cssi/SciSciGPT-SciSciNet huggingface dataset:
Occupational Cohort Studies

Retrospective occupational mortality studies have been one of the most effective mechanisms for investigating the relationship of exposure to chronic disease. Occupational cohorts are well suited to investigate such exposure-disease relationships because exposure is usually better characterized in the workplace than in most other settings. In this chapter, we address issues related to the design, conduct, analysis, and interpretation of retrospective occupational mortality studies. We summarize aspects of cohort definition and verification, determination of vital status, coding of death certificates, and incorporation of exposure measurements. Selected mortality summary indices are reviewed, followed by a discussion of regression models appropriate for the analysis. Finally, we briefly describe some of the designs that combine the advantages of case–control and cohort studies. Keywords: occupational cohort; mortality study; case–cohort; nested case–control
Aggregate income risks and hedging mechanisms

Abstract Estimates are made, from time series data on real gross domestic products, of the standard deviation of returns in markets for perpetual claims on countries' incomes. The results indicate that there is much fundamental uncertainty to be hedged. Evidence is shown that there may be only minimal ability to cross hedge these returns in existing capital markets. Methods of establishing markets for perpetual claims on aggregate incomes are examined. Such markets, by allowing hedging of these aggregate income risks, might make for dramatically more effective international macroeconomic risk sharing than is possible today. Retail institutions are described that might develop such markets and help the public with their risk management. However, the establishment of such markets would also incur the risk of major financial bubbles and panics.
Guidelines for the management of severe head injury

The practice of medicine is characteristically based on a combination of clinical reports, laboratory investigation, the lessons imparted by professors and colleagues, and anecdotal experience. Unfortunately, a clear hierarchy of scientific validity has not been developed to guide the physician in weighing these various sources of knowledge when deciding upon a management strategy. The difficulty inherent in carefully analyzing individual papers, coupled with the extreme frequency at which publications of uncontrolled quality appear, seems to mitigate against the practice of evidence-based medicine.
A NICMOS-3 based spectrograph for near infrared astronomy

We describe a design for a compact cooled spectrograph which incorporates two cameras and two collimators to achieve high throughput from 0.8 to 2.5μm. The spectrograph utilizes multiple NICMOS 3 arrays, specially mounted to minimize their separation in the spectral dimension. This accommodates the broad wavelength range while providing extensive coverage in the spatial dimension.
Comovement in GDP trends and cycles among trading partners

Abstract It has long been recognized that business cycle comovement is greater between countries that trade more intensively with one another. However, nations face shocks to both the cyclical and trend components of their GDP series. Contrary to the result for cyclical fluctuations, we find comovement of shocks to the trend component of real GDP is weaker among countries that trade more intensively with one another. We simulate changes in ten-year output growth correlations corresponding to the estimated effects of trade and show that the impact of trade on trend comovement is quantitatively more important than its effect on cyclical comovement.
Public Plug-in Electric Vehicles + Grid Data: Is a New Cyberattack Vector Viable?

High-wattage demand-side appliances such as Plug-in Electric Vehicles (PEVs) are proliferating. As a result, information on the charging patterns of PEVs is becoming accessible via smartphone applications, which aggregate real-time availability and historical usage of public PEV charging stations. Moreover, information on the power grid infrastructure and operations has become increasingly available in technical documents and real-time dashboards of the utilities, affiliates, and the power grid operators. The research question that this study explores is: Can one combine high-wattage demand-side appliances with public information to launch cyberattacks on the power grid? To answer this question and report a proof of concept demonstration, the study scrapes data from public sources for Manhattan, NY, USA using the electric vehicle charging station smartphone application and the power grid data circulated by the U.S. Energy Information Administration, New York Independent System Operator, and the local utility in New York. It then designs a novel data-driven cyberattack strategy using state-feedback based partial eigenvalue relocation, which targets frequency stability of the power grid. The study establishes that while such an attack is not possible at the current penetration level of PEVs, it will be practical once the number of PEVs increases.
Budget Windows, Sunsets and Fiscal Control

Abstract In recent years, the United States has evaluated policy changes using a multi-year budget window. A budget window that is too short permits the shifting of costs beyond the window's endpoint, while a window that is too long includes years for which current legislation is essentially meaningless, and allows the shifting of fiscal burdens to those whom budget rules are supposed to protect. This paper characterizes the “optimal” budget window. An appropriately designed budget window eliminates the incentive to use sunsets to avoid budget restrictions. The analysis also has implications for how to account for long-term term budget commitments.
Multimodal mapping of neural activity and cerebral blood flow reveals long-lasting neurovascular dissociations after small-scale strokes

Neurovascular coupling, the close spatial and temporal relationship between neural activity and hemodynamics, is disrupted in pathological brain states. To understand the altered neurovascular relationship in brain disorders, longitudinal, simultaneous mapping of neural activity and hemodynamics is critical yet challenging to achieve. Here, we use a multimodal neural platform in a mouse model of stroke and realize long-term, spatially resolved tracking of intracortical neural activity and cerebral blood flow in the same brain regions. We observe a pronounced neurovascular dissociation that occurs immediately after small-scale strokes, becomes the most severe a few days after, lasts into chronic periods, and varies with the level of ischemia. Neuronal deficits extend spatiotemporally, whereas restoration of cerebral blood flow occurs sooner and reaches a higher relative value. Our findings reveal the neurovascular impact of ministrokes and inform the limitation of neuroimaging techniques that infer neural activity from hemodynamic responses.
Residual dimension of nilpotent groups

The functions $F_{G}(n)$ measures the asymptotic behavior of residual finiteness for a finitely generated group $G$. In previous work \cite{Pengitore_1}, the author claimed a characterization for $F_{N}(n)$ when $N$ is a finitely generated nilpotent group. However, a counterexample to the above claim was communicated to the author, and subsequently, the statement of the asymptotic characterization of $F_{N}(n)$ is incorrect. In this article, we introduce new tools to provide lower asymptotic bounds for $F_{N}(n)$ when $N$ is a finitely generated nilpotent group. Moreover, we introduce a class of finitely generated nilpotent groups for which the upper bound of \cite{Pengitore_1} can be improved. Finally, we construct of a class of finitely generated nilpotent groups $N$ for which the asymptotic behavior of $F_N(n)$ can be fully characterized.
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion

Datasets containing sensitive information are often sequentially analyzed by many algorithms. This raises a fundamental question in differential privacy regarding how the overall privacy bound degrades under composition. To address this question, we introduce a family of analytical and sharp privacy bounds under composition using the Edgeworth expansion in the framework of the recently proposed f-differential privacy. In contrast to the existing composition theorems using the central limit theorem, our new privacy bounds under composition gain improved tightness by leveraging the refined approximation accuracy of the Edgeworth expansion. Our approach is easy to implement and computationally efficient for any number of compositions. The superiority of these new bounds is confirmed by an asymptotic error analysis and an application to quantifying the overall privacy guarantees of noisy stochastic gradient descent used in training private deep neural networks.
Sources of Transfer in Multilingual Named Entity Recognition

Named-entities are inherently multilingual, and annotations in any given language may be limited. This motivates us to consider polyglot named-entity recognition (NER), where one model is trained using annotated data drawn from more than one language. However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data. The starting point of this paper is a simple solution to this problem, in which polyglot models are fine-tuned on monolingual data to consistently and significantly outperform their monolingual counterparts. To explain this phenomena, we explore the sources of multilingual transfer in polyglot NER models and examine the weight structure of polyglot models compared to their monolingual counterparts. We find that polyglot models efficiently share many parameters across languages and that fine-tuning may utilize a large number of those parameters.
Bridging Anaphora Resolution as Question Answering

Most previous studies on bridging anaphora resolution (Poesio et al., 2004; Hou et al., 2013b; Hou, 2018a) use the pairwise model to tackle the problem and assume that the gold mention information is given. In this paper, we cast bridging anaphora resolution as question answering based on context. This allows us to find the antecedent for a given anaphor without knowing any gold mention information (except the anaphor itself). We present a question answering framework (BARQA) for this task, which leverages the power of transfer learning. Furthermore, we propose a novel method to generate a large amount of “quasi-bridging” training data. We show that our model pre-trained on this dataset and fine-tuned on a small amount of in-domain dataset achieves new state-of-the-art results for bridging anaphora resolution on two bridging corpora (ISNotes (Markert et al., 2012) and BASHI (Ro siger, 2018)).
Estimating and interpreting nonlinear receptive field of sensory neural responses with deep neural network models

Our understanding of nonlinear stimulus transformations by neural circuits is hindered by the lack of comprehensive yet interpretable computational modeling frameworks. Here, we propose a data-driven approach based on deep neural networks to directly model arbitrarily nonlinear stimulus-response mappings. Reformulating the exact function of a trained neural network as a collection of stimulus-dependent linear functions enables a locally linear receptive field interpretation of the neural network. Predicting the neural responses recorded invasively from the auditory cortex of neurosurgical patients as they listened to speech, this approach significantly improves the prediction accuracy of auditory cortical responses, particularly in nonprimary areas. Moreover, interpreting the functions learned by neural networks uncovered three distinct types of nonlinear transformations of speech that varied considerably from primary to nonprimary auditory regions. The ability of this framework to capture arbitrary stimulus-response mappings while maintaining model interpretability leads to a better understanding of cortical processing of sensory signals.
When Second Best Might Be the Best: Using Hospitalization Data to Monitor the Novel Coronavirus Pandemic

The novel coronavirus' high rate of asymptomatic transmission combined with a lack of testing kits call for a different approach to monitor its spread and severity. We proposed the use of hospitalizations and hospital utilization data to monitor the spread and severity. A proposed threshold of a declining 7-day moving average over a 14-day period, "7&14" was set to communicate when a wave of the novel coronavirus may have passed. The state of Ohio was chosen to illustrate this threshold. While not the ideal solution for monitoring the spread of the epidemic, the proposed approach is an easy to implement framework accounting for limitations of the data inherent in the current epidemic. Hospital administrators and policy makers may benefit from incorporating this approach into their decision making.
Personalized Policy Learning using Longitudinal Mobile Health Data.

Personalized policy represents a paradigm shift one decision rule for all users to an individualized decision rule for each user. Developing personalized policy in mobile health applications impose...
Trust and the Media: Perceptions of Climate Change News Sources Among US College Students

With increasing fake news and polarizing politics, Americans have been exposed to false or misinterpreted scientific information. A disconnect between the scientific community and news outlets has perpetuated public uncertainty about climate change. With the widening of such disconnect, it is crucial to understand how youth, who mainly use digital sources for information, comprehend climate change, as such a demographic will be a vehicle for climate change mitigation. We aim to understand climate change knowledge and attitudes among college students and their trustworthiness of six news outlets as sources of information about climate change. Results from a survey show that students care and are aware of climate change. Moreover, students are hesitant about news sources for climate change information. While students trust more the New York Times and The Wall Street Journal, local news sources to the college, they overall neither trust nor distrust CNN, The Washington Post, Fox News, and Breitbart. This hesitation to trust or distrust such contrasting news regarding climate change may be explained by the overabundance of misinformation, the usage of cognitive heuristics, the rise of anti-intellectualism, and the lack of digital literacy, which make processing information more challenging in this postdigital era. We conclude by emphasizing the need to develop different information literacies in higher education. As digital platforms continue to grow, it is important to understand how youth receive and process information about topics like climate change in a complex information ecosystem.
Learning the Associations of MITRE ATT & CK Adversarial Techniques

The MITRE ATT&CK Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures (TTP). However, this information would be highly useful for attack diagnosis (i.e., forensics) and mitigation (i.e., intrusion response) if we can reliably construct technique associations that will enable predicting unobserved attack techniques based on observed ones. In this paper, we present our statistical machine learning analysis on APT and Software attack data reported by MITRE ATT&CK to infer the technique clustering that represents the significant correlation that can be used for technique prediction. Due to the complex multidimensional relationships between techniques, many of the traditional clustering methods could not obtain usable associations. Our approach, using hierarchical clustering for inferring attack technique associations with 95% confidence, provides statistically significant and explainable technique correlations. Our analysis discovers 98 different technique associations (i.e., clusters) for both APT and Software attacks. Our evaluation results show that 78% of the techniques associated by our algorithm exhibit significant mutual information that indicates reasonably high predictability.
Enhanced nutrient uptake is sufficient to drive emergent cross-feeding between bacteria in a synthetic community

Interactive microbial communities are ubiquitous, influencing biogeochemical cycles and host health. One widespread interaction is nutrient exchange, or cross-feeding, wherein metabolites are transferred between microbes. Some cross-fed metabolites, such as vitamins, amino acids, and ammonium (NH4+), are communally valuable and impose a cost on the producer. The mechanisms that enforce cross-feeding of communally valuable metabolites are not fully understood. Previously we engineered a cross-feeding coculture between N2-fixing Rhodopseudomonas palustris and fermentative Escherichia coli. Engineered R. palustris excretes essential nitrogen as NH4+ to E. coli, while E. coli excretes essential carbon as fermentation products to R. palustris. Here, we sought to determine whether a reciprocal cross-feeding relationship would evolve spontaneously in cocultures with wild-type R. palustris, which is not known to excrete NH4+. Indeed, we observed the emergence of NH4+ cross-feeding, but driven by adaptation of E. coli alone. A missense mutation in E. coli NtrC, a regulator of nitrogen scavenging, resulted in constitutive activation of an NH4+ transporter. This activity likely allowed E. coli to subsist on the small amount of leaked NH4+ and better reciprocate through elevated excretion of fermentation products from a larger E. coli population. Our results indicate that enhanced nutrient uptake by recipients, rather than increased excretion by producers, is an underappreciated yet possibly prevalent mechanism by which cross-feeding can emerge.
Auto-G-Computation of Causal Effects on a Network.

Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) the intervention received by one unit cannot causally influence the outcome of another; and (ii) u...
Graph Prolongation Convolutional Networks: Explicitly Multiscale Machine Learning on Graphs with Applications to Modeling of Cytoskeleton

We define a novel type of ensemble Graph Convolutional Network (GCN) model. Using optimized linear projection operators to map between spatial scales of graph, this ensemble model learns to aggregate information from each scale for its final prediction. We calculate these linear projection operators as the infima of an objective function relating the structure matrices used for each GCN. Equipped with these projections, our model (a Graph Prolongation-Convolutional Network) outperforms other GCN ensemble models at predicting the potential energy of monomer subunits in a coarse-grained mechanochemical simulation of microtubule bending. We demonstrate these performance gains by measuring an estimate of the FLOPs spent to train each model, as well as wall-clock time. Because our model learns at multiple scales, it is possible to train at each scale according to a predetermined schedule of coarse vs. fine training. We examine several such schedules adapted from the Algebraic Multigrid (AMG) literature, and quantify the computational benefit of each. We also compare this model to another model which features an optimized coarsening of the input graph. Finally, we derive backpropagation rules for the input of our network model with respect to its output, and discuss how our method may be extended to very large graphs.
Multiple monoenergetic gamma radiography (MMGR) with a compact superconducting cyclotron

Smuggling of special nuclear materials and nuclear devices through borders and ports of entry constitutes a major risk to global security. Technologies are needed to reliably screen the flow of commerce for the presence of high- Z materials such as uranium and plutonium. Here, we present an experimental proof-of-concept of a technique that uses inelastic ( p , p ′) nuclear reactions to generate monoenergetic photons, which provide means to measure the areal density and the effective- Z ( Z eff) of an object with an accuracy surpassing that achieved by current methods. We use an ION-12 SC superconducting 12 MeV proton cyclotron to produce 4.4, 6.1, 6.9, and 7.1 MeV photons from a variety of nuclear reactions. Using these photons in a transmission mode, we show that we are able to accurately reconstruct the areal densities and Z eff of a test object. This methodology could enable mobile applications to screen commercial cargoes with high material specificity, providing a means of distinguishing common cargo materials from high-Z materials that include uranium and plutonium.
Wasserstein GANs for MR Imaging: From Paired to Unpaired Training

Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this article leverages unpaired adversarial training for reconstruction networks, where the inputs are undersampled k-space and naively reconstructed images from one dataset, and the labels are high-quality images from another dataset. The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality. The generator is an unrolled neural network – a cascade of convolutional and data consistency layers. The discriminator is also a multilayer CNN that plays the role of a critic scoring the quality of reconstructed images based on the Wasserstein distance. Our experiments with knee MRI datasets demonstrate that the proposed unpaired training enables diagnostic-quality reconstruction when high-quality image labels are not available for the input types of interest, or when the amount of labels is small. In addition, our adversarial training scheme can achieve better image quality (as rated by expert radiologists) compared with the paired training schemes with pixel-wise loss.
MapGL: inferring evolutionary gain and loss of short genomic sequence features by phylogenetic maximum parsimony

Comparative genomics studies are growing in number partly because of their unique ability to provide insight into shared and divergent biology between species. Of particular interest is the use of phylogenetic methods to infer the evolutionary history of cis-regulatory sequence features, which contribute strongly to phenotypic divergence and are frequently gained and lost in eutherian genomes. Understanding the mechanisms by which cis-regulatory element turnover generate emergent phenotypes is crucial to our understanding of adaptive evolution. Ancestral reconstruction methods can place species-specific cis-regulatory features in their evolutionary context, thus increasing our understanding of the process of regulatory sequence turnover. However, applying these methods to gain and loss of cis-regulatory features historically required complex workflows, preventing widespread adoption by the broad scientific community. MapGL simplifies phylogenetic inference of the evolutionary history of short genomic sequence features by combining the necessary steps into a single piece of software with a simple set of inputs and outputs. We show that MapGL can reliably disambiguate the mechanisms underlying differential regulatory sequence content across a broad range of phylogenetic topologies and evolutionary distances. Thus, MapGL provides the necessary context to evaluate how genomic sequence gain and loss contribute to species-specific divergence. MapGL makes phylogenetic inference of species-specific sequence gain and loss easy for both expert and non-expert users, making it a powerful tool for gaining novel insights into genome evolution.
Evolutionary Dynamics of Transposable Elements Following a Shared Polyploidization Event in the Tribe Andropogoneae

Both polyploidization and transposable element (TE) activity are known to be major drivers of plant genome evolution. Here, we utilize the Zea-Tripsacum clade to investigate TE activity and accumulation after a shared polyploidization event. Comparisons of TE evolutionary dynamics in various Zea and Tripsacum species, in addition to two closely related diploid species, Urelytrum digitatum and Sorghum bicolor, revealed variation in repeat content among all taxa included in the study. The repeat composition of Urelytrum is more similar to that of Zea and Tripsacum compared to Sorghum, despite the similarity in genome size with the latter. Although LTR-retrotransposons were abundant in all species, we observed an expansion of the copia superfamily, specifically in Z. mays and T. dactyloides, species that have adapted to more temperate environments. Additional analyses of the genomic distribution of these retroelements provided evidence of biased insertions near genes involved in various biological processes including plant development, defense, and macromolecule biosynthesis. Specifically, copia insertions in Zea and T. dactyloides were significantly enriched near genes involved in abiotic stress response, suggesting independent evolution post Zea-Tripsacum divergence. The lack of copia insertions near the orthologous genes in S. bicolor suggests that duplicate gene copies generated during polyploidization may offer novel neutral sites for TEs to insert, thereby providing an avenue for subfunctionalization via TE insertional mutagenesis.
Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs

Aiming to understand how human (false-)belief— a core socio-cognitive ability—would affect human interactions with robots, this paper proposes to adopt a graphical model to unify the representation of object states, robot knowledge, and human (false-)beliefs. Specifically, a parse graph (pg) is learned from a single-view spatiotemporal parsing by aggregating various object states along the time; such a learned representation is accumulated as the robot’s knowledge. An inference algorithm is derived to fuse individual pg from all robots across multi-views into a joint pg, which affords more effective reasoning and inference capability to overcome the errors originated from a single view. In the experiments, through the joint inference over pgs, the system correctly recognizes human (false-)belief in various settings and achieves better cross-view accuracy on a challenging small object tracking dataset.
Monadic pseudo BE-algebras

In this paper we define the monadic pseudo BE-algebras and investigate their properties. We prove that the existential and universal quantifiers of a monadic pseudo BE-algebra form a residuated pair. Special properties are studied for the particular case of monadic bounded commutative pseudo BE-algebras. Monadic classes of pseudo BE-algebras are investigated and it is proved that the quantifiers on bounded commutative pseudo BE-algebras are also quantifiers on the corresponding pseudo MV-algebras. The monadic deductive systems and monadic congruences of monadic pseudo BE-algebras are defined and their properties are studied. It is proved that, in the case of a monadic distributive commutative pseudo BE-algebra there is a one-to-one correspondence between monadic congruences and monadic deductive systems, and the monadic quotient pseudo BE-algebra algebra is also defined.
How Do People Describe Locations During a Natural Disaster: An Analysis of Tweets from Hurricane Harvey

Social media platforms, such as Twitter, have been increasingly used by people during natural disasters to share information and request for help. Hurricane Harvey was a category 4 hurricane that devastated Houston, Texas, USA in August 2017 and caused catastrophic flooding in the Houston metropolitan area. Hurricane Harvey also witnessed the widespread use of social media by the general public in response to this major disaster, and geographic locations are key information pieces described in many of the social media messages. A geoparsing system, or a geoparser, can be utilized to automatically extract and locate the described locations, which can help first responders reach the people in need. While a number of geoparsers have already been developed, it is unclear how effective they are in recognizing and geo-locating the locations described by people during natural disasters. To fill this gap, this work seeks to understand how people describe locations during a natural disaster by analyzing a sample of tweets posted during Hurricane Harvey. We then identify the limitations of existing geoparsers in processing these tweets, and discuss possible approaches to overcoming these limitations.
{MEUZZ}: Smart Seed Scheduling for Hybrid Fuzzing

Seed scheduling is a prominent factor in determining the yields of hybrid fuzzing. Existing hybrid fuzzers schedule seeds based on fixed heuristics that aim to predict input utilities. However, such heuristics are not generalizable as there exists no one-size-fits-all rule applicable to different programs. They may work well on the programs from which they were derived, but not others. To overcome this problem, we design a Machine learning-Enhanced hybrid fUZZing system (MEUZZ), which employs supervised machine learning for adaptive and generalizable seed scheduling. MEUZZ determines which new seeds are expected to produce better fuzzing yields based on the knowledge learned from past seed scheduling decisions made on the same or similar programs. MEUZZ's learning is based on a series of features extracted via code reachability and dynamic analysis, which incurs negligible runtime overhead (in microseconds). Moreover, MEUZZ automatically infers the data labels by evaluating the fuzzing performance of each selected seed. As a result, MEUZZ is generally applicable to, and performs well on, various kinds of programs. Our evaluation shows MEUZZ significantly outperforms the state-of-the-art grey-box and hybrid fuzzers, achieving 27.1% more code coverage than QSYM. The learned models are reusable and transferable, which boosts fuzzing performance by 7.1% on average and improves 68% of the 56 cross-program fuzzing campaigns. MEUZZ discovered 47 deeply hidden and previously unknown bugs--with 21 confirmed and fixed by the developers--when fuzzing 8 well-tested programs with the same configurations as used in previous work.
Magneto-Mechanical Metamaterials with Widely Tunable Mechanical Properties and Acoustic Bandgaps

Mechanical metamaterials are architected manmade materials that allow for unique behaviors not observed in nature, making them promising candidates for a wide range of applications. Existing metamaterials lack tunability as their properties can only be changed to a limited extent after the fabrication. In this paper, we present a new magneto-mechanical metamaterial that allows great tunability through a novel concept of deformation mode branching. The architecture of this new metamaterial employs an asymmetric joint design using hard-magnetic soft active materials that permits two distinct actuation modes (bending and folding) under opposite-direction magnetic fields. The subsequent application of mechanical forces leads to the deformation mode branching where the metamaterial architecture transforms into two distinctly different shapes, which exhibit very different deformations and enable great tunability in properties such as mechanical stiffness and acoustic bandgaps. Furthermore, this metamaterial design can be incorporated with magnetic shape memory polymers with global stiffness tunability, which further enables the global shift of the acoustic behaviors. The combination of magnetic and mechanical actuations, as well as shape memory effects, imbue unmatched tunable properties to a new paradigm of metamaterials.
Bonus properties of states of low energy

States of Low Energy (SLEs) are exact Hadamard states defined on arbitrary Friedmann–Lemaitre spacetimes. They are constructed from a fiducial state by minimizing the Hamiltonian’s expectation value after averaging with a temporal window function. We show the SLE to be expressible solely in terms of the (state independent) commutator function. They also admit a convergent series expansion in powers of the spatial momentum, both for massive and for massless theories. In the massless case, the leading infrared behavior is found to be Minkowski-like for all scale factors. This provides a new cure for the infrared divergences in Friedmann–Lemaitre spacetimes with accelerated expansion. As a consequence, massless SLEs are viable candidates for pre-inflationary vacua, and in a soluble model, they are shown to entail a qualitatively correct primordial power spectrum.
Low E2F2 activity is associated with high genomic instability and PARPi resistance

The E2F family, classically known for a central role in cell cycle, has a number of emerging roles in cancer including angiogenesis, metabolic reprogramming, metastasis and DNA repair. E2F1 specifically has been shown to be a critical mediator of DNA repair; however, little is known about DNA repair and other E2F family members. Here we present an integrative bioinformatic and high throughput drug screening study to define the role of E2F2 in maintaining genomic integrity in breast cancer. We utilized in vitro E2F2 ChIP-chip and over expression data to identify transcriptional targets of E2F2. This data was integrated with gene expression from E2F2 knockout tumors in an MMTV-Neu background. Finally, this data was compared to human datasets to identify conserved roles of E2F2 in human breast cancer through the TCGA breast cancer, Cancer Cell Line Encyclopedia, and CancerRx datasets. Through these methods we predict that E2F2 transcriptionally regulates mediators of DNA repair. Our gene expression data supports this hypothesis and low E2F2 activity is associated with a highly unstable tumor. In human breast cancer E2F2, status was also correlated with a patient's response to PARP inhibition therapy. Taken together this manuscript defines a novel role of E2F2 in cancer progression beyond cell cycle and could impact patient treatment.
Mobility of Lower MA-Helices for Ion Conduction through Lateral Portals in 5-HT3A Receptors

Abstract The intracellular domain of the serotonin type 3A receptor, a pentameric ligand-gated ion channel, is crucial for regulating conductance. Ion permeation through the extracellular vestibule and the transmembrane channel is well understood, whereas the specific ion conduction pathway through the intracellular domain is less clear. The intracellular domain starts with a short loop after the third transmembrane segment, followed by a short α-helical segment, a large unstructured loop, and finally, the membrane-associated MA-helix that continues into the last transmembrane segment. The MA-helices from all five subunits form the extension of the transmembrane ion channel and shape what has been described as a “closed vestibule,” with their lateral portals obstructed by loops and their cytosolic ends forming a tight hydrophobic constriction. The question remains whether the lateral portals or cytosolic constriction conduct ions upon channel opening. In our study, we used disulfide bond formation between pairs of engineered cysteines to probe the proximity and mobility of segments of the MA-helices most distal to the membrane bilayer. Our results indicate that the proximity and orientation for cysteine pairs at I409C/R410C, in close proximity to the lateral windows, and L402C/L403C, at the cytosolic ends of the MA-helices, are conducive for disulfide bond formation. Although conformational changes associated with gating promote cross-linking for I409C/R410C, which in turn decreases channel currents, cross-linking of L402C/L403C is functionally silent in macroscopic currents. These results support the hypothesis that concerted conformational changes open the lateral portals for ion conduction, rendering ion conduction through the vertical portal unlikely.
Characterizing COVID-19 misinformation communities using a novel twitter dataset

From conspiracy theories to fake cures and fake treatments, COVID-19 has become a hotbed for the spread of misinformation online It is more important than ever to identify methods to debunk and correct false information online In this paper, we present a methodology and analyses to characterize the two competing COVID-19 misinformation communities online: (i) misinformed users or users who are actively posting misinformation, and (ii) informed users or users who are actively spreading true information, or calling out misinformation The goals of this study are twofold: (i) collecting a diverse set of annotated COVID-19 Twitter dataset that can be used by the research community to conduct meaningful analysis;and (ii) characterizing the two target communities in terms of their network structure, linguistic patterns, and their membership in other communities Our analyses show that COVID-19 misinformed communities are denser, and more organized than informed communities, with a possibility of a high volume of the misinformation being part of disinformation campaigns Our analyses also suggest that a large majority of misinformed users may be anti-vaxxers Finally, our sociolinguistic analyses suggest that COVID-19 informed users tend to use more narratives than misinformed users © 2020 CEUR-WS All rights reserved
Cubes and their centers

We study the relationship between the sizes of sets B, S in $${\mathbb{R}^n}$$ where B contains the k-skeleton of an axes-parallel cube around each point in S, generalizing the results of Keleti, Nagy, and Shmerkin [6] about such sets in the plane. We find sharp estimates for the possible packing and box-counting dimensions for B and S. These estimates follow from related cardinality bounds for sets containing the discrete skeleta of cubes around a finite set of a given size. The Katona–Kruskal Theorem from hypergraph theory plays an important role. We also find partial results for the Haussdorff dimension and settle an analogous question for the dual polytope of the cube, the orthoplex.
Lower algebraic K-theory of certain reflection groups

For P ⊂ 3 a finite volume geodesic polyhedron, with the property that all interior angles between incident faces are of the form π/mij (mij ≥ 2 an integer), there is a naturally associated Coxeter group ΓP. Furthermore, this Coxeter group is a lattice inside the semi-simple Lie group O+(3, 1) = Isom(3), with fundamental domain the original polyhedron P. In this paper, we provide a procedure for computing the lower algebraic K-theory of the integral group ring of such groups ΓP in terms of the geometry of the polyhedron P. As an ingredient in the computation, we explicitly calculate the K−1 and Wh of the groups Dn and Dn × 3, and we also summarize what is known about the 0.
Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

Aided by technology, people are increasingly able to communicate across geographical, cultural, and language barriers. This ability also results in new challenges, as interlocutors need to adapt their communication approaches to increasingly diverse circumstances. In this work, we take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance. As a case study, we focus on facilitating the accurate transmission of pragmatic intentions and introduce a methodology for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance. We demonstrate the feasibility of this approach by evaluating our method in two realistic communication scenarios and show that it can reduce the potential for misalignment between the speaker's intentions and the listener's perceptions in both cases.
Laser-machined ultra-high-Q microrod resonators for nonlinear optics

Optical whispering-gallery microresonators are useful tools in microphotonics and non-linear optics at very low threshold powers. Here, we present details about the fabrication of ultra-high-Q whispering-gallery-mode resonators made by CO2-laser lathe machining of fused-quartz rods. The resonators can be fabricated in less than 1 min and the obtained optical quality factors exceed Q = 1 × 109. Demonstrated resonator diameters are in the range between 170 μm and 8 mm (free spectral ranges between 390 GHz and 8 GHz). Using these microresonators, a variety of optical nonlinearities are observed, including Raman scattering, Brillouin scattering, and four-wave mixing.
Shock Waves in Dispersive Eulerian Fluids

The long-time behavior of an initial step resulting in a dispersive shock wave (DSW) for the one-dimensional isentropic Euler equations regularized by generic, third-order dispersion is considered by use of Whitham averaging. Under modest assumptions, the jump conditions (DSW locus and speeds) for admissible, weak DSWs are characterized and found to depend only upon the sign of dispersion (convexity or concavity) and a general pressure law. Two mechanisms leading to the breakdown of this simple wave DSW theory for sufficiently large jumps are identified: a change in the sign of dispersion, leading to gradient catastrophe in the modulation equations, and the loss of genuine nonlinearity in the modulation equations. Large amplitude DSWs are constructed for several particular dispersive fluids with differing pressure laws modeled by the generalized nonlinear Schrodinger equation. These include superfluids (Bose–Einstein condensates and ultracold fermions) and “optical fluids.” Estimates of breaking times for smooth initial data and the long-time behavior of the shock tube problem are presented. Detailed numerical simulations compare favorably with the asymptotic results in the weak to moderate amplitude regimes. Deviations in the large amplitude regime are identified with breakdown of the simple wave DSW theory.
Global spacetime similarity

There are two classes of topologies most often placed on the space of Lorentz metrics on a fixed manifold. As I interpret a complaint of Geroch [Relativity (Plenum, 1970), p. 259 and Gen. Relativ. Gravitation 2, 61 (1971)], however, neither of these standard classes correctly capture a notion of global spacetime similarity. In particular, Geroch presents examples to illustrate that one, the compact-open topologies, in general seems to be too coarse, while another, the open (Whitney) topologies, in general seems to be too fine. After elaborating further the mathematical and physical reasons for these failures, I then construct a topology that succeeds in capturing a notion of global spacetime similarity and investigate some of its mathematical and physical properties.
Spitzer MIPS imaging of NGC 650 : Probing the history of mass loss on the asymptotic giant branch

We present the far-infrared (IR) maps of a bipolar planetary nebula (PN), NGC 650, at 24, 70, and 160 μm, taken with the Multiband Imaging Photometer for Spitzer (MIPS) on board the Spitzer Space Telescope. While the two-peak emission structure seen in all MIPS bands suggests the presence of a near edge-on dusty torus, the distinct emission structure between the 24 μm map and the 70/160 μm maps indicates the presence of two distinct emission components in the central torus. Based on the spatial correlation of these two far-IR emission components with respect to various optical line emission, we conclude that the 24 μm emission is largely due to the [O IV] line at 25.9 μm arising from highly ionized regions behind the ionization front, whereas the 70 and 160 μm emissions are due to dust continuum arising from low-temperature dust in the remnant asymptotic giant branch (AGB) wind shell. The far-IR nebula structure also suggests that the enhancement of mass loss at the end of the AGB phase has occurred isotropically, but has ensued only in the equatorial directions while ceasing in the polar directions. The present data also show evidence for the prolate spheroidal distribution of matter in this bipolar PN. The AGB mass-loss history reconstructed in this PN is thus consistent with what has been previously proposed based on the past optical and mid-IR imaging surveys of the post-AGB shells.
Interactive tutorial to improve student understanding of single photon experiments involving a Mach–Zehnder interferometer

We have developed and evaluated a quantum interactive learning tutorial (QuILT) on a Mach–Zehnder interferometer with single photons to expose upper-level students in quantum mechanics courses to contemporary quantum optics applications. The QuILT strives to help students develop the ability to apply fundamental quantum principles to physical situations in quantum optics and explore the differences between classical and quantum ideas. The QuILT adapts visualization tools to help students build physical intuition about counter-intuitive quantum optics phenomena with single photons including a quantum eraser setup and focuses on helping them integrate qualitative and quantitative understanding. We discuss findings from in-class evaluations.
Azimuthal and Kinematic Segregation of Neutral and Molecular Gas in Arp 118: The Yin-Yang Galaxy NGC 1144

We present new high-resolution H I observations of the disk of the collisional infrared luminous (LIR = 2.2 × 1011 L☉) galaxy NGC 1144, which reveal an apparent large-scale azimuthal and kinematic segregation of neutral hydrogen relative to the molecular gas distribution. Even among violently collisional galaxies, the CO/H I asymmetry in NGC 1144 is unusual, both in the inner regions and in the outer disk. We suggest that we are observing Arp 118 at a special moment, shortly after a high-speed collision between NGC 1144 and its elliptical companion NGC 1143. H I emission with an average molecular fraction fmol 400 km s-1) rotational velocities can explain (1) the CO/H I asymmetries, (2) a large velocity jump (185 km s-1) across the arm as measured by H I absorption against a radio bright continuum source that straddles the arm, and (3) the asymmetric distribution of star formation and off-nuclear molecular gas resulting from likely streaming motions associated with the strong shock. The new results provide for the first time a coherent picture of Arp 118's many peculiarities and underline the potentially complex changes in the gas phase that can accompany large gravitational perturbations of gas-rich galaxies.
Simultaneous Multicolor Detection of Faint Galaxies in the Hubble Deep Field

We present a novel way to detect objects when multiband images are available. Typically, object detection is performed in one of the available bands or on a somewhat arbitrarily co-added image. Our technique provides an almost optimal way to use all the color information available. We build up a composite image of the N passbands where each pixel value corresponds to the probability that the given pixel is just sky. By knowing the probability distribution of sky pixels (a χ2 distribution with N degrees of freedom), the data can be used to derive the distribution of pixels dominated by object flux. From the two distributions an optimal segmentation threshold can be determined. Clipping the probability image at this threshold yields a mask, where pixels unlikely to be sky are tagged. After using a standard connected-pixel criterion, the regions of this mask define the detected objects. Applying this technique to the Hubble Deep Field data, we find that we can extend the detection limit of the data below that possible using linearly co-added images. We also discuss possible ways of enhancing object detection probabilities for certain well-defined classes of objects by using various optimized linear combinations of the pixel fluxes (optimal subspace filtering).
Achronal averaged null energy condition for extremal horizons and (A)dS

We prove the achronal averaged null energy condition for general quantum field theories in the near horizon geometry of spherical extremal black holes (i.e. AdS2 × Sd−2), de Sitter and anti-de Sitter. The derivation follows from monotonicity of relative entropy after computing the modular hamiltonian of a null deformed region. For incomplete (but maximally extended) achronal null geodesics in AdS2 × Sd−2, we prove the positivity of a different light-ray operator for arbitrary CFTs. This agrees with a constraint recently derived for the Lorentzian cylinder.