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Wilson loop in general representation and RG flow in 1D defect QFT
No full textAbstractThe generalized Wilson loop operator interpolating between the supersymmetric and the ordinary Wilson loop inN=4SYM theory provides an interesting example of renormalization group flow on a line defect: the scalar coupling parameterζhas a non-trivial beta function and may be viewed as a running coupling constant in a 1D defect QFT. In this paper we continue the study of this operator, generalizing previous results for the beta function and Wilson loop expectation value to the case of an arbitrary representation of the gauge group and beyond the planar limit. Focusing on the scalar ladder limit where the generalized Wilson loop reduces to a purely scalar line operator in a free adjoint theory, and specializing to the case of the rankksymmetric representation ofSU(N), we also consider a certain ‘semiclassical’ limit wherekis taken to infinity with the productkζ2fixed. This limit can be conveniently studied using a 1D defect QFT representation in terms ofNcommuting bosons. Using this representation, we compute the beta function and the circular loop expectation value in the largeklimit, and use it to derive constraints on the structure of the beta function for general representation. We discuss the corresponding 1D RG flow and comment on the consistency of the results with the 1D defect version of the F-theorem.</jats:p
Compressed Python likelihood for large scale temperature and polarization from Planck
No full textWe present Planck-low-py, a binned low-` temperature and E-mode polarization likelihood, as
an option to facilitate ease of use of the Planck 2018 large-scale data in joint-probe analysis and
forecasting. It is written in Python and compresses the ` < 30 temperature and polarization
angular power spectra information from Planck into two log-normal bins in temperature and three
in polarization. These angular scales constrain the optical depth to reionization and provide a lever
arm to constrain the tilt of the primordial power spectrum. We show that cosmological constraints
on ΛCDM model parameters using Planck-low-py are consistent with those derived with the full
Commander and SimAll likelihoods from the Planck legacy release
Metal-Insulator Transition and Anomalous Lattice Parameters Changes in Ru-doped VO2
No full textVO2, of interest for decades due to both its phenomenology and its potential applications, has a monoclinic distortion of the rutile crystal structure at ambient temperature that is coupled to its metal-insulator transition. In contrast, RuO2 has three electrons more per formula unit, is a metallic conductor, and has an undistorted rutile structure. Here, we report a systematic study of Ru-doped VO2 (V1−RuO2, 0.01≤≤0.9), generally characterizing its crystal structure, magnetic and electronic properties, and heat capacity. The composition-dependent Wilson ratio is determined. We find that an unusually high Ru doping value (80%, =0.8) is required to achieve a metallic state in V1−RuO2. No superconductivity was observed down to 0.1 K in the metallic materials. We propose a possible understanding for how the insulating state can exist in V1−RuO2 at high Ru contents
A deep potential model with long-range electrostatic interactions
No full textMachine learning models for the potential energy of multi-atomic systems, such as the deep potential (DP) model, make molecular simulations with the accuracy of quantum mechanical density functional theory possible at a cost only moderately higher than that of empirical force fields. However, the majority of these models lack explicit long-range interactions and fail to describe properties that derive from the Coulombic tail of the forces. To overcome this limitation, we extend the DP model by approximating the long-range electrostatic interaction between ions (nuclei + core electrons) and valence electrons with that of distributions of spherical Gaussian charges located at ionic and electronic sites. The latter are rigorously defined in terms of the centers of the maximally localized Wannier distributions, whose dependence on the local atomic environment is modeled accurately by a deep neural network. In the DP long-range (DPLR) model, the electrostatic energy of the Gaussian charge system is added to short-range interactions that are represented as in the standard DP model. The resulting potential energy surface is smooth and possesses analytical forces and virial. Missing effects in the standard DP scheme are recovered, improving on accuracy and predictive power. By including long-range electrostatics, DPLR correctly extrapolates to large systems the potential energy surface learned from quantum mechanical calculations on smaller systems. We illustrate the approach with three examples: the potential energy profile of the water dimer, the free energy of interaction of a water molecule with a liquid water slab, and the phonon dispersion curves of the NaCl crystal
A Class of Magnetic Topological Material Candidates with Hypervalent Bi Chains
No full textThe link between crystal and electronic structure is crucial for understanding structure property relations in solid-state chemistry. In particular, it has been instrumental in understanding topological materials, where electrons behave differently than they would in conventional solids. Herein, we identify 1D Bi chains as a structural motif of interest for topological materials. We focus on Sm3ZrBi5, a new quasi-one-dimensional (1D) compound in the Ln3MPn5 (Ln = lanthanide; M = metal; Pn = pnictide) family that
crystallizes in the P 63/mcm space group. Density functional theory calculations indicate a complex, topologically non-trivial electronic structure that changes significantly in the presence of spin-orbit coupling. Magnetic measurements show a quasi-1D an-tiferromagnetic structure with two magnetic transitions at 11.7 and 10.7 K that are invariant to applied field up to 9 T, indicating magnetically frustrated spins. Heat capacity, electrical, and thermoelectric measurements support this claim and suggest complex scattering behavior in Sm3ZrBi5. This work highlights 1D chains as an unexplored structural motif for identifying topological materials, as well as the potential for rich physical phenomena in the Ln3MPn5 family
An analytical framework for interpretable and generalizable single-cell data analysis
No full textThe scaling of single-cell data exploratory analysis with the rapidly growing diversity and quantity of single-cell omics datasets demands more interpretable and robust data representation that is generalizable across datasets. Here, we have developed a ‘linearly interpretable’ framework that combines the interpretability and transferability of linear methods with the representational power of non-linear methods. Within this framework we introduce a data representation and visualization method, GraphDR, and a structure discovery method, StructDR, that unifies cluster, trajectory and surface estimation and enables their confidence set inference
Data-plane security applications in adversarial settings
No full textHigh-speed programmable switches have emerged as a promising building block for developing performant data-plane applications. In this paper, we argue that the resource constraints and programming model of hardware switches have led to developers adopting problematic design patterns, whose security implications are not widely understood. We bridge the gap by identifying the major challenges and common design pitfalls in switch-based applications in adversarial settings. Examining five recently-proposed switch-based security applications, we find that adversaries can exploit these design pitfalls to completely bypass the protection these applications were designed to provide, or disrupt system operations by introducing collateral damage
Author Correction: Lead federated neuromorphic learning for wireless edge artificial intelligence
No full textA Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations
No full textRecently there has been a surge of interest in adopting deep neural networks (DNNs) for solving the optimal power flow (OPF) problem in power systems. Computing optimal generation dispatch decisions using a trained DNN takes significantly less time when compared to conventional optimization solvers. However, a major drawback of existing work is that the machine learning models are trained for a specific system topology. Hence, the DNN predictions are only useful as long as the system topology remains unchanged. Changes to the system topology (initiated by the system operator) would require retraining the DNN, which incurs significant training overhead and requires an extensive amount of training data (corresponding to the new system topology). To overcome this drawback, we propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach. The key idea behind this approach is to find a common initialization vector that enables fast training for any system topology. The developed OPF-predictor is validated through simulations using benchmark IEEE bus systems. The results show that the MTL approach achieves significant training speed-ups and requires only a few gradient steps with a few data samples to achieve high OPF prediction accuracy and outperforms other pretraining techniques
Spectroscopy of Twisted Bilayer Graphene Correlated Insulators
No full textWe analytically compute the scanning tunneling microscopy (STM) signatures of integer-filled correlated ground states of the magic angle twisted bilayer graphene (TBG) narrow bands. After experimentally validating the strong-coupling approach at ±4 electrons/moiré unit cell, we consider the spatial features of the STM signal for 14 different many-body correlated states and assess the possibility of Kekulé distortion (KD) emerging at the graphene lattice scale. Remarkably, we find that coupling the two opposite graphene valleys in the intervalley-coherent (IVC) TBG insulators does not always result in KD. As an example, we show that the Kramers IVC state and its nonchiral U (4) rotations do not exhibit any KD, while the time-reversal-symmetric IVC state does. Our results, obtained over a large range of energies and model parameters, show that the STM signal and Chern number of a state can be used to uniquely determine the nature of the TBG ground state