cssi/SciSciGPT-SciSciNet huggingface dataset:
Are anonymity-seekers just like everybody else? An analysis of contributions to Wikipedia from Tor

User-generated content sites routinely block contributions from users of privacy-enhancing proxies like Tor because of a perception that proxies are a source of vandalism, spam, and abuse. Although these blocks might be effective, collateral damage in the form of unrealized valuable contributions from anonymity seekers is invisible. One of the largest and most important user-generated content sites, Wikipedia, has attempted to block contributions from Tor users since as early as 2005. We demonstrate that these blocks have been imperfect and that thousands of attempts to edit on Wikipedia through Tor have been successful. We draw upon several data sources and analytical techniques to measure and describe the history of Tor editing on Wikipedia over time and to compare contributions from Tor users to those from other groups of Wikipedia users. Our analysis suggests that although Tor users who slip through Wikipedia’s ban contribute content that is more likely to be reverted and to revert others, their contributions are otherwise similar in quality to those from other unregistered participants and to the initial contributions of registered users.
Can the COVID-19 Interstate Travel Restrictions Help Lift the FDA’s Blood Ban?

Optimism among health law scholars is rare in the time of coronavirus. Yet this piece suggests that the crisis might be helpful in overruling one controversial health law policy that predates the virus: the FDA’s blood donation ban for gay and bisexual men. The blood ban was developed in response to the 1980s HIV-AIDS outbreak. Scholars have criticized this policy for years now as being outdated and unconstitutional. A step in the right direction occurred on April 2, 2020, when the FDA issued new recommendations to blood banks changing the one-year deferral for donations from men who have sex with men to a three-month deferral due to the shortage in the blood supply and after a public outcry on the issue. Yet, the policy is still problematic as it expresses disdain about sex between men. In response to the COVID-19 pandemic, states have issued travel restrictions on travelers from severely impacted states. This piece argues that outside of the dwindling blood supply, experience with stigma as a result of the travel restrictions has the potential to change public perceptions about the ban. Both policies, the blood ban and the COVID-19 interstate travel restrictions, are necessary for the short term, are based on activities connected with a disease, and create stigma with respect to the affected populations. Although no one can imagine the travel restrictions continuing after the pandemic is under control, the FDA’s ban has existed for decades after the end of the AIDS crisis. Drawing parallels between the policies and their stigmatizing effects could help mobilize the public against the blood ban and eventually have it lifted entirely.
Productivity, computerization, and skill change

Robert Solow was, perhaps, the first to point out the anomaly between productivity growth and computerization. Indeed, he quipped that we see computers everywhere except in the productivity statistics. As we shall see, industries that have had the greatest investment in computers (namely, financial services) have ranked among the lowest in terms of conventionally measured productivity growth. Moreover, at least until recently, there has been little evidence of a payoff to computer investment in terms of productivity growth.
On the growth rate of chromatic numbers of finite subgraphs

Abstract We prove that, for every function f : N → N , there is a graph G with uncountable chromatic number such that, for every k ≥ 3 , every subgraph of G with fewer than f ( k ) vertices has chromatic number less than k. This answers a question of Erdős, Hajnal, and Szemeredi. We also show that, if ⋄ holds, then we can take this graph G to be a Hajnal-Mate graph on ω 1 .
9 – Stem Diseases

Publisher Summary Stem diseases are caused by the formation of lesions or dead areas on the bark-cambium tissues. The death of these tissues is associated with wound, such as a broken branch or some type of mechanical injury. This localized lesion is called “canker.” The stem diseases occur on the trunk, the branches, and the twigs. Cankers vary in size and shape. The development of a canker on a woody plant represents the host–parasite interaction on a stem for several years. Most cankers can be placed into three groups: target cankers, diffuse cankers, and canker blights. Most canker fungi are restricted to the invasion of bark tissues. However, some fungi have the unique ability to attack both the bark and the underlying xylem. The stem diseases that result from a simultaneous canker and woody decay are called “canker rots.” This chapter describes the life cycle of stem disease fungi. The fungi that cause cankers are facultative parasites. They are found growing as saprophytes on the bark of living trees and on dead branches, both on the tree and on the ground.
Three-dimensional radiative transfer on a massively parallel computer

We perform 3D radiative transfer calculations in non-local thermodynamic equilibrium (NLTE) in the simple two-level atom approximation on the Mas-Par MP-1, which contains 8192 processors and is a single instruction multiple data (SIMD) machine, an example of the new generation of massively parallel computers. On such a machine, all processors execute the same command at a given time, but on different data. To make radiative transfer calculations efficient, we must re-consider the numerical methods and storage of data. To solve the transfer equation, we adopt the short characteristic method and examine different acceleration methods to obtain the source function. We use the ALI method and test local and non-local operators. Furthermore, we compare the Ng and the orthomin methods of acceleration. We also investigate the use of multi-grid methods to get fast solutions for the NLTE case. In order to test these numerical methods, we apply them to two problems with and without periodic boundary conditions.
High-Field Effects

Electrons and holes contribute to the charge transport in semiconductors, while ordinary charge transport in metals is restricted to electrons only. Another distinctive feature of semiconductor transport is its inherent nonlinearity in high electric fields. The basic phenomena of high field transport are nonlinear conductance; changed responses to magnetic fields; changed high-frequency response, including the dielectric function; and changed confinement in potential wells or at heterojunctions between different semiconductors. Keywords: nonlinearity; high electric fields; semiconductors; Botlzmann equations; hot electrons
Chapter 8 – Structured Programming

Publisher Summary A program is a listing of the series of steps or operations to be performed in solving a problem and the order in which those steps are to be performed. Programming languages have certain types of instructions in common: input instructions, output instructions, arithmetic instructions, comparison instructions, control instructions, data movement instructions, data definition instructions, and file and record definition instructions. The steps to writing a good program are studying the problem to be solved, writing a clear specification of the problem, developing the solution, writing the program, testing and debugging the program, and documenting the program. Programs should be efficient, correct, reliable, robust, and maintainable. In a flowchart, the shapes of the symbols indicate the nature of the operations to be performed, and the flowlines indicate the order in which the operations should be performed. Pseudocode, or structured English, allows a programmer to use English-like sentences to write an explanation of what a program is supposed to do.
Assessing financial stability: The Capital and Loss Assessment under Stress Scenarios (CLASS) model

Abstract The CLASS model is a top-down capital stress testing framework that uses public data, simple econometric models and auxiliary assumptions to project the effect of macroeconomic scenarios on U.S. banking firms. Through the lens of the model, we find that the total banking system capital shortfall under stressful macroeconomic conditions began to rise 4 years before the financial crisis, peaking in the fourth quarter of 2008. The capital gap has since fallen sharply, and is now significantly below pre-crisis levels. In the cross-section, banking firms estimated to be most sensitive to macroeconomic conditions also have higher capital ratios, consistent with a “precautionary” view of bank capital, though this behavior is evident only since the crisis. We interpret our results as evidence that the resiliency of the U.S. banking system has improved since the financial crisis, and also as an illustration of the value of stress testing as a macroprudential policy tool.
Compactification of Superstring Theory

This article will appear in the Encyclopedia of Mathematical Physics (Elsevier, 2006) and follows its referencing guidelines.
Bayesian estimation of spatial filters with Moran's eigenvectors and hierarchical shrinkage priors

Abstract This paper proposes a Bayesian method for spatial regression using eigenvector spatial filtering (ESF) and Piironen and Vehtari (2017)’s regularized horseshoe (RHS) prior. ESF models are most often estimated using variable selection procedures such as stepwise selection, but in the absence of a Bayesian model averaging procedure variable selection methods cannot properly account for parameter uncertainty. Hierarchical shrinkage priors such as the RHS address the foregoing concern in a computationally efficient manner by encoding prior information about spatial filters into an adaptive prior distribution which shrinks posterior estimates towards zero in the absence of a strong signal while only minimally regularizing coefficients of important eigenvectors. This paper presents results from a large simulation study which compares the performance of the proposed Bayesian model (RHS-ESF) to alternative spatial models under a variety of spatial autocorrelation scenarios. The RHS-ESF model performance matched that of the SAR model from which the data was generated. The study also highlights that reliable uncertainty estimates require greater attention to spatial autocorrelation in covariates than is typically given. A demonstration analysis of 2016 U.S. Presidential election results further evidences robustness of results to hyper-prior specifications as well as the advantages of estimating spatial models using the Stan probabilistic programming language.
Automated thermal mapping techniques using chromatic image analysis

Thermal imaging techniques are introduced using a chromatic image analysis system and temperature sensitive coatings. These techniques are used for thermal mapping and surface heat transfer measurements on aerothermodynamic test models in hypersonic wind tunnels. Measurements are made on complex vehicle configurations in a timely manner and at minimal expense. The image analysis system uses separate wavelength filtered images to analyze surface spectral intensity data. The system was initially developed for quantitative surface temperature mapping using two-color thermographic phosphors but was found useful in interpreting phase change paint and liquid crystal data as well.
Violin: A Large-Scale Dataset for Video-and-Language Inference

We introduce a new task, Video-and-Language Inference, for joint multimodal understanding of video and text. Given a video clip with aligned subtitles as premise, paired with a natural language hypothesis based on the video content, a model needs to infer whether the hypothesis is entailed or contradicted by the given video clip. A new large-scale dataset, named Violin (VIdeO-and-Language INference), is introduced for this task, which consists of 95,322 video-hypothesis pairs from 15,887 video clips, spanning over 582 hours of video. These video clips contain rich content with diverse temporal dynamics, event shifts, and people interactions, collected from two sources: (i) popular TV shows, and (ii) movie clips from YouTube channels. In order to address our new multimodal inference task, a model is required to possess sophisticated reasoning skills, from surface-level grounding (e.g., identifying objects and characters in the video) to in-depth commonsense reasoning (e.g., inferring causal relations of events in the video). We present a detailed analysis of the dataset and an extensive evaluation over many strong baselines, providing valuable insights on the challenges of this new task.
Telling Left From Right: Learning Spatial Correspondence of Sight and Sound

Self-supervised audio-visual learning aims to capture useful representations of video by leveraging correspondences between visual and audio inputs. Existing approaches have focused primarily on matching semantic information between the sensory streams. We propose a novel self-supervised task to leverage an orthogonal principle: matching spatial information in the audio stream to the positions of sound sources in the visual stream. Our approach is simple yet effective. We train a model to determine whether the left and right audio channels have been flipped, forcing it to reason about spatial localization across the visual and audio streams. To train and evaluate our method, we introduce a large-scale video dataset, YouTube-ASMR-300K, with spatial audio comprising over 900 hours of footage. We demonstrate that understanding spatial correspondence enables models to perform better on three audio-visual tasks, achieving quantitative gains over supervised and self-supervised baselines that do not leverage spatial audio cues. We also show how to extend our self-supervised approach to 360 degree videos with ambisonic audio.
Laser Sintering Control for Metal Additive Manufacturing by PDE Backstepping

Metal additive manufacturing (AM) has been intensively advanced due to numerous industrial applications, such as automobiles, aerospace, consumer electronics, and medical devices. The dynamics of the melt pool via laser sintering for metal AM has been studied by means of the thermodynamic phase change model known as the “Stefan problem”. In this article, we develop a control design for the laser power to drive the depth of the melt pool to the desired set point. The governing equation is described by a partial differential equation (PDE) defined on a time-varying spatial domain, which is dependent on the PDE state, and the optical penetration of the laser energy affects the PDE dynamics in the domain as well as at the surface boundary. First, we design the full-state feedback control law utilizing the entire spatial profile of the temperature in the melt pool and the moving interface position. The closed-loop system is proven to satisfy some conditions to validate the physical model, and its origin is shown to be exponentially stable. Next, we propose an observer-based output feedback control law by reconstructing the temperature profile with the availability of only the measured interface position and prove the analogous properties of the closed-loop system. Numerical simulation for a controller designed on a single-phase Stefan model is conducted on a more complex and realistic two-phase Stefan model, which incorporates the cooling effect from the solid phase. In addition, a bias in the interface location measurement is considered. The numerical results illustrate the robustness of the proposed feedback. By lowering the initial temperature in the solid and by increasing the interface sensor bias to more extreme levels, which leads to the controller’s failure (where the failure is exhibited through the entire metal freezing and the melt pool disappearing), we explore the limits of how much uncertainty our control law can handle.
Axisymmetric Diffeomorphisms and Ideal Fluids on Riemannian 3-Manifolds

We study the Riemannian geometry of 3D axisymmetric ideal fluids. We prove that the $L^2$ exponential map on the group of volume-preserving diffeomorphisms of a $3$-manifold is Fredholm along axisymmetric flows with sufficiently small swirl. Along the way, we define the notions of axisymmetric and swirl-free diffeomorphisms of any manifold with suitable symmetries and show that such diffeomorphisms form a totally geodesic submanifold of infinite $L^2$ diameter inside the space of volume-preserving diffeomorphisms whose diameter is known to be finite. As examples we derive the axisymmetric Euler equations on $3$-manifolds equipped with each of Thurston's eight model geometries.
The Importance of Category Labels in Grammar Induction with Child-directed Utterances

Recent progress in grammar induction has shown that grammar induction is possible without explicit assumptions of language specific knowledge. However, evaluation of induced grammars usually has ignored phrasal labels, an essential part of a grammar. Experiments in this work using a labeled evaluation metric, RH, show that linguistically motivated predictions about grammar sparsity and use of categories can only be revealed through labeled evaluation. Furthermore, depth-bounding as an implementation of human memory constraints in grammar inducers is still effective with labeled evaluation on multilingual transcribed child-directed utterances.
Perceptual Evaluation of Speech Naturalness in Speakers of Varying Gender Identities

Purpose The purpose of this study was to investigate how speech naturalness relates to masculinity–femininity and gender identification (accuracy and reaction time) for cisgender male and female sp...
Precise Tradeoffs in Adversarial Training for Linear Regression

Despite breakthrough performance, modern learning models are known to be highly vulnerable to small adversarial perturbations in their inputs. While a wide variety of recent \emph{adversarial training} methods have been effective at improving robustness to perturbed inputs (robust accuracy), often this benefit is accompanied by a decrease in accuracy on benign inputs (standard accuracy), leading to a tradeoff between these often competing objectives. Complicating matters further, recent empirical evidence suggest that a variety of other factors (size/quality of training data, model size, etc.) affect this tradeoff in somewhat surprising ways. In this paper we provide a precise and comprehensive understanding of the role of adversarial training in the context of linear regression with Gaussian features. In particular, we characterize the fundamental tradeoff between the accuracies achievable by any algorithm regardless of computational power or size of the training data. Furthermore, we precisely characterize the standard/robust accuracy and the corresponding tradeoff achieved by a contemporary mini-max adversarial training approach in a high-dimensional regime where the number of data points and the parameters of the model grow in proportion to each other. Our theory for adversarial training algorithms also facilitates the rigorous study of how a variety of factors (size/quality of training data, model overparametrization etc.) affect the tradeoff between these two competing accuracies.
Multi-Fidelity Sensor Selection: Greedy Algorithms to Place Cheap and Expensive Sensors With Cost Constraints

We develop greedy algorithms to approximate the optimal solution to the multi-fidelity sensor selection problem, which is a cost constrained optimization problem prescribing the placement and number of cheap (low signal-to-noise) and expensive (high signal-to-noise) sensors in an environment or state space. Specifically, we evaluate the composition of cheap and expensive sensors, along with their placement, required to achieve accurate reconstruction of a high-dimensional state. We use the column-pivoted QR decomposition to obtain preliminary sensor positions. How many of each type of sensor to use is highly dependent upon the sensor noise levels, sensor costs, overall cost budget, and the singular value spectrum of the data measured. Such nuances allow us to provide sensor selection recommendations based on computational results for asymptotic regions of parameter space. We also present a systematic exploration of the effects of the number of modes and sensors on reconstruction error when using one type of sensor. Our extensive exploration of multi-fidelity sensor composition as a function of data characteristics is the first of its kind to provide guidelines towards optimal multi-fidelity sensor selection.
Design of Millimeter-Wave Single-Shot Beam Training for True-Time-Delay Array

Beam training is one of the most important and challenging tasks in millimeter-wave and sub-terahertz communications. Novel transceiver architectures and signal processing techniques are required to avoid prohibitive training overhead when large antenna arrays with narrow beams are used. In this work, we leverage recent developments in wide range true-time- delay (TTD) analog arrays and frequency dependent probing beams to accelerate beam training. We propose an algorithm that achieves high-accuracy angle of arrival estimation with a single training symbol. Further, the impact of TTD front- end impairments on beam training accuracy is investigated, including the impact of gain, phase, and delay errors. Lastly, the study on impairments and required specifications of resolution and range of analog delay taps are used to provide a design insight of energy efficient TTD array, which employs a novel architecture with discrete-time sampling based TTD elements.
Female mice are resilient to age-related decline of substantia nigra dopamine neuron firing parameters.

Abstract Degeneration of substantia nigra pars compacta dopamine neurons is a central feature in the pathology of Parkinson’s disease, which is characterized by progressive loss of motor and cognitive functions. The largest risk factors for Parkinson’s disease are age and sex; most cases occur after age 60 and males have nearly twice the incidence as females. Preclinical work has scarcely considered the influence of these 2 factors to disease risk and presentation. Here, we observed a progressive decline in dopamine neuron firing activity in male C57BL/6 mice by 18 months of age, while dopamine neurons from females remained largely unaffected. This was accompanied by increased mRNA expression of PINK1 in both males and females, and PARK2 primarily in males, both of which have been linked to Parkinson’s. Since the declining cell properties were accompanied by only slight decreases in locomotion in both sexes, it is likely that these age-related impairments in males represent a vulnerability to further insults that could predispose the neurons to neurodegenerative processes such as in Parkinson’s.
An Active Self-Optimizing Multiplayer Gaming Architecture

Multiplayer games are representative of a large class of distributed applications that suffer from redundant communication, bottlenecks, single points of failure and poor reactivity to changing network conditions. Many of these problems can be alleviated through simple network adaptations at the infrastructure level. In this paper, we describe a model in which game packets are directed along the edges of a rooted tree connecting the players, aggregated during the upstream flight and multicast from the root to the leaves. This tree is constructed based on a heuristic, and can dynamically adjust itself in response to changes in network conditions. This gaming infrastructure is built and maintained using active networks, which is currently the only open architecture suitable for these types of applications. We have designed and implemented a prototype using ANTS that performs these adaptations for unmodified DOOM clients. We present analytical and simulation results that illustrate the reduction in communication overhead, and show that the multicast tree can quickly adjust to changing network conditions. The overhead of the active network-based middleware is acceptable, especially in wide-area networks.
AV-Net: deep learning for fully automated artery-vein classification in optical coherence tomography angiography

This study is to demonstrate deep learning for automated artery-vein (AV) classification in optical coherence tomography angiography (OCTA). The AV-Net, a fully convolutional network (FCN) based on modified U-shaped CNN architecture, incorporates enface OCT and OCTA to differentiate arteries and veins. For the multi-modal training process, the enface OCT works as a near infrared fundus image to provide vessel intensity profiles, and the OCTA contains blood flow strength and vessel geometry features. A transfer learning process is also integrated to compensate for the limitation of available dataset size of OCTA, which is a relatively new imaging modality. By providing an average accuracy of 86.75%, the AV-Net promises a fully automated platform to foster clinical deployment of differential AV analysis in OCTA.
Clinical sensitivity and interpretation of PCR and serological COVID-19 diagnostics for patients presenting to the hospital.

The diagnosis of COVID-19 requires integration of clinical and laboratory data. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnostic assays play a central role in diagnosis and have fixed technical performance metrics. Interpretation becomes challenging because the clinical sensitivity changes as the virus clears and the immune response emerges. Our goal was to examine the clinical sensitivity of two most common SARS-CoV-2 diagnostic test modalities, polymerase chain reaction (PCR) and serology, over the disease course to provide insight into their clinical interpretation in patients presenting to the hospital. We conducted a single-center, retrospective study. To derive clinical sensitivity of PCR, we identified 209 PCR-positive SARS-CoV-2 patients with multiple PCR test results (624 total PCR tests) and calculated daily sensitivity from date of symptom onset or first positive test. Clinical sensitivity of PCR decreased with days post symptom onset with >90% clinical sensitivity during the first 5 days after symptom onset, 70%-71% from Days 9 to 11, and 30% at Day 21. To calculate daily clinical sensitivity by serology, we utilized 157 PCR-positive patients with a total of 197 specimens tested by enzyme-linked immunosorbent assay for IgM, IgG, and IgA anti-SARS-CoV-2 antibodies. In contrast to PCR, serological sensitivity increased with days post symptom onset with >50% of patients seropositive by at least one antibody isotype after Day 7, >80% after Day 12, and 100% by Day 21. Taken together, PCR and serology are complimentary modalities that require time-dependent interpretation. Superimposition of sensitivities over time indicate that serology can function as a reliable diagnostic aid indicating recent or prior infection.
Identification of Phosphatidylcholine Isomers in Imaging Mass Spectrometry Using Gas-Phase Charge Inversion Ion/Ion Reactions

Gas-phase ion/ion reactions have been enabled on a commercial dual source, hybrid QhFT-ICR mass spectrometer for use during imaging mass spectrometry experiments. These reactions allow for the transformation of the ion type most readily generated from the tissue surface to an ion type that gives improved chemical structural information upon tandem mass spectrometry (MS/MS) without manipulating the tissue sample. This process is demonstrated via the charge inversion reaction of phosphatidylcholine (PC) lipid cations generated from rat brain tissue via matrix-assisted laser desorption/ionization (MALDI) with 1,4-phenylenedipropionic acid (PDPA) reagent dianions generated via electrospray ionization (ESI). Collision-induced dissociation (CID) of the resulting demethylated PC product anions allows for the determination of the lipid fatty acyl tail identities and positions, which is not possible via CID of the precursor lipid cations. The abundance of lipid isomers revealed by this workflow is found to vary significantly in different regions of the brain. As each isoform may have a unique cellular function, these results underscore the importance of accurately separating and identifying the many isobaric and isomeric lipids and metabolites that can complicate image interpretation and spectral analysis.
The ClpX and ClpP2 Orthologs of Chlamydia trachomatis Perform Discrete and Essential Functions in Organism Growth and Development.

ABSTRACT Chlamydia trachomatis is an obligate intracellular bacterium that undergoes a complex developmental cycle in which the bacterium differentiates between two functionally and morphologically distinct forms, the elementary body (EB) and reticulate body (RB), each of which expresses its own specialized repertoire of proteins. Both primary (EB to RB) and secondary (RB to EB) differentiations require protein turnover, and we hypothesize that proteases are critical for mediating differentiation. The Clp protease system is well conserved in bacteria and important for protein turnover. Minimally, the system relies on a serine protease subunit, ClpP, and an AAA+ ATPase, such as ClpX, that recognizes and unfolds substrates for ClpP degradation. In Chlamydia, ClpX is encoded within an operon 3′ to clpP2. We present evidence that the chlamydial ClpX and ClpP2 orthologs are essential to organism viability and development. We demonstrate here that chlamydial ClpX is a functional ATPase and forms the expected homohexamer in vitro. Overexpression of a ClpX mutant lacking ATPase activity had a limited impact on DNA replication or secondary differentiation but, nonetheless, reduced EB viability with observable defects in EB morphology noted. Conversely, overexpression of a catalytically inactive ClpP2 mutant significantly impacted developmental cycle progression by reducing the overall number of organisms. Blocking clpP2X transcription using CRISPR interference led to a decrease in bacterial growth, and this effect was complemented in trans by a plasmid copy of clpP2. Taken together, our data indicate that ClpX and the associated ClpP2 serve distinct functions in chlamydial developmental cycle progression and differentiation. IMPORTANCEChlamydia trachomatis is the leading cause of infectious blindness globally and the most reported bacterial sexually transmitted infection both domestically and internationally. Given the economic burden, the lack of an approved vaccine, and the use of broad-spectrum antibiotics for treatment of infections, an understanding of chlamydial growth and development is critical for the advancement of novel targeted antibiotics. The Clp proteins comprise an important and conserved protease system in bacteria. Our work highlights the importance of the chlamydial Clp proteins to this clinically important bacterium. Additionally, our study implicates the Clp system playing an integral role in chlamydial developmental cycle progression, which may help establish models of how Chlamydia spp. and other bacteria progress through their respective developmental cycles. Our work also contributes to a growing body of Clp-specific research that underscores the importance and versatility of this system throughout bacterial evolution and further validates Clp proteins as drug targets.
JAK-inhibitor and type I interferon ability to produce favorable clinical outcomes in COVID-19 patients: a systematic review and meta-analysis.

The spread of a highly pathogenic, novel coronavirus (SARS-CoV-2) has emerged as a once-in-a-century pandemic, having already infected over 63 million people worldwide. Novel therapies are urgently needed. Janus kinase-inhibitors and Type I interferons have emerged as potential antiviral candidates for COVID-19 patients due to their proven efficacy against diseases with excessive cytokine release and their direct antiviral ability against viruses including coronaviruses, respectively. A search of MEDLINE and MedRxiv was conducted by three investigators from inception until July 30th 2020 and included any study type that compared treatment outcomes of humans treated with Janus kinase-inhibitor or Type I interferon against controls. Inclusion necessitated data with clearly indicated risk estimates or those that permitted their back-calculation. Outcomes were synthesized using RevMan. Of 733 searched studies, we included four randomized and eleven non-randomized trials. Five of the studies were unpublished. Those who received Janus kinase-inhibitor had significantly reduced odds of mortality (OR, 0.12; 95% CI, 0.03–0.39, p< 0.001) and ICU admission (OR, 0.05; 95% CI, 0.01–0.26, p< 0.001), and had significantly increased odds of hospital discharge (OR, 22.76; 95% CI, 10.68–48.54, p< 0.00001) when compared to standard treatment group. Type I interferon recipients had significantly reduced odds of mortality (OR, 0.19; 95% CI, 0.04–0.85, p< 0.05), and increased odds of discharge bordering significance (OR, 1.89; 95% CI, 1.00–3.59, p=0.05). Janus kinase-inhibitor treatment is significantly associated with positive clinical outcomes in terms of mortality, ICU admission, and discharge. Type I interferon treatment is associated with positive clinical outcomes in regard to mortality and discharge. While these data show promise, additional well-conducted RCTs are needed to further elucidate the relationship between clinical outcomes and Janus kinase-inhibitors and Type I interferons in COVID-19 patients.
Non-uniqueness for the ab-family of equations

Abstract We study the cubic ab-family of equations, which includes both the Fokas-Olver-Rosenau-Qiao (FORQ) and the Novikov (NE) equations. For a ≠ 0 , it is proved that there exist initial data in the Sobolev space H s , s 3 / 2 , with nonunique solutions. Multiple solutions are constructed by studying the collision of 2-peakon solutions. Furthermore, we prove the novel phenomenon that for some members of the family, collision between 2-peakons can occur even if the “faster” peakon is in front of the “slower” peakon.
BRD4 Prevents R-Loop Formation and Transcription-Replication Conflicts by Ensuring Efficient Transcription Elongation

Effective spatio-temporal control of transcription and replication during S-phase is paramount to maintaining genomic integrity and cell survival. Dysregulation of these systems can lead to conflicts between the transcription and replication machinery, causing DNA damage and cell death. BRD4 allows efficient transcriptional elongation by stimulating phosphorylation of RNA polymerase II (RNAPII). We report that bromodomain and extra-terminal domain (BET) protein loss of function (LOF) causes RNAPII pausing on the chromatin and DNA damage affecting cells in S-phase. This persistent RNAPII-dependent pausing leads to an accumulation of RNA:DNA hybrids (R-loops) at sites of BRD4 occupancy, leading to transcription-replication conflicts (TRCs), DNA damage, and cell death. Finally, our data show that the BRD4 C-terminal domain, which interacts with P-TEFb, is required to prevent R-loop formation and DNA damage caused by BET protein LOF.
A semi-analytical solution to the maximum-likelihood fit of Poisson data to a linear model using the Cash statistic

The Cash statistic, also known as the C statistic, is commonly used for the analysis of low-count Poisson data, including data with null counts for certain values of the independent variable. The u...
Player Modeling via Multi-Armed Bandits

This paper focuses on building personalized player models solely from player behavior in the context of adaptive games. We present two main contributions: The first is a novel approach to player modeling based on multi-armed bandits (MABs). This approach addresses, at the same time and in a principled way, both the problem of collecting data to model the characteristics of interest for the current player and the problem of adapting the interactive experience based on this model. Second, we present an approach to evaluating and fine-tuning these algorithms prior to generating data in a user study. This is an important problem, because conducting user studies is an expensive and labor-intensive process; therefore, an ability to evaluate the algorithms beforehand can save a significant amount of resources. We evaluate our approach in the context of modeling players’ social comparison orientation (SCO) and present empirical results from both simulations and real players.
Adversarial Perturbations Fool Deepfake Detectors

This work uses adversarial perturbations to enhance deepfake images and fool common deepfake detectors. We created adversarial perturbations using the Fast Gradient Sign Method and the Carlini and Wagner L 2 norm attack in both blackbox and whitebox settings. Detectors achieved over 95% accuracy on unperturbed deepfakes, but less than 27% accuracy on perturbed deepfakes. We also explore two improvements to deep-fake detectors: (i) Lipschitz regularization, and (ii) Deep Image Prior (DIP). Lipschitz regularization constrains the gradient of the detector with respect to the input in order to increase robustness to input perturbations. The DIP defense removes perturbations using generative convolutional neural networks in an unsupervised manner. Regularization improved the detection of perturbed deepfakes on average, including a 10% accuracy boost in the blackbox case. The DIP defense achieved 95% accuracy on perturbed deepfakes that fooled the original detector while retaining 98% accuracy in other cases on a 100 image subsample.
Equivalent noise characterization of human lightness constancy

A goal of visual perception is to provide stable representations of task-relevant scene properties (e.g. object reflectance) despite variation in task-irrelevant scene properties (e.g. illumination, reflectance of other nearby objects). To study such representational stability in the context of lightness representations in humans, we introduce a threshold-based psychophysical paradigm. We measure how thresholds for discriminating the achromatic reflectance of a target object (task-relevant property) in rendered naturalistic scenes are impacted by variation in the reflectance functions of background objects (task-irrelevant property). We refer to these thresholds as lightness discrimination thresholds. Our approach has roots in the equivalent noise paradigm. This paradigm relates signals to internal and external sources of noise and has been traditionally used to investigate contrast coding. For low variation in background reflectance, lightness discrimination thresholds were nearly constant, indicating that observers' internal noise determines threshold in this regime. As background object reflectance variation increases, its effects start to dominate performance. We report lightness discrimination thresholds as a function of the amount of variability in the background object reflectance to determine the equivalent noise - the smallest level of task-irrelevant (i.e. background reflectance) variation that substantially corrupts the visual representation (i.e. perceived object lightness) of the task-relevant variable (i.e. achromatic reflectance). A linear receptive field model, which employs a single center-surround receptive field tailored to our stimulus set, captures human behavior in this task. Our approach provides a method for characterizing the effect of task-irrelevant scene variations on the perceptual representation of a task-relevant scene property.
VASOSPASTIC ARREST: A HEART-STOPPING CASE OF PRINZMETAL ANGINA

Vasospastic angina due to coronary vasospasm can lead to cardiac arrest. A 60-year-old man, active smoker, presented to the emergency department after being found unresponsive. An initial electrocardiogram showed a wide QRS, lateral ST-elevations, and massive reciprocal ST-depressions, which
Active Tuning of Hybridized Modes in a Heterogeneous Photonic Molecule

From fundamental discovery to practical application, advances in the optical and quantum sciences rely upon precise control of light-matter interactions. Systems of coupled optical cavities are ubiquitous in these efforts, yet the design and active modification of the hybridized mode properties remains challenging. Here, we demonstrate the design, fabrication, and analysis of a tunable heterogeneous photonic molecule consisting of a ring resonator strongly coupled to a nanobeam photonic crystal cavity. Leveraging the disparity in mode volume between these two strongly coupled cavities, we combine theory and experiment to establish the ability to actively tune the mode volume of the resulting supermodes over a full order of magnitude. As the mode volume determines the strength of light-matter interactions, this work illustrates the potential for strongly coupled cavities with dissimilar mode volumes in applications requiring designer photonic properties and tunable light-matter coupling, such as photonics-based quantum simulation.
Constructing thin subgroups commensurable with the figure-eight knot group

In this paper we find infinitely many lattices in $SL(4,\mathbb{R})$ each of which contains thin subgroups commensurable with the figure-eight knot group.
The Design of The CCAT-prime Epoch of Reionization Spectrometer Instrument

The epoch of reionization spectrometer (EoR-Spec) is an instrument module for the Prime-Cam receiver of the 6-m aperture CCAT-prime Telescope at 5600 m in Chile. EoR-Spec will perform 158 $$\upmu $$m [CII] line intensity mapping of star-forming regions at redshifts between 3.5 and 8 (420–210 GHz), tracing the evolution of structure during early galaxy formation. At lower redshifts, EoR-Spec will observe galaxies near the period of peak star formation—when most stars in today’s universe were formed. At higher redshifts, EoR-Spec will trace the late stages of reionization, the early stages of galaxy assembly, and the formation of large-scale, three-dimensional clustering of star-forming galaxies. To achieve its science goals, EoR-Spec will utilize CCAT-prime’s exceptionally low water vapor site, large field of view ($$\sim 5^\circ $$ at 210 GHz), and narrow beam widths ($$\sim 1$$ arcminute at 210 GHz). EoR-Spec will be outfitted with a cryogenic, metamaterial, silicon substrate-based Fabry–Perot interferometer operating at a resolving power ($$\lambda /\varDelta \lambda $$) of 100. Monolithic dichroic arrays of cryogenic, feedhorn-coupled transition edge sensor bolometers provide approximately 6000 detectors, which are read out using a frequency division multiplexing system based on microwave SQUIDs. The novel design allows the measurement of the [CII] line at two redshifts simultaneously using dichroic pixels and two orders of the Fabry–Perot. Here we present the design and science goals of EoR-Spec, with emphasis on the spectrometer, detector array, and readout designs.
The maximum likelihood degree of a very affine variety

We show that the maximum likelihood degree of a smooth very affine variety is equal to the signed topological Euler characteristic. This generalizes Orlik and Terao’s solution to Varchenko’s conjecture on complements of hyperplane arrangements to smooth very affine varieties. For very affine varieties satisfying a genericity condition at infinity, the result is further strengthened to relate the variety of critical points to the Chern–Schwartz–MacPherson class. The strengthened version recovers the geometric deletion–restriction formula of Denham et al. for arrangement complements, and generalizes Kouchnirenko’s theorem on the Newton polytope for nondegenerate hypersurfaces.
Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms

This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing vehicles, if properly coordinated, do not lead to an increase in congestion (in stark contrast to common belief). From an algorithmic standpoint, such theoretical insight suggests that the problem of routing customers and rebalancing vehicles can be decoupled, which leads to a computationally-efficient routing and rebalancing algorithm for the autonomous vehicles. Numerical experiments and case studies corroborate our theoretical insights and show that the proposed algorithm outperforms state-of-the-art point-to-point methods by avoiding excess congestion on the road. Collectively, this paper provides a rigorous approach to the problem of congestion-aware, system-wide coordination of autonomously driving vehicles, and to the characterization of the sustainability of such robotic systems.
Formation, fractionation, and excitation of carbon monoxide in diffuse clouds

Context. A wealth of observations of CO in absorption in diffuse clouds has accumulated in the past decade at uv and mm-wavelengths Aims. Our aims are threefold: a) To compare the uv and mm-wave results; b) to interpret 13 CO and 12 CO abundances in terms of the physical processes which separately and jointly determine them; c) to interpret observed J = 1−0 rotational excitation and line brightness in terms of ambient gas properties. Methods. A simple phenomenological model of CO formation as the immediate descendant of quiescently-recombining HCO + is used to study the accumulation, fractionation and rotational excitation of CO in more explicit and detailed models of H2-bearing diffuse/H I clouds Results. The variation of N(CO) with N(H2) is explained by quiescent recombination of a steady fraction n(HCO + )/n(H2) = 2 × 10 −9 .O bservedN( 12 CO))/N( 13 CO) ratios generally do not require a special chemistry but result from competing processes and do not provide much insight into the local gas properties, especially the temperature. J = 1−0 CO line brightnesses directly represent N(CO), not N(H2), so the CO-H2 conversion factor varies widely; it attains typical values at N( 12 CO) < 10 16 cm −2 . Models of CO rotational excitation account for the line brightnesses and CO-H2conversion factors but readily reproduce the observed excitation temperatures and optical depths of the rotational transitions only if excitation by H-atoms is weak – as seems to be the case for the very most recent calculations of these excitation rates. Conclusions. Mm-wave and uv results generally agree well but the former show somewhat more enhancement of 13 Ci n 13 CO. In any case, fractionation may seriously bias 12 C/ 13 C ratios measured in CO and other co-spatial molecules. Complete C→CO conversion must occur over a very narrow range of AV and N(H2) just beyond the diffuse regime. For N(H2) < 7 × 10 19 cm −2 the character of the
Is classical flat Kasner spacetime flat in quantum gravity

Quantum nature of classical flat Kasner spacetime is studied using effective spacetime description in loop quantum cosmology (LQC). We find that even though the spacetime curvature vanishes at the classical level, nontrivial quantum gravitational effects can arise. For the standard loop quantization of Bianchi-I spacetime, which uniquely yields universal bounds on expansion and shear scalars and results in a generic resolution of strong singularities, we find that a flat Kasner metric is not a physical solution of the effective spacetime description, except in a limit. The lack of a flat Kasner metric at the quantum level results from a novel feature of the loop quantum Bianchi-I spacetime: quantum geometry induces nonvanishing spacetime curvature components, making it not Ricci flat even when no matter is present. The noncurvature singularity of the classical flat Kasner spacetime is avoided, and the effective spacetime transits from a flat Kasner spacetime in asymptotic future, to a Minkowski spacetime in asymptotic past. Interestingly, for an alternate loop quantization which does not share some of the fine features of the standard quantization, flat Kasner spacetime with expected classical features exists. In this case, even with nontrivial quantum geometric effects, the spacetime curvature vanishes. These examples show that the character of even a flat classical vacuum spacetime can alter in a fundamental way in quantum gravity and is sensitive to the quantization procedure.