cssi/SciSciGPT-SciSciNet huggingface dataset:
Salivary Digestion Extends the Range of Sugar-Aversions in the German Cockroach

Saliva has diverse functions in feeding behavior of animals. However, the impact of salivary digestion of food on insect gustatory information processing is poorly documented. Glucose-aversion (GA) in the German cockroach, Blattella germanica, is a highly adaptive heritable behavioral resistance trait that protects the cockroach from ingesting glucose-containing-insecticide-baits. In this study, we confirmed that GA cockroaches rejected glucose, but they accepted oligosaccharides. However, whereas wild-type cockroaches that accepted glucose also satiated on oligosaccharides, GA cockroaches ceased ingesting the oligosaccharides within seconds, resulting in significantly lower consumption. We hypothesized that saliva might hydrolyze oligosaccharides, releasing glucose and terminating feeding. By mixing artificially collected cockroach saliva with various oligosaccharides, we demonstrated oligosaccharide-aversion in GA cockroaches. Acarbose, an alpha-glucosidase inhibitor, prevented the accumulation of glucose and rescued the phagostimulatory response and ingestion of oligosaccharides. Our results indicate that pre-oral and oral hydrolysis of oligosaccharides by salivary alpha-glucosidases released glucose, which was then processed by the gustatory system of GA cockroaches as a deterrent and caused the rejection of food. We suggest that the genetic mechanism of glucose-aversion support an extended aversion phenotype that includes glucose-containing oligosaccharides. Salivary digestion protects the cockroach from ingesting toxic chemicals and thus could support the rapid evolution of behavioral and physiological resistance in cockroach populations.
#Election2020: the first public Twitter dataset on the 2020 US Presidential election

Credible evidence-based political discourse is a critical pillar of democracy and is at the core of guaranteeing free and fair elections. The study of online chatter is paramount, especially in the wake of important voting events like the recent November 3, 2020 U.S. Presidential election and the inauguration on January 21, 2021. Limited access to social media data is often the primary obstacle that limits our abilities to study and understand online political discourse. To mitigate this impediment and empower the Computational Social Science research community, we are publicly releasing a massive-scale, longitudinal dataset of U.S. politics- and election-related tweets. This multilingual dataset encompasses over 1.2 billion tweets and tracks all salient U.S. political trends, actors, and events from 2019 to the time of this writing. It predates and spans the entire period of the Republican and Democratic primaries, with real-time tracking of all presidential contenders on both sides of the aisle. The dataset also focuses on presidential and vice-presidential candidates, the presidential elections and the transition from the Trump administration to the Biden administration. Our dataset release is curated, documented, and will continue to track relevant events. We hope that the academic community, computational journalists, and research practitioners alike will all take advantage of our dataset to study relevant scientific and social issues, including problems like misinformation, information manipulation, conspiracies, and the distortion of online political discourse that has been prevalent in the context of recent election events in the United States. Our dataset is available at: https://github.com/echen102/us-pres-elections-2020.
Accounting for Derivative Instruments and Hedging Activities

The goal of this research was to investigate the reasons behind the plethora of amendments of the FASB Accounting Pronouncements for Financial Instruments from 2002 to 2008. Entities have communicated their apprehensions that the existent disclosure requirements in SFAS No. 133, “Accounting for Derivative Instruments and Hedging Activities”, do not furnish sufficient input about how derivative and hedging activities influence an entity’s financial position, financial performance, and cash flows. Correspondently, in 2008 the FASB issued Statement No. 161, “Disclosures about Derivative Instruments and Hedging Activities—an amendment of FASB Statement No. 133”. The purpose of the study was to investigate the extent to which the thirty companies that comprise the Dow Jones Industrial Average complied with the new qualitative and quantitative disclosure requirements for derivative financial instruments of SFAS No. 161. Following the theoretical framework of corporate risk management, the quarterly financial statements (10Qs) of the thirty companies that comprise the Dow Jones Industrial Average were examined to determine whether companies complied with the qualitative requirements of SFAS No.161 to disclose their objectives for holding or issuing derivative financial instruments and their risk management policy as well as a description of the items being hedged. A surprising finding was that most companies failed with the requirements of SFAS No. 161 to disclose the required information about cash flow hedges, net investments in foreign operations and, fair value hedges. These findings suggest that although the FASB issued SFAS No. 161 to enhance derivative disclosures to enable users of financial statements to evaluate the success and significance of derivative instruments and hedging transactions on an entity’s financial statements, companies might need additional time to implement the standard.
Ultrafast dephasing in hydrogen-bonded pyridine–water mixtures

Hydrogen-bonded mixtures with varying concentration are a complicated networked system that demands a detection technique with both time and frequency resolutions. Hydrogen-bonded pyridine-water mixtures are studied by a time-frequency resolved coherent Raman spectroscopic technique. Femtosecond broadband dual-pulse excitation and delayed picosecond probing provide sub-picosecond time resolution in the mixtures temporal evolution. For different pyridine concentrations in water, asymmetric blue versus red shifts (relative to pure pyridine spectral peaks) were observed by simultaneously recording both the coherent anti-Stokes and Stokes Raman spectra. Macroscopic coherence dephasing times for the perturbed pyridine ring modes were observed in ranges of 0.9 - 2.6 picoseconds for both 18 and 10 cm-1 broad probe pulses. For high pyridine concentrations in water, an additional spectral broadening (or escalated dephasing) for a triangular ring vibrational mode was observed. This can be understood as a result of ultrafast collective emissions from coherently excited ensemble of pairs of pyridine molecules bound to water molecules.
Management Information Systems for the Information Age

From the Publisher: This text will fit best in an MIS course where students have to do a lot of hands-on,problem-solving projects,especially on the Internet. Haag works well for professors who want an interactive,engaging experience. If a professor has large classes or wants a way to get everyone involved (bus/non-business majors),this is the book for them. The overall content and organization of the book has been retained,providing a clearly focused overview of Information Systems. The Second Edition continues to cover Management Information Systems concepts with a different approach and organization of topics compared to traditional introductory I. S. books for example. Electronic Commerce and its effect on organizations is a major theme in this text,threaded through each chapter. The authors assume that students know a little about hardware and software and focus more on the way organizations use information systems. There are many sidebars,boxes,and examples pulled from real-world organizations and many individual and group projects for students to work on-tying theory to practice. This is perhaps our most practical,hands-on text.
Improving Streaming Automatic Speech Recognition with Non-Streaming Model Distillation on Unsupervised Data

Streaming end-to-end automatic speech recognition (ASR) models are widely used on smart speakers and on-device applications. Since these models are expected to transcribe speech with minimal latency, they are constrained to be causal with no future context, compared to their non-streaming counterparts. Consequently, streaming models usually perform worse than non-streaming models. We propose a novel and effective learning method by leveraging a non-streaming ASR model as a teacher to generate transcripts on an arbitrarily large data set, which is then used to distill knowledge into streaming ASR models. This way, we scale the training of streaming models to up to 3 million hours of YouTube audio. Experiments show that our approach can significantly reduce the word error rate (WER) of RNN-T models not only on LibriSpeech but also on YouTube data in four languages. For example, in French, we are able to reduce the WER by 16.4% relatively to a baseline streaming model by leveraging a non-streaming teacher model trained on the same amount of labeled data as the baseline.
Single-family rentals and neighborhood racial integration✰

Abstract Neighborhood racial segregation continues to be a major social problem within America's metropolitan areas. One factor possibly accounting for segregation is the inability of minority households to afford housing in White neighborhoods, where housing units historically have been largely owner-occupied single-family homes. In recent years there has been a dramatic shift in the housing makeup of many of these neighborhoods, with single-family rentals increasing in share. Rentals lower the cost of neighborhood entry. Our results, which suggest that these rentals reduce neighborhood racial segregation for Blacks, support policies that seek to maintain and grow single-family rentals within White neighborhoods.
Learn2Hop: Learned Optimization on Rough Landscapes

Optimization of non-convex loss surfaces containing many local minima remains a critical problem in a variety of domains, including operations research, informatics, and material design. Yet, current techniques either require extremely high iteration counts or a large number of random restarts for good performance. In this work, we propose adapting recent developments in meta-learning to these many-minima problems by learning the optimization algorithm for various loss landscapes. We focus on problems from atomic structural optimization--finding low energy configurations of many-atom systems--including widely studied models such as bimetallic clusters and disordered silicon. We find that our optimizer learns a 'hopping' behavior which enables efficient exploration and improves the rate of low energy minima discovery. Finally, our learned optimizers show promising generalization with efficiency gains on never before seen tasks (e.g. new elements or compositions). Code will be made available shortly.
Experimental evaluation of the ignition process of carbon monoxide and oxygen in a rocket engine

Carbon monoxide and oxygen ignition boundaries were determined in a spark torch igniter as a function of propellant inlet temperatures. The oxygen temperature was varied from ambient to -258 F, and the carbon monoxide temperature was varied from ambient to -241 F. With the oxygen and carbon monoxide at -253 F and -219 F, respectively, they successfully ignited between mixture ratios of 2.42 and 3.10. Analysis of the results indicated that the lower ignition boundary was more sensitive to oxygen temperature than to carbon monoxide temperature. Another series of tests was performed in a small simulated rocket engine with oxygen at -197 F and carbon monoxide at -193 F. An oxygen/hydrogen flame was used to initiate combustion of the oxygen and carbon monoxide. Tests performed at the optimum operating mixture ratio of 0.55 obtained steady-state combustion in every test.
Drift-diffusion on a Cayley tree with stochastic resetting: the localization–delocalization transition

In this paper we develop the theory of drift-diffusion on a semi-infinite Cayley tree with stochastic resetting. In the case of a homogeneous tree with a closed terminal node and no resetting, it is known that the system undergoes a classical localization-delocalization (LD) transition at a critical mean velocity $v_c= -(D/L)\ln (z-1)$ where $D$ is the diffusivity, $L$ is the branch length and $z$ is the coordination number of the tree. If $v v_c $ (diffusion-dominated delocalized state). This is equivalent to the transition between recurrent and transient transport on the tree, with the mean first passage time (MFPT) to be absorbed by an open terminal node switching from a finite value to infinity. Here we show how the LD transition provides a basic framework for understanding analogous phase transitions in optimal resetting rates. First, we establish the existence of an optimal resetting rate $r^{**}(z)$ that maximizes the steady-state solution at a closed terminal node. In addition, we show that there is a phase transition at a critical velocity $v_c^{**}(z)$ such that $r^{**}>0$ for $v>v_c^{**}$ and $r^{**}=0$ for $v<v_c^{**}$. We then identify a critical velocity $v^*(z)$ for a phase transition in a second optimal resetting rate $r^*$ that minimizes the MFPT to be absorbed by an open terminal node. Previous results for the semi-infinite line are recovered on setting $z=2$. The critical velocity of the LD transition provides an upper bound for the other critical velocities such that $v_c^*(z)<v_c^{**}(z)<v_c(z)$ for all finite $z$. Only $v_c(z)$ has a simple universal dependence on the coordination number $z$. We end by considering the combined effects of quenched disorder and stochastic resetting.
A stabilized computational nonlocal poromechanics model for dynamic analysis of saturated porous media

In this article we formulate a stable computational nonlocal poromechanics model for dynamic analysis of saturated porous media. As a novelty, the stabilization formulation eliminates zero-energy modes associated with the original multiphase correspondence constitutive models in the coupled nonlocal poromechanics model. The two-phase stabilization scheme is formulated based on an energy method that incorporates inhomogeneous solid deformation and fluid flow. In this method, the nonlocal formulations of skeleton strain energy and fluid flow dissipation energy equate to their local formulations. The stable coupled nonlocal poromechanics model is solved for dynamic analysis by an implicit time integration scheme. As a new contribution, we validate the coupled stabilization formulation by comparing numerical results with analytical and finite element solutions for one-dimensional and two-dimensional dynamic problems in saturated porous media. Numerical examples of dynamic strain localization in saturated porous media are presented to demonstrate the efficacy of the stable coupled poromechanics framework for localized failure under dynamic loads.
FUDGE: Controlled Text Generation With Future Discriminators

We propose Future Discriminators for Generation (FUDGE), a flexible and modular method for controlled text generation. Given a pre-existing model G for generating text from a distribution of interest, FUDGE enables conditioning on a desired attribute a (for example, formality) while requiring access only to G’s output logits. FUDGE learns an attribute predictor operating on a partial sequence, and uses this predictor’s outputs to adjust G’s original probabilities. We show that FUDGE models terms corresponding to a Bayesian decomposition of the conditional distribution of G given attribute a. Moreover, FUDGE can easily compose predictors for multiple desired attributes. We evaluate FUDGE on three tasks — couplet completion in poetry, topic control in language generation, and formality change in machine translation — and observe gains in all three tasks.
Analytical Stability Bound for a Class of Delayed Fractional-Order Dynamic Systems

Delayed Linear Time-Invariant (LTI) fractional-order dynamic systems areconsidered. The analytical stability bound is obtained by using Lambertfunction. Two examples are presented to illustrate the obtainedanalytical results.
Koopman Operator Based Modeling for Quadrotor Control on SE (3)

In this letter, we propose a Koopman operator based approach to describe the nonlinear dynamics of a quadrotor on $SE(3)$ in terms of an infinite-dimensional linear system which evolves in the space of observables (lifted space) and which is more appropriate for control design purposes. The major challenge when using the Koopman operator is the characterization of a set of observables that can span the lifted space. Most of the existing methods either start from a set of dictionary functions and then search for a subset that best fits the underlying nonlinear dynamics or they rely on machine learning algorithms to learn these observables. Instead of guessing or learning the observables, in this work we derive them in a systematic way for the quadrotor dynamics on $SE(3)$ . In addition, we prove that the proposed sequence of observables converges pointwise to the zero function, which allows us to select only a finite set of observables to form (an approximation of) the lifted space. Our theoretical analysis is also confirmed by numerical simulations which demonstrate that by increasing the dimension of the lifted space, the derived linear state space model can approximate the nonlinear quadrotor dynamics more accurately.
Sequential Phenomena in Psychophysical Judgments: A Theoretical Analysis

This paper deals with an analysis of psychophysical detection experiments designed to assess the limit of a human observer's level of sensitivity. A mathematical theory or the detection process is introduced that, in contrast to previous theories, provides an analysis of the sequential effects observed in psychophysical data. Two variations of the detection task are considered: information feedback and no-information feedback. In the feedback situation the subject is given information concerning the correctness of his responses, whereas in the no-feedback situation he is not. Data from a visual detection experiment with no-information feedback, and from an auditory detection experiment with information feedback are analyzed in terms of the theory. Finally, some general results are derived concerning the relationship between performance in the feedback situation and the no-feedback situation.
Factoring Statutory Reasoning as Language Understanding Challenges

Statutory reasoning is the task of determining whether a legal statute, stated in natural language, applies to the text description of a case. Prior work introduced a resource that approached statutory reasoning as a monolithic textual entailment problem, with neural baselines performing nearly at-chance. To address this challenge, we decompose statutory reasoning into four types of language-understanding challenge problems, through the introduction of concepts and structure found in Prolog programs. Augmenting an existing benchmark, we provide annotations for the four tasks, and baselines for three of them. Models for statutory reasoning are shown to benefit from the additional structure, improving on prior baselines. Further, the decomposition into subtasks facilitates finer-grained model diagnostics and clearer incremental progress.
Learning with Retrospection.

Deep neural networks have been successfully deployed in various domains of artificial intelligence, including computer vision and natural language processing. We observe that the current standard procedure for training DNNs discards all the learned information in the past epochs except the current learned weights. An interesting question is: is this discarded information indeed useless? We argue that the discarded information can benefit the subsequent training. In this paper, we propose learning with retrospection (LWR) which makes use of the learned information in the past epochs to guide the subsequent training. LWR is a simple yet effective training framework to improve accuracies, calibration, and robustness of DNNs without introducing any additional network parameters or inference cost, but only with a negligible training overhead. Extensive experiments on several benchmark datasets demonstrate the superiority of LWR for training DNNs.
COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic

We introduce a FEVER-like dataset COVID-Fact of 4,086 claims concerning the COVID-19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence. Unlike previous approaches, we automatically detect true claims and their source articles and then generate counter-claims using automatic methods rather than employing human annotators. Along with our constructed resource, we formally present the task of identifying relevant evidence for the claims and verifying whether the evidence refutes or supports a given claim. In addition to scientific claims, our data contains simplified general claims from media sources, making it better suited for detecting general misinformation regarding COVID-19. Our experiments indicate that COVID-Fact will provide a challenging testbed for the development of new systems and our approach will reduce the costs of building domain-specific datasets for detecting misinformation.
HEALTH OUTCOMES AMONG FORMER FEMALE COLLEGIATE GYMNASTS: THE INFLUENCE OF SPORT SPECIALIZATION, CONCUSSION, AND DISORDERED EATING:

Background:Over 1,500 collegiate gymnasts compete each year. Injury rates among these athletes are high, yet little is known about their long-term health outcomes.Hypothesis/Purpose:The purpose of ...
Brexit Referendum and Business Investment in the UK

In this paper I apply firm-level analysis to examine how the Brexit process has affected business investment in the UK. An interaction term of potential trade costs after exiting the EU and a measure of firms' participation in global trade is used as a proxy for firm-level exposure to Brexit-related effects. The results suggest that potential trade costs have had a considerable and statistically significant negative impact on firm investment in the UK after the referendum. At the same time, the post-referendum sterling depreciation has likely contributed positively to investment expenditure by more foreign-oriented firms.
Doing good when doing well: evidence on real earnings management

We provide evidence on earnings management by exploiting temporary exogenous shocks to utility firms’ sales arising from weather variation. We find that sample firms’ sales are highly sensitive to annual changes in average temperatures in the region where the firm operates, but this sensitivity disappears quickly as one moves down the income statement. This evidence, while indirect, is suggestive of earnings management activities. In search of direct evidence, we study charitable giving decisions by sample firms and uncover a significant positive sensitivity of charitable spending to weather-driven demand shocks, behavior that is highly consistent with the presence of real earnings management efforts. We find no convincing evidence supporting possible alternative explanations for this evidence, but we do find limited support for the presence of a larger giving–weather relation when earnings management incentives are likely to be elevated. If other real decisions with similar characteristics scale proportionally to charitable giving, our findings suggest that the overall magnitude of real earnings management activities could be quite substantial.
Simulated packing and cracking

We introduce simulated packing and cracking as a technique for evaluating partisan-gerrymandering measures. We apply it to historical congressional and legislative elections to evaluate four measur...
Influenza-Induced Oxidative Stress Sensitizes Lung Cells to Bacterial Toxin-Mediated Necroptosis

Pneumonia caused by Influenza A virus (IAV) co- and secondary bacterial infections are characterized by their severity and high mortality rate. Previously we have shown that bacterial pore-forming toxins (PFT)-mediated necroptosis is a key driver of acute lung injury during bacterial pneumonia. Here, we evaluate the impact of IAV on PFT-induced acute lung injury during co- and secondary Streptococcus pneumoniae (Spn) infection. IAV synergistically sensitized lung epithelial cells for PFT-mediated necroptosis in vitro and in murine models of Spn co-infection and secondary infection. Pharmacological induction of oxidative stress sans virus sensitized cells for PFT-mediated necroptosis. Inhibition of MLKL, the effector of necroptosis reduced disease severity during secondary bacterial infection. Our results advance our understanding on the molecular basis of co- and secondary bacterial infection to influenza and identifies necroptosis inhibition and antioxidant therapy as potential intervention strategies.
Learning of Causal Observable Functions for Koopman-DFL Lifting Linearization of Nonlinear Controlled Systems and Its Application to Excavation Automation

Effective and causal observable functions for low-order lifting linearization of nonlinear controlled systems are learned from data by using neural networks. While Koopman operator theory allows us to represent a nonlinear system as a linear system in an infinite-dimensional space of observables, exact linearization is guaranteed only for autonomous systems with no input, and finding effective observable functions for approximation with a low-order linear system remains an open question. Dual-Faceted Linearization uses a set of effective observables for low-order lifting linearization, but the method requires knowledge of the physical structure of the nonlinear system. Here, a data-driven method is presented for generating a set of nonlinear observable functions that can accurately approximate a nonlinear control system to a low-order linear control system. A caveat in using data of measured variables as observables is that the measured variables may contain input to the system, which incurs a causality contradiction when lifting the system, i.e., taking derivatives of the observables. The current work presents a method for eliminating such anti-causal components of the observables and lifting the system using only causal observables. The method is applied to excavation automation, a complex nonlinear dynamical system, to obtain a low-order lifted linear model for control design.
PEREGRiNN: Penalized-Relaxation Greedy Neural Network Verifier

Neural Networks (NNs) have increasingly apparent safety implications commensurate with their proliferation in real-world applications: both unanticipated as well as adversarial misclassifications can result in fatal outcomes. As a consequence, techniques of formal verification have been recognized as crucial to the design and deployment of safe NNs. In this paper, we introduce a new approach to formally verify the most commonly considered safety specifications for ReLU NNs – i.e. polytopic specifications on the input and output of the network. Like some other approaches, ours uses a relaxed convex program to mitigate the combinatorial complexity of the problem. However, unique in our approach is the way we use a convex solver not only as a linear feasibility checker, but also as a means of penalizing the amount of relaxation allowed in solutions. In particular, we encode each ReLU by means of the usual linear constraints, and combine this with a convex objective function that penalizes the discrepancy between the output of each neuron and its relaxation. This convex function is further structured to force the largest relaxations to appear closest to the input layer; this provides the further benefit that the most “problematic” neurons are conditioned as early as possible, when conditioning layer by layer. This paradigm can be leveraged to create a verification algorithm that is not only faster in general than competing approaches, but is also able to verify considerably more safety properties; we evaluated PEREGRiNN on a standard MNIST robustness verification suite to substantiate these claims.
Sufficiently dense Kuramoto networks are globally synchronizing.

Consider any network of n identical Kuramoto oscillators in which each oscillator is coupled bidirectionally with unit strength to at least μ ( n − 1 ) other oscillators. There is a critical value of the connectivity, μ c, such that whenever μ > μ c, the system is guaranteed to converge to the all-in-phase synchronous state for almost all initial conditions, but when μ 0.6838. This paper proves that μ c ≤ 0.75 and explain why this is the best upper bound that one can obtain by a purely linear stability analysis.
Resource use efficiency among rice farmers around fragile ecosystems: evidence from Kilombero Wetland, Tanzania

Farmers have been encroaching fragile wetlands as a strategy to increase their rice production thus threatening wetlands’ existence and capacity to other critical ecosystem services. This calls for efficient production to strike the balance between food rice production and wetlands’ sustainable existence. The current study sought to provide assess rice farmers’ technical efficiency of resource use by detecting the determinants of rice yield and further identify the determinants of technical efficiency of the resources used by rice farmers in Kilombero wetland. A cross-sectional survey of 145 randomly sampled farmers aided in achieving the study objective. A stochastic frontier analysis (SFA) model was used to analyze data. The mean technical efficiency among farmers was at 60.54% level. The positive determinants of rice yield were land and fertilizers while labor influenced it negatively. Age, education, farming experience, group membership, and credit access reduced inefficiency while the distance to the extension agent and off-farm income increased farmers’ inefficiency. The study concludes that there is a possibility of expanding rice production without threatening the wetland’s existence. It recommends that government and other stakeholders to ensure that rice farmers are up-to-date with optimal use of fertilizers in rice production since it will assist in improving rice yield while the rate of expansion of rice lands in the wetlands will lower. Policy implementers ought to establish initiatives that inspire rice farmers to capitalize on farmer groups and join education programs to take full advantage of their potential efficiency and might participate in community development activities. JEL code: Q15
Investigation of finite-volume methods to capture shocks and turbulence spectra in compressible flows

Author(s): Motheau, E; Wakefield, J | Abstract: The aim of the present paper is to provide a comparison between several finitevolume methods of different numerical accuracy: the second-order Godunov method with PPM interpolation and the high-order finite-volume WENO method. The results show that while on a smooth problem the high-order method performs better than the second-order one, when the solution contains a shock all the methods collapse to first-order accuracy. In the context of the decay of compressible homogeneous isotropic turbulence with shocklets, the actual overall order of accuracy of the methods reduces to second-order, despite the use of fifth-order reconstruction schemes at cell interfaces. Most important, results in terms of turbulent spectra are similar regardless of the numerical methods employed, except that the PPM method fails to provide an accurate representation in the high-frequency range of the spectra. It is found that this specific issue comes from the slope-limiting procedure and a novel hybrid PPM/WENO method is developed that has the ability to capture the turbulent spectra with the accuracy of a high-order method, but at the cost of the second-order Godunov method. Overall, it is shown that virtually the same physical solution can be obtained much faster by refining a simulation with the second-order method and carefully chosen numerical procedures, rather than running a coarse high-order simulation. Our results demonstrate the importance of evaluating the accuracy of a numerical method in terms of its actual spectral dissipation and dispersion properties on mixed smooth/shock cases, rather than by the theoretical formal order of convergence rate.
Imprecision in Engineering Design

Methods for incorporating imprecision in engineering design decision-making are briefly reviewed and compared. A tutorial is presented on the Method of Imprecision (MoI), a formal method, based on the mathematics of fuzzy sets, for representing and manipulating imprecision in engineering design. The results of a design cost estimation example, utilizing a new informal cost specification, are presented. The MoI can provide formal information upon which to base decisions during preliminary engineering design and can facilitate set-based concurrent design.
Gerstenhaber brackets for skew group algebras in positive characteristic

The deformation theory of an algebra is controlled by the Gerstenhaber bracket, a Lie bracket on Hochschild cohomology. We develop techniques for evaluating Gerstenhaber brackets of semidirect product algebras recording actions of finite groups over fields of positive characteristic. The Hochschild cohomology and Gerstenhaber bracket of these skew group algebras can be complicated when the characteristic of the underlying field divides the group order. We show how to investigate Gerstenhaber brackets using twisted product resolutions, which are often smaller and more convenient than the cumbersome bar resolution typically used. These resolutions provide a concrete description of the Gerstenhaber bracket suitable for exploring questions in deformation theory. We demonstrate with the prototypical example of a Drinfeld Hecke algebra (graded Hecke algebra) in positive characteristic.
How do most low ETR firms avoid paying taxes

Evidence suggests a large proportion of profitable U.S. firms have low effective tax rates (i.e., an ETR between 0 and 10%). Despite widespread interest in how firms avoid paying taxes, we do not know how most firms attain low ETRs and whether they are primarily benefiting from benign or aggressive tax positions. Using a research design that explicitly examines low ETR firms, we predict and find that the majority are primarily benefiting from a benign tax position: large net operating loss carryforwards (NOLs). We also find that large NOLs allow firms to persistently retain low ETRs year after year. In contrast, we find that multinationals and tax haven firms, which should have more opportunities for aggressive tax planning, have a lower probability of attaining a low ETR (relative to domestic and non-tax haven firms). Collectively, these findings suggest that the typical low ETR firm does not incur significant tax risk. Consistent with this, we find that low ETR firms accrue unrecognized tax benefits at a similar rate as firms that pay the statutory tax rate and do not experience higher future tax rate volatility. Overall, the results shed light on the profile of the average low ETR firm and provide evidence that the majority are utilizing large NOLs rather than aggressive tax planning.
Output-Lifted Learning Model Predictive Control

Abstract We propose a computationally efficient Learning Model Predictive Control (LMPC) scheme for constrained optimal control of a class of nonlinear systems where the state and input can be reconstructed using lifted outputs. For the considered class of systems, we show how to use historical trajectory data collected during iterative tasks to construct a convex value function approximation along with a convex safe set in a lifted space of virtual outputs. These constructions are iteratively updated with historical data and used to synthesize predictive control policies. We show that the proposed strategy guarantees recursive constraint satisfaction, asymptotic stability, and non-decreasing closed-loop performance at each policy update. Finally, simulation results demonstrate the effectiveness of the proposed strategy on the kinematic unicycle.
Emerging Domains in the Ecology of Influence: Implications for Coaching and Management Education

This study investigates an emergent shift in influence at the leader-member and leader-team levels using qualitative data triangulation. Findings surface in a detailed codebook and suggest that the influence construct is shifting to form two fundamental domains that differ at the leader and member levels. It is believed that the findings will have important practical implications for coaching and management education. An exploration into how influence is being experienced in teams nestled in an increasingly complex work environment may serve to challenge the traditional distinction between leader and member when it comes to who exhibits or enacts influence.
3D Shape Generation and Completion Through Point-Voxel Diffusion

We propose a novel approach for probabilistic generative modeling of 3D shapes. Unlike most existing models that learn to deterministically translate a latent vector to a shape, our model, Point-Voxel Diffusion (PVD), is a unified, probabilistic formulation for unconditional shape generation and conditional, multi-modal shape completion. PVD marries denoising diffusion models with the hybrid, point-voxel representation of 3D shapes. It can be viewed as a series of denoising steps, reversing the diffusion process from observed point cloud data to Gaussian noise, and is trained by optimizing a variational lower bound to the (conditional) likelihood function. Experiments demonstrate that PVD is capable of synthesizing high-fidelity shapes, completing partial point clouds, and generating multiple completion results from single-view depth scans of real objects.
Gender Wage Gap: Within and across Borders

In this paper, we study a multi-country gender wage inequality with a focus on the decile level wage distribution. We study "Big Mac real" wages (wages equivalent to the number of Big Mac burgers per day) of individuals of each gender belonging to different deciles, for 21 countries during 2000-2013. We find that, across countries, the higher the GDP per capita, the larger the gender wage gap. The "wealthy" European countries have the lowest female to male wage ratio. High female participation in part-time jobs may be a reason for that. Meanwhile, Latin American countries with the lowest GDP per capita in our study have the highest ratio of female to male wages. As expected, we also find that within a country, the higher the wage decile, the larger the gender wage inequality.
Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture Classification

Elbow fracture diagnosis often requires patients to take both frontal and lateral views of elbow X-ray radiographs. In this paper, we propose a multiview deep learning method for an elbow fracture subtype classification task. Our strategy leverages transfer learning by first training two single-view models, one for frontal view and the other for lateral view, and then transferring the weights to the corresponding layers in the proposed multiview network architecture. Meanwhile, quantitative medical knowledge was integrated into the training process through a curriculum learning framework, which enables the model to first learn from “easier” samples and then transition to “harder” samples to reach better performance. In addition, our multiview network can work both in a dual-view setting and with a single view as input. We evaluate our method through extensive experiments on a classification task of elbow fracture with a dataset of 1,964 images. Results show that our method outperforms two related methods on bone fracture study in multiple settings, and our technique is able to boost the performance of the compared methods. The code is available at https://github.com/ljaiverson/multiview-curriculum.
Challenges for variational reduced-density-matrix theory with three-particle N-representability conditions

The direct variational optimization of the two-electron reduced density matrix (2RDM) can provide a reference-independent description of the electronic structure of many-electron systems that naturally capture strong or nondynamic correlation effects. Such variational 2RDM approaches can often provide a highly accurate description of strong electron correlation, provided that the 2RDMs satisfy at least partial three-particle N-representability conditions (e.g., the T2 condition). However, recent benchmark calculations on hydrogen clusters [N. H. Stair and F. A. Evangelista, J. Chem. Phys. 153, 104108 (2020)] suggest that even the T2 condition leads to unacceptably inaccurate results in the case of two- and three-dimensional clusters. We demonstrate that these failures persist under the application of full three-particle N-representability conditions (3POS). A variety of correlation metrics are explored in order to identify regimes under which 3POS calculations become unreliable, and we find that the relative squared magnitudes of the cumulant three- and two-particle reduced density matrices correlate reasonably well with the energy error in these systems. However, calculations on other molecular systems reveal that this metric is not a universal indicator for the reliability of the reduced-density-matrix theory with 3POS conditions.
Limits of Probabilistic Safety Guarantees when Considering Human Uncertainty

When autonomous robots interact with humans, such as during autonomous driving, explicit safety guarantees are crucial in order to avoid potentially life-threatening accidents. Many data-driven methods have explored learning probabilistic bounds over human agents’ trajectories (i.e. confidence tubes that contain trajectories with probability δ), which can then be used to guarantee safety with probability 1− δ. However, almost all existing works consider δ ≥ 0.001. The purpose of this paper is to argue that (1) in safety-critical applications, it is necessary to provide safety guarantees with δ < 10−8, and (2) current learning-based methods are illequipped to compute accurate confidence bounds at such low δ. Using human driving data (from the highD dataset), as well as synthetically generated data, we show that current uncertainty models use inaccurate distributional assumptions to describe human behavior and/or require infeasible amounts of data to accurately learn confidence bounds for δ ≤ 10−8. These two issues result in unreliable confidence bounds, which can have dangerous implications if deployed on safety-critical systems.
Gauge-free duality in pure square spin ice: Topological currents and monopoles

We consider pure square spin ice, that is, square ice, where only nearest neighbors are coupled. The gauge-free duality between the perpendicular and collinear structure leads to a natural description in terms of topological currents and charges as the relevant degrees of freedom. That, in turn, can be expressed via a continuous field theory where the discrete spins are subsumed into entropic interactions among charges and currents. This approach produces structure factors, correlations, and susceptibilities for spins, monopoles, and currents. It also generalizes the height formalism of the disordered ground state to non-zero temperature. The framework can be applied to the zoology of recent experimental results, especially realizations on quantum annealers, and can be expanded to include longer range interactions.
Energy-based time derivative damage accumulation model under uniaxial and multiaxial random loadings

A new fatigue life prediction method using the energy-based approach under uniaxial and multiaxial random loadings is proposed in this paper. One unique characteristic of the proposed method is that it uses time-derivative damage accumulation model compared to the classical cycle-based damage accumulation model. Thus, damage under arbitrary random loading can be directly obtained using time-domain integration without cycle counting (e.g., rain-flow cycle counting method in classical fatigue analysis). First, a brief review of existing models is given focusing on their applicability to uniaxial/multiaxial, constant/random, and high cycle fatigue/low cycle fatigue loading regimes. It is observed that most existing models are only applicable to certain loading conditions and many of them are not applicable/validated under random loadings. Next, a time-derivative damage accumulation model under uniaxial random loading is proposed. The proposed damage function is inspired by a time-domain fatigue crack growth model. The fatigue life is obtained by integrating the damage function following random energy loading histories. Following this, an equivalent energy concept for general multiaxial loading conditions is used to convert the random multiaxial loading to an equivalent random uniaxial loading, where the time-derivative damage model can be used. Finally, the proposed model is validated with extensive experimental data from open literature and in-house testing data under various constant and random spectrum loadings. Conclusions and future work are suggested based on the findings from this study.
Emergence of Lagrangian Field Theory from Self-Organized Criticality

Self-organized criticality (SOC) is a universal mechanism for self-sustained critical behavior in large-scale systems evolving outside equilibrium. Our report explores a tentative link between SOC and Lagrangian field theory, with the long-term goal of bridging the gap between complex dynamics and the non-perturbative behavior of quantum fields.
A quantum Leray–Hirsch theorem for banded gerbes

For a gerbe $\mathcal{Y}$ over a smooth proper Deligne–Mumford stack $\mathcal{B}$ banded by a finite group $G$, we prove a structure result on the Gromov–Witten theory of $\mathcal{Y}$, expressing Gromov–Witten invariants of $\mathcal{Y}$ in terms of Gromov–Witten invariants of $\mathcal{B}$ twisted by various flat $U(1)$‑gerbes on $\mathcal{B}$. This can be viewed as a Leray–Hirsch type of result for Gromov–Witten theory of gerbes.
Energy harvesting from anisotropic fluctuations

We consider a rudimentary model for a heat engine, known as the Brownian gyrator, that consists of an overdamped system with two degrees of freedom in an anisotropic temperature field. Whereas the hallmark of the gyrator is a nonequilibrium steady-state curl-carrying probability current that can generate torque, we explore the coupling of this natural gyrating motion with a periodic actuation potential for the purpose of extracting work. We show that path lengths traversed in the manifold of thermodynamic states, measured in a suitable Riemannian metric, represent dissipative losses, while area integrals of a work density quantify work being extracted. Thus, the maximal amount of work that can be extracted relates to an isoperimetric problem, trading off area against length of an encircling path. We derive an isoperimetric inequality that provides a universal bound on the efficiency of all cyclic operating protocols, and a bound on how fast a closed path can be traversed before it becomes impossible to extract positive work. The analysis presented provides guiding principles for building autonomous engines that extract work from anisotropic fluctuations.