cssi/SciSciGPT-SciSciNet huggingface dataset:
Justification Defenses in Situations of Unavoidable Uncertainty: A Reply to Professor Ferzan

The objective (or deeds) theory of justification has been attacked on the ground that one can never know for sure whether the circumstances for justification actually exist. One can only speculate as to whether the conditions exist. This is true not only for the actor at the time of the conduct for which a justification is sought but can also be true for the adjudicator after all available evidence has been gathered.The attack contains a useful insight about the nature of justifying circumstances: they necessarily contain some degree of unavoidable uncertainty. But it does not follow from this insight that justification defenses must necessarily be subjective in their formulation or conceptualized as based upon a reasons theory.This brief essay explains why this is so and what the inevitably speculative insight tells us about the objective theory of justification.
Geometric aspects of the Kapustin-Witten equations

This expository paper introduces the Kapustin–Witten equations to mathematicians. We discuss the connections between the complex Yang–Mills equations and the Kapustin–Witten equations. In addition, we show the relation between the Kapustin–Witten equations, the moment map condition and the gradient Chern–Simons flow. The new results in the paper correspond to estimates on the solutions to the Kapustin–Witten equations given an estimate on the complex part of the connection. This leaves open the problem of obtaining global estimates on the complex part of the connection.
Empirical identification of perceived congestion

This paper investigates the empirical identification of perceived congestion and mitigating behavior using observational data. Congestion effects are identified using a procedure based upon a nonlinear function of the choice attributes combined with an equilibrium condition on the sorting behavior of participants. Results suggest that congestion effects can be identified, under certain assumptions, using only revealed preference data, and that ignoring these effects underestimates the strength of preferences for other attributes. The model is applied to data on rock climbers, and the resulting estimates used to simulate the reopening of a currently closed section of a popular NY rock-climbing area and the re-sorting of climbers that would result.
Charles Kindleberger: An Impressionist in a Minimalist World

Minimalist economists stubbornly resist Charles Kindleberger’s characterization of investor expectations in a financial bubble as “irrational.” This paper seeks to resolve the controversy by imbedding Kindleberger’s well-researched, impressionistic theory of financial crises into an expanded, but still-minimalist model of rational expectations. Introducing the concepts of malicious disinformation and rational overpromotion creates an informational environment in which it is time-consuming and costly to distinguish fact from fiction. Rationality still requires that expectations and market fundamentals move together over long periods of time, but dishonorable overpromoters can earn substantial profits in the interim.
Eminent Domain and the Sanctity of Home

The Supreme Court's decision in Kelo v. New London has caused alarm over how easy it is for government to take private homes through eminent domain. The image of government officials ousting people from their homes for unnecessary projects has sparked movements to reform eminent domain in many states. Typically, such reform efforts are focused on the public use doctrine and what government agencies plan to do with property after taking it, rather than the unique nature of home property and its value to homeowners. This article concludes that the root of the problem lies beyond the public use doctrine. Government takes too many homes from unwilling homeowners, and for too cheaply, even for traditional public use projects. The article explores potential methods of protecting homes against unjustified condemnation, including substantive and procedural methods. The article concludes that while various methods have advantages, adjusting the compensation structure of eminent domain to require significantly greater compensation in the case of home takings is most likely to produce optimal results. A system of compensation that recognizes more accurately what homes are typically worth to their owners would produce greater deterrence against government takings, and would improve the bargaining process between government officials and homeowners, while still allowing governments to overcome holdouts in cases of genuine public necessity.
Anchoring on Historical High Prices and Seasoned Equity Offerings

We document that firms’ financing decisions are affected by historical high prices. The ratio of the monthly high price to the 12-month historical high price positively affects the probability of a seasoned equity offering (SEO). Furthermore, the postannouncement market reaction is muted and the offering discount is smaller if the preannouncement stock price is high relative to its historical high price. The results suggest that historical high price reference points may help managers rationally time SEOs to take advantage of market reception and minimize issuance costs.
Integration and cospecialization of emerging complementary technologies by startups

We analyze the market entry problem faced by startups that must integrate their service or product with one or more complementary technologies. The problem is especially challenging when the complementary technologies have uncertain cost reduction potentials. The entrepreneurship literature suggests that startups should pursue focused strategies for various reasons, including bounded rationality and budget constraints, but generally overlooks startups entering markets with complementary technologies. The advice for mature firms investing in complementary technologies is often to diversify investment across multiple complements to manage technological uncertainty. Given competing guidance, we seek to extend the entrepreneurship literature by modeling startups' entry decisions for markets in which complementary technologies exhibit strong learning effects. We find that, consistent with the extant entrepreneurship literature, startups generally achieve higher expected returns by channeling their integration investment to only one complementary technology. However, the mechanisms driving our results differ significantly by hinging on nonlinear feedback effects that occur when firms concentrate integration investment in only one complementary technology. Interestingly, this focused strategy often does not yield the highest market share or the lowest likelihood of bankruptcy. We characterize the situations under which each finding holds and describe the implications of these findings for theory, practice, and policy.
The Capitalization of Income Streams and the Effects of Open Market Policy under Fixed Exchange Rates

Abstract This paper investigates the long- and short-run neutrality of open-market monetary policy in a world of fixed exchange rates and imperfect substitutability between bonds denominated in different currencies. Using an illustrative portfolio-balance model, it shows that when the public discounts the future tax liabilities associated with the national debt and the central bank supports the exchange rate by trading non-interest-bearing foreign assets, open-market policy has a short-run effect, but no long-run effect, on the domestic price level and interest rate. When the foreign-exchange intervention assets earn interest that is rebated to and capitalized by the public, open-market policy loses even its short-run efficacy — the capital-account offset to monetary policy is complete.
Informing the Electorate? How Party Cues and Policy Information Affect Public Opinion about Initiatives

Citizens in representative democracies receive party endorsements and policy information when choosing candidates or making policy decisions via the initiative process. What effects do these sources of information have on public opinion? We address this important question by conducting survey experiments where citizens express opinions about initiatives in a real-world electoral context. We manipulate whether they receive party cues, policy information, both, or neither type of information. We find that citizens do not simply ignore policy information when they are also exposed to party cues. Rather, citizens respond by shifting their opinions away from their party's positions when policy information provides a compelling reason for doing so. These results challenge the prominent claim in public opinion research that citizens blindly follow their party when also exposed to policy information. They also suggest that efforts to inform the electorate can influence opinions, provided that citizens actually receive the information being disseminated.
Diversification cones, trade costs and factor market linkages

Abstract This paper finds non-uniform differences in the distribution functions of factor usage intensities among 10 rich OECD countries. The 10 countries form three distinct groups such that the between-group differences are more pronounced than within-group differences and capital-abundant countries are in capital-abundant groups. The estimation works even if the same industry codes represent different goods across countries in the data. The finding is consistent with the multiple-cone factor proportions theory with zero trade costs with each group being one cone. An alternative interpretation is non-zero trade costs. Both interpretations imply weak factor market linkages between the countries in different groups.
What Elena Kagan Could Have and Should Have Said (and Still Have Been Confirmed)

During her confirmation hearings, Justice Kagan backed away from numerous critical comments she had previously made about the nomination process. No one knows why she changed her mind but it is likely that the shift resulted more from a political calculation than a change of heart about the nature of the process. This Commentary suggests that Justice Kagan could have testified consistently with her previously expressed views and still have been confirmed.
The option to quit: The effect of employee stock options on turnover

Abstract We show that in the years following a large broad-based employee stock option (BBSO) grant, employee turnover falls at the granting firm. We find evidence consistent with a causal relation by exploiting unexpected changes in the value of unvested options. A large fraction of the reduction in turnover appears to be temporary with turnover increasing in the third year following the year of the adoption of the BBSO plan. The increase three years post-grant is equal in magnitude to the cumulative decrease in turnover over the three prior years, suggesting that long-vesting BBSO plans delay, instead of prevent, turnover.
Disruptive Technology and Common Law Lawmaking: A Brief Analysis of A&M Records, Inc. v. Napster, Inc.

This symposium Article analyzes the Ninth Circuit's decision in AM (2) the court's treatment of vicarious liablility for copyright infringement is doctrinally incoherent because it suggests that liability depends on whether a third party has "turn[ed] a blind eye" toward infringement or has exercised its supervisory power over the infringer to the "fullest extent"; and (3) the court's choice to sidestep analysis of the "service provider" safe harbors in Section 512 of the Copyright Act should have been better explained but was understandable given the state of the record.
The Lame Ducks of Marbury

Part of Marbury at 200: A Bicentennial Celebration of Marbury v. Madison. Marbury's Errors?
Risk and return in the Tehran stock exchange

Abstract This paper analyzes market index returns in the Tehran stock exchange (TSE) within the context of three variants of the Capital Asset Pricing Model: the static international; the constant-parameter intertemporal; and a Markov-switching intertemporal CAPM, which allows for time-varying degree of integration with regional and international equity markets. We find that TSE returns are CAPM-efficient at monthly frequency with respect to several international market indices. Moreover, we find evidence in support of international integration of the TSE with respect to international markets. In addition, we conduct an extensive investigation for the direction of causality between TSE returns, international market index returns, and those in neighboring countries.
THE PRIORITY OF RESPECT OVER REPAIR

Contemporary tort theory is dominated by a debate between legal economists and corrective-justice theorists. Legal economists suppose that tortfeasors and tortious wrongs are false targets for cheapest cost-avoiders and avoidable future losses. Corrective-justice theorists argue powerfully that the economic account of tort as search for cheapest cost-avoiders with respect to future accidents does not capture the most fundamental fact about tort adjudication, namely, that the reason we hold defendants liable in tort is that they have wronged their victims and should therefore repair the harm they have done. Deterring cheapest cost-avoiders from committing future harms no more justifies imposing liability in tort than deterring future crime justifies hanging the innocent. This is a powerful critique of the economic theory of tort, but it overshoots the mark. As an account of tort law, corrective justice puts the cart before the horse Tort is a law of wrongs, not just a law of redress for wrongs. Repairing harm wrongly done is the next best way of complying with an obligation not to do harm wrongly in the first place. Rights and remedies form a unity in which rights have priority. Corrective justice is thus an essential but subordinate aspect of tort. This paper develops this line of criticism of corrective-justice theory and offers an alternative account of tort that places primary norms of harm avoidance and respect for rights at its center. On this conception, tort is—as the corrective-justice theorists rightly insist—a law of wrongs, but its distinctiveness lies in the content and character of the wrongs with which it is concerned. At its core, tort is concerned with protecting essential conditions of individual agency.
The information content in implied idiosyncratic volatility and the cross-section of stock returns: Evidence from the option markets

Current literature is inconclusive as to whether idiosyncratic risk influences future stock returns and the direction of the impact. Earlier studies are based on historical realized volatility. Implied volatilities from option prices represent the market's assessment of future risk and are likely a superior measure to historical realized volatility. Implied idiosyncratic volatilities on firms with traded options are used to examine the relationship between idiosyncratic volatility and future returns. A strong positive link was found between implied idiosyncratic risk and future returns. After considering the impact of implied idiosyncratic volatility, historical realized idiosyncratic volatility is unimportant. This performance is strongly tied to small size and high book-to-market equity firms. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28: 1013–1039, 2008
Increasing firm value through detection and prevention of white-collar crime

White-collar crime can cost a company from 1 per cent to 6 per cent of annual sales, yet little is known about the organisational conditions that can reduce this cost. I find that the role of block holders, boards of directors or CEO compensation have little impact on fraud occurrence. Instead, operational governance, including clarity of policies and procedures, formal cross-company communication and performance-based pay for both the board and employees, significantly reduces the likelihood of a crime being committed.
How Big Are the Tax Benefits of Debt

I integrate under firm-specific benefit functions to estimate that the capitalized tax benefit of debt equals 9.7 percent of firm value (or as low as 4.3 percent, net of personal taxes). The typical firm could double tax benefits by issuing debt until the marginal tax benefit begins to decline. I infer how aggressively a firm uses debt by observing the shape of its tax benefit function. Paradoxically, large, liquid, profitable firms with low expected distress costs use debt conservatively. Product market factors, growth options, low asset collateral, and planning for future expenditures lead to conservative debt usage. Conservative debt policy is persistent.
Regulators and Redskins

We examine the correlation between federal government activity and performance of the capital's National Football League team, the Washington Redskins. We find a posi- tive, non-spurious, and robust correlation between the Redskins' winning percentage and bureaucratic output, measured by pages published in the Federal Register. Because the Red- skins' performance is prototypically exogenous, we give this result a causal interpretation and provide a plausible, causal mechanism: bureaucrats must make "logrolling" deals to expand their regulatory power, and a winning football team offers a shared source of opti- mism to lubricate such negotiations. We do not find the same correlation when examining congressional activity.
Trade, wages, and the political economy of trade protection: evidence from the Colombian trade reforms

Abstract Worker industry affiliation plays a crucial role in how trade policy affects wages in many trade models. Yet, most research has focused on how trade policy affects wages by altering the economy-wide returns to a specific worker characteristic (i.e., skill or education) rather than through worker industry affiliation. This paper exploits drastic trade liberalizations in Colombia in the 1980s and 1990s to investigate the relationship between protection and industry wage premiums. We relate wage premiums to trade policy in an empirical framework that accounts for the political economy of trade protection. Accounting for time-invariant political economy factors is critical. When we do not control for unobserved time-invariant industry characteristics, we find that workers in protected sectors earn less than workers with similar observable characteristics in unprotected sectors. Allowing for industry fixed effects reverses the result: trade protection increases relative wages. This positive relationship persists when we instrument for tariff changes. Our results are in line with short- and medium-run models of trade where labor is immobile across sectors or, alternatively, with the existence of industry rents that are reduced by trade liberalization. In the context of the current debate on the rising income inequality in developing countries, our findings point to a source of disparity beyond the well-documented rise in the economy-wide skill premium: because tariff reductions were proportionately larger in sectors employing a high fraction of less-skilled workers, the decrease in the wage premiums in these sectors affected such workers disproportionately.
Did the 2003 Tax Act Increase Capital Investments by Corporations

ABSTRACT: On May 28, 2003, the Jobs and Growth Tax Relief Reconciliation Act of 2003 (2003 Tax Act) reduced shareholder-level taxes on dividends and capital gains. One of the goals of the 2003 Tax Act was to encourage capital investment by corporations. We investigate whether firms increased investment in response to the Act. We first document that capital expenditures increase after the 2003 Tax Act. We then use a difference-in-differences research design to show that this increase in capital expenditures varies predictably with two shareholder-level tax-motivated hypotheses. First, we find that the increase in investment is smaller for firms largely held by investors that are less sensitive to shareholder-level taxes. Second, we find that the increase in investment is larger for firms most likely to fund investment from new equity issuances rather than internal funds. Additional analysis suggests that while the majority of firms increase investment after the tax cut, a small subset of larger, older, and...
Do enterprise zones create jobs? Evidence from California’s enterprise zone program

We use new establishment-level data and geographic mapping methods to improve upon evaluations of the effectiveness of state enterprise zones, focusing on California’s program. Because zone boundaries do not follow census tracts or zip codes, we created digitized maps of original zone boundaries and later expansions. We combine these maps with geocoded observations on most businesses located in California. The evidence indicates that enterprise zones do not increase employment. We also find no shift of employment toward the lower-wage workers targeted by enterprise zone incentives. We conclude that the program is ineffective in achieving its primary goals.
One small step towards a metatheory of evidence and proof

Many challenges to a probabilistic account of evidence fail to distinguish two separate questions. One is the question of whether the evaluation of individual items of evidence is probabilistic or ...
The Dual Foundation of Universal Jurisdiction: Towards a Jurisprudence for the ‘Court of Critique’

This article revisits the case of The State of Israel v Adolph Eichmann and calls for renewed attention to the analysis of universal jurisdiction in this early example of it. Precisely because the ...
Transport provision to tourists by a cost minimizing firm: a stochastic characterization

We first delineate a general model that accounts for the common features of sightseeing trips to city attractions and to locations such as lakes and parks. Next, we use the theory of discrete-time Markov chains (DTMCs) to formalize this general model and then we compute the one-step transition probabilities of our Markov chain theoretic model. Finally, we show how our Markovian modeling approach can be used to derive the long run expected cost per day that a private firm, in the business of providing transport to tourists, might minimize.
GAT, Solvay, and the Centralization of Patent Litigation in Europe

As business has become global, so have disputes about patents, patent infringement, and patent validity. In many ongoing disputes between patent holders and alleged infringers, parties are engaging in parallel court proceedings in multiple countries to litigate infringements of parallel patents and to contest the validity of patents. Concentrating litigation involving identical inventions and identical or similar conduct into one national or multinational court could result in faster, more efficient, and more consistent enforcement of patents. There are a number of obstacles to any centralization of litigation. As the debate about a unified patent litigation system in Europe has shown, even countries that are geographically, economically, culturally, and historically proximate face substantial challenges when attempting to adopt a single centralized patent litigation mechanism, even if they link the introduction of the mechanism to the creation of a unitary patent.
Noise or Signal: The Role of Image Backgrounds in Object Recognition

We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds. We create a toolkit for disentangling foreground and background signal on ImageNet images, and find that (a) models can achieve non-trivial accuracy by relying on the background alone, (b) models often misclassify images even in the presence of correctly classified foregrounds--up to 88% of the time with adversarially chosen backgrounds, and (c) more accurate models tend to depend on backgrounds less. Our analysis of backgrounds brings us closer to understanding which correlations machine learning models use, and how they determine models' out of distribution performance.
MALI: A memory efficient and reverse accurate integrator for Neural ODEs

Neural ordinary differential equations (Neural ODEs) are a new family of deep-learning models with continuous depth. However, the numerical estimation of the gradient in the continuous case is not well solved: existing implementations of the adjoint method suffer from inaccuracy in reverse-time trajectory, while the naive method and the adaptive checkpoint adjoint method (ACA) have a memory cost that grows with integration time. In this project, based on the asynchronous leapfrog (ALF) solver, we propose the Memory-efficient ALF Integrator (MALI), which has a constant memory cost w.r.t integration time similar to the adjoint method, and guarantees accuracy in reverse-time trajectory (hence accuracy in gradient estimation). We validate MALI in various tasks: on image recognition tasks, to our knowledge, MALI is the first to enable feasible training of a Neural ODE on ImageNet and outperform a well-tuned ResNet, while existing methods fail due to either heavy memory burden or inaccuracy; for time series modeling, MALI significantly outperforms the adjoint method; and for continuous generative models, MALI achieves new state-of-the-art performance. We provide a pypi package: https://jzkay12.github.io/TorchDiffEqPack
FlexPD: A Flexible Framework of First-Order Primal-Dual Algorithms for Distributed Optimization

In this paper, we study the problem of minimizing a sum of convex objective functions, which are locally available to agents in a network. Distributed optimization algorithms make it possible for the agents to cooperatively solve the problem through local computations and communications with neighbors. Lagrangian-based distributed optimization algorithms have received significant attention in recent years, due to their exact convergence property. However, many of these algorithms have slow convergence or are expensive to execute. In this paper, we propose a flexible framework of first-order primal-dual algorithms (FlexPD), which allows for an arbitrary number of primal steps per iteration. This framework includes three algorithms, FlexPD-F, FlexPD-G, and FlexPD-C that can be customized for various applications with different computation and communication requirements. For strongly convex and Lipschitz gradient objective functions, by carefully controlling the stepsize choices, we establish linear convergence of our proposed framework to the optimal solution. Simulation results confirm the superior performance of our framework compared to the existing methods.
Timing CEO turnovers: Evidence from delegation in mergers and acquisitions

Abstract We examine the role of delegation in predicting CEO successions. Using a novel proxy for delegation in mergers and acquisitions, we find that overall CEO turnover rates are about one third higher following deals where the CEO delegates to a senior manager versus deals with no observable delegation. The delegation-turnover relation is strongest when deals are delegated to heirs apparent, the CEO is older, or the delegation decision is unexpected. Voluntary turnovers are more frequent following delegated deals than non-delegated deals, consistent with delegation signaling an orderly succession. The delegation-turnover relation fades over time while other predictors of turnover such as profitability, CEO age, and the presence of an heir apparent in the corporate hierarchy continue to remain significant up to five years after the deal. Our findings suggest that delegation is unique among our predictors of turnover in the sense that it captures near term orderly successions.
Generalized k-means in GLMs with applications to the outbreak of COVID-19 in the United States

Abstract Generalized k -means can be combined with any similarity or dissimilarity measure for clustering. Using the well known likelihood ratio or F -statistic as the dissimilarity measure, a generalized k -means method is proposed to group generalized linear models (GLMs) for exponential family distributions. Given the number of clusters k , the proposed method is established by the uniform most powerful unbiased (UMPU) test statistic for the comparison between GLMs. If k is unknown, then the proposed method can be combined with generalized liformation criterion (GIC) to automatically select the best k for clustering. Both AIC and BIC are investigated as special cases of GIC. Theoretical and simulation results show that the number of clusters can be correctly identified by BIC but not AIC. The proposed method is applied to the state-level daily COVID-19 data in the United States, and it identifies 6 clusters. A further study shows that the models between clusters are significantly different from each other, which confirms the result with 6 clusters.
Political Costs of Tax-Based Consolidations

This paper studies the impact of tax-based consolidations on reelection outcomes. Using a granular database of tax-based consolidations for a panel of 10 OECD countries over the last 40 years, we find that tax reforms are politically costly but some reforms are costlier than others. Measures aimed primarily at reducing existing deficits and debt are costlier than tax consolidation policies for improving long-term growth prospects. Electoral costs are particularly high for broad-based indirect tax and corporate tax reforms. Voters tend to penalize governments less if tax consolidations are announced early in the government's term or if the government has a strong political mandate. Favorable economic conditions increase public support for tax-based consolidations. Personal income tax reforms are electorally salient if the reforms are frontloaded, announced during recessions, and in less progressive tax systems.
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting

The COVID-19 pandemic represents the most significant public health disaster since the 1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatio-temporal forecasting of epidemic dynamics is crucial. Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting. Here we focus on the design and analysis of deep learning-based models for COVID-19 forecasting. We implement multiple recurrent neural network-based deep learning models and combine them using the stacking ensemble technique. In order to incorporate the effects of multiple factors in COVID-19 spread, we consider multiple sources such as COVID-19 confirmed and death case count data and testing data for better predictions. To overcome the sparsity of training data and to address the dynamic correlation of the disease, we propose clustering-based training for high-resolution forecasting. The methods help us to identify the similar trends of certain groups of regions due to various spatio-temporal effects. We examine the proposed method for forecasting weekly COVID-19 new confirmed cases at county-, state-, and country-level. A comprehensive comparison between different time series models in COVID-19 context is conducted and analyzed. The results show that simple deep learning models can achieve comparable or better performance when compared with more complicated models. We are currently integrating our methods as a part of our weekly forecasts that we provide state and federal authorities.
Numerical solution for the stress near a hole with corners in an infinite plate under biaxial loading

We consider the elastic stress near a hole with corners in an infinite plate under biaxial stress. The elasticity problem is formulated using complex Goursat functions, resulting in a set of singular integro-differential equations on the boundary. The resulting boundary integral equations are solved numerically using a Chebyshev collocation method which is augmented by a fractional power term, derived by asymptotic analysis of the corner region, to resolve stress singularities at corners of the hole. We apply our numerical method to the test case of the hole formed by two partially overlapping circles, which can include either a corner pointing into the solid or a corner pointing out of the solid. Our numerical results recover the exact stress on the boundary to within relative error $$10^{-3}$$ for modest computational effort.
Control and readout of a superconducting qubit using a photonic link.

Delivering on the revolutionary promise of a universal quantum computer will require processors with millions of quantum bits (qubits)1–3. In superconducting quantum processors4, each qubit is individually addressed with microwave signal lines that connect room-temperature electronics to the cryogenic environment of the quantum circuit. The complexity and heat load associated with the multiple coaxial lines per qubit limits the maximum possible size of a processor to a few thousand qubits5. Here we introduce a photonic link using an optical fibre to guide modulated laser light from room temperature to a cryogenic photodetector6, capable of delivering shot-noise-limited microwave signals directly at millikelvin temperatures. By demonstrating high-fidelity control and readout of a superconducting qubit, we show that this photonic link can meet the stringent requirements of superconducting quantum information processing7. Leveraging the low thermal conductivity and large intrinsic bandwidth of optical fibre enables the efficient and massively multiplexed delivery of coherent microwave control pulses, providing a path towards a million-qubit universal quantum computer. High-fidelity control and readout of a superconducting qubit is performed with a low-noise optical fibre link that delivers microwave signals directly to the millikelvin quantum computing environment.
Reconstruction algorithms for low-rank tensors and depth-3 multilinear circuits

We give new and efficient black-box reconstruction algorithms for some classes of depth-3 arithmetic circuits. As a consequence, we obtain the first efficient algorithm for computing the tensor rank and for finding the optimal tensor decomposition as a sum of rank-one tensors when then input is a constant-rank tensor. More specifically, we provide efficient learning algorithms that run in randomized polynomial time over general fields and in deterministic polynomial time over and for the following classes: 1) Set-multilinear depth-3 circuits of constant top fan-in ((k) circuits). As a consequence of our algorithm, we obtain the first polynomial time algorithm for tensor rank computation and optimal tensor decomposition of constant-rank tensors. This result holds for d dimensional tensors for any d, but is interesting even for d=3. 2) Sums of powers of constantly many linear forms ((k) circuits). As a consequence we obtain the first polynomial-time algorithm for tensor rank computation and optimal tensor decomposition of constant-rank symmetric tensors. 3) Multilinear depth-3 circuits of constant top fan-in (multilinear (k) circuits). Our algorithm works over all fields of characteristic 0 or large enough characteristic. Prior to our work the only efficient algorithms known were over polynomially-sized finite fields (see. Karnin-Shpilka 09’). Prior to our work, the only polynomial-time or even subexponential-time algorithms known (deterministic or randomized) for subclasses of (k) circuits that also work over large/infinite fields were for the setting when the top fan-in k is at most 2 (see Sinha 16’ and Sinha 20’).
Cloud–Precipitation Hybrid Regimes and Their Projection onto IMERG Precipitation Data

We extend and enhance the concept of the Cloud Regimes (CRs) developed from two-dimensional joint histograms of cloud optical thickness and cloud top pressure from the Moderate Resolution Imaging Spectroradiometer (MODIS), by adding precipitation information in order to better understand cloud-precipitation relationships. Taking advantage of the high-resolution Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation dataset, cloud-precipitation “hybrid” regimes are derived by implementing the k-means clustering algorithm with advanced initialization and objective measures to determine the most optimal clusters. By expressing precipitation rates within 1-degree grid cell as histograms and making choices on the relative weight of cloud and precipitation, we could obtain several editions of hybrid cloud-precipitation regimes (CPRs), and examine their characteristics. In the deep tropics, when precipitation is weighted weakly, the cloud part of the hybrid entroids resembles the centroid of cloud-only regimes, but still tightens the cloud-precipitation relationship by decreasing the precipitation variability of each regime. As precipitation weight progressively increases, the shape of the cloudy part of the hybrid centroids becomes blunter, while the precipitation part of the centroids sharpens. In the case where cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct features in the precipitation parts of the centroids, which allows us to project them onto the 30-minly IMERG domain. Such a projection can be used to overcome the temporal sparseness of MODIS cloud observations, which leads to great application potential for various convection-focused studies, including diurnal cycle analysis.
Social Responsibility Auditing in Supply Chain Networks

We study a buyer’s problem of auditing suppliers within an existing network to ensure social responsibility compliance. The buyer suffers economic damages if a violation at a supplier is exposed (w...
Primal Superlinear Convergence of Sqp Methods in Piecewise Linear-Quadratic Composite Optimization

This paper mainly concerns with the primal superlinear convergence of the quasi-Newton sequential quadratic programming (SQP) method for piecewise linear-quadratic composite optimization problems. We show that the latter primal superlinear convergence can be justified under the noncriticality of Lagrange multipliers and a version of the Dennis-More condition. Furthermore, we show that if we replace the noncriticality condition with the second-order sufficient condition, this primal superlinear convergence is equivalent with an appropriate version of the Dennis-More condition. We also recover Bonnans’ result in (Appl. Math. Optim. 29, 161–186, 1994) for the primal-dual superlinear of the basic SQP method for this class of composite problems under the second-order sufficient condition and the uniqueness of Lagrange multipliers. To achieve these goals, we first obtain an extension of the reduction lemma for convex Piecewise linear-quadratic functions and then provide a comprehensive analysis of the noncriticality of Lagrange multipliers for composite problems. We also establish certain primal estimates for KKT systems of composite problems, which play a significant role in our local convergence analysis of the quasi-Newton SQP method.
The distribution of indirect cost recovery in academic research

Research universities rely heavily on external funding to advance knowledge and generate economic growth. In the US, tens of billions of dollars are spent each year on research and development with the federal government contributing over half of these funds. Yet a decline in relative federal funding highlights the role of other funders and their varying contractual terms. Specifically, non-federal funders provide lower recovery of indirect costs. Using project-level university sponsored research administrative records from four institutions, we examine indirect cost recovery. We find significant variation in the amount of indirect funding recovered – both across and within funders, as well as to different academic fields within a university. The distribution of sponsors in the overall research funding portfolio also impacts indirect cost recovery. The recovery variation has important implications for the sustainability and cross-subsidization of the university research enterprise. Together, our results show where universities are under-recovering indirect costs.
Homo Sacer, Homosexual: Some Thoughts on Waging Tax Guerrilla Warfare

In fact, it is now clear to most that the social and political forces now holding power are beyond simply opposing issues supportive to LGBT people and have now moved to open warfare against all that they hold in contempt, including and especially the LGBT community. It is then little surprise that LGBT communities are experiencing not only unprecedented attacks politically, but have also been living through an unprecedented and sustained increase in anti-LGBT violence. —National Coalition of Anti-Violence Programs1
Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Reading

We propose a simple and effective strategy for data augmentation for low-resource machine reading comprehension (MRC). Our approach first pretrains the answer extraction components of a MRC system on the augmented data that contains approximate context of the correct answers, before training it on the exact answer spans. The approximate context helps the QA method components in narrowing the location of the answers. We demonstrate that our simple strategy substantially improves both document retrieval and answer extraction performance by providing larger context of the answers and additional training data. In particular, our method significantly improves the performance of BERT based retriever (15.12%), and answer extractor (4.33% F1) on TechQA, a complex, low-resource MRC task. Further, our data augmentation strategy yields significant improvements of up to 3.9% exact match (EM) and 2.7% F1 for answer extraction on PolicyQA, another practical but moderate sized QA dataset that also contains long answer spans.
Sub-second and ppm-level optical sensing of hydrogen using templated control of nano-hydride geometry and composition.

The use of hydrogen as a clean and renewable alternative to fossil fuels requires a suite of flammability mitigating technologies, particularly robust sensors for hydrogen leak detection and concentration monitoring. To this end, we have developed a class of lightweight optical hydrogen sensors based on a metasurface of Pd nano-patchy particle arrays, which fulfills the increasing requirements of a safe hydrogen fuel sensing system with no risk of sparking. The structure of the optical sensor is readily nano-engineered to yield extraordinarily rapid response to hydrogen gas (<3 s at 1 mbar H2) with a high degree of accuracy (<5%). By incorporating 20% Ag, Au or Co, the sensing performances of the Pd-alloy sensor are significantly enhanced, especially for the Pd80Co20 sensor whose optical response time at 1 mbar of H2 is just ~0.85 s, while preserving the excellent accuracy (<2.5%), limit of detection (2.5 ppm), and robustness against aging, temperature, and interfering gases. The superior performance of our sensor places it among the fastest and most sensitive optical hydrogen sensors.
A billiards-like dynamical system for attacking chess pieces

Abstract We apply a one-dimensional discrete dynamical system originally considered by Arnol’d reminiscent of mathematical billiards to the study of two-move riders, a type of fairy chess piece. In this model, particles travel through a bounded convex region along line segments of one of two fixed slopes. We apply this dynamical system to characterize the vertices of the inside-out polytope arising from counting placements of nonattacking chess pieces and also to give a bound for the period of the counting quasipolynomial. The analysis focuses on points of the region that are on trajectories that contain a corner or on cycles of full rank, or are crossing points thereof. As a consequence, we give a simple proof that the period of the bishops’ counting quasipolynomial is 2, and provide formulas bounding periods of counting quasipolynomials for many two-move riders including all partial nightriders. We draw parallels to the theory of mathematical billiards and pose many new open questions.
Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

The ability to regulate emotions is key to goal attainment and well-being. Although much has been discovered about neurodevelopment and the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks. Here we employed a network neuroscience framework to parse the neural underpinnings of emotion regulation skill acquisition, while accounting for age, in a sample of children and adolescents (N = 70, 34 female, aged 8-17 years). Focusing on three key network metrics-network differentiation, modularity, and community number differences between active regulation and a passive emotional baseline-we found that the control network, the default mode network, and limbic network were each related to emotion regulation ability while controlling for age. Greater network differentiation in the control and limbic networks was related to better emotion regulation ability. With regards to network community structure (modularity and community number), more communities and more crosstalk between modules (i.e., less modularity) in the control network were associated with better regulatory ability. By contrast, less crosstalk (i.e., greater modularity) between modules in the default mode network was associated with better regulatory ability. Together, these findings highlight whole-brain connectome features that support the acquisition of emotion regulation in youth.
Diagnosis of Acute Poisoning using explainable artificial intelligence

Abstract Introduction Medical toxicology is the clinical specialty that treats the toxic effects of substances, for example, an overdose, a medication error, or a scorpion sting. The volume of toxicological knowledge and research has, as with other medical specialties, outstripped the ability of the individual clinician to entirely master and stay current with it. The application of machine learning/artificial intelligence (ML/AI) techniques to medical toxicology is challenging because initial treatment decisions are often based on a few pieces of textual data and rely heavily on experience and prior knowledge. ML/AI techniques, moreover, often do not represent knowledge in a way that is transparent for the physician, raising barriers to usability. Logic-based systems are more transparent approaches, but often generalize poorly and require expert curation to implement and maintain. Methods We constructed a probabilistic logic network to model how a toxicologist recognizes a toxidrome, using only physical exam findings. Our approach transparently mimics the knowledge representation and decision-making of practicing clinicians. We created a library of 300 synthetic cases of varying clinical complexity. Each case contained 5 physical exam findings drawn from a mixture of 1 or 2 toxidromes. We used this library to evaluate the performance of our probabilistic logic network, dubbed Tak, against 2 medical toxicologists, a decision tree model, as well as its ability to recover the actual diagnosis. Results The inter-rater reliability between Tak and the consensus of human raters was κ = 0.8432 for straightforward cases, 0.4396 for moderately complex cases, and 0.3331 for challenging cases. The inter-rater reliability between the decision tree classifier and the consensus of human raters was, κ = 0.2522 for straightforward cases, 0.1963 for moderately complex cases and 0.0331 for challenging cases. Conclusions The software, dubbed Tak, performs comparably to humans on straightforward cases and intermediate difficulty cases, but is outperformed by humans on challenging clinical cases. Tak outperforms a decision tree classifier at all levels of difficulty. Our results are a proof-of-concept that, in a restricted domain, probabilistic logic networks can perform medical reasoning comparably to humans.