cssi/SciSciGPT-SciSciNet huggingface dataset:
The Changing Behavior of Trading Volume Reactions to Earnings Announcements: Evidence of the Increasing Use of Accounting Earnings News by Investors

The increase in investor diversity over the last 35–40 years prompted us to revisit trading volume reactions to earnings announcements and how these reactions vary with firm size. We argue that this increase in investor diversity would likely increase differences in the precision of pre-announcement information around earnings announcements, particularly for large firms. This suggests that the role of earnings announcements in resolving investor disagreement, as reflected in trading volume reactions, has increased. Over the 35-year period 1977–2011, we find a dramatic increase in the magnitude and frequency of volume reactions to earnings announcements, particularly for large firms. The increase in large firms’ trading volume reactions is so pronounced that the relation between volume reactions and firm size has turned positive in recent years, reversing Bamber's (1986, 1987) previously documented negative relation. We provide intuition and empirical evidence that our results are attributable to the resolution of differential prior precision among increasingly diverse investors following large firms. This article is protected by copyright. All rights reserved.
The effects of U.S. monetary policy on Colombia and Panama (2002–2007)

Abstract I study the economies of Colombia (floating exchange rate) and Panama (dollarized) to illustrate how the monetary policy of a large economy can export capital structure distortions to small open economies that follow different exchange rate regimes. The paper contributes to the literature on international business cycles in two ways. First, it adds to recent research that extends the Mises–Hayek business cycle theory to an international context. Second, most current research abstracts from effects on the production structures of emerging market economies when analyzing the transmission of monetary policy shocks. This paper seeks to fill this gap by studying structural effects of U.S. monetary policy on the economies of Colombia (floating exchange rate) and Panama (dollarized).
Booms, Busts, and Fraud

Firms sometimes commit fraud by altering publicly reported information to be more favorable, and investors can monitor flrms to obtain more accurate information. We study equilibrium fraud and monitoring decisions. Fraud is most likely to occur in relatively good times, and the link between fraud and good times becomes stronger as monitoring costs decrease. Nevertheless, improving business conditions may sometimes diminish fraud. We provide an explanation for why fraud peaks towards the end of a boom and is then revealed in the ensuing bust. We also show that fraud can increase if flrms make more information available to the public.
Mitigate, tolerate or relocate? Offshoring challenges, strategic imperatives and resource constraints

This paper examines key firm-level factors influencing initial strategic responses to offshoring implementation challenges. Comparative case findings indicate that firms are likely to try to mitigate challenges if they perceive to have control over their cause; if strategic objectives are diverse; and if firms have abundant resources available. By comparison, firms tolerate challenges if cost is a strategic imperative, or if resource endowments are limited. Firms relocate operations temporarily or permanently in particular if challenges are externally caused, whereby temporary relocation requires investments into flexible global infrastructures. Findings reveal critical contingencies of capability development and learning in offshoring and beyond.
The size and power of the variance ratio test in finite samples: A Monte Carlo investigation

We examine the finite-sample properties of the variance ratio test of the random walk hypothesis via Monte Carlo simulations under two null and three alternative hypotheses. These results are compared to the performance of the Dickey–Fuller t and the Box–Pierce Q statistics. Under the null hypothesis of a random walk with independent and identically distributed Gaussian increments, the empirical size of all three tests are comparable. Under a heteroscedastic random walk null, the variance ratio test is more reliable than either the Dickey–Fuller or Box–Pierce tests. We compute the power of these three tests against three alternatives of recent empirical interest: a stationary AR(1), the sum of this AR(1) and a random walk, and an integrated AR(1). By choosing the sampling frequency appropriately, the variance ratio test is shown to be as powerful as the Dickey–Fuller and Box–Pierce tests against the stationary alternative and is more powerful than either of the two tests against the two unit root alternatives.
International Credit Flows and Pecuniary Externalities

This paper develops a dynamic two-country neoclassical stochastic growth model with incomplete markets. Short-term credit flows can be excessive and reverse suddenly. The equilibrium outcome is constrained inefficient due to pecuniary externalities. First, an undercapitalized country borrows too much since each firm does not internalize that an increase in production capacity undermines their output price, worsening their terms of trade. From an ex ante perspective each firm undermines the natural “terms of trade hedge.” Second, sudden stops and fire sales lead to sharp price drops of illiquid capital. Capital controls or domestic macro-prudential measures that limit short-term borrowing can improve welfare. ( JEL F32, F43, G15, O41)
Criminal Case Complexity: An Empirical Perspective

Criminal case complexity persists as a central tenet in many academic and public critiques of our legal system even though little is known about two critical questions. One question is whether key actors (juries, attorneys, and judges) view case complexity similarly. In other words, do juries, attorneys, and judges agree on whether a case is complex? A second question involves the determinants of case complexity for each group. That is, what factors make a case more (or less) complex for juries, attorneys, and judges. This article explores both questions from an empirical perspective with the benefit of recent data from four jurisdictions. The data are important because, within the context of criminal cases, they permit analyses of agreement levels among the three key actors. Results suggest that the three sets of actors possess slightly different views on whether cases are complex. Judges reported the lowest levels of case complexity; jurors the highest. Moreover, important variation exists in terms of what made cases complex for each group. The results implicate legal reform efforts. No clear consensus exists among the critical actors on complexity perceptions. Many of the variables that influence case complexity fall outside of reformers’ reach. Variables within the reach of policy do not appear to systematically reduce case complexity.
A cross-firm analysis of the impact of corporate governance on the East Asian financial crisis

Abstract In a sample of 398 firms from Indonesia, Korea, Malaysia, the Philippines, and Thailand, firm-level differences in variables related to corporate governance had a strong impact on firm performance during the East Asian financial crisis of 1997–1998. Significantly better stock price performance is associated with firms that had indicators of higher disclosure quality (ADRs and auditors from Big Six accounting firms), with firms that had higher outside ownership concentration, and with firms that were focused rather than diversified. The results suggest that individual firms have some power to preclude expropriation of minority shareholders if legal protection is inadequate.
Achy But Healthy; Lessons From the Israeli Health Sector

Health policy has been for some time high on the agenda of many countries--and where it has not, it should be. Since no ideal model of health services has ever been devised 9 one may look for favorable elements in the health sector of a given country and examine their applicability to other countries. This paper analyzes Israel’s health sector in this context.
ETF Premiums and Liquidity Segmentation

Exchange traded funds (ETFs) provide a means for investors to access assets indirectly that may be accessible at a high cost otherwise. I show that liquidity segmentation can explain the tendency for ETFs to trade at a premium to net asset value (NAV) as well as the lifeâ€ cycle pattern in premiums. ETFs with larger NAV tracking error standard deviations (TESDs) tend to trade at higher premiums and the liquidity benefits offered by foreign ETFs and fixed income ETFs are revealed to be the most valuable to investors. Further tests validate that TESD has the desirable properties of a liquidity segmentation measure.
The Minimum Wage as Industrial Policy: A Forgotten Role

In the welter of arguments being debated in connection with amending minimum wage legislation, the protagonists have lost sight of the original intent of such state intervention. That purpose was to help -- exclusively -- those workers whose wage formation process was subject to “market failure” by forcing their employers to internalize the minimum social costs of maintaining a worker, which they had succeeded in shifting onto the worker or society.Although the minimum wage was obviously also designed to create micro-welfare effects, its primary function lay in removing labor costs from competition, increasing productivity macro-economically by driving “parasitic” firms out of business and concentrating production in the most competent firms, and steering capital-labor relations.The chief objective of this Article is to recover the missing theoretical and political underpinnings of the current controversy by examining earlier political and economic debates in which arguments of principle tended not to be subordinated to those of expediency.
Growing Efficient Deep Networks by Structured Continuous Sparsification

We develop an approach to growing deep network architectures over the course of training, driven by a principled combination of accuracy and sparsity objectives. Unlike existing pruning or architecture search techniques that operate on full-sized models or supernet architectures, our method can start from a small, simple seed architecture and dynamically grow and prune both layers and filters. By combining a continuous relaxation of discrete network structure optimization with a scheme for sampling sparse subnetworks, we produce compact, pruned networks, while also drastically reducing the computational expense of training. For example, we achieve 49.7% inference FLOPs and 47.4% training FLOPs savings compared to a baseline ResNet-50 on ImageNet, while maintaining 75.2% top-1 validation accuracy --- all without any dedicated fine-tuning stage. Experiments across CIFAR, ImageNet, PASCAL VOC, and Penn Treebank, with convolutional networks for image classification and semantic segmentation, and recurrent networks for language modeling, demonstrate that we both train faster and produce more efficient networks than competing architecture pruning or search methods.
Is It Immoral To Punish The Heedless And Clueless? A Comment On Alexander, Ferzan And Morse: Crime And Culpability

In a typical scene from the 1960s retro television series Mad Men, a mother watches her children putting plastic bags from the drycleaners over their heads, chastising them... for messing up the clothes inside. This, as one critic notes, is one of those 'retrospective winks at past ignorance' that make Mad Men 'so funny and, at times, so chilling'.1 The joke is premised on a measurable national shift in knowledge about the dangers of allowing children to play with dry cleaning bags. Perceptions of risk both shape and are shaped by the criminal law. Risks that were unknown or underestimated at one time (for example, certain vehicular risks) now give rise to criminal liability.2 The criminal law reflects knowledge of risk and assessments of what risks are reasonable. At the same time, criminal sanctions communicate norms and educate the public about blameworthy risk-taking.
On Social and Moral Revival

A SSUMING that American society (and to a lesser extent other Western societies) has experienced a breakdown of community, how may it be resurrected? Will it suffice to reweave the frayed social bonds, or is the recreation of a moral culture also essential? If such a culture is needed, does the breakdown allow us to fashion one from scratch, or must it be constructed on the old foundations? Can this culture be formed through reasoned deliberations or must it be formed through some rather different social processes? And can there be a moral culture yet one that is "nonjudgmental"? I will draw upon the relevant points of three important books that speak to these issues but no comprehensive review of their texts is provided.
Understanding the heterogeneous effects of gasoline taxes across income and location

Abstract Using confidential household-level data, we estimate spatial and demographic heterogeneity in household-level gasoline price elasticities and gasoline tax burdens from a 60-cent federal gasoline tax increase. In order to address unobserved consumer heterogeneity, we employ a parametric adaptation of the maximum score methodology to model the household’s choice of vehicles and driving decisions, allowing for disaggregated vehicle choices, vehicle-specific fixed effects, and interactions among the multiple vehicles in each household. We estimate average price elasticities of demand for gasoline at -0.74, with significant heterogeneity across space and demographics. We also find that rural and poor households bear the greater share of the burden from increasing taxes. Finally, we suggest a revenue recycling policy that helps mitigate the gasoline tax's resulting economic inequalities across household location and income. In addition, though a gasoline tax is an effective policy for reducing the externalities associated with driving, our policy also allows the government to retain some revenue from the tax even after the revenue recycling.
FastTrack, a strategy to shorten time to degree

Recent reports express concern about the sustainability of the biomedical research enterprise in its current form. Recurring concerns include the predictability and sustainability of funding for research, regulatory burden and training the next generation in the biomedical workforce. One specific concern is the duration of training periods during pre-doctoral and post-doctoral studies. This article addresses the issue of time-to-degree (TTD) for doctorates. Many reports stress the importance of shortening the TTD, but provide no recommendations to achieve this goal. Herein, factors potentially affecting TTD are discussed and one mechanism that harmonizes undergraduate and graduate programs is proposed as a strategy to reduce the TTD.
Initial determination of COVID-19 seroprevalence among outpatients and healthcare workers in Minnesota using a novel SARS-CoV-2 total antibody ELISA.

Abstract Objectives To avoid the significant risks posed by the use of COVID-19 serology tests with supply chain constraints or poor performance characteristics, we developed an in-house SARS-CoV-2 total antibody test . Our test was compared with three commercial methods, and was used to determine COVID-19 seroprevalence among healthcare workers and outpatients in Minnesota. Methods Seventy-nine plasma and serum samples from 50 patients 4–69 days after symptom onset who tested positive by a SARS-CoV-2 PCR method using a nasopharyngeal (NP) swab were used to evaluate our test’s clinical performance. Seropositive samples were analyzed for IgG titers in a follow-up assay. Thirty plasma and serum from 12 patients who tested negative by a SARS-CoV-2 PCR method using a nasopharyngeal (NP) swab and 210 negative pre-pandemic serum samples were also analyzed. Among samples from patients > 14 days after symptom onset, the assay had 100% clinical sensitivity and 100% clinical specificity, 100% positive predictive value and 100% negative predictive value. Analytical specificity was 99.8%, indicating minimal cross-reactivity. A screening study was conducted to ascertain COVID-19 seroprevalence among healthcare workers and outpatients in Minnesota. Results Analysis of serum collected between April 13 and May 21, 2020 indicated a COVID-19 seroprevalence of 2.96% among 1,282 healthcare workers and 4.46% among 2,379 outpatients. Conclusions Our in-house SARS-CoV-2 total antibody test can be used to conduct reliable epidemiological studies to inform public health decisions during the COVID-19 pandemic.
Collision dominated, ballistic, and viscous regimes of terahertz plasmonic detection by graphene

The terahertz detection performance and operating regimes of graphene plasmonic field-effect transistors (FETs) were investigated by a hydrodynamic model. Continuous wave detection simulations showed that the graphene response sensitivity is similar to that of other materials including Si, InGaAs, GaN, and diamond-based FETs. However, the pulse detection results indicated a very short response time, which favors rapid/high-sensitively detection. The analysis on the mobility dependence of the response time revealed the same detection regimes as the traditional semiconductor materials, i.e., the non-resonant (collision dominated) regime, the resonant ballistic regime, and the viscous regime. When the kinematic viscosity (ν) is above a certain critical viscosity value, νNR, the plasmonic FETs always operates in the viscous non-resonant regime, regardless of channel length (L). In this regime, the response time rises monotonically with the increase of L. When ν < νNR, the plasmonic resonance can be reached in a certain range of L (i.e., the resonant window). Within this window, the carrier transport is ballistic. For a sufficiently short channel, the graphene devices would always operate in the non-resonant regime, regardless of the field-effect mobility, corresponding to another viscous regime. The above work mapped the operating regimes of graphene plasmonic FETs and demonstrated the significance of the viscous effects for the graphene plasmonic detection. These results could be used for the extraction of the temperature dependences of viscosity in graphene.
COVID-19 Vaccine Acceptance among Health Care Workers in the United States.

Background: Acceptance of the COVID-19 vaccine will play a major role in combating the pandemic. Healthcare workers (HCWs) are among the first group to receive vaccination, so it is important to consider their attitudes about COVID-19 vaccination to better address barriers to widespread vaccination acceptance. Methods: We conducted a cross sectional study to assess the attitude of HCWs toward COVID-19 vaccination. Data were collected between 7 October and 9 November 2020. We received 4080 responses out of which 3479 were complete responses and were included in the final analysis. Results: 36% of respondents were willing to take the vaccine as soon as it became available while 56% were not sure or would wait to review more data. Only 8% of HCWs do not plan to get vaccine. Vaccine acceptance increased with increasing age, education, and income level. A smaller percentage of female (31%), Black (19%), Lantinx (30%), and rural (26%) HCWs were willing to take the vaccine as soon as it became available than the overall study population. Direct medical care providers had higher vaccine acceptance (49%). Safety (69%), effectiveness (69%), and speed of development/approval (74%) were noted as the most common concerns regarding COVID-19 vaccination in our survey.
Visual spoofing in content-based spam detection

Although the problem of spam classification seems to be solved, there are still vulnerabilities in the current spam filters that could be easily exploited. We present one such vulnerability, in which one could replace some characters with corresponding characters from a different alphabet. These characters are visually similar, yet have a different Unicode encoding. With this approach spammers can create messages that bypass existing spam filters. Moreover, we show that this approach can be used to avoid plagiarism detection, and in other applications that use natural language processing for automatic analysis of text documents.
Incentivising Exploration and Recommendations for Contextual Bandits with Payments

We propose a contextual bandit based model to capture the learning and social welfare goals of a web platform in the presence of myopic users. By using payments to incentivize these agents to explore different items/recommendations, we show how the platform can learn the inherent attributes of items and achieve a sublinear regret while maximizing cumulative social welfare. We also calculate theoretical bounds on the cumulative costs of incentivization to the platform. Unlike previous works in this domain, we consider contexts to be completely adversarial, and the behavior of the adversary is unknown to the platform. Our approach can improve various engagement metrics of users on e-commerce stores, recommendation engines and matching platforms.
Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection

Recent advances in the area of plane segmentation from single RGB images show strong accuracy improvements and now allow a reliable segmentation of many indoor scenes into planes. Nonetheless, fine-grained details of these segmentation masks are still lacking accuracy, thus restricting the usability of such techniques on a larger scale in numerous applications, such as inpainting for Augmented Reality use cases. We propose to enhance deep learning based methods with classical model-based approaches by aligning the segmented plane masks with edges detected in the image. This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects. Our approach is motivated by logistics, where this assumption is valid and refined planes can be used to perform robust object detection without the need for supervised learning. Results for two baselines and our approach are reported on our own dataset, which we made publicly available. The results show a consistent improvement over the state-of-the-art. The influence of the prior segmentation and the edge detection is investigated and finally, areas for future research are proposed.
Real-Time Topology Optimization in 3D via Deep Transfer Learning

Abstract The published literature on topology optimization has exploded over the last two decades to include methods that use shape and topological derivatives or evolutionary algorithms formulated on various geometric representations and parametrizations. One of the key challenges of all these methods is the massive computational cost associated with 3D topology optimization problems. We introduce a transfer learning method based on a convolutional neural network that (1) can handle high-resolution 3D design domains of various shapes and topologies; (2) supports real-time design space explorations as the domain and boundary conditions change; (3) requires a much smaller set of high-resolution examples for the improvement of learning in a new task compared to traditional deep learning networks; (4) is multiple orders of magnitude more efficient than the established gradient-based methods, such as SIMP. We provide numerous 2D and 3D examples to showcase the effectiveness and accuracy of our proposed approach, including for design domains that are unseen to our source network, as well as the generalization capabilities of the transfer learning-based approach. Our experiments achieved an average binary accuracy around 95% at real-time prediction rates. These properties, in turn, suggest that the proposed transfer-learning method may serve as the first practical underlying framework for real-time 3D design exploration based on topology optimization.
Training artificial neural networks for precision orientation and strain mapping using 4D electron diffraction datasets.

Abstract Techniques for training artificial neural networks (ANNs) and convolutional neural networks (CNNs) using simulated dynamical electron diffraction patterns are described. The premise is based on the following facts. First, given a suitable crystal structure model and scattering potential, electron diffraction patterns can be simulated accurately using dynamical diffraction theory. Secondly, using simulated diffraction patterns as input, ANNs can be trained for the determination of crystal structural properties, such as crystal orientation and local strain. Further, by applying the trained ANNs to four-dimensional diffraction datasets (4D-DD) collected using the scanning electron nanodiffraction (SEND) or 4D scanning transmission electron microscopy (4D-STEM) techniques, the crystal structural properties can be mapped at high spatial resolution. Here, we demonstrate the ANN-enabled possibilities for the analysis of crystal orientation and strain at high precision and benchmark the performance of ANNs and CNNs by comparing with previous methods. A factor of thirty improvement in angular resolution at 0.009˚ (0.16 mrad) for orientation mapping, sensitivity at 0.04% or less for strain mapping, and improvements in computational performance are demonstrated.
Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach

Individualized treatment rules (ITRs) are considered a promising recipe to deliver better policy interventions. One key ingredient in optimal ITR estimation problems is to estimate the average treatment effect conditional on a subject's covariate information, which is often challenging in observational studies due to the universal concern of unmeasured confounding. Instrumental variables (IVs) are widely-used tools to infer the treatment effect when there is unmeasured confounding between the treatment and outcome. In this work, we propose a general framework of approaching the optimal ITR estimation problem when a valid IV is allowed to only partially identify the treatment effect. We introduce a novel notion of optimality called "IV-optimality". A treatment rule is said to be IV-optimal if it minimizes the maximum risk with respect to the putative IV and the set of IV identification assumptions. We derive a bound on the risk of an IV-optimal rule that illuminates when an IV-optimal rule has favorable generalization performance. We propose a classification-based statistical learning method that estimates such an IV-optimal rule, design computationally-efficient algorithms, and prove theoretical guarantees. We contrast our proposed method to the popular outcome weighted learning (OWL) approach via extensive simulations, and apply our method to study which mothers would benefit from traveling to deliver their premature babies at hospitals with high level neonatal intensive care units.
Confinement study of an SU(4) gauge theory with fermions in multiple representations

We discuss the phase diagnostics used in our finite-temperature study of an SU(4) gauge theory with dynamical fermions in both the fundamental and two-index antisymmetric representations. Beyond the usual Polyakov loop diagnostics of confinement, we employ several Wilson flow phase diagnostics. The first, what we call the ‘‘flow anisotropy’’, is known in the literature: the deconfinement transition introduces anisotropy between the spatial and temporal directions, to which the flow is extremely sensitive. The second, the ‘‘long flow time Polyakov loop,’’ is related but novel. While we do not claim to fully understand this diagnostic, we have empirically found it to be useful as an unusually sharp diagnostic of phase.
Mis)conceptions of the Corporation

Common conceptions of the corporation are wrong. Contrary to contemporary jurisprudence, a corporation—a piece of paper that is given legal legitimacy by a state—is not a person worthy of constitutional rights. A corporation, as a legislative creature, should only enjoy those rights bestowed upon it by its creator. This Article is structured into three principal sections. Part I argues that the only appropriate theoretical construct with which to conceptualize a corporation is one that posits that the corporation is an artificial creation of the state. First, it outlines three competing theories—artificial, associational, and real entity—as well as the apparently increasingly popular notion that theory simply does not matter. It argues that as Supreme Court precedent evolved, it became sadly muddled and that today the Court has essentially given up on theorizing the corporation. Second, it argues for the artificial entity theory on the bases of common sense, constitutional history, and the continuing role of the state in chartering corporations. Why has the artificial entity theory fallen deeply out of favor? Part II, which explores the political economy of corporate theory, argues that instrumental reasons explain the decline of artificial entity theory: anti-regulatory fervor and a desire to privilege a managerial class. Next, it addresses some concerns that might emerge to my conceptualization; notably, that not offering constitutional rights to corporations is too stark and reductionist an approach, as well as the notion that such a restrictive conception of corporate personhood might jeopardize attempts to find corporations liable under criminal or international law. Finally, Part III discusses whether the law has been headed in precisely the wrong direction: rather than asking whether * Professor of Law, University of San Francisco. 1 Dibadj: (Mis)Conceptions of the Corporation Published by Reading Room, 2013 732 GEORGIA STATE UNIVERSITY LAW REVIEW [Vol. 29:3 corporations deserve constitutional rights, should the real question be whether constitutional rights should be asserted against corporations?
Endogenous Inverse Demand Functions

In this work we present an equilibrium formulation for price impacts. This is motivated by the Buhlmann equilibrium in which assets are sold into a system of market participants and can be viewed as a generalization of the Esscher premium. Existence and uniqueness of clearing prices for the liquidation of a portfolio are studied. We also investigate other desired portfolio properties including monotonicity and concavity. Price per portfolio unit sold is also calculated. In special cases, we study price impacts generated by market participants who follow the exponential utility and power utility.
A closer look at CP-violating Higgs portal dark matter as a candidate for the GCE

A statistically significant excess of gamma rays has been reported and robustly confirmed in the Galactic Center over the past decade. Large local dark matter densities suggest that this Galactic Center Excess (GCE) may be attributable to new physics, and indeed it has been shown that this signal is well-modelled by annihilations dominantly into $$ b\overline{b} $$ with a WIMP-scale cross section. In this paper, we consider Majorana dark matter annihilating through a Higgs portal as a candidate source for this signal, where a large CP-violation in the Higgs coupling may serve to severely suppress scattering rates. In particular, we explore the phenomenology of two UV completions, a singlet-doublet model and a doublet-triplet model, and map out the available parameter space which can give a viable signal while respecting current experimental constraints.
The Code of Data Capital:: A Distributional Analysis of Law in the Global Data Economy

The global data economy has become an integral part of the global economy. It plays a fundamental role in issues of economic distribution and inequality, which has much to do with the legal arrangements and entitlements that shape it. Yet data and data-driven technologies are often conceptualized in terms that do not seem adequate to capture the role they play in global distribution, or which overlook the law as a key mechanism shaping distributional outcomes. Using a law and political economy approach, this article argues that conceiving of data as capital that is coded by legal mechanisms allows new ways of imagining alternative distributions of the value it generates in the global economy today. The article maps some of the legal entitlements that shape the global data economy and its distributive effects, as well as proposals for alternatives. It concludes that analyses of the distributive effects of the global data economy ought to take into account the fundamental roles that both law and technology play in shaping those effects.
Geographic information systems and cartography

The GIS phenomenon shows no sign of weakening, but cannot last for ever. The paper addresses the long-term significance of GIS from three perspectives. First, the various application fields are reviewed and used to define four views of GIS. Second, the paper looks at some new and continuing debates in GIS and their significance for cartography. The third section of the paper discusses the search for an intellectual core for GIS, and the role of the US National Science Foundation's National Center for Geographic Information and Analysis.
Boundary control of the beam equation by linear quadratic regulation

Abstract We present and solve a Linear Quadratic Regulator (LQR) for the boundary control of the beam equation. We use the simple technique of completing the square to get an explicit solution. By decoupling the spatial frequencies we are able to reduce an infinite dimensional LQR to an infinite family of two dimensional LQRs each of which can be solved explicitly.
Analogues of Alladi's formula over global function fields

Abstract In this paper, we show an analogue of Kural, McDonald and Sah's result on Alladi's formula for global function fields. Explicitly, we show that for a global function field K, if a set S of prime divisors has a natural density δ ( S ) within prime divisors, then − lim n → ∞ ⁡ ∑ 1 ≤ deg ⁡ D ≤ n D ∈ D ( K , S ) μ ( D ) | D | = δ ( S ) , where μ ( D ) is the Mobius function on divisors and D ( K , S ) is the set of all effective distinguishable divisors whose smallest prime factors are in S. As applications, we get the analogue of Dawsey's and Sweeting and Woo's results to the Chebotarev Density Theorem for function fields, and the analogue of Alladi's result to the Prime Polynomial Theorem for arithmetic progressions. We also display a connection between the Mobius function and the Fourier coefficients of modular form associated to elliptic curves. The proof of our main theorem is similar to the approach in Kural et al.'s article.
Tiling Generating Functions of Halved Hexagons and Quartered Hexagons

We prove exact product formulas for the tiling generating functions of various halved hexagons and quartered hexagons with defects on boundary. Our results generalize the previous work of the first author and the work of Ciucu.
Developing Functional Network Connectivity of the Dorsal Anterior Cingulate Cortex Mediates Externalizing Psychopathology in Adolescents with Child Neglect

Childhood adversity has been associated with elevated risk for psychopathology. We investigated whether development of functional brain networks important for executive function (EF) could serve as potential mediators of this association. We analyzed data of 475 adolescents, a subsample of the multisite longitudinal NCANDA (National Consortium on Alcohol and Neurodevelopment in Adolescence) cohort with completed measures of childhood trauma, resting-state functional brain connectivity data, and symptoms of internalizing and externalizing psychopathology at baseline and follow-up years 1-4. Using parallel process latent growth models, we found that childhood adversity was associated with increased risk for externalizing/internalizing behaviors. We specifically investigated whether functional connectivity of the dorsal anterior cingulate cortex (dACC) to brain regions within the cingulo-opercular (CO) network, a well-known EF network that underlies control of attention and self-regulation, mediates the association between adversity and symptoms of psychopathology. We found that childhood adversity, specifically child neglect was negatively associated with functional connectivity of the dACC within the CO network, and that this connectivity mediated the association between neglect and externalizing behaviors. Our study advances a mechanistic understanding of how childhood adversity may impact the development of psychopathology, highlighting the relevance of dACC functional networks particularly for externalizing psychopathology.
Automatic Document Sketching: Generating Drafts from Analogous Texts

The advent of large pre-trained language models has made it possible to make high-quality predictions on how to add or change a sentence in a document. However, the high branching factor inherent to text generation impedes the ability of even the strongest language models to offer useful editing suggestions at a more global or document level. We introduce a new task, document sketching, which involves generating entire draft documents for the writer to review and revise. These drafts are built from sets of documents that overlap in form - sharing large segments of potentially reusable text - while diverging in content. To support this task, we introduce a Wikipedia-based dataset of analogous documents and investigate the application of weakly supervised methods, including use of a transformer-based mixture of experts, together with reinforcement learning. We report experiments using automated and human evaluation methods and discuss relative merits of these models.
Postvaccination SARS-CoV-2 infections among skilled nursing facility residents and staff members - Chicago, Illinois, December 2020-March 2021.

Early studies suggest that COVID-19 vaccines protect against severe illness (1); however, postvaccination SARS-CoV-2 infections (i.e., breakthrough infections) can occur because COVID-19 vaccines do not offer 100% protection (2,3). Data evaluating the occurrence of breakthrough infections and impact of vaccination in decreasing transmission in congregate settings are limited. Skilled nursing facility (SNF) residents and staff members have been disproportionately affected by SARS-CoV-2, the virus that causes COVID-19 (4,5), and were prioritized for COVID-19 vaccination (6,7). Starting December 28, 2020, all 78 Chicago-based SNFs began COVID-19 vaccination clinics over several weeks through the federal Pharmacy Partnership for Long-Term Care Program (PPP).† In February 2021, through routine screening, the Chicago Department of Public Health (CDPH) identified a SARS-CoV-2 infection in a SNF resident >14 days after receipt of the second dose of a two-dose COVID-19 vaccination series. SARS-CoV-2 cases, vaccination status, and possible vaccine breakthrough infections were identified by matching facility reports with state case and vaccination registries. Among 627 persons with SARS-CoV-2 infection across 75 SNFs since vaccination clinics began, 22 SARS-CoV-2 infections were identified among 12 residents and 10 staff members across 15 facilities ≥14 days after receiving their second vaccine dose (i.e., breakthrough infections in fully vaccinated persons). Nearly two thirds (14 of 22; 64%) of persons with breakthrough infections were asymptomatic; two residents were hospitalized because of COVID-19, and one died. No facility-associated secondary transmission occurred. Although few SARS-CoV-2 infections in fully vaccinated persons were observed, these cases demonstrate the need for SNFs to follow recommended routine infection prevention and control practices and promote high vaccination coverage among SNF residents and staff members.
Load Balancing Under Strict Compatibility Constraints

Consider a system with N identical single-server queues and M(N) task types, where each server is able to process only a small subset of possible task types. Arriving tasks select d≥2 random compatible servers, and join the shortest queue among them. The compatibility constraints are captured by a fixed bipartite graph GN between the servers and the task types. When GN is complete bipartite, the meanfield approximation is accurate. However, such dense compatibility graphs are infeasible for large-scale implementation. We characterize a class of sparse compatibility graphs for which the meanfield approximation remains valid. For this, we introduce a novel notion, called proportional sparsity, and establish that systems with proportionally sparse compatibility graphs asymptotically match the performance of a fully flexible system. Furthermore, we show that proportionally sparse random compatibility graphs can be constructed, which reduce the server-degree almost by a factor N/ln(N) compared to the complete bipartite compatibility graph.
Dynamic Control of a Fiber Manufacturing Process using Deep Reinforcement Learning

This paper presents a model-free deep reinforcement learning (DRL) approach for controlling a fiber drawing system. The custom DRL-based control system predictively regulates fiber diameter and produces a fiber with a desired, constant or non-constant, diameter trajectory, i.e. diameter variation along the fiber length. Physical models of the system are not used. The system was trained and tested on a small-scale fiber drawing system, which has non-linear delayed dynamics and stochastic behaviors. For a reference trajectory with random step changes, after 1 hour of training, the DRL controller showed the same root mean squared error (RMSE) as an optimized PI controller; after 3 hours of training, it achieved the performance of a quadratic dynamic matrix controller (QDMC). While the PI feedback controller showed 3.5 seconds of time lag in a step response, the DRL controller showed less than a second of time lag. Controller performance tests on trajectories not used in the training process are conducted; for a sine sweep reference trajectory, the DRL controller maintained an RMSE under 40 m up to a frequency of 45 mHz, compared to 25 mHz for QDMC.
NeRD: Neural 3D Reflection Symmetry Detector

Recent advances have shown that symmetry, a structural prior that most objects exhibit, can support a variety of single-view 3D understanding tasks. However, detecting 3D symmetry from an image remains a challenging task. Previous works either assume the symmetry is given or detect the symmetry with a heuristic-based method. In this paper, we present NeRD, a Neural 3D Reflection Symmetry Detector, which combines the strength of learning-based recognition and geometry-based reconstruction to accurately recover the normal direction of objects’ mirror planes. Specifically, we enumerate the symmetry planes with a coarse-to-fine strategy and find the best ones by building 3D cost volumes to examine the intra-image pixel correspondence from the symmetry. Our experiments show that the symmetry planes detected with our method are significantly more accurate than the planes from direct CNN regression on both synthetic and real datasets. More importantly, we also demonstrate that the detected symmetry can be used to improve the performance of downstream tasks such as pose estimation and depth map regression by a wide margin over existing methods. The code of this paper has been made public at https://github.com/zhou13/nerd.
Rel–$C^\infty$ structures on Gromov–Witten moduli spaces

We show that moduli spaces of transversely cut-out (perturbed) pseudo-holomorphic curves in an almost complex manifold carry canonical relative smooth structures ("relative to the moduli space of domain curves"). The main point is that these structures can be characterized by a universal property. The tools required are ordinary gluing analysis combined with some fundamental results from the polyfold theory of Hofer--Wysocki--Zehnder.
An ontological metamodel for cyber-physical system safety, security, and resilience coengineering

Cyber-physical systems are complex systems that require the integration of diverse software, firmware, and hardware to be practical and useful. This increased complexity is impacting the management of models necessary for designing cyber-physical systems that are able to take into account a number of “-ilities”, such that they are safe and secure and ultimately resilient to disruption of service. We propose an ontological metamodel for system design that augments an already existing industry metamodel to capture the relationships between various model elements (requirements, interfaces, physical, and functional) and safety, security, and resilient considerations. Employing this metamodel leads to more cohesive and structured modeling efforts with an overall increase in scalability, usability, and unification of already existing models. In turn, this leads to a mission-oriented perspective in designing security defenses and resilience mechanisms to combat undesirable behaviors. We illustrate this metamodel in an open-source GraphQL implementation, which can interface with a number of modeling languages. We support our proposed metamodel with a detailed demonstration using an oil and gas pipeline model.
Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics

This paper introduces an alternative approach to sampling from autoregressive models. Autoregressive models are typically sampled sequentially, according to the transition dynamics defined by the model. Instead, we propose a sampling procedure that initializes a sequence with white noise and follows a Markov chain defined by Langevin dynamics on the global log-likelihood of the sequence. This approach parallelizes the sampling process and generalizes to conditional sampling. Using an autoregressive model as a Bayesian prior, we can steer the output of a generative model using a conditional likelihood or constraints. We apply these techniques to autoregressive models in the visual and audio domains, with competitive results for audio source separation, super-resolution, and inpainting.
Unfazed: parent-of-origin detection for large and small de novo variants.

SUMMARY Unfazed is a command-line tool to determine the parental gamete of origin for de novo mutations from paired-end Illumina DNA sequencing reads. Unfazed uses variant information for a sequenced trio to identify the parental gamete of origin by linking phase-informative inherited variants to de novo mutations using read-based phasing. It achieves a high success rate by chaining reads into haplotype groups, thus increasing the search space for informative sites. Unfazed provides a simple command-line interface and scales well to large inputs, determining parent-of-origin for nearly 30,000 de novo variants in under 60 hours. AVAILABILITY Unfazed is available at https://github.com/jbelyeu/unfazed. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
On pointwise decay of waves

This paper introduces some of the basic mechanisms relating the behavior of the spectral measure of Schrodinger operators near zero energy to the long-term decay and dispersion of the associated Schrodinger and wave evolutions. These principles are illustrated by means of the author’s work on decay of Schrodinger and wave equations under various types of perturbations, including those of the underlying metric. In particular, we consider local decay of solutions to the linear Schrodinger and wave equations on curved backgrounds that exhibit trapping. A particular application is waves on a Schwarzschild black hole spacetime. We elaborate on Price’s law of local decay that accelerates with the angular momentum, which has recently been settled by Hintz, also in the much more difficult Kerr black hole setting. While the author’s work on the same topic was conducted ten years ago, the global semiclassical representation techniques developed there have recently been applied by Krieger, Miao, and the author [“A stability theory beyond the co-rotational setting for critical wave maps blow up,” arXiv:2009.08843 (2020)] to the nonlinear problem of stability of blowup solutions to critical wave maps under non-equivariant perturbations.