cssi/SciSciGPT-SciSciNet huggingface dataset:
Development and Validation of a Web-Based Severe COVID-19 Risk Prediction Model.

Abstract Background Coronavirus disease 2019 (COVID-19) carries high morbidity and mortality globally. Identification of patients at risk for clinical deterioration upon presentation would aid in triaging, prognostication, and allocation of resources and experimental treatments. Research Question Can we develop and validate a web-based risk prediction model for identification of patients who may develop severe COVID-19, defined as intensive care unit (ICU) admission, mechanical ventilation, and/or death? Methods This retrospective cohort study reviewed 415 patients admitted to a large urban academic medical center and community hospitals. Covariates included demographic, clinical, and laboratory data. The independent association of predictors with severe COVID-19 was determined using multivariable logistic regression. A derivation cohort (n=311, 75%) was used to develop the prediction models. The models were tested by a validation cohort (n=104, 25%). Results The median age was 66 years (Interquartile range [IQR] 54-77) and the majority were male (55%) and non-White (65.8%). The 14-day severe COVID-19 rate was 39.3%; 31.7% required ICU, 24.6% mechanical ventilation, and 21.2% died. Machine learning algorithms and clinical judgment were used to improve model performance and clinical utility, resulting in the selection of eight predictors: age, sex, dyspnea, diabetes mellitus, troponin, C-reactive protein, D-dimer, and aspartate aminotransferase. The discriminative ability was excellent for both the severe COVID-19 (training area under the curve [AUC]=0.82, validation AUC=0.82) and mortality (training AUC= 0.85, validation AUC=0.81) models. These models were incorporated into a mobile-friendly website. Conclusions This web-based risk prediction model can be used at the bedside for prediction of severe COVID-19 using data mostly available at the time of presentation.
Cold tolerance of mountain stoneflies (Plecoptera: Nemouridae) from the high Rocky Mountains

How aquatic insects cope with cold temperatures is poorly understood. This is particularly true for high-elevation species, which often experience a seasonal risk of freezing. In the Rocky Mountains, nemourid stoneflies (Plecoptera: Nemouridae) are a major component of mountain stream biodiversity and are typically found in streams fed by glaciers and snowfields, which are rapidly receding due to climate change. Predicting the effects of climate change on mountain stoneflies is difficult because their thermal physiology is largely unknown. We investigated cold tolerance of several alpine stoneflies (Lednia tumana, Lednia tetonica, and Zapada spp.) from the Rocky Mountains, USA. We measured the supercooling point (SCP) and tolerance to ice enclosure of late-instar nymphs collected from a range of thermal regimes. SCPs varied among species and populations, with the lowest SCP measured for nymphs from an alpine pond, which was much more likely to freeze solid in winter than flowing streams. We also show that L. tumana cannot survive being enclosed in ice, even for short periods of time (<3 h) at relatively mild temperatures (–0.5 °C). Our results indicate that high-elevation stoneflies at greater risk of freezing may have correspondingly lower SCPs, and despite their common association with glacial meltwater, these stoneflies appear to be living near their lower thermal limits.
Reversible blood-brain barrier opening utilizing the membrane active peptide melittin in vitro and in vivo

Abstract The blood-brain barrier (BBB) tightly controls entry of molecules and cells into the brain, restricting the delivery of therapeutics. Blood-brain barrier opening (BBBO) utilizes reversible disruption of cell-cell junctions between brain microvascular endothelial cells to enable transient entry into the brain. Here, we demonstrate that melittin , a membrane active peptide present in bee venom, supports transient BBBO. From endothelial and neuronal viability studies, we first identify the accessible concentration range for BBBO. We then use a tissue-engineered model of the human BBB to optimize dosing and elucidate the mechanism of opening. Melittin and other membrane active variants transiently increase paracellular permeability via disruption of cell-cell junctions that result in transient focal leaks. To validate the results from the tissue-engineered model, we then demonstrate that transient BBBO can be reproduced in a mouse model. We identify a minimum clinically effective intra-arterial dose of 3 μM min melittin, which is reversible within one day and neurologically safe. Melittin-induced BBBO represents a novel technology for delivery of therapeutics into the brain.
A political-economy perspective on mayoral elections and urban crime

Abstract We provide a political-economy analysis of crime prevention in an arbitrary city in the United States. City residents (voters) elect mayors (politicians) and elected mayors determine the resources to be allocated to crime prevention. Between the two time periods, there is an election. Politicians are either honest or dishonest. The marginal cost of public monies ψ measures how efficiently an elected mayor converts tax receipts into crime prevention. Voters have identical per period utility functions. We ascertain the equilibrium outcome and per period voter well-being. Second, we show that an increase in ψ reduces the equilibrium allocation of resources to crime prevention and voter well-being. Third, a dishonest politician can delay the revelation of his dishonesty. A critical value of ψ , ψ ∗ , exists such that a dishonest incumbent separates and loses the election if and only if ψ > ψ ∗ and he pools and is re-elected otherwise. Finally, we note that an increase in ψ can raise voter well-being when politicians are more likely to be dishonest.
A Regret Minimization Approach to Iterative Learning Control

We consider the setting of iterative learning control, or model-based policy learning in the presence of uncertain, time-varying dynamics. In this setting, we propose a new performance metric, planning regret, which replaces the standard stochastic uncertainty assumptions with worst case regret. Based on recent advances in non-stochastic control, we design a new iterative algorithm for minimizing planning regret that is more robust to model mismatch and uncertainty. We provide theoretical and empirical evidence that the proposed algorithm outperforms existing methods on several benchmarks.
Roughness-dominated transition on nosetips, attachment lines and lifting-entry vehicles

Modeling of roughness-dominated transition is a critical design issue for both ablating and non-ablating thermal protection systems (TPS). Ablating TPS, used for planetary-entry and earth-return missions, first experience recession under high-altitude, low-Reynolds-number conditions. Such laminar-flow ablation causes the formation of a surface microroughness pattern characteristic of the TPS material composition and fabrication process. For non-ablating TPS, such as the overlapping-tile, metallic heatshields proposed for future reusable launch vehicles, the surface roughness pattern is established a priori by the engineering design and assembly procedure. In both cases, these distributed surface roughness patterns create disturbances within, and alter the mean velocity profile of, the laminar boundary layer flowing over the surface. As altitude decreases, Reynolds number increases, and flow field conditions capable of amplifying these roughness-induced perturbations are eventually achieved, i.e., transition onset occurs. Boundary layer transition to turbulence results in more severe heat-transfer rates. Ablating TPS experience increased recession rates, leading to potential bum-through, while non-ablating TPS experience accelerated temperature rise, leading to potential melting of key components.
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling

We present a novel approach to the problem of text style transfer. Unlike previous approaches requiring style-labeled training data, our method makes use of readily-available unlabeled text by relying on the implicit connection in style between adjacent sentences, and uses labeled data only at inference time. We adapt T5 (Raffel et al., 2020), a strong pretrained text-to-text model, to extract a style vector from text and use it to condition the decoder to perform style transfer. As our label-free training results in a style vector space encoding many facets of style, we recast transfers as “targeted restyling” vector operations that adjust specific attributes of the input while preserving others. We demonstrate that training on unlabeled Amazon reviews data results in a model that is competitive on sentiment transfer, even compared to models trained fully on labeled data. Furthermore, applying our novel method to a diverse corpus of unlabeled web text results in a single model capable of transferring along multiple dimensions of style (dialect, emotiveness, formality, politeness, sentiment) despite no additional training and using only a handful of exemplars at inference time.
Building a laboratory at a Primarily Undergraduate Institution (PUI).

Scientists who are interested in building research programs at primarily-undergraduate institutions (PUIs) have unique considerations compared to colleagues at research-intensive (R1) institutions. Maintaining a research program at a PUI holds unique challenges that should be considered before prospective faculty go on the job market, as they negotiate a job offer, and after they begin a new position. In this article we describe some of the considerations that aspiring and newly hired faculty should keep in mind as they plan out how they will set up a laboratory as a new Principle Investigator (PI) at a PUI. Anyone hoping to start a research program at a PUI should understand both the timeframe of interviews, job offers, and negotiations and the challenges and rewards of working with undergraduate researchers. Once a job is offered, candidates should be aware of the range of negotiable terms that can be part of a start-up package. Space and equipment considerations are also important, and making the most of shared spaces, existing infrastructure, and deals can extend the purchasing power of start-up funds as a new PIs builds their lab. PUIs’ focus on undergraduate education and mentorship leads to important opportunities for collaboration, funding, and bringing research projects directly into undergraduate teaching laboratories. A major focus of any new laboratory leader must be on building a productive, equitable, and supportive laboratory community. Equitable onboarding, mentorship plans, and formalized expectations, can all help build a productive and sustainable laboratory research program. However, important considerations about safety, inclusion, student schedules, and a PI’s own professional commitments are also extremely important concerns when working with undergraduates in research. A successful research program at a PUI will bring students into meaningful scientific inquiry and requires insights and skills that are often not the focus of scientific training. This article aims to describe the scope of setting up a new laboratory as a way to alleviate some of the burden that new and prospective faculty often feel.
Spectral causality and the scattering of waves

Causality—the principle stating that the output of a system cannot temporally precede the input—is a universal property of nature. Here, we show that analogous input-output relations can also be realized in the spectral domain by leveraging the peculiar properties of time-modulated non-Hermitian photonic systems. Specifically, we uncover the existence of a broad class of complex time-modulated metamaterials that obey the time-domain equivalent of the well-established frequency-domain Kramers–Kronig relations (a direct consequence of causality). We find that, in the scattering response of such time-modulated systems, the output frequencies are inherently prohibited from spectrally preceding the input frequencies, and hence we refer to these systems as “spectrally causal.” We explore the consequences of this newly introduced concept for several relevant applications, including broadband perfect absorption, temporal cloaking of an “event,” and truly unidirectional propagation along a synthetic dimension. By emulating the concept of causality in the spectral domain and providing new tools to extend the field of temporally modulated metamaterials (“chrono-metamaterials”) into the complex realm, our findings may open unexplored opportunities and enable relevant technological advances in various areas of photonics and, more broadly, of wave physics and engineering.
Uniqueness of the minimizer of the normalized volume function

We confirm a conjecture of Chi Li which says that the minimizer of the normalized volume function for a klt singularity is unique up to rescaling. This is achieved by defining stability thresholds for valuations, and then showing that a valuation is a minimizer if and only if it is K-semistable, and that K-semistable valuation is unique up to rescaling. As applications, we prove a finite degree formula for volumes of klt singularities and an effective bound of the local fundamental group of a klt singularity.
The Physical Meaning of Phase Coefficients

The question of what information about an asteroid's surface is contained in a measurement of the phase coefficient between phase angles of 10° and 30° is examined in detail. Contrary to some past claims it is shown that absolute reflectivities cannot be derived from phase coefficients. Furthermore, typical asteroid phase coefficients cannot be interpreted unambiguously. This is because the observed phase coefficient may depend as much on the photometric properties of an individual surface element as on the degree of large-scale surface roughness, and these two effects are impossible to separate if only disk integrated measurements are available. The wavelength dependence of asteroid phase coefficients should be small and should contain little information about the surface. In the case of irregular asteroids with macroscopically rough surfaces, the importance of large-scale shadowing, and hence the observed phase coefficient, will depend on the aspect of the asteroid. In such cases, therefore, phase coefficients must be carefully defined to be meaningful It should be possible, in some cases, to estimate the relative surface roughness of two quasi-spherical asteroids by combining photometric and polarimetric observations. For example, if the two asteroids have almost identical polarization curves but quite different phase coefficients, it is likely that the asteroid with the larger phase coefficient has a macroscopically rougher surface.
Distributionally weighted least squares in structural equation modeling.

n real data analysis with structural equation modeling, data are unlikely to be exactly normally distributed. If we ignore the non-normality reality, the parameter estimates, standard error estimates, and model fit statistics from normal theory based methods such as maximum likelihood (ML) and normal theory based generalized least squares estimation (GLS) are unreliable. On the other hand, the asymptotically distribution free (ADF) estimator does not rely on any distribution assumption but cannot demonstrate its efficiency advantage with small and modest sample sizes. The methods which adopt misspecified loss functions including ridge GLS (RGLS) can provide better estimates and inferences than the normal theory based methods and the ADF estimator in some cases. We propose a distributionally weighted least squares (DLS) estimator, and expect that it can perform better than the existing generalized least squares, because it combines normal theory based and ADF based generalized least squares estimation. Computer simulation results suggest that model-implied covariance based DLS (DLSM) provided relatively accurate and efficient estimates in terms of RMSE. In addition, the empirical standard errors, the relative biases of standard error estimates, and the Type I error rates of the Jiang-Yuan rank adjusted model fit test statistic (TJY) in DLSM were competitive with the classical methods including ML, GLS, and RGLS. The performance of DLSM depends on its tuning parameter a. We illustrate how to implement DLSM and select the optimal a by a bootstrap procedure in a real data example. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
The medium amplitude response of nonlinear Maxwell–Oldroyd type models in simple shear

Abstract A general framework for Maxwell–Oldroyd type differential constitutive models is examined, in which an unspecified nonlinear function of the stress tensor and rate-of-deformation tensor is incorporated into the well-known corotational version of the Jeffreys model discussed by Oldroyd. For medium amplitude simple shear deformations, the recently developed mathematical framework of medium amplitude parallel superposition (MAPS) rheology reveals that this generalized nonlinear Maxwell model can produce only a limited number of distinct signatures, which combine linearly in a well-posed basis expansion for the third order complex viscosity. This basis expansion represents a library of MAPS signatures for distinct constitutive models that are contained within the generalized nonlinear Maxwell model. We describe a framework for quantitative model identification using this basis expansion for the third order complex viscosity, and discuss its limitations in distinguishing distinct nonlinear features of the underlying constitutive models from medium amplitude shear stress data. The leading order contributions to the normal stress differences are also considered, revealing that only the second normal stress difference provides distinct information about the weakly nonlinear response space of the model. After briefly considering the conditions for time-strain separability within the generalized nonlinear Maxwell model, we apply the basis expansion of the third order complex viscosity to derive the medium amplitude signatures of the model in specific shear deformation protocols. Finally, we use these signatures for estimation of model parameters from rheological data obtained by these different deformation protocols, revealing that three-tone oscillatory shear deformations produce data that is readily able to distinguish all features of the medium amplitude, simple shear response space of this generalized class of constitutive models.
Development of Thermal Barriers for Solid Rocket Motor Nozzle Joints

The Space Shuttle solid rocket motor case assembly joints are sealed using conventional 0-ring seals. The 5500+F combustion gases are kept a safe distance away from the seals by thick layers of insulation. Special joint-fill compounds are used to fill the joints in the insulation to prevent a direct flowpath to the seals. On a number of occasions. NASA has observed in several of the rocket nozzle assembly joints hot gas penetration through defects in the joint- fill compound. The current nozzle-to-case joint design incorporates primary, secondary and wiper (inner-most) 0-rings and polysulfide joint-fill compound. In the current design, 1 out of 7 motors experience hot gas to the wiper 0-ring. Though the condition does not threaten motor safety, evidence of hot gas to the wiper 0-ring results in extensive reviews before resuming flight. NASA and solid rocket motor manufacturer Thiokol are working to improve the nozzle-to-case joint design by implementing a more reliable J-leg design and a thermal barrier, This paper presents burn-resistance, temperature drop, flow and resiliency test results for several types of NASA braided carbon-fiber thermal barriers. Burn tests were performed to determine the time to burn through each of the thermal barriers when exposed to the flame of an oxy-acetylene torch (5500 F), representative of the 5500 F solid rocket motor combustion temperatures. Thermal barriers braided out of carbon fibers endured the flame for over 6 minutes, three times longer than solid rocket motor burn time. Tests were performed on two thermal barrier braid architectures, denoted Carbon-3 and Carbon-6, to measure the temperature drop across and along the barrier in a compressed state when subjected to the flame of an oxyacetylene torch. Carbon-3 and Carbon-6 thermal barriers were excellent insulators causing temperature drops through their diameter of up to a 2800 and 2560 F. respectively. Gas temperature 1/4" downstream of the thermal barrier were within the downstream Viton 0-ring temperature limit of 600 F. Carbon-6 performed extremely well in subscale rocket "char" motor tests when subjected to hot gas at 3200 F for an 11 second rocket firing, simulating the maximum downstream joint cavity fill time. The thermal barrier reduced the incoming hot gas temperature by 2200 F in an intentionally oversized gap defect, spread the incoming jet flow, and blocked hot slag, thereby offering protection to the downstream 0-rings.
A decade of discoveries by the Daya Bay reactor neutrino experiment

With the end of Daya Bay experimental operations in December 2020, I review the history, discoveries, measurements and impact of the Daya Bay reactor neutrino experiment in China.
Cooperative Path Integral Control for Stochastic Multi-Agent Systems

A distributed stochastic optimal control solution is presented for cooperative multi-agent systems. The network of agents is partitioned into multiple factorial subsystems, each of which consists of a central agent and neighboring agents. Local control actions that rely only on agents' local observations are designed to optimize the joint cost functions of subsystems. When solving for the local control actions, the joint optimality equation for each subsystem is cast as a linear partial differential equation and solved using the Feynman-Kac formula. The solution and the optimal control action are then formulated as path integrals and approximated by a Monte-Carlo method. Numerical verification is provided through a simulation example consisting of a team of cooperative UAVs.
Positivity of vector bundles and Hodge theory

It is well known that positivity properties of the curvature of a vector bundle have implications on the algebro-geometric properties of the bundle, such as numerical positivity, vanishing of higher cohomology leading to existence of global sections etc. It is also well known that bundles arising in Hodge theory tend to have positivity properties. From these considerations several issues arise: (i) In general for bundles that are semi-positive but not strictly positive; what further natural conditions lead to the existence of sections of its symmetric powers? (ii) In Hodge theory the Hodge metrics generally have singularities; what can be said about these and their curvatures, Chern forms etc.? (iii) What are some algebro-geometric applications of positivity of Hodge bundles? The purpose of these partly expository notes is fourfold. One is to summarize some of the general measures and types of positivity that have arisen in the literature. A second is to introduce and give some applications of norm positivity. This is a concept that implies the di_erent notions of metric semi-positivity that are present in many of the standard examples and one that has an algebro-geometric interpretation in these examples. A third purpose is to discuss and compare some of the types of metric singularities that arise in algebraic geometry and in Hodge theory. Finally we shall present some applications of the theory from both the classical and recent literature.
Faster algorithms for rooted connectivity in directed graphs

We consider the fundamental problems of determining the rooted and global edge and vertex connectivities (and computing the corresponding cuts) in directed graphs. For rooted (and hence also global) edge connectivity with small integer capacities we give a new randomized Monte Carlo algorithm that runs in time O(n²). For rooted edge connectivity this is the first algorithm to improve on the Ω(n³) time bound in the dense-graph high-connectivity regime. Our result relies on a simple combination of sampling coupled with sparsification that appears new, and could lead to further tradeoffs for directed graph connectivity problems. We extend the edge connectivity ideas to rooted and global vertex connectivity in directed graphs. We obtain a (1+e)-approximation for rooted vertex connectivity in O(nW/e) time where W is the total vertex weight (assuming integral vertex weights); in particular this yields an O(n²/e) time randomized algorithm for unweighted graphs. This translates to a O(KnW) time exact algorithm where K is the rooted connectivity. We build on this to obtain similar bounds for global vertex connectivity. Our results complement the known results for these problems in the low connectivity regime due to work of Gabow [Harold N. Gabow, 1995] for edge connectivity from 1991, and the very recent work of Nanongkai et al. [Nanongkai et al., 2019] and Forster et al. [Sebastian Forster et al., 2020] for vertex connectivity.
Patterns of Cataract Surgery Related Litigations in the United States from 2000-2020

Background: Awareness of the outcomes of cataract surgery-related malpractice litigation in the United States allows for ophthalmologists to better understand how to improve patient care and reduce...
Degradation Aware Predictive Energy Management Strategy for Ship Power Systems

Integration of modern defence weapons into ship power systems poses a challenge in terms of meeting the high ramp rate requirements of those loads. It might be demanding for the generators to meet the ramp rates of these loads. Failure to meet so, might lead to stability issues. This is addressed by conglomeration of generators and energy storage elements to handle the required power demand posed by loads. This paper proposes an energy management strategy based on model predictive control that incorporates the uncertainty in the load prediction. The proposed controller optimally coordinates the power split between the generators and energy storage elements to guarantee that the power demand is met taking into considerations the ramp rate limitations and the load uncertainty. A low bandwidth model consisting of a single generator and a single energy storage element is used to validate the results of the proposed energy management strategy. The results demonstrate the robustness of the controller under load prediction uncertainty and demonstrate the effect of load uncertainty on battery capacity loss.
Sub-Gaussian Error Bounds for Hypothesis Testing

We interpret likelihood-based test functions from a geometric perspective where the Kullback-Leibler (KL) divergence is adopted to quantify the “distance” from a distribution to another. Such a test function can be seen as a sub-Gaussian random variable, and we propose a principled way to calculate its corresponding sub-Gaussian norm. Then an error bound for binary hypothesis testing can be obtained in terms of the subGaussian norm and the KL divergence, which is more informative than Pinsker's bound when the significance level is prescribed. For M-ary hypothesis testing, we also derive an error bound which is complementary to Fano's inequality by being more informative when the number of hypotheses or the sample size is not large.
City-wide school-located influenza vaccination: A retrospective cohort study.

Author(s): Benjamin-Chung, Jade; Arnold, Benjamin F; Mishra, Kunal; Kennedy, Chris J; Nguyen, Anna; Pokpongkiat, Nolan N; Djajadi, Stephanie; Seth, Anmol; Klein, Nicola P; Hubbard, Alan E; Reingold, Arthur; Colford, John M | Abstract: BackgroundWe measured the effectiveness of a city-wide school-located influenza vaccination (SLIV) program implemented in over 102 elementary schools in Oakland, California.MethodsWe conducted a retrospective cohort study among Kaiser Permanente Northern California (KPNC) members of all ages residing in either the intervention or a multivariate-matched comparison site from September 2011 - August 2017. Outcomes included medically attended acute respiratory illness (MAARI), influenza hospitalization, and Oseltamivir prescriptions. We estimated difference-in-differences (DIDs) in 2014-15, 2015-16, and 2016-17 using generalized linear models and adjusted for race, ethnicity, age, sex, health plan, and language.ResultsPre-intervention member characteristics were similar between sites. The proportion of KPNC members vaccinated for influenza by KPNC or the SLIV program was 8-11% higher in the intervention site than the comparison site during the intervention period. Among school-aged children, SLIV was associated with lower Oseltamivir prescriptions per 1,000 (DIDs: -3.5 (95% CI -5.5, -1.5) in 2015-16; -4.0 (95% CI -6.5, -1.6) in 2016-17) but not with other outcomes. SLIV was associated with lower MAARI per 1,000 in adults 65n+nyears (2014-15: -13.2, 95% CI -23.2, -3.2; 2015-16: -21.5, 95% CI -31.1, -11.9; 2016-17: -13.0, 95% CI -23.2, -2.9). There were few significant associations with other outcomes among adults.ConclusionsA city-wide SLIV intervention was associated with higher influenza vaccination coverage, lower Oseltamivir prescriptions in school-aged children, and lower MAARI among people over 65nyears, suggesting possible indirect effects of SLIV among older adults.
Simultaneous Non-Gaussian Component Analysis (SING) for Data Integration in Neuroimaging

As advances in technology allow the acquisition of complementary information, it is increasingly common for scientific studies to collect multiple datasets. Large-scale neuroimaging studies often include multiple modalities (e.g., task functional MRI, resting-state fMRI, diffusion MRI, and/or structural MRI) with the aim to understand the relationships between datasets. In this study, we seek to understand whether regions of the brain activated in a working memory task relate to resting-state correlations. In neuroimaging, a popular approach uses principal component analysis for dimension reduction prior to canonical correlation analysis with joint independent component analysis, but this may discard biological features with low variance and/or spuriously associate structure unique to a dataset with joint structure. We introduce SImultaneous Non-Gaussian component analysis (SING) in which dimension reduction and feature extraction are achieved simultaneously, and shared information is captured via subject scores. We apply our method to a working memory task and resting-state correlations from the Human Connectome Project. We find joint structure as evident from joint scores whose loadings highlight resting-state correlations involving regions associated with working memory. Moreover, some of the subject scores are related to fluid intelligence.
Gender differences in motivated reasoning

Abstract Men and women systematically differ in their beliefs about their performance relative to others; in particular, men tend to be more overconfident. This paper provides support for one explanation for gender differences in overconfidence, performance-motivated reasoning, in which people distort how they process new information in ways that make them believe they outperformed others. Using a large online experiment, I find that male subjects distort information processing in ways that favor their performance, while female subjects do not systematically distort information processing in either direction. These statistically-significant gender differences in performance-motivated reasoning mimic gender differences in overconfidence; beliefs of male subjects are systematically overconfident, while beliefs of female subjects are well-calibrated on average. The experiment also includes political questions, and finds that politically-motivated reasoning is similar for both men and women. These results suggest that, while men and women are both susceptible to motivated reasoning in general, men find it particularly attractive to believe that they outperformed others.
Extrinsic curvature and conformal Gauss–Bonnet for four-manifolds with corner

This paper defines two new extrinsic curvature quantities on the corner of a four-dimensional Riemannian manifold with corner. One of these is a pointwise conformal invariant, and the conformal transformation of the other is governed by a new linear second-order pointwise conformally invariant partial differential operator. The Gauss-Bonnet theorem is then stated in terms of these quantities.
Beyond the Signs: Nonparametric Tensor Completion via Sign Series

We consider the problem of tensor estimation from noisy observations with possibly missing entries. A nonparametric approach to tensor completion is developed based on a new model which we coin as sign representable tensors. The model represents the signal tensor of interest using a series of structured sign tensors. Unlike earlier methods, the sign series representation effectively addresses both low- and high-rank signals, while encompassing many existing tensor models -- including CP models, Tucker models, single index models, several hypergraphon models -- as special cases. We show that the sign tensor series is theoretically characterized, and computationally estimable, via classification tasks with carefully-specified weights. Excess risk bounds, estimation error rates, and sample complexities are established. We demonstrate the outperformance of our approach over previous methods on two datasets, one on human brain connectivity networks and the other on topic data mining.
Barycentric gluing and geometry of stable metrics

We discuss various aspects of a local-to-global embedding technique and the metric geometry of stable metric spaces, in particular two of its important subclasses: locally finite spaces and proper spaces. We explain how the barycentric gluing technique, which has been mostly applied to bi-Lipschitz embedding problems pertaining to locally finite spaces, can be implemented successfully in a much broader context. For instance, we show that the embeddability of an arbitrary metric space into $$\ell _p$$ is determined by the embeddability of its balls. We also introduce the notion of upper stability. This new metric invariant lies formally between Krivine–Maurey (isometric) notion of stability and Kalton’s property $${\mathcal {Q}}$$ . We show that several results of Raynaud and Kalton for stable metrics can be extended to the broader context of upper stable metrics and we point out the relevance of upper stability to a long standing embedding problem raised by Kalton. Applications to compression exponent theory are highlighted and we recall old, and state new, important open problems. This article was written in a style favoring clarity over conciseness in order to make the material appealing, accessible, and reusable to geometers from a variety of backgrounds, and not only to Banach space geometers.
The core of a transferable utility game as the solution to a public good market demand problem

The core of a monotonic transferable utility (TU) game is shown to be the set of prices that incentivize each individual to demand the grand coalition in a market demand problem in which the goods being demanded are coalitions viewed as excluable public goods. It is also shown that the core is the intersection of superdifferentials evaluated at the grand coalition of the covers of person-specific TU games derived from the original game. These characterizations of the core demonstrate how a market demand approach to coalition formation in the spirit of Baldwin and Klemperer (Tropical geometry to analyze demand, University of Oxford, 2014; Econometrica, 87: 867–932, 2019) is related to the approach to the core using the cover of a TU game and it superdifferential at the grand coalition developed by Shapley and Shubik (J Econ Theory, 1: 9–25, 1969), Aubin (Math Oper Res, 6:1–13, 1981), and Danilov and Koshevoy (J Math Anal Appl, 247: 1–14, 2000).
FAID Diversity via Neural Networks

Decoder diversity is a powerful error correction framework in which a collection of decoders collaboratively correct a set of error patterns otherwise uncorrectable by any individual decoder. In this paper, we propose a new approach to design the decoder diversity of finite alphabet iterative decoders (FAIDs) for Low-Density Parity Check (LDPC) codes over the binary symmetric channel (BSC), for the purpose of lowering the error floor while guaranteeing the waterfall performance. The proposed decoder diversity is achieved by training a recurrent quantized neural network (RQNN) to learn/design FAIDs. We demonstrated for the first time that a machine-learned decoder can surpass in performance a man-made decoder of the same complexity. As RQNNs can model a broad class of FAIDs, they are capable of learning an arbitrary FAID. To provide sufficient knowledge of the error floor to the RQNN, the training sets are constructed by sampling from the set of most problematic error patterns - trapping sets. In contrast to the existing methods that use the cross-entropy function as the loss function, we introduce a frame-error-rate (FER) based loss function to train the RQNN with the objective of correcting specific error patterns rather than reducing the bit error rate (BER). The examples and simulation results show that the RQNN-aided decoder diversity increases the error correction capability of LDPC codes and lowers the error floor.
Synaptic vesicle pools are a major hidden resting metabolic burden of nerve terminals.

The human brain is a uniquely vulnerable organ as interruption in fuel supply leads to acute cognitive impairment on rapid time scales. The reasons for this vulnerability are not well understood, but nerve terminals are likely loci of this vulnerability as they do not store sufficient ATP molecules and must synthesize them on-demand during activity or suffer acute degradation in performance. The requirements for on-demand ATP synthesis however depends in part on the magnitude of resting metabolic rates. We show here that, at rest, synaptic vesicle (SV) pools are a major source of presynaptic basal energy consumption. This basal metabolism arises from SV-resident V-ATPases compensating for a hidden resting H+ efflux from the SV lumen. We show that this steady-state H+ efflux is 1) mediated by vesicular neurotransmitter transporters, 2) independent of the SV cycle, 3) accounts for ~half of the resting synaptic energy consumption and 4) contributes to nerve terminal intolerance of fuel deprivation.
Competing auctions: Finite markets and convergence

The literature on competing auctions offers a model where sellers compete for buyers by setting reserve prices. An outstanding conjecture (e.g., Peters and Severinov 1997) is that the sellers post prices close to their marginal costs when the market becomes large. This conjecture is confirmed in this paper: we show that if all sellers have zero costs, then the equilibrium reserve price converges to 0 in distribution. Under further conditions there is a symmetric pure strategy equilibrium. In this equilibrium, if the ratio of buyers to sellers increases, then the equilibrium reserve price increases, and the reserve price is decreasing in the size of the market. Convergence of reserve prices occurs at the fast rate of 1/n if the ratio of buyers to sellers is held constant.
Titration of the gene sites on DNA by DNA-RNA hybridization. II. The Escherichia coli chromosome.

Abstract Escherichia coli RNA, having the same specific radioactivity in all molecules (long-labeled RNA), or labeled by exposing bacteria to [ 3 H]uracil for 30 seconds (pulse-labeled RNA), was hybridized to E. coli DNA over a wide range of RNA/ DNA inputs † : from ratios so low (less than 1 1000 ) that not all copies of any DNA site were filled, to RNA/DNA inputs so high (greater than 1000) that no more RNA could be bound. RNA with radioactivity only in stable components was also hybridized over a wide range of RNA/DNA inputs. (a) The maximum fraction of input RNA detectable as authentic hybrid was about 75%; it is difficult to exceed this figure because of simultaneous formation of partial hybrids subsequently removed by ribonuclease. (b) The first DNA sites to become saturated did so at RNA/DNA inputs of about 1 1000 . All sites for stable RNA were occupied at an RNA/DNA input of about 1 160 . (c) At any RNA/DNA input between about 1 4 and 1 160 , virtually all unstable RNA is hybridized, and the size of this fraction equals the difference between the fraction of radioactivity hybridized from total RNA and from stable RNA. Such measurements suggested that unstable RNA is 2.5 to 3% of the bacterial RNA while 50% of the synthesized RNA is unstable. Competition experiments with excess unlabeled rRNA gave the same figures. (d) There is a wide variation in the numbers of molecules of different mRNA species per cell; 10% of the active genes are homologous to 70% of the mRNA mass . (e) Over the entire range of abundances, the same frequency distribution was found for the radioactivity of pulse-labeled mRNA. This suggests that variable life-times do not contribute to the abundance distribution of mRNA species. (f) Only 10% of the DNA was observed to be complementary to virtually all (more than 99.85%) of the label from both pulse-labeled or long-labeled mRNA; this suggests that in these cultures most of the potential E. coli gene sites make extremely little if any RNA.
Glyconeogenesis in Tetrahymena pyriformis RELATIONSHIP OF ENZYME ADAPTATION TO THE CARBON PATHWAY

Abstract Phosphoenolpyruvate carboxykinase in the cytosol of Tetrahymena pyriformis is closely related to the high rate of glyconeogenesis in that organism, while the enzyme in the mitochondria does not show such a correlation. This finding, together with the observation of depression of phosphoenolpyruvate carboxykinase and malate dehydrogenase activities by glucose in the cytosol, indicates an extramitochondrial pathway for phosphoenolpyruvate synthesis from dicarboxylic acids which is similar to that postulated for rat liver and may, therefore, be of general metabolic significance. A change of oxygen tension which influences glyconeogenesis in Tetrahymena also has an effect on phosphoenolpyruvate carboxykinase and possibly other glyconeogenic enzymes. One factor in the repression and derepression of phosphoenolpyruvate carboxykinase was found to be related to oxygen tension and to occur independently of any carbohydrate in the growth medium. A phosphoenolpyruvate carboxylase has been identified in this organism. Although its exact metabolic significance is somewhat unclear, the enzyme may function as the primary CO2 fixation mechanism in Tetrahymena.
A Convergent Feasible Algorithm for the Output Covariance Constraint Problem

We consider the optimal control problem of minimizing control effort subject to multiple output covariance inequality constraints Y i ? ? i , where ? i , is the given bound on Y i , related to the performance requirement on the i-th output group. The contribution of this paper is an iterative controller design algorithm for finding a controller that satisfies the first order necessary conditions for optimality corresponding to this control problem. We establish the convergence of the algorithm for both continuous and discrete time problems.
Creativity Is More Than Silly, More Than Art, More Than Good: The Diverse Career of Arthur Cropley

In this article, I highlight some of Arthur Cropley's work that has been the most influential to me as a scholar. Cropley's work has continually pushed the boundaries by exploring what creativity is and isn't, squashing myths, and not making assumptions. I specifically discuss Cropley's research and theory on the importance of convergent thinking, on the role of creativity in science and engineering, and on the potential for evil with creativity.
Long-term Incidence of Hematuria, Urethral Stricture and Bladder Cancer after Radiation Therapy for Prostate Cancer

Abstract Introduction Approximately 25% of patients diagnosed with prostate cancer choose radiation therapy as the primary treatment for this malignancy. Urinary tract toxicity after radiation therapy impacts patients years after treatment of the malignancy. We describe the incidence of hematuria, urethral stricture and bladder cancer after radiation therapy, and measure the effect of the radiation therapy modality in patients with prostate cancer. Methods We performed a retrospective review of 886 consecutive patients who received radiation therapy for prostate cancer between 1992 and 2013. Prostate cancer clinical characteristics, radiation therapy treatment modality and events of interest (hematuria, urethral stricture disease and bladder cancer) were recorded. The Kaplan-Meier method was used to estimate the incidence of events of interest and multivariate stepwise Cox regression was performed to analyze associations. Results Radiation therapy modalities included external beam radiation therapy (379), brachytherapy (225), combination therapy (35) or post-prostatectomy radiation therapy (adjuvant 47 or salvage 201). Overall the 5 and 10-year risk (95% CI) of hematuria was 23% (19–27) and 42% (36–48), urethral stricture 7% (5–9) and 12% (8–16), and bladder cancer 2% (1–3) and 5% (3–7), respectively. On multivariate regression smoking was associated with hematuria (HR 2.5, p Conclusions Hematuria, urethral stricture and bladder cancer continue to develop several years after radiation therapy for prostate cancer, thereby warranting continued, long-term followup for these conditions.
On the influence of initial geometry on the evolution of fluid filaments

In this work, the influence of the initial geometry on the evolution of a fluid filament deposited on a substrate is studied, with a particular focus on the thin fluid strips of nano-scale thickness. Based on the analogy to the classical Rayleigh–Plateau (R–P) instability of a free-standing fluid jet, an estimate of the minimal distance between the final states (sessile droplets) can be obtained. However, this numerical study shows that while the prediction based on the R–P instability mechanism is highly accurate for an initial perturbation of a sinusoidal shape, it does not hold for a rectangular waveform perturbation. The numerical results are obtained by directly solving fully three-dimensional Navier–Stokes equations, based on a Volume of Fluid interface tracking method. The results show that (i) rectangular-wave perturbations can lead to the formation of patterns characterized by spatial scales that are much smaller than what is expected based on the R–P instability mechanism; (ii) the nonlinear sta...
Unique role of consumer studies in nonprescription drug development

Abstract Objective To inform the health care community about nonprescription drug labeling and consumer studies unique and integral to the nonprescription drug development process. Summary Data from consumer studies are essential for many nonprescription drug approvals. These studies are conducted to help predict actual consumer behavior in the marketplace if the product is approved. They test whether potential consumers understand a label (label comprehension study), can properly decide if a product is appropriate for them to use (self-selection study), and can use a product in accordance with the label (actual use study). The design and interpretation of these studies often pose unique challenges. Conclusion Consumer studies are often part of a nonprescription drug development program. Continued efforts to improve consumer research should result in greater access to safe and effective nonprescription drug products for the American public.
Coupled Clouds and Chemistry of the Giant Planets — A Case for Multiprobes

In seeking to understand the formation of the giant planets and the origin of their atmospheres, the heavy element abundance in well-mixed atmosphere is key. However, clouds come in the way. Thus, composition and condensation are intimately intertwined with the mystery of planetary formation and atmospheric origin. Clouds also provide important clues to dynamical processes in the atmosphere. In this chapter we discuss the thermochemical processes that determine the composition, structure, and characteristics of the Jovian clouds. We also discuss the significance of clouds in the big picture of the formation of giant planets and their atmospheres. We recommend multiprobes at all four giant planets in order to break new ground.