cssi/SciSciGPT-SciSciNet huggingface dataset:
Is there a diversification discount in financial conglomerates

Abstract This paper investigates whether the diversity of activities conducted by financial institutions influences their market valuations. We find that there is a diversification discount: The market values of financial conglomerates that engage in multiple activities, e.g., lending and non-lending financial services, are lower than if those financial conglomerates were broken into financial intermediaries that specialize in the individual activities. While difficult to identify a single causal factor, the results are consistent with theories that stress intensified agency problems in financial conglomerates engaged in multiple activities and indicate that economies of scope are not sufficiently large to produce a diversification premium.
Mutual Fund Herding and the Impact on Stock Prices

We analyze the trading activity of the mutual fund industry from 1975 through 1994 to determine whether funds “herd” when they trade stocks and to investigate the impact of herding on stock prices. Although we find little herding by mutual funds in the average stock, we find much higher levels in trades of small stocks and in trading by growth-oriented funds. Stocks that herds buy outperform stocks that they sell by 4 percent during the following six months; this return difference is much more pronounced among small stocks. Our results are consistent with mutual fund herding speeding the price-adjustment process. DO INSTITUTIONAL INVESTORS “F LOCK TOGETHER” ~or “herd,” as it is often called! when they trade securities? Do some investors follow the lead of others when they trade? Such questions have interested researchers for some time, and are central to understanding the impact of institutional trading on securities markets and to understanding the way in which information becomes incorporated into market prices. 1
Trade and Labor Standards

The purpose of our paper is to explore the different views of the issues of international labor standards and to explore the available options for addressing these issues. We conclude that: (1) there is no convincing case on theoretical or empirical grounds for incorporating labor standards into the WTO and into U.S. trade agreements; (2) the surest way to improve labor standards is for the United States and other industrialized countries to maintain open markets and to encourage the economic growth of their developing country trading partners; and (3) steps should be taken to support the activities of the International Labour Organization (ILO) to provide inducements and technical assistance to help developing countries raise their labor standards.
Tullock’s Challenge: A Reconsideration of Constitutional Monarchy

Tullock (2005, p. 160) notes that the perceived robust relationship between democracy and economic progress is due mostly to assumption, rather than analysis. Taking up Tullock’s challenge to consider the relationship bet - ween economic progress and other political forms, we re-assess the relation-ship between monarchy and economic progress. Our analysis specifically focuses on the possibility of a «monarchical» constitution creating the insti-tutions within which, compared to democracy, a larger social surplus can be enjoyed. After summarizing the existing conversation on monarchy vs. democracy, we outline a constitutional political economy of monarchy and apply it to the European country of Liechtenstein, which has enjoyed both rapid development and non-discriminatory governance under the kind of constitution we envision. We conclude by responding to anticipated objec-tions and proposing further avenues of inquiry on the political economy of monarchy. Key words: Constitutional Political Economy, Democracy, Efficiency, Monarchy, Liechtenstein. JEL Classification: H1, H77, P1. Resumen: De acuerdo a Tullock (2005, p. 160) la solida relacion entre demo-cracia y progreso economico descansa mas en supuestos que en analisis. Tomando el desafio de Tullock de considerar la relacion entre progreso eco-nomico y diversas estructuras politicas, re-evaluamos la relacion entre mo-narquia y progreso economico. Nuestro analisis se enfoca especificamente en la posibilidad de una «monarquia» constitucional que crea la instituciones en las cuales, comparado con una democracia, un mayor beneficio social es posible. Luego de resumir la conversacion monarquia vs. democracia, pre-sentamos una politica economica constitucional de la monarquia y la apli-camos al pais europeo de Lichtenstein, que ha presentado un rapido desa-rrollo y una gobernanza no discriminatoria bajo el tipo de constitucion que presentamos. Concluimos respondiendo a anticipadas objeciones y pro po - niendo siguientes caminos de investigacion en el tema de la economia po-litica de la monarquia. Palabras clave: Economia Politica Constitutional, Democracia, Eficiencia, Monarquia, Liechtenstein. Clasificacion JEL: H1, H77, P1.
What Drives Credit Growth in Emerging Asia

Abstract This paper seeks to uncover the main drivers of credit growth in emerging Asia using a multi-country structural vector autoregressive (SVAR) model. We propose an innovative identification strategy in a two-block SVAR model whereby shocks within blocks are identified using sign restrictions, whereas shocks across the blocks are identified using a block-recursive structure. We find that domestic factors are more dominant than external factors in terms of driving rapid credit growth in emerging Asia. This is particularly true for domestic monetary policy, which can play a pivotal role in managing rapid credit growth in emerging Asia.
Tax-related mandatory risk factor disclosures, future profitability, and stock returns

Prior research finds that mandatory risk factor disclosures are informative in that they increase investors’ assessments of the volatility of a firm’s cash flows. However, the literature is silent as to whether these disclosures provide information about the level of future cash flows and, ultimately, their implications for firm value. We address this question by examining the association between Form 10-K risk factor disclosures and future cash flows levels and stock returns. We use the setting of taxes because it is relatively easier to identify the specific income and cash flow statement line items to which these risks relate, and offer two main results. First, we find that tax risk factor disclosures are positively associated with future cash flows. This suggests that, on average, tax risk factor disclosures relate to tax positions that are rewarded with future tax savings. Second, we find that investors incorporate this relation into stock prices. In additional analysis, we find no evidence of a drift in stock prices, suggesting that investors incorporate the implications of tax risk factor disclosures in a timely manner. Overall, our results suggest that risk factor disclosures provide information about the level of a firm’s future cash flows, that the risks discussed in these disclosures are – on average – value-increasing, and that investors incorporate this information into current stock prices.
Russian Fiscal Federalism: Impact of Political and Fiscal (De)centralization

The extent of political and fiscal centralization in Russia has experienced dramatic changes since the end of the Soviet era. The heavily centralized, both politically and economically, federal structures became dysfunctional and unstable until the introduction of the Budget and Tax Codes over the last decade induced a relative clarity to revenue assignments and expenditure and management responsibilities of different levels of government. While the creation of federal districts and the elimination of elections of regional governors in 2005 have brought a substantial rise of political centralization, the reforms had an ambiguous effect on fiscal centralization and fiscal independence of the regions, the estimation of which is addressed in this paper. We use an updated and extensive dataset and apply a novel estimation technique by evaluating the response of regional governments expenditures to changes in the size of the GRP (gross regional product) and to changes in the regions tax collections. While the results related to regional shares of tax revenues and expenditures are somewhat ambiguous, the examination of marginal fiscal incentives suggests an increase in fiscal centralization in Russia over the last decade. Our investigation also indicates that, contrary to Treismans (2000) conjecture, no decline in the variability of tax revenues had taken place in the last decade. We also show that the recent variability of GRP has been smaller than for tax revenues but greater than for budget expenditures, which stresses the effectiveness of budget equalization policies of the central government. Finally, we briefly addressed the issue of intra-regional fiscal relations, which is of crucial importance for political and economic progress of the country.
Institutional industry herding.

Abstract We examine whether institutional investors follow each other into and out of the same industries. Our empirical results reveal strong evidence of institutional industry herding. The cross-sectional correlation between the fraction of institutional traders buying an industry this quarter and the fraction buying last quarter, for example, averages 40%. Additional tests suggest that correlated signals primarily drive institutional industry herding. Our results also provide empirical support for “style investing” models.
Investor Protection and Corporate Governance: Evidence from Worldwide CEO Turnover

Recent research asserts that an essential feature of good corporate governance is strong investor protection, where investor protection is defined as the extent of the laws that protect investors' rights and the strength of the legal institutions that facilitate law enforcement. The purpose of this study is to test this assertion by investigating whether these measures of investor protection are associated with an important role of good corporate governance: identifying and terminating poorly performing CEOs. Our tests indicate that strong law enforcement institutions significantly improve the association between CEO turnover and poor performance, whereas extensive investor protection laws do not. In addition, we find that in countries with strong law enforcement, CEO turnover is more likely to be associated with poor stock returns when stock prices are more informative. Finding that strong law enforcement institutions are associated with improved CEO turnover-performance sensitivity is consistent with good corporate governance requiring law enforcement institutions capable of protecting shareholders' property rights (i.e., protecting shareholders from expropriation by insiders). Finding that investor protection laws are not associated with improved CEO turnover-performance sensitivity is open to several explanations. For example, investor protection laws may not be as important as strong law enforcement in fostering good governance, the set of laws we examine may not be the set that are most important in promoting good governance, or measurement error in our surrogate for extensive investor protection laws may reduce the power of our test of this variable.
Style drift: Evidence from small-cap mutual funds

Abstract This paper documents that small-cap mutual funds allocate on average 27% of their portfolio to mid- and large-cap stocks. We find that larger and older small-cap funds are more likely to hold mid- and large-cap stocks, consistent with funds straying from their objective over time. Funds that invest heavily in mid- and large-cap stocks expose their investors to unanticipated risks but investors do not experience higher abnormal returns or performance persistence overall. These funds did outperform their peers by 3% annually in the most recent period between January 2003 and March 2010.
Dual Dirac Liquid on the Surface of the Electron Topological Insulator

Topological insulators possess properties of both conductors and insulators. A theoretical study demonstrates that the surface state of a Fu-Kane-Mele topological insulator can access all of the other surface states of the material.
SpreadsheetCoder: Formula Prediction from Semi-structured Context

Spreadsheet formula prediction has been an important program synthesis problem with many real-world applications. Previous works typically utilize input-output examples as the specification for spreadsheet formula synthesis, where each input-output pair simulates a separate row in the spreadsheet. However, such a formulation does not fully capture the rich context in real-world spreadsheets. First, spreadsheet data entries are organized as tables, thus rows and columns are not necessarily independent from each other. In addition, many spreadsheet tables include headers, which provide high-level descriptions of the cell data. However, previous synthesis approaches do not consider headers as part of the specification. In this work, we present the first approach for synthesizing spreadsheet formulas from tabular context, which includes both headers and semi-structured tabular data. In particular, we propose SpreadsheetCoder, a BERT-based model architecture to represent the tabular context in both row-based and column-based formats. We train our model on a large dataset of spreadsheets, and demonstrate that SpreadsheetCoder achieves top-1 prediction accuracy of 42.51%, which is a considerable improvement over baselines that do not employ rich tabular context.
A Hidden Challenge of Link Prediction: Which Pairs to Check?

The traditional setup of link prediction in networks assumes that a test set of node pairs, which is usually balanced, is available over which to predict the presence of links. However, in practice, there is no test set: the ground-truth is not known, so the number of possible pairs to predict over is quadratic in the number of nodes in the graph. Moreover, because graphs are sparse, most of these possible pairs will not be links. Thus, link prediction methods, which often rely on proximity-preserving embeddings or heuristic notions of node similarity, face a vast search space, with many pairs that are in close proximity, but that should not be linked. To mitigate this issue, we introduce LinkWaldo, a framework for choosing from this quadratic, massively-skewed search space of node pairs, a concise set of candidate pairs that, in addition to being in close proximity, also structurally resemble the observed edges. This allows it to ignore some high-proximity but low-resemblance pairs, and also identify high-resemblance, lower-proximity pairs. Our framework is built on a model that theoretically combines Stochastic Block Models (SBMs) with node proximity models. The block structure of the SBM maps out where in the search space new links are expected to fall, and the proximity identifies the most plausible links within these blocks, using locality sensitive hashing to avoid expensive exhaustive search. LinkWaldo can use any node representation learning or heuristic definition of proximity, and can generate candidate pairs for any link prediction method, allowing the representation power of current and future methods to be realized for link prediction in practice. We evaluate LinkWaldo on 13 networks across multiple domains, and show that on average it returns candidate sets containing 7-33% more missing and future links than both embedding-based and heuristic baselines' sets.
Machine-learning interatomic potentials for materials science

Abstract Large-scale atomistic computer simulations of materials rely on interatomic potentials providing computationally efficient predictions of energy and Newtonian forces. Traditional potentials have served in this capacity for over three decades. Recently, a new class of potentials has emerged, which is based on a radically different philosophy. The new potentials are constructed using machine-learning (ML) methods and a massive reference database generated by quantum-mechanical calculations. While the traditional potentials are derived from physical insights into the nature of chemical bonding, the ML potentials utilize a high-dimensional mathematical regression to interpolate between the reference energies. We review the current status of the interatomic potential field, comparing the strengths and weaknesses of the traditional and ML potentials. A third class of potentials is introduced, in which an ML model is coupled with a physics-based potential to improve the transferability to unknown atomic environments. The discussion is focused on potentials intended for materials science applications. Possible future directions in this field are outlined.
Larger workers outperform smaller workers across resource environments: An evaluation of demographic data using functional linear models

Behavior and organization of social groups is thought to be vital to the functioning of societies, yet the contributions of various roles within social groups toward population growth and dynamics have been difficult to quantify. A common approach to quantifying these role-based contributions is evaluating the number of individuals conducting certain roles, which ignores how behavior might scale up to effects at the population-level. Manipulative experiments are another common approach to determine population-level effects, but they often ignore potential feedbacks associated with these various roles.Here, we evaluate the effects of worker size distribution in bumblebee colonies on worker production in 24 observational colonies across three environments, using functional linear models. Functional linear models are an underused correlative technique that has been used to assess lag effects of environmental drivers on plant performance. We demonstrate potential applications of this technique for exploring high-dimensional ecological systems, such as the contributions of individuals with different traits to colony dynamics.We found that more larger workers had mostly positive effects and more smaller workers had negative effects on worker production. Most of these effects were only detected under low or fluctuating resource environments suggesting that the advantage of colonies with larger-bodied workers becomes more apparent under stressful conditions.We also demonstrate the wider ecological application of functional linear models. We highlight the advantages and limitations when considering these models, and how they are a valuable complement to many of these performance-based and manipulative experiments.
Prototyping Machine-Learning-Supported Lead Time Prediction Using AutoML

Abstract Many Small and Medium Enterprises in the domain of Make-To-Order- and Small-Series-Production struggle with accurately predicting lead times of highly customisable orders. This paper investigates an approach using AutoML integrated into existing enterprise systems in order to enable Lead Time Prediction based on Machine Learning models. This prediction is based on both order data from an ERP system as well as real-time factory state informed by an IIoT platform. We used simulation data to feed the AutoML model generation and developed a lightweight web-based microservice around it to infer lead times of incoming orders during live production. Using industry standards, this microservice can be seamlessly integrated into existing system landscapes. The simplicity of AutoML systems allows for swift (re)training and benchmarking of models but potentially comes at the cost of overall lower model quality.
Mergers and labor market outcomes in the US airline industry

This article examines US airline mergers between 1993 and 2018 and studies their impact on the labor market. Our difference‐in‐differences estimates indicate a significant reduction in the merging airlines' long‐term wage and fringe benefits following the mergers. The effect is particularly salient among large‐scale mergers involving major airlines and low cost carriers. The results also suggest a negative short‐term employment impact of mergers that varies by occupation types. Our findings are consistent with the impact of merger‐induced monopsony power discussed in recent literature and offer important policy implications regarding how to account for employer monopsony power during mergers and acquisitions.
Cyclic gallium-68 labeled peptides for specific detection of human angiotensin-converting enzyme 2.

In this study, we developed ACE2-specific, peptide-derived 68Ga-labeled radiotracers, motivated by the hypotheses that (1) ACE2 is an important determinant of SARS-CoV-2 susceptibility, and (2) that modulation of ACE2 in COVID-19 drives severe organ injury. Methods: A series of NOTA-conjugated peptides derived from the known ACE2 inhibitor DX600 were synthesized, with variable linker identity. Since DX600 bears two cystine residues, both linear and cyclic peptides were studied. An ACE2 inhibition assay was used to identify lead compounds, which were labeled with 68Ga to generate peptide radiotracers ([68Ga]NOTA-PEP). The aminocaproate-derived radiotracer [68Ga]NOTA-PEP4 was subsequently studied in a humanized ACE2 (hACE2) transgenic model. Results: Cyclic DX-600 derived peptides had markedly lower IC50's than their linear counterparts. The three cyclic peptides with triglycine, aminocaproate, and polyethylene glycol linkers had calculated IC50's similar to, or lower than the parent DX600 molecule. Peptides were readily labeled with 68Ga, and the biodistribution of [68Ga]NOTA-PEP4 was determined in a hACE2 transgenic murine cohort. Pharmacologic concentrations of co-administered NOTA-PEP ("blocking") showed significant reduction of [68Ga]NOTA-PEP4 signals in the in the heart, liver, lungs, and small intestine. Ex vivo hACE2 activity in these organs was confirmed as a correlate to in vivo results. Conclusion: NOTA-conjugated, cyclic peptides derived from the known ACE2 inhibitor DX600 retain their activity when N-conjugated for 68Ga chelation. In vivo studies in a transgenic hACE2 murine model using the lead tracer [68Ga]NOTA-PEP4 showed specific binding in the heart, liver, lungs and intestine - organs known to be affected in SARS-CoV-2 infection. These results suggest that [68Ga]NOTA-PEP4 could be used to detect organ-specific suppression of ACE2 in SARS-CoV-2 infected murine models and COVID-19 patients.
Everyday Use: A History of Civil Rights in Black Churches

After the Civil War, African Americans began to exercise civil rights of contract, property, and standing, a set of rights with significance that scholars of the Black freedom struggle have not fully appreciated. By the Jim Crow era, African Americans were putting these civil rights of the nineteenth century to everyday use. Dylan C. Penningroth seeks to revise the history of “civil rights” by examining one strand of Black people’s long engagement with legal rules, legal ideas, and legal institutions: the private law of religion. Throughout the twentieth century, Black male church leaders fought over the role churches should play in the Black freedom struggle, while ordinary church members, both women and men, seized the new meaning of civil rights as racial justice and redirected it to their concerns about church injustice.
A Theory of ‘Why and How’ Audit Firms Choose to Specialize

We examine analytically the strategic path auditors take towards becoming industry specialists in terms of their investments in the acquisition of industry specific knowledge and their pricing beha...
Integrated omics analyses reveal differential gene expression and potential for cooperation between denitrifying polyphosphate and glycogen accumulating organisms.

Unusually high accumulation of the potent greenhouse gas nitrous oxide (N2 O) has previously been documented in denitrifying biological phosphorus (P) removal bioprocesses, but the roles of differential denitrification gene expression patterns and ecological interactions between key functional groups in driving these emissions are not well understood. To address these knowledge gaps, we applied genome-resolved metagenomics and metatranscriptomics to a denitrifying bioprocess enriched in as-yet-uncultivated denitrifying polyphosphate accumulating organisms (PAOs) affiliated with Candidatus Accumulibacter. The 6 transcriptionally most active populations in the community included three co-occurring Accumulibacter strains affiliated with clades IF (a novel clade identified in this study), IA, and IC, a competing glycogen accumulating organism (GAO) affiliated with Competibacteraceae (GAO1), a Gammaproteobacteria PR6, and a Anaerolineae CH7. Strongly elevated expression of nitrite reductase compared to nitrous oxide reductase was observed in the overall community and in Accumulibacter populations, suggesting a strong role for differential gene expression in driving N2 O accumulation. Surprisingly, while ~90% of the nirS gene transcripts were expressed by the three co-occurring PAO populations, ~93% of the norB gene transcripts were expressed by GAO1 and ~75% of the norZ gene transcripts mapped to PR6 and several other non-PAO flanking populations. This suggests the potential for cooperation between flanking populations and PAOs in reducing denitrification intermediates. Such cooperation may benefit the community by reducing the accumulation of toxic nitric oxide. This article is protected by copyright. All rights reserved.
Technology for Developing Regions

Moore's Law and the wave of technologies it enabled have led to tremendous improvements in productivity and the quality of life in industrialized nations. Yet, technology has had almost no effect on the other five billion people on our planet. In this talk I argue that decreasing costs of computing and wireless networking make this the right time to spread the benefits of technology, and that the biggest missing piece is a lack of focus on the problems that matter. After covering some example applications that have shown very high impact, I present some our own preliminary results, including the use of novel low-cost telemedicine to improve the vision of real people, with over 20,000 patients examined so far. I conclude with some discussion on the role of database researchers in this new area.
Golden mean renormalization for the almost Mathieu operator and related skew products

Considering SL(2,R) skew-product maps over circle rotations, we prove that a renormalization transformation associated with the golden mean α* has a nontrivial periodic orbit of length 3. We also present some numerical results, including evidence that this period 3 describes scaling properties of the Hofstadter butterfly near the top of the spectrum at α* and scaling properties of the generalized eigenfunction for this energy.
Cytoplasmic Volume and Limiting Nucleoplasmin Scale Nuclear Size During Xenopus Laevis Development

How nuclear size is regulated relative to cell size is a fundamental cell biological question. Reductions in both cell and nuclear sizes during Xenopus laevis embryogenesis provide a robust scaling system to study mechanisms of nuclear size regulation. To test if the volume of embryonic cytoplasm is limiting for nuclear growth, we encapsulated gastrula stage embryonic cytoplasm and nuclei in droplets of defined volume using microfluidics. Nuclei grew and reached new steady-state sizes as a function of cytoplasmic volume, supporting a limiting component mechanism of nuclear size control. Through biochemical fractionation, we identified the histone chaperone nucleoplasmin (Npm2) as a putative nuclear size-scaling factor. Cellular amounts of Npm2 decrease over development, and nuclear size was sensitive to Npm2 levels both in vitro and in vivo, affecting nuclear histone levels and chromatin organization. Thus, reductions in cell volume with concomitant decreases in Npm2 amounts represent a developmental mechanism of nuclear size-scaling that may also be relevant to cancers with increased nuclear size.
Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement

This work introduces sequential neural beamforming, which alternates between neural network based spectral separation and beamforming based spatial separation. Our neural networks for separation use an advanced convolutional architecture trained with a novel stabilized signal-to-noise ratio loss function. For beamforming, we explore multiple ways of computing time-varying covariance matrices, including factorizing the spatial covariance into a time-varying amplitude component and a time-invariant spatial component, as well as using block-based techniques. In addition, we introduce a multi-frame beamforming method which improves the results significantly by adding contextual frames to the beamforming formulations. We extensively evaluate and analyze the effects of window size, block size, and multi-frame context size for these methods. Our best method utilizes a sequence of three neural separation and multi-frame time-invariant spatial beamforming stages, and demonstrates an average improvement of 2.75 dB in scale-invariant signal-to-noise ratio and 14.2% absolute reduction in a comparative speech recognition metric across four challenging reverberant speech enhancement and separation tasks. We also use our three-speaker separation model to separate real recordings in the LibriCSS evaluation set into non-overlapping tracks, and achieve a better word error rate as compared to a baseline mask based beamformer.
Stable homotopy refinement of quantum annular homology

We construct a stable homotopy refinement of quantum annular homology, a link homology theory introduced by Beliakova, Putyra and Wehrli. For each . The construction relies on an equivariant version of the Burnside category approach of Lawson, Lipshitz and Sarkar. The quotient under the cyclic group action is shown to recover the stable homotopy refinement of annular Khovanov homology. We study spectrum level lifts of structural properties of quantum annular homology.
DATA-DRIVEN FAILURE PREDICTION IN BRITTLE MATERIALS: A PHASE FIELD-BASED MACHINE LEARNING FRAMEWORK

Failure in brittle materials led by the evolution of micro- to macro-cracks under repetitive or increasing loads is often catastrophic with no significant plasticity to advert the onset of fracture. Early failure detection with respective location are utterly important features in any practical application, both of which can be effectively addressed using artificial intelligence. In this paper, we develop a supervised machine learning (ML) framework to predict failure in an isothermal, linear elastic and isotropic phase-field model for damage and fatigue of brittle materials. Time-series data of the phase-field model is extracted from virtual sensing nodes at different locations of the geometry. A pattern recognition scheme is introduced to represent time-series data/sensor nodes responses as a pattern with a corresponding label, integrated with ML algorithms, used for damage classification with identified patterns. We perform an uncertainty analysis by superposing random noise to the time-series data to assess the robustness of the framework with noise-polluted data. Results indicate that the proposed framework is capable of predicting failure with acceptable accuracy even in the presence of high noise levels. The findings demonstrate satisfactory performance of the supervised ML framework, and the applicability of artificial intelligence and ML to a practical engineering problem, i.,e, data-driven failure prediction in brittle materials.
ChainCQG: Flow-Aware Conversational Question Generation.

Conversational systems enable numerous valuable applications, and question-answering is an important component underlying many of these. However, conversational question-answering remains challenging due to the lack of realistic, domain-specific training data. Inspired by this bottleneck, we focus on conversational question generation as a means to generate synthetic conversations for training and evaluation purposes. We present a number of novel strategies to improve conversational flow and accommodate varying question types and overall fluidity. Specifically, we design ChainCQG as a two-stage architecture that learns question-answer representations across multiple dialogue turns using a flow propagation training strategy. ChainCQG significantly outperforms both answer-aware and answer-unaware SOTA baselines (e.g., up to 48% BLEU-1 improvement). Additionally, our model is able to generate different types of questions, with improved fluidity and coreference alignment.
Mechanical and Fracture Properties of Polycrystalline Graphene with Hydrogenated Grain Boundaries

Molecular dynamics (MD) is employed to study the mechanical and fracture properties of polycrystalline graphene with hydrogenated grain boundaries. Polycrystalline graphene sheets with an average grain size of 4, 6, and 8 nm are considered. The impact of hydrogenation percentage on the mechanical strength and fracture toughness of polycrystalline graphene sheets is investigated. Polycrystalline graphene with and without an initial crack are considered. The results show that due to the hydrogenation the strength of polycrystalline graphene sheets reduces. The impact of hydrogenation is significantly greater on the strength of polycrystalline graphene without an initial crack. The strength of polycrystalline graphene sheets with hydrogenated grain boundaries decreases with increase in temperature. The impact of temperature on the strength increases with increase in hydrogenation percentage at the grain boundaries. The results also show that the hydrogenation of the polycrystalline graphene affects the crack propagation path. As the hydrogenation percentage of grain boundaries increases, the crack path changes from intragranular to intergranular. Furthermore, the increase in the hydrogenation reduces the critical stress intensity factor of polycrystalline graphene. Such changes in the strength, fracture toughness, and crack path are due to the hydrogen embrittlement of grain boundaries which reduces their fracture energy.
Oxytocin excites BNST interneurons and inhibits BNST output neurons to the central amygdala.

Abstract The dorsolateral bed nucleus of the stria terminalis (BNSTDL) has high expression of oxytocin (OT) receptors (OTR), which were shown to facilitate cued fear. However, the role of OTR in the modulation of BNSTDL activity remains elusive. BNSTDL contains GABA-ergic neurons classified based on intrinsic membrane properties into three types. Using in vitro patch-clamp recordings in male rats, we demonstrate that OT selectively excites and increases spontaneous firing rate of Type I BNSTDL neurons. As a consequence, OT increases the frequency, but not amplitude, of spontaneous inhibitory post-synaptic currents (sIPSCs) selectively in Type II neurons, an effect abolished by OTR antagonist or tetrodotoxin, and reduces spontaneous firing rate in these neurons. These results suggest an indirect effect of OT in Type II neurons, which is mediated via OT-induced increase in firing of Type I interneurons. As Type II BNSTDL neurons were shown projecting to the central amygdala (CeA), we also recorded from retrogradely labeled BNST→CeA neurons and we show that OT increases the frequency of sIPSC in these Type II BNST→CeA output neurons. In contrast, in Type III neurons, OT reduces the amplitude, but not frequency, of both sIPSCs and evoked IPSCs via a postsynaptic mechanism without changing their intrinsic excitability. We present a model of fine-tuned modulation of BNSTDL activity by OT, which selectively excites BNSTDL interneurons and inhibits Type II BNST→CeA output neurons. These results suggest that OTR in the BNST might facilitate cued fear by inhibiting the BNST→CeA neurons.
Recovering individual-level spatial inference from aggregated binary data

Abstract Binary regression models are commonly used in disciplines such as epidemiology and ecology to determine how spatial covariates influence individuals. In many studies, binary data are shared in a spatially aggregated form to protect privacy. For example, rather than reporting the location and result for each individual that was tested for a disease, researchers may report that a disease was detected or not detected within geopolitical units. Often, the spatial aggregation process obscures the values of response variables, spatial covariates, and locations of each individual, which makes recovering individual-level inference difficult. We show that applying a series of transformations, including a change of support, to a bivariate point process model allows researchers to recover individual-level inference for spatial covariates from spatially aggregated binary data. The series of transformations preserves the convenient interpretation of desirable binary regression models that are commonly applied to individual-level data. Using a simulation experiment, we compare the performance of our proposed method under varying types of spatial aggregation against the performance of standard approaches using the original individual-level data. We illustrate our method by modeling individual-level probability of infection using a data set that has been aggregated to protect an at-risk and endangered species of bats. Our simulation experiment and data illustration demonstrate the utility of the proposed method when access to original non-aggregated data is impractical or prohibited.
An interplanetary targeting and orbit insertion maneuver design technique

The paper describes a tradeoff in selecting a planetary encounter aimpoint and a spacecraft propulsive maneuver strategy in the Pioneer Venus Orbiter Mission. The method uses parametric data spanning a region of acceptable targeting aimpoints in the delivery space and the geometric considerations. Real-time maneuver adjustments accounted for known attitude control errors, orbit determination updates, and late changes in a targeting specification.
When Things Go Awry: Command Responsibility, Death Marches, and Unforeseeable Circumstances

Although the events of the past year are in many ways unprecedented, they have resulted in circumstances that are common throughout history. The rise of a global pandemic has led to suffering in many forms, political powers shifting, militant coups rising, and countries facing protests as civil unrest becomes more prevalent. In these uncertain times, political leaders and the role of militaries have been even more scrutinized, revealing flaws that might have remained undetected if it was not for circumstances going awry. These current events have caused us to reflect upon incidents of the past when commanders have faced the uncertainty of how to complete their mission. History is wrought with instances in which the commander, despite having a “Plan B,” still fails to succeed in his role, thus resulting in hundreds of thousands of unnecessary lives lost. Specifically, this article focuses on three death marches—The Long Walk of the Navajo, The Bataan Death March, and Holocaust Death Marches—and the international law of command responsibility. In comparing and contrasting these three historic events through the lens of this law, we analyze the imposition of a commander’s criminal liability when unexpected events occur and he or she is called upon to make difficult decisions. In doing so, we also provide a historical backdrop of each commander’s ethical, moral, and tactical decisions, allowing us to explore what else could have been done, and who should be held liable for the actions of the commander’s soldiers. Ultimately, we call on national leaders and military commanders alike to evaluate our uncomfortable contemporary reality, look back in history, and ask themselves one question: am I truly prepared to make the right decisions when things go wrong?
Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling

Plug-and-play prirs (PnP) is an image reconstruction framework that uses an image denoiser as an imaging prior. Unlike traditional regularized inversion, PnP does not require the prior to be expressible in the form of a regularization function. This flexibility enables PnP algorithms to exploit the most effective image denoisers, leading to their state-of-the-art performance in various imaging tasks. However, many powerful denoisers, such as the ones based on convolutional neural networks (CNNs), do not have tunable parameters that would allow controlling their influence within PnP. To address this issue, in this paper, we introduce a scaling parameter that adjusts the magnitude of the denoiser input and output. We theoretical justify the denoiser scaling from the perspectives of proximal optimization, statistical estimation, and consensus equilibrium. Finally, we provide numerical experiments demonstrating the ability of denoiser scaling to systematically improve the performance of PnP for denoising CNN priors that do not have explicitly tunable parameters.
Intelligence - Consider This and Respond!

Regarding intelligence as a ‘considered response’ phenomenon is the key notion that is presented in this paper. Applied to human-level intelligence, it seems to be a useful definition that can lend clarity to the following related aspects as well: mind, self/I, awareness, self-awareness, consciousness, sentience, thoughts and feelings, free will, perception, attention, cognition, expectation, prediction, learning. Also, embodiment is argued to be an essential component of an AGI’s agent architecture, in order for it to attain grounded cognition, a sense of self and social learning - via direct physical experience and mental processes, all based on considered response.
A comparison of flow rates and pressure profiles for N-sequential inlets and three related seal configurations

Experimental and analytical results are presented for choked flows of fluid nitrogen over a range of reduced inlet stagnation temperatures (from 0.67 to ambient) and reduced inlet stagnation pressures to 2. Flow rate and pressure profile comparisons are made between N aligned sequential orifice inlets, a 33-tooth labyrinth seal, a 3-step seal, a cylindrical seal and the classic venturi. Seal effectiveness appears strongly dependent on upstream losses and geometry configuration.
PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design

Engineering design tasks often require synthesizing new designs that meet desired performance requirements. The conventional design process, which requires iterative optimization and performance evaluation, is slow and dependent on initial designs. Past work has used conditional generative adversarial networks (cGANs) to enable direct design synthesis for given target performances. However, most existing cGANs are restricted to categorical conditions. Recent work on Continuous conditional GAN (CcGAN) tries to address this problem, but still faces two challenges: 1) it performs poorly on non-uniform performance distributions, and 2) the generated designs may not cover the entire design space. We propose a new model, named Performance Conditioned Diverse Generative Adversarial Network (PcDGAN), which introduces a singular vicinal loss combined with a Determinantal Point Processes (DPP) based loss function to enhance diversity. PcDGAN uses a new self-reinforcing score called the Lambert Log Exponential Transition Score (LLETS) for improved conditioning. Experiments on synthetic problems and a real-world airfoil design problem demonstrate that PcDGAN outperforms state-of-the-art GAN models and improves the conditioning likelihood by 69% in an airfoil generation task and up to 78% in synthetic conditional generation tasks and achieves greater design space coverage. The proposed method enables efficient design synthesis and design space exploration with applications ranging from CAD model generation to metamaterial selection.
Model Light Curves for Type Ib and Ic Supernovae

Using the Monte Carlo code, SEDONA, multiband photometry and spectra are calculated for supernovae derived from stripped helium stars with presupernova masses from 2.2 to 10.0 $M_\odot$. The models are representative of evolution in close binaries and have previously been exploded using a parametrized one-dimensional model for neutrino-transport. A subset, those with presupernova masses in the range 2.2 - 5.6 $M_\odot$, have many properties in common with observed Type Ib and Ic supernovae, including a median ejected mass near 2 $M_\odot$, explosion energies near $1 \times 10^{51}$ erg, typical $^{56}$Ni masses 0.07 - 0.09 $M_\odot$, peak times of about 20 days, and a narrow range for the $V$-$R$ color index 10 days post $V$-maximum near 0.3 mag. The median peak bolometric luminosity, near 10$^{42.3}$ erg s$^{-1}$, is fainter, however, than for several observational tabulations and the brightest explosion has a bolometric luminosity of only 10$^{42.50}$ erg s$^{-1}$. The brightest absolute $B$, $V$, and $R$ magnitudes at peak are $-17.2$, $-17.8$, and $-18.0$. These limits are fainter than some allegedly typical Type Ib and Ic supernovae and could reflect problems in our models or the observational analysis. Helium stars with lower and higher masses also produce interesting transients that may have been observed including fast, faint, blue transients and long, red, faint Type Ic supernovae. New models are specifically presented for SN 2007Y, SN 2007gr, SN 2009jf, LSQ13abf, SN 2008D, and SN 2010X.
Bijective Proofs of Proper Coloring Theorems

The chromatic polynomial and its generalization, the chromatic symmetric function, are two important graph invariants. Celebrated theorems of Birkhoff, Whitney, and Stanley show how both objects ca...
Parallel Index-Based Structural Graph Clustering and Its Approximation

SCAN (Structural Clustering Algorithm for Networks) is a well-studied, widely used graph clustering algorithm. For large graphs, however, sequential SCAN variants are prohibitively slow, and parallel SCAN variants do not effectively share work among queries with different SCAN parameter settings. Since users of SCAN often explore many parameter settings to find good clusterings, it is worthwhile to precompute an index that speeds up queries. This paper presents a practical and provably efficient parallel index-based SCAN algorithm based on GS*-Index, a recent sequential algorithm. Our parallel algorithm improves upon the asymptotic work of the sequential algorithm by using integer sorting. It is also highly parallel, achieving logarithmic span (parallel time) for both index construction and clustering queries. Furthermore, we apply locality-sensitive hashing (LSH) to design a novel approximate SCAN algorithm and prove guarantees for its clustering behavior. We present an experimental evaluation of our algorithms on large real-world graphs. On a 48-core machine with two-way hyper-threading, our parallel index construction achieves 50--151× speedup over the construction of GS*-Index. In fact, even on a single thread, our index construction algorithm is faster than GS*-Index. Our parallel index query implementation achieves 5--32× speedup over GS*-Index queries across a range of SCAN parameter values, and our implementation is always faster than ppSCAN, a state-of-the-art parallel SCAN algorithm. Moreover, our experiments show that applying LSH results in faster index construction while maintaining good clustering quality.
Heating Augmentation in Laminar Flow Due to Heat-Shield Cavities on the Project Orion CEV

An experimental study has been conducted to assess the effects of compression pad cavities on the aeroheating environment of the Project Orion CEV heat-shield at laminar conditions. Testing was conducted in Mach 6 and Mach 10 perfect-gas wind tunnels to obtain heating measurements on and around the compression pads using global phosphor thermography. Consistent trends in heating augmentation levels were observed in the data and correlations of average and maximum heating at the cavities were formulated in terms of the local boundary-layer parameters and cavity dimensions. Additional heating data from prior testing of Genesis and Mars Science Laboratory models were also examined to extend the parametric range of cavity heating correlations.