cssi/SciSciGPT-SciSciNet huggingface dataset:
Global connections between El Nino and landslide impacts.

El Nino is a critical part of global inter-annual climate variability, and the intensity of El Nino has major implications for rainfall-induced natural hazards in many vulnerable countries. The impact of landslides triggered by rainfall is likely to be modulated by the strength of El Nino, but the nature of this connection and the places where it is most relevant remains unconstrained. Here we combine new satellite rainfall data with a global landslide exposure model to show that El Nino has far-reaching effects on landslide impacts to people and infrastructure. We find that the impact of El Nino on landslide exposure can be greater in parts of Southeast Asia and Latin America than that due to seasonal rainfall variability. These findings improve our understanding of hazard variability around the world and can assist disaster mitigation efforts on seasonal timescales. This study investigates the relation between El Nino and landslide impacts. The authors show how El Nino and La Nina can cause swings in exposure of population to landslides that are as large as those due to rainy-season/dry-season variability in key locations, particularly South America.
The Korean government’s public health responses to the COVID-19 epidemic through the lens of industrial policy

The paper explores the idea that the success of the Korean government’s non-pharmaceutical interventions in response to the COVID-19 epidemic can be better understood through the lens of industrial...
On asymptotic socle degrees of local cohomology modules

Abstract Let R be a standard graded algebra over a field k and I be a homogeneous ideal of R. We study the question whether there is a constant c such that Soc ( H m j ( R / I t ) ) − c t = 0 for all t ≥ 1 and a variation of this question. We also draw a connection between this question and the notion of gauge-boundedness in prime characteristic.
How Do Member Countries Receive IMF Policy Advice: Results from a State-of-the-Art Sentiment Index

This paper applies state-of-the-art deep learning techniques to develop the first sentiment index measuring member countries' reception of IMF policy advice at the time of Article IV Consultations. This paper finds that while authorities of member countries largely agree with Fund advice, there is variation across country size, external openness, policy sectors and their assessed riskiness, political systems, and commodity export intensity. The paper also looks at how sentiment changes during and after a financial arrangement or program with the Fund, as well as when a country receives IMF technical assistance. The results shed light on key aspects on Fund surveillance while redefining how the IMF can view its relevance, value added, and traction with its member countries.
Remote Learners, Home Makers: How Digital Fabrication Was Taught Online During a Pandemic

Digital fabrication courses that relied on physical makerspaces were severely disrupted by COVID-19. As universities shut down in Spring 2020, instructors developed new models for digital fabrication at a distance. Through interviews with faculty and students and examination of course materials, we recount the experiences of eight remote digital fabrication courses. We found that learning with hobbyist equipment and online social networks could emulate using industrial equipment in shared workshops. Furthermore, at-home digital fabrication offered unique learning opportunities including more iteration, machine tuning, and maintenance. These opportunities depended on new forms of labor and varied based on student living situations. Our findings have implications for remote and in-person digital fabrication instruction. They indicate how access to tools was important, but not as critical as providing opportunities for iteration; they show how remote fabrication exacerbated student inequities; and they suggest strategies for evaluating trade-offs in remote fabrication models with respect to learning objectives.
An Investigation into the Approximations Used in Wave Packet Molecular Dynamics for the Study of Warm Dense Matter

Wave packet molecular dynamics (WPMD) has recently received a lot of attention as a computationally fast tool with which to study dynamical processes in warm dense matter beyond the Born–Oppenheimer approximation. These techniques, typically, employ many approximations to achieve computational efficiency while implementing semi-empirical scaling parameters to retain accuracy. We investigated three of the main approximations ubiquitous to WPMD: a restricted basis set, approximations to exchange, and the lack of correlation. We examined each of these approximations in regard to atomic and molecular hydrogen in addition to a dense hydrogen plasma. We found that the biggest improvement to WPMD comes from combining a two-Gaussian basis with a semi-empirical correction based on the valence-bond wave function. A single parameter scales this correction to match experimental pressures of dense hydrogen. Ultimately, we found that semi-empirical scaling parameters are necessary to correct for the main approximations in WPMD. However, reducing the scaling parameters for more ab-initio terms gives more accurate results and displays the underlying physics more readily.
Will investments in human resources during the COVID-19 pandemic crisis pay off after the crisis?

[...]training primarily seeks to develop specific human capital resources based on employees’ job- or firm-specific KSAOs. [...]whereas staffing builds KSAOs that are valuable across various jobs and organizations, training develops KSAOs that are mainly valuable to a particular job or firm. [...]if the training is balanced by emphasizing general skills as well, which can be transferrable to many different contexts and thus help to develop workforce flexibility, it will have a greater potential to contribute to the firm’s quick recovery and performance after the crisis. [...]although firms in the current circumstances may be tempted to prioritize immediate skill needs in designing online training, if they are also seeking longer term, postcrisis benefits of the training, they would need to find a way to incorporate elements that can help build generic human capital resources. [...]it may also enhance the public relations of the firm and improve the firm’s overall reputation among customers and investors who value corporate social responsibility (Aguinis etal., 2020).1 Further, decades of industrial-organizational psychology research suggest that such a move will enhance employees’ perceptions of organizational support and collective commitment to the firm, thereby increasing workforce productivity (Rhoades etal., 2001). [...]it is important to broaden the criterion domain and examine the extent to which investments in various HR practices (designed to improve employees’ performance and commitment to their job and firm) during the COVID-19 crisis pay off by improving various postcrisis firm outcomes (e.g., human capital resources, collective employee motivation and involvement, turnover rate, productivity, profitability, and market responses to the firm such as stock price) in order to better gauge the postcrisis effects of HR investments during the COVID-19 crisis.
Low regularity conservation laws for the Benjamin–Ono equation

We obtain conservation laws at negative regularity for the Benjamin-Ono equation on the line and on the circle. These conserved quantities control the $H^s$ norm of the solution for $-\frac{1}{2} < s < 0$. The conservation laws are obtained from a study of the perturbation determinant associated to the Lax pair of the equation.
Mission Operations Cost Estimation Tool (MOCET) 2020/2021 Updates

The Mission Operations Cost Estimation Tool (MOCET) team will present a summary of updates made to MOCET in 2020 as well as planned updates for 2021. These updates include additional mission data added to the existing Cost Estimating Relationships (CERs) and enhancements like the addition of a reserves estimations capability, modeling for International Space Station (ISS) hosted instruments, Level 2 Work Breakdown Structure (WBS) modeling, and modeling for extended missions. An overview of the state of the user community will be presented including statistics from the One NASA Cost Engineering (ONCE) model portal and software.nasa.gov. MOCET is a model developed by The Aerospace Corporation in partnership with NASA's Science Office for Mission Assessments (SOMA), which provides the capability to generate cost estimates for the operational, or Phase E, portion of NASA science missions. MOCET is comprised of CERs that have been derived from historical data for Planetary, Earth Science, and Explorer missions. The resulting CERs and accompanying documentation have been implemented as a standalone Excel based tool which is now available via the One NASA Cost Engineering (ONCE) model portal and software.nasa.gov.
Age-related type I collagen modifications reveal tissue-defining differences between ligament and tendon

Tendons and ligaments tend to be pooled into a single category as dense elastic bands of collagenous connective tissue. They do have many similar properties, for example both tissues are flexible cords of fibrous tissue that join bone to either muscle or bone. Tendons and ligaments are both prone to degenerate and rupture with only limited capacity to heal, although tendons tend to heal faster than ligaments. Type I collagen constitutes about 80% of the dry weight of tendons and ligaments and is principally responsible for the core strength of each tissue. Collagen synthesis is a complex process with multiple steps and numerous post-translational modifications including proline and lysine hydroxylation, hydroxylysine glycosylation and covalent cross-linking. The chemistry, placement and quantity of intramolecular and intermolecular cross-links are believed to be key contributors to the tissue-specific variations in material strength and biological properties of collagens. As tendons and ligaments grow and develop, the collagen cross-links are known to chemically mature, strengthen and change in profile. Accordingly, changes in cross-linking and other post-translational modifications are likely associated with tissue development and degeneration. Using mass spectrometry, we have compared tendon and ligaments from fetal and adult bovine knee joints to investigate changes in collagen post-translational properties. Although hydroxylation levels at the type I collagen helical cross-linking lysine residues were similar in all adult tissues, ligaments had significantly higher levels of glycosylation at these sites compared to tendon. Differences in lysine hydroxylation were also found between the tissues at the telopeptide cross-linking sites. Total collagen cross-linking analysis, including mature trivalent cross-links and immature divalent cross-links, revealed unique cross-linking profiles between tendon and ligament tissues. Tendons were found to have a significantly higher frequency of smaller diameter collagen fibrils compared with ligament, which we suspect is functionally associated with the unique cross-linking profile of each tissue. Understanding the specific molecular characteristics that define and distinguish these specialized tissues will be important to improving the design of orthopedic treatment approaches.
Large-Scale Multi-Agent Deep FBSDEs

In this paper we present a scalable deep learning framework for finding Markovian Nash Equilibria in multi-agent stochastic games using fictitious play. The motivation is inspired by theoretical analysis of Forward Backward Stochastic Differential Equations (FBSDE) and their implementation in a deep learning setting, which is the source of our algorithm's sample efficiency improvement. By taking advantage of the permutation-invariant property of agents in symmetric games, the scalability and performance is further enhanced significantly. We showcase superior performance of our framework over the state-of-the-art deep fictitious play algorithm on an inter-bank lending/borrowing problem in terms of multiple metrics. More importantly, our approach scales up to 3000 agents in simulation, a scale which, to the best of our knowledge, represents a new state-of-the-art. We also demonstrate the applicability of our framework in robotics on a belief space autonomous racing problem.
A high frequency model of cascade noise

Closed form asymptotic expressions for computing high frequency noise generated by an annular cascade in an infinite duct containing a uniform flow are presented. There are two new elements in this work. First, the annular duct mode representation does not rely on the often-used Bessel function expansion resulting in simpler expressions for both the radial eigenvalues and eigenfunctions of the duct. In particular, the new representation provides an explicit approximate formula for the radial eigenvalues obviating the need for solutions of the transcendental annular duct eigenvalue equation. Also, the radial eigenfunctions are represented in terms of exponentials eliminating the numerical problems associated with generating the Bessel functions on a computer. The second new element is the construction of an unsteady response model for an annular cascade. The new construction satisfies the boundary conditions on both the cascade and duct walls simultaneously adding a new level of realism to the noise calculations. Preliminary results which demonstrate the effectiveness of the new elements are presented. A discussion of the utility of the asymptotic formulas for calculating cascade discrete tone as well as broadband noise is also included.
A cross–nearest neighbor/Monte Carlo algorithm for single molecule localization microscopy defines interactions between p53, Mdm2, and MEG3

The functions of long noncoding (lnc)RNAs, such as MEG3, are defined by their interactions with other RNAs and proteins. These interactions, in turn, are shaped by their subcellular localization and temporal context. Therefore, it is important to be able to analyze the relationships of lncRNAs while preserving cellular architecture. The ability of MEG3 to suppress cell proliferation led to its recognition as a tumor suppressor. MEG3 has been proposed to activate p53 by disrupting the interaction of p53 with mouse double minute 2 homolog (Mdm2). To test this mechanism in the native cellular context, we employed two-color direct stochastic optical reconstruction microscopy, a single-molecule localization microscopy technique, to detect and quantify the localizations of p53, Mdm2, and MEG3 in U2OS cells. We developed a new cross-nearest neighbor/Monte Carlo algorithm to quantify the association of these molecules. Proof of concept for our method was obtained by examining the association between FKBP1A and mTOR, MEG3 and p53, and Mdm2 and p53. In contrast to previous models, our data support a model in which MEG3 modulates p53 independently of the interaction with Mdm2.
Algorithmic foundations for the diffraction limit

For more than a century and a half it has been widely-believed (but was never rigorously shown) that the physics of diffraction imposes certain fundamental limits on the resolution of an optical system. However our understanding of what exactly can and cannot be resolved has never risen above heuristic arguments which, even worse, appear contradictory. In this work we remedy this gap by studying the diffraction limit as a statistical inverse problem and, based on connections to provable algorithms for learning mixture models, we rigorously prove upper and lower bounds on the statistical and algorithmic complexity needed to resolve closely spaced point sources. In particular we show that there is a phase transition where the sample complexity goes from polynomial to exponential. Surprisingly, we show that this does not occur at the Abbe limit, which has long been presumed to be the true diffraction limit.
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates

We study the problem of differentially private (DP) matrix completion under user-level privacy. We design a joint differentially private variant of the popular Alternating-Least-Squares (ALS) method that achieves: i) (nearly) optimal sample complexity for matrix completion (in terms of number of items, users), and ii) the best known privacy/utility trade-off both theoretically, as well as on benchmark data sets. In particular, we provide the first global convergence analysis of ALS with noise introduced to ensure DP, and show that, in comparison to the best known alternative (the Private Frank-Wolfe algorithm by Jain et al. (2018)), our error bounds scale significantly better with respect to the number of items and users, which is critical in practical problems. Extensive validation on standard benchmarks demonstrate that the algorithm, in combination with carefully designed sampling procedures, is significantly more accurate than existing techniques, thus promising to be the first practical DP embedding model.
Lifting coarse homotopies

Coarse geometry, and in particular coarse homotopy theory, has proven to be a powerful tool for approaching problems in geometric group theory and higher index theory. In this paper, we continue to develop theory in this area by proving a Coarse Lifting Lemma with respect to a certain class of bornologous surjective maps. This class is wide enough to include quotients by coarsely discontinuous group actions, which allows us to obtain results concerning the coarse fundamental group of quotients which are analogous to classical topological results for the fundamental group. As an application, we compute the fundamental group of metric cones over negatively curved compact Riemannian manifolds.
Risk Exposures, Risk Perceptions, Negative Attitudes Toward General Vaccination, and COVID-19 Vaccine Acceptance Among College Students in south Carolina.

Purpose:The current study investigated how risk exposures, risk perceptions of COVID-19, and negative attitudes toward general vaccination were related to COVID vaccine acceptance among college stu...
Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction

We propose a transition-based bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. Bubble representations were proposed in the formal linguistics literature decades ago; they enhance dependency trees by encoding coordination boundaries and internal relationships within coordination structures explicitly. In this paper, we introduce a transition system and neural models for parsing these bubble-enhanced structures. Experimental results on the English Penn Treebank and the English GENIA corpus show that our parsers beat previous state-of-the-art approaches on the task of coordination structure prediction, especially for the subset of sentences with complex coordination structures.
“Champion Man-Hater of All Time”: Feminism, Insanity, and Property Rights in 1940s America

Legions of law students in property or trust and estates courses have studied the will dispute, In re Strittmater’s Estate. The cases, casebooks, and treatises that cite Strittmater present the 1947 decision from New Jersey's highest court as a model of the “insane delusion” doctrine. Readers learn that snubbed relatives successfully invalidated Louisa Strittmater’s will, which left her estate to the Equal Rights Amendment campaign, by convincing the court that her radical views on gender equality amounted to insanity and, thus, testamentary incapacity. By failing to provide any commentary or context on the overt sexism, these sources affirm the court’s portrait of Louisa Strittmater as an eccentric landlady and fanatical feminist. This is troubling. Strittmater should be a well-known case, but not for the proposition that feminism is an insane delusion. Despite the decision’s popularity on law school syllabi, no scholar has interrogated the case’s broader historical background. Through original archival research, this Article centers Strittmater as a case study in how social views on gender, psychology, and the law shaped one another in the immediate aftermath of World War II, hampering women’s property rights and efforts to achieve constitutional equality. More than just a problematic precedent, the case exposes a world in which the “Champion Man-Hater of All Time”—newspapers’ epithet for Strittmater—was not only a humorous headline but also a credible threat to the postwar order that courts were helping to erect. The Article thus challenges the textbook understanding of “insane delusion” and shows that postwar culture was conducive to a strengthening of the longstanding suspicion that feminist critiques of gender inequality were, simply put, crazy.
Effects of Urban Development Patterns on Municipal Water Shortage

While urban areas are being threatened by water shortage due to climate change and rapid population growth, effects of urban development patterns on future municipal water shortage are rarely investigated. We address this aspect of urbanization by assessing the impacts of sprawl versus high-density patterns on future changes in the sub-annual water shortage intensity-duration-frequency (IDF) relationships. The City of Fort Collins, Colorado, water supply system is chosen as a representative region that is rapidly developing over the last decades. The future water supply is estimated using the Soil and Water Assessment Tool (SWAT) driven with a hot-dry climate model from the statistically downscaled Coupled Model Intercomparison Project, phase 5 (CMIP5) projections. Future water demand is projected using the Integrated Urban Water Model (IUWM) under both sprawl and high-density development patterns. The demonstration study reveals that urban areas under the sprawl development pattern are likely to experience water shortage events with higher intensity, duration, and frequency compared to the high-density pattern. Characterizing impacts of urban development patterns on future water shortage conditions is required for sustainable water management and smart urban growth and can help urban planners and water managers to develop an adaptive path to meet future water demand and decrease the vulnerability of municipal water supply systems to shortage.
Passive and active immunity in infants born to mothers with SARS-CoV-2 infection during pregnancy: prospective cohort study.

OBJECTIVE: To investigate maternal immunoglobulins' (IgM, IgG) response to SARS-CoV-2 infection during pregnancy and IgG transplacental transfer, to characterise neonatal antibody response to SARS-CoV-2 infection, and to longitudinally follow actively and passively acquired antibodies in infants. DESIGN: A prospective observational study. SETTING: Public healthcare system in Santa Clara County (California, USA). PARTICIPANTS: Women with symptomatic or asymptomatic SARS-CoV-2 infection during pregnancy and their infants were enrolled between 15 April 2020 and 31 March 2021. OUTCOMES: SARS-CoV-2 serology analyses in the cord and maternal blood at delivery and longitudinally in infant blood between birth and 28 weeks of life. RESULTS: Of 145 mothers who tested positive for SARS-CoV-2 during pregnancy, 86 had symptomatic infections: 78 with mild-moderate symptoms, and 8 with severe-critical symptoms. The seropositivity rates of the mothers at delivery was 65% (95% CI 0.56% to 0.73%) and the cord blood was 58% (95% CI 0.49% to 0.66%). IgG levels significantly correlated between the maternal and cord blood (Rs=0.93, p<0.0001). IgG transplacental transfer ratio was significantly higher when the first maternal positive PCR was 60-180 days before delivery compared with <60 days (1.2 vs 0.6, p<0.0001). Infant IgG seroreversion rates over follow-up periods of 1-4, 5-12, and 13-28 weeks were 8% (4 of 48), 12% (3 of 25), and 38% (5 of 13), respectively. The IgG seropositivity in the infants was positively related to IgG levels in the cord blood and persisted up to 6 months of age. Two newborns showed seroconversion at 2 weeks of age with high levels of IgM and IgG, including one premature infant with confirmed intrapartum infection. CONCLUSIONS: Maternal SARS-CoV-2 IgG is efficiently transferred across the placenta when infections occur more than 2 months before delivery. Maternally derived passive immunity may persist in infants up to 6 months of life. Neonates are capable of mounting a strong antibody response to perinatal SARS-CoV-2 infection.
Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone

Transcribing meetings containing overlapped speech with only a single distant microphone (SDM) has been one of the most challenging problems for automatic speech recognition (ASR). While various approaches have been proposed, all previous studies on the monaural overlapped speech recognition problem were based on either simulation data or small-scale real data. In this paper, we extensively investigate a two-step approach where we first pre-train a serialized output training (SOT)-based multi-talker ASR by using large-scale simulation data and then fine-tune the model with a small amount of real meeting data. Experiments are conducted by utilizing 75 thousand (K) hours of our internal single-talker recording to simulate a total of 900K hours of multi-talker audio segments for supervised pre-training. With fine-tuning on the 70 hours of the AMI-SDM training data, our SOT ASR model achieves a word error rate (WER) of 21.2% for the AMI-SDM evaluation set while automatically counting speakers in each test segment. This result is not only significantly better than the previous state-of-the-art WER of 36.4% with oracle utterance boundary information but also better than a result by a similarly fine-tuned single-talker ASR model applied to beamformed audio.
Even Galois representations and the Fontaine–Mazur conjecture

We prove, under mild hypotheses, that there are no irreducible two-dimensional ordinary even Galois representations of $\mathrm{Gal}(\overline{\mathbf{Q}}/\mathbf{Q})$ with distinct Hodge–Tate weights. This is in accordance with the Fontaine–Mazur conjecture. If K/Q is an imaginary quadratic field, we also prove (again, under certain hypotheses) that $\mathrm{Gal}(\overline{\mathbf{Q}}/K)$ does not admit irreducible two-dimensional ordinary Galois representations of non-parallel weight.
Electric field effects on fiber alignment using an auxiliary electrode during electrospinning

Control of electrospun fiber placement and distribution was investigated by examining the effect of electric field parameters on the electrospinning of fibers. The experimental set-up used in this study eliminated the bending instability and whipping, allowing the jet to be modeled as a stable trajectory. Coupling of experimental and computational results suggests the potential for predicting aligned fiber distribution in electrospun mats.
A User-Powered American Sign Language Dictionary

Students learning American Sign Language (ASL) have trouble searching for the meaning of unfamiliar signs. ASL signs can be differentiated by a small set of simple features including hand shape, orientation, location, and movement. In a feature-based ASL-to-English dictionary, users search for a sign by providing a query, which is a set of observed features. Because there is natural variability in the way signs are executed, and observations are error-prone, an approach other than exact matching of features is needed. We propose ASL-Search, an ASL-to-English dictionary entirely powered by its users. ASL-Search utilizes Latent Semantic Analysis (LSA) on a database of feature-based user queries to account for variability. To demonstrate ASL-Search's viability, we created ASL-Flash, a learning tool that presents online flashcards to ASL students and provides query data. Our simulations on this data serve as a proof of concept, demonstrating that our dictionary's performance improves with use and performs well for users with varied levels of ASL experience.
Global eigenvalue distribution of matrices defined by the skew-shift

We consider large Hermitian matrices whose entries are defined by evaluating the exponential function along orbits of the skew-shift $\binom{j}{2} \omega+jy+x \mod 1$ for irrational $\omega$. We prove that the eigenvalue distribution of these matrices converges to the corresponding distribution from random matrix theory on the global scale, namely, the Wigner semicircle law for square matrices and the Marchenko-Pastur law for rectangular matrices. The results evidence the quasi-random nature of the skew-shift dynamics which was observed in other contexts by Bourgain-Goldstein-Schlag and Rudnick-Sarnak-Zaharescu.
Complexity of virtual multistrings

A virtualn-string (M,α) consists of a closed, oriented surface M and a collection α of n oriented, closed curves immersed in M. We consider virtual n-strings up to virtual homotopy, i.e. stabilizat...
Human genetic analyses of organelles highlight the nucleus in age-related trait heritability

Getting older increases our risk of experiencing a wide range of diseases, such as diabetes, heart disease and neurodegenerative disease. The genetic variations that we inherit from our parents play a major role in predicting this risk. However, the biological networks involved in this process are extremely complex and remain challenging to decipher. Prior studies have suggested that specialised structures inside our body’s cells, called organelles, may have an important role to play in aging. Organelles represent self-contained biological factories inside each cell, designed to perform specific tasks. Examples include the nucleus, which harbours most of the cell’s genetic material, and mitochondria, which help provide cells with energy. Organelles tend to deteriorate and become dysfunctional with age, and mitochondria in particular are badly affected by the ageing process. A decline in organelle activity has been thought to explain ageing and the development of age-related diseases. However, this has never been systematically tested on a large scale at the inherited genetic level. Gupta et al. assessed whether common inherited genetic variation in genes associated with ten different organelles could affect the risk of age-related disease, using a database of DNA samples from more than 300,000 individuals. They considered 24 diseases and traits that become more common with advanced age. Gupta et al. discovered that inherited variants in or near genes associated with the nucleus were consistently linked to age-related disease risks. Most of this signal arose from genes encoding the nuclear transcription factors, proteins that help to control the rate at which genes are expressed. However, variants in genes associated with other organelles, including mitochondria, did not appear to be linked to age-related diseases. This research suggests that inherited variation in transcription factors in the nucleus could act as genetic levers that increase the risk of common, age-related diseases. It also suggests that common genetic variation in other cellular organelles may not be as heavily involved in the development of such diseases. Such insights into the cellular structures and biological pathways involved in ageing and age-related disease also establish new targets for drugs to prevent or treat disease.
Electronic structure of NdO via slow photoelectron velocity-map imaging spectroscopy of NdO--.

Electronically excited NdO is a possible product of the chemistry associated with the release of Nd into the ionosphere, and emission from these states may contribute to the observations following such experiments. To better characterize the energetics and spectroscopy of NdO, we report a combined experimental and theoretical study using slow photoelectron velocity-map imaging spectroscopy of cryogenically cooled NdO− anions (cryo-SEVI) supplemented by wave function-based quantum-chemical calculations. Using cryo-SEVI, we measure the electron affinity of NdO to be 1.0091(7) eV and resolve numerous transitions to low-lying electronic and vibrational states of NdO that are assigned with the aid of the electronic structure calculations. Additionally, temperature-dependent data suggest contributions from the (2)4.5 state of NdO− residing 2350 cm−1 above the ground anion state. Photodetachment to higher-lying excited states of NdO is also reported, which may help to clarify observations from prior release experiments.
The Smallest Singular Value of Dense Random Regular Digraphs

Let $A$ be the adjacency matrix of a uniformly random $d$-regular digraph on $n$ vertices, and suppose that $\min(d,n-d)\geq\lambda n$. We show that for any $\kappa \geq 0$, \[\mathbb{P}[s_n(A)\leq\kappa]\leq C_\lambda\kappa\sqrt{n}+2e^{-c_\lambda n}.\] Up to the constants $C_\lambda, c_\lambda > 0$, our bound matches optimal bounds for $n\times n$ random matrices, each of whose entries is an i.i.d $\text{Ber}(d/n)$ random variable. The special case $\kappa = 0$ of our result confirms a conjecture of Cook regarding the probability of singularity of dense random regular digraphs.
Probability bound analysis: A novel approach for quantifying parameter uncertainty in decision-analytic modeling and cost-effectiveness analysis

Decisions about health interventions are often made using limited evidence. Mathematical models used to inform such decisions often include uncertainty analysis to account for the effect of uncertainty in the current evidence base on decision-relevant quantities. However, current uncertainty quantification methodologies, including probabilistic sensitivity analysis (PSA), require modelers to specify a precise probability distribution to represent the uncertainty of a model parameter. This study introduces a novel approach for representing and propagating parameter uncertainty, probability bounds analysis (PBA), where the uncertainty about the unknown probability distribution of a model parameter is expressed in terms of an interval bounded by lower and upper bounds on the unknown cumulative distribution function (p-box) and without assuming a particular form of the distribution function. We give the formulas of the p-boxes for common situations (given combinations of data on minimum, maximum, median, mean, or standard deviation), describe an approach to propagate p-boxes into a black-box mathematical model, and introduce an approach for decision-making based on the results of PBA. We demonstrate the characteristics and utility of PBA vs PSA using two case studies. In sum, this study provides modelers with practical tools to conduct parameter uncertainty quantification given the constraints of available data and with the fewest assumptions.
Efficient Large Scale Inlier Voting for Geometric Vision Problems

Outlier rejection and equivalently inlier set optimization is a key ingredient in numerous applications in computer vision such as filtering point-matches in camera pose estimation or plane and normal estimation in point clouds. Several approaches exist, yet at large scale we face a combinatorial explosion of possible solutions and state-of-the-art methods like RANSAC, Hough transform or Branch&Bound require a minimum inlier ratio or prior knowledge to remain practical. In fact, for problems such as camera posing in very large scenes these approaches become useless as they have exponential runtime growth if these conditions aren't met. To approach the problem we present a efficient and general algorithm for outlier rejection based on "intersecting" $k$-dimensional surfaces in $R^d$. We provide a recipe for casting a variety of geometric problems as finding a point in $R^d$ which maximizes the number of nearby surfaces (and thus inliers). The resulting algorithm has linear worst-case complexity with a better runtime dependency in the approximation factor than competing algorithms while not requiring domain specific bounds. This is achieved by introducing a space decomposition scheme that bounds the number of computations by successively rounding and grouping samples. Our recipe (and open-source code) enables anybody to derive such fast approaches to new problems across a wide range of domains. We demonstrate the versatility of the approach on several camera posing problems with a high number of matches at low inlier ratio achieving state-of-the-art results at significantly lower processing times.
A Method to Analyze Multiple Social Identities in Twitter Bios

Twitter users signal social identity in their profile descriptions, or bios, in a number of important but complex ways that are not well-captured by existing characterizations of how identity is expressed in language. Better ways of defining and measuring these expressions may therefore be useful both in understanding how social identity is expressed in text, and how the self is presented on Twitter. To this end, the present work makes three contributions. First, using qualitative methods, we identify and define the concept of a personal identifier, which is more representative of the ways in which identity is signaled in Twitter bios. Second, we propose a method to extract all personal identifiers expressed in a given bio. Finally, we present a series of validation analyses that explore the strengths and limitations of our proposed method. Our work opens up exciting new opportunities at the intersection between the social psychological study of social identity and the study of how we compose the self through markers of identity on Twitter and in social media more generally.
Intermittent Visual Servoing: Efficiently Learning Policies Robust to Instrument Changes for High-precision Surgical Manipulation

Assisting surgeons with automation of surgical subtasks is challenging due to backlash, hysteresis, and variable tensioning in cable-driven robots. These issues are exacerbated as surgical instruments are changed during an operation. In this work, we propose a framework for automation of high- precision surgical subtasks by learning local, sample-efficient, accurate, closed-loop policies that use visual feedback instead of robot encoder estimates. This framework, which we call deep Intermittent Visual Servoing (IVS), switches to a learned visual servo policy for high-precision segments of repetitive surgical tasks while relying on a coarse open-loop policy for the segments where precision is not necessary. We train the policy using only 180 human demonstrations that are roughly 2 seconds each. Results on a da Vinci Research Kit suggest that combining the coarse policy with half a second of corrections from the learned policy during each high-precision segment improves the success rate on the Fundamentals of Laparoscopic Surgery peg transfer task from 72.9% to 99.2%, 31.3% to 99.2%, and 47.2% to 100.0% for 3 instruments with differing cable properties. In the contexts we studied, IVS attains the highest published success rates for automated surgical peg transfer and is significantly more reliable than previous techniques when instruments are changed. Supplementary material is available at https://tinyurl.com/ivs-icra.
Beautiful Cycles: A Theory and a Model Implying a Curious Role for Interest

Abstract Where do economic cycles come from? This paper contemplates an utmost minimalistic model and an underlying theory that rest on two assumptions that let them emerge endogenously: (1) the presence of interest-bearing debt, and (2) a degree of downward nominal wage rigidity. Despite its parsimony, the model generates well-behaved, self-evolving limit cycles and replicates six essential empirical facts: (1) booms are long, while recessions short-lived; (2) leverage is procyclical; (3) firm profit and wage shares are countercyclical and procyclical, respectively; (4) Phillips curves are downward-sloping and convex, and Okun’s law is replicated; (5) default cascades arise at the turning points to recessions; (6) lending spreads are countercyclical. One can refer to the model as being of a dynamic stochastic general disequilibrium (DSGD) kind.
Auditor conservatism, audit quality, and real consequences for clients

Auditors tend to be more conservative in their audits when they face greater litigation risk. However, it is unclear whether this conservatism is always desirable or whether it can be excessive. To evaluate the usefulness of greater auditor conservatism, we examine whether auditor conservatism improves audit quality and imposes real operating consequences for clients. We examine auditor behavior in a sample of auditors’ client banks, when one of the auditor’s other client banks fails (as deemed by the FDIC). We find that auditors that experience a bank failure within their portfolio become more conservative for surviving clients. However, we find that the larger loan loss provisions at surviving clients are no more timely, are less accurate, and reverse in subsequent periods. This finding suggests that auditors’ initial portfolio-wide response to a specific client failure may be overly conservative. We also document that surviving client banks face real consequences in terms of constraints on their lending because of the auditor’s excessive conservatism. Overall, we conclude that, in some situations, auditors may overreact, leading to excessive conservatism, which in turn has adverse consequences for clients.
Effective Solid Angle Model and Monte Carlo Method: Improved Estimations to Measure Cosmic Muon Intensity at Sea Level in All Zenith Angles

Cosmic muons are highly energetic and penetrative particles and these figures are used for imaging of large and dense objects such as spent nuclear fuels in casks and special nuclear materials in cargo. Cosmic muon intensity depends on the incident angle (zenith angle). The low intensity of cosmic muon requires a long measurement time to acquire statistically meaningful counts. Therefore, high-energy particle simulations e.g., GEANT4, are often used to guide measurement studies. However, the measurable cosmic muon count rate changes upon detector geometry and configuration. Here we develop an effective solid angle model to estimate experimental results more accurately than the simple cosine-squared model. We show that the cosine-squared model has a large error at high zenith angles, whereas our model provides improved estimations at all zenith angles. We anticipate our model will enhance the ability to estimate actual measurable cosmic muon count rates in muon imaging applications by reducing the gap between simulation and measurement results. This will increase the value of modeling results and improve the quality of experiments and applications in muon detection and imaging.
Form 10-Q Itemization

The quarterly financial statement, or Form 10-Q, is one of the most frequently required filings for US public companies to disclose financial and other important business information. Due to the massive volume of 10-Q filings and the enormous variations in the reporting format, it has been a long-standing challenge to retrieve item-specific information from 10-Q filings that lack machine-readable hierarchy. This paper presents a solution for itemizing 10-Q files by complementing a rule-based algorithm with a Convolutional Neural Network (CNN) image classifier. This solution demonstrates a pipeline that can be generalized to a rapid data retrieval solution among a large volume of textual data using only typographic items. The extracted textual data can be used as unlabeled content-specific data to train transformer models (e.g., BERT) or fit into various field-focus natural language processing (NLP) applications.
Stable polar oxynitrides through epitaxial strain

We investigate energetically favorable structures of $AB{\mathrm{O}}_{2}\mathrm{N}$ oxynitrides as functions of pressure and strain via swarm-intelligence-based structure prediction methods, density functional theory (DFT) lattice dynamics and first-principles molecular dynamics. We predict several thermodynamically stable polar oxynitride perovskites under high pressures. In addition, we find that ferroelectric polar phases of perovskite-structured oxynitrides can be thermodynamically stable and synthesized at high pressure on appropriate substrates. The dynamical stability of the ferroelectric oxynitrides under epitaxial strain at ambient pressure also implies the possibility to synthesize them using pulsed laser deposition or other atomic layer deposition methods. Our results have broad implications for further exploration of other oxynitride materials as well. We performed first-principles molecular dynamics and find that the polar perovskite of ${\mathrm{YSiO}}_{2}\mathrm{N}$ ($I4cm$) is metastable up to at least 600 K under compressive epitaxial strain before converting to the stable wollastonitelike structures ($I4/mcm$). We predict that ${\mathrm{YGeO}}_{2}\mathrm{N}, {\mathrm{LaSiO}}_{2}\mathrm{N}$, and ${\mathrm{LaGeO}}_{2}\mathrm{N}$ are metastable as ferroelectric perovskites ($P4mm$) at zero pressure even without epitaxial strain.
The Los Alamos Computing Facility During the Manhattan Project

This paper describes the history of the computing facility at Los Alamos during the Manhattan Project, 1944 to 1946. While the hand computations are briefly discussed, the paper’s main focus is on ...
On the Theory of Reinforcement Learning with Once-per-Episode Feedback

We study a theory of reinforcement learning (RL) in which the learner receives binary feedback only once at the end of an episode. While this is an extreme test case for theory, it is also arguably more representative of real-world applications than the traditional requirement in RL practice that the learner receive feedback at every time step. Indeed, in many real-world applications of reinforcement learning, such as self-driving cars and robotics, it is easier to evaluate whether a learner's complete trajectory was either "good" or "bad," but harder to provide a reward signal at each step. To show that learning is possible in this more challenging setting, we study the case where trajectory labels are generated by an unknown parametric model, and provide a statistically and computationally efficient algorithm that achieves sub-linear regret.
Tax and Cross-Collateralized Nonrecourse Liability

This Article explores the tax treatment of cross-collateral nonrecourse debt. When using the term cross-collateral debt, we are referring to nonrecourse debt that is connected with more than one piece of property. While tax issues concerning cross-collateralized properties can arise in several circumstances, the focus of this Article is on the tax treatment of a transfer of property subject to a cross-collateralizednonrecourse liability to a controlled corporation in exchange for stock that qualifies for some or all nonrecognition under § 351. The Article also discusses two other tax issues involving cross-collateralizednonrecourse liability—namely, cancellation of debt and determination of basis issues.
A Unified Treatment of Partial Stragglers and Sparse Matrices in Coded Matrix Computation.

The overall execution time of distributed matrix computations is often dominated by slow worker nodes (stragglers) over the clusters. Recently, different coding techniques have been utilized to mitigate the effect of stragglers where worker nodes are assigned the task of processing encoded submatrices of the original matrices. In many machine learning or optimization problems the relevant matrices are often sparse. Several coded computation methods operate with dense linear combinations of the original submatrices; this can significantly increase the worker node computation times and consequently the overall job execution time. Moreover, several existing techniques treat the stragglers as failures (erasures) and discard their computations. In this work, we present a coding approach which operates with limited encoding of the original submatrices and utilizes the partial computations done by the slower workers. Our scheme continues to have the optimal threshold of prior work. Extensive numerical experiments done in AWS (Amazon Web Services) cluster confirm that the proposed approach enhances the speed of the worker computations (and thus the whole process) significantly.