cssi/SciSciGPT-SciSciNet huggingface dataset:
Lessons Learned in Looking Back: Perspectives from Trailblazers

Although this is in many ways a good era for women, many things have yet to improve. Women continue to lag behind men with regard to salaries, are under-represented in position of leadership, and still take on greater responsibility for child rearing and family responsibilities. Careers dominated by women tend to be associated with lower salaries and lower prominence and by the same token, women tend to dominate leadership positions when the field becomes less prestigious. Although women now outnumber men in graduating classes, the feminization of clinical psychology brings new challenges. This special series comes out of the 2009 trailblazer panel held at the ABCT convention in New York City, which explored the experiences faced by pioneering women in behavior therapy and discussed ways to help women overcome the glass ceiling. This introductory paper summarizes the lessons learned by these trailblazers with a view toward helping women in academia today.
An experimental test of the crowding out hypothesis

We report the results of laboratory experiments that examine whether third-party contributions crowd out private giving to charity. Subjects play a single dictator game with a charity as the recipient. The subject chooses his preferred charity from a list. There are four treatment combinations: two initial allocations and two frames. Initial allocations are either US$18 for the subject and US$2 for the charity, or US$15 and US$5, respectively, and the subject is then given the opportunity to allocate additional funds if desired. The decision frame is also varied to affect subjects’ perceptions of the task. In one frame, subjects are simply informed of the initial allocations between themselves and their chosen charity. In the other, subjects are told that their US$20 allocation has been taxed, and the amount allocated to their chosen charity. The structure of payoffs is identical in both frames. In the first frame, we see a level of crowding out that is close to zero, far less than other experimental studies; in the second frame, we observe nearly 100% crowding out. D 2004 Elsevier B.V. All rights reserved.
Trade credit and the joint effects of supplier and customer financial characteristics

Abstract We examine how access to bank credit affects trade credit in the supplier–customer relationships of U.S. public firms. For identification, we use exogenous liquidity shocks to supplier firms in the form of staggered changes to interstate bank branching laws. Using a variety of tests, we show that supplier firms with greater access to banking liquidity offer more trade credit to their customers. We also show that when bank branching restrictions are relaxed in the supplier’s state, the supplier–customer relationship is more likely to survive.
Roscoe Pound in China: A Lost Precedent for the Liabilities of American Legal Exceptionalism

INTRODUCTION 77 I. POUND AND AMERICA IN REPUBLICAN CHINA 85 A. Pound’s Introduction to Chinese Law 85 B. Pound’s Arrival in China 91 II. POUND AS FOREIGN LEGAL REFORMER 99 A. Pound’s Evolutionary Legal Science 99 B. Pound’s Frustrations and Growing Ambitions 113 III. POUND AS STATESIDE PROPAGANDIST 118 A. Pound’s Public/Private Split 118 B. Pound’s Strategic Distortions 124 IV. POUND & LAW IN COLD WAR FOREIGN POLICY 128 A. Pound’s Anti-Communist Legalism 128 B. Pound’s Legacy as Failed Exporter 134 CONCLUSION 138
A moral solution to the moral hazard problem

Abstract In agency theory, offering a flat salary contract under unobservable effort creates a moral hazard problem because the agent is motivated to shirk and provide less than a previously agreed-upon level of effort. We examine a moral solution to this moral hazard problem. In particular, we present a principal-agent model where the agent possesses some level of moral sensitivity that causes him disutility if he provides less than the agreed-upon level of effort. We examine the interplay between moral sensitivity and firm productivity in determining the optimal salary contract, and contrast our moral solution with the traditional incentive solution that becomes necessary when moral sensitivity is assumed to be zero. This allows us to highlight the benefits of the agent’s moral sensitivity to both the principal and the agent, and thereby, point out the potential cost of ignoring this moral sensitivity. We conclude that adding moral sensitivity increases the descriptive, prescriptive, and pedagogical usefulness of the principal-agent model.
EQUILIBRIUM WAGE DISTRIBUTIONS

The observation that different firms charge different prices for what appears to be the same commodity or pay different wages for what appears to be equivalent labour has long been explained in economics by a reference to 'imperfect information'. This paper is concerned with characterising market equilibrium with imperfect information. We do not present a general theory; rather, we develop in some detail an example of importance in its own right -imperfect information in the labour market. Several properties of our example, in particular, the existence of equilibria with price (wage) dispersion, unemployment (excess supply of labour), positive profits, multiple equilibria and the nonoptimality or some of all of the equilibria, are of more general validity; other results may not be. This paper is concerned with two kinds of imperfect information: (a) Individuals may not know the wage paid in any particular firm, or whether there is a vacancy in any particular firm, until they apply for a job.1 (b) There are several characteristics besides the wage rate associated with a job which are important to the individual. Some, like the normal length of a work week, become known when the individual applies for the job; information about other characteristics (e.g. personalities of colleagues) is acquired only gradually. There is one important difference between the two kinds of imperfect in
Computer Network Attack and the Use of Force in International Law: Thoughts on a Normative Framework

This Chapter explores the acceptability under the jus ad bellum, that body of international law governing the resort to force as an instrument of national policy, of computer network attack. Analysis centers on the United Nations Charter’s prohibition of the use of force in Article 2(4), its Chapter VII security scheme, and the inherent right to self-defense codified in Article 51. Concluding that traditional applications of the use of force prohibition fail to adequately safeguard shared community values threatened by CNA, the Chapter proposes an alternative normative framework based on scrutiny of the consequences caused by such operations.
Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games

Firms in durable good product markets face incentives to intertemporally price discriminate, by setting high initial prices to sell to consumers with the highest willingness to pay, and cutting prices thereafter to appeal to those with lower willingness to pay. A critical determinant of the profitability of such pricing policies is the extent to which consumers anticipate future price declines, and delay purchases. I develop a framework to investigate empirically the optimal pricing over time of a firm selling a durable-good product to such strategic consumers. Prices in the model are equilibrium outcomes of a game played between forward-looking consumers who strategically delay purchases to avail of lower prices in the future, and a forward-looking firm that takes this consumer behavior into account in formulating its optimal pricing policy. The model outlines first, a dynamic model of demand incorporating forward-looking consumer behavior, and second, an algorithm to compute the optimal dynamic sequence of prices given these demand estimates. The model is solved using numerical dynamic programming techniques. I present an empirical application to the market for video-games in the US. The results indicate that consumer forward-looking behavior has a significant effect on optimal pricing of games in the industry. Simulations reveal that the profit losses of ignoring forward-looking behavior by consumers are large and economically significant, and suggest that market research that provides information regarding the extent of discounting by consumers is valuable to video-game firms.
Optimal Risk Management Using Options

This article provides an analytical solution to the problem of an institution optimally managing the market risk of a given exposure by minimizing its Value-atRisk using options. The optimal hedge consists of a position in a single option whose strike price is independent of the level of expense the institution is willing to incur for its hedging program. This optimal strike price depends on the distribution of the asset exposure, the horizon of the hedge, and the level of protection desired by the institution. Moreover, the costs associated with a suboptimal choice of exercise price are economically significant.
Clathrin senses membrane curvature

The ability of proteins to assemble at sites of high membrane curvature is essential to diverse membrane remodeling processes, including clathrin-mediated endocytosis. Multiple adaptor proteins within the clathrin pathway have been shown to sense regions of high membrane curvature, leading to local recruitment of the clathrin coat. Because clathrin triskelia do not bind to the membrane directly, it has remained unclear whether the clathrin coat plays an active role in sensing membrane curvature or is passively recruited by adaptor proteins. Using a synthetic tag to assemble clathrin directly on membrane surfaces, here we show that clathrin is a strong sensor of membrane curvature, comparable with previously studied adaptor proteins. Interestingly, this sensitivity arises from clathrin assembly rather than from the properties of unassembled triskelia, suggesting that triskelia have preferred angles of interaction, as predicted by earlier structural data. Furthermore, when clathrin is recruited by adaptors, its curvature sensitivity is amplified by 2- to 10-fold, such that the resulting protein complex is up to 100 times more likely to assemble on a highly curved surface compared with a flatter one. This exquisite sensitivity points to a synergistic relationship between the coat and its adaptor proteins, which enables clathrin to pinpoint sites of high membrane curvature, an essential step in ensuring robust membrane traffic. More broadly, these findings suggest that protein networks, rather than individual protein domains, are likely the most potent drivers of membrane curvature sensing.
A Spanning Tree-based Genetic Algorithm for Distribution Network Reconfiguration

This paper presents a spanning tree-based genetic algorithm (GA) for the reconfiguration of electrical distribution systems with the objective of minimizing active power losses. Due to low voltage levels at distribution systems, power losses are very high and sensitive to system configuration. Therefore, optimal reconfiguration is an important factor in the operation of distribution systems to minimize active power losses. Smart and automated electric distribution systems should be able to reconfigure as a response to changes in load levels to minimize active power losses. The proposed method searches spanning trees of potential configurations and finds the optimal spanning tree using genetic algorithm in two steps. In the first step, all the invalid combinations of branches and tie-lines (e.g. combinations which are not supplying power to some of loads) generated by initial population of GA are filtered out with the help of spanning tree search algorithm. In this second step, power flow analyses are performed for only those combinations that form spanning trees and the optimal configuration is determined based on the amount of active power losses (optimal configuration is one which results minimum power losses). The proposed method is implemented on several systems including the well-known 33-node and 69-node systems. The results show that the proposed method is accurate and efficient in comparison with existing methods.
Laissez les bons temps rouler? The persistent effect French civil law has on corruption, institutions, and incomes in Louisiana

Louisiana consistently ranks as one of the most corrupt states in the nation. In fact, the Pelican State is the most corrupt state when looking at the most common indicator of corruption: corruption convictions per 100,000. What is less clear about Louisiana is how the state became corrupt. This paper seeks to provide the missing link. I argue that the high levels of corruption in the state can be explained by its origins in French civil law. This historical influence has perverse and persistent effects on the state, despite occurring over 200 years ago. Through these origins in civil law, corruption in Louisiana impacts its economic institutions. These institutions then lead to a variety of other bad outcomes in the state such as a high dependency on oil and low incomes. This argument implies that resource dependency is bad for development only when institutional quality is low. By linking legal origins to corruption, institutions, and economic outcomes, I seek to offer a clearer explanation for why Louisiana sets itself apart from other states in its politically corrupt environment.
Analysis of the Alternating Direction Method of Multipliers for Nonconvex Problems

This work investigates the theoretical performance of the alternating-direction method of multipliers (ADMM) as it applies to nonconvex optimization problems, and in particular, problems with nonconvex constraint sets. The alternating direction method of multipliers is an optimization method that has largely been analyzed for convex problems. The ultimate goal is to assess what kind of theoretical convergence properties the method has in the nonconvex case, and to this end, theoretical contributions are twofold. First, this work analyzes the method with local optimal solution of the ADMM subproblems, which contrasts with much analysis that requires global solutions of the subproblems. Such a consideration is important to practical implementations. Second, it is established that the method still satisfies a local convergence result. The work concludes with some more detailed discussion of how the analysis relates to previous work.
A Single Cell Atlas of the Healthy Breast Tissues Reveal Clinically Relevant Clusters of Breast Epithelial Cells

Single cell RNA sequencing is an evolving field to elucidate cellular architecture of adult organs. Using normal breast tissues from healthy volunteers and a rapid procurement/processing/sequencing protocol, 13 breast epithelial cell clusters were identified. Approximately 90% of breast cancers were enriched for cell-of-origin signatures derived from differentiated luminal clusters and two minor luminal progenitor clusters. Expression of cell cycle and chromosome segregation-related genes were higher in one of the minor clusters and breast tumors with this cluster signature displayed the highest mutation rate and poor outcome. We identified TBX3 and PDK4 as genes co-expressed with estrogen receptor (ER) in the normal breasts and their expression analyses in >550 breast cancers enabled prognostically relevant cell-of-origin based subclassification of ER+ breast cancers.
Clinical signs associated with earlier diagnosis of children with autism Spectrum disorder

The objective of this study is to gain new insights into the relationship between clinical signs and age at diagnosis. We utilize a new, large, online survey of 1743 parents of children diagnosed with ASD, and use multiple statistical approaches. These include regression analysis, factor analysis, and machine learning (regression tree). We find that clinical signs that most strongly predict early diagnosis are not necessarily specific to autism, but rather those that initiate the process that eventually leads to an ASD diagnosis. Given the high correlations between symptoms, only a few signs are found to be important in predicting early diagnosis. For several clinical signs we find that their presence and intensity are positively correlated with delayed diagnosis (e.g., tantrums and aggression). Even though our data are drawn from parents’ retrospective accounts, we provide evidence that parental recall bias and/or hindsight bias did not play a significant role in shaping our results. In the subset of children without early deficits in communication, diagnosis is delayed, and this might be improved if more attention will be given to clinical signs that are not necessarily considered as ASD symptoms. Our findings also suggest that careful attention should be paid to children showing excessive tantrums or aggression, as these behaviors may interfere with an early ASD diagnoses.
Mechanical Responses of Primary-α Ti Grains in Polycrystalline Samples: Part I—Measurements of Spherical Indentation Stress–Strain Curves

Recently established spherical indentation stress–strain protocols have demonstrated the feasibility of measuring reliably the mechanical responses at different material structure length scales in a broad range of structural alloys. In the present study, we apply these high-throughput protocols on the primary α phase grains in polycrystalline samples of Ti-5Al-2.5Sn, Ti-8Al-1Mo-1 V, Ti-6Al-4 V, Ti-6Al-2Sn-4Zr-2Mo and Ti-6Al-2Sn-4Zr-6Mo to aggregate a large experimental dataset that documents systematically the effects of α-phase chemical composition and grain orientation on the measured values of indentation modulus and the indentation yield strength. This dataset is being offered to the materials community in an open repository to allow further analyses of the effect of chemical composition of the α -phase on its single-crystal elastic and plastic properties. This study establishes the feasibility and tremendous value of spherical indentation stress–strain protocols for documenting the grain-scale anisotropic mechanical responses of different α -phase compositions in high-throughput assays. In Part II of this paper, the dataset is analyzed with advanced statistical approaches to estimate the single-crystal elastic stiffness constants and the critical resolved shear strengths (CRSS) of the different Ti alloys studied.
The Performance of Time-Preference and Risk-Preference Measures in Surveys

Time preferences and risk preferences play an important role in a wide range of behavior, including financial decisions, entrepreneurship, and the proper incentivizing of agents. Numerous methods h...
A Natural Language Processing Tool to Extract Quantitative Smoking Status from Clinical Narratives

This study presents a natural language processing (NLP) tool to extract quantitative smoking information (e.g., Pack-Year, Quit Year, Smoking Year, and Pack per Day) from clinical notes and standardized them into Pack-Year unit. We annotated a corpus of 200 clinical notes from patients who had low-dose CT imaging procedures for lung cancer screening and developed an NLP system using a two-layer rule-engine structure. We divided the 200 notes into a training set and a test set and developed the NLP system only using the training set. The experimental results on the test set showed that our NLP system achieved the best F1 scores of 0.963 and 0.946 for lenient and strict evaluation, respectively.
Structural Tax Exceptionalism

Following the Supreme Court’s landmark decision in *Mayo Foundation for Medical Education and Research v. United States*, many scholars of tax law have declared that *Mayo* marks the death of tax exceptionalism. The tax exceptionalist view holds that because tax is different or special, generally applicable administrative law procedural rules and doctrines do not apply in the tax context. In *Mayo*, however, the Supreme Court held that generally applicable administrative law rules and doctrines do apply to the Treasury Department and the IRS. Contrary to the prevailing narrative that proclaims the death of tax exceptionalism, this Article posits that the reports of the death of tax exceptionalism are significantly exaggerated. Although it is certainly true that the *Mayo* Court rejected a tax-specific approach to administrative review, important aspects of tax procedure depart significantly from the standard template that Congress usually deploys to govern administrative action. For example, the Internal Revenue Code provides special rules for tax rulemaking as well as adjudication of tax controversies. The existing literature generally has examined these departures from the Administrative Procedure Act’s template largely in isolation from one another. This Article, in contrast, explores such tax-exceptional features as an interlocking whole and posits that the unique structural features of tax administration revive tax exceptionalism as a practical matter. Moreover, these departures arguably serve important policy goals. The Code’s relatively flexible approach to rulemaking, including a grant of retroactive rulemaking authority, helps the Treasury Department and the IRS provide taxpayers with the guidance that they need, prevent abuse, and treat similarly situated taxpayers equally; while the Code’s relatively ungenerous approach toward IRS fact finding affords taxpayers greater opportunity for individualized justice in court that reinforces taxpayer morale. In sum, though tax exceptionalism may technically be dead, the reality is much more complicated, and the tradeoffs of structural tax exceptionalism should not lightly be rejected.
Bayesian Restricted Likelihood Methods: Conditioning on Insufficient Statistics in Bayesian Regression

Bayesian methods have proven themselves to be successful across a wide range of scientific problems and have many well-documented advantages over competing methods. However, these methods run into difficulties for two major and prevalent classes of problems: handling data sets with outliers and dealing with model misspecification. We outline the drawbacks of previous solutions to both of these problems and propose a new method as an alternative. When working with the new method, the data is summarized through a set of insufficient statistics, targeting inferential quantities of interest, and the prior distribution is updated with the summary statistics rather than the complete data. By careful choice of conditioning statistics, we retain the main benefits of Bayesian methods while reducing the sensitivity of the analysis to features of the data not captured by the conditioning statistics. For reducing sensitivity to outliers, classical robust estimators (e.g., M-estimators) are natural choices for conditioning statistics. A major contribution of this work is the development of a data augmented Markov chain Monte Carlo (MCMC) algorithm for the linear model and a large class of summary statistics. We demonstrate the method on simulated and real data sets containing outliers and subject to model misspecification. Success is manifested in better predictive performance for data points of interest as compared to competing methods.
Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation

In this paper, we propose a general framework for distribution-free nonparametric testing in multi-dimensions, based on a notion of multivariate ranks defined using the theory of measure transportation. Unlike other existing proposals in the literature, these multivariate ranks share a number of useful properties with the usual one-dimensional ranks; most importantly, these ranks are distribution-free. This crucial observation allows us to design nonparametric tests that are exactly distribution-free under the null hypothesis. We demonstrate the applicability of this approach by constructing exact distribution-free tests for two classical nonparametric problems: (I) testing for mutual independence between random vectors, and (II) testing for the equality of multivariate distributions. In particular, we propose (multivariate) rank versions of distance covariance ((Szekely et al. [117]) and energy statistic (Szekely and Rizzo [116]) for testing scenarios (I) and (II) respectively. In both these problems we derive the asymptotic null distribution of the proposed test statistics. We further show that our tests are consistent against all fixed alternatives. Moreover, the proposed tests are computationally feasible and are well-defined under minimal assumptions on the underlying distributions (e.g., they do not need any moment assumptions). We also demonstrate the efficacy of these procedures via extensive simulations. In the process of analyzing the theoretical properties of our procedures, we end up proving some new results in the theory of measure transportation and in the limit theory of permutation statistics using Stein’s method for exchangeable pairs, which may be of independent interest.
Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently

Multi-channel sparse blind deconvolution, or convolutional sparse coding, refers to the problem of learning an unknown filter by observing its circulant convolutions with multiple input signals that are sparse. This problem finds numerous applications in signal processing, computer vision, and inverse problems. However, it is challenging to learn the filter efficiently due to the bilinear structure of the observations with respect to the unknown filter and inputs, as well as the sparsity constraint. In this paper, we propose a novel approach based on nonconvex optimization over the sphere manifold by minimizing a smooth surrogate of the sparsity-promoting loss function. It is demonstrated that manifold gradient descent with random initializations will probably recover the filter, up to scaling and shift ambiguity, as soon as the number of observations is sufficiently large under an appropriate random data model. Numerical experiments are provided to illustrate the performance of the proposed method with comparisons to existing ones.
Deep neural networks for choice analysis: A statistical learning theory perspective

Abstract Although researchers increasingly use deep neural networks (DNN) to analyze individual choices, overfitting and interpretability issues remain obstacles in theory and practice. This study presents a statistical learning theoretical framework to examine the tradeoff between estimation and approximation errors, and between the quality of prediction and of interpretation. It provides an upper bound on the estimation error of the prediction quality in DNN, measured by zero-one and log losses, shedding light on why DNN models do not overfit. It proposes a metric for interpretation quality by formulating a function approximation loss that measures the difference between true and estimated choice probability functions. It argues that the binary logit (BNL) and multinomial logit (MNL) models are the specific cases of DNNs, since the latter always has smaller approximation errors. We explore the relative performance of DNN and classical choice models through three simulation scenarios comparing DNN, BNL, and binary mixed logit models (BXL), as well as one experiment comparing DNN to BNL, BXL, MNL, and mixed logit (MXL) in analyzing the choice of trip purposes based on the National Household Travel Survey 2017. The results indicate that DNN can be used for choice analysis beyond the current practice of demand forecasting because it has the inherent utility interpretation and the power of automatically learning utility specification. Our results suggest DNN outperforms BNL, BXL, MNL, and MXL models in both prediction and interpretation when the sample size is large ( ≥ O ( 10 4 ) ), the input dimension is high, or the true data generating process is complex, while performing worse when the opposite is true. DNN outperforms BNL and BXL in zero-one, log, and approximation losses for most of the experiments, and the larger sample size leads to greater incremental value of using DNN over classical discrete choice models. Overall, this study introduces the statistical learning theory as a new foundation for high-dimensional data, complex statistical models, and non-asymptotic data regimes in choice analysis, and the experiments show the effective prediction and interpretation of DNN for its applications to policy and behavioral analysis.
Losing to Blackouts: Evidence from Firm Level Data

Many developing economies are often hit by electricity crises either because of supplyconstraints or lacking in broader energy market reforms. This study uses manufacturingfirm census data from Ethiopia to identify productivity losses attributable to powerdisruptions. Our estimates show that these disruptions, on average, result in productivitylosses of about 4-10 percent. We found nonlinear productivity losses at different quantilesalong the productivity distribution. Firms at higher quantiles faced higher losses comparedto firms around the median. We observed patterns of systematic shutdowns as firmsattempt to minimize losses.
The Rule of Advice in International Human Rights Law

Advisory jurisdiction is a ubiquitous feature of international human rights adjudication. Yet the attention of legal scholars is almost entirely devoted to contentious jurisdiction. This Article aims to fill that gap in the literature. By introducing two models of advisory jurisdiction, and analyzing the example of the Inter-American Court of Human Rights—the world's most active international advice-giver—the Article shows how international human rights courts may utilize advisory proceedings to influence state conduct, in a mechanism the Article calls “ruling through advice.” The Article also shows how human rights courts may attempt to guide states and national courts by means of an “anticipatory adjudication” mechanism. Using the Inter-American Court's groundbreaking advisory opinion on same-sex marriage as a case study, the Article argues that, despite the domestic implementation of its opinion, the Court misused its advisory powers, putting the regional human rights system at risk. The insights that both the conceptual model and the case study offer contribute to a broader conversation about international courts’ advisory role.
Effect of RNA on Morphology and Dynamics of Membraneless Organelles

Membraneless organelles (MLOs) are spatiotemporally regulated structures that concentrate multivalent proteins or RNA, often in response to stress. The proteins enriched within MLOs are often classified as high-valency "scaffolds" or low-valency "clients", with the former being associated with a phase-separation promoting role. In this study, we employ a minimal model for P-body components, with a defined protein-protein interaction network, to study their phase separation at biologically realistic low protein concentrations. Without RNA, multivalent proteins can assemble into solid-like clusters only in the regime of high concentration and stable interactions. RNA molecules promote cluster formation in an RNA-length-dependent manner, even in the regime of weak interactions and low protein volume fraction. Our simulations reveal that long RNA chains act as superscaffolds that stabilize large RNA-protein clusters by recruiting low-valency proteins within them while also ensuring functional "liquid-like" turnover of components. Our results suggest that RNA-mediated phase separation could be a plausible mechanism for spatiotemporally regulated phase separation in the cell.
Trace Reconstruction Problems in Computational Biology

The problem of reconstructing a string from its error-prone copies, the trace reconstruction problem , was introduced by Vladimir Levenshtein two decades ago. While there has been considerable theoretical work on trace reconstruction, practical solutions have only recently started to emerge in the context of two rapidly developing research areas: immunogenomics and DNA data storage. In immunogenomics, traces correspond to mutated copies of genes, with mutations generated naturally by the adaptive immune system. In DNA data storage, traces correspond to noisy copies of DNA molecules that encode digital data, with errors being artifacts of the data retrieval process. In this paper, we introduce several new trace generation models and open questions relevant to trace reconstruction for immunogenomics and DNA data storage, survey theoretical results on trace reconstruction, and highlight their connections to computational biology. Throughout, we discuss the applicability and shortcomings of known solutions and suggest future research directions.
Quantile Regression Neural Networks: A Bayesian Approach

This article introduces a Bayesian neural network estimation method for quantile regression assuming an asymmetric Laplace distribution (ALD) for the response variable. It is shown that the posterior distribution for feedforward neural network quantile regression is asymptotically consistent under a misspecified ALD model. This consistency proof embeds the problem from density estimation domain and uses bounds on the bracketing entropy to derive the posterior consistency over Hellinger neighborhoods. This consistency result is shown in the setting where the number of hidden nodes grow with the sample size. The Bayesian implementation utilizes the normal-exponential mixture representation of the ALD density. The algorithm uses Markov chain Monte Carlo (MCMC) simulation technique - Gibbs sampling coupled with Metropolis–Hastings algorithm. We have addressed the issue of complexity associated with the afore-mentioned MCMC implementation in the context of chain convergence, choice of starting values, and step sizes. We have illustrated the proposed method with simulation studies and real data examples.
Scalability of Streaming Anomaly Detection in an Unbounded Key Space Using Migrating Threads

Applications where streams of data are passed through large data structures are becoming of increasing importance. For instance network intrusion detection and cyber security as a whole rely on real time analysis of network traffic. Unfortunately, when implemented on conventional architectures such applications become horribly inefficient, especially when attempts are made to scale up performance via some sort of parallelism. An earlier paper discussed an implementation of the Firehose streaming benchmark that assumed only a bounded number of keys and datums. This paper discusses a significantly more complex (and more realistic) variant that analyzes continuously streaming samples from an unbounded range of keys. We utilize a novel migrating thread architecture in which threads may migrate as needed through a single system wide shared memory space, thereby avoiding conventional inefficiencies. As with the earlier paper, results are promising, with both far better scaling and increased performance over previously reported implementations, on a platform with considerably less intrinsic hardware computational resources.
Train tracks and measured laminations on infinite surfaces

Let $X$ be an infinite Riemann surface equipped with its conformal hyperbolic metric such that the action of the covering group $\pi_1(X)$ on $\tilde{X}$ is of the first kind-i.e., the surface $X$ is equal to its convex core. We first prove that any geodesic lamination on $X$ is nowhere dense. Given a fixed geodesic pants decomposition of $X$ we define a family of train tracks on $X$ such that any geodesic lamination of $X$ is weakly carried by at least one train track. Then we parametrize all measured laminations on $X$ carried by a train track by the corresponding edge weight systems on the train track. Furthermore, we show that the weak* topology on the measured laminations weakly carried by a train track corresponds to a pointwise (weak) convergence of the edge weight systems. When one considers the Teichmuller space $T(X)$ of the Riemann surface $X$, it is natural to restrict the attention to the space $ML_b(X)$ of bounded measured laminations. When $X$ has a bounded geometry, we prove that a measured lamination weakly carried by a train track is bounded if and only if the corresponding edge weight system has a finite supremum norm. The Teichmuller space considerations lead to a natural uniform weak* topology on the space of bounded measured laminations on $X$. We prove that the correspondence between bounded measured laminations weakly carried by a train track and their edge weight systems is a homeomorphism when $ML_b(X)$ is equipped with the uniform weak* topology and the edge weight system is equipped with the topology induced by the supremum norm.
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering.

The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG. Here we propose a new model, QA-GNN, which addresses the above challenges through two key innovations: (i) relevance scoring, where we use LMs to estimate the importance of KG nodes relative to the given QA context, and (ii) joint reasoning, where we connect the QA context and KG to form a joint graph, and mutually update their representations through graph-based message passing. We evaluate QA-GNN on the CommonsenseQA and OpenBookQA datasets, and show its improvement over existing LM and LM+KG models, as well as its capability to perform interpretable and structured reasoning, e.g., correctly handling negation in questions.
Understanding the Diverging User Trajectories in Highly-related Online Communities during the COVID-19 Pandemic.

As the COVID-19 pandemic is disrupting life worldwide, related online communities are popping up. In particular, two “new” communities, /r/China flu and /r/Coronavirus, emerged on Reddit and have been dedicated to COVID- related discussions from the very beginning of this pandemic. With /r/Coronavirus promoted as the official community on Reddit, it remains an open question how users choose between these two highly-related communities. In this paper, we characterize user trajectories in these two communities from the beginning of COVID-19 to the end of September 2020. We show that new users of /r/China flu and /r/Coronavirus were similar from January to March. After that, their differences steadily increase, both in language distance and membership prediction, as the pandemic continues to unfold. Furthermore, users who started at /r/China flu from January to March were more likely to leave, while those who started in later months tend to remain highly “loyal”. To understand this difference, we develop a movement analysis framework to understand membership changes in these two communities and identify a significant proportion of /r/China flu members (around 50%) that moved to /r/Coronavirus in February. This movement turns out to be highly predictable based on other subreddits that users were previously active in. Our work demonstrates how two highly-related communities emerge and develop their own identity in a crisis, and highlights the important role of existing communities in understanding such an emergence.
Dynamic Surface Measurements on a Model Helicopter Rotor During Blade Slap at High Angles of Attack

The modern helicopter offers a unique operational capability to both the public and private sectors. However, the use of the helicopter may become severely limited due to the radiated noise generated by the rotor system. A description is presented of some of the experimental results obtained with a model helicopter rotor in an anechoic wind tunnel with regard to blade stall as a source mechanism of blade slap. Attention is given to dynamic rotor blade surface phenomena and the resulting far field impulsive noise from the model helicopter rotor at high angles of attack and low tip speed. The results of the investigation strongly implicates the boundary layer as playing an important role in blade slap due to blade/vortex interaction (BVI) in a highly loaded rotor. Intermittent stall cannot be ruled out as a possible source mechanism for blade slap. This implies that blade surface characteristics, airfoil shape and local Reynolds number may now be used as tools to reduce the resultant far-field sound pressure levels in helicopters.
Excessive matrix metalloproteinase-1 and hyperactivation of endothelial cells occurred in COVID-19 patients and were associated with the severity of COVID-19.

Systemic vascular injury occurs in COVID-19 patients, yet the underlying mechanisms remain unknown. To clarify the role of inflammatory factors in COVID-19 vascular injury, we used a multiplex immunoassay to profile 65 inflammatory cytokines/chemokines/growth factors in plasma samples from 24 hospitalized (severe/critical) COVID-19 patients, 14 mild/moderate cases, and 13 healthy controls (HCs). COVID-19 patients had significantly higher plasma levels of 20 analytes than HCs. Surprisingly, only one cytokine (MIF) was among these altered analytes, while the rest were chemokines/growth factors. Additionally, only MMP-1 and VEGF-A were significantly elevated in hospitalized COVID-19 patients when compared to mild/moderate cases. We further studied MMP-1 enzymatic activity and multiple endothelial cell (EC) activation markers (soluble forms of CD146, ICAM-1, and VCAM-1) and found that they were highly dysregulated in COVID-19 patients. Thus, COVID-19 patients have a unique inflammatory profile, and excessive MMP-1 and hyperactivation of ECs are associated with the severity of COVID-19.
Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization

Microbiome studies have uncovered associations between microbes and human, animal, and plant health outcomes. This has led to an interest in developing microbial interventions for treatment of disease and optimization of crop yields which requires identification of microbiome features that impact the outcome in the population of interest. That task is challenging because of the high dimensionality of microbiome data and the confounding that results from the complex and dynamic interactions among host, environment, and microbiome. In the presence of such confounding, variable selection and estimation procedures may have unsatisfactory performance in identifying microbial features with an effect on the outcome. In this manuscript, we aim to estimate population-level effects of individual microbiome features while controlling for confounding by a categorical variable. Due to the high dimensionality and confounding-induced correlation between features, we propose feature screening, selection, and estimation conditional on each stratum of the confounder followed by a standardization approach to estimation of population-level effects of individual features. Comprehensive simulation studies demonstrate the advantages of our approach in recovering relevant features. Utilizing a potential-outcomes framework, we outline assumptions required to ascribe causal, rather than associational, interpretations to the identified microbiome effects. We conducted an agricultural study of the rhizosphere microbiome of sorghum in which nitrogen fertilizer application is a confounding variable. In this study, the proposed approach identified microbial taxa that are consistent with biological understanding of potential plant-microbe interactions. Standardization enables more accurate identification of individual microbiome features with an effect on the outcome of interest compared to other variable selection and estimation procedures when there is confounding by a categorical variable.
Performance of a Magnetic Multipole Line-Cusp Argon Ion Thruster

A 17 cm diameter line cusp ion thruster was evaluated with inert gases which are candidate propellants for on orbit and orbit transfer propulsion functions for Large Space Systems. A semiempirical relationship was generated to predict thruster beam current in terms of plasma parameters which would allow initial thruster optimization without ion extraction and the associated large vacuum facilities. The sensitivity of performance to changes in discharge electrode configurations and magnetic circuit was evaluated and is presented. After final optimization a propellant utilization efficiency of 0.9 at a discharge chamber power expenditure of about 260 w per beam ampere was obtained. These performance parameters are the highest yet achieved with argon propellant.
Spatial Signal Strength Prediction using 3D Maps and Deep Learning

Machine learning (ML) and artificial neural networks (ANNs) have been successfully applied to simulating complex physics by learning physics models thanks to large data. Inspired by the successes of ANNs in physics modeling, we use deep neural networks (DNNs) to predict the radio signal strength field in an urban environment. Our algorithm relies on samples of signal strength collected across the prediction space and a 3D map of the environment, which enables it to predict the scattering of radio waves through the environment. While already extensive body of research exists in spatial signal strength prediction, our approach differs from most existing approaches in that it does not require the knowledge of the transmitter location, it does not require side channel information such as attenuation and shadowing parameters, and it is the first work, to the best of our knowledge, to use 3D maps to accomplish the task of signal strength prediction.
Multi-resolution Outlier Pooling for Sorghum Classification

Automated high throughput plant phenotyping involves leveraging sensors, such as RGB, thermal and hyperspectral cameras (among others), to make large scale and rapid measurements of the physical properties of plants for the purpose of better understanding the difference between crops and facilitating rapid plant breeding programs. One of the most basic phenotyping tasks is to determine the cultivar, or species, in a particular sensor product. This simple phenotype can be used to detect errors in planting and to learn the most differentiating features between cultivars. It is also a challenging visual recognition task, as a large number of highly related crops are grown simultaneously, leading to a classification problem with low inter-class variance. In this paper, we introduce the Sorghum-100 dataset, a large dataset of RGB imagery of sorghum captured by a state-of-the-art gantry system, a multi-resolution network architecture that learns both global and fine-grained features on the crops, and a new global pooling strategy called Dynamic Outlier Pooling which outperforms standard global pooling strategies on this task.