cssi/SciSciGPT-SciSciNet huggingface dataset:
Is the Price Level Determined by the Needs of Fiscal Solvency

A new theory of price determination suggests that if primary surpluses are independent of the level of debt, the price level has to jump' to assure fiscal solvency. In this regime (which we call Fiscal Dominant), monetary policy has to work through seignorage to control the price level. If on the other hand primary surpluses are expected to respond to the level of debt in a way that assures fiscal solvency (a regime we call Money Dominant), then the price level is determined in more conventional ways. In this paper we develop testable restrictions that differentiate between the two regimes. Using post war data, we present what we think is overwhelming evidence that the United States is in a Money Dominant regime; even the post Reagan data (1980 to 1995) seem to support that contention.
Welcoming Women: Recent Changes in U.S. Asylum Law

In the summer of 2009, the Obama Administration made public a Department of Homeland Security (“DHS”) brief submitted in the case of a Mexican woman who requested asylum based on the fear she would be killed by her former domestic partner if she returned to Mexico. The government’s brief in the case, the Matter of L-R-, proposed new legal justifications for granting battered women asylum, but stopped short of advocating a full grant of protected status. In December 2009, a U.S. immigration court granted asylum to a Guatemalan woman, Rody Alvarado, who suffered ten years of abuse from her husband in her native country. Ms. Alvarado’s case, the Matter of R-A-, had been in limbo for more than fourteen years. She was initially granted asylum in 1996, but the Board of Immigration Appeals (“BIA”) overturned that decision three years later, finding that battered women were not a cognizable social group under the legal definition of refugee. While these two recent developments are extremely encouraging for women’s rights advocates, the U.S. government has yet to finalize the legal rules governing asylum claims for victims of gender-based violence. This essay endorses a specific legal regime, based on the arguments made in the Matter of L-R- and Matter of R-A-, and relevant international treaties governing asylum, and argues that deserving women should receive asylum protection in the United States.
Quantifying cognitive biases in analyst earnings forecasts

This paper develops a formal model of analyst earnings forecasts that discriminates between rational behavior and that induced by cognitive biases. In the model, analysts are Bayesians who issue sequential forecasts that combine new information with the information contained in past forecasts. The model enables us to test for cognitive biases, and to quantify their magnitude. We estimate the model and find strong evidence that analysts are overconfident about the precision of their own information and also subject to cognitive dissonance bias, but they are able to make corrections for bias in the forecasts of others. We show that our measure of overconfidence varies with book-to-market ratio in a way consistent with the findings of Daniel and Titman [1999. Market efficiency in an irrational world. Financial Analysts’ Journal 55, 28–40]. We also demonstrate the existence of these biases in international data.
Spectral phase transitions in optical parametric oscillators

Driven nonlinear resonators provide a fertile ground for phenomena related to phase transitions far from equilibrium, which can open opportunities unattainable in their linear counterparts. Here, we show that optical parametric oscillators (OPOs) can undergo second-order phase transitions in the spectral domain between degenerate and non-degenerate regimes. This abrupt change in the spectral response follows a square-root dependence around the critical point, exhibiting high sensitivity to parameter variation akin to systems around an exceptional point. We experimentally demonstrate such a phase transition in a quadratic OPO. We show that the divergent susceptibility of the critical point is accompanied by spontaneous symmetry breaking and distinct phase noise properties in the two regimes, indicating the importance of a beyond nonlinear bifurcation interpretation. We also predict the occurrence of first-order spectral phase transitions in coupled OPOs. Our results on non-equilibrium spectral behaviors can be utilized for enhanced sensing, advanced computing, and quantum information processing.
The actin networks of chytrid fungi reveal evolutionary loss of cytoskeletal complexity in the fungal kingdom

Cells from across the eukaryotic tree use actin polymer networks for a wide variety of functions, including endocytosis, cytokinesis, and cell migration. Despite this functional conservation, the actin cytoskeleton has undergone significant diversification, highlighted by the differences in the actin networks of mammalian cells and yeast. Chytrid fungi diverged before the emergence of the Dikarya (multicellular fungi and yeast) and therefore provide a unique opportunity to study actin cytoskeletal evolution. Chytrids have two life stages: zoospore cells that can swim with a flagellum and sessile sporangial cells that, like multicellular fungi, are encased in a chitinous cell wall. Here, we show that zoospores of the amphibian-killing chytrid Batrachochytrium dendrobatidis (Bd) build dynamic actin structures resembling those of animal cells, including an actin cortex, pseudopods, and filopodia-like spikes. In contrast, Bd sporangia assemble perinuclear actin shells and actin patches similar to those of yeast. The use of specific small-molecule inhibitors indicate that nearly all of Bd's actin structures are dynamic and use distinct nucleators: although pseudopods and actin patches are Arp2/3 dependent, the actin cortex appears formin dependent and actin spikes require both nucleators. Our analysis of multiple chytrid genomes reveals actin regulators and myosin motors found in animals, but not dikaryotic fungi, as well as fungal-specific components. The presence of animal- and yeast-like actin cytoskeletal components in the genome combined with the intermediate actin phenotypes in Bd suggests that the simplicity of the yeast cytoskeleton may be due to evolutionary loss.
NITES: A Non-Parametric Interpretable Texture Synthesis Method

A non-parametric interpretable texture synthesis method, called NITES, is proposed in this work. Although automatic synthesis of visually pleasant texture can currently be achieved by deep neural networks, the associated generation models are mathematically intractable and their training demands higher computational cost. NITES offers a new texture synthesis solution to address these shortcomings. NITES is mathematically transparent and efficient in training and inference. The input is a single exemplary texture image. The NITES method crops out patches from the input and analyzes the statistical properties of these texture patches to obtain their joint spatial-spectral representations. Then, the probabilistic distributions of samples in the joint spatial-spectral spaces are characterized. Finally, numerous texture images that are visually similar to the exemplary texture image can be generated automatically. Experimental results are provided to show the superior quality of generated texture images and efficiency of the proposed NITES method in terms of both training and inference time.
A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing

To control part quality, it is critical to analyze pore generation mechanisms, laying theoretical foundation for future porosity control. Current porosity analysis models use machine setting parameters, such as laser angle and part pose. However, these setting-based models are machine dependent; hence, they often do not transfer to analysis of porosity for a different machine. To address the first problem, a physics-informed, data-driven model (PIM) is used, which instead of directly using machine setting parameters to predict porosity levels of printed parts, first interprets machine settings into physical effects, such as laser energy density and laser radiation pressure. Then, these physical, machine-independent effects are used to predict porosity levels according to “pass,” “flag,” and “fail” categories instead of focusing on quantitative pore size prediction. With six learning methods’ evaluation, PIM proved to achieve good performances with prediction error of 10 $\sim $ 26%. Finally, pore-encouraging influence and pore-suppressing influence were analyzed for quality analysis.
A Simulation Study on the Performance of Different Reliability Estimation Methods.

The accuracy of certain internal consistency estimators have been questioned in recent years. The present study tests the accuracy of six reliability estimators (Cronbach’s alpha, omega, omega hier...
Training General Representations for Remote Sensing Using in-Domain Knowledge

Automatically finding good and general remote sensing representations allows to perform transfer learning on a wide range of applications - improving the accuracy and reducing the required number of training samples. This paper investigates development of generic remote sensing representations, and explores which characteristics are important for a dataset to be a good source for representation learning. For this analysis, five diverse remote sensing datasets are selected and used for both, disjoint upstream representation learning and downstream model training and evaluation. A common evaluation protocol is used to establish baselines for these datasets that achieve state-of-the-art performance. As the results indicate, especially with a low number of available training samples a significant performance enhancement can be observed when including additionally in-domain data in comparison to training models from scratch or fine-tuning only on ImageNet (up to 11% and 40%, respectively, at 100 training samples). All datasets and pretrained representation models are published online.
Investor emotions and earnings announcements

Abstract Armed with a decade of social media data, I explore the impact of investor emotions on earnings announcements. In particular, I test whether the emotional content of firm-specific messages posted on social media just prior to a firm’s earnings announcement predicts its earnings and announcement returns. I find that investors are typically excited about firms that end up exceeding expectations, yet their enthusiasm results in lower announcement returns. Specifically, a standard deviation increase in excitement is associated with an 7.8 basis points lower announcement return, which translates into an approximately −5.8% annualized loss. My findings confirm that emotions and market dynamics are closely related and highlight the importance of considering investor emotions when assessing a firm’s short-term value.
Rank Uncertainty in Organizations

A principal incentivizes a team of agents to work by privately offering them bonuses contingent on team success. We study the principal's optimal incentive scheme that implements work as a unique equilibrium. This scheme leverages rank uncertainty to address strategic uncertainty. Each agent is informed only of a ranking distribution and his own bonus, the latter making work dominant provided that higher-rank agents work. If agents are symmetric, their bonuses are identical. Thus, discrimination is strictly suboptimal, in sharp contrast with the case of public contracts (Winter 2004). We characterize how agents' ranking and compensation vary with asymmetric effort costs.
Efficient iterative solutions to complex-valued nonlinear least-squares problems with mixed linear and antilinear operators

We consider a setting in which it is desired to find an optimal complex vector $${\mathbf {x}}\in {\mathbb {C}}^N$$ that satisfies $${\mathcal {A}}({\mathbf {x}}) \approx {\mathbf {b}}$$ in a least-squares sense, where $${\mathbf {b}} \in {\mathbb {C}}^M$$ is a data vector (possibly noise-corrupted), and $${\mathcal {A}}(\cdot ): {\mathbb {C}}^N \rightarrow {\mathbb {C}}^M$$ is a measurement operator. If $${\mathcal {A}}(\cdot )$$ were linear, this reduces to the classical linear least-squares problem, which has a well-known analytic solution as well as powerful iterative solution algorithms. However, instead of linear least-squares, this work considers the more complicated scenario where $${\mathcal {A}}(\cdot )$$ is nonlinear, but can be represented as the summation and/or composition of some operators that are linear and some operators that are antilinear. Some common nonlinear operations that have this structure include complex conjugation or taking the real-part or imaginary-part of a complex vector. Previous literature has shown that this kind of mixed linear/antilinear least-squares problem can be mapped into a linear least-squares problem by considering $${\mathbf {x}}$$ as a vector in $${\mathbb {R}}^{2N}$$ instead of $${\mathbb {C}}^N$$ . While this approach is valid, the replacement of the original complex-valued optimization problem with a real-valued optimization problem can be complicated to implement, and can also be associated with increased computational complexity. In this work, we describe theory and computational methods that enable mixed linear/antilinear least-squares problems to be solved iteratively using standard linear least-squares tools, while retaining all of the complex-valued structure of the original inverse problem. An illustration is provided to demonstrate that this approach can simplify the implementation and reduce the computational complexity of iterative solution algorithms.
Turbomachinery Blade Row Interaction Aerodynamics and Forced Response Experiments

One of the major challenges in the development of fan and compressor airfoils is high cycle fatigue (HCF) due to flow-induced resonant response. These resonant vibrations occur when a periodic forcing function, with a frequency that coincides with an airfoil natural frequency, acts on a blade row. The most common forcing functions are viscous wakes and shock waves generated by transonic blading. Resonant response is predicted by first determining the natural frequencies of the design rotor. With these airfoil frequencies able to be accurately predicted with finite element models, the Campbell diagram is a key design tool. However, Campbell diagrams provide no measure of the amplitude of the resonant vibration or stress. Thus, the numerous HCF failures in modern and mature aircraft engines have necessitated the development of design analyses that predict the vibration amplitude/stress at the resonant speeds. The driving phenomenon for these flow-induced vibrations is the blade row unsteady aerodynamics. Timeaccurate multi-blade row CFD analyses applied to a sector of the interacting airfoil rows predict the forcing function and the resulting blade row unsteady aerodynamics. Prior to implementing such analyses into the design system, they must first be validated with appropriate data. This is especially true for aggressive designs that feature highlyloaded closely-spaced blade rows as they operate in a highly unsteady flow environment. This paper describes experiments designed to provide benchmark unsteady aerodynamic blade row interaction and rotor response data. Detailed unsteady aerodynamic rotor-IGV and rotor-stator interaction data, as well as rotor response data are obtained in an advanced design transonic research compressor. These detailed data quantify the rotor-generated forcing functions to the upstream IGV and downstream stator vane rows, the resultant IGV and vane row unsteady aerodynamic loading, the vane-to-vane flow fields, as well as the rotor blade vibratory response.
Rejoinder: New Objectives for Policy Learning

I would like thank the discussants, Oliver Dukes and Stijn Vansteelandt (DV), Sijia Li, Xiudi Li, Alex Luedtkeand (LLL), and Muxuan Liang and Yingqi Zhao (LZ), for a very thoughtful discussion both...
High-Frequency Dynamics Modulated by Collective Magnetization Reversal in Artificial Spin Ice

In a network of ferromagnetic nanowires, a magnetic metamaterial known as $a\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}f\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}l$ $s\phantom{\rule{0}{0ex}}p\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}n$ $i\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}e$, the collective magnetization behavior strongly affects the dynamics and the magnetoresistive behavior. The authors use micromagnetic simulations to provide a microscopic picture of this unexpected response, and find that the sharp features in their experimental data are due to a collective change in the magnetization configuration. Given the geometric freedom enabled by modern lithography, these results offer the possibility to design innovative, reconfigurable microwave and magnetoresistive devices based on artificial spin ice, for $e.g.$ neuromorphic computing.
Cluster-based distributed augmented Lagrangian algorithm for a class of constrained convex optimization problems

Author(s): Moradian, Hossein; Kia, Solmaz S | Abstract: We propose a distributed solution for a constrained convex optimization problem over a network of clustered agents each consisted of a set of subagents. The communication range of the clustered agents is such that they can form a connected undirected graph topology. The total cost in this optimization problem is the sum of the local convex costs of the subagents of each cluster. We seek a minimizer of this cost subject to a set of affine equality constraints, and a set of affine inequality constraints specifying the bounds on the decision variables if such bounds exist. We design our distributed algorithm in a cluster-based framework which results in a significant reduction in communication and computation costs. Our proposed distributed solution is a novel continuous-time algorithm that is linked to the augmented Lagrangian approach. It converges asymptotically when the local cost functions are convex and exponentially when they are strongly convex and have Lipschitz gradients. Moreover, we use an $\epsilon$-exact penalty function to address the inequality constraints and derive an explicit lower bound on the penalty function weight to guarantee convergence to $\epsilon$-neighborhood of the global minimum value of the cost. A numerical example demonstrates our results.
Increased Transparency in Procurement: The Role of Peer Effects

Motivated by recent initiatives to increase transparency in procurement, we study the effects of disclosing information about previous purchases in a setting where an organization delegates its pur...
The Role of Dectin-1 Signaling in Altering Tumor Immune Microenvironment in the Context of Aging

An increased accumulation of immune-dysfunction-associated CD4+Foxp3+ regulatory T cells (Tregs) is observed in aging oral mucosa during infection. Here we studied the function of Tregs during oral cancer development in aging mucosa. First, we found heightened proportions of Tregs and myeloid-derived suppressor cells (MDSC) accumulating in mouse and human oral squamous cell carcinoma (OSCC) tissues. Using the mouse 4-Nitroquinoline 1-oxide(4-NQO) oral carcinogenesis model, we found that tongues of aged mice displayed increased propensity for epithelial cell dysplasia, hyperplasia, and accelerated OSCC development, which coincided with significantly increased abundance of IL-1β, Tregs, and MDSC in tongues. Partial depletion of Tregs reduced tumor burden. Moreover, fungal abundance and dectin-1 signaling were elevated in aged mice suggesting a potential role for dectin-1 in modulating immune environment and tumor development. Confirming this tenet, dectin-1 deficient mice showed diminished IL-1β, reduced infiltration of Tregs and MDSC in the tongues, as well as slower progression and reduced severity of tumor burden. Taken together, these data identify an important role of dectin-1 signaling in establishing the intra-tumoral immunosuppressive milieu and promoting OSCC tumorigenesis in the context of aging.
Learning with Multiplicative Perturbations

Adversarial Training (AT) and Virtual Adversarial Training (VAT) are the regularization techniques that train Deep Neural Networks (DNNs) with adversarial examples generated by adding small but worst-case perturbations to input examples. In this paper, we propose xAT and xVAT, new adversarial training algorithms that generate multiplicative perturbations to input examples for robust training of DNNs. Such perturbations are much more perceptible and interpretable than their additive counterparts exploited by AT and VAT. Furthermore, the multiplicative perturbations can be generated transductively or inductively, while the standard AT and VAT only support a transductive implementation. We conduct a series of experiments that analyze the behavior of the multiplicative perturbations and demonstrate that xAT and xVAT match or outperform state-of-the-art classification accuracies across multiple established benchmarks while being about 30% faster than their additive counterparts. Our source code can be found at https://github.com/sndnyang/xvat
Payout policy and the interaction of firm-level and country-level governance

For a panel of 1880 firms across 21 countries from 2004 to 2008, the impact of firm-level and country-level governance on payout policy is consistent with the La Porta et al. (J Finance 60(1):1–33, 2000) outcome model. In weak legal regimes, dividends increase in firm-level governance. In strong legal regimes, dividends decrease in firm-level governance while share repurchases increase, substituting tax-efficient, flexible share repurchases for rigid dividends. Consistent with the outcome model, payout decreases in growth opportunities when firm-level and country-level governance are high. These results are robust to an instrumental variable approach and alternative firm-level and country-level governance measures.
Model-Assisted Machine-Code Synthesis

Binary rewriters are tools that are used to modify the functionality of binaries lacking source code. Binary rewriters can be used to rewrite binaries for a variety of purposes including optimization, hardening, and extraction of executable components. To rewrite a binary based on semantic criteria, an essential primitive to have is a machine-code synthesizer—a tool that synthesizes an instruction sequence from a specification of the desired behavior, often given as a formula in quantifier-free bit-vector logic (QFBV). However, state-of-the-art machine-code synthesizers such as McSynth++ employ naive search strategies for synthesis: McSynth++ merely enumerates candidates of increasing length without performing any form of prioritization. This inefficient search strategy is compounded by the huge number of unique instruction schemas in instruction sets (e.g., around 43,000 in Intel's IA-32) and the exponential cost inherent in enumeration. The effect is slow synthesis: even for relatively small specifications, McSynth++ might take several minutes or a few hours to find an implementation. In this paper, we describe how we use machine learning to make the search in McSynth++ smarter and potentially faster. We converted the linear search in McSynth++ into a best-first search over the space of instruction sequences. The cost heuristic for the best-first search comes from two models—used together—built from a corpus of pairs: (i) a language model that favors useful instruction sequences, and (ii) a regression model that correlates features of instruction sequences with features of QFBV formulas, and favors instruction sequences that are more likely to implement the input formula. Our experiments for IA-32 showed that our model-assisted synthesizer enables synthesis of code for 6 out of 50 formulas on which McSynth++ times out, speeding up the synthesis time by at least 549X, and for the remaining formulas, speeds up synthesis by 4.55X.
Vaccination reshapes the virus-specific T cell repertoire in unexposed adults.

Summary We examined how baseline CD4+ T cell repertoire and precursor states impact responses to pathogen infection in humans using primary immunization with yellow fever virus (YFV) vaccine. YFV-specific T cells in unexposed individuals were identified by peptide-MHC tetramer staining and tracked pre- and post-vaccination by tetramers and TCR sequencing. A substantial number of YFV-reactive T cells expressed memory phenotype markers and contained expanded clones in the absence of exposure to YFV. After vaccination, pre-existing YFV-specific T cell populations with low clonal diversity underwent limited expansion, but rare populations with a reservoir of unexpanded TCRs generated robust responses. These altered dynamics reorganized the immunodominance hierarchy and resulted in an overall increase in higher avidity T cells. Thus, instead of further increasing the representation of dominant clones, YFV vaccination recruits rare and more responsive T cells. Our findings illustrate the impact of vaccines in prioritizing T cell responses and reveal repertoire reorganization as a key component of effective vaccination.
Disclosure and the outcome of securities litigation

We examine the impact of disclosure by defendant firms on the outcome of securities fraud class actions. We hypothesize that firms issuing a higher quantity of disclosure will experience more adverse litigation outcomes, given the strict pleading standards of the Private Securities Litigation Reform Act (PSLRA). Using broad measures of disclosure derived from press releases issued during the class period, we find that more disclosure increases the likelihood that the judge will allow a lawsuit to proceed rather than dismissing it. Our results provide new insights to the literature on disclosure and litigation by studying the outcome rather than incidence of litigation, or the plaintiffs’ decision to sue. We strengthen the inference that the features of the PSLRA create a positive relationship between overall disclosure and the likelihood of settlement by showing that the relationship holds when controlling for forward-looking disclosure and ex ante litigation risk, and by providing evidence that more public disclosures allow plaintiffs to present more extensive cases.
COVID-19 Disease Trajectories Among Nursing Home Residents

INTRODUCTION: Older adults are at greater risk of both infection with and mortality from COVID-19 Many United States nursing homes have been devastated by the COVID-19 pandemic, yet little has been described regarding the typical disease course in this population The objective of this study is to describe and identify patterns in the disease course of nursing home residents infected with COVID-19 SETTING AND METHODS: This is a case series of 74 residents with COVID-19 infection in a nursing home in central Indiana between March 28(th) - June 17(th) 2020 Data were extracted from the electronic medical record and from nursing home medical director tracking notes from the time of the index infection through August 31(st) , 2020 The clinical authorship team reviewed the data to identify patterns in the disease course of the residents RESULTS: The most common symptoms were fever, hypoxia, anorexia, and fatigue/malaise The duration of symptoms was extended, with an average of over three weeks Of those infected 25 died;23 of the deaths were considered related to COVID-19 infection A subset of residents with COVID-19 infection experienced a rapidly progressive, fatal course DISCUSSION/CONCLUSIONS: Nursing home residents infected with COVID-19 from the facility we studied experienced a prolonged disease course regardless of the severity of their symptoms, with implications for the resources needed to care for and support of these residents during active infection and post-disease Future studies should combine data from nursing home residents across the country to identify the risk factors for disease trajectories identified in this case series
Structural Changes in Investment and the Waning Power of Monetary Policy

We argue that secular change in both the production and composition of investment goods has weakened private investment's role in the transmission of monetary policy to labor earnings and consumption. We show analytically that fluctuations in the production of investment goods amplify the response of consumption to monetary policy shocks by varying labor income for hand-to-mouth agents. We document three secular changes that weaken this channel: (i) labor's share of value added in investment goods production has declined, (ii) the import share of investment goods has risen, and (iii) the composition of investment has shifted towards components that are less responsive to monetary policy. A small open economy, two agent New Keynesian model calibrated to match these facts implies a 38% and 26% weaker response of labor income and aggregate consumption, respectively, to real interest rate shocks in a 2010's economy relative to a 1960's economy.
The Surprising Effectiveness of Linear Models for Visual Foresight in Object Pile Manipulation

In this paper, we tackle the problem of pushing piles of small objects into a desired target set using visual feedback. Unlike conventional single-object manipulation pipelines, which estimate the state of the system parametrized by pose, the underlying physical state of this system is difficult to observe from images. Thus, we take the approach of reasoning directly in the space of images, and acquire the dynamics of visual measurements in order to synthesize a visual-feedback policy. We present a simple controller using an image-space Lyapunov function, and evaluate the closed-loop performance using three different class of models for image prediction: deep-learning-based models for image-to-image translation, an object-centric model obtained from treating each pixel as a particle, and a switched-linear system where an action-dependent linear map is used. Through results in simulation and experiment, we show that for this task, a linear model works surprisingly well – achieving better prediction error, downstream task performance, and generalization to new environments than the deep models we trained on the same amount of data. We believe these results provide an interesting example in the spectrum of models that are most useful for vision-based feedback in manipulation, considering both the quality of visual prediction, as well as compatibility with rigorous methods for control design and analysis. Project site: https://sites.google.com/view/linear-visual-foresight/home
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks

Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features. However, for models with text pairs as inputs (e.g., paraphrase identification), existing methods are not sufficient to capture feature interactions between two texts and their simple extension of computing all word-pair interactions between two texts is computationally inefficient. In this work, we propose the Group Mask (GMASK) method to implicitly detect word correlations by grouping correlated words from the input text pair together and measure their contribution to the corresponding NLP tasks as a whole. The proposed method is evaluated with two different model architectures (decomposable attention model and BERT) across four datasets, including natural language inference and paraphrase identification tasks. Experiments show the effectiveness of GMASK in providing faithful explanations to these models.
A Quick Route to Unique Factorization in Quadratic Orders

We give a short proof—not relying on ideal classes or the geometry of numbers—of a known criterion for quadratic orders to possess unique factorization.
Multilingual Contextual Affective Analysis of LGBT People Portrayals in Wikipedia

Specific lexical choices in narrative text reflect both the writer's attitudes towards people in the narrative and influence the audience's reactions. Prior work has examined descriptions of people in English using contextual affective analysis, a natural language processing (NLP) technique that seeks to analyze how people are portrayed along dimensions of power, agency, and sentiment. Our work presents an extension of this methodology to multilingual settings, which is enabled by a new corpus that we collect and a new multilingual model. We additionally show how word connotations differ across languages and cultures, highlighting the difficulty of generalizing existing English datasets and methods. We then demonstrate the usefulness of our method by analyzing Wikipedia biography pages of members of the LGBT community across three languages: English, Russian, and Spanish. Our results show systematic differences in how the LGBT community is portrayed across languages, surfacing cultural differences in narratives and signs of social biases. Practically, this model can be used to identify Wikipedia articles for further manual analysis---articles that might contain content gaps or an imbalanced representation of particular social groups.
Establishing the minimal clinically important difference for the Hernia-Related Quality of Life Survey (HerQLes).

Abstract Background Hernia-Related Quality of Life Survey (HerQLes) assesses quality of life (QoL) after hernia repair, but the minimal clinically important difference (MCID) is unknown. Methods Using 2013–2019 data from the Abdominal Core Health Quality Collaborative, HerQLes summary scores were calculated for VHR patients at baseline and 1-year. MCID was calculated using distribution-based method. Multivariate regression identified factors associated with exceeding MCID at 1 year. Results 1817 patients met criteria. MCID was identified as a change in HerQLes of at least 15.6 points. Mean 1-year post-op score was 74.9 (SD ± 26.2), which exceeded the MCID threshold (p Conclusion Using MCID can help identify patients who may significantly improve QoL after VHR, as well as power clinical trials with QoL as primary outcome.
Differentially Private Decomposable Submodular Maximization

We study the problem of differentially private constrained maximization of decomposable submodular functions. A submodular function is decomposable if it takes the form of a sum of submodular functions. The special case of maximizing a monotone, decomposable submodular function under cardinality constraints is known as the Combinatorial Public Projects (CPP) problem (Papadimitriou, Schapira, and Singer 2008). Previous work by Gupta et al. (2010) gave a differentially private algorithm for the CPP problem. We extend this work by designing differentially private algorithms for both monotone and non-monotone decomposable submodular maximization under general matroid constraints, with competitive utility guarantees. We complement our theoretical bounds with experiments demonstrating improved empirical performance.
Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels

High-resolution (HR) information of fluid flows, although preferable, is usually less accessible due to limited computational or experimental resources. In many cases, fluid data are generally sparse, incomplete, and possibly noisy. How to enhance spatial resolution and decrease the noise level of flow data is essential and practically useful. Deep learning (DL) techniques have been demonstrated to be effective for super-resolution (SR) tasks, which, however, primarily rely on sufficient HR labels for training. In this work, we present a novel physics-informed DL-based SR solution using convolutional neural networks (CNNs), which is able to produce HR flow fields from low-resolution (LR) inputs in high-dimensional parameter space. By leveraging the conservation laws and boundary conditions of fluid flows, the CNN-SR model is trained without any HR labels. Moreover, the proposed CNN-SR solution unifies the forward SR and inverse data assimilation for the scenarios where the physics is partially known, e.g., unknown boundary conditions. A new network structure is designed to enable not only the parametric SR but also the parametric inference for the first time. Several flow SR problems relevant to cardiovascular applications have been studied to demonstrate the proposed method's effectiveness and merit. A series of different LR scenarios, including LR input with Gaussian noises, non-Gaussian magnetic resonance imaging noises, and downsampled measurements given either well-posed or ill-posed physics, are investigated to illustrate the SR, denoising, and inference capabilities of the proposed method.
How Do Anti-discrimination Laws Affect Firm Performance and Financial Policies? Evidence from the Post-World War II Period

We exploit the staggered passage of state-level fair-employment laws in the post-World War II period to examine how stronger worker protection against racial discrimination affects firm profitabili...
Fine-Grained Complexity and Algorithms for the Schulze Voting Method

We study computational aspects of a well-known single-winner voting rule called the Schulze method [Schulze, 2003] which is used broadly in practice. In this method the voters give (weak) ordinal preference ballots which are used to define the weighted majority graph of direct comparisons between pairs of candidates. The choice of the winner comes from indirect comparisons in the graph, and more specifically from considering directed paths instead of direct comparisons between candidates. When the input is the weighted majority graph, to our knowledge, the fastest algorithm for computing all winners in the Schulze method uses a folklore reduction to the All-Pairs Bottleneck Paths (APBP) problem and runs in $Oh(m2.69) time, where m is the number of candidates. It is an interesting open question whether this can be improved. Our first result is a combinatorial algorithm with a nearly quadratic running time for computing all winners. This running time is essentially optimal as it is nearly linear in the size of the weighted majority graph. If the input to the Schulze winners problem is not the weighted majority graph but the preference profile, then constructing the weighted majority graph is a bottleneck that increases the running time significantly; in the special case when there are m candidates and n=Oh(m) voters, the running time is Oh(m2.69), or $Oh(m2.5) if there is a nearly-linear time algorithm for multiplying dense square matrices. To address this bottleneck, we prove a formal equivalence between the well-studied Dominance Product problem and the problem of computing the weighted majority graph. As the Dominance Product problem is believed to require at least time r2.5-o(1) on r x r matrices, our equivalence implies that constructing the weighted majority graph in $Oh(m2.499) time for m candidates and n = Oh(m) voters would imply a breakthrough in the study of "intermediate" problems [Lincoln et al., 2020] in fine-grained complexity. We prove a similar connection between the so called Dominating Pairs problem and the problem of verifying whether a given candidate is a winner. Our paper is the first to bring fine-grained complexity into the field of computational social choice. Previous approaches say nothing about lower bounds for problems that already have polynomial time algorithms. By bringing fine-grained complexity into the picture we can identify voting protocols that are unlikely to be practical for large numbers of candidates and/or voters, as their complexity is likely, say at least cubic.
Pathogenic Mechanisms of Hypertrophic Cardiomyopathy beyond Sarcomere Dysfunction

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disorder, affecting 1 in 500 people in the general population. Although characterized by asymmetric left ventricular hypertrophy, cardiomyocyte disarray, and cardiac fibrosis, HCM is in fact a highly complex disease with heterogenous clinical presentation, onset, and complications. While HCM is generally accepted as a disease of the sarcomere, variable penetrance in families with identical genetic mutations challenges the monogenic origin of HCM and instead implies a multifactorial cause. Furthermore, large-scale genome sequencing studies revealed that many genes previously reported as causative of HCM in fact have little or no evidence of disease association. These findings thus call for a re-evaluation of the sarcomere-centered view of HCM pathogenesis. Here, we summarize our current understanding of sarcomere-independent mechanisms of cardiomyocyte hypertrophy, highlight the role of extracellular signals in cardiac fibrosis, and propose an alternative but integrated model of HCM pathogenesis.
Linear-Threshold Dynamics for the Study of Epileptic Events

In this letter, we provide a detailed characterization of the equilibria and bifurcations of two-dimensional linear-threshold models. Using the input to the system as the bifurcation parameter, we characterize the location of the admissible equilibria, show that bifurcations can arise only when equilibria lie on the boundary of well-defined regions of the state space, and prove that (codimension-one) bifurcations can only be of three different types: persistent, non-smooth fold, and Hopf. We show how these bifurcations change the qualitative properties of the system trajectories, and how these behaviors resemble prototypical patterns of EEG activity observed before, during, and after seizure events in the human brain. Our findings suggest that low-dimensional linear threshold models can effectively be used to model, analyze, predict, and ultimately regulate the interactions of neuronal populations in the human brain.
A Computational Framework for Lithium Ion Cell-Level Model Predictive Control Using a Physics-Based Reduced-Order Model

Most state-of-the art battery-control strategies rely on voltage-based design limits to address performance and lifetime concerns. Such approaches are inherently conservative. However, by exploiting internal electrochemical quantities, it is possible to control battery performance right up to true physical bounds. This paper develops an extensible framework that combines model predictive control (MPC) with computationally efficient realization algorithm (xRA)-generated reduced-order electrochemical models for the advanced control of lithium-ion batteries. The approach is demonstrated on the fast-charge problem where hard constraints are imposed on problem variables to avoid lithium plating induced performance degradation. This work establishes a general mathematical foundation for the incorporation of electrochemically rich reduced-order models directly into an MPC framework.
Robust Control Barrier and Control Lyapunov Functions with Fixed-Time Convergence Guarantees

This paper studies control synthesis for a general class of nonlinear, control-affine dynamical systems under additive disturbances and state-estimation errors. We enforce forward invariance of static and dynamic safe sets and convergence to a given goal set within a user-defined time in the presence of input constraints. We use robust variants of control barrier functions (CBF) and fixed-time control Lyapunov functions (FxT-CLF) to incorporate a class of additive disturbances in the system dynamics, and state-estimation errors. To solve the underlying constrained control problem, we formulate a quadratic program and use the proposed robust CBF-FxT-CLF conditions to compute the control input. We showcase the efficacy of the proposed method on a numerical case study involving multiple underactuated marine vehicles.
Submilliarcsecond VLBI observations of the close pair GC 1342 + 662 and GC 1342 + 663

Differential VLBI was simultaneously performed on the source pair GC 1342 + 662 and GC 1342 + 663 (4.4-arcminute separation) using S-band on the Goldstone/Madrid baseline. These measurements were acquired on two separate observing sessions: 30 December 1982 and 14 May 1983. The change in separation of GC 1342 + 662 relative to GC 1342 + 663 between the two epochs was 0.03 + or - 0.08 milliarcsecond. The differences of the relative position measurements between epochs of GC 1342 + 662 relative to GC 1342 + 663 were -0.29 + or - 0.05 milliarcsecond in right ascension and 0.14 + or 0.09 milliarcsecond in declination. These measurements demonstrate submilliarcsecond accuracy and repeatability. The discrepancies outside of the formal uncertainties could be attributed to the intrinsic properties of the sources such as structure and to a lesser probability, proper motion. These discrepancies could also be attributed to excursions in UT1-UTC of about four times the quoted BIH uncertainty. 20 references.
Neuronal Dystroglycan regulates postnatal development of CCK/cannabinoid receptor-1 interneurons.

Background The development of functional neural circuits requires the precise formation of synaptic connections between diverse neuronal populations. The molecular pathways that allow GABAergic interneuron subtypes in the mammalian brain to initially recognize their postsynaptic partners remain largely unknown. The transmembrane glycoprotein Dystroglycan is localized to inhibitory synapses in pyramidal neurons, where it is required for the proper function of CCK+ interneurons. However, the precise temporal requirement for Dystroglycan during inhibitory synapse development has not been examined. Methods In this study, we use NEXCre or Camk2aCreERT2 to conditionally delete Dystroglycan from newly-born or adult pyramidal neurons, respectively. We then analyze forebrain development from postnatal day 3 through adulthood, with a particular focus on CCK+ interneurons. Results In the absence of postsynaptic Dystroglycan in developing pyramidal neurons, presynaptic CCK+ interneurons fail to elaborate their axons and largely disappear from the cortex, hippocampus, amygdala, and olfactory bulb during the first two postnatal weeks. Other interneuron subtypes are unaffected, indicating that CCK+ interneurons are unique in their requirement for postsynaptic Dystroglycan. Dystroglycan does not appear to be required in adult pyramidal neurons to maintain CCK+ interneurons. Bax deletion did not rescue CCK+ interneurons in Dystroglycan mutants during development, suggesting that they are not eliminated by canonical apoptosis. Rather, we observed increased innervation of the striatum, suggesting that the few remaining CCK+ interneurons re-directed their axons to neighboring areas where Dystroglycan expression remained intact. Conclusion Together these findings show that Dystroglycan functions as part of a synaptic partner recognition complex that is required early for CCK+ interneuron development in the forebrain.