BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Sonia Zhang (Princeton) DTSTART:20200508T170000Z DTEND:20200508T180000Z DTSTAMP:20260404T094600Z UID:PrincetonFPO/1 DESCRIPTION:Title: Topology in quantum magnets and superconductors\nby Sonia Zhang (Princeton) as part of Princeton FPO\n\n\nAbstract\nA unifying theme in understanding the macroscopic properties of quantum materials is the c oncept of emergence\, where novel effects and unusual phases arise from th e interplay of spin-orbit effects\, band topology and strong correlation. In this dissertation\, using scanning tunnelling microscopy and spectrosco py (STM/S)\, we explore a series of unconventional spin-orbit materials in cluding magnets and superconductors. First\, we provide a brief introducti on to the working principles of low temperature atomic resolution STM/S op erating in conjunction with a vector magnetic field capability. Utilising this state-of-the-art capability we explore the effects of artificial qua ntum impurity and vortex defects on topological superconductor candidates LiFeAs and PbTaSe2. We find that a controlled deposition of a carefully c hosen class of atomic scale magnetic impurities on their surfaces generate s zero-bias peaks exhibiting signatures of Majorana zero modes\, despite b eing absent in vortices in pristine samples. In a second line of research\ , we explore wavefunction topology in correlated kagome magnets. In Fe3Sn2 \, we discover a giant and anisotropic many-body spin-orbit tunability who se origin remains unclear in current theoretical models. In Co3Sn2S2 we find an unexpected negative magnetic response in the kagome flat band ar ising from the topology. Finally\, we explore topological magnet Mn3Sn and show that the unique geometry of the kagome lattice leads to a remarkable manifestation of an apparent Kondo lattice-type effect\, usually observed in strongly correlated heavy fermion materials. Our results taken collect ively feature novel effects and phases arising from rich interplay among s pin-orbit effects\, band topology and many-body interactions in quantum ma gnets and exotic superconductors\, that may potentially lead to new fronti ers in condensed matter physics.\n LOCATION:https://stable.researchseminars.org/talk/PrincetonFPO/1/ END:VEVENT BEGIN:VEVENT SUMMARY:Yunqin Zheng DTSTART:20200504T170000Z DTEND:20200504T180000Z DTSTAMP:20260404T094600Z UID:PrincetonFPO/2 DESCRIPTION:Title: Tensor network states\, entanglement\, and anomalies of topolo gical phases of matter\nby Yunqin Zheng as part of Princeton FPO\n\n\n Abstract\nThis dissertation investigates two aspects of topological phases of matter: 1) the tensor network state (TNS) representations of the groun d states as well as their en- tanglement entropies of gapped Hamiltonians in diverse dimensions\; 2) the anomalies and dynamics of strongly coupled quantum field theories.\n\nFor the first aspect\, we first show an efficie nt method of analytically deriving the translation invariant TNS and matri x product state (MPS) representation for the ground state of translation i nvariant stabilizer code Hamiltonians in both 1d and higher dimensions. Th ese TNS/MPS states have minimal virtual bond dimension. Using the TNS\, we derive the entanglement entropy for a variety of stabilizer codes\, inclu ding the fracton models the Haah code. We further go beyond the stabilizer codes and study the structure of entanglement entropy for generic 3d gapp ed Hamiltonians. In particular\, an explicit formula for a universal physi cal observable – topological entanglement entropy (TEE) – has been der ived\, which sharpens previous results. Our formula shows that the TEE acr oss an arbitrary entanglement surface is linearly proportional to the TEE across a torus.\n\nFor the second aspect\, we use the global symmetries an d their ’t Hooft anomalies of the SU(2) Yang-Mills theory with a theta t erm to constrain its dynamics. In particular\, we point out that there are four different such theories\, distinguished by Lorentz symmetry enrichme nts of the Wilson loops in the SU(2) fundamental representation. We furthe r derive a new mixed anomaly between time reversal and one form symmetry w hich can only be seen on an unorientable manifold. We further use the anom alies to explore various possible dynamics\, such as nontrivial degrees of freedom localized on the domain wall due to spontaneously broken time rev ersal symmetry\, as well as a potentially possible but exotic quantum phas e transition — Gauge Enhanced Quantum Critical Point.\n LOCATION:https://stable.researchseminars.org/talk/PrincetonFPO/2/ END:VEVENT BEGIN:VEVENT SUMMARY:Jaan Altosaar DTSTART:20200515T170000Z DTEND:20200515T180000Z DTSTAMP:20260404T094600Z UID:PrincetonFPO/4 DESCRIPTION:Title: Probabilistic Modeling of Structure in Science: Statistical Ph ysics to Recommender Systems\nby Jaan Altosaar as part of Princeton FP O\n\n\nAbstract\nApplied machine learning relies on translating the struct ure of a problem into a computational model. This arises in applications a s diverse as statistical physics and food recommender systems. The pattern of connectivity in an undirected graphical model or the fact that datapoi nts in food recommendation are un- ordered collections of features can inf orm the structure of a model. First\, consider undi- rected graphical mode ls from statistical physics like the ubiquitous Ising model. Basic researc h in physics requires scalable simulations for comparing the behavior of a model to its experimental counterpart. The Ising model consists of binary random variables with local connectivity\; interactions between neighbori ng nodes can lead to long-range correlations. Modeling these correlations is necessary to capture physical phenomena such as phase transitions. To m irror the local structure of these models\, we use flow- based convolutio nal generative models that can capture long-range correlations. Com- binin g flow-based models designed for continuous variables with recent work on hier- archical variational approximations enables the modeling of discret e random variables. Compared to existing variational inference methods\, t his approach scales to statistical physics models with tens of thousands o f correlated random variables and uses fewer pa- rameters. Just as computa tional choices can be made by considering the structure of an undirected g raphical model\, model construction itself can be guided by the structure of individual datapoints. Consider a recommendation task where datapoints consist of un- ordered sets\, and the objective is to maximize top-K recal l\, a common recommendation metric. Simple results show that a classifier with zero worst-case error achieves maxi- mum top-K recall. Further\, the unordered structure of the data suggests the use of a permutation-invaria nt classifier for statistical and computational efficiency. We evalu- ate such a classifier on human dietary behavior data\, where every meal is a n unordered collection of ingredients\, and find that it outperforms prob abilistic matrix factorization methods. Finally\, we show that building pr oblem structure into an approximate infer- ence algorithm improves the acc uracy of probabilistic modeling methods.\n\nZoom ID: 95950675768 Password: 246827 \n\n(Please disregard regular ID and password for the seminar seri es.)\n LOCATION:https://stable.researchseminars.org/talk/PrincetonFPO/4/ END:VEVENT END:VCALENDAR