: Reconstructing initial conditions from galaxy surveys (2:00 PM - 3:15 PM)

+ Abstract:

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, HEP Journal Club (12:30 PM - 1:45 PM)

Peizhi Du, Dark Radiation Isocurvature from Cosmological Phase Transitions (2:00 PM - 3:15 PM)

+ Abstract:

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Hogg/Blanton (2:00 PM - 3:45 PM)

, Pullen Group Meeting (3:00 PM - 4:00 PM)

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Andrea Ghez (3:00 PM - 6:00 PM)

Q. Weller and M. Calkins (4:30 PM - 6:00 PM)

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Anna Suliga, Non-conservation of Lepton Numbers in the Neutrino Sector Could Change the Prospects for Core Collapse Supernova Explosions (12:30 PM - 1:30 PM)

, Astro Journal Club (2:00 PM - 3:15 PM)

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David Hogg et. al, Machine Learning Chalk Talk (11:00 AM - 12:15 PM)

+ Abstract:

Keith Hawkins, Galactic Archaeology in the Gaia Era (2:00 PM - 3:15 PM)

+ Abstract:

11
, HEP Journal Club (12:30 PM - 1:45 PM)

Nathaniel Craig, Broken higher-form symmetries in particle physics (2:00 PM - 3:15 PM)

+ Abstract:

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Amir Levinson, Fast radio bursts, monster shocks and chaos (2:00 PM - 3:00 PM)

+ Abstract:

Hogg/Blanton (2:00 PM - 3:45 PM)

, Pullen Group Meeting (3:00 PM - 4:00 PM)

Vincenzo Vitelli, Learning and Active Mechanics (4:00 PM - 5:30 PM)

-- Abstract: Physical learning is an emerging paradigm whereby materials acquire behaviors by exposure to examples. So far, it has been applied to static properties encoded in energy minima. In this talk, we extend it to dynamic functionalities, such as motion and shape change. Using a generalized Hopfield model, we delineate the key physical ingredients needed and illustrate them with LEGO toys as well as potential active matter platforms based on oil droplets with chemotactic signaling that learn life-like functionalities. Next, we turn to investigate how living organisms themselves exploit active mechanics to change their shape. Using machine learning, we infer an interpretable model of morphogenesis in Drosophila embryos that captures how tissue flow is regulated by protein dynamics and validate it with a mutant analysis. This data driven model taken together with experiments on human stem cells suggest that our machine-learned mechanism for early neuroectoderm morphogenesis is conserved across species.

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, Holiday Party (3:30 PM - 5:30 PM)

Q. Weller and M. Calkins (4:30 PM - 6:00 PM)

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Giovaneti, CU*IP (4:00 PM - 6:00 PM)

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, Holiday Recess; University Closed

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, Holiday Recess; University Closed

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, Holiday Recess; University Closed

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, Holiday Recess; University Closed

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, Holiday Recess; University Closed

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, Holiday Recess; University Closed

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, Holiday Recess; University Closed

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, Holiday Recess; University Closed

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