cssi/SciSciGPT-SciSciNet huggingface dataset:
Constraint-Driven Coordinated Control of Multi-Robot Systems

In this paper we present a reformulation-framed as a constrained optimization problem-of multi-robot tasks which are encoded through a cost function that is to be minimized. The advantages of this approach are multiple. The constraint-based formulation provides a natural way of enabling long-term robot autonomy applications, where resilience and adaptability to changing environmental conditions are essential. Moreover., under certain assumptions on the cost function, the resulting controller is guaranteed to be decentralized. Furthermore, finite-time convergence can be achieved, while using local information only, and therefore preserving the decentralized nature of the algorithm. The developed control framework has been tested on a team of ground mobile robots implementing long-term environmental monitoring.
Associations between motivation to learn, basic skills, and adult education and training participation among older adults in the USA

ABSTRACTThe objective of this study is to examine the associations between the motivation to learn, basic skills (i.e. literacy and numeracy), and organised adult education and training (AET) parti...
The VLA Low Band Ionospheric and Transient Experiment (VLITE): A Commensal Sky Survey

The US Naval Research Laboratory (NRL) and the National Radio Astronomy Observatory (NRAO) have collaborated to develop, install, and commission a new commensal system on the Karl G. Jansky Very Large Array (VLA). The VLA Low Band Ionospheric and Transient Experiment (VLITE) makes use of dedicated samplers and fibers to tap the signal from 10 VLA low band receivers and correlate those through a real-time DiFX correlator. VLITE allows for the simultaneous use of the VLA to observe primary science using the higher frequencies receivers (1−50 GHz) through the NRAO WIDAR correlator and lower frequencies through the DiFX correlator. VLITE operates during nearly all observing programs and provides 64 MHz of bandwidth centered at 352 MHz. The operation of VLITE requires no additional resources from the VLA system running the primary science and produces an ad-hoc sky survey. The commensal system greatly expands the capabilities of the VLA through value-added PI science, stand-alone astrophysics, the opening of a new window on transient searches, and serendipity. In the first year of operation we have recorded more than 6300 hours spread across the sky. We present an overview of the VLITE program, discuss the sky coverage and depth obtained during the first year of operation, and briefly outline the astrophysics and transients programs.
A Distributed and Integrated Method of Moments for High-Dimensional Correlated Data Analysis.

This article is motivated by a regression analysis of electroencephalography (EEG) neuroimaging data with high-dimensional correlated responses with multilevel nested correlations. We develop a div...
Cu-Catalyzed Hydroboration of Benzylidenecyclopropanes: Reaction Optimization, (Hetero)Aryl Scope, and Origins of Pathway Selectivity.

The copper-catalyzed hydroboration of benzylidenecyclopropanes, conveniently accessed in one step from readily available benzaldehydes, is reported. Under otherwise identical reaction conditions, t...
Arithmetic lattices in unipotent algebraic groups

Fixing an arithmetic lattice $\Gamma$ in an algebraic group $G$, the commensurability growth function assigns to each $n$ the cardinality of the set of subgroups $\Delta$ with $[\Gamma : \Gamma \cap \Delta] [\Delta: \Gamma \cap \Delta] = n$. This growth function gives a new setting where methods of F. Grunewald, D. Segal, and G. C. Smith's "Subgroups of finite index in nilpotent groups" apply to study arithmetic lattices in an algebraic group. In particular, we show that for any unipotent algebraic $\mathbb{Z}$-group with arithmetic lattice $\Gamma$, the Dirichlet function associated to the commensurability growth function satisfies an Euler decomposition. Moreover, the local parts are rational functions in $p^{-s}$, where the degrees of the numerator and denominator are independent of $p$. This gives regularity results for the set of arithmetic lattices in $G$.
Regional dynamics of fractal dimension of the left ventricular endocardium from cine computed tomography images.

We present a method to leverage the high fidelity of computed tomography (CT) to quantify regional left ventricular function using topography variation of the endocardium as a surrogate measure of strain. 4DCT images of 10 normal and 10 abnormal subjects, acquired with standard clinical protocols, are used. The topography of the endocardium is characterized by its regional values of fractal dimension ( FD ), computed using a box-counting algorithm developed in-house. The average FD in each of the 16 American Heart Association segments is calculated for each subject as a function of time over the cardiac cycle. The normal subjects show a peak systolic percentage change in FD of 5.9%±2% in all free-wall segments, whereas the abnormal cohort experiences a change of 2%±1.2% ( p<0.00001 ). Septal segments, being smooth, do not undergo large changes in FD . Additionally, a principal component analysis is performed on the temporal profiles of FD to highlight the possibility for unsupervised classification of normal and abnormal function. The method developed is free from manual contouring and does not require any feature tracking or registration algorithms. The FD values in the free-wall segments correlated well with radial strain and with endocardial regional shortening measurements.
Are we there yet?: evaluating state-of-the-art neural network based geoparsers using EUPEG as a benchmarking platform

Geoparsing is an important task in geographic information retrieval. A geoparsing system, known as a geoparser, takes some texts as the input and outputs the recognized place mentions and their location coordinates. In June 2019, a geoparsing competition, Toponym Resolution in Scientific Papers, was held as one of the SemEval 2019 tasks. The winning teams developed neural network based geoparsers that achieved outstanding performances (over 90% precision, recall, and F1 score for toponym recognition). This exciting result brings the question "are we there yet?", namely have we achieved high enough performances to possibly consider the problem of geoparsing as solved? One limitation of this competition is that the developed geoparsers were tested on only one dataset which has 45 research articles collected from the particular domain of Bio-medicine. It is known that the same geoparser can have very different performances on different datasets. Thus, this work performs a systematic evaluation of these state-of-the-art geoparsers using our recently developed benchmarking platform EUPEG that has eight annotated datasets, nine baseline geoparsers, and eight performance metrics. The evaluation result suggests that these new geoparsers indeed improve the performances of geoparsing on multiple datasets although some challenges remain.
Are These Pairing Elements Correct?: Automated Verification and Applications

Using a set of pairing product equations (PPEs) to verify the correctness of an untrusted set of pairing elements with respect to another set of trusted elements has numerous cryptographic applications. These include the design of basic and structure-preserving signature schemes, building oblivious transfer schemes from "blind" IBE, finding new verifiable random functions and keeping the IBE/ABE authority "accountable" to the user. A natural question to ask is: are all trusted-untrusted pairing element groups in the literature PPE testable? We provide original observations demonstrating that the answer is no, and moreover, it can be non-trivial to determine whether or not there exists a set of PPEs that can verify some pairing elements with respect to others. Many IBE schemes have PPE-testable private keys (with respect to the public parameters), while others, such as those based on dual-system encryption, provably do not. To aid those wishing to use PPE-based element verification in their cryptosystems, we devised rules to systematically search for a set of PPEs that can verify untrusted elements with respect to a set of trusted elements. We prove the correctness of each rule and combine them into a main searching algorithm for which we also prove correctness. We implemented this algorithm in a new software tool, called AutoPPE. Tested on over two dozen case studies, AutoPPE found a set of PPEs (on schemes where they exist) usually in just a matter of seconds. This work represents an important step towards the larger goal of improving the speed and accuracy of pairing-based cryptographic design via computer automation.
Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record

The growth of DNA biobanks linked to data from electronic health records (EHRs) has enabled the discovery of numerous associations between genomic variants and clinical phenotypes. Nonetheless, although clinical data are generally longitudinal, standard approaches for detecting genotype-phenotype associations in such linked data, notably logistic regression, do not naturally account for variation in the period of follow-up or the time at which an event occurs. Here we explored the advantages of quantifying associations using Cox proportional hazards regression, which can account for the age at which a patient first visited the healthcare system (left truncation) and the age at which a patient either last visited the healthcare system or acquired a particular phenotype (right censoring). In comprehensive simulations, we found that, compared to logistic regression, Cox regression had greater power at equivalent Type I error. We then scanned for genotype-phenotype associations using logistic regression and Cox regression on 50 phenotypes derived from the EHRs of 49,792 genotyped individuals. Consistent with the findings from our simulations, Cox regression had approximately 10% greater relative sensitivity for detecting known associations from the NHGRI-EBI GWAS Catalog. In terms of effect sizes, the hazard ratios estimated by Cox regression were strongly correlated with the odds ratios estimated by logistic regression. As longitudinal health-related data continue to grow, Cox regression may improve our ability to identify the genetic basis for a wide range of human phenotypes.
Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies.

Causal mediation analysis aims to examine the role of a mediator or a group of mediators that lie in the pathway between an exposure and an outcome. Recent biomedical studies often involve a large number of potential mediators based on high-throughput technologies. Most of the current analytic methods focus on settings with one or a moderate number of potential mediators. With the expanding growth of -omics data, joint analysis of molecular-level genomics data with epidemiological data through mediation analysis is becoming more common. However, such joint analysis requires methods that can simultaneously accommodate high-dimensional mediators and that are currently lacking. To address this problem, we develop a Bayesian inference method using continuous shrinkage priors to extend previous causal mediation analysis techniques to a high-dimensional setting. Simulations demonstrate that our method improves the power of global mediation analysis compared to simpler alternatives and has decent performance to identify true nonnull contributions to the mediation effects of the pathway. The Bayesian method also helps us to understand the structure of the composite null cases for inactive mediators in the pathway. We applied our method to Multi-Ethnic Study of Atherosclerosis and identified DNA methylation regions that may actively mediate the effect of socioeconomic status on cardiometabolic outcomes.
Numerical simulation of the development of vortices over the Qinghai-Xizang (Tibet) Plateau

Some of the vortices which develop over the Tibetan plateau during summer give rise to severe weather over eastern China. These weather developments can be difficult to forecast, but have been simulated successfully in a recently developed numerical model. It will be shown that different vortices respond differently to topographic effects, to nonlinear processes and to sensible and latent heating in their formation, maintenance, and motion. Elevated terrain appears to enhance the influence of thermodynamic processes, block airflow, and increase frictional dissipation. Sensible heating sometimes collaborates with topography in blocking cold air intrusions and is not only a function of terrain elevation but also of the synoptic situation. Without the input of latent heating, vortices over the eastern part of the Tibetan plateau tend to degenerate.
Secure Integration of Electric Vehicles with the Power Grid

This paper focuses on the secure integration of distributed energy resources (DERs), especially pluggable electric vehicles (EVs), with the power grid. We consider the vehicle-to-grid (V2G) system where EVs are connected to the power grid through an ‘aggregator’ In this paper, we propose a novel Cyber-Physical Anomaly Detection Engine that monitors system behavior and detects anomalies almost instantaneously (worst case inspection time for a packet is 0.165 seconds1). This detection engine ensures that the critical power grid component (viz., aggregator) remains secure by monitoring (a) cyber messages for various state changes and data constraints along with (b) power data on the V2G cyber network using power measurements from sensors on the physical/power distribution network. Since the V2G system is time-sensitive, the anomaly detection engine also monitors the timing requirements of the protocol messages to enhance the safety of the aggregator. To the best of our knowledge, this is the first piece of work that combines (a) the EV charging/discharging protocols, the (b) cyber network and (c) power measurements from physical network to detect intrusions in the EV to power grid system.1Minimum latency on V2G network is 2 seconds
Anatomy of a Chicago slum

The Addams area is probably a more orderly slum than many others, and it departs sharply from the common image of an atomized and unruly urban rabble. For all its historical uniqueness, the neighborhood does establish the possibility of a moral order within its population. The recurrence of the circumstances that led to its organization is as uncertain as the future of the Addams area itself. In spite of all these uncertainties, the Addams area shows that slum residents are intent upon finding a moral order and are sometimes successful in doing so.
Enhanced Electrochemical Storage Properties of Na- and Mg-Intercalated B-Doped-Graphene Based Heterostructures and Bilayers

Modern technology requires novel materials to develop efficient storage systems, which offer high storage capacities and charging/discharging rates while maintaining good mechanical stability and c...
The communication complexity of optimization

We consider the communication complexity of a number of distributed optimization problems. We start with the problem of solving a linear system. Suppose there is a coordinator together with s servers P1, ..., Ps, the i-th of which holds a subset A(i)x = b (i) of ni constraints of a linear system in d variables, and the coordinator would like to output an x ∈ Rd for which A(i)x = b (i) for i = 1, ..., s. We assume each coefficient of each constraint is specified using L bits. We first resolve the randomized and deterministic communication complexity in the point-to-point model of communication, showing it is [MATH HERE] and [MATH HERE], respectively. We obtain similar results for the blackboard communication model. As a result of independent interest, we show the probability a random matrix with integer entries in {−2L, ..., 2L} is invertible is 1 − 2−Θ(dL), whereas previously only 1 − 2−Θ(d) was known. When there is no solution to the linear system, a natural alternative is to find the solution minimizing the ep loss, which is the ep regression problem. While this problem has been studied, we give improved upper or lower bounds for every value of p ≥ 1. One takeaway message is that sampling and sketching techniques, which are commonly used in earlier work on distributed optimization, are neither optimal in the dependence on d nor on the dependence on the approximation e, thus motivating new techniques from optimization to solve these problems. Towards this end, we consider the communication complexity of optimization tasks which generalize linear systems, such as linear, semidefinite, and convex programming. For linear programming, we first resolve the communication complexity when d is constant, showing it is [MATH HERE] in the point-to-point model. For general d and in the point-to-point model, we show an O(sd3 L) upper bound and an [MATH HERE] lower bound. In fact, we show if one perturbs the coefficients randomly by numbers as small as 2−Θ(L), then the upper bound is O(sd2 L) +poly(dL), and so this bound holds for almost all linear programs. Our study motivates understanding the bit complexity of linear programming, which is related to the running time in the unit cost RAM model with words of O(log(nd)) bits, and we give the fastest known algorithms for linear programming in this model.
A Stochastic Decision-Making Model for an Electricity Retailer with Intermittent Renewable Energy and Short-term Demand Response

This paper presents a short-term decision-making model for an electricity retailer with self-production of renewable energy. In the proposed model, a new trading mechanism for short-term demand response (DR) between retail customers equipped with smart grid technology and an electricity retailer is presented. Through the proposed trading mechanism, retail customers submit short-term DR offer curves to the retailer in order to increase or decrease their energy consumption for a given time period at favorable prices. On the other hand, the retailer decides its involvement in the short-term DR market for every time period to avoid unfavorable prices in the real-time market and, thus, maximize its profit. The stochastic nature of day-ahead and real-time prices, renewable energy production, electricity demand, and DR participation of retail customers is taken into account in the formulation of this work. The resulting model is a mixed-integer linear programming (MILP) which can be solved by existing commercial solvers. Case studies using real-world data are performed to demonstrate the effectiveness of the proposed model.
Inferring Distributions Over Depth from a Single Image

When building a geometric scene understanding system for autonomous vehicles, it is crucial to know when the system might fail. Most contemporary approaches cast the problem as depth regression, whose output is a depth value for each pixel. Such approaches cannot diagnose when failures might occur. One attractive alternative is a deep Bayesian network, which captures uncertainty in both model parameters and ambiguous sensor measurements. However, estimating uncertainties is often slow and the distributions are often limited to be uni-modal. In this paper, we recast the continuous problem of depth regression as discrete binary classification, whose output is an un-normalized distribution over possible depths for each pixel. Such output allows one to reliably and efficiently capture multi-modal depth distributions in ambiguous cases, such as depth discontinuities and reflective surfaces. Results on standard benchmarks show that our method produces accurate depth predictions and significantly better uncertainty estimations than prior art while running near real-time. Finally, by making use of uncertainties of the predicted distribution, we significantly reduce streak-like artifacts and improves accuracy as well as memory efficiency in 3D map reconstruction. Video and code can be found on the project website1.
Sequential Chance Optimization For Flow-Tube Based Control Of Probabilistic Nonlinear Systems

In this paper, we address the problem of closed-loop control of nonlinear dynamical systems subjected to probabilistic uncertainties. More precisely, we design time-varying polynomial feedback controllers to follow the given nominal trajectory and also, for safety purposes, remain in the tube around the nominal trajectory, despite all uncertainties. We formulate this problem as a chance optimization problem where we maximize the probability of achieving control objectives. To address control problems with long planning horizons, we formulate the single large chance optimization problem as a sequence of smaller chance optimization problems. To solve the obtained chance optimization problems, we leverage the theory of measures and moments and obtain convex relaxations in the form of semidefinite programs. We provide numerical examples on stabilizing controller design and motion planning of uncertain nonlinear systems to illustrate the performance of the proposed approach.
2019 Novel coronavirus (CoviD-19) pandemic: Built environment considerations to reduce transmission

With the rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in coronavirus disease 2019 (COVID-19), corporate entities, federal, state, county, and city governments, universities, school districts, places of worship, prisons, health care facilities, assisted living organizations, daycares, homeowners, and other building owners and occupants have an opportunity to reduce the potential for transmission through built environment (BE)-mediated pathways. Over the last decade, substantial research into the presence, abundance, diversity, function, and transmission of microbes in the BE has taken place and revealed common pathogen exchange pathways and mechanisms. In this paper, we synthesize this microbiology of the BE research and the known information about SARS-CoV-2 to provide actionable and achievable guidance to BE decision makers, building operators, and all indoor occupants attempting to minimize infectious disease transmission through environmentally mediated pathways. We believe this information is useful to corporate and public administrators and individuals responsible for building operations and environmental services in their decision-making process about the degree and duration of social-distancing measures during viral epidemics and pandemics.
Weighted Speech Distortion Losses for Neural-Network-Based Real-Time Speech Enhancement

This paper investigates several aspects of training a RNN (recurrent neural network) that impact the objective and subjective quality of enhanced speech for real-time single-channel speech enhancement. Specifically, we focus on a RNN that enhances short-time speech spectra on a single-frame-in, single-frame-out basis, a framework adopted by most classical signal processing methods. We propose two novel mean-squared-error-based learning objectives that enable separate control over the importance of speech distortion versus noise reduction. The proposed loss functions are evaluated by widely accepted objective quality and intelligibility measures and compared to other competitive online methods. In addition, we study the impact of feature normalization and varying batch sequence lengths on the objective quality of enhanced speech. Finally, we show subjective ratings for the proposed approach and a state-of-the-art real-time RNN-based method.
Accelerating Monte Carlo modeling of structured-light-based diffuse optical imaging via "photon sharing".

The increasing use of spatially modulated imaging and single-pixel detection techniques demands computationally efficient methods for light transport modeling. Herein, we report an easy-to-implement yet significantly more efficient Monte Carlo (MC) method for simultaneously simulating spatially modulated illumination and detection patterns accurately in 3D complex domains. We have implemented this accelerated algorithm, named “photon sharing,” in our open-source MC simulators, reporting 13.6× and 5.5× speedups in mesh- and voxel-based MC benchmarks, respectively. In addition, the proposed algorithm is readily used to accelerate the solving of inverse problems in spatially modulated imaging systems by building Jacobians of all illumination-detection pattern pairs concurrently, resulting in a 12.4-fold speed improvement.
Deterrence in sequential contests: An experimental study

Abstract Many contests are sequential, with leaders making decisions first, and followers observing those decisions and responding to them. The theory predicts that, unlike in standard Stackelberg duopoly settings, in two-player sequential contests the leader has no strategic advantage. However, this is no longer the case for sequential contests with multiple leaders. Applications include political competition with two established parties and a possibility for a third party entry, or R&D competition with multiple incumbents and a new entrant. We conduct a lab experiment testing the equilibrium predictions for two- and three-player sequential contests, with the corresponding simultaneous contests as controls. Consistent with theory, we find evidence of entry deterrence by leaders in the three-player sequential contest, but not in the two-player version.
Altruistic Autonomy: Beating Congestion on Shared Roads

Traffic congestion has large economic and social costs. The introduction of autonomous vehicles can potentially reduce this congestion, both by increasing network throughput and by enabling a social planner to incentivize users of autonomous vehicles to take longer routes that can alleviate congestion on more direct roads. We formalize these effects of altruistic autonomy on roads shared between human drivers and autonomous vehicles. In this work, we develop a formal model of road congestion on shared roads based on the fundamental diagram of traffic. We consider a network of parallel roads and provide algorithms that compute optimal equilibria that are robust to additional unforeseen demand. We further plan for optimal routings when users have varying degrees of altruism. We find that even with arbitrarily small altruism, total latency can be unboundedly better than without altruism, and that the best selfish equilibrium can be unboundedly better than the worst selfish equilibrium. We validate our theoretical results through microscopic traffic simulations and show average latency decrease of a factor of 4 from worst-case selfish equilibrium to the optimal equilibrium when autonomous vehicles are altruistic.
Incomplete Follow-up†

This article describes a fallacy associated with longitudinal (follow-up) data analysis. Bias that can occur from not accounting for study duration and from inappropriate management of missing data are considered. Keywords: average survival time; clinical trial; failure; fallacy; incomplete data; longitudinal study; lost to follow-up; missing data
Some Aspects of the Hydrodynamics of the Microencapsulation Route to NIF Mandrels

Spherical plastic shells for use as mandrels for the fabrication of ICF (Inertial Confinement Fusion) target capsules can be produced by solution-based microencapsulation techniques. The specifications for these mandrels in terms of sphericity are extremely rigorous, and it is clear that various aspects of the solution hydrodynamics associated with their production are important in controlling the quality of the final product. This paper explores what we know (and need to know) about the hydrodynamics of the microencapsulation process in order to lay the foundation for process improvements as well as identify inherent limits.
The Zα2 domain of ZBP1 is a molecular switch regulating influenza-induced PANoptosis and perinatal lethality during development

Z-DNA-binding protein 1 (ZBP1) is an innate immune sensor of nucleic acids that regulates host defense responses and development. ZBP1 activation triggers inflammation and pyroptosis, necroptosis, and apoptosis (PANoptosis) by activating receptor-interacting Ser/Thr kinase 3 (RIPK3), caspase-8, and the NLRP3 inflammasome. ZBP1 is unique among innate immune sensors because of its N-terminal Zα1 and Zα2 domains, which bind to nucleic acids in the Z-conformation. However, the specific role of these Zα domains in orchestrating ZBP1 activation and subsequent inflammation and cell death is not clear. Here we generated Zbp1ΔZα2/ΔZα2 mice that express ZBP1 lacking the Zα2 domain and demonstrate that this domain is critical for influenza A virus–induced PANoptosis and underlies perinatal lethality in mice in which the RIP homotypic interaction motif domain of RIPK1 has been mutated (Ripk1mRHIM/mRHIM). Deletion of the Zα2 domain in ZBP1 abolished influenza A virus–induced PANoptosis and NLRP3 inflammasome activation. Furthermore, deletion of the Zα2 domain of ZBP1 was sufficient to rescue Ripk1mRHIM/mRHIM mice from perinatal lethality caused by ZBP1-driven cell death and inflammation. Our findings identify the essential role of the Zα2 domain of ZBP1 in several physiological functions and establish a link between Z-RNA sensing via the Zα2 domain and promotion of influenza-induced PANoptosis and perinatal lethality.
Decriminalizing Indoor Prostitution: Implications for Sexual Violence and Public Health

Most governments in the world, including the U.S., prohibit sex work. Given these types of laws rarely change and are fairly uniform across regions, our knowledge about the impact of decriminalizing sex work is largely conjectural. We exploit the fact that a Rhode Island District Court judge unexpectedly decriminalized indoor sex work to provide causal estimates of the impact of decriminalization on the composition of the sex market, reported rape offences, and sexually transmitted infections. While decriminalization increases the size of the indoor sex market, reported rape offences fall by 30% and female gonorrhoea incidence declines by over 40%.
Have Institutional Investors Destabilized Emerging Markets

In the past few years, there has been a large increase in portfolio capital flows into emerging markets, mostly fueled by mutual funds and other institutional investors. Based on a simple variance ratio test, this paper finds that emerging stock markets as a group experienced a sharp increase in autocorrelation in total returns at a time when institutional investors began to expand significantly their holdings in these markets. These results are consistent with the view that institutional investor sentiment toward emerging markets as an asset class can at times play a critical role in determining asset prices, with shifts in sentiment resulting in periods of bubble-like booms and busts and asset price overshooting.
1.01 Carbanions of Alkali and Alkaline-Earth Cations: (II) Selectivity of Carbonyl Addition Reactions

The nucleophilic addition of Grignard and organolithium reagents to carbonyl compounds is one of the most widely used reactions in organic chemistry. Selectivity in these reactions to generate diastereomeric or enantiomeric products is influenced by the structure of the substrate and the reactant, as well as by the reaction mechanism. Advances in this area include a better understanding of the steric and electronic factors that govern addition of nucleophiles to chiral substrates; the development of chiral auxiliaries that consistently and efficiently generate one diastereomer over another and their conversion to enantiomerically pure products; the improvements in chiral reagents that predictably add to a carbonyl to afford one enantiomer; and the development of chiral ligands that can be used to control the stereoselectivity of reactions between achiral substrates and achiral ligands.
Fat Tails, Thin Tails, and Climate Change Policy

AbstractClimate policy is complicated by the considerable uncertainties concerning the benefits and costs of abatement. We do not even know the probability distributions for future temperatures and...
Biomechanics of vascular plant as template for engineering design

Abstract Plants are biocomposites with a hierarchical organization and multifunctionality with several unique characteristics different from animals (lack of motion and neurological control). Consequently, a stronger correlation is expected between plant structure, function, and mechanical response. Insights into these changes can have broad implications in bioinspired design, biobased material development, and precision agriculture. We use nanomechanical methods to investigate structural and compositional changes in sunflower plants at longitudinal stages of growth. Specifically, we use Scanning Electron Microscopy for microstructural analysis and Raman Spectroscopy and Optical Microscopy for compositional analysis. These studies together revealed several aspects of longitudinal growth. Specifically, the rapid plant growth during the vegetative stage (28% from week 4 to week 6) significantly slows (8%) during the transition to the reproductive stage (week 6 to week 8) and beyond. During the transition period, the thickness of the vascular zone increases significantly (28%), accompanied by modest thickening of cell walls (8%) and increases in cell diameter (4%), all of which impacting fluid flow dynamics. The increase in the vascular zone comes with a corresponding decrease internal flexible hydraulic chamber (17%), indicating the underlying mechanics of loss of flexibility. Raman spectroscopy revealed a high concentration of metabolites (molybdenum sulfide and crocetin), which are responsible for stress signaling and plant defense in the early stages of growth. Together, the study is first of its kind to quantify structure-composition changes of stem growth, Several engineering implications of the insight is discussed for applications varying from bioinspired design, biocomposites, and devices for precision agriculture.
Repeated Hurricanes Reveal Risks and Opportunities for Social-Ecological Resilience to Flooding and Water Quality Problems.

Hurricanes that damage lives and property can also impact pollutant sources and trigger poor water quality. Yet, these water quality impacts that affect both human and natural communities are diffi...
A framework for effective application of machine learning to microbiome-based classification problems

ABSTRACT Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs) (n = 490 patients, 261 controls and 229 cases). We developed a reusable open-source pipeline to train, validate, and interpret ML models. To show the effect of model selection, we assessed the predictive performance, interpretability, and training time of L2-regularized logistic regression, L1- and L2-regularized support vector machines (SVM) with linear and radial basis function kernels, a decision tree, random forest, and gradient boosted trees (XGBoost). The random forest model performed best at detecting SRNs with an area under the receiver operating characteristic curve (AUROC) of 0.695 (interquartile range [IQR], 0.651 to 0.739) but was slow to train (83.2 h) and not inherently interpretable. Despite its simplicity, L2-regularized logistic regression followed random forest in predictive performance with an AUROC of 0.680 (IQR, 0.625 to 0.735), trained faster (12 min), and was inherently interpretable. Our analysis highlights the importance of choosing an ML approach based on the goal of the study, as the choice will inform expectations of performance and interpretability. IMPORTANCE Diagnosing diseases using machine learning (ML) is rapidly being adopted in microbiome studies. However, the estimated performance associated with these models is likely overoptimistic. Moreover, there is a trend toward using black box models without a discussion of the difficulty of interpreting such models when trying to identify microbial biomarkers of disease. This work represents a step toward developing more-reproducible ML practices in applying ML to microbiome research. We implement a rigorous pipeline and emphasize the importance of selecting ML models that reflect the goal of the study. These concepts are not particular to the study of human health but can also be applied to environmental microbiology studies.
Planarity in higher-dimensional contact manifolds

We obtain several results for (iterated) planar contact manifolds in higher dimensions: (1) Iterated planar contact manifolds are not weakly symplectically semi-fillable. This generalizes a 3-dimensional result of Etnyre to a higher-dimensional setting. (2) They do not arise as nonseparating weak contact-type hypersurfaces in closed symplectic manifolds. This generalizes a result by Albers-Bramham-Wendl. (3) They satisfy the Weinstein conjecture, i.e. every contact form admits a closed Reeb orbit. This is proved by an alternative approach as that of [Acu], and is a higher-dimensional generalization of a result of Abbas-Cieliebak-Hofer. The results follow as applications from a suitable symplectic handle attachment, which bears some independent interest.
Repeat hepatic resections for colorectal metastases

We identified 106 patients who had undergone complete resection of isolated colorectal hepatic metastases. Nine of these patients subsequently underwent repeat liver resections for isolated hepatic recurrences. The median follow-up for these patients was 21 months. One postoperative death was related to the second hepatectomy. At the time of last follow-up, five patients were alive and free of recurrent disease at 9, 19, 31, 50, and 67 months after their second hepatic resection. The remaining three patients were alive, but disease had recurred 11 months after resection in the first patient, 12 months after resection in the second, and 18 months after resection in the third. Among these three patients, two had solitary pulmonary nodules, which were resected, and one had unresectable liver disease. Our experience and a review of the literature suggest that repeat hepatic resection for isolated colorectal metastases can result in long-term survival in selected patients. (SURGERY 1990;107:101-4.)
Beyond content knowledge: transferable skills connected to experience as a peer-leader in a PLTL program and long-term impacts

Being a successful peer-led team learning (PLTL) workshop leader involves developing content knowledge and workshop facilitation skills. These skills connected to being a peer leader, however, do not terminate at the end of one’s undergraduate program. In fact, many former peer leaders denote having been a peer leader on their LinkedIn profile. This study examines the transferable skills that former peer leaders identified as being valuable in their current positions. We conducted semi-structured interviews with former peer leaders from varying disciplines, universities, ages, and years since being a peer leader. Interview questions captured leadership experiences including successes and challenges of being peer leaders, roles and responsibilities, and specific transferable skills further developed by being peer leaders and how they are being utilized in the leaders’ current position. Thematic analyses of these interviews indicate that former peer leaders recognize leadership skills, coping with many challenges (including not having the right answer), collaboration/teamwork skills, self-confidence, and problem-solving skills as being relevant and frequently used in their current work.
Privately Answering Classification Queries in the Agnostic PAC Model.

We revisit the problem of differentially private release of classification queries. In this problem, the goal is to design an algorithm that can accurately answer a sequence of classification queries based on a private training set while ensuring differential privacy. We formally study this problem in the agnostic PAC model and derive a new upper bound on the private sample complexity. Our results improve over those obtained in a recent work [BTT18] for the agnostic PAC setting. In particular, we give an improved construction that yields a tighter upper bound on the sample complexity. Moreover, unlike [BTT18], our accuracy guarantee does not involve any blow-up in the approximation error associated with the given hypothesis class. Given any hypothesis class with VC-dimension $d$, we show that our construction can privately answer up to $m$ classification queries with average excess error $\alpha$ using a private sample of size $\approx \frac{d}{\alpha^2}\,\max\left(1, \sqrt{m}\,\alpha^{3/2}\right)$. Using recent results on private learning with auxiliary public data, we extend our construction to show that one can privately answer any number of classification queries with average excess error $\alpha$ using a private sample of size $\approx \frac{d}{\alpha^2}\,\max\left(1, \sqrt{d}\,\alpha\right)$. When $\alpha=O\left(\frac{1}{\sqrt{d}}\right)$, our private sample complexity bound is essentially optimal.
Budget-Constrained Bandits over General Cost and Reward Distributions

We consider a budget-constrained bandit problem where each arm pull incurs a random cost, and yields a random reward in return. The objective is to maximize the total expected reward under a budget constraint on the total cost. The model is general in the sense that it allows correlated and potentially heavy-tailed cost-reward pairs that can take on negative values as required by many applications. We show that if moments of order $(2+\gamma)$ for some $\gamma > 0$ exist for all cost-reward pairs, $O(\log B)$ regret is achievable for a budget $B>0$. In order to achieve tight regret bounds, we propose algorithms that exploit the correlation between the cost and reward of each arm by extracting the common information via linear minimum mean-square error estimation. We prove a regret lower bound for this problem, and show that the proposed algorithms achieve tight problem-dependent regret bounds, which are optimal up to a universal constant factor in the case of jointly Gaussian cost and reward pairs.
A branch-and-cut algorithm for mixed integer bilevel linear optimization problems and its implementation

In this paper, we describe a comprehensive algorithmic framework for solving mixed integer bilevel linear optimization problems (MIBLPs) using a generalized branch-and-cut approach. The framework presented merges features from existing algorithms (for both traditional mixed integer linear optimization and MIBLPs) with new techniques to produce a flexible and robust framework capable of solving a wide range of bilevel optimization problems. The framework has been fully implemented in the open-source solver MibS. The paper describes the algorithmic options offered by MibS and presents computational results evaluating the effectiveness of the various options for the solution of a number of classes of bilevel optimization problems from the literature.
Gender Differences in Hispanic Patients of Mexican Origin Hospitalized with Heart Failure

Abstract Background More than 3 million women in the United States die of heart failure (HF) annually. Women are significantly underrepresented in studies that inform practice guidelines, especially women hospitalized for HF despite the associated negative outcomes. HF is common in Hispanic people, the largest ethnic minority group in the United States, who are mostly of Mexican origin. There are no studies of gender differences in Mexican-Hispanic persons hospitalized for HF. We sought to describe gender differences in demographic and clinical characteristics, clinical presentation, treatment, in-hospital outcomes, and discharge status in Mexican-Hispanic patients hospitalized for HF. Methods We conducted a secondary analysis of data collected for a study examining readmission in patients hospitalized with HF in a 107-bed community; hospital near the U.S.–Mexico border. Results Of 155 self-identified Hispanic patients, 43.2% (n = 67) were women. Compared with men, women were equally affected by obesity, on average 6 years older (p Conclusions Key gender differences in chronic illness burden, treatment, and discharge status were found, highlighting the heterogeneity of women with HF and the need for further gender-specific research to develop care strategies specific to women of all races and ethnicities.
A quantitative assessment of communicating extra-terrestrial intelligent civilizations in the galaxy and the case of FRB-like signals

A formula is proposed to quantitatively estimate the signal emission rate of Communicating Extra-Terrestrial Intelligent civilizations (CETIs) in the Galaxy. I suggest that one possible type of CETI signal would be brief radio bursts similar to fast radio bursts (FRBs). A dedicated search for FRB-like artificial signals in the Galaxy for decades may pose a meaningful upper limit on the emission rate of these signals by CETIs. The Fermi-Hart paradox is answered in terms of not having enough observing times for this and other types of signals. Whether humans should send FRB-like signals in the far future is briefly discussed.
A positive characterization of rational maps

When is a topological branched self-cover of the sphere equivalent to a rational map on CP^1? William Thurston gave one answer in 1982, giving a negative criterion (an obstruction to a map being rational). We give a complementary, positive criterion: the branched self-cover is equivalent to a rational map if and only if there is an elastic spine that gets "looser" under backwards iteration. This completes a series announced in arXiv:1502.02561 and started in arXiv:1507.05294 and arXiv:1607.00340.
“Playing the whole game”: A data collection and analysis exercise with Google Calendar

We provide a computational exercise suitable for early introduction in an undergraduate statistics or data science course that allows students to ‘play the whole game’ of data science: performing b...
Testing influences of APOE and BDNF genes and heart failure on cognitive function

Abstract Background Apolipoprotein E (APOE) e2, e4 and brain-derived neurotrophic factor (BDNF) Val66Met alleles have been associated with cognition. Associations of these alleles with cognition in heart failure (HF) and influences of HF across the cognitive spectrum (i.e., cognitively normal to Alzheimer's dementia [AD]) remain unexplored. Objectives To investigate influences of APOE e2, e4, BDNF Met and HF on cognition among participants across the cognitive spectrum. Methods Genetic association study using national databases (N = 7,166). Results APOE e2 frequencies were similar across the cognitive spectrum among participants with HF. APOE e4 frequency was lower among participants with HF and AD than non-HF participants with AD. BDNF Met frequencies did not differ across the spectrum. HF was associated with worse attention and language. In the HF subsample, e4 was associated with worse memory. Conclusion Associations between APOE and cognition may differ in HF but need to be tested in a larger sample.
On locally primitively universal quadratic forms

A positive definite integral quadratic form is said to be almost (primitively) universal if it (primitively) represents all but at most finitely many positive integers. In general, almost primitive universality is a stronger property than almost universality. The two main results of this paper are (1) every primitively universal form nontrivially represents zero over every ring $$\mathbb {Z}_p$$ of p-adic integers, and (2) every almost universal form in five or more variables is almost primitively universal.