cssi/SciSciGPT-SciSciNet huggingface dataset:
Bayesian wavelet-packet historical functional linear models

Historical functional linear models (HFLMs) quantify associations between a functional predictor and functional outcome where the predictor is an exposure variable that occurs before, or at least concurrently with, the outcome. Prior work on the HFLM has largely focused on estimation of a surface that represents a time-varying association between the functional outcome and the functional exposure. This existing work has employed frequentist and spline-based estimation methods, with little attention paid to formal inference or adjustment for multiple testing and no approaches that implement wavelet bases. In this work, we propose a new functional regression model that estimates the time-varying, lagged association between a functional outcome and a functional exposure. Building off of recently developed function-on-function regression methods, the model employs a novel use the wavelet-packet decomposition of the exposure and outcome functions that allows us to strictly enforce the temporal ordering of exposure and outcome, which is not possible with existing wavelet-based functional models. Using a fully Bayesian approach, we conduct formal inference on the time-varying lagged association, while adjusting for multiple testing. We investigate the operating characteristics of our wavelet-packet HFLM and compare them to those of two existing estimation procedures in simulation. We also assess several inference techniques and use the model to analyze data on the impact of lagged exposure to particulate matter finer than 2.5 $$\upmu $$ g, or PM $$_{2.5}$$ , on heart rate variability in a cohort of journeyman boilermakers during the morning of a typical day’s shift.
What Are We Comparing in Comparative Negligence

As the majority of jurisdictions in the country have moved from contributory negligence to comparative negligence in tort cases, judges commonly ask juries to assign a percentage of negligence to each of the parties involved in a negligence tort. While the practice of apportionment has become commonplace, the theory behind it is little understood. What exactly does it mean to assign percentages of negligence in a negligence case? The nature of the fault may be quite different for each party, making it quite difficult for the jury to combine them. Do the units used to measure the amount of negligence matter? Should the jury apportion negligence in some absolute way or in some relative way? None of these questions have obvious answers, and no scholar has addressed how juries should make these assessments.
Four Challenges to Financial Regulatory Reform

The recent global financial crisis represented a failure of regulation to the extent that regulatory responses and strategies existed, but were not deployed, to prevent or mitigate the factors that contributed to the crisis. Recognizing the failure of regulation in preventing the recent financial crisis and the need to pursue more effective regulatory strategies, policymakers in the United Kingdom, United States and European Union set forth concurrent proposals for substantial reform of their respective regulatory systems. The reforms emphasized the reorganization of regulatory agencies and expansion of powers of surviving agencies. The proposals, however, differed significantly from each other. Successful financial regulatory reform must address four challenges. First, how should regulatory systems be structured? Second, should there be a separation of prudential supervision and consumer protection regulation? Third, which entity should be responsible for monitoring and managing systemic risk and what should be its powers? Fourth, how should cross-border financial services and transactions be supervised and regulated? The paper considers the four challenges to financial regulatory reform and evaluates whether the UK, US and EU reform proposals succeeded in addressing the challenges.Given their weaknesses, the paper concludes that the reform proposals do not go far enough to prevent future regulatory failures.
Greenwash: Corporate Environmental Disclosure under Threat of Audit ∗

We develop an economic model of “greenwash,” in which a firm strategically disclosesenvironmentalinformationandanactivistmayauditandpenalizethe firmfordisclosingpositivebutnotnegativeaspectsofitsenvironmentalprofile. We fully characterize the model’s equilibria, and derive a variety of predictions about disclosure behavior. We rationalize conflicting results in the empirical literature, finding a nonmonotonic relationship between a firm’s expected environmental performance and its environmental disclosures. Greater activist pressure deters greenwash, but induces some firms to disclose less about their environmental performance. Environmental management systems discourage firms with poor expected environmental performance from greenwashing, which may justify public policies encouraging firms to adopt them.
Large countries, small countries and the enlargement of trade blocs

Abstract Are there systematic forces such that countries of different sizes participating in a free trade bloc benefit differently from the entry of new members? If economies of scale imply that firms located in large countries enjoy lower costs, then the gains from enlarging the bloc will fall disproportionately on small countries, because the entrance of new members diminishes the importance of the domestic market and improves the small countries' relative competitiveness. The theoretical prediction is clear, but the empirical analysis of trade flows towards Spain and Portugal after their 1986 entry into the European Community yields mixed results. France and the U.K. appear to have lost market shares relative to the small countries in the Community, but the same is not true for Italy and, to a lesser degree, for Germany.
Electoral Silver Linings after Shelby, Citizens United and Bennett

This short essay addresses severability in recent U.S. Supreme Court cases about election law: Shelby Co. v. Holder, Citizens United v. FEC and Arizona Free Enter. Club's Freedom Club PAC v. Bennett. The glass is actually half full because the high court tends to leave most of the laws they are reviewing intact.
Audit Committee Incentives and the Resolution of Detected Misstatements

SUMMARY: We investigate the role of audit committee economic incentives in judgments involving the resolution of detected misstatements. The results reveal a positive association between audit committee short-term stock option compensation and the likelihood that managers are allowed to waive income-decreasing misstatements that, if corrected, would have caused the company to miss its analyst forecast. Complementary results reveal a positive association between the audit committee long-term stock option compensation and the likelihood that managers are allowed to waive income-increasing misstatements when the company reports just missing, meeting, or beating its analyst forecast. These findings illustrate agency conflicts that can arise when compensating audit committees with options. We obtain these results while controlling for CEO option compensation and audit committee characteristics, along with indicators of corporate governance, auditor incentives, and company characteristics. Data Availability: D...
Officers’ fiduciary duties and acquisition outcomes

A Delaware case law recognized officers’ distinct fiduciary duties (OFDs) for the first time in 2009. I use this legal event as a natural experiment to examine the effect of OFDs on corporate acquisition decisions. I find that firms whose officers were protected from market discipline prior to 2009 experienced increased announcement-period abnormal stock returns after the event, mainly because post-event acquisitions by these firms created more synergies and reduced officers’ control. These firms also increased liability insurance premium expenditures, but reduced value-decreasing acquisition frequencies after the event. Furthermore, I find that the effect of OFDs is more pronounced at firms where officers are not directors (thus, do not have access to liability insurance), have wealth risk, face less product market competition, are insulated from the market for corporate control, or are able to avoid board monitoring. Overall results suggest that OFDs are a critical corporate governance mechanism that works in tandem with other disciplinary mechanisms.
Peer group ties and executive compensation networks

Publicly traded firms in the US typically determine C.E.O. compensation by benchmarking the pay of their C.E.O.s against the pay of C.E.O.s in "peer" firms. Consequently, executive compensation is influenced not only by firm-level characteristics, but also by the selection and actions of the firm's immediate peers as well as by the structure of the executive compensation network overall. Analyzing compensation peer group choices made by the same 1183 firms for F.Y. 2007, 2008 and 2009, we find that while the typical compensation peer is similar in size and industry to the firm that chose it, deviations from this norm are common, especially among larger firms, and tend to be towards larger firms with better paid CEOs. Further analysis shows that firms who pay CEOs well relative to the pay that would be predicted from their revenues, return on assets, and industry tend to have greater aspiration bias in their group of named peers.
Developing a Taste for Not Being Discriminated Against

'" Arnold 1. Shure Professor of Law, University of Chicago Law School. I am grateful to the other symposium participants for their encouragement and patience; to organizer Clark Freshman for putting it all together; to the Square Table of Bigelow fellows, Douglas Baird, Gary Becker, Lauren Berlant, Robyn Borok, Adam Cox, Adrienne Davis, Mary Duros, Margaret Schilt, Richard Epstein, Eric Hurst, Jenia Iontcheva, Frances Kamm, Saul Levmore, Martha McCluskey, Tracey Meares, Marsha Nagorsky, Carl Nightingale, Mary Patillo-McCoy, Dick Posner, Eric Posner, Todd Preuss, Gina Sanders, Cass Sunstein, David Weisbach, and Iris Young for brainstorming and bibliographic help; and to Virginia Kim for valuable research assistance. Research support for this project came from the Arnold and Frieda Shure Research Fund of the University of Chicago. ** Townsend Professor, Yale Law School. t Professor of Law, University of Miami School of Law. t Professor of Law, Duke University School of Law. § Professor of Law, University of California-Berkeley School of Law. I. Although everything I have to say in this piece was sparked by Review Symposium organizer Clark Freshman's mission of reading the two books in conjunction with one another, this piece is not in any conventional sense a book review, but rather an exploration of what, as I see it, lies at the intersection of the two books and the significance I see in the contrasts between them. Let me at the outset stress that the task of considering these two works in relationship to one another will inevitably mean that I cannot consider either one in all its richness and will be forced to omit mention of those valuable portions of both books that do not fall at the intersection of the two as I have construed that intersection for the purposes of this Review. The only consolation I can offer for this is that the other symposium participants, having each focused on a different intersection, highlight some of the portions of both works I overlook. Additionally, I have not attempted to summarize the
Global market integration: An alternative measure and its application

Global markets seem to be increasingly integrated but there is no well-accepted measure of integration. We show that the correlation across markets is a poor measure; perfectly integrated markets can exhibit weak correlation. We derive a new integration measure based on the explanatory power of a multi-factor model and use it empirically to investigate recent trends in global integration. For most countries, there has been a marked increase in measured integration over the past three decades, but this is not indicated by correlations among country indexes.
Market reaction to equity offer reasons: What information do managers reveal?

Prior studies have had limited success explaining the negative market reaction to common stock announcements using firm and offer specific variables. We employ apiecewise linear model to test the relationship between announcement returns and firm and offer specific variables by specific offer reason as stated by management. We find evidence that managers are signalling the quality of the new investment when issuing equity for the offer-motive capital expenditures; this is support for the announcement of the equity issue being a signal of wasteful investment. We also find that the announcement of equity issues signals overvaluation when the equity offer is for general purposes.
Rate Regulation, Competition, and Loss Reserve Discounting by Property-Casualty Insurers

This study examines whether the reported loss reserves of property‐casualty insurers contain an implicit discount for the time value of money. Reporting the present value of loss reserves enables insurers to justify the competitive level of insurance premiums to regulators. The evidence indicates that there is a positive and significant discount rate implicit in the relation between reported loss reserves and expected future claim payments. Moreover, insurers subject to relatively stringent rate regulation discount to a greater extent than do other insurers. The results also suggest that implicit discounting is distinct from solvency and tax motives to exercise discretion over the loss reserve.
A Delicate Task: Balancing the Rights of Children and Mothers in Parental Termination Proceedings

This article considers the independent liberty interests of children in foster care and their mothers in parental termination proceedings. Recent federal reforms impose a mandatory deadline for the state to terminate parental rights. That policy erroneously presumes that the passage of time alone establishes parental fault and satisfies a parent’s due process rights. It also fails to protect the minority of children who assert an interest in preserving a safe relationship with mothers who are unlikely to meet the state’s schedule – including many substance abusers and victims of domestic violence.
The Freedom–Responsibility Nexus in Management Philosophy and Business Ethics

This article pursues the question whether and inasmuch theories of corporate responsibility are dependent on conceptions of managerial freedom. I argue that neglect of the idea of freedom in economic theory has led to an inadequate conceptualization of the ethical responsibilities of corporations within management theory. In a critical review of the history of economic ideas, I investigate why and how the idea of freedom was gradually removed from the canon of economics. This reconstruction aims at a deconstruction of certain axioms of neoclassical economics that hamper contemporary efforts in integrating ethics firmly into management education. I intend to show by these deconstructive endeavors that a constructive use of the idea of responsible freedom could correct and complement the current quantitative focus in business theory through qualitative orientations. I argue that with the help of qualitative success criteria, the strategic integration of the tenets of both business ethics and Corporate Social Responsibility into business practice can be advanced by further research.
Who Wins in the Supreme Court? An Examination of Attorney and Law Firm Influence

Who are the most successful attorneys in the Supreme Court? A novel way to answer this question is by looking at attorneys’ relative influence on the course of the law. This article performs macro and micro-level analyses of the most successful Supreme Court litigators by examining the amount of language shared between nearly 9,500 Supreme Court merits briefs and their respective Supreme Court opinions from 1946 through 2013. The article also includes analyses of the most successful law firms according to the same metric. Who Wins in the Supreme Court? An Examination of Attorney and Law Firm Influence
The World Economic Crisis and Transnational Corporations

AbstractAlthough the world economic crisis has slowed the flow of global investments and production, transnational capitalism has become more centralized through greater monopolization. We can trace this development in the auto industry with an examination of state intervention, transnational alliances and global competition. Far from developing a nation-centric recovery plan, the Obama bail-out deepens the auto industry’s global character. This example shows how the transnational capitalist class works through the State to strengthen its dominant position over national capital.
Limited-memory BFGS with displacement aggregation

A displacement aggregation strategy is proposed for the curvature pairs stored in a limited-memory BFGS (a.k.a. L-BFGS) method such that the resulting (inverse) Hessian approximations are equal to those that would be derived from a full-memory BFGS method. This means that, if a sufficiently large number of pairs are stored, then an optimization algorithm employing the limited-memory method can achieve the same theoretical convergence properties as when full-memory (inverse) Hessian approximations are stored and employed, such as a local superlinear rate of convergence under assumptions that are common for attaining such guarantees. To the best of our knowledge, this is the first work in which a local superlinear convergence rate guarantee is offered by a quasi-Newton scheme that does not either store all curvature pairs throughout the entire run of the optimization algorithm or store an explicit (inverse) Hessian approximation. Numerical results are presented to show that displacement aggregation within an adaptive L-BFGS scheme can lead to better performance than standard L-BFGS.
Gas-Phase Ion/Ion Chemistry for Structurally Sensitive Probes of Gaseous Protein Ion Structure: Electrostatic and Electrostatic to Covalent Cross-Linking.

Abstract Intramolecular interactions within a protein are key in maintaining protein tertiary structure and understanding how proteins function. Ion mobility-mass spectrometry (IM-MS) has become a widely used approach in structural biology since it provides rapid measurements of collision cross sections (CCS), which inform on the gas-phase conformation of the biomolecule under study. Gas-phase ion/ion reactions target amino acid residues with specific chemical properties and the modified sites can be identified by MS. In this study, electrostatically reactive, gas-phase ion/ion chemistry and IM-MS are combined to characterize the structural changes between ubiquitin electrosprayed from aqueous and denaturing conditions. The electrostatic attachment of sulfo-NHS acetate to ubiquitin via ion/ion reactions and fragmentation by electron-capture dissociation (ECD) provide the identification of the most accessible protonated sites within ubiquitin as the sulfonate group forms an electrostatic complex with accessible protonated side chains. The protonated sites identified by ECD from the different solution conditions are distinct and, in some cases, reflect the disruption of interactions such as salt bridges that maintain the native protein structure. This agrees with previously published literature demonstrating that a high methanol concentration at low pH causes the structure of ubiquitin to change from a native (N) state to a more elongated A state. Results using gas-phase, electrostatic cross-linking reagents also point to similar structural changes and further confirm the role of methanol and acid in favoring a more unfolded conformation. Since cross-linking reagents have a distance constraint for the two reactive sites, the data is valuable in guiding computational structures generated by molecular dynamics. The research presented here describes a promising strategy that can detect subtle changes in the local environment of targeted amino acid residues to inform on changes in the overall protein structure.
Neural Combinatorial Deep Reinforcement Learning for Age-Optimal Joint Trajectory and Scheduling Design in UAV-Assisted Networks

In this article, an unmanned aerial vehicle (UAV)-assisted wireless network is considered in which a battery-constrained UAV is assumed to move towards energy-constrained ground nodes to receive status updates about their observed processes. The UAV’s flight trajectory and scheduling of status updates are jointly optimized with the objective of minimizing the normalized weighted sum of Age of Information (NWAoI) values for different physical processes at the UAV. The problem is first formulated as a mixed-integer program. Then, for a given scheduling policy, a convex optimization-based solution is proposed to derive the UAV’s optimal flight trajectory and time instants on updates. However, finding the optimal scheduling policy is challenging due to the combinatorial nature of the formulated problem. Therefore, to complement the proposed convex optimization-based solution, a finite-horizon Markov decision process (MDP) is used to find the optimal scheduling policy. Since the state space of the MDP is extremely large, a novel neural combinatorial-based deep reinforcement learning (NCRL) algorithm using deep Q-network (DQN) is proposed to obtain the optimal policy. However, for large-scale scenarios with numerous nodes, the DQN architecture cannot efficiently learn the optimal scheduling policy anymore. Motivated by this, a long short-term memory (LSTM)-based autoencoder is proposed to map the state space to a fixed-size vector representation in such large-scale scenarios while capturing the spatio-temporal interdependence between the update locations and time instants. A lower bound on the minimum NWAoI is analytically derived which provides system design guidelines on the appropriate choice of importance weights for different nodes. Furthermore, an upper bound on the UAV’s minimum speed is obtained to achieve this lower bound value. The numerical results also demonstrate that the proposed NCRL approach can significantly improve the achievable NWAoI per process compared to the baseline policies, such as weight-based and discretized state DQN policies.
The Riskiness of Credit Allocation and Financial Stability

We explore empirically how the time-varying allocation of credit across firms with heterogeneous credit quality matters for financial stability outcomes. Using firm-level data for 55 countries over 1991-2016, we show that the riskiness of credit allocation, captured by Greenwood and Hanson (2013)'s ISS indicator, helps predict downside risks to GDP growth and systemic banking crises, two to three years ahead. Our analysis indicates that the riskiness of credit allocation is both a measure of corporate vulnerability and of investor sentiment. Economic forecasters wrongly predict a positive association between the riskiness of credit allocation and future growth, suggesting a flawed expectations process.
"Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study.

Background: The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. Objective: The aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic. Methods: We began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time. Results: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. Conclusions: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will need to target evolving conspiracy theories and the new facets of each as they become incorporated.
Deep levels in cesium lead bromide from native defects and hydrogen

Lead halide perovskites such as CsPbBr3 have achieved widespread attention as optoelectronic materials, due in large part to their good performance despite significant defect densities. This “defect tolerance” has often been explained by hypothesizing that there is negligible trap-assisted non-radiative recombination in these materials because none of the dominant defects give rise to deep levels in the gap. We refer to this as the “shallow defect hypothesis”. In this work, we reject the shallow defect hypothesis for CsPbBr3. Via a thorough first-principles inventory of native defects and hydrogen impurities, we show that a number of relevant defects do in fact have deep levels, most notably the bromine interstitial and hydrogen interstitial. This adds to a growing body of evidence against the shallow defect hypothesis, suggesting that the observed defect tolerance may be due instead to relatively low recombination rates at deep levels. Guided by the theoretical identification of these defects, experiments can take steps to mitigate trap-assisted non-radiative recombination, further boosting the efficiency of lead halide perovskite optoelectronics.
Methodological Reconstruction of Historical Seismic Events From Anecdotal Accounts of Destructive Tsunamis: A Case Study for the Great 1852 Banda Arc Mega-Thrust Earthquake and Tsunami

We demonstrate the efficacy of a Bayesian statistical inversion framework for reconstructing the likely characteristics of large pre-instrumentation earthquakes from historical records of tsunami observations. Our framework is designed and implemented for the estimation of the location and magnitude of seismic events from anecdotal accounts of tsunamis including shoreline wave arrival times, heights, and inundation lengths over a variety of spatially separated observation locations. As an initial test case we use our framework to reconstruct the great 1852 earthquake and tsunami of eastern Indonesia. Relying on the assumption that these observations were produced by a subducting thrust event, the posterior distribution indicates that the observables were the result of a massive mega-thrust event with magnitude near 8.8 Mw and a likely rupture zone in the north-eastern Banda arc. The distribution of predicted epicentral locations overlaps with the largest major seismic gap in the region as indicated by instrumentally recorded seismic events. These results provide a geologic and seismic context for hazard risk assessment in coastal communities experiencing growing population and urbanization in Indonesia. In addition, the methodology demonstrated here highlights the potential for applying a Bayesian approach to enhance understanding of the seismic history of other subduction zones around the world.
Deal or No Deal: Why Courts Should Allow Defendants to Present Evidence that They Rejected Favorable Plea Bargains

Robert Woodsum is charged with two counts of felonious sexual assault and faces up to fourteen years incarceration if found guilty of both counts. Woodsum enters into plea negotiations with the prosecutor, who offers a deal under which Woodsum would plead guilty to two misdemeanor charges of simple assault in exchange for the prosecutor recommending to the judge that Woodsum receive a two year sentence that would be deferred, contingent on Woodsum’s good behavior and pursuit of counseling. Woodsum, however, rejects this plea bargain, which the State later characterizes as “extraordinarily beneficial” to Woodsum. When his case proceeds to trial, Woodsum seeks to admit evidence that he turned down this plea bargain, claiming that his rejection of such an “extraordinarily beneficial” deal and willingness to risk fourteen years incarceration is evidence of his consciousness of innocence. The trial court rebuffs his attempt, and Woodsum is convicted of both counts. Woodsum’s ensuing appeal eventually reaches the Supreme Court of New Hampshire in State v. Woodsum. One of Woodsum’s arguments is that evidence of the prosecutor’s offer and his rejection is not precluded under New Hampshire Rule of Evidence 410, which, like Federal Rule
Relating Church-Style and Curry-Style Subtyping

Type theories with higher-order subtyping or singleton types are examples of systems where computation rules for variables are affected by type information in the context. A complication for these systems is that bounds declared in the context do not interact well with the logical relation proof of completeness or termination. This paper proposes a natural modification to the type syntax for Fω ≤ , adding a variable’s bound to the variable type constructor, thereby separating the computational behavior of the variable from the context. The algorithm for subtyping in Fω ≤ can then be given on types without context or kind information. As a consequence, the metatheory follows the general approach for type systems without computational information in the context, including a simple logical relation definition without Kripke-style indexing by context. This new presentation of the system is shown to be equivalent to the traditional presentation without bounds on the variable type constructor.
Semantics-based Automated Web Testing

We present TAO, a software testing tool performing automated test and oracle generation based ona semantic approach. TAO entangles grammar-based test generation with automated semantics eval-uation using a denotational semantics framework. We show how TAO can be incorporated withthe Selenium automation tool for automated web testing, and how TAO can be further extended tosupport automated delta debugging, where a failing web test script can be systematically reducedbased on grammar-directed strategies. A real-life parkingwebsite is adopted throughout the paper todemonstrate the effectivity of our semantics-based web testing approach.
Ribbonlength and crossing number for folded ribbon knots

We study Kauffman’s model of folded ribbon knots: knots made of a thin strip of paper folded flat in the plane. The ribbonlength is the length to width ratio of such a folded ribbon knot. We show f...
The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation.

This paper evaluates the performance of several modern subword segmentation methods in a low-resource neural machine translation setting. We compare segmentations produced by applying BPE at the token or sentence level with morphologically-based segmentations from LMVR and MORSEL. We evaluate translation tasks between English and each of Nepali, Sinhala, and Kazakh, and predict that using morphologically-based segmentation methods would lead to better performance in this setting. However, comparing to BPE, we find that no consistent and reliable differences emerge between the segmentation methods. While morphologically-based methods outperform BPE in a few cases, what performs best tends to vary across tasks, and the performance of segmentation methods is often statistically indistinguishable.
TCN: Table Convolutional Network for Web Table Interpretation

Information extraction from semi-structured webpages provides valuable long-tailed facts for augmenting knowledge graph. Relational Web tables are a critical component containing additional entities and attributes of rich and diverse knowledge. However, extracting knowledge from relational tables is challenging because of sparse contextual information. Existing work linearize table cells and heavily rely on modifying deep language models such as BERT which only captures related cells information in the same table. In this work, we propose a novel relational table representation learning approach considering both the intra- and inter-table contextual information. On one hand, the proposed Table Convolutional Network model employs the attention mechanism to adaptively focus on the most informative intra-table cells of the same row or column; and, on the other hand, it aggregates inter-table contextual information from various types of implicit connections between cells across different tables. Specifically, we propose three novel aggregation modules for (i) cells of the same value, (ii) cells of the same schema position, and (iii) cells linked to the same page topic. We further devise a supervised multi-task training objective for jointly predicting column type and pairwise column relation, as well as a table cell recovery objective for pre-training. Experiments on real Web table datasets demonstrate our method can outperform competitive baselines by of F1 for column type prediction and by of F1 for pairwise column relation prediction.
Fast Statistical Leverage Score Approximation in Kernel Ridge Regression

Nystrom approximation is a fast randomized method that rapidly solves kernel ridge regression (KRR) problems through sub-sampling the n-by-n empirical kernel matrix appearing in the objective function. However, the performance of such a sub-sampling method heavily relies on correctly estimating the statistical leverage scores for forming the sampling distribution, which can be as costly as solving the original KRR. In this work, we propose a linear time (modulo poly-log terms) algorithm to accurately approximate the statistical leverage scores in the stationary-kernel-based KRR with theoretical guarantees. Particularly, by analyzing the first-order condition of the KRR objective, we derive an analytic formula, which depends on both the input distribution and the spectral density of stationary kernels, for capturing the non-uniformity of the statistical leverage scores. Numerical experiments demonstrate that with the same prediction accuracy our method is orders of magnitude more efficient than existing methods in selecting the representative sub-samples in the Nystrom approximation.
Cultural cartography with word embeddings

Abstract Using the frequency of keywords is a classic approach in the formal analysis of text, but has the drawback of glossing over the relationality of word meanings. Word embedding models overcome this problem by constructing a standardized and continuous “meaning space” where words are assigned a location based on relations of similarity to other words depending on how they are used in natural language samples. We show how word embeddings are commensurate with prevailing theories of meaning in sociology and can be put to the task of interpretation via two kinds of navigation. First, one can hold terms constant and measure how the embedding space moves around them—much like astronomers measured the changing of celestial bodies with the seasons. Second, one can also hold the embedding space constant and see how documents or authors move relative to it—just as ships use the stars on a given night to determine their location. Using the empirical case of immigration discourse in the United States, we demonstrate the merits of these two broad strategies for advancing important topics in cultural theory, including social marking, media fields, echo chambers, and cultural diffusion and change more broadly.
Intel HEXL: Accelerating Homomorphic Encryption with Intel AVX512-IFMA52

Modern implementations of homomorphic encryption (HE) rely heavily on polynomial arithmetic over a finite field. This is particularly true of the BGV, BFV, and CKKS HE schemes. Two of the biggest performance bottlenecks in HE primitives and applications are polynomial modular multiplication and the forward and inverse number-theoretic transform (NTT). Here, we introduce Intel® Homomorphic Encryption Acceleration Library (Intel® HEXL), a C++ library which provides optimized implementations of polynomial arithmetic for Intel® processors. Intel HEXL takes advantage of the recent Intel® Advanced Vector Extensions 512 (Intel® AVX512) instruction set to provide state-of-the-art implementations of the NTT and modular multiplication, measuring up to 7.2x single-threaded speedup over a native C++ baseline. Intel HEXL is available open-source at https://github.com/intel/hexl under the Apache 2.0 license and has been adopted by the Microsoft SEAL and PALISADE homomorphic encryption libraries
Cruise ship travel in the era of COVID-19: A summary of outbreaks and a model of public health interventions.

BACKGROUND: Cruise travel contributed to SARS-CoV-2 transmission when there were relatively few cases in the United States. By March 14, 2020, the Centers for Disease Control and Prevention (CDC) issued a No Sail Order suspending U.S. cruise operations; the last U.S. passenger ship docked on April 16. METHODS: We analyzed SARS-CoV-2 outbreaks on cruises in U.S. waters or carrying U.S. citizens and used regression models to compare voyage characteristics. We used compartmental models to simulate the potential impact of four interventions (screening for COVID-19 symptoms; viral testing on two days and isolation of positive persons; reduction of passengers by 40%, crew by 20%, and port visits to one) for 7-day and 14-day voyages. RESULTS: During January 19-April 16, 2020, 89 voyages on 70 ships had known SARS-CoV-2 outbreaks; 16 ships had recurrent outbreaks. There were 1,669 RT-PCR-confirmed SARS-CoV-2 infections and 29 confirmed deaths. Longer voyages were associated with more cases (adjusted incidence rate ratio, 1.10, 95% CI: 1.03-1.17, p < 0.0001). Mathematical models showed that 7-day voyages had about 70% fewer cases than 14-day voyages. On 7-day voyages, the most effective interventions were reducing the number of individuals onboard (43-49% reduction in total infections) and testing passengers and crew (42-43% reduction in total infections). All four interventions reduced transmission by 80%, but no single intervention or combination eliminated transmission. Results were similar for 14-day voyages. CONCLUSIONS: SARS-CoV-2 outbreaks on cruises were common during January-April 2020. Despite all interventions modeled, cruise travel still poses a significant SARS-CoV-2 transmission risk.
Factors Associated with Reported Likelihood to Get Vaccinated for COVID-19 in a Nationally Representative US Survey

Abstract Objectives Although general principles related to vaccination hesitancy have been well researched, reports on reluctance to be vaccinated for coronavirus disease 2019 (COVID-19) in the United States are somewhat surprising, given the disease's substantive disruption of everyday life. However, the landscape in which people are making COVID-19 vaccination decisions has recently evolved with releases of encouraging vaccine-related data and changes to official messaging about the virus. Therefore, this study sought to identify factors associated with reported likelihood to get vaccinated for COVID-19 among US adults in late January 2021. Study design We used the Prolific online research panel to survey a nationally representative sample of 1017 US adults. Methods Respondents were asked about their behavioral intentions toward COVID-19 vaccination, trust in science, perceptions related to COVID-19, and selected sociodemographic factors. We computed associations between those 11 independent variables and likelihood to get vaccinated for COVID-19 using multiple linear regression. Results Around 73.9% of respondents indicated at least some likelihood to get vaccinated for COVID-19. Trust in science and perceived seriousness of COVID-19 were positively associated with intention to get vaccinated, and identifying as Black or African American was negatively associated with intention to get vaccinated. Other factors were moderately, weakly, or not at all associated with intention. Conclusions Building trust in science and truthfully emphasizing the seriousness of catching COVID-19 should be further researched for their potential to support campaigns to encourage COVID-19 vaccination. Data continue to suggest the importance of dialogue with Black communities about COVID-19 vaccination.
Ultra-compact dual-band smart NEMS magnetoelectric antennas for simultaneous wireless energy harvesting and magnetic field sensing.

Ultra-compact wireless implantable medical devices are in great demand for healthcare applications, in particular for neural recording and stimulation. Current implantable technologies based on miniaturized micro-coils suffer from low wireless power transfer efficiency (PTE) and are not always compliant with the specific absorption rate imposed by the Federal Communications Commission. Moreover, current implantable devices are reliant on differential recording of voltage or current across space and require direct contact between electrode and tissue. Here, we show an ultra-compact dual-band smart nanoelectromechanical systems magnetoelectric (ME) antenna with a size of 250 × 174 µm2 that can efficiently perform wireless energy harvesting and sense ultra-small magnetic fields. The proposed ME antenna has a wireless PTE 1–2 orders of magnitude higher than any other reported miniaturized micro-coil, allowing the wireless IMDs to be compliant with the SAR limit. Furthermore, the antenna’s magnetic field detectivity of 300–500 pT allows the IMDs to record neural magnetic fields. Wireless implantable medical devices (IMDs) are hamstrung by both size and efficiency required for wireless power transfer. Here, Zaeimbashi et al. present a magnetoelectric nano-electromechanical systems that can harvest energy and sense weak magnetic fields like those arising from neural activity.
Pneumonia Detection On Chest X-Ray Using Radiomic Features And Contrastive Learning

Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still has been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era. With the rise of deep learning, the explainability of deep neural networks on chest X-ray diagnosis remains opaque. In this study, we proposed a novel framework that leverages radiomics features and contrastive learning to detect pneumonia in chest X-ray. Experiments on the RSNA Pneumonia Detection Challenge dataset show that our model achieves superior results to several state-of-the-art models ($\gt 10$ % in F1-score) and increases the model’s interpretability.
Symmetry and unification from soft theorems and unitarity

We argue that symmetry and unification can emerge as byproducts of certain physical constraints on dynamical scattering. To accomplish this we parameterize a general Lorentz invariant, four-dimensional theory of massless and massive scalar fields coupled via arbitrary local interactions. Assuming perturbative unitarity and an Adler zero condition, we prove that any finite spectrum of massless and massive modes will necessarily unify at high energies into multiplets of a linearized symmetry. Certain generators of the symmetry algebra can be derived explicitly in terms of the spectrum and three-particle interactions. Furthermore, our assumptions imply that the coset space is symmetric.
Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Natural-language prompts have recently been used to coax pretrained language models into performing other AI tasks, using a fill-in-the-blank paradigm (Petroni et al., 2019) or a few-shot extrapolation paradigm (Brown et al., 2020). For example, language models retain factual knowledge from their training corpora that can be extracted by asking them to “fill in the blank” in a sentential prompt. However, where does this prompt come from? We explore the idea of learning prompts by gradient descent—either fine-tuning prompts taken from previous work, or starting from random initialization. Our prompts consist of “soft words,” i.e., continuous vectors that are not necessarily word type embeddings from the language model. Furthermore, for each task, we optimize a mixture of prompts, learning which prompts are most effective and how to ensemble them. Across multiple English LMs and tasks, our approach hugely outperforms previous methods, showing that the implicit factual knowledge in language models was previously underestimated. Moreover, this knowledge is cheap to elicit: random initialization is nearly as good as informed initialization.
Quantum Mechanics and the Continuum Limit of an Emergent Geometry

Recent advances in emergent geometry have identified a new class of models that represent spacetime as the graph obtained as the ground state of interacting Ising spins. These models have many desirable features, including stable excitations possessing many of the characteristics of a quantum particle. We analyze the dynamics of such excitations, including a detailed treatment of the edge states not previously addressed. Using a minimal prescription for the interaction of defects we numerically investigate approximate bounds to the speed of propagation of such a “particle”. We discover, using numerical simulations, that there may be a Lieb-Robinson bound to propagation that could point the way to how a causal structure could be accommodated in this class of emergent geometry models.
ACE: Explaining cluster from an adversarial perspective

Abstract A common workflow in single-cell RNA-seq analysis is to project the data to a latent space, cluster the cells in that space, and identify sets of marker genes that explain the differences among the discovered clusters. A primary drawback to this three-step procedure is that each step is carried out independently, thereby neglecting the effects of the nonlinear embedding and inter-gene dependencies on the selection of marker genes. Here we propose an integrated deep learning framework, Adversarial Clustering Explanation (ACE), that bundles all three steps into a single work-flow. The method thus moves away from the notion of “marker genes” to instead identify a panel of explanatory genes. This panel may include genes that are not only enriched but also depleted relative to other cell types, as well as genes that exhibit differences between closely related cell types. Empirically, we demonstrate that ACE is able to identify gene panels that are both highly discriminative and nonredundant, and we demonstrate the applicability of ACE to an image recognition task. 1
Non-interactive Distributional Indistinguishability (NIDI) and Non-malleable Commitments

We introduce non-interactive distributionally indistinguishable arguments (NIDI) to address a significant weakness of NIWI proofs: namely, the lack of meaningful secrecy when proving statements about \(\mathsf {NP}\) languages with unique witnesses.