1967 Publications

Black Hole Growth and Feedback in Isolated Romulus25 Dwarf Galaxies

Ray Sharma, Alyson Brooks, R. Somerville, et. al.

We investigate the effects of massive black hole growth on the structural evolution of dwarf galaxies within the Romulus25 cosmological hydrodynamical simulation. We study a sample of 228 central, isolated dwarf galaxies with stellar masses Mstar<1010M⊙ and a central BH. We find that the local MBH−Mstar relation exhibits a high degree of scatter below Mstar109M⊙ are more likely to exhibit lower central stellar mass density, lower HI gas content, and lower star formation rates than their undermassive BH counterparts. Our results suggest that overmassive BHs in isolated galaxies above Mstar>109M⊙ are capable of driving feedback, in many cases suppressing and even quenching star formation by late times.

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SARS-CoV-2 titers in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases

F Wu, A Xiao, J Zhang, K Moniz, N Endo, F Armas, R. Bonneau, M Brown, M Bushman, P Chai, C Duvallet, T Erickson, K Foppe, N Ghaeli, X Gu, W Hanage, K Huang, W Lee, M Matus, K McElroy, J Nagler, S Rhode, M Santillana, J Tucker, S Wuertz, S Zhao, J Thompson, E Alm

Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we use longitudinal wastewater analysis to track SARS-CoV-2 dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. Viral titers in wastewater increased exponentially from mid-March to mid-April, after which they began to decline. Viral titers in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral titers as a convolution of back-dated new clinical cases with the viral shedding function of an individual. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. Finally, we found that wastewater viral titers at the neighborhood level correlate better with demographic variables than with population size. This work suggests that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and may shed light on infection characteristics that are difficult to capture in clinical investigations, such as early viral shedding dynamics.

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Accurate quadrature of nearly singular line integrals in two and three dimensions by singularity swapping

L. af Klinteberg, A. Barnett

The method of Helsing and co-workers evaluates Laplace and related layer potentials generated by a panel (composite) quadrature on a curve, efficiently and with high-order accuracy for arbitrarily close targets. Since it exploits complex analysis, its use has been restricted to two dimensions (2D). We first explain its loss of accuracy as panels become curved, using a classical complex approximation result of Walsh that can be interpreted as "electrostatic shielding" of a Schwarz singularity. We then introduce a variant that swaps the target singularity for one at its complexified parameter preimage; in the latter space the panel is flat, hence the convergence rate can be much higher. The preimage is found robustly by Newton iteration. This idea also enables, for the first time, a near-singular quadrature for potentials generated by smooth curves in 3D, building on recurrences of Tornberg-Gustavsson. We apply this to accurate evaluation of the Stokes flow near to a curved filament in the slender body approximation. Our 3D method is several times more efficient (both in terms of kernel evaluations, and in speed in a C implementation) than the only existing alternative, namely, adaptive integration.

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July 3, 2020

Algorithms for Tensor Network Contraction Ordering

Frank Schindler, A. Jermyn

Contracting tensor networks is often computationally demanding. Well-designed contraction sequences can dramatically reduce the contraction cost. We explore the performance of simulated annealing and genetic algorithms, two common discrete optimization techniques, to this ordering problem. We benchmark their performance as well as that of the commonly-used greedy search on physically relevant tensor networks. Where computationally feasible, we also compare them with the optimal contraction sequence obtained by an exhaustive search. We find that the algorithms we consider consistently outperform a greedy search given equal computational resources, with an advantage that scales with tensor network size. We compare the obtained contraction sequences and identify signs of highly non-local optimization, with the more sophisticated algorithms sacrificing run-time early in the contraction for better overall performance.

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Femtosecond exciton dynamics in WSe2 optical waveguides

Aaron J. Sternbach, Simone Latini, Sanghoon Chae, Hannes Hübener, Umberto De Giovannini, Yinming Shao, Lin Xiong, Zhiyuan Sun, Norman Shi, Peter Kissin, Guang-Xin Ni, Daniel Rhodes, Brian Kim, Nanfang Yu, Andrew J. Millis, Michael M. Fogler, Peter J. Schuck, Michal Lipson, X.-Y. Zhu, James Hone, Richard D. Averitt, A. Rubio, D. N. Basov
Van-der Waals (vdW) atomically layered crystals can act as optical waveguides over a broad range of the electromagnetic spectrum ranging from Terahertz to visible. Unlike common Si-based waveguides, vdW semiconductors host strong excitonic resonances that may be controlled using non-thermal stimuli including electrostatic gating and photoexcitation. Here, we utilize waveguide modes to examine photo-induced changes of excitons in the prototypical vdW semiconductor, WSe
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Neurons as Canonical Correlation Analyzers

D. Chklovskii, A. Sengupta, C. Pehlevan

Normative models of neural computation offer simplified yet lucid mathematical descriptions of murky biological phenomena. Previously, online Principal Component Analysis (PCA) was used to model a network of single-compartment neurons accounting for weighted summation of upstream neural activity in the soma and Hebbian/anti-Hebbian synaptic learning rules. However, synaptic plasticity in biological neurons often depends on the integration of synaptic currents over a dendritic compartment rather than total current in the soma. Motivated by this observation, we model a pyramidal neuronal network using online Canonical Correlation Analysis (CCA). Given two related datasets represented by distal and proximal dendritic inputs, CCA projects them onto the subspace which maximizes the correlation between their projections. First, adopting a normative approach and starting from a single-channel CCA objective function, we derive an online gradient-based optimization algorithm whose steps can be interpreted as the operation of a pyramidal neuron. To model networks of pyramidal neurons, we introduce a novel multi-channel CCA objective function, and derive from it an online gradient-based optimization algorithm whose steps can beinterpreted as the operation of a pyramidal neuron network including its architecture, dynamics, and synaptic learning rules. Next, we model a neuron with more than two dendritic compartments by deriving its operation from a known objective function for multi-view CCA. Finally, we confirm the functionality of our networks via numerical simulations. Overall, our work presents a simplified but informative abstraction of learning in a pyramidal neuron network, and demonstrates how such networks can integrate multiple sources of inputs.

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Frontiers in Computational Neuroscience
June 30, 2020

Force-Induced Formation of Twisted Chiral Ribbons

A. Balchunas, L. Jia, M. Zakhary, J. Robaszewski, T. Gibaud, Z. Dogic, R. Pelcovits, T. Powers

We demonstrate that an achiral stretching force transforms disk-shaped colloidal membranes composed of chiral rods into twisted ribbons with handedness opposite the preferred twist of the rods. Using an experimental technique that enforces torque-free boundary conditions we simultaneously measure the force-extension curve and the ribbon shape. An effective theory that accounts for the membrane bending energy and uses geometric properties of the edge to model the internal liquid crystalline degrees of freedom explains both the measured force-extension curve and the force-induced twisted shape.

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SU(3)_1 Chiral Spin Liquid on the Square Lattice: a View from Symmetric PEPS

J.Y. Chen, S. Capponi, A. Wietek, M. Mambrini, N. Schuch, D. Poilblanc

Quantum spin liquids can be faithfully represented and efficiently characterized within the framework of Projected Entangled Pair States (PEPS). Guided by extensive exact diagonalization and density matrix renormalization group calculations, we construct an optimized symmetric PEPS for a SU(3)1 chiral spin liquid on the square lattice. Characteristic features are revealed by the entanglement spectrum (ES) on an infinitely long cylinder. In all three ℤ3 sectors, the level counting of the linear dispersing modes is in full agreement with SU(3)1 Wess-Zumino-Witten conformal field theory prediction. Special features in the ES are shown to be in correspondence with bulk anyonic correlations, indicating a fine structure in the holographic bulk-edge correspondence. Possible universal properties of topological SU(N)k chiral PEPS are discussed.

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Genomic analyses implicate noncoding de novo variants in congenital heart disease

F Richter, S Morton, S Kim, A Kitaygorodsky, L Wasson, K. Chen

A genetic etiology is identified for one-third of patients with congenital heart disease (CHD), with 8% of cases attributable to coding de novo variants (DNVs). To assess the contribution of noncoding DNVs to CHD, we compared genome sequences from 749 CHD probands and their parents with those from 1,611 unaffected trios. Neural network prediction of noncoding DNV transcriptional impact identified a burden of DNVs in individuals with CHD (n = 2,238 DNVs) compared to controls (n = 4,177; P = 8.7 × 10−4). Independent analyses of enhancers showed an excess of DNVs in associated genes (27 genes versus 3.7 expected, P = 1 × 10−5). We observed significant overlap between these transcription-based approaches (odds ratio (OR) = 2.5, 95% confidence interval (CI) 1.1–5.0, P = 5.4 × 10−3). CHD DNVs altered transcription levels in 5 of 31 enhancers assayed. Finally, we observed a DNV burden in RNA-binding-protein regulatory sites (OR = 1.13, 95% CI 1.1–1.2, P = 8.8 × 10−5). Our findings demonstrate an enrichment of potentially disruptive regulatory noncoding DNVs in a fraction of CHD at least as high as that observed for damaging coding DNVs.

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