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DTSTART;TZID=America/New_York:20220928T090000
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DTSTAMP:20260408T034358
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SUMMARY:Extracting the quantum Hall conductance from a single bulk wavefunction from the modular flow
DESCRIPTION:Topological Quantum Matter Seminar \nSpeaker: Ruihua Fan\, Harvard University \nTitle: Extracting the quantum Hall conductance from a single bulk wavefunction from the modular flow\n\nAbstract: One question in the study of topological phases is to identify the topological data from the ground state wavefunction without accessing the Hamiltonian. Since local measurement is not enough\, entanglement becomes an indispensable tool. Here\, we use modular Hamiltonian (entanglement Hamiltonian) and modular flow to rephrase previous studies on topological entanglement entropy and motivate a natural generalization\, which we call the entanglement linear response. We will show how it embraces a previous work by Kim&Shi et al on the chiral central charge\, and furthermore\, inspires a new formula for the quantum Hall conductance.\n\nReferences: https://arxiv.org/abs/2206.02823\, https://arxiv.org/abs/2208.11710
URL:https://cmsa.fas.harvard.edu/event/tqm92822/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Topological Quantum Matter Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Topological-Seminar-09.28.22.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220928T123000
DTEND;TZID=America/New_York:20220928T133000
DTSTAMP:20260408T034358
CREATED:20230817T172722Z
LAST-MODIFIED:20240229T110654Z
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SUMMARY:The Tree Property and uncountable cardinals
DESCRIPTION:Colloquium \nSpeaker: Dima Sinapova (Rutgers University) \nTitle: The Tree Property and uncountable cardinals \nAbstract: In the late 19th century Cantor discovered that there are different levels of infinity. More precisely he showed that there is no bijection between the natural numbers and the real numbers\, meaning that the reals are uncountable. He then went on to discover a whole hierarchy of infinite cardinal numbers. It is natural to ask if finitary and countably infinite combinatorial objects have uncountable analogues. It turns out that the answer is yes. \nWe will focus on one such key combinatorial property\, the tree property. A classical result from graph theory (König’s infinity lemma) shows the existence of this property for countable trees. We will discuss what happens in the case of uncountable trees.\n\n 
URL:https://cmsa.fas.harvard.edu/event/collquium-title-tba-2-2/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Colloquium
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Colloquium-09.28.22.png
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DTSTART;TZID=America/New_York:20220928T140000
DTEND;TZID=America/New_York:20220928T150000
DTSTAMP:20260408T034358
CREATED:20230808T184138Z
LAST-MODIFIED:20240214T110335Z
UID:10001211-1664373600-1664377200@cmsa.fas.harvard.edu
SUMMARY:Statistical mechanics of neural networks: From the geometry of high dimensional error landscapes to beating power law neural scaling
DESCRIPTION:New Technologies in Mathematics \nSpeaker: Surya Ganguli\, Stanford University \n\nTitle: Statistical mechanics of neural networks: From the geometry of high dimensional error landscapes to beating power law neural scaling\n\n\n\n\nAbstract: Statistical mechanics and neural network theory have long enjoyed fruitful interactions.  We will review some of our recent work in this area and then focus on two vignettes. First we will analyze the high dimensional geometry of neural network error landscapes that happen to arise as the classical limit of a dissipative many-body quantum optimizer.  In particular\, we will be able to use the Kac-Rice formula and the replica method to calculate the number\, location\, energy levels\, and Hessian eigenspectra of all critical points of any index.  Second we will review recent work on neural power laws\, which reveal that the error of many neural networks falls off as a power law with network size or dataset size.  Such power laws have motivated significant societal investments in large scale model training and data collection efforts.  Inspired by statistical mechanics calculations\, we show both in theory and in practice how we can beat neural power law scaling with respect to dataset size\, sometimes achieving exponential scaling\, by collecting small carefully curated datasets rather than large random ones.\n\n\n\nReferences: Y. Bahri\, J. Kadmon\, J. Pennington\, S. Schoenholz\, J. Sohl-Dickstein\, and S. Ganguli\, Statistical mechanics of deep learning\, Annual Reviews of Condensed Matter Physics\, 2020.\n\nSorscher\, Ben\, Robert Geirhos\, Shashank Shekhar\, Surya Ganguli\, and Ari S. Morcos. 2022. Beyond Neural Scaling Laws: Beating Power Law Scaling via Data Pruning https://arxiv.org/abs/2206.14486 (NeurIPS 2022).
URL:https://cmsa.fas.harvard.edu/event/8303/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-09.28.2022.png
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