cssi/SciSciGPT-SciSciNet huggingface dataset:
Race, Identity, and Professional Responsibility: Why Legal Services Organizations Need African American Staff Attorneys

Given the fundamental importance of the attorney-client relationship in securing favorable outcomes for clients, legal services organizations that serve large populations of African Americans should employ African American staff attorneys because: (1) African American lawyers and clients share a group identity that makes it more likely that a black attorney will be able to gain a black client’s trust; (2) black attorneys communicate more effectively with black clients; and (3) the perception of a judicial system that is unfair and racist is likely to encourage black clients to trust black lawyers more than white lawyers, who are more likely to be perceived as part of “the system.”Empirical evidence from the legal and medical fields show that African American clients are more likely to trust and communicate effectively with African American service providers. This Article also explores, however, the reasons why some African Americans may not want a black attorney. One reason is that black clients may feel “better off” with a white lawyer precisely because racism infects the criminal justice system. Another reason may be that some African Americans may believe that white lawyers are better lawyers. Finally, in some circumstances, a black client may not want a black lawyer if he perceives the lawyer as “not black enough.” Notwithstanding some of these preferences, however, the empirical evidence strongly suggests that more often than not, black clients prefer black lawyers.Because race consideration in staffing implicates discrimination law, this Article also considers recent Supreme Court precedent that affects the ability of certain organizations to engage in color-conscious actions. According to a plurality of the Court in Parents Involved in Community Schools v. Seattle School District No. 1, the school assignment programs at issue conflicted with the premise of Brown, which requires strict adherence to colorblindness. Unfortunately, this approach ignores the continuing power of race and is a stark departure from Justice Blackmun’s defense of affirmative action in Bakke. While a definitive conclusion as to when the law allows color-consciousness is difficult in light of the Court’s recent decisions, the theme of this Article echoes Justice Blackmun. In essence, this Article argues that we cannot solve the problems that face African Americans by removing race-consciousness from the dialogue about diversity in the legal profession.
Making a Nuisance of Takings Law

Nearly a decade ago, the Supreme Court in Lucas v. South Carolina Coastal Council held that a regulation that denies to a landowner all economically beneficial use of the regulated land is a per se taking that requires the payment of just compensation under the Fifth or Fourteenth Amendments. The Court added, however, that the regulator can avoid takings liability if the use interests proscribed by the regulation were not part of the title to begin with because they were reflected in "background principles" of the state's law of property and nuisance. This article addresses whether those background principles must inhere in the common law or may have statutory or regulatory derivation. It argues that regulatory legislation is just as relevant to the determination of an owner's pre-regulation property interest as is the common law. The article then surveys both federal and state court decisions handed down since "Lucas" and concludes that most have agreed that restrictions not based in the common law are relevant to takings analysis as limiting background principles. Finally, the article analyzes a recent Iowa Supreme Court decision declaring a right-to-farm statute to be a taking of neighboring property owners' land. The article argues that the result in the Iowa case is justifiable as a means of protecting the legitimate expectations of the neighbors that they would be able to seek redress for activities causing unreasonable interference with the use and enjoyment of their properties.
The politics and economics of offshore outsourcing

Abstract This paper reviews the political uproar over offshore outsourcing connected with the release of the Economic Report of the President (ERP) in February 2004, examines the differing ways in which economists and non-economists talk about offshore outsourcing, and assesses the empirical evidence on the importance of offshore outsourcing in accounting for the weak labor market from 2001 to 2004. Even with important gaps in the data, the empirical literature is able to conclude that offshore outsourcing is unlikely to have accounted for a meaningful part of the job losses in the recent downturn or contributed much to the slow labor market rebound. The empirical evidence to date, while still tentative, actually suggests that increased employment in the overseas affiliates of US multinationals is associated with more employment in the US parent rather than less.
RUNNING FOR THE EXIT: DISTRESSED SELLING AND ENDOGENOUS CORRELATION IN FINANCIAL MARKETS

We propose a simple multiperiod model of price impact from trading in a market with multiple assets, which illustrates how feedback effects due to distressed selling and short selling lead to endogenous correlations between asset classes. We show that distressed selling by investors exiting a fund and short selling of the fund’s positions by traders may have nonnegligible impact on the realized correlations between returns of assets held by the fund. These feedback effects may lead to positive realized correlations between fundamentally uncorrelated assets, as well as an increase in correlations across all asset classes and in the fund’s volatility which is exacerbated in scenarios in which the fund undergoes large losses. By studying the diffusion limit of our discrete time model, we obtain analytical expressions for the realized covariance and show that the realized covariance may be decomposed as the sum of a fundamental covariance and a liquidity-dependent “excess” covariance. Finally, we examine the impact of these feedback effects on the volatility of other funds. Our results provide insight into the nature of spikes in correlation associated with the failure or liquidation of large funds.
Sovereign debt and corporate borrowing costs in emerging markets

Abstract We document that the corporate sector faces higher borrowing costs when the external debt of the public sector is higher. By contrast, no significant relationship is found between domestic public debt and corporate borrowing costs. An increase in sovereign debt by one standard deviation from its sample mean is associated with 9% higher loan yield spreads. The correlation is considerably higher in countries with weak creditor rights and past sovereign default episodes. Overall, these findings suggest substantial adverse linkages between public external debt and private financing costs.
Chronicle of a Death Foretold: The Future of U.S. Human Rights Litigation Post-Kiobel

For thirty years, the Alien Tort Statute (ATS) has provided U.S. courts with civil jurisdiction over human rights abuses committed abroad and a small group of victims a modest measure of justice. The Supreme Court’s April 2013 decision to limit the extraterritorial reach of the ATS in Kiobel v. Royal Dutch Petroleum prompted declarations from experts that human rights litigation in the United States is dead. This view overstates the value of ATS litigation to human rights victims and ignores the availability of other legal strategies. This Article explores the implications of Kiobel for the interests of those most affected by human rights violations—its victims—and identifies strategies for how advocates can best use existing legal remedies in the United States to vindicate victims’ rights. First, the Article defines a metric to evaluate the significance of U.S. legal strategies from a victim-centered perspective. The metric is based on international standards related to victims’ rights: the rights to truth, justice, and reparations. Second, the Article catalogues the multiple legal strategies available in the United States to hold perpetrators accountable for human rights abuses committed abroad. Even after the Supreme Court’s holding in Kiobel, the United States remains the only country in the world where a nonnational victim can bring a civil action against a nonnational perpetrator for human rights abuses committed on foreign soil. U.S. courts also have extraterritorial jurisdiction
The Ducks Stop Here? The Environmental Challenge to Federalism

In Solid Waste Association of Northern Cook County v. U. S. Army Corps of Engineers ("SWANCC"), the Supreme Court considered whether federal regulatory authority reaches isolated wetlands and ponds due to the potential presence of migratory birds. In rejecting such an expansive view of federal authority, the Court's majority underlined its devotion to federalism, despite the dissent's complaint that the decision would frustrate environmental protection. This paper argues that SWANNC is not an obstacle to environmental protection. There is little reason to believe that interstate competition amongst states will produce a "race to the bottom" in environmental regulation today, if it ever did. The presence of interstate externalities can, in certain circumstances, justify federal environmental regulation. However, the presence of such externalities is often overstated, and the costs of addressing such externalities through federal regulation may well be greater than maintaining state primacy. Insofar as wetl...
TARP funds distribution and bank loan supply.

Abstract This paper investigates the determinants of the Troubled Asset Relief Program (TARP) funds distribution to banks and the stimulus effect of TARP investments on credit supply in the economy. Using banks’ political and regulatory connections as instruments, this paper finds that TARP investments increased bank loan supply by an annualized rate of 6.36% for banks with below median Tier 1 capital ratios. This increase is found in all major types of loans and can be translated into $404 billion of additional loans for all TARP banks. On average, TARP banks employed about one-third of their TARP capital to support new loans and kept the rest to strengthen their balance sheets. Furthermore, there is little evidence that loans made by TARP banks had lower quality than those by non-TARP banks. In sum, this paper shows a positive stimulus effect of TARP on credit supply during the 2008–2009 financial crisis.
Balancing Procedures and Outcomes Within Democratic Theory: Core Values and Judicial Review

Democratic theorists often distinguish between two views of democratic procedures. ‘Outcomes theorists’ emphasize the instrumental nature of these procedures and argue that they are only valuable because they tend to produce good outcomes. In contrast, ‘proceduralists’ emphasize the intrinsic value of democratic procedures, for instance, on the grounds that they are fair. In this paper. I argue that we should reject pure versions of these two theories in favor of an understanding of the democratic ideal that recognizes a commitment to both intrinsically valuable democratic procedures and democratic outcomes. In instances in which there is a conflict between these two commitments, I suggest they must be balanced. This balancing approach offers a justification of judicial review on the grounds that it potentially limits outcomes that undermine democracy. But judicial review is not justifiable in any instance in which a bad democratic outcome results from democratic procedures. When the loss that would result from overturning a democratic procedure is greater than the gain to democracy that would result from ensuring against an undemocratic outcome; judicial review is not justifiable. Loss or gain to democracy is defined by the negative or positive impact of each action on the core democratic values of equality and autonomy, aspects of the democratic ideal. Even when judicial review is justified, the fact that it overturns intrinsically valuable procedures suggests that such review is never ideal from the standpoint of democracy.
Consumer Finance and Insolvency Law in India: A Case Study

In February 2007, justices of the Supreme Court of India issued two remarkable opinions in the case of ICICI Bank v. Kaur. 1 Prakash Kaur, the complainant in that case, had borrowed money from ICICI to purchase a truck. When Kaur failed to pay installments on the loan, agents hired by the bank took possession of the truck ―by use of force.‖ 2 Approving a settlement of the parties’ claims, 3 Justice Altamas Kabir wrote for the Court:
Freakonomics and the Tax Gap: An Applied Perspective

Over the past thirty years, a significant amount of research from a variety of social science disciplines has considered tax compliance. Economists, psychologists, and sociologists have contributed to the discussion, offering research and, at times, conflicting explanations regarding whether a person is likely to comply with his obligation to file an accurate tax return. The unifying theme among this research is a search for explanatory reasons which are the factors that lead to non-compliance. In broad terms, the economic models of tax compliance assume rational behavior, and that people will coldly consider compliance from the perspective as to whether the expected utility of non-compliance exceeds the utility of complying. To that end, researchers relying on the economic model have looked to a variety of independent variables likely to affect the calculus, including penalty rates, the likelihood of an audit, and complexity. There are numerous studies testing the variables that economists believe contribute to taxpayers’ decisions to comply with the tax laws. Psychologists and sociologists have rightly pointed out that the economic model is insufficient as an explanatory tool. Sociologists and psychologists alike argue that some economic models fail to capture the complexities of human behavior and relationships, and fail to explain why compliance rates exceed what would otherwise be expected if people were solely evaluating compliance in terms of dollars and cents. The challenge among policymakers mining the social science research is that the research is at times inconsistent and incomplete. This article considers how the current earned income tax credit (EITC} creates opportunities for individuals to affirmatively misstate eligibility. It examines insights from the popular book Freakonomics and argues that despite the confusing and sometimes inconsistent state of research relating to tax compliance, policymakers concerned with reducing the tax gap should consider structural incentives and visibility as key factors relating to the decision to intentionally comply with the tax laws. While more empirical research must be done that examines and considers the relationship between various independent variables that may affect the decision to comply with the tax laws, policymakers can limit opportunities for noncompliance by considering (i) how taxpayers and return preparers perceive the visibility of their conduct, and (ii) how structural incentives of particular provisions might contribute to the willingness to cheat. The article examines how structural incentives and visibility operate in the context of the EITC, and create the conditions that allow individuals to intentionally misstate eligibility on tax returns.
EMPLOYEE STOCK OWNERSHIP IN ECONOMIC TRANSITIONS: THE CASE OF UNITED AIRLINES

This article argues that employee stock ownership transactions (ESOTs) may have decisive advantages in addressing the transition problems associated with significant economic change. Equity ownership by employees can increase value not only because of the better incentive alignment achieved by making employees major stockholders, but also because equity has special value in solving some of the bargaining (i.e., information) problems that can make it especially difficult to renegotiate the em-ployees' contracts with the firm in volatile economic environments. One important question raised by ESOTs is whether they produce a merely transitional organizational form or a relatively permanent form for managing economic change. Do they amount to simply a set of one-time adjustments of economic claims so that we should expect a reversion back to public shareholder ownership? Or do these transactions produce a durable organizational form that proves more effective in negotiating an ongoing series of transition problems? After a general analysis of the potential advantages of ESOTs in cases of economic transition, this article explores this question by examining one particularly interesting example, the recent employee acquisition of majority equity in United Air Lines. The parties in the UAL transaction, which can be traced to the transition shock of airline deregulation in 1978 and the series of competitive struggles that followed, contemplated employee ownership as more than a transitional device. The deal is structured to lock up employee stock in an ESOP and to provide strong employee governance rights for the next 20 years. One of the objectives of the ESOT was to catalyze a cultural change in UAL's operations so that the airline could become more competitive, not just through wage reductions but through operational efficiencies that would require a higher level of employee cooperation. The evidence to date suggests that UAL has become a more efficient competitor, but also that governance pressure from the employees where their economic interests are directly at stake is potentially destabilizing. The UAL case shows that adjustment to prosperity can raise problems almost as difficult as adjustment to economic adversity.
Active Social Media Management: The Case of Health Care

Given the demand for authentic personal interactions over social media, it is unclear how much firms should actively manage their social media presence. We study this question empirically in a health care setting. We show that active social media management drives more user-generated content. However, we find that this is due to an incremental increase in user postings from an organization's employees rather than from its clients. This result holds when we explore exogenous variation in social media policies, employees, and clients that are explained by medical marketing laws, medical malpractice laws, and distortions in Medicare incentives. Further examination suggests that content being generated mainly by employees can be avoided if a firm's postings are entirely client focused. However, most firm postings seem not to be specifically targeted to clients' interests, instead highlighting more general observations or achievements of the firm itself. We show that untargeted postings like these provoke acti...
Faith-based charity and crowd-out during the great depression

Abstract Interest in religious organizations as providers of social services has increased dramatically in recent years. Churches in the U.S. were a crucial provider of social services through the early part of the twentieth century, but their role shrank dramatically with the expansion in government spending under the New Deal. In this paper, we investigate the extent to which the New Deal crowded-out church charitable spending in the 1930s. We do so using a new nationwide data set of charitable spending for six large Christian denominations, matched to data on local New Deal spending. We instrument for New Deal spending using measures of the political strength of a state's congressional delegation, and confirm our findings using a different instrument based on institutional constraints on state relief spending. With both instruments we find that higher government spending leads to lower church charitable activity. Crowd-out was small as a share of total New Deal spending (3%), but large as a share of church spending: our estimates suggest that benevolent church spending fell by 30% in response to the New Deal, and that government relief spending can explain virtually all of the decline in charitable church activity observed between 1933 and 1939.
Urban Informality as a Commons Dilemma

The phenomenon of informal housing settlements on the outer periphery of cities in the developing world is by now well known, popularized in part by movies like City of God1 and by books like Planet of Slums2 and Shadow Cities.3 The buying and selling of land in these settlements takes place largely outside of formal housing markets and formal planning and land use processes. An informal market and its accompanying norms fuel what is now regarded as the predominant mode of urbanization in much of the developing world's metropolitan cities.4 Informal settlements in Latin America ("barrios piratas" or "colonias illegales"), particularly, have varied origins. It is impor tant to emphasize that most informal settlements in Latin Ameri can cities are not "squatter settlements" involving the invasion by potential inhabitants of privately held land.5 Rather, they have sprung from subdivisions in which a "pirate" developer or land speculator acquires a relatively large piece of "raw" or unserviced
Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW

Over the last decade, time series motif discovery has emerged as a useful primitive for many downstream analytical tasks, including clustering, classification, rule discovery, segmentation, and summarization. In parallel, there has been an increased understanding that Dynamic Time Warping (DTW) is the best time series similarity measure in a host of settings. Surprisingly however, there has been virtually no work on using DTW to discover motifs. The most obvious explanation of this is the fact that both motif discovery and the use of DTW can be computationally challenging, and the current best mechanisms to address their lethargy are mutually incompatible. In this work, we present the first scalable exact method to discover time series motifs under DTW. Our method automatically performs the best trade-off between time-to-compute and tightness-of-lower-bounds for a novel hierarchy of lower bounds representation we introduce. We show that under realistic settings, our algorithm can admissibly prune up to 99.99% of the DTW computations.
Opposing functions of the plant TOPLESS gene family during SNC1-mediated autoimmunity

Regulation of the plant immune system is important for controlling the specificity and amplitude of responses to pathogens and in preventing growth-inhibiting autoimmunity that leads to reductions in plant fitness. In previous work, we reported that SRFR1, a negative regulator of effector-triggered immunity, interacts with SNC1 and EDS1. When SRFR1 is non-functional in the Arabidopsis accession Col-0, SNC1 levels increase, causing a cascade of events that lead to autoimmunity phenotypes. Previous work showed that some members of the transcriptional co-repressor family TOPLESS interact with SNC1 to repress negative regulators of immunity. Therefore, to explore potential connections between SRFR1 and TOPLESS family members, we took a genetic approach that examined the effect of each TOPLESS member in the srfr1 mutant background. The data indicated that an additive genetic interaction exists between SRFR1 and two members of the TOPLESS family, TPR2 and TPR3, as demonstrated by increased stunting and elevated PR2 expression in srfr1 tpr2 and srfr1 tpr2 tpr3 mutants. Furthermore, the tpr2 mutation intensifies autoimmunity in the auto-active snc1-1 mutant, indicating a novel role of these TOPLESS family members in negatively regulating SNC1-dependent phenotypes. This negative regulation can also be reversed by overexpressing TPR2 in the srfr1 tpr2 background. Similar to TPR1 that positively regulates snc1-1 phenotypes by interacting with SNC1, we show here that TPR2 directly binds the N-terminal domain of SNC1. In addition, TPR2 interacts with TPR1 in vivo, suggesting that the opposite functions of TPR2 and TPR1 are based on titration of SNC1-TPR1 complexes by TPR2 or altered functions of a SNC1-TPR1-TPR2 complex. Thus, this work uncovers diverse functions of individual members of the TOPLESS family in Arabidopsis and provides evidence for the additive effect of transcriptional and post-transcriptional regulation of SNC1.
A Chebyshev-Based High-Order-Accurate Integral Equation Solver for Maxwell’s Equations

This article introduces a new method for discretizing and solving integral equation formulations of Maxwell’s equations, which achieves spectral accuracy for smooth surfaces. The approach is based on a hybrid Nystrom-collocation method using Chebyshev polynomials to expand the unknown current densities over curvilinear quadrilateral surface patches. As an example, the proposed strategy is applied to the magnetic field integral equation (MFIE) and the N-Muller formulation for scattering from metallic and dielectric objects, respectively. The convergence is studied for several different geometries, including spheres, cubes, and complex NURBS geometries imported from CAD software, and the results are compared against a commercial Method-of-Moments solver using RWG basis functions.
Tetrahedral projection using vertex shaders

Projective methods for volume rendering currently represent the best approach for interactive visualization of unstructured data sets. We present a technique for tetrahedral projection using the programmable vertex shaders on current generation commodity graphics cards. The technique is based on Shirley and Tuchman's Projected Tetrahedra (PT) algorithm and allows tetrahedral elements to be volume scan converted within the graphics processing unit. Our technique requires no pre-processing of the data and no additional data structures. Our initial implementation allows interactive viewing of large unstructured datasets on a desktop personal computer.
Poliovirus-nonsusceptible Vero cell line for the World Health Organization global action plan

Polio or poliomyelitis is a disabling and life-threatening disease caused by poliovirus (PV). As a consequence of global polio vaccination efforts, wild PV serotypes 2 and 3 have been eradicated around the world, and wild PV serotype 1-transmitted cases have been largely eliminated except for limited regions. However, vaccine-derived PV, pathogenically reverted live PV vaccine strains, has become a serious issue. For the global eradication of polio, the World Health Organization is conducting the third edition of the Global Action Plan, which is requesting stringent control of potentially PV-infected materials. To facilitate the mission, we generated a PV-nonsusceptible Vero cell subline, which may serve as an ideal replacement of standard Vero cells to isolate emerging/re-emerging viruses without the risk of generating PV-infected materials.
Thalamic state influences timing precision in the thalamocortical circuit

The majority of sensory signals are transmitted through the thalamus. There is growing evidence of complex thalamic gating through coordinated firing modes that have a strong impact on cortical sen...
A polynomial time primal network simplex algorithm for minimum cost flows

Developing a polynomial time primal network simplex algorithm for the minimum cost flow problem has been a long standing open problem. In this paper, we develop one such algorithm that runs in O(min(n 2m lognC, n 2m2 logn)) time, wheren is the number of nodes in the network,m is the number of arcs, andC denotes the maximum absolute arc costs if arc costs are integer and ∞ otherwise. We first introduce a pseudopolynomial variant of the network simplex algorithm called the “premultiplier algorithm”. We then develop a cost-scaling version of the premultiplier algorithm that solves the minimum cost flow problem in O(min(nm lognC, nm 2 logn)) pivots. With certain simple data structures, the average time per pivot can be shown to be O(n). We also show that the diameter of the network polytope is O(nm logn).
Efficient CRISPR/Cas9 mutagenesis for neurobehavioral screening in adult zebrafish

Adult zebrafish are increasingly used to interrogate mechanisms of disease development and tissue regeneration. Yet, the prospect of large-scale genetics in adult zebrafish has traditionally faced a host of biological and technical challenges, including inaccessibility of adult tissues to high-throughput phenotyping and the spatial and technical demands of adult husbandry. Here, we describe an experimental pipeline that combines high-efficiency CRISPR/Cas9 mutagenesis with functional phenotypic screening to identify genes required for spinal cord repair in adult zebrafish. Using CRISPR/Cas9 dual-guide ribonucleic proteins, we show selective and combinatorial mutagenesis of 17 genes at 28 target sites with efficiencies exceeding 85% in adult F0 'crispants'. We find that capillary electrophoresis is a reliable method to measure indel frequencies. Using a quantifiable behavioral assay, we identify seven single- or duplicate-gene crispants with reduced functional recovery after spinal cord injury. To rule out off-target effects, we generate germline mutations that recapitulate the crispant regeneration phenotypes. This study provides a platform that combines high-efficiency somatic mutagenesis with a functional phenotypic readout to perform medium- to large-scale genetic studies in adult zebrafish.
Tunable Human Myocardium Derived Decellularized Extracellular Matrix for 3D Bioprinting and Cardiac Tissue Engineering

The generation of 3D tissue constructs with multiple cell types and matching mechanical properties remains a challenge in cardiac tissue engineering. Recently, 3D bioprinting has become a powerful tool to achieve these goals. Decellularized extracellular matrix (dECM) is a common scaffold material due to providing a native biochemical environment. Unfortunately, dECM's low mechanical stability prevents usage for bioprinting applications alone. In this study, we developed bioinks composed of decellularized human heart ECM (dhECM) with either gelatin methacryloyl (GelMA) or GelMA-methacrylated hyaluronic acid (MeHA) hydrogels dual crosslinked with UV light and microbial transglutaminase (mTGase). We characterized the bioinks' mechanical, rheological, swelling, printability, and biocompatibility properties. Composite GelMA-MeHA-dhECM (GME) hydrogels demonstrated improved mechanical properties by an order of magnitude compared to the GelMA-dhECM (GE) hydrogels. All hydrogels were extrudable and compatible with human induced pluripotent stem cell derived cardiomyocytes (iCMs) and human cardiac fibroblasts (hCFs). Tissue-like beating of the printed constructs with striated sarcomeric alpha-actinin and connexin 43 expression was observed. The order of magnitude difference between the elastic modulus of these hydrogel composites offers applications in in vitro modeling of the myocardial infarct boundary. Here, as a proof of concept, we created an infarct boundary region with control over the mechanical properties along with the cellular and macromolecular content through printing iCMs with GE bioink and hCFs with GME bioink.
Big Data in Finance

Big data is revolutionizing the finance industry and has the potential to significantly shape future research in finance. This special issue contains articles following the 2019 NBER/ RFS conference on big data. In this Introduction to the special issue, we define the “Big Data” phenomenon as a combination of three features: large size, high dimension, and complex structure. Using the articles in the special issue, we discuss how new research builds on these features to push the frontier on fundamental questions across areas in finance – including corporate finance, market microstructure, and asset pricing. Finally, we offer some thoughts for future research directions.
Detecting fake news spreaders in social networks using inductive representation learning

An important aspect of preventing fake news dissemination is to proactively detect the likelihood of its spreading. Research in the domain of fake news spreader detection has not been explored much from a network analysis perspective. In this paper, we propose a graph neural network based approach to identify nodes that are likely to become spreaders of false information. Using the community health assessment model and interpersonal trust we propose an inductive representation learning framework to predict nodes of densely-connected community structures that are most likely to spread fake news, thus making the entire community vulnerable to the infection. Using topology and interaction based trust properties of nodes in real-world Twitter networks, we are able to predict false information spreaders with an accuracy of over 90%.
Deductive Stability Proofs for Ordinary Differential Equations

Stability is required for real world controlled systems as it ensures that those systems can tolerate small, real world perturbations around their desired operating states. This paper shows how stability for continuous systems modeled by ordinary differential equations (ODEs) can be formally verified in differential dynamic logic (dL). The key insight is to specify ODE stability by suitably nesting the dynamic modalities of dL with first-order logic quantifiers. Elucidating the logical structure of stability properties in this way has three key benefits: i) it provides a flexible means of formally specifying various stability properties of interest, ii) it yields rigorous proofs of those stability properties from dL’s axioms with dL’s ODE safety and liveness proof principles, and iii) it enables formal analysis of the relationships between various stability properties which, in turn, inform proofs of those properties. These benefits are put into practice through an implementation of stability proofs for several examples in KeYmaera X, a hybrid systems theorem prover based on dL.
Sharing R&D Risk in Healthcare via FDA Hedges

The high cost of capital for firms conducting medical research and development (R&D) has been partly attributed to the government risk facing investors in medical innovation. This risk slows down medical innovation because investors must be compensated for it. We analyze new and simple financial instruments, Food and Drug Administration (FDA) hedges, to allow medical R&D investors to better share the pipeline risk associated with FDA approval with broader capital markets. Using historical FDA approval data, we discuss the pricing of FDA hedges and mechanisms under which they can be traded and estimate issuer returns from offering them. Using various unique data sources, we find that FDA approval risk has a low correlation across drug classes as well as with other assets and the overall market. We argue that this zero-beta property of scientific FDA risk could be a main source of gains from trade between issuers of FDA hedges looking for diversified investments and developers looking to offload the FDA approval risk. We offer proof of concept of the feasibility of trading this type of pipeline risk by examining related securities issued around mergers and acquisitions activity in the drug industry. Overall, our argument is that, by allowing better risk sharing between those investing in medical innovation and capital markets more generally, FDA hedges could ultimately spur medical innovation and improve the health of patients.
Li-ion Battery Fault Detection in Large Packs Using Force and Gas Sensors

Abstract Internal short circuits are a leading cause of battery thermal runaway, and hence a major safety issue for electric vehicles. An internal short circuit with low resistance is called a hard internal short, which causes a high internal current flow that leads to an extremely fast temperature rise, gas generation, cell swelling, and ultimately battery rupture and failure. Thus it is crucial to detect these faults immediately after they get triggered. In large battery packs with many cells in parallel, detecting an internal short circuit event using voltage is difficult due to suppression of the voltage signal from the faulty cell by the other healthy cells connected in parallel. In contrast, analyzing the gas composition in the pack enclosure can provide a robust single cell failure detection method. At elevated temperature, decomposition of the battery materials results in gas generation and cell swelling. The cell structure is designed to rupture at a critical gas pressure and vent the accumulated CO2 gas, in order to prevent explosive forces. In this paper, we extend our previous work by combining the models of cell thermal dynamics, swelling, and CO2 gas generation. In particular, we developed a fast and high confidence level detection method of hard internal short circuit events for a battery pack by measuring cell expansion force and monitoring CO2 concentrations in a pack enclosure.
Sparse estimation of Laplacian eigenvalues in multiagent networks

Abstract We propose a method to efficiently estimate the Laplacian eigenvalues of an arbitrary, unknown network of interacting dynamical agents. The inputs to our estimation algorithm are measurements about the evolution of a collection of agents (potentially one) during a finite time horizon; notably, we do not require knowledge of which agents are contributing to our measurements. We propose a scalable algorithm to exactly recover a subset of the Laplacian eigenvalues from these measurements. These eigenvalues correspond directly to those Laplacian modes that are observable from our measurements. We show how our technique can be applied to networks of multiagent systems with arbitrary dynamics in both continuous- and discrete-time. Finally, we illustrate our results with numerical simulations.
Numerical continuum tensor networks in two dimensions

We describe the use of tensor networks to numerically determine wave functions of interacting two-dimensional fermionic models in the continuum limit. We use two different tensor network states: one based on the numerical continuum limit of fermionic projected entangled pair states obtained via a tensor network formulation of multi-grid, and another based on the combination of the fermionic projected entangled pair state with layers of isometric coarse-graining transformations. We first benchmark our approach on the two-dimensional free Fermi gas then proceed to study the two-dimensional interacting Fermi gas with an attractive interaction in the unitary limit, using tensor networks on grids with up to 1000 sites.
More than idle talk: Examining the effects of positive and negative team gossip

Supported by meta-analytic evidence, the general consensus in the teams literature is that the more a team communicates, the better. However, one type of communication that appears at odds with thi...
Empirical Models of Industry Dynamics with Endogenous Market Structure

This article reviews recent developments in the study of firm and industry dynamics, with a special emphasis on the econometric endogeneity of market structure. The endogeneity of market structure ...
Leviathan’s Latent Dimensions: Measuring State Capacity for Comparative Political Research

State capacity is a core concept in political science research, and it is widely recognized that state institutions exert considerable influence on outcomes such as economic development, civil conf...
EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models

The advent of larger machine learning (ML) models have improved state-of-the-art (SOTA) performance in various modeling tasks, ranging from computer vision to natural language. As ML models continue increasing in size, so does their respective energy consumption and computational requirements. However, the methods for tracking, reporting, and comparing energy consumption remain limited. We present EnergyVis, an interactive energy consumption tracker for ML models. Consisting of multiple coordinated views, EnergyVis enables researchers to interactively track, visualize and compare model energy consumption across key energy consumption and carbon footprint metrics (kWh and CO2), helping users explore alternative deployment locations and hardware that may reduce carbon footprints. EnergyVis aims to raise awareness concerning computational sustainability by interactively highlighting excessive energy usage during model training; and by providing alternative training options to reduce energy usage.
Short Exposure to Intermittent Hypoxia Increases Erythropoietin Levels in Healthy Individuals

The objective of this study was to identify the shortest intermittent hypoxia protocol necessary to increase serum erythropoietin levels in healthy individuals. Eight 4-min bouts of intermittent hy...
UPR: A Model-Driven Architecture for Deep Phase Retrieval

The problem of phase retrieval has been intriguing researchers for decades due to its appearance in a wide range of applications. The task of a phase retrieval algorithm is typically to recover a signal from linear phase-less measurements. In this paper, we approach the problem by proposing a hybrid model-based data-driven deep architecture, referred to as Unfolded Phase Retrieval (UPR), that shows potential in improving the performance of the state-of-the-art phase retrieval algorithms. Specifically, the proposed method benefits from versatility and interpretability of well established model-based algorithms, while simultaneously benefiting from the expressive power of deep neural networks. Our numerical results illustrate the effectiveness of such hybrid deep architectures and showcase the untapped potential of data-aided methodologies to enhance the existing phase retrieval algorithms.
Double/debiased machine learning for logistic partially linear model

We propose double/debiased machine learning approaches to infer (at the parametric rate) the parametric component of a logistic partially linear model with the binary response following a conditional logistic model of a low dimensional linear parametric function of some key (exposure) covariates and a nonparametric function adjusting for the confounding effect of other covariates. We consider a Neyman orthogonal (doubly robust) score equation consisting of two nuisance functions: nonparametric component in the logistic model and conditional mean of the exposure on the other covariates and with the response fixed. To estimate the nuisance models, we separately consider the use of high dimensional (HD) sparse parametric models and more general (typically nonparametric) machine learning (ML) methods. In the HD case, we derive certain moment equations to calibrate the first-order bias of the nuisance models and grant our method a model double robustness property in the sense that our estimator achieves the desirable rate when at least one of the nuisance models is correctly specified and both of them are ultra-sparse. In the ML case, the non-linearity of the logit link makes it substantially harder than the partially linear setting to use an arbitrary conditional mean learning algorithm to estimate the nuisance component of the logistic model. We handle this obstacle through a novel full model refitting procedure that is easy-to-implement and facilitates the use of nonparametric ML algorithms in our framework. Our ML estimator is rate doubly robust in the same sense as Chernozhukov et al. (2018a). We evaluate our methods through simulation studies and apply them in assessing the effect of emergency contraceptive (EC) pill on early gestation foetal with a policy reform in Chile in 2008 (Bentancor and Clarke, 2017).
Deterministic mincut in almost-linear time

We present a deterministic (global) mincut algorithm for weighted, undirected graphs that runs in m1+o(1) time, answering an open question of Karger from the 1990s. To obtain our result, we de-randomize the construction of the skeleton graph in Karger’s near-linear time mincut algorithm, which is its only randomized component. In particular, we partially de-randomize the well-known Benczur-Karger graph sparsification technique by random sampling, which we accomplish by the method of pessimistic estimators. Our main technical component is designing an efficient pessimistic estimator to capture the cuts of a graph, which involves harnessing the expander decomposition framework introduced in recent work by Goranci et al. (SODA 2021). As a side-effect, we obtain a structural representation of all approximate mincuts in a graph, which may have future applications.
Quantifying Pilot Contribution to Flight Safety During Dual Generator Failure

Accident statistics cite flight crew error in over 60% of accidents involving transport category aircraft. Yet, a well-trained and well-qualified pilot is acknowledged as the critical center point of aircraft systems safety and an integral safety component of the entire commercial aviation system. No data currently exists that quantifies the contribution of the flight crew in this role. Neither does data exist for how often the flight crew handles non-normal procedures or system failures on a daily basis in the National Airspace System. A pilot-in-the-loop high fidelity motion simulation study was conducted by the NASA Langley Research Center in partnership with the Federal Aviation Administration (FAA) to evaluate the pilot's contribution to flight safety during normal flight and in response to aircraft system failures. Eighteen crews flew various normal and non-normal procedures over a two-day period and their actions were recorded in response to failures. To quantify the human's contribution, crew complement was used as the experiment independent variable in a between-subjects design. Pilot actions and performance when one of the flight crew was unavailable were also recorded for comparison against the nominal two-crew operations. This paper details diversion decisions, perceived safety of flight, workload, time to complete pertinent checklists, and approach and landing results while dealing with a complete loss of electrical generators. Loss of electrical power requires pilots to complete the flight without automation support of autopilots, flight directors, or auto throttles. For reduced crew complements, the additional workload and perceived safety of flight was considered unacceptable.
The Role of Elevated Terrain and the Gulf of Mexico in the Production of Severe Local Storm Environments over North America

The prevailing conceptual model for the production of severe local storm (SLS) environments over North America asserts that upstream elevated terrain and the Gulf of Mexico are both essential to their formation. This work tests this hypothesis using two prescribed-ocean climate model experiments with North American topography removed or the Gulf of Mexico converted to land and analyzes how SLS environments and associated synoptic-scale drivers (southerly Great Plains low-level jets, drylines, elevated mixed layers, and extratropical cyclones) change relative to a control historical run. Overall, SLS environments depend strongly on upstream elevated terrain but more weakly on the Gulf of Mexico. Removing elevated terrain substantially reduces SLS environments especially over the continental interior due to broad reductions in both thermodynamic instability and vertical wind shear, leaving a more zonally-uniform residual distribution that is maximized near the Gulf coast and decays toward the continental interior. This response is associated with a strong reduction in synoptic-scale drivers and a cooler and drier mean-state atmosphere. Replacing the Gulf of Mexico with land modestly reduces SLS environments over the Great Plains (driven primarily thermodynamically) and increases them over the eastern U.S. (driven primarily kinematically), shifting the primary local maximum eastward into Illinois; it also eliminates the secondary, smaller local maximum over southern Texas. This response is associated with modest changes in synoptic-scale drivers and a warmer and drier lower troposphere. These experiments provide insight into the role of elevated terrain and the Gulf of Mexico in modifying the spatial distribution and seasonality of SLS environments.