cssi/SciSciGPT-SciSciNet huggingface dataset:
Oxygenation Profiles of Human Blood, Cell Culture Medium, and Water for Perfusion of 3D-Bioprinted Tissues using the FABRICA Bioreactor Platform.

Persistent and saturated oxygen distribution from perfusion media (i.e., blood, or cell culture media) to cells within cell-dense, metabolically-active biofabricated tissues is required to keep them viable. Improper or poor oxygen supply to cells within the tissue bulk severely limits the tissue culturing potential of many bioreactors. We added an oxygenator module to our modular FABRICA bioreactor in order to provide stable oxygenation to biofabricated tissues during culture. In this proof of concept study of an oxygenated and perfused bioreactor, we characterized the oxygenation of water, cell culture medium, and human blood in the FABRICA as functions of augmenting vacuum (air inlet) pressure, perfusion (volumetric flow) rate, and tubing/oxygenator components. The mean oxygen levels for water and cell culture media were 27.7 ± 2.1% and 27.6 ± 4.1%, respectively. The mean oxygen level for human blood was 197.0 ± 90.0 mmHg, with near-physiologic levels achieved with low-permeability PharMed tubing alone (128.0 ± 14.0 mmHg). Hematologic values pre- and post-oxygenation, respectively were (median ± IQR): Red blood cell: 6.0 ± 0.5 (106/μL) and 6.5 ± 0.4 (106/μL); Hemoglobin: 17.5 ± 1.2 g/dL and 19.2 ± 3.0 g/dL; and Hematocrit: 56.7 ± 2.4% and 61.4 ± 7.5%. The relative stability of the hematologic parameters indicates that blood function and thus blood cell integrity were maintained throughout oxygenation. Already a versatile research tool, the now oxygenated FABRICA provides easy-to-implement, in vivo-like perfusion and stable oxygenation culture conditions in vitro semi-independently of one another, which means the bioreactor has the potential to serve as a platform for investigating the behavior of 3D tissue models (regardless of biofabrication method), performing drug toxicity-testing, and testing pharmaceutical efficacy/safety.
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks

Building rich machine learning datasets in a scalable manner often necessitates a crowd-sourced data collection pipeline. In this work, we use human studies to investigate the consequences of employing such a pipeline, focusing on the popular ImageNet dataset. We study how specific design choices in the ImageNet creation process impact the fidelity of the resulting dataset---including the introduction of biases that state-of-the-art models exploit. Our analysis pinpoints how a noisy data collection pipeline can lead to a systematic misalignment between the resulting benchmark and the real-world task it serves as a proxy for. Finally, our findings emphasize the need to augment our current model training and evaluation toolkit to take such misalignments into account. To facilitate further research, we release our refined ImageNet annotations at this https URL.
On Clustering on Graphs with Multiple Edge Types

We study clustering on graphs with multiple edge types. Our main motivation is that similarities between objects can be measured by many different metrics. For instance, similarity between two papers can be based on common authors, where they were published, keyword similarity, citations, etc. As such, graphs with multiple edges give a more accurate model to describe similarities between objects than models using single-edge graphs. Each edge/metric provides only partial information about the data; recovering full information requires aggregation of all the similarity metrics. Clustering becomes much more challenging in this context, since in addition to the difficulties of the traditional clustering problem, we have to deal with a space of clusterings. Reducing the multidimensional space into a single dimension poses significant challenges. At the same time, the multidimensional space can contain latent structures, and searching this multidimensional space can reveal important information about the graph...
Background adaptive faster R-CNN for semi-supervised convolutional object detection of threats in x-ray images

Recently, progress has been made in the supervised training of Convolutional Object Detectors (e.g. Faster RCNN) for threat recognition in carry-on luggage using X-ray images. This is part of the Transportation Security Administration's (TSA's) mission to ensure safety for air travelers in the United States. Collecting more data reliably improves performance for this class of deep algorithm, but requires time and money to produce training data with threats staged in realistic contexts. In contrast to these hand-collected data containing threats, data from the real-world, known as the Stream-of-Commerce (SOC), can be collected quickly with minimal cost; while technically unlabeled, in this work we make a practical assumption that these are without threat objects. Because of these data constraints, we will use both labeled and unlabeled sources of data for the automatic threat recognition problem. In this paper, we present a semi-supervised approach for this problem which we call Background Adaptive Faster R-CNN. This approach is a training method for two-stage object detectors which uses Domain Adaptation methods from the field of deep learning. The data sources described earlier are considered two “domains": one a hand-collected data domain of images with threats, and the other a real-world domain of images assumed without threats. Two domain discriminators, one for discriminating object proposals and one for image features, are adversarially trained to prevent encoding domain-specific information. Penalizing this encoding is important because otherwise the Convolutional Neural Network (CNN) can learn to distinguish images from the two sources based on superficial characteristics, and minimize a purely supervised loss function without improving its ability to recognize objects. For the hand-collected data, only object proposals and image features completely outside of areas corresponding to ground truth object bounding boxes (background) are used. The losses for these domain-adaptive discriminators are added to the Faster R-CNN losses of images from both domains. This technique enables threat recognition based on examples from the labeled data, and can reduce false alarm rates by matching the statistics of extracted features on the hand-collected backgrounds to that of the real world data. Performance improvements are demonstrated on two independently-collected datasets of labeled threats.
Disagree to Agree: Forming Consensus Around Basic Income in Times of Political Divisiveness

In recent years, political polarization in the USA reached new and alarming levels. As a result, political activists face new challenges in their effort to mobilize around struggles and demands for policy changes. In light of this, we argue that universal basic income can serve as a key policy around which social movements and political activists (both within the progressive movement and across the ideological spectrum) could form an “overlapping consensus.” Using this Rawlsian framework, we focus on the comprehensive doctrines of feminist activists, racial justice activists, liberal egalitarians, Marxists-socialists, and classical liberals (libertarians). We argue that forming an overlapping consensus around UBI would enable activists to have a common political goal without necessarily having to face the challenge of reaching agreement over fundamental values. This, in turn, could enable them to gain more political visibility and thus increase their ability to promote sociopolitical change.
A Probabilistic Framework for Moving-Horizon Estimation: Stability and Privacy Guarantees

This article proposes a probabilistic framework for the design of robustly asymptotically stable moving-horizon estimators (MHE) for discrete-time nonlinear systems, and a mechanism to incorporate differential privacy in moving-horizon estimation. We formulate the moving-horizon estimator as an iterative proximal descent scheme in the space of probability measures with respect to the $L^2$ -Wasserstein metric, which we name $W_2$ -MHE. We then investigate asymptotic stability and robustness properties of the $W_2$ -MHE against the backdrop of the classical notion of strong local observability. Motivated by applications where the measurement data used by the estimator is to be kept private, we then propose a mechanism to incorporate differential privacy in the estimation method, based on an entropy regularization of the MHE objective functional. In particular, we find sufficient bounds on the regularization parameter to achieve the desired level of differential privacy. We then demonstrate the performance of the $W_2$ -MHE in numerical simulations.
CoMut: visualizing integrated molecular information with comutation plots.

Motivation Large-scale sequencing studies have created a need to succinctly visualize genomic characteristics of patient cohorts linked to widely variable phenotypic information. This is often done by visualizing the co-occurrence of variants with comutation plots. Current tools lack the ability to create highly customizable and publication quality comutation plots from arbitrary user data. Results We developed CoMut, a stand-alone, object-oriented Python package that creates comutation plots from arbitrary input data, including categorical data, continuous data, bar graphs, side bar graphs, and data that describes relationships between samples. Availability and implementation The CoMut package is open source and is available at https://github.com/vanallenlab/comut under the MIT License, along with documentation and examples. A no installation, easy-to-use implementation is available on Google Colab (see GitHub).
A Method for Detecting Text of Arbitrary Shapes in Natural Scenes That Improves Text Spotting

Understanding the meaning of text in images of natural scenes like highway signs or store front emblems is particularly challenging if the text is foreshortened in the image or the letters are artistically distorted. We introduce a pipeline-based text spotting framework that can both detect and recognize text in various fonts, shapes, and orientations in natural scene images with complicated backgrounds. The main contribution of our work is the text detection component, which we call UHT, short for UNet, Heatmap, and Textfill. UHT uses a UNet to compute heatmaps for candidate text regions and a textfill algorithm to produce tight polygonal boundaries around each word in the candidate text. Our method trains the UNet with groundtruth heatmaps that we obtain from text bounding polygons provided by groundtruth annotations. Our text spotting framework, called UHTA, combines UHT with the state-of-the-art text recognition system ASTER. Experiments on four challenging and public scene-text-detection datasets (Total-Text, SCUT-CTW1500, MSRA-TD500, and COCO-Text) show the effectiveness and generalization ability of UHT in detecting not only multilingual (potentially rotated) straight but also curved text in scripts of multiple languages. Our experimental results of UHTA on the TotalText dataset show that UHTA outperforms four state-of-the- art text spotting frameworks by at least 9.1 percent points in the F-measure, which suggests that UHTA may be used as a complete text detection and recognition system in real applications.
Finding Universal Grammatical Relations in Multilingual BERT

Recent work has found evidence that Multilingual BERT (mBERT), a transformer-based multilingual masked language model, is capable of zero-shot cross-lingual transfer, suggesting that some aspects of its representations are shared cross-lingually. To better understand this overlap, we extend recent work on finding syntactic trees in neural networks' internal representations to the multilingual setting. We show that subspaces of mBERT representations recover syntactic tree distances in languages other than English, and that these subspaces are approximately shared across languages. Motivated by these results, we present an unsupervised analysis method that provides evidence mBERT learns representations of syntactic dependency labels, in the form of clusters which largely agree with the Universal Dependencies taxonomy. This evidence suggests that even without explicit supervision, multilingual masked language models learn certain linguistic universals.
Low Degree Points on Curves

In this paper we investigate an arithmetic analogue of the gonality of a smooth projective curve $C$ over a number field $k$: the minimal $e$ such there are infinitely many points $P \in C(\bar{k})$ with $[k(P):k] \leq e$. Developing techniques that make use of an auxiliary smooth surface containing the curve, we show that this invariant can take any value subject to constraints imposed by the gonality. Building on work of Debarre--Klassen, we show that this invariant is equal to the gonality for all sufficiently ample curves on a surface $S$ with trivial irregularity.
The environmental stress response causes ribosome loss in aneuploid yeast cells

Aneuploidy, a condition characterized by whole chromosome gains and losses, is often associated with significant cellular stress and decreased fitness. However, how cells respond to the aneuploid state has remained controversial. In aneuploid budding yeast, two opposing gene-expression patterns have been reported: the "environmental stress response" (ESR) and the "common aneuploidy gene-expression" (CAGE) signature, in which many ESR genes are oppositely regulated. Here, we investigate this controversy. We show that the CAGE signature is not an aneuploidy-specific gene-expression signature but the result of normalizing the gene-expression profile of actively proliferating aneuploid cells to that of euploid cells grown into stationary phase. Because growth into stationary phase is among the strongest inducers of the ESR, the ESR in aneuploid cells was masked when stationary phase euploid cells were used for normalization in transcriptomic studies. When exponentially growing euploid cells are used in gene-expression comparisons with aneuploid cells, the CAGE signature is no longer evident in aneuploid cells. Instead, aneuploid cells exhibit the ESR. We further show that the ESR causes selective ribosome loss in aneuploid cells, providing an explanation for the decreased cellular density of aneuploid cells. We conclude that aneuploid budding yeast cells mount the ESR, rather than the CAGE signature, in response to aneuploidy-induced cellular stresses, resulting in selective ribosome loss. We propose that the ESR serves two purposes in aneuploid cells: protecting cells from aneuploidy-induced cellular stresses and preventing excessive cellular enlargement during slowed cell cycles by down-regulating translation capacity.
A global Newton-type scheme based on a simplified Newton-type approach

Globalization concepts for Newton-type iteration schemes are widely used when solving nonlinear problems numerically. Most of these schemes are based on a predictor/corrector step size methodology with the aim of steering an initial guess to a zero of f without switching between different attractors. In doing so, one is typically able to reduce the chaotic behavior of the classical Newton-type iteration scheme. In this note we propose a globalization methodology for general Newton-type iteration concepts which changes into a simplified Newton iteration as soon as the transformed residual of the underlying function is small enough. Based on Banach’s fixed-point theorem, we show that there exists a neighborhood around a suitable iterate $$x_{n}$$ such that we can steer the iterates—without any adaptive step size control but using a simplified Newton-type iteration within this neighborhood—arbitrarily close to an exact zero of f. We further exemplify the theoretical result within a global Newton-type iteration procedure and discuss further an algorithmic realization. Our proposed scheme will be demonstrated on a low-dimensional example thereby emphasizing the advantage of this new solution procedure.
Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data. Part I: Maximum A Posteriori Estimate

Characterizing the properties of groundwater aquifers is essential for predicting aquifer response and managing groundwater resources. In this work, we develop a high-dimensional scalable Bayesian inversion framework governed by a three-dimensional quasi-static linear poroelastic model to characterize lateral permeability variations in groundwater aquifers. We determine the maximum a posteriori (MAP) point of the posterior permeability distribution from centimeter-level surface deformation measurements obtained from Interferometric Synthetic Aperture Radar (InSAR). The scalability of our method to high parameter dimension is achieved through the use of adjoint-based derivatives, inexact Newton methods to determine the MAP point, and a Matern class sparse prior precision operator. Together, these guarantee that the MAP point is found at a cost, measured in number of forward/adjoint poroelasticity solves, that is independent of the parameter dimension. We apply our methodology to a test case for a municipal well in Mesquite, Nevada, in which InSAR and GPS surface deformation data are available. We solve problems with up to 320,824 state variable degrees of freedom (DOFs) and 16,896 parameter DOFs. A consistent treatment of noise level is employed so that the aquifer characterization result does not depend on the pixel spacing of surface deformation data. Our results show that the use of InSAR data significantly improves characterization of lateral aquifer heterogeneity, and the InSAR-based aquifer characterization recovers complex lateral displacement trends observed by independent daily GPS measurements.
Lifetime Maximization for UAV-assisted Data Gathering Networks in the Presence of Jamming

Deployment of unmanned aerial vehicles (UAVs) is recently getting significant attention due to a variety of practical use cases, such as surveillance, data gathering, and commodity delivery. Since UAVs are powered by batteries, energy efficient communication is of paramount importance. In this paper, we investigate the problem of lifetime maximization of a UAV-assisted network in the presence of multiple sources of interference, where the UAVs are deployed to collect data from a set of wireless sensors. We demonstrate that the placement of the UAVs play a key role in prolonging the lifetime of the network since the required transmission powers of the UAVs are closely related to their locations in space. In the proposed scenario, the UAVs transmit the gathered data to a primary UAV called leader, which is in charge of forwarding the data to the base station (BS) via a backhaul UAV network. We deploy tools from spectral graph theory to tackle the problem due to its high non-convexity. Simulation results demonstrate that our proposed method can significantly improve the lifetime of the UAV network.
The Sec1/Munc18 protein Vps45 holds the Qa-SNARE Tlg2 in an open conformation

Fusion of intracellular trafficking vesicles is mediated by the assembly of SNARE proteins into membrane-bridging complexes. SNARE-mediated membrane fusion requires Sec1/Munc18-family (SM) proteins, SNARE chaperones that can function as templates to catalyze SNARE complex assembly. Paradoxically, the SM protein Munc18-1 traps the Qa-SNARE protein syntaxin-1 in an autoinhibited closed conformation. Here we present the structure of a second SM-Qa-SNARE complex, Vps45-Tlg2. Strikingly, Vps45 holds Tlg2 in an open conformation, with its SNARE motif disengaged from its Habc domain and its linker region unfolded. The domain 3a helical hairpin of Vps45 is unfurled, exposing the presumptive R-SNARE binding site to allow template complex formation. Although Tlg2 has a pronounced tendency to form homo-tetramers, Vps45 can rescue Tlg2 tetramers into stoichiometric Vps45-Tlg2 complexes. Our findings demonstrate that SM proteins can engage Qa-SNAREs using at least two different modes, one in which the SNARE is closed and one in which it is open.
PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously

Today’s blockchain designs suffer from a trilemma claiming that no blockchain system can simultaneously achieve decentralization, security, and performance scalability. For current blockchain systems, as more nodes join the network, the efficiency of the system (computation, communication, and storage) stays constant at best. A leading idea for enabling blockchains to scale efficiency is the notion of sharding: different subsets of nodes handle different portions of the blockchain, thereby reducing the load for each individual node. However, existing sharding proposals achieve efficiency scaling by compromising on trust - corrupting the nodes in a given shard will lead to the permanent loss of the corresponding portion of data. In this paper, we settle the trilemma by demonstrating a new protocol for coded storage and computation in blockchains. In particular, we propose PolyShard: "polynomially coded sharding" scheme that achieves information-theoretic upper bounds on the efficiency of the storage, system throughput, as well as on trust, thus enabling a truly scalable system.
Factors Associated with Good Patient Outcomes Following Convalescent Plasma in COVID-19: A Prospective Phase II Clinical Trial.

Coronavirus disease 2019 (COVID-19) is a viral respiratory syndrome caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This novel virus was discovered in Wuhan City, Hubei Province, China, in December 2019. As of September 6, 2020, confirmed cases have risen to more than 27,000,000 worldwide and more than 885,000 people have died. Currently, no cure or standard treatment for COVID-19 exists. We conducted a prospective single-arm open-label phase II clinical trial assessing the safety and efficacy of convalescent plasma in hospitalized patients with COVID-19. Convalescent plasma with sufficient total anti-SARS-CoV-2 IgG titer (1:320) obtained from recovered donors was administered to adult patients with either severe or critical COVID-19 illness. Primary outcomes were adverse events in association with plasma administration, and hospital mortality. Secondary outcomes included disease progression, recovery, length of stay, and hospital discharge. Of the 38 patients included in the analysis, 24 (63%) recovered and were discharged, and 14 (37%) died. Patients who received convalescent plasma early in the disease course (severe illness group) as compared to the patients that received convalescent plasma later in the disease progression (critical illness group) had significantly lower hospital mortality 13% vs 55% (p < 0.02) and shorter mean hospital length of stay 15.4 vs 33 days (p < 0.01). One patient experienced a transient transfusion reaction. No other adverse effects of convalescent plasma infusion were observed. Our results suggest that convalescent plasma with adequate anti-SARS-CoV-2 antibody titer is safe and has the potential for positive impact on clinical outcomes including recovery and survival if given to patients early in the course of COVID-19 disease. ClinicalTrials.gov. Identifier, NCT04343261, IND #19805.
Invariants related to the tree property

We consider cardinal invariants related to Shelah’s model-theoretic tree properties and the relations that obtain between them. From strong colorings, we construct theories T with κcdt(T) > κsct(T) + κinp(T). We show that these invariants have distinct structural consequences, by investigating their effect on the decay of saturation in ultrapowers. This answers some questions of Shelah.
A Concise Route to MK-4482 (EIDD-2801) from Cytidine: Part 2

A new route to MK-4482 was developed. The route replaces uridine with the more available and less expensive cytidine. Low-cost, simple reagents are used for the chemical transformations, and the yield is improved from 17% to 44%. A step is removed from the longest linear sequence, and these advancements are expected to expand access to MK-4482 should it become a viable drug substance.
State Preemption of Local Laws: Origins and Modern Trends

American cities are creatures of their states, with states both granting and limiting the power of their cities. This relationship is characterized by how cooperative or competitive states are with cities in their legislation. Despite the recent attention given to state preemption of local laws, this is not a new phenomenon. Part of the confusion surrounding preemption is that there is no shared definition or understanding of its forms. The purpose of this article is to begin to create that shared conception. In doing so, we define preemption according to its historic origins as the use of coercive methods to substitute state priorities for local policymaking. We argue modern state preemption of local laws can be divided into four phases, each with their own policies and mechanisms. We show how preemption has changed over time, shifting the functional, legal, and political relationship between states and their cities. Together, these phases help assist policymakers and administrators in understanding the nature of state preemption, and thus how to create and implement local policies in an environment where the distribution of power between governments is competitive and changing.
A Bayesian approach to restricted latent class models for scientifically-structured clustering of multivariate binary outcomes

This paper presents a model-based method for clustering multivariate binary observations that incorporates constraints consistent with the scientific context. The approach is motivated by the precision medicine problem of identifying autoimmune disease patient subsets or classes who may require different treatments. We start with a family of restricted latent class models or RLCMs (e.g., Xu and Shang, 2018). However, in the motivating example and many others like it, the unknown number of classes and the definition of classes using binary states are among the targets of inference. We use a Bayesian approach to RCLMs in order to use informative prior assumptions on the number and definitions of latent classes to be consistent with scientific knowledge so that the posterior distribution tends to concentrate on smaller numbers of clusters and sparser binary patterns. The paper derives a posterior sampling algorithm based on Markov chain Monte Carlo with split-merge updates to efficiently explore the space of clustering allocations. Through simulations under the assumed model and realistic deviations from it, we demonstrate greater interpretability of results and superior finite-sample clustering performance for our method compared to common alternatives. The methods are illustrated with an analysis of protein data to detect clusters representing autoantibody classes among scleroderma patients. This article is protected by copyright. All rights reserved.
Anatomical Data Augmentation via Fluid-Based Image Registration

We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpolation from the geodesic subspace underlying given samples. Our approach consists of three steps: 1) given a source image and a set of target images, we construct a geodesic subspace using the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model; 2) we sample transformations from the resulting geodesic subspace; 3) we obtain deformed images and segmentations via interpolation. Experiments on brain (LPBA) and knee (OAI) data illustrate the performance of our approach on two tasks: 1) data augmentation during training and testing for image segmentation; 2) one-shot learning for single atlas image segmentation. We demonstrate that our approach generates anatomically meaningful data and improves performance on these tasks over competing approaches. Code is available at https://github.com/uncbiag/easyreg.
Disruption of Zika Virus xrRNA1-Dependent sfRNA1 Production Results in Tissue-Specific Attenuated Viral Replication.

The Zika virus (ZIKV), like other flaviviruses, produces several species of sub-genomic RNAs (sfRNAs) during infection, corresponding to noncoding RNA fragments of different lengths that result from the exonuclease degradation of the viral 3′ untranslated region (UTR). Over the course of infection, these sfRNAs accumulate in the cell as a result of an incomplete viral genome degradation of the 3′ UTR by the host 5′ to 3′ exoribonuclease, Xrn1. The halting of Xrn1 in the 3′ UTR is due to two RNA pseudoknot structures in the 3′ UTR, termed exoribonuclease-resistant RNA1 and 2 (xrRNA1&2). Studies with related flaviviruses have shown that sfRNAs are important for pathogenicity and inhibiting both mosquito and mammalian host defense mechanisms. However, these investigations have not included ZIKV and there is very limited data addressing how sfRNAs impact infection in a whole animal model or specific tissues. In this study, we generate a sfRNA1-deficient ZIKV (X1) by targeted mutation in the xrRNA1 3′ UTR structure. We find that the X1 virus lacks the production of the largest ZIKV sfRNA species, sfRNA1. Using the X1 virus to infect adult Ifnar1−/− mice, we find that while the lack of sfRNA1 does not alter ZIKV replication in the spleen, there is a significant reduction of ZIKV genome replication in the brain and placenta compared to wild-type ZIKV infection. Despite the attenuated phenotype of the X1 ZIKV, mice develop a robust neutralizing antibody response. We conclude that the targeted disruption of xrRNA1 results in tissue-specific attenuation while still supporting robust neutralizing antibody responses. Future studies will need to investigate the tissue-specific mechanisms by which ZIKV sfRNAs influence infection and may utilize targeted xrRNA mutations to develop novel attenuated flavivirus vaccine approaches.
Differential COVID-19 case positivity in New York City neighborhoods: socioeconomic factors and mobility

Background New York City (NYC) has been one of the hotspots of the COVID-19 pandemic in the United States. By the end of April 2020, close to 165 000 cases and 13 000 deaths were reported in the city with considerable variability across the city's ZIP codes. Objectives In this study, we examine: (a) the extent to which the variability in ZIP code-level case positivity can be explained by aggregate markers of socioeconomic status (SES) and daily change in mobility; and (b) the extent to which daily change in mobility independently predicts case positivity. Methods COVID-19 case positivity by ZIP code was modeled using multivariable linear regression with generalized estimating equations to account for within-ZIP clustering. Daily case positivity was obtained from NYC Department of Health and Mental Hygiene and measures of SES were based on data from the American Community Survey. Changes in human mobility were estimated using anonymized aggregated mobile phone location systems. Results Our analysis indicates that the socioeconomic markers considered together explained 56% of the variability in case positivity through April 1 and their explanatory power decreased to 18% by April 30. Changes in mobility during this time period are not likely to be acting as a mediator of the relationship between ZIP-level SES and case positivity. During the middle of April, increases in mobility were independently associated with decreased case positivity. Conclusions Together, these findings present evidence that heterogeneity in COVID-19 case positivity during NYC's spring outbreak was largely driven by residents' SES.
Optimal Re-centering Bounds, with Applications to Rosenthal-type Concentration of Measure Inequalities

For any nonnegative Borel-measurable function f such that \(f(x)\;=\;0\) if and only if \(x\;=\;0\), the best constant c f in the inequality \(Ef(X\;-\;E\;X)\leqslant c_f E\;f(X)\) for all random variables X with a finite mean is obtained. Properties of the constant c f in the case when \(f\;=\;|\cdot|^p\) for \(p>0\) are studied. Applications to concentration of measure in the form of Rosenthal-type bounds on the moments of separately Lipschitz functions on product spaces are given.
New Observations of the Very Luminous Supernova 2006gy: Evidence for Echoes

Supernova (SN) 2006gy was a hydrogen-rich core-collapse SN that remains one of the most luminous optical supernovae ever observed. The total energy budget (> 2×10 51 erg radiated in the optical alone) poses many challenges for standard SN theory. We present new ground-based near-infrared (NIR) observations of SN 2006gy, as well as a single epoch of Hubble Space Telescope (HST) imaging obtained more than two years after the explosion. Our NIR data taken around peak optical emission show an evolution that is largely consistent with a cooling blackbody, with tentative evidence for a growing NIR excess starting around day ∼130. Our late-time Keck adaptive optics NIR image, taken on day 723, shows little change from previous NIR observations taken around day 400. Furthermore, the optical HST observations show a reduced decline rate after day 400, and the SN is bluer on day 810 than it was at peak. This late-time decline is inconsistent with 56 Co decay, and thus is problematic for the various pair-instability SN models used to explain the nature of SN 2006gy. The slow decline of the NIR emission can be explained with a light echo, and we confirm that the late-time NIR excess is the result of a massive (&10 M⊙) dusty shell heated by the SN peak luminosity. The late-time optical observations require the existence of a scattered light echo, which may be generated by the same dust that contributes to the NIR echo. Both the NIR and optical echoes originate in the proximity of the progenitor, ∼10 18 cm for the NIR echo and .10–40 pc for the optical echo, which provides further evidence that the progenitor of SN 2006gy was a very massive star. Subject headings: supernovae: general — supernovae: individual (SN 2006gy)
MASSIVE STAR FORMATION IN THE LMC. I. N159 AND N160 COMPLEXES

We present images and spectral energy distributions (SEDs) of massive young stellar objects (YSOs) in three star-forming H II regions of the Large Magellanic Cloud: N159A, N159 Papillon, and N160. We use photometry from SOFIA/FORCAST at 25.3--37.1 um to constrain model fits to the SEDs and determine luminosities, ages, and dust content of the embedded YSOs and their local environments. By placing these sources on mid-infrared color-magnitude and color-color diagrams, we analyze their dust properties and consider their evolutionary status. Since each object in the FORCAST images has an obvious bright near-infrared counterpart in Spitzer Space Telescope images, we do not find any evidence for new, very cool, previously-undiscovered Class 0 YSOs. Additionally, based on its mid-infrared colors and model parameters, N159A is younger than N160 and the Papillon. The nature of the first extragalactic protostars in N159, P1 and P2, is also discussed.
Stochastic Matrix Factorization

This paper considers a restriction to non-negative matrix factorization in which at least one matrix factor is stochastic. That is, the elements of the matrix factors are non-negative and the columns of one matrix factor sum to 1. This restriction includes topic models, a popular method for analyzing unstructured data. It also includes a method for storing and finding pictures. The paper presents necessary and sufficient conditions on the observed data such that the factorization is unique. In addition, the paper characterizes natural bounds on the parameters for any observed data and presents a consistent least squares estimator. The results are illustrated using a topic model analysis of PhD abstracts in economics and the problem of storing and retrieving a set of pictures of faces.
Fitting birth–death processes to panel data with applications to bacterial DNA fingerprinting

Continuous-time linear birth-death-immigration (BDI) processes are frequently used in ecology and epidemiology to model stochastic dynamics of the population of interest. In clinical settings, multiple birth-death processes can describe disease trajectories of individual patients, allowing for estimation of the effects of individual covariates on the birth and death rates of the process. Such estimation is usually accomplished by analyzing patient data collected at unevenly spaced time points, referred to as panel data in the biostatistics literature. Fitting linear BDI processes to panel data is a nontrivial optimization problem because birth and death rates can be functions of many parameters related to the covariates of interest. We propose a novel expectation-maximization (EM) algorithm for fitting linear BDI models with covariates to panel data. We derive a closed-form expression for the joint generating function of some of the BDI process statistics and use this generating function to reduce the E-step of the EM algorithm, as well as calculation of the Fisher information, to one-dimensional integration. This analytical technique yields a computationally efficient and robust optimization algorithm that we implemented in an open-source R package. We apply our method to DNA fingerprinting of Mycobacterium tuberculosis, the causative agent of tuberculosis, to study intrapatient time evolution of IS6110 copy number, a genetic marker frequently used during estimation of epidemiological clusters of Mycobacterium tuberculosis infections. Our analysis reveals previously undocumented differences in IS6110 birth-death rates among three major lineages of Mycobacterium tuberculosis, which has important implications for epidemiologists that use IS6110 for DNA fingerprinting of Mycobacterium tuberculosis.
Dynamical Patterns in Individual Trajectories Toward Extremism

Society faces a fundamental global problem of understanding which individuals are currently developing strong support for some extremist entity such as ISIS (Islamic State) - even if they never end up doing anything in the real world. The importance of online connectivity in developing intent has been confirmed by recent case-studies of already convicted terrorists. Here we identify dynamical patterns in the online trajectories that individuals take toward developing a high level of extremist support - specifically, for ISIS. Strong memory effects emerge among individuals whose transition is fastest, and hence may become ‘out of the blue’ threats in the real world. A generalization of diagrammatic expansion theory helps quantify these characteristics, including the impact of changes in geographical location, and can facilitate prediction of future risks. By quantifying the trajectories that individuals follow on their journey toward expressing high levels of pro-ISIS support - irrespective of whether they then carry out a real-world attack or not - our findings can help move safety debates beyond reliance on static watch-list identifiers such as ethnic background or immigration status, and/or post-fact interviews with already-convicted individuals. Given the broad commonality of social media platforms, our results likely apply quite generally: for example, even on Telegram where (like Twitter) there is no built-in group feature as in our study, individuals tend to collectively build and pass through so-called super-group accounts.
Probing the Quench Dynamics of Antiferromagnetic Correlations in a 2D Quantum Ising Spin System

New experiments explore quantum dynamics in a synthetic 2D magnet, an essential step for testing theoretical tools for calculating properties of nonequilibrium quantum systems against real world data.
Density modulation-induced absolute laser-plasma-instabilities: Simulations and theory

Fluid simulations show that when a sinusoidal density modulation is superimposed on a linear density profile, convective instabilities can become absolutely unstable. This conversion can occur for two-plasmon-decay and stimulated Raman Scattering instabilities under realistic direct-drive inertial confinement fusion conditions and can affect hot electron generation and laser energy deposition. Analysis of the three-wave model shows that a sufficiently large change of the density gradient in a linear density profile can turn convective instabilities into absolute ones. An analytical expression is given for the threshold of the gradient change for a two-slope density profile, which depends on the convective gain only.
Transversal Fluctuations of the ASEP, Stochastic Six Vertex Model, and Hall-Littlewood Gibbsian Line Ensembles

We consider the ASEP and the stochastic six vertex model started with step initial data. After a long time, T, it is known that the one-point height function fluctuations for these systems are of order T1/3. We prove the KPZ prediction of T2/3 scaling in space. Namely, we prove tightness (and Brownian absolute continuity of all subsequential limits) as T goes to infinity of the height function with spatial coordinate scaled by T2/3 and fluctuations scaled by T1/3. The starting point for proving these results is a connection discovered recently by Borodin–Bufetov–Wheeler between the stochastic six vertex height function and the Hall-Littlewood process (a certain measure on plane partitions). Interpreting this process as a line ensemble with a Gibbsian resampling invariance, we show that the one-point tightness of the top curve can be propagated to the tightness of the entire curve.
Effects of Turbulence on Cosmic Ray Propagation in Protostars and Young Star/Disk Systems

The magnetic fields associated with young stellar objects are expected to have an hour-glass geometry, i.e., the magnetic field lines are pinched as they thread the equatorial plane surrounding the forming star but merge smoothly onto a background field at large distances. With this field configuration, incoming cosmic rays experience both a funneling effect that acts to enhance the flux impinging on the circumstellar disk and a magnetic mirroring effect that acts to reduce that flux. To leading order, these effects nearly cancel out for simple underlying magnetic field structures. However, the environments surrounding young stellar objects are expected to be highly turbulent. This paper shows how the presence of magnetic field fluctuations affects the process of magnetic mirroring, and thereby changes the flux of cosmic rays striking circumstellar disks. Turbulence has two principle effects: (1) the (single) location of the magnetic mirror point found in the absence of turbulence is replaced with a wide distribution of values. (2) The median of the mirror point distribution moves outward for sufficiently large fluctuation amplitudes (roughly when δB/B 0 > 0.2 at the location of the turbulence-free mirror point); the distribution becomes significantly non-Gaussian in this regime as well. These results may have significant consequences for the ionization fraction of the disk, which in turn dictates the efficiency with which disk material can accrete onto the central object. A similar reduction in cosmic ray flux can occur during the earlier protostellar stages; the decrease in ionization can help alleviate the magnetic braking problem that inhibits disk formation.
THE MASS AND SIZE DISTRIBUTION OF PLANETESIMALS FORMED BY THE STREAMING INSTABILITY. I. THE ROLE OF SELF-GRAVITY

NASA [NNX13AI58G, NNX16AB42G, NAS 5-26555]; NSF [AST 1313021]; Space Telescope Science Institute [HST-AR-12814]; XSEDE grant [TG-AST120062]; California Institute of Technology (Caltech); Jet Propulsion Laboratory (JPL) - NASA
Examining The Fermi-LAT Third Source Catalog in search of dark matter subhalos

Dark matter annihilations taking place in nearby subhalos could appear as gamma-ray sources without detectable counterparts at other wavelengths. In this study, we consider the collection of unassociated gamma-ray sources reported by the Fermi Collaboration in an effort to identify the most promising dark matter subhalo candidates. While we identify 24 bright, high-latitude, non-variable sources with spectra that are consistent with being generated by the annihilations of ~ 20–70 GeV dark matter particles (assuming annihilations to bbar b), it is not possible at this time to distinguish these sources from radio-faint gamma-ray pulsars. Deeper multi-wavelength observations will be essential to clarify the nature of these sources. It is notable that we do not find any such sources that are well fit by dark matter particles heavier than ~100 GeV. We also study the angular distribution of the gamma-rays from this set of subhalo candidates, and find that the source 3FGL J2212.5+0703 prefers a spatially extended profile (of width ~ 0.15°) over that of a point source, with a significance of 4.2σ (3.6σ after trials factor). Although not yet definitive, this bright and high-latitude gamma-ray source is well fit as a nearby subhalo of mχ ≃ 20–50 GeV dark matter particlesmore » (annihilating to bb¯) and merits further multi-wavelength investigation. As a result, based on the subhalo distribution predicted by numerical simulations, we derive constraints on the dark matter annihilation cross section that are competitive to those resulting from gamma-ray observations of dwarf spheroidal galaxies, the Galactic Center, and the extragalactic gamma-ray background.« less
Eigenvalue-based cyclostationary spectrum sensing using multiple antennas

In this paper, we propose a signal-selective spectrum sensing method for cognitive radio networks and specifically targeted for receivers with multiple-antenna capability. This method is used for detecting the presence or absence of primary users based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. The analytical threshold for achieving constant false alarm rate using this detection method is presented, verified through simulations, and shown to be independent of both the number of samples used and the noise variance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown, through numerical simulations, to outperform existing multiple-antenna cyclostationary-based spectrum sensing algorithms under a quasi-static Rayleigh fading channel, in both spatially correlated and uncorrelated noise environments. The algorithm also has significantly lower computational complexity than these other approaches.
Why Is Landau-Ginzburg Link Cohomology Equivalent To Khovanov Homology?

In this note we make an attempt to compare a cohomological theory of Hilbert spaces of ground states in the $$ \mathcal{N}=\left(2,2\right) $$ 2d Landau-Ginzburg theory in models describing link embeddings in ℝ3 to Khovanov and Khovanov-Rozansky homologies. To confirm the equivalence we exploit the invariance of Hilbert spaces of ground states for interfaces with respect to homotopy. In this attempt to study solitons and instantons in the LandauGiznburg theory we apply asymptotic analysis also known in the literature as exact WKB method, spectral networks method, or resurgence. In particular, we associate instantons in LG model to specific WKB line configurations we call null-webs.
Extracting Energy from Black Holes: The Relative Importance of the Blandford-Znajek Mechanism

We critically assess the role of the Blandford-Znajek mechanism in the powering of outflows from accretion-disk-fed black holes. We argue that there is no reason to suppose that the magnetic field threading the central spinning black hole differs significantly in strength from that threading the central regions of the disk. In this case, we show that the electromagnetic output from the inner disk regions is expected in general to dominate over that from the hole. Thus the spin (or not) of the hole is probably irrelevant to the expected electromagnetic power output from the system. We also point out that the strength of the poloidal field in the center of a standard accretion disk has been generally overestimated, and we discuss scenarios that might give rise to more significant central poloidal fields.