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DTSTART;TZID=America/New_York:20231018T123000
DTEND;TZID=America/New_York:20231018T133000
DTSTAMP:20260423T212340
CREATED:20240223T113304Z
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UID:10002866-1697632200-1697635800@cmsa.fas.harvard.edu
SUMMARY:Composite fermions and the fractional quantum anomalous Hall effect
DESCRIPTION:Topological Quantum Matter Seminar \nSpeaker: Hart Goldman\, University of Chicago \nTitle: Composite fermions and the fractional quantum anomalous Hall effect \nAbstract: Recent experiments have revealed evidence for fractional quantum anomalous Hall (FQAH) states at zero magnetic field in a growing number of moire materials. In this talk\, I will argue that a composite fermion description\, already a unifying framework for the phenomenology of 2d electron gases at high magnetic fields\, provides a similarly powerful perspective in this new zero-field context. In particular\, a central prediction of the composite fermion framework is a non-Fermi liquid metal of composite fermions at even-denominator fillings. To this end\, I will present exact diagonalization evidence for such composite Fermi liquid states at zero magnetic field in twisted MoTe2 bilayers\, at fillings n = 1/2 and n = 3/4. Dubbing these states anomalous composite Fermi liquids (ACFLs)\, I will argue that they play a central organizing role in the FQAH phase diagram. I will also develop a long wavelength theory for this ACFL state\, which offers concrete experimental predictions that I will discuss in relation to current measurements. For example\, upon doping the composite Fermi sea\, one obtains a Jain sequence of FQAH states consistent with those observed experimentally\, as well as a new type of commensurability oscillations originating from the superlattice potential intrinsic to the system. Finally\, I will discuss opportunities for new physics not possible in quantum Hall systems at finite magnetic field.
URL:https://cmsa.fas.harvard.edu/event/tqms_101823/
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-10.18.23.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231018T140000
DTEND;TZID=America/New_York:20231018T150000
DTSTAMP:20260423T212340
CREATED:20240223T114049Z
LAST-MODIFIED:20240223T114049Z
UID:10002867-1697637600-1697641200@cmsa.fas.harvard.edu
SUMMARY:Physics of Language Models: Knowledge Storage\, Extraction\, and Manipulation
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Yuanzhi Li\, CMU Dept. of Machine Learning and Microsoft Research \nTitle: Physics of Language Models: Knowledge Storage\, Extraction\, and Manipulation \nAbstract: Large language models (LLMs) can memorize a massive amount of knowledge during pre-training\, but can they effectively use this knowledge at inference time? In this work\, we show several striking results about this question. Using a synthetic biography dataset\, we first show that even if an LLM achieves zero training loss when pretraining on the biography dataset\, it sometimes can not be finetuned to answer questions as simple as “What is the birthday of XXX” at all. We show that sufficient data augmentation during pre-training\, such as rewriting the same biography multiple times or simply using the person’s full name in every sentence\, can mitigate this issue. Using linear probing\, we unravel that such augmentation forces the model to store knowledge about a person in the token embeddings of their name rather than other locations. \nWe then show that LLMs are very bad at manipulating knowledge they learn during pre-training unless a chain of thought is used at inference time. We pretrained an LLM on the synthetic biography dataset\, so that it could answer “What is the birthday of XXX” with 100% accuracy.  Even so\, it could not be further fine-tuned to answer questions like “Is the birthday of XXX even or odd?” directly.  Even using Chain of Thought training data only helps the model answer such questions in a CoT manner\, not directly. \nWe will also discuss preliminary progress on understanding the scaling law of how large a language model needs to be to store X pieces of knowledge and extract them efficiently. For example\, is a 1B parameter language model enough to store all the knowledge of a middle school student? \n  \n 
URL:https://cmsa.fas.harvard.edu/event/nt-101823/
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/NTM-10.18.2023.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231018T153000
DTEND;TZID=America/New_York:20231018T163000
DTSTAMP:20260423T212340
CREATED:20240223T075212Z
LAST-MODIFIED:20240223T075212Z
UID:10002829-1697643000-1697646600@cmsa.fas.harvard.edu
SUMMARY:Geometry of the doubly periodic Aztec dimer model
DESCRIPTION:Probability Seminar \nSpeaker: Tomas Berggren (MIT) \nTitle: Geometry of the doubly periodic Aztec dimer model \nAbstract: Random dimer models (or equivalently tiling models) have been a subject of extensive research in mathematics and physics for several decades. In this talk\, we will discuss the doubly periodic Aztec diamond dimer model of growing size\, with arbitrary periodicity and only mild conditions on the edge weights. In this limit\, we see three types of macroscopic regions — known as rough\, smooth and frozen regions. We will discuss how the geometry of the arctic curves\, the boundary of these regions\, can be described in terms of an associated amoeba and an action function. In particular\, we determine the number of frozen and smooth regions and the number of cusps on the arctic curves. We will also discuss the convergence of local fluctuations to the appropriate translation-invariant Gibbs measures. Joint work with Alexei Borodin. \n  \n 
URL:https://cmsa.fas.harvard.edu/event/probability-101123/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Probability Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Probability-Seminar-10.18.23.png
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