cssi/SciSciGPT-SciSciNet huggingface dataset:
Does Auditor Explanatory Language in Unqualified Audit Reports Indicate Increased Financial Misstatement Risk

ABSTRACT: According to auditing standards, explanatory language added at the auditor's discretion to unqualified audit reports should not indicate increased financial misstatement risk. However, an auditor is unlikely to add language that would strain the auditor-client relationship absent concerns about the client's financial statements. Using a sample of 30,825 financial statements issued with unqualified audit opinions during 2000–2009, we find that financial statements with audit reports containing explanatory language are significantly more likely to be subsequently restated than financial statements without such language. We find that this positive association is driven by language that references the division of responsibility for performance of the audit, adoption of new accounting principles, and previous restatements. In addition, we find that (1) “emphasis of matter” language that discusses mergers, related-party transactions, and management's use of estimates predicts restatements related to ...
A model of liability dollarization and myopic governments

Abstract Liability dollarization of the domestic banking system represents a source of vulnerability for emerging market countries. The root cause is a lack of faith in the domestic currency, which ultimately stems from the belief that the government will not follow policies that promote long-run currency stability. This paper presents a model in which government myopia determines the unofficial dollarization of bank credit. Specifically, myopic politicians will choose low interest rates to expand short-run output in order to get re-elected, but this choice has the long-run consequence of increasing dollar lending. Increased liability dollarization is shown to force the hand of future decision-makers into choosing fixed exchange rates because of the fear that large depreciations will destroy balance sheets. The results imply that institutional reforms are necessary to reverse liability dollarization.
Red Alert: Prenatal Stress and Plans to Close Military Bases

In May 2005 the US military announced a restructuring plan called Base Realignment and Closure (BRAC). Some areas were projected to lose 20 percent of employment, sparking much distress. Previous r...
Board Reforms with a Japanese Twist: Viewing the Japanese Board of Directors with a Delaware Lens

Japan has a unique and often mystifying corporate governance structure. Still, foreign investors put money in their companies, hedge funds hound large inefficient entities, and American lawyers inundate Tokyo. Shedding its agricultural backbone in the late 19th century, Japan has always been in the spotlight either because of its militarist agenda, its phenomenal recovery, or its growth and dominance of the economic center stage. After its defeat in the Second World War, Japan pioneered a program that permitted it to recover quickly from its ruins. Other than a strong government and a highly subservient people, it needed a stable and reliable corporate atmosphere to fuel its growth. The result was the J-Form: Intertwined or cross-shareholding, dependence on a main bank for financing, the keiretsu system or the horizontal network of interlinked corporations, mutual protection against hostile takeovers, constant government monitoring and patriarchy, suppression of shareholder interest, rubberstamp and overstaffed boards of directors, exorbitant derivative suit filing fees, and extreme trust on managers. It was stakeholder-oriented model that allocated the corporate pie to all of the stakeholders.The result was a stable atmosphere that led to a gradual enlargement of its economy. But Japan’s corporate governance was seen as too paternalistic, managerialist and inefficient. Its board of directors was turned into the dumping ground of loyal senior managers who concurrently held both director and employee positions. The board was top heavy with insiders and grey directors. It could not and did not perform any monitoring function.This paper researches the evolution of the Japanese board of directors and pinpoints the factors that hinder its development. It tackles the composition of the board, whether it is monitoring or managing, and the existence of grey directors. It discusses the reforms that have been underway in the last few decades. It resolves that despite the legislative reforms and adoption of the committee system (with optional implementation), the Japanese board is still not independent, filled with insiders, and convoluted with grey directors. It is a managing board instead of a monitoring one. This paper prescribes remedies that may be attainable within a reasonable timeframe, provided that more reformists enter the scene. The unmistakable grey hues permeating the Japanese boards must be replaced by the neutrally-toned independent directors, lest Japan wants to be known as the new Potemkin Village.
Investor Relations, Firm Visibility, and Investor Following

ABSTRACT: We examine the actions and outcomes of investor relations (IR) programs in smaller, less-visible firms. Through interviews with IR professionals, we learn that IR strategies have a common goal of attracting institutional investors and that direct access to management, rather than increased disclosure, is viewed as the key driver of the strategy's success. We test for the effects of IR programs by examining small-cap companies that hired IR firms in a differences-in-differences research design with controls for changes in disclosure and determinants of the decision to initiate IR. Relative to a matched sample of control firms, we find that companies initiating IR programs exhibit greater increases in institutional investor ownership and a shift toward investors that normally would not follow the companies. We also find greater improvements in analyst following, media coverage, and the book-to-price ratio. Our results indicate that IR activities successfully improve visibility, investor following,...
Is Smaller Better? A Comment on "Comparative Economic Impact Analyses" by Michael Mondello and Patrick Rishe

In a recent article in Economic Development Quarterly, “Comparative Economic Impact Analyses: Differences Across Cities, Events, and Demographics” (November 2004), Michael Mondello and Patrick Rishe examined the economic impact of small, amateur sporting events on host economies. This response to their work suggests four reasons why ex ante economic impact estimates for smaller sporting events may come closer to matching ex post reality than those for “mega-events” including less crowding out, lower hosting costs, higher multipliers, and less incentive for boosters to published inflated figures.
How I Decide Depends on What I Spend: Use of Heuristics Is Greater for Time than for Money

We demonstrate that decision making is more heuristic in situations that involve spending time rather than money. Relative to participants in the money condition, those in the time condition show a higher propensity to choose a compromise option (experiment 1) and to rely on an arbitrary anchor (experiment 2). We propose that such heuristics are used more for time because, compared to monetary expenditures, temporal expenditures are harder to account for. Consistent with this proposition, when participants in both time and money conditions are primed to account for their expenditures, they no longer differ in their use of heuristics. The associated response times offer additional process evidence (experiment 3).
Are Voters Rational on the Margin? A Spatial Analysis of Voter Turnout in U.S. Presidential Elections

This study examines voter turnout in U.S. presidential elections from 1960-2008. Specifically, this study contributes to the literature by using a panel spatial econometric model to investigate whether, on the margin, voters behave rationally (increased voter turnout) when the probability of their vote being decisive is greater. The results show that voter turnout is greater when the margin of victory is small, when individual votes have a greater weight in the election (smaller ratio of electoral votes to number of voters), and when states switched away from a winner-take-all system to a congressional district system of allocating their electoral votes.
Risk-Constrained Dynamic Active Portfolio Management

Active portfolio management is concerned with objectives related to the outperformance of the return of a target benchmark portfolio. In this paper, we consider a dynamic active portfolio management problem where the objective is related to the tradeoff between the achievement of performance goals and the risk of a shortfall. Specifically, we consider an objective that relates the probability of achieving a given performance objective to the time it takes to achieve the objective. This allows a new direct quantitative analysis of the risk/return tradeoff, with risk defined directly in terms of probability of shortfall relative to the benchmark, and return defined in terms of the expected time to reach investment goals relative to the benchmark. The resulting optimal policy is a state-dependent policy that provides new insights. As a special case, our analysis includes the case where the investor wants to minimize the expected time until a given performance goal is reached subject to a constraint on the shortfall probability.
Good, bad, and ugly colonial activities : studying development across the Americas

Levels of economic development vary widely within countries in the Americas. We argue that this variation can be explained by differences in institutions which in turn have their roots in the colonial era. Colonizers engaged in different economic activities in different regions of a country, depending on the local conditions and the supply of native labor. Some activities, such as mining and sugar cultivation, where "bad" in the sense that they depended heavily on the exploitation of labor and created extractive institutions, while "good" activities created inclusive institutions. We show that areas with bad colonial activities have 13 percent lower GDP per capita today than areas with good colonial activities. Moreover, areas that had high pre-colonial population density have lower output per capita today, independent of the type of colonial activity. We attribute this to the "ugly" fact that colonizers used the pre-colonial population as an exploitable resource, thereby also creating extractive institutions. We present some evidence that the intermediating factor between history and current development is related to institutional differences across regions and not to income inequality or the current ethnic composition of the population.
Modeling Customer Lifetimes with Multiple Causes of Churn

Customer retention and customer churn are key metrics of interest to marketers, but little attention has been placed on linking the different reasons for which customers churn to their value to a contractual service provider. In this paper, we put forth a hierarchical competing-risk model to jointly model when customers choose to terminate their service and why. Some of these reasons for churn can be influenced by the firm e.g., service problems or price--value trade-offs, but others are uncontrollable e.g., customer relocation and death. Using this framework, we demonstrate that the impact of a firm's efforts to reduce customer churn for controllable reasons is mitigated by the prevalence of uncontrollable ones, resulting in a “damper effect” on the return from a firm's retention marketing efforts. We use data from a provider of land-based telecommunication services to demonstrate how the competing-risk model can be used to derive a measure of the incremental customer value that a firm can expect to accrue through its efforts to delay churn, taking this damper effect into account. In addition to varying across customers based on geodemographic information, the magnitude of the damper effect depends on a customer's tenure to date. We discuss how our framework can be used to tailor the firm's retention strategy to individual customers, both in terms of which customers to target and when retention efforts should be deployed.
Nifty Futures Rollover Strategies

As expiration dates for Nifty stock index futures approach, trading volume in calendar spreads rises at times to over 50% of the total daily volume, making this transaction particularly important to execute efficiently for investors rolling over sizable positions into deferred month contracts. In this study, futures data covering 12 recent Nifty futures expirations was used for rollover strategy construction, analysis, and testing. We reported here on the comparison of findings for three different strategies: optimally rolling an amount that minimized average calendar spread volatility subject to practical trading constraints, rolling an equal number of Nifty futures daily, and rolling all contracts on a single day. We found, in all cases, that the optimal strategy outperformed the other two commonly employed rollover strategies. Practical guidelines for rolling Nifty futures positions were also discussed.
Judging Multidistrict Litigation

High-stakes multidistrict litigations saddle the transferee judges who manage them with an odd juxtaposition of power and impotence. On one hand, judges appoint and compensate lead lawyers (who effectively replace parties’ chosen counsel) and promote settlement with scant appellate scrutiny or legislative oversight. But on the other, without the arsenal that class certification once afforded, judges are relatively powerless to police the private settlements they encourage. Of course, this power shortage is of little concern since parties consent to settle.
The Wonderful World of Disney Visas

International workers play an important role in perpetuating the carefully crafted fantasy that to visit the Walt Disney World Resort in Orlando, Florida is to be transported to far-off destinations around the globe. This Article examines how Disney has filled its need for these workers in two ways. For one, Disney has used a blend of chutzpah and ingenuity to forge new federal law establishing the Q visa. Additionally, Disney has dexterously used the existing J visa, along with an on-resort academic program, to bring international workers to Florida as students. An examination of Disney‘s immigration practices offers insight into the larger questions of who designs and benefits from immigration laws. These questions are particularly worthy of attention given the current call for federal immigration reform. I proceed by detailing the history of the Q visa law, which was designed by Disney for its own needs—namely, to authorize ―cultural representatives‖ to travel to the United States for short durations and to work in jobs where they share aspects of their home countries with the American public. This present study is the first historical treatment of the Q visa in the literature. I then discuss what Disney has appropriated from its custom-designed immigration program. Next, I look at the J visa and how Disney has exploited it by analyzing the history of the J visa, which was created during the Cold War to cultivate an appreciation for and familiarity with American society. I then look at Disney‘s International College Program, which is intended to provide compliance with the J visa law while ensuring a ready stream of available labor for Disney‘s mammoth Florida resort operations. A thorough exploration of the facts shows that Disney‘s International College Program is not consistent with the original statutory intent. Scrutiny of Disney‘s Q and J visa programs highlights weaknesses in our current immigration system and illustrates how those flaws might affect future immigration reforms.
a dynamic model of the limit order book

of a Markov equilibrium in which the bid and ask prices depend only on the numbers of buy and sell orders in the book, and which can be characterized in closed-form in several cases of interest. My model generates empirically verified implications for the shape of the limit-order book and the dynamics of prices and trades. In particular, I show that buy and sell orders can cluster away from the bid-ask spread, thus generating a hump-shaped limit-order book. Also, following a market buy order, both the ask and bid prices increase, with the ask increasing more than the bid—hence the spread widens.
Bayesian Model Averaging for Spatial Econometric Models

We extend the literature on Bayesian model comparison for ordinary least-squares regression models to include spatial autoregressive and spatial error models. Our focus is on comparing models that consist of different matrices of explanatory variables. A Markov Chain Monte Carlo model composition methodology labeled MC3 by Madigan and York is developed for two types of spatial econometric models that are frequently used in the literature. The methodology deals with cases where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. Estimates and inferences are produced by averaging over models using the posterior model probabilities as weights, a procedure known as Bayesian model averaging. We illustrate the methods using a spatial econometric model of origin–destination population migration flows between the 48 U.S. states and the District of Columbia during the 1990–2000 period.
Does multinationality matter? Implications of operational hedging for the exchange risk exposure

An important issue in global corporate risk management is whether the multinationality of a firm matters in terms of its effect on exchange risk exposure. In this paper, we examine the exchange risk exposure of US firms during 1983–2006, comparing multinational and non-multinational firms and focusing on the role of operational hedging. Since MNCs and non-multinationals differ in size and other characteristics, we construct matched samples of MNCs and non-multinationals based on the propensity score method. We find that the multinationality in fact matters for a firm’s exchange exposure but not in the way usually presumed – the exchange risk exposures are actually smaller and less significant for MNCs than nonmultinationals. The results are robust with respect to different samples and model specifications. There is evidence that operational hedging decreases a firm’s exchange risk exposure and increases its stock returns. The effective deployment of operational risk management strategies provides one reason why MNCs may have insignificant exchange risk exposure estimates.
Foreword: Critical Race Theory and Empirical Methods Conference

Everyone seems to be talking about race. From the protests that erupted in cities across the country over the failure of grand juries in Missouri and New York to indict police officers in the killing of two unarmed black men, to the racially charged statements made by the owners of professional sports teams; and the college fraternity members captured on film singing a racist lynching song; race exploded into the nation’s collective consciousness. Even the Starbucks Coffee chain’s recent “Race Together” campaign, intended to promote discussion about race, sparked a controversy and was quickly withdrawn. These and other events have propelled race to the top of the national media and policy agendas and made it the topic of dinner table and water cooler conversations throughout the United States. Still, broad disagreement remains, particularly between whites and racial minorities, over what these events mean with respect to contemporary race relations.This dissonance reveals the country’s deep and persistent racial divide. It also raises questions about the operation of race in social relationships and government practices, including the influence of race on public perceptions of criminality and the availability of socioeconomic opportunity — issues that go to the heart of democracy in America. Indeed, despite claims that the election of the nation’s first black President signals the declining significance of race, current events lay bare its continued relevance and suggest the need for a means of measuring, analyzing, and addressing the complicated ways in which race and racial bias remain powerful forces in twenty-first-century America.This is, therefore, a particularly opportune time for the Fordham Law Review to publish this Critical Race Theory and Empirical Methods symposium, which brings together scholars from the law, humanities, and social sciences to engage critically and articulate innovative analytical frameworks for the examination of race and identity. This essay explores the origins of Critical race theory and empirical methods (“eCRT”) as an area of academic inquiry and introduces the symposium articles that showcase the incredible diversity of this scholarship.
Law versus Morality as Regulators of Conduct

It is evident that both law and morality serve to channel our behavior. Law accomplishes this primarily through the threat of sanctions if we disobey legal rules. Morality too involves incentives: bad acts may result in guilt and disapprobation, and good acts may result in virtuous feelings and praise. These two very different avenues of effect on our actions are examined in this article from an instrumental perspective. The analysis focuses on various social costs associated with law and morality, and on their effectiveness, as determined by the magnitude and likelihood of sanctions and by certain informational factors. After the relative character of law and of morality as means of control of conduct is assessed, consideration is given to their theoretically optimal domains—to where morality alone would appear to be best to control behavior, to where morality and the law would likely be advantageous to employ jointly, and to where solely the law would seem desirable to utilize. The observed pattern of use of morality and of law is discussed, and it is tentatively suggested that the observed and the optimal patterns are in rough alignment with one another.
Fitness Tax Credits: Costs, Benefits, and Viability

As the number of overweight and obese Americans rises, it becomes increasingly clear that Americans need further incentives to stimulate lasting lifestyle changes. Tax incentives focused on exercise, which have been largely unexplored to this point, are an effective response to the growing obesity problem in the United States that would largely avoid the special-interest opposition that tax policies focused on diet have encountered. In addition, they would also provide a more palatable solution for the taxpayer beneficiaries with a relatively low impact on government revenues. Viable tax incentives to encourage greater fitness include tax credits and sales tax breaks, and in the alternative direct subsidies can be used to facilitate similar behavior. This Article posits that from this menu of options, tax credits to encourage exercise stand to create the most lasting positive results. This Article further presents a proposed fitness tax credit usable by state, local, or national government bodies and demonstrates that the most effective way to garner support for a fitness-based tax credit in the United States is to couple it with a broader emphasis on healthier lifestyles and to emphasize its potential long-term revenue gains.
Electromagnetic plasma wave emissions from the auroral field lines.

Several types of electromagnetic waves are known to be emitted by charged particles on the auroral field lines. In this paper we review the most important types of auroral radio emissions, both from a historical perspective as well as considering the latest results. Particular emphasis is placed on four types of electromagnetic emissions which are directly associated with the plasma on the auroral field lines. These emissions are (1) auroral hiss, (2) saucers, (3) ELF noise bands, and (4) auroral kilometric radiation. Ray tracing and radio direction finding measurements indicate that both the auroral hiss and auroral kilometric radiation are generated along the auroral field lines relatively close to the earth, at radial distances from about 2.5 to 5 RE, probably in direct association with the acceleration of auroral particles by parallel electric fields. The exact mechanism by which these radio emissions are generated has not been firmly established. For the auroral hiss the favored mechanism appears to be amplified Cerenkov radiation. For the auroral kilometric radiation several mechanisms have been proposed, usually involving the intermediate generation of electrostatic waves by the precipitating electrons.
Online AUC Optimization for Sparse High-Dimensional Datasets

The Area Under the ROC Curve (AUC) is a widely used performance measure for imbalanced classification arising from many application domains where high-dimensional sparse data is abundant. In such cases, each $d$ dimensional sample has only $k$ non-zero features with $k\ll d$ , and data arrives sequentially in a streaming form. Current online AUC optimization algorithms have high per-iteration cost $\mathcal{O}(d)$ and usually produce non-sparse solutions in general, and hence are not suitable for handling the data challenge mentioned above. In this paper, we aim to directly optimize the AUC score for high-dimensional sparse datasets under online learning setting and propose a new algorithm, FTRL-AUC. Our proposed algorithm can process data in an online fashion with a much cheaper per-iteration cost $\mathcal{O}(k)$ , making it amenable for high-dimensional sparse streaming data analysis. Our new algorithmic design critically depends on a novel reformulation of the U-statistics AUC objective function as the empirical saddle point reformulation, and the innovative introduction of the “lazy update” rule so that the per-iteration complexity is dramatically reduced from $\mathcal{O}(d)$ to $\mathcal{O}(k)$ . Furthermore, FTRL-AUC can inherently capture sparsity more effectively by applying a generalized Follow-The-Regularized-Leader (FTRL) framework. Experiments on real-world datasets demonstrate that FTRL-AUC significantly improves both run time and model sparsity while achieving competitive AUC scores compared with the state-of-the-art methods. Comparison with the online learning method for logistic loss demonstrates that FTRL-AUC achieves higher AUC scores especially when datasets are imbalanced. Experiments on real-world datasets demonstrate that FTRL-AUC significantly improves both run time and model sparsity while achieving competitive AUC scores compared with the state-of-the-art methods. Comparison with the online learning method for logistic loss demonstrates that FTRL-AUC achieves higher AUC scores especially when datasets are imbalanced.
Statistical Methods in Astronomy

We present a review of data types and statistical methods often encountered in astronomy. The aim is to provide an introduction to statistical applications in astronomy for statisticians and computer scientists. We highlight the complex, often hierarchical, nature of many astronomy inference problems and advocate for cross-disciplinary collaborations to address these challenges.
Reducing Confusion in Active Learning for Part-Of-Speech Tagging

Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. This is now an essential tool for building low-resource syntactic analyzers such as part-of-speech (POS) taggers. Existing AL heuristics are generally designed on the principle of selecting uncertain yet representative training instances, where annotating these instances may reduce a large number of errors. However, in an empirical study across six typologically diverse languages (German, Swedish, Galician, North Sami, Persian, and Ukrainian), we found the surprising result that even in an oracle scenario where we know the true uncertainty of predictions, these current heuristics are far from optimal. Based on this analysis, we pose the problem of AL as selecting instances which maximally reduce the confusion between particular pairs of output tags. Extensive experimentation on the aforementioned languages shows that our proposed AL strategy outperforms other AL strategies by a significant margin. We also present auxiliary results demonstrating the importance of proper calibration of models, which we ensure through cross-view training, and analysis demonstrating how our proposed strategy selects examples that more closely follow the oracle data distribution.
Implicit Multirate GARK Methods

This work considers multirate generalized-structure additively partitioned Runge–Kutta methods for solving stiff systems of ordinary differential equations with multiple time scales. These methods treat different partitions of the system with different timesteps for a more targeted and efficient solution compared to monolithic single rate approaches. With implicit methods used across all partitions, methods must find a balance between stability and the cost of solving nonlinear equations for the stages. In order to characterize this important trade-off, we explore multirate coupling strategies, problems for assessing linear stability, and techniques to efficiently implement Newton iterations for stage equations. Unlike much of the existing multirate stability analysis which is limited in scope to particular methods, we present general statements on stability and describe fundamental limitations for certain types of multirate schemes. New implicit multirate methods up to fourth order are derived, and their accuracy and efficiency properties are verified with numerical tests.
Not-so-normal variants

In contrast to the widely held view that radiologic normal variants are of no clinical significance, many of the anatomical variants of the skeletal system may, in fact, be responsible for symptoms. Three groups of reputedly normal variants are presented: Group 1 consists of ossicles that, from the onset, may represent the sequelae of chronic repetitious trauma; group 2 is composed of normal variants in which the presence of a congenital synchondrosis may predispose to injury from chronic repetitious trauma; and group 3 consists of ossicles that in a variety of ways predispose to local and premature degenerative changes. These ossicles may be asymptomatic, however, radionuclide bone scanning is crucial in evaluating the significance of the ossicles in the symptomatic patient.
Stochastic Limit-Cycle Oscillations of a Nonlinear System Under Random Perturbations

Dynamical systems with $$\varepsilon $$ small random perturbations appear in both continuous mechanical motions and discrete stochastic chemical kinetics. The present work provides a detailed analysis of the central limit theorem (CLT), with a time-inhomogeneous Gaussian process, near a deterministic limit cycle in $$\mathbb {R}^n$$ . Based on respectively the theory of random perturbations of dynamical systems and the WKB approximation that codes the large deviations principle (LDP), results are developed in parallel from both standpoints of stochastic trajectories and transition probability density and their relations are elucidated. We show rigorously the correspondence between the local Gaussian fluctuations and the curvature of the large deviation rate function near its infimum, connecting the CLT and the LDP of diffusion processes. We study uniform asymptotic behavior of stochastic limit cycles through the interchange of limits of time $$t\rightarrow \infty $$ and $$\varepsilon \rightarrow 0$$ . Three further characterizations of stochastic limit cycle oscillators are obtained: (i) An approximation of the probability flux near the cycle; (ii) Two special features of the vector field for the cyclic motion; (iii) A local entropy balance equation along the cycle with clear physical meanings. Lastly and different from the standard treatment, the origin of the $$\varepsilon $$ in the theory is justified by a novel scaling hypothesis via constructing a sequence of stochastic differential equations.
Rapid Interfacial Exchange of Li Ions Dictates High Coulombic Efficiency in Li Metal Anodes

Although Li metal anodes offer the highest possible specific energy density for Li-based battery chemistries, practical application is plagued by Li filament growth, with adverse effects on both Co...
The Effect of the Rooney Rule on Implicit Bias in the Long Term

The Rooney Rule, originally proposed to counter implicit bias in hiring, has been implemented in the private and public sector in various settings. This rule requires that a decision-maker include at least one candidate from an underrepresented group in their shortlist of candidates. Recently, [42] proposed a mathematical model of implicit bias and studied the effectiveness of the Rooney Rule when applied to a single selection decision. However, selection decisions often occur repeatedly over time; e.g., a software firm is continuously hiring employees or a university makes admissions decisions every year. Further, it has been observed that, given consistent counterstereotypical feedback, implicit biases against underrepresented candidates can change. In this paper, we propose a model of how a decision-maker's implicit bias changes over time given their hiring decisions either with or without the Rooney Rule in place. Our model draws from the work of [42] and the literature on opinion dynamics. Our main result is that, for this model, when the decision-maker is constrained by the Rooney Rule, their implicit bias roughly reduces at a rate that is inverse of the size of the shortlist---independent of the total number of candidates, whereas without the Rooney Rule, the rate is inversely proportional to the number of candidates. Thus, our model predicts that when the number of candidates is much larger than the size of the shortlist, the Rooney Rule enables a significantly faster reduction in implicit bias, providing additional reason in favor of instating it as a strategy to mitigate implicit bias. Towards empirically evaluating the long-term effect of the Rooney Rule in repeated selection decisions, we conduct an iterative candidate selection experiment on Amazon Mechanical Turk. We observe that, indeed, decision-makers subject to the Rooney Rule select more minority candidates in addition to those required by the rule itself than they would if no rule is in effect, and in fact are able to do so without considerably decreasing the utility of candidates selected.
Extremals in nonlinear potential theory

We consider the PDE $-\Delta_pu=\rho$, where $\rho$ is a signed Borel measure on $\mathbb{R}^n$. For each $p>n$, we characterize solutions as extremals of a generalized Morrey inequality determined by $\rho$.
Relaxed peephole optimization: a novel compiler optimization for quantum circuits

As in classical computing, compilers play an important role in quantum computing. Quantum processors typically support a limited set of primitive operations or quantum gates and have certain hardware-related limitations. A quantum compiler is responsible for adapting a quantum program to these constraint environments and decomposing quantum gates into a sequence of the primitive ones. During the compilation process, it is also critical for the compiler to optimize the quantum circuits in order to reduce the noise in the computation results. Since the noise is introduced by operations and decoherence, reducing the gate count is the key for improving performance. In this paper, we propose a novel quantum compiler optimization, named relaxed peephole optimization (RPO) for quantum computers. RPO leverages the single-qubit state information that can be determined statically by the compiler. We define that a qubit is in a basis state when, at a given point in time, its state is either in the X-, Y-, or Z-basis (|+〉 / |-〉, |L〉 / |R〉 and |0〉 / |1〉). When basis qubits are used as inputs to quantum gates, there exist opportunities for strength reduction, which replaces quantum operations with equivalent but less expensive ones. Compared to the existing peephole optimization for quantum programs, the difference is that our proposed optimization does not require an identical unitary matrix, thereby named 'relaxed' peephole optimization. We also extend our approach to optimize the quantum gates when some input qubits are in known pure states. Both optimizations, namely the Quantum Basis-state Optimization (QBO) and the Quantum Pure-state Optimization (QPO), are implemented in the IBM's Qiskit transpiler. Our experimental results show that our proposed optimization pass is fast and effective. The circuits optimized with our compiler optimizations obtain up to 18.0% (11.7% on average) fewer CNOT gates and up to 8.2% (7.1% on average) lower transpilation time than that of the most aggressive optimization level in the Qiskit compiler. When running on real quantum computers, the success rates of 3-qubit quantum phase estimation algorithm improve by 2.30X due to the reduced gate counts.
CheXtransfer: performance and parameter efficiency of ImageNet models for chest X-Ray interpretation

Deep learning methods for chest X-ray interpretation typically rely on pretrained models developed for ImageNet. This paradigm assumes that better ImageNet architectures perform better on chest X-ray tasks and that ImageNet-pretrained weights provide a performance boost over random initialization. In this work, we compare the transfer performance and parameter efficiency of 16 popular convolutional architectures on a large chest X-ray dataset (CheXpert) to investigate these assumptions. First, we find no relationship between ImageNet performance and CheXpert performance for both models without pretraining and models with pretraining. Second, we find that, for models without pretraining, the choice of model family influences performance more than size within a family for medical imaging tasks. Third, we observe that ImageNet pretraining yields a statistically significant boost in performance across architectures, with a higher boost for smaller architectures. Fourth, we examine whether ImageNet architectures are unnecessarily large for CheXpert by truncating final blocks from pretrained models, and find that we can make models 3.25x more parameter-efficient on average without a statistically significant drop in performance. Our work contributes new experimental evidence about the relation of ImageNet to chest x-ray interpretation performance.
Accelerating Text Mining Using Domain-Specific Stop Word Lists

Text preprocessing is an essential step in text mining. Removing words that can negatively impact the quality of prediction algorithms or are not informative enough is a crucial storage-saving technique in text indexing and results in improved computational efficiency. Typically, a generic stop word list is applied to a dataset regardless of the domain. However, many common words are different from one domain to another but have no significance within a particular domain. Eliminating domain-specific common words in a corpus reduces the dimensionality of the feature space, and improves the performance of text mining tasks. In this paper, we present a novel mathematical approach for the automatic extraction of domain-specific words called the hyperplane-based approach. This new approach depends on the notion of low dimensional representation of the word in vector space and its distance from hyperplane. The hyperplane-based approach can significantly reduce text dimensionality by eliminating irrelevant features. We compare the hyperplane-based approach with other feature selection methods, namely χ2 and mutual information. An experimental study is performed on three different datasets and five classification algorithms, and measure the dimensionality reduction and the increase in the classification performance. Results indicate that the hyperplane-based approach can reduce the dimensionality of the corpus by 90% and outperforms mutual information. The computational time to identify the domain-specific words is significantly lower than mutual information.Reproducibility: code and results can be found at https://github.com/FarahAlshanik/Domain-Specific-Word-List
Predicting Kováts Retention Indices Using Graph Neural Networks.

Abstract The Kovats retention index is a dimensionless quantity that characterizes the rate at which a compound is processed through a gas chromatography column. This quantity is independent of many experimental variables and, as such, is considered a near-universal descriptor of retention time on a chromatography column. The Kovats retention indices of a large number of molecules have been determined experimentally. The “NIST 20: GC Method/Retention Index Library” database has collected and, more importantly, curated retention indices of a subset of these compounds resulting in a highly valued reference database. The experimental data in the library form an ideal data set for training machine learning models for the prediction of retention indices of unknown compounds. In this article, we describe the training of a graph neural network model to predict the Kovats retention index for compounds in the NIST library and compare this approach with previous work [1]. We predict the Kovats retention index with a mean unsigned error of 28 index units as compared to 44, the putative best result using a convolutional neural network [1] . The NIST library also incorporates an estimation scheme based on a group contribution approach that achieves a mean unsigned error of 114 compared to the experimental data. Our method uses the same input data source as the group contribution approach, making its application straightforward and convenient to apply to existing libraries. Our results convincingly demonstrate the predictive powers of systematic, data-driven approaches leveraging deep learning methodologies applied to chemical data and for the data in the NIST 20 library outperform previous models.
Learning Skill Equivalencies Across Platform Taxonomies

Assessment and reporting of skills is a central feature of many digital learning platforms. With students often using multiple platforms, cross-platform assessment has emerged as a new challenge. While technologies such as Learning Tools Interoperability (LTI) have enabled communication between platforms, reconciling the different skill taxonomies they employ has not been solved at scale. In this paper, we introduce and evaluate a methodology for finding and linking equivalent skills between platforms by utilizing problem content as well as the platform’s clickstream data. We propose six models to represent skills as continuous real-valued vectors, and leverage machine translation to map between skill spaces. The methods are tested on three digital learning platforms: ASSISTments, Khan Academy, and Cognitive Tutor. Our results demonstrate reasonable accuracy in skill equivalency prediction from a fine-grained taxonomy to a coarse-grained one, achieving an average recall@5 of 0.8 between the three platforms. Our skill translation approach has implications for aiding in the tedious, manual process of taxonomy to taxonomy mapping work, also called crosswalks, within the tutoring as well as standardized testing worlds.
Galvanic Vestibular Stimulation Produces Cross-Modal Improvements in Visual Thresholds

Background Stochastic resonance (SR) refers to a faint signal being enhanced with the addition of white noise. Previous studies have found that vestibular perceptual thresholds are lowered with noisy galvanic vestibular stimulation (i.e., "in-channel" SR). Auditory white noise has been shown to improve tactile and visual thresholds, suggesting "cross-modal" SR. Objective We investigated galvanic vestibular white noise (nGVS) (n = 9 subjects) to determine the cross-modal effects on visual and auditory thresholds. Methods We measured auditory and visual perceptual thresholds of human subjects across a swath of different nGVS levels in order to determine if some individual-subject determined best nGVS level elicited a reduction in thresholds as compared the no noise condition (sham). Results We found improvement in visual thresholds (by an average of 18%, p = 0.014). Subjects with higher (worse) visual thresholds with no stimulation (sham) improved more than those with lower thresholds (p = 0.04). Auditory thresholds were unchanged by vestibular stimulation. Conclusion These results are the first demonstration of cross-modal improvement with galvanic vestibular stimulation, indicating galvanic vestibular white noise can produce cross-modal improvements in some sensory channels, but not all.
Trajectory Control of Rendezvous with Maneuver Target Spacecraft

In this paper, a nonlinear trajectory control algorithm of rendezvous with maneuvering target spacecraft is presented. The disturbance forces on the chaser and target spacecraft and the thrust forces on the chaser spacecraft are considered in the analysis. The control algorithm developed in this paper uses the relative distance and relative velocity between the target and chaser spacecraft as the inputs. A general formula of reference relative trajectory of the chaser spacecraft to the target spacecraft is developed and applied to four different proximity maneuvers, which are in-track circling, cross-track circling, in-track spiral rendezvous and cross-track spiral rendezvous. The closed-loop differential equations of the proximity relative motion with the control algorithm are derived. It is proven in the paper that the tracking errors between the commanded relative trajectory and the actual relative trajectory are bounded within a constant region determined by the control gains. The prediction of the tracking errors is obtained. Design examples are provided to show the implementation of the control algorithm. The simulation results show that the actual relative trajectory tracks the commanded relative trajectory tightly. The predicted tracking errors match those calculated in the simulation results. The control algorithm developed in this paper can also be applied to interception of maneuver target spacecraft and relative trajectory control of spacecraft formation flying.
Agenda-Setting in the Regulatory State: Theory and Evidence

Government officials who run administrative agencies must make countless decisions every day about what issues and work to prioritize. These agenda-setting decisions hold enormous implications for the shape of law and public policy, but they have received remarkably little attention by either administrative law scholars or social scientists who study the bureaucracy. Existing research offers few insights about the institutions, norms, and inputs that shape and constrain agency discretion over their agendas or about the strategies that officials employ in choosing to elevate certain issues while putting others on the back burner. In this article, we advance the study of agency agenda-setting by offering concepts and frameworks that emerged from a Penn Program on Regulation workshop we organized at which more than two dozen leading scholars, practitioners, and government official discussed the relatively hidden world of agency agendas. Addressing the concept of agenda-setting itself, we distinguish between mandatory and discretionary agendas as well as between agenda-setting over high-salience regulatory initiatives versus workaday regulatory activities. We also recount what workshop participants shared about the influence of courts, Congress, the White House, and interest groups over administrative agendas. Finally, we consider a variety of possible reforms that might make agency agenda-setting more democratic, open, and responsive. Overall, we show that agency agenda-setting, notwithstanding its relative opacity, is a distinctive and important process that warrants greater study by legal and empirical scholars.
A kinetic theory approach for 2D crowd dynamics with emotional contagion

In this paper, we present a computational modeling approach for the dynamics of human crowds, where the spreading of an emotion (specifically fear) has an influence on the pedestrians’ behavior. Ou...