cssi/SciSciGPT-SciSciNet huggingface dataset:
On the Utility of Religious Toleration

Brian Leiter’s Why Tolerate Religion? valuably clarifies the issues involved in granting religion-specific accommodations (and thus exceptions or exemptions) to laws and policies of general application. His arguments are careful, rigorous, and fair, and in rejecting the deontological arguments for religion-specific accommodations he seems to me largely correct. But when he turns to arguing against the utilitarian case for such accommodations, he employs a seemingly non-standard sense of utilitarianism in which demands of principled consistency constrain what would otherwise be utilitarian welfare-maximization. A more traditional and stronger version of utilitarianism, however, has room for seemingly unprincipled or even irrational distinctions as long as employing those distinctions is utility- or welfare-maximizing. And thus although Leiter’s arguments against the deontological justifications for religion-specific accommodations are largely successful, his arguments against utilitarian justifications, by relying more heavily on the notion of “principle” than a utilitarian should accept, are open to challenge.
All Work Cultures Discriminate

Traditionalists often explain the glass ceiling by arguing that women lack the competitive drive to succeed in high level environments. Current research, I argue, suggests that women may be just as motivated to succeed as men but that the average woman and the average man function best in very different environments. The average man’s performance deteriorates without the spur of competition, while women avoid competition yet appear to be more self-disciplined and more motivated than men by the intrinsic value of a task. In this paper I attempt to examine the complex implications of statistical sex differences in personality for the law of employment discrimination. When defendants invoke lack of interest as a defense, plaintiff should abandon their current strategy of denying the existence of sex differences and instead question whether sex differences take the particular form asserted by defendants.A more difficult question is whether the law should allow plaintiffs to raise the question of sex-biased work culture as an element of their claim. Such claims, I argue, should be considered only as applied to extreme behavior whose regulation can be justified on more general grounds. Such claims would be better regarded as a form of disparate impact than as a form of disparate treatment. Finally and most importantly, the law must not interfere with voluntary employer efforts to devise work cultures that are congenial to women. Such experiments have been made more difficult by the Supreme Court’s 2009 decision in Ricci v. de Stefano and I address how a modified affirmative action defense might permit desirable innovation in work cultures.
NRA v. City of Chicago: Does the Second Amendment Bind Frank Easterbrook?

There is little doubt that Frank Easterbrook will go down as one of the great appellate judges in the history of the United States. As those of us who know him well can testify, he is a judge who brings his immense intelligence and fierce dedication to his judicial work. Easterbrook also produces opinions that are always a pleasure to readshort and incisive, without pointless verbiage. One can disagree with their conclusions. But it is impossible to mistake their meaning. I agree wholeheartedly with just about everything he writes on a wide range of issues that deal with antitrust, contracts, corporations, and securities law. I have had more disagreements on his approach to constitutional law. Easterbrook does not like, nor does he need, praise. So I shall write about constitutional law.
Debating the Direction of Comparative Politics An Analysis of Leading Journals

This article contributes to ongoing debates about the direction of comparative politics through an analysis of new data on the scope, objectives, and methods of research in the field. The results of the analysis are as follows. Comparative politics is a rich and diverse field that cannot be accurately characterized on the basis of just one dimension or even summarized in simple terms. In turn, the tendency to frame choices about the direction of the field in terms of a stark alternative between an old area studies approach and a new economic approach relies on largely unsupported assumptions. It is therefore advisable to focus on problematic methodological practices that, as this study shows, are widespread in comparative research and thus pose serious impediments to the production of knowledge.
Do Disability Laws Impair Firm Performance

Much has been written about the impact of disability laws on employers and employees. Yet there is little empirical support available on whether disability laws impact employers in a negative or positive way. We fill this gap in the literature by studying the impact of state disability laws adopted by legislatures and courts between 1976 and 1988. By examining the data of approximately 18,000 commercial banks and after controlling for local state economic conditions, we examine whether disability laws impact firm employment, salaries, profitability, and capital investments. We find a small decline in employee salaries and a large decline in fixed asset investment associated with the passage of state disability laws. Our results are significant and surprising, and have policy implications for both state and federal disability protections.
SPITZER OBSERVATIONS OF EXOPLANETS DISCOVERED WITH THE KEPLER K2 MISSION

We have used the Spitzer Space Telescope to observe two transiting planetary systems orbiting low-mass stars discovered in the Kepler K2 mission. The system K2-3 (EPIC 201367065) hosts three planets, while K2-26 (EPIC 202083828) hosts a single planet. Observations of all four objects in these two systems confirm and refine the orbital and physical parameters of the planets. The refined orbital information and more precise planet radii possible with Spitzer will be critical for future observations of these and other K2 targets. For K2-3b we find marginally significant evidence for a transit timing variation between the K2 and Spitzer epochs.
Uncertainty and liquidity

Abstract This paper studies a model where money is valued for the liquidity services it provides in the future. Changes in expectations of the value of future liquidity services affect the desired proportions of money and other assets in agents' portfolios, and, as a result, they change nominal interest rates and real stock prices. I find that changes in dividend risk have effects opposite to those in portfolio models without money. Furthermore, nominal interest rates are the best (least noisy) indicator of changes in uncertainty about future dividends.
Discrimination in Baby Making: The Unconstitutional Treatment of Prospective Parents Through Surrogacy

INTRODUCTION 1187 I. THE UNITED STATES’ EXPERIENCE WITH SURROGACY 1189 II. PENALIZING SINGLETONS AND COHABITANTS—THE MARRIAGE REQUIREMENT 1193 III. THE STANDARD OF SCRUTINY SUBTERFUGE 1194 A. ANSWERING THE UNANSWERED QUESTION: IS ACCESS TO ART A CONSTITUTIONALLY PROTECTED RIGHT? 1195 B. STANDARDS OF SCRUTINY IN THE FACE OF UNCERTAINTY 1197 IV. THE CASE AGAINST THE LEGALITY OF MARITAL STATUS-BASED DISCRIMINATION 1198 A. SEX AND MORALITY 1200 B. PHYSICAL AND MENTAL HEALTH 1200 C. DUAL-PARENT CHILD REARING AND CHILD SUPPORT 1203 CONCLUSION 1206
Incomplete Financial Markets and the Booming Housing Sector in China

Housing is by far the most important asset in households’ balance sheets across many countries including China, and the policy relevance of the housing sector has been increasingly recognized (as evident by the inclusion of the housing cost into ECB’s inflation measure proposed in its new monetary policy strategy published in July 2021). However, despite forceful and frequent government interventions, the rise in Chinese housing prices has not been contained as much as intended, a trend that has not been reversed by the COVID-19 shock. In this paper, we first provide some stylized facts and then a DSGE model (encompassing both demand and supply channels) to highlight the impact of financial market incompleteness (e.g., inadequate investment channels) on China’s housing prices. The model implies that to eradicate the root causes of the rising housing price, policymakers need to go beyond the housing market itself; instead, it would be desirable to deepen financial markets because these markets would help channel financial resources to productive sectors rather than to housing speculation. This is particularly important in the COVID era because without addressing this structural vulnerability, the higher household savings and the government stimulus may fuel the housing bubble and sow seeds for a future crisis. The paper can also shed light on the housing markets in other economies that face similar vulnerabilities.
Hayden-Preskill decoding from noisy Hawking radiation

In the Hayden-Preskill thought experiment, the Hawking radiation emitted before a quantum state is thrown into the black hole is used along with the radiation collected later for the purpose of decoding the quantum state. A natural question is how the recoverability is affected if the stored early radiation is damaged or subject to decoherence, and/or the decoding protocol is imperfectly performed. We study the recoverability in the thought experiment in the presence of decoherence or noise in the storage of early radiation.
Predicting Nonlinear Seismic Response of Structural Braces Using Machine Learning

Numerical modeling of different structural materials that have highly nonlinear behaviors has always been a challenging problem in engineering disciplines. Experimental data is commonly used to characterize this behavior. This study aims to improve the modeling capabilities by using state of the art Machine Learning techniques, and attempts to answer several scientific questions: (i) Which ML algorithm is capable and is more efficient to learn such a complex and nonlinear problem? (ii) Is it possible to artificially reproduce structural brace seismic behavior that can represent real physics? (iii) How can our findings be extended to the different engineering problems that are driven by similar nonlinear dynamics? To answer these questions, the presented methods are validated by using experimental brace data. The paper shows that after proper data preparation, the long-short term memory (LSTM) method is highly capable of capturing the nonlinear behavior of braces. Additionally, the effects of tuning the hyperparameters on the models, such as layer numbers, neuron numbers, and the activation functions, are presented. Finally, the ability to learn nonlinear dynamics by using deep neural network algorithms and their advantages are briefly discussed.
Transmembrane 163 (TMEM163) Protein: A New Member of the Zinc Efflux Transporter Family

A growing body of evidence continues to demonstrate the vital roles that zinc and its transporters play on human health. The mammalian solute carrier 30 (SLC30) family, with ten current members, controls zinc efflux transport in cells. TMEM163, a recently reported zinc transporter, has similar characteristics in both predicted transmembrane domain structure and function to the cation diffusion facilitator (CDF) protein superfamily. This review discusses past and present data indicating that TMEM163 is a zinc binding protein that transports zinc in cells. We provide a brief background on TMEM163's discovery, transport feature, protein interactome, and similarities, as well as differences, with known SLC30 (ZnT) protein family. We also examine recent reports that implicate TMEM163 directly or indirectly in various human diseases such as Parkinson's disease, Mucolipidosis type IV and diabetes. Overall, the role of TMEM163 protein in zinc metabolism is beginning to be realized, and based on current evidence, we propose that it is likely a new CDF member belonging to mammalian SLC30 (ZnT) zinc efflux transporter proteins.
Can I lift it? Humanoid robot reasoning about the feasibility of lifting a heavy box with unknown physical properties

A robot cannot lift up an object if it is not feasible to do so. However, in most research on robot lifting, "feasibility" is usually presumed to exist a priori. This paper proposes a three-step method for a humanoid robot to reason about the feasibility of lifting a heavy box with physical properties that are unknown to the robot. Since feasibility of lifting is directly related to the physical properties of the box, we first discretize a range for the unknown values of parameters describing these properties and tabulate all valid optimal quasi-static lifting trajectories generated by simulations over all combinations of indices. Second, a physical-interaction-based algorithm is introduced to identify the robust gripping position and physical parameters corresponding to the box. During this process, the stability and safety of the robot are ensured. On the basis of the above two steps, a third step of mapping operation is carried out to best match the estimated parameters to the indices in the table. The matched indices are then queried to determine whether a valid trajectory exists. If so, the lifting motion is feasible; otherwise, the robot decides that the task is beyond its capability. Our method efficiently evaluates the feasibility of a lifting task through simple interactions between the robot and the box, while simultaneously obtaining the desired safe and stable trajectory. We successfully demonstrated the proposed method using a NAO humanoid robot.
Directional-TV algorithm for image reconstruction from limited-angular-range data.

Abstract Investigation of image reconstruction from data collected over a limited-angular range in X-ray CT remains a topic of active research because it may yield insight into the development of imaging workflow of practical significance. This reconstruction problem is well-known to be challenging, however, because it is highly ill-conditioned. In the work, we investigate optimization-based image reconstruction from data acquired over a limited-angular range that is considerably smaller than the angular range in short-scan CT. We first formulate the reconstruction problem as a convex optimization program with directional total-variation (TV) constraints applied to the image, and then develop an iterative algorithm, referred to as the directional-TV (DTV) algorithm for image reconstruction through solving the optimization program. We use the DTV algorithm to reconstruct images from data collected over a variety of limited-angular ranges for breast and bar phantoms of clinical- and industrial-application relevance. The study demonstrates that the DTV algorithm accurately recovers the phantoms from data generated over a significantly reduced angular range, and that it considerably diminishes artifacts observed otherwise in reconstructions of existing algorithms. We have also obtained empirical conditions on minimal-angular ranges sufficient for numerically accurate image reconstruction with the DTV algorithm.
K3 carpets on minimal rational surfaces and their smoothings

In this paper, we study K3 double structures on minimal rational surfaces Y. The results show there are infinitely many non-split abstract K3 double structures on Y = &#x1d53d;e parametrized by ℙ1, countab...
Vertebral Fractures After Denosumab Discontinuation in Breast Cancer Survivors: A Single Institution Experience:

Background: Denosumab is approved to prevent fragility fractures in patients with osteoporosis at high risk for fracture and to prevent bone loss in patients with breast and prostate cancer who rec...
Applying Transfer Learning for Improving Domain-Specific Search Experience Using Query to Question Similarity

Search is one of the most common platforms used to seek information. However, users mostly get overloaded with results whenever they use such a platform to resolve their queries. Nowadays, direct answers to queries are being provided as a part of the search experience. The question-answer (QA) retrieval process plays a significant role in enriching the search experience. Most off-the-shelf Semantic Textual Similarity models work fine for well-formed search queries, but their performances degrade when applied to a domain-specific setting having incomplete or grammatically ill-formed search queries in prevalence. In this paper, we discuss a framework for calculating similarities between a given input query and a set of predefined questions to retrieve the question which matches to it the most. We have used it for the financial domain, but the framework is generalized for any domain-specific search engine and can be used in other domains as well. We use Siamese network [6] over Long Short-Term Memory (LSTM) [3] models to train a classifier which generates un-normalized and normalized similarity scores for a given pair of questions. Moreover, for each of these question pairs, we calculate three other similarity scores: cosine similarity between their average word2vec embeddings [15], cosine similarity between their sentence embeddings [7] generated using RoBERTa [17] and their customized fuzzy-match score. Finally, we develop a meta-classifier using Support Vector Machines [19] for combining these five scores to detect if a given pair of questions is similar. We benchmark our model's performance against existing State Of The Art (SOTA) models on Quora Question Pairs (QQP) dataset1 as well as a dataset specific to the financial domain. After evaluating its performance on the financial domain specific data, we conclude that it not only outperforms several existing SOTA models on F1 score but also has decent accuracy.
Machine Learning Prediction and Experimental Validation of Antigenic Drift in H3 Influenza A Viruses in Swine.

ABSTRACT The antigenic diversity of influenza A viruses (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat and pandemic potential. High-throughput sequencing technologies can quantify IAV genetic diversity, but there are no accurate approaches to adequately describe antigenic phenotypes. This study evaluated an ensemble of nonlinear regression models to estimate virus phenotype from genotype. Regression models were trained with a phenotypic data set of pairwise hemagglutination inhibition (HI) assays, using genetic sequence identity and pairwise amino acid mutations as predictor features. The model identified amino acid identity, ranked the relative importance of mutations in the hemagglutinin (HA) protein, and demonstrated good prediction accuracy. Four previously untested IAV strains were selected to experimentally validate model predictions by HI assays. Errors between predicted and measured distances of uncharacterized strains were 0.35, 0.61, 1.69, and 0.13 antigenic units. These empirically trained regression models can be used to estimate antigenic distances between different strains of IAV in swine by using sequence data. By ranking the importance of mutations in the HA, we provide criteria for identifying antigenically advanced IAV strains that may not be controlled by existing vaccines and can inform strain updates to vaccines to better control this pathogen. IMPORTANCE Influenza A viruses (IAV) in swine constitute a major economic burden to an important global agricultural sector, impact food security, and are a public health threat. Despite significant improvement in surveillance for IAV in swine over the past 10 years, sequence data have not been integrated into a systematic vaccine strain selection process for predicting antigenic phenotype and identifying determinants of antigenic drift. To overcome this, we developed nonlinear regression models that predict antigenic phenotype from genetic sequence data by training the model on hemagglutination inhibition assay results. We used these models to predict antigenic phenotype for previously uncharacterized IAV, ranked the importance of genetic features for antigenic phenotype, and experimentally validated our predictions. Our model predicted virus antigenic characteristics from genetic sequence data and provides a rapid and accurate method linking genetic sequence data to antigenic characteristics. This approach also provides support for public health by identifying viruses that are antigenically advanced from strains used as pandemic preparedness candidate vaccine viruses.
Force-Based Reasoning in Assembly Planning

Assembly planning based on force-based reasoning of decomposable subassemblies, clusters of parts that are naturally broken apart from an assembly by decomposition forces is presented. The proposed force-based approach does not depend on combinatorial cut-set enumerations, but it generates only the minimal cut-sets of force-flow networks through a max-flow/min-cut algorithm, which incurs only polynomial time complexity. The key features of the proposed approach are as follows: (1) force-flow networks are constructed from the liaison graph of an assembly to represent the maximum amount of forces that individual parts can deliver to their mating parts for the chosen disassembly directions, fixtured and grasped parts, and removable connectors, (2) decomposable subassemblies are identified from the minimal cut-sets of force- flow networks based on a max-flow/min-cut algorithm, (3) the assemblability of the identified decomposable subassemblies are evaluated based on a force-based stability analysis, and (4) planning is done by the recursive decomposition of an assembly into subassemblies and verification of its reversibility.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Deep graph similarity learning: a survey

In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search. Recently, there has been an increasing interest in deep graph similarity learning, where the key idea is to learn a deep learning model that maps input graphs to a target space such that the distance in the target space approximates the structural distance in the input space. Here, we provide a comprehensive review of the existing literature of deep graph similarity learning. We propose a systematic taxonomy for the methods and applications. Finally, we discuss the challenges and future directions for this problem.
Nitration of metal ion coordinated imidazole and the crystal structure of pentaammine (4-nitroimidazolato)cobalt(III)chloride

Der Titelkomplex wurde durch Reaktion von Pentammin-imidazol-cobalt(III) mit einer Standardnitrierungsmischung 135 aus HNO3 und H2SO4 mit 97% Ausb. dargestellt und durch 1H-NMR- und Elektronenspektren charakterisiert.
Understanding Worldwide Private Information Collection on Android.

Mobile phones enable the collection of a wealth of private information, from unique identifiers (e.g., email addresses), to a user's location, to their text messages. This information can be harvested by apps and sent to third parties, which can use it for a variety of purposes. In this paper we perform the largest study of private information collection (PIC) on Android to date. Leveraging an anonymized dataset collected from the customers of a popular mobile security product, we analyze the flows of sensitive information generated by 2.1M unique apps installed by 17.3M users over a period of 21 months between 2018 and 2019. We find that 87.2% of all devices send private information to at least five different domains, and that actors active in different regions (e.g., Asia compared to Europe) are interested in collecting different types of information. The United States (62% of the total) and China (7% of total flows) are the countries that collect most private information. Our findings raise issues regarding data regulation, and would encourage policymakers to further regulate how private information is used by and shared among the companies and how accountability can be truly guaranteed.
Fast Sparse Grid Simulations of Fifth Order WENO Scheme for High Dimensional Hyperbolic PDEs

The weighted essentially non-oscillatory (WENO) schemes, especially the fifth order WENO schemes, are a popular class of high order accurate numerical methods for solving hyperbolic partial differential equations (PDEs). However when the spatial dimensions are high, the number of spatial grid points increases significantly. It leads to large amount of operations and computational costs in the numerical simulations by using nonlinear high order accuracy WENO schemes such as a fifth order WENO scheme. How to achieve fast simulations by high order WENO methods for high spatial dimension hyperbolic PDEs is a challenging and important question. In the literature, sparse-grid technique has been developed as a very efficient approximation tool for high dimensional problems. In a recent work [Lu, Chen and Zhang, Pure and Applied Mathematics Quarterly, 14 (2018) 57-86], a third order finite difference WENO method with sparse-grid combination technique was designed to solve multidimensional hyperbolic equations including both linear advection equations and nonlinear Burgers’ equations. Numerical experiments showed that WENO computations on sparse grids achieved comparable third order accuracy in smooth regions of the solutions and nonlinear stability as that for computations on regular single grids. In application problems, higher than third order WENO schemes are often preferred in order to efficiently resolve the complex solution structures. In this paper, we extend the approach to higher order WENO simulations specifically the fifth order WENO scheme. A fifth order WENO interpolation is applied in the prolongation part of the sparse-grid combination technique to deal with discontinuous solutions. Benchmark problems are first solved to show that significant CPU times are saved while both fifth order accuracy and stability of the WENO scheme are preserved for simulations on sparse grids. The fifth order sparse grid WENO method is then applied to kinetic problems modeled by high dimensional Vlasov based PDEs to further demonstrate large savings of computational costs by comparing with simulations on regular single grids. Several open problems are discussed at last.
The Viability of the $30 Casebook: Intellectual Property, Voluntary Payment, Open Distribution, and Author Incentives

It is not uncommon for a new hardbound copy of today’s law school casebooks to exceed $200. And, each year, the prices inch ever higher. After exploring the various dynamics in the traditional publishing market that have led to the current prices for casebooks, this article describes the experiences of Semaphore Press, a publisher of law school casebooks that offers a very different approach to providing law school casebooks. Semaphore Press offers digital copies of required textbooks for law school classes, (in pdf format with no digit rights management (DRM) restrictions), at a suggested price of $30. In addition, students can alter the price they pay, paying less or more than the suggested price, or they even download a copy of a required casebook for free. Semaphore Press’s commitment, embodied in this design, is that - whatever else happens - the student obtains access to the course materials. This article explores the viability of this model, sharing data gathered over past semesters. Approximately 83% of students enrolled in courses in which a Semaphore Press book was assigned paid for the book. The article details the amounts of student payments, and explores why students may be paying for digital books that they know they can obtain for free. The Semaphore Press experience with digital casebooks containing no DRM suggests that under current market conditions, a voluntary payment distribution model with a suggested $30 price is not only a model welcomed by law students, but it is a viable option both for authors and adopting professors.
The Information Externality of Public Firms’ Financial Information in the State-Bond Secondary Market

This study provides evidence on the role of public firms’ financial reports in the state‐bond secondary market. I investigate the informational role of corporate earnings announcements and find that public firms’ monthly earnings signals aggregated to the state level are positively associated with contemporaneous state‐bond returns. Further analyses reveal that public firms’ earnings announcements predict traditional economic indicators and contain incremental information that is independent of the traditional economic indicators. In cross‐sectional analyses, I show that the earnings–returns relation is especially pronounced when bondholders face longer investment horizons and higher credit risks. Taken together, the evidence indicates a positive externality of corporate financial reports in alleviating the opacity in the municipal bond secondary market.
Impact of Incentive Mechanism in Online Referral Programs: Evidence from Randomized Field Experiments

Despite the growing popularity of online referral programs, a minimal amount is known regarding the theoretical foundations that drive the key actions associated with successful referrals. In this ...
Copyright, Containers, and the Court: A Reply to Professor Leaffer

I find little with which to be pleased in the Court’s recent copyright cases. The Court seems to be fighting a holding action, fending off the future by resolutely gazing backward. While the Court has not itself enlarged copyright, it has not meaningfully evaluated Congress’s power to do so, and its decisions freeze copyright into a moment in time long past. Until copyright law recognizes that content is no longer container-bound, it will continue to flounder, desperately seeking analogies to the past and missing the significance of the technological changes all around us. That said, I agree with Professor Leaffer that the future is not black but gray. I believe that there is still much that can be done about the expansion of copyright and its increasing concentration into the hands of a media oligopoly, beginning with an awakening of public concern with those vital rights that are eroded as copyright expands. As long as that can happen, there is hope. Sadly, if that does not happen, then I don’t think either Congress or the Court will save us from ourselves.
CDA: a Cost Efficient Content-based Multilingual Web Document Aligner.

We introduce a Content-based Document Alignment approach (CDA), an efficient method to align multilingual web documents based on content in creating parallel training data for machine translation (MT) systems operating at the industrial level. CDA works in two steps: (i) projecting documents of a web domain to a shared multilingual space; then (ii) aligning them based on the similarity of their representations in such space. We leverage lexical translation models to build vector representations using TF×IDF. CDA achieves performance comparable with state-of-the-art systems in the WMT-16 Bilingual Document Alignment Shared Task benchmark while operating in multilingual space. Besides, we created two web-scale datasets to examine the robustness of CDA in an industrial setting involving up to 28 languages and millions of documents. The experiments show that CDA is robust, cost-effective, and is significantly superior in (i) processing large and noisy web data and (ii) scaling to new and low-resourced languages.
Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT.

We investigate how Multilingual BERT (mBERT) encodes grammar by examining how the high-order grammatical feature of morphosyntactic alignment (how different languages define what counts as a “subject”) is manifested across the embedding spaces of different languages. To understand if and how morphosyntactic alignment affects contextual embedding spaces, we train classifiers to recover the subjecthood of mBERT embeddings in transitive sentences (which do not contain overt information about morphosyntactic alignment) and then evaluate them zero-shot on intransitive sentences (where subjecthood classification depends on alignment), within and across languages. We find that the resulting classifier distributions reflect the morphosyntactic alignment of their training languages. Our results demonstrate that mBERT representations are influenced by high-level grammatical features that are not manifested in any one input sentence, and that this is robust across languages. Further examining the characteristics that our classifiers rely on, we find that features such as passive voice, animacy and case strongly correlate with classification decisions, suggesting that mBERT does not encode subjecthood purely syntactically, but that subjecthood embedding is continuous and dependent on semantic and discourse factors, as is proposed in much of the functional linguistics literature. Together, these results provide insight into how grammatical features manifest in contextual embedding spaces, at a level of abstraction not covered by previous work.
Re-imagining the Dignitary Torts

Tort law is cluttered with different causes of action that permit financial recovery for emotional harms arising from what we call wrongful affronts to human dignity. These include the common law torts of offensive battery, assault, false imprisonment, privacy invasion, defamation, some nuisance claims, and the more broadly labeled actions for intentional and negligent infliction of emotional distress. We don’t need all this clutter. Plus, simplifying our approach to dignitary harm would eliminate unjustified inconsistencies found in this group of torts. This article paves the way for a more coherent approach to the protection of personal dignity.
Hybridized Summation-by-Parts Finite Difference Methods

We present a hybridization technique for summation-by-parts finite difference methods with weak enforcement of interface and boundary conditions for second order, linear elliptic partial differential equations. The method is based on techniques from the hybridized discontinuous Galerkin literature where local and global problems are defined for the volume and trace grid points, respectively. By using a Schur complement technique the volume points can be eliminated, which drastically reduces the system size. We derive both the local and global problems, and show that the resulting linear systems are symmetric positive definite. The theoretical stability results are confirmed with numerical experiments as is the accuracy of the method.
Content-defined Merkle Trees for Efficient Container Delivery

Containerization simplifies the sharing and deployment of applications when environments change in the software delivery chain. To deploy an application, container delivery methods push and pull container images. These methods operate on file and layer (set of files) granularity, and introduce redundant data within a container. Several container operations such as upgrading, installing, and maintaining become inefficient, because of copying and provisioning of redundant data. In this paper, we reestablish recent results that block-level deduplication reduces the size of individual containers, by verifying the result using content-defined chunking. Block-level deduplication, however, does not improve the efficiency of push/pull operations which must determine the specific blocks to transfer. We introduce a content-defined Merkle Tree (CDMT) over deduplicated storage in a container. CDMT indexes deduplicated blocks and determines changes to blocks in logarithmic time on the client. CDMT efficiently pushes and pulls container images from a registry, especially as containers are upgraded and (re-)provisioned on a client. We also describe how a registry can efficiently maintain the CDMT index as new image versions are pushed. We show the scalability of CDMT over Merkle Trees in terms of disk and network I/O savings using 15 container images and 233 image versions from Docker Hub.
End-to-End Multi-Channel Transformer for Speech Recognition

Transformers are powerful neural architectures that allow integrating different modalities using attention mechanisms. In this paper, we leverage the neural transformer architectures for multi-channel speech recognition systems, where the spectral and spatial information collected from different microphones are integrated using attention layers. Our multi-channel transformer network mainly consists of three parts: channel-wise self attention layers (CSA), cross-channel attention layers (CCA), and multi-channel encoder-decoder attention layers (EDA). The CSA and CCA layers encode the contextual relationship "within" and "between" channels and across time, respectively. The channel-attended outputs from CSA and CCA are then fed into the EDA layers to help decode the next token given the preceding ones. The experiments show that in a far-field in-house dataset, our method outperforms the baseline single-channel transformer, as well as the super-directive and neural beamformers cascaded with the transformers.
Early Life Cycle Software Defect Prediction: Why? How?

Many researchers assume that, for software analytics, "more data is better." We write to show that, at least for learning defect predictors, this may not be true. To demonstrate this, we analyzed hundreds of popular GitHub projects. These projects ran for 84 months and contained 3,728 commits (median values). Across these projects, most of the defects occur very early in their life cycle. Hence, defect predictors learned from the first 150 commits and four months perform just as well as anything else. This means that, at least for the projects studied here, after the first few months, we need not continually update our defect prediction models. We hope these results inspire other researchers to adopt a "simplicity-first" approach to their work. Some domains require a complex and data-hungry analysis. But before assuming complexity, it is prudent to check the raw data looking for "short cuts" that can simplify the analysis.
Rates of protoplanetary accretion and differentiation set nitrogen budget of rocky planets

The effect of protoplanetary differentiation on the fate of life-essential volatiles such as nitrogen and carbon and its subsequent effect on the dynamics of planetary growth is unknown. Because the dissolution of nitrogen in magma oceans depends on its partial pressure and oxygen fugacity, it is an ideal proxy to track volatile redistribution in protoplanets as a function of their sizes and growth zones. Using high-pressure/temperature experiments in graphite-undersaturated conditions, here we show that the siderophilic (iron-loving) character of nitrogen is an order of magnitude higher than previous estimates across a wide range of oxygen fugacity. The experimental data combined with metal–silicate–atmosphere fractionation models suggest that asteroid-sized protoplanets, and planetary embryos that grew from them, were nitrogen depleted. However, protoplanets that grew to planetary embryo size before undergoing differentiation had nitrogen-rich cores and nitrogen-poor silicate reservoirs. Bulk silicate reservoirs of large Earth-like planets obtained nitrogen from the cores of the latter type of planetary embryos. Therefore, to satisfy the volatile budgets of Earth-like planets during the main stage of their growth, the timescales of planetary embryo accretion had to be shorter than their differentiation timescales; that is, Moon- to Mars-sized planetary embryos grew rapidly within ~1–2 Myrs of the Solar System’s formation. Rates of protoplanetary accretion and differentiation control the depletion of nitrogen in rocky planets, according to high-pressure/temperature experiments that show that nitrogen is extremely siderophilic.
COVID-19 and excess mortality in the United States: A county-level analysis.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) excess deaths refer to increases in mortality over what would normally have been expected in the absence of the COVID-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to COVID-19. In this study, we take advantage of county-level variation in COVID-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to COVID-19 varies across subsets of counties defined by sociodemographic and health characteristics. METHODS AND FINDINGS: In this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct COVID-19 and all-cause mortality occurring in US counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a 10-week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more COVID-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black, and 59.6% non-Hispanic White. A total of 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and COVID-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to COVID-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than COVID-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to COVID-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to COVID-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics. CONCLUSIONS: In this study, we found that direct COVID-19 death counts in the US in 2020 substantially underestimated total excess mortality attributable to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.
Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data

We propose instance segmentation as a useful tool for image analysis in materials science. Instance segmentation is an advanced technique in computer vision which generates individual segmentation masks for every object of interest that is recognized in an image. Using an out-of-the-box implementation of Mask R-CNN, instance segmentation is applied to images of metal powder particles produced through gas atomization. Leveraging transfer learning allows for the analysis to be conducted with a very small training set of labeled images. As well as providing another method for measuring the particle size distribution, we demonstrate the first direct measurements of the satellite content in powder samples. After analyzing the results for the labeled data dataset, the trained model was used to generate measurements for a much larger set of unlabeled images. The resulting particle size measurements showed reasonable agreement with laser scattering measurements. The satellite measurements were self-consistent and showed good agreement with the expected trends for different samples. Finally, we present a small case study showing how instance segmentation can be used to measure spheroidite content in the UltraHigh Carbon Steel DataBase, demonstrating the flexibility of the technique.
The Hodge ring of varieties in positive characteristic

Let k be a field of positive characteristic. We prove that the only linear relations between the Hodge numbers hi,j(X)=dimHj(X,ΩXi) that hold for every smooth proper variety X over k are the ones given by Serre duality. We also show that the only linear combinations of Hodge numbers that are birational invariants of X are given by the span of the hi,0(X) and the h0,j(X) (and their duals hi,n(X) and hn,j(X)). The corresponding statements for compact Kahler manifolds were proven by Kotschick and Schreieder.
The friendship paradox in real and model networks

The friendship paradox is the observation that the degrees of the neighbours of a node in any network will, on average, be greater than the degree of the node itself. In common parlance, your friends have more friends than you do. In this article, we develop the mathematical theory of the friendship paradox, both in general as well as for specific model networks, focusing not only on average behaviour but also on variation about the average and using generating function methods to calculate full distributions of quantities of interest. We compare the predictions of our theory with measurements on a large number of real-world network datasets and find remarkably good agreement. We also develop equivalent theory for the generalized friendship paradox, which compares characteristics of nodes other than degree to those of their neighbours.
ExSol: Collaboratively Assessing Cybersecurity Risks for Protecting Energy Delivery Systems

No longer just prophesied about, cyber-attacks to Energy Delivery Systems (EDS) (e.g., the power grid, gas and oil industries) are now very real dangers that result in non-trivial economical losses and inconveniences to modern societies. In such a context, risk analysis has been proposed as a valuable way to identify, analyze, and mitigate potential vulnerabilities, threats, and attack vectors. However, performing risk analysis for EDS is difficult due to their innate structural diversity and interdependencies, along with an always-increasing threatscape. Therefore, there is a need for a methodology to evaluate the current system state, identify vulnerabilities, and qualify risk at multiple granularities in a collaborative manner among different actors in the context of EDS. With this in mind, this article presents ExSol, a collaborative, real-time, risk assessment ecosystem that features an approach for modeling real-life EDS infrastructures, an ontology traversal technique that retrieves well-defined security requirements from well-reputed documents on cyber-protection for EDS infrastructures, as well as a methodology for calculating risk for a single asset and for an entire system. Moreover, we also provide experimental evidence involving a series of attack scenarios in both simulated and real-world EDS environments, which ultimately encourage the adoption of ExSol in practice.
Shape evolution of detached Bridgman crystals grown in microgravity

Detached (or dewetted) Bridgman crystal growth defines that process in which a gap exists between a growing crystal and the crucible wall. In microgravity, the parameters that influence the existence of a stable gap are the growth angle of the solidifying crystal, the contact angle between the melt and the crucible wall, and the pressure difference across the meniscus. During actual crystal growth, the initial crystal radius will not have the precise value required for stable detached growth. Beginning with a crystal diameter that differs from stable conditions, numerical calculations are used to analyze the transient crystal growth process. Depending on the initial conditions and growth parameters, the crystal shape will either evolve towards attachment at the crucible wall, towards a stable gap width, or inwards towards eventual collapse of the meniscus. Dynamic growth stability is observed only when the sum of the growth and contact angles exceeds 180°.