cssi/SciSciGPT-SciSciNet huggingface dataset:
Compensation or feedback: Motivating performance in multidimensional tasks

Employees often perform tasks with multiple dimensions. In this study, we examine how employees' performance on multidimensional tasks differs under different control structures. We conduct two experiments in which we manipulate the presence of compensation controls and the presence of feedback controls on multiple task dimensions. Our findings suggest that when employees are compensated on multiple dimensions they commit to multiple goals and divide their attention among those task dimensions. However, when feedback controls are implemented on one task dimension with compensation controls on another dimension, employees can improve performance on individual dimensions as well as their overall task performance. As a result, we find that employee performance on a multidimensional task can be higher when firms compensate employees on one task dimension and provide feedback on the other task dimension than when firms compensate on both task dimensions. This study highlights the benefits of complementing compensation-based controls (i.e., incentive pay) with non-compensation based controls (e.g., feedback), and provides a theoretical basis to help explain the prevalence of this approach in practice.
Blind Justice: "Seeing" Race and Gender in Cases of Violent Crime

Racial disparities in the justice system, particularly as they relate to the death penalty, have received increasing scholarly and public-policy scrutiny recently. Comparatively little attention has been paid to the role of the defendant’s sex in cases of violent crime, though research has been conducted on how the victim’s sex affects court decisions. This article seeks to extend this line of inquiry by asking how women accused of killing their spouses or nonspousal intimate partners are treated by the judicial system. I present a theoretical framework that elucidates the impact of intersectionality and sexual stratification on structuring outcomes for women defendants in cases of violent crimes. To test implications derived from this framework, I utilize an original data set of homicide cases from Oakland County, Michigan, from 1986 to 1988. I find that female defendants were convicted more frequently than were male defendants, and that there is an interactive effect with race. Further, I find that the conviction rate was higher if the victim was an alleged batterer of the defendant. Finally, my data indicate that sentencing decisions have a clear racial aspect to them. I conclude with suggestions for future research.
Breaking Bad? The Uneasy Case for Regulatory Breakeven Analysis

Often regulatory benefits can be hard to quantify because they deal with harms that are not traded in markets or because the probability of harm is not well understood. Breakeven analysis offers one plausible way of addressing the problem. But it is no panacea. It may fail to improve the rationality of decisions, especially in hard cases. Alternative approaches may have greater advantages. Thus, the argument for breakeven analysis remains unproven.
Does It Really Hurt to Be Out of Step

Scholars have seemingly established that constituents hold “out of step” legislators electorally accountable. Empirically, however, such claims have not been based on measures placing districts and...
Competition in Price and Availability when Availability is Unobservable

This paper presents a strategic model of competition in both price and availability when firms can publicly commit to prices but not inventories (or capacities). Demand is uncertain and firms may stock out in equilibrium. Consumers choose where to shop given the price and expected availability at each firm (the probability of being served). In a one period model, I show that firms can use higher prices to “signal” higher availability (regardless of whether price or inventory is chosen first). This generates a floor on equilibrium prices and industry profits that exists regardless of the number of firms in the industry. In a repeated game, firms that maintain reputations for higher service rates may earn even higher profits. The model sheds light on the relationship between price, availability, and reputations in the video rental industry.
Learning to Open Monty Hall's Doors

The analysis in this paper searches for individual and group determinants of learning behavior in Monty Hall’s Three Door problem examined in Friedman (1998, American Economic Review. 88, 933‐946). The results show that the size of monetary incentives, individuals’ initial abilities, and social interactions with others are all important determinants of initial choices and subsequent learning in this problem: (i) More able students have a greater initial propensity to make the right choice than less able students, and their learning curves are initially steeper; (ii) Individual learning can also be enhanced through social interactions; (iii) Interestingly, less able students benefit more than more able students from social interactions in the sample. These findings support the argument that learning models that take into account individuals’ abilities and that allow for social interactions where agents can exchange information hold a great deal of promise for enhancing our understanding of actual learning environments, learning processes, and the formation of rationality.
Match or Mismatch? Automatic Admissions and College Preferences of Low- and High-Income Students:

We examine the role of information in college matching behavior of low- and high-income students, exploiting a state automatic admissions policy that provides some students with perfect a priori ce...
'O Sole Mio: An Experimental Analysis of Weather and Risk Attitudes in Financial Decisions

While weather has been shown to affect financial markets and financial deci- sion making, a still open question is the channel through which such influence is exerted. By employing the multiple price list method of Holt and Laury (2002), this paper provides direct experimental evidence that sunshine and good weather promote risk taking behavior, after controlling for other variables such as gender, religion and income. This effect is present whether relying on objective measures of meteorological conditions or subjective weather assessments. Finally, employ- ing a psychological test, we find evidence that weather may affect individual risk tolerance through its effect on mood.
Are Reservations Recommended

We examine the role of reservations in capacity-constrained services with a focus on restaurants. Although customers value reservations, restaurants typically neither charge for them nor impose penalties for failing to keep them. However, reservations impose costs on firms offering them. We offer a novel motivation for offering reservations that emphasizes the way in which reservations can alter customer behavior. We focus on a market in which demand is uncertain and the firm has limited capacity. There is a positive chance that the firm will not have enough capacity to serve all potential customers. Customers are unable to observe how many potential diners are in the market before incurring a cost to request service. Hence, if reservations are not offered, some may choose to stay home rather than risk being denied service. This lowers the firm's sales when realized demand is low. Reservations increase sales on a slow night by guaranteeing reservations holders service. However, some reservation holders may choose not to use their reservations resulting in no-shows. The firm must then trade off higher sales in a soft market with sales lost to no-shows on busy nights. We consequently evaluate various no-show mitigation strategies, all of which serve to make reservations more likely in equilibrium. Competition also makes reservations more attractive; when there are many small firms in the market, reservations are always offered.
Why Do Firms with Diversification Discounts Have Higher Expected Returns

A diversified firm can trade at a discount to a matched portfolio of single-segment firms if the diversified firm has either lower expected cash flows or higher expected returns than the single-segment firms. We study whether firms with diversification discounts have higher expected returns in order to compensate investors for offering less upside potential (or skewness exposure) than focused firms. Our empirical tests support this hypothesis. First, we find that focused firms offer greater skewness exposure than diversified firms. Second, we find that diversified firms have significantly larger discounts when the diversified firm offers less skewness than matched single-segment firms. Finally, we find that up to 53% of the excess returns received on diversification-discount firms relative to diversification-premium firms can be explained by differences in exposure to skewness.
Knowing What Others Know: Common Knowledge, Accounting, and Capital Markets

The concept of common knowledge concerning higher orders of knowledge has seen exciting new developments in the fields of philosophy, game theory, statistics, economics, and cognitive science in the recent decades. Even though information lies at the heart of accounting and capital markets research, these new developments have remained at the periphery of these fields. Common knowledge thinking may significantly advance our understanding of financial reporting, analysis, securities valuation, managerial control, auditing, and information systems. Such accounting and business applications will also make important contributions in the form of concrete, real‐life examples and applications to the basic fields where the idea of common knowledge originated. This paper is an overview of common knowledge and its actual and potential applications to accounting and capital markets research.
Monetary policy in a data-rich environment

Abstract Most empirical analyses of monetary policy have been confined to frameworks in which the Federal Reserve is implicitly assumed to exploit only a limited amount of information, despite the fact that the Fed actively monitors literally thousands of economic time series. This article explores the feasibility of incorporating richer information sets into the analysis, both positive and normative, of Fed policymaking. We employ a factor-model approach, developed by Stock, J.H., Watson, M.W., Diffusion Indices, Journal of Business & Economic Statistics 2002, 20 (2) 147, Forecasting Inflation, 1999, Journal of Monetary Economics 44 (2) 293, that permits the systematic information in large data sets to be summarized by relatively few estimated factors. With this framework, we reconfirm Stock and Watson's result that the use of large data sets can improve forecast accuracy, and we show that this result does not seem to depend on the use of finally revised (as opposed to “real-time”) data. We estimate policy reaction functions for the Fed that take into account its data-rich environment and provide a test of the hypothesis that Fed actions are explained solely by its forecasts of inflation and real activity. Finally, we explore the possibility of developing an “expert system” that could aggregate diverse information and provide benchmark policy settings.
Transformation or structural change? What polanyi can teach us about the platform economy

The rise of the platform economy marks the latest phase in the ongoing digital revolution Indeed, the platform is to this digital era what the factory was to the industrial era, both a symbol and an organizing mechanism Gernot Grabher and Jonas Konig (2020) used Karl Polanyi's analysis of what he termed the “great transformation” to frame the rise of platform economy The platform economy is remarkable as it confirms Polanyi's (and Marx's before him) insight that the reach of the market is based upon increased commodification as it has been able to reach into ever more parts of social life We introduced the term “platform economy” in 2015 because we recognized that the digital platforms were changing the dynamics of capitalist accumulation - an analysis framed by regulationist school of political economy The intuition was that the socio-technical innovation of digital online platforms was the critical fulcrum for an economic restructuring that would rewire the flows of data and ultimately money and power The firms we have termed the “mega-platforms”, Apple, Amazon, Facebook, Google, and Microsoft, have become the most valuable and powerful firms in the world Importantly, the reach of these platforms is global and yet local and personal Moreover, this platform power has only been reinforced during the COVID-19 pandemic Copyright © 2021 Martin F Kenney, John Zysman, Dafna Bearson
String Theory, the Dark Sector, and the Hierarchy Problem

We discuss dark energy, dark matter, and the hierarchy problem in the context of a general noncommutative formulation of string theory. In this framework, dark energy is generated by the dynamical geometry of the dual spacetime while dark matter, on the other hand, comes from the degrees of freedom dual to the visible matter. This formulation of string theory is sensitive to both the IR and UV scales, and the Higgs scale is radiatively stable by being a geometric mean of radiatively stable UV and IR scales. We also comment on various phenomenological signatures of this novel approach to dark energy, dark matter, and the hierarchy problem. We find that this new view on the hierarchy problem is realized in a toy model based on a nonholomorphic deformation of the stringy cosmic string. Finally, we discuss a proposal for a new nonperturbative formulation of string theory, which sheds light on M-theory and F-theory, as well as on supersymmetry and holography.
From "the Moon Is Rounder Abroad" to "Bravo, My Country": How China Misperceives the World.

For a long time, since China’s opening to the outside world in the late 1970s, admiration for foreign socioeconomic prosperity and quality of life characterized much of the Chinese society, which contributed to dissatisfaction with the country’s development and government and a large-scale exodus of students and emigrants to foreign countries. More recently, however, overestimating China’s standing and popularity in the world has become a more conspicuous feature of Chinese public opinion and the social backdrop of the country’s overreach in global affairs in the last few years. This essay discusses the effects of these misperceptions about the world, their potential sources, and the outcomes of correcting misperceptions. It concludes that while the world should get China right and not misinterpret China’s intentions and actions, China should also get the world right and have a more balanced understanding of its relationship with the world.
A RETROSPECTIVE LOOK AT “THE HAYEK STORY”: ROUNDABOUTNESS, STICKY CONSUMPTION, AND SEQUESTERED CAPITAL

Friedrich Hayek’s business cycle theory withered throughout the 1930s as he admitted that its underlying model of Bohm-Bawerkian roundaboutness was incomplete and inadequate. In 1934, Hayek started a two-volume book on capital theory, completing only one volume in 1941. Curiously, Hayek ([1941] 2009) cites John Hicks’s (1939) Value and Capital but not the financial measure of roundaboutness that Hicks suggested as a substitute for Bohm-Bawerkian roundaboutness. In 1967, in “The Hayek Story,” Hicks criticized the inexplicable lags. Hayek maintained his view that consumption was sticky and responded to Hicks with a mound-of-honey analogy. Nevertheless, Hayek maintained that his business cycle theory was fundamentally correct and continued to hope that others might someday discover a capital structure theory to undergird it. Toward fulfilling Hayek’s hope, we suggest augmenting the canonical stages of production with a sequestered-capital stage where products are invented, productized, and inventoried prior to launch, uncoordinated by observable prices.
A spinoparabrachial circuit defined by Tacr1 expression drives pain

Painful stimuli evoke a mixture of sensations, negative emotions and behaviors. These myriad effects are thought to be produced by parallel ascending circuits working in combination. Here, we describe a pathway from spinal cord to brain for ongoing pain. Activation of a subset of spinal neurons expressing Tacr1 evokes a full repertoire of somatotopically directed pain-related behaviors in the absence of noxious input. Tacr1 projection neurons (expressing NKR1) target a tiny cluster of neurons in the superior lateral parabrachial nucleus (PBN-SL). We show that these neurons, which also express Tacr1 (PBN-SLTacr1), are responsive to sustained but not acute noxious stimuli. Activation of PBN-SLTacr1 neurons alone did not trigger pain responses but instead served to dramatically heighten nocifensive behaviors and suppress itch. Remarkably, mice with silenced PBN-SLTacr1 neurons ignored long-lasting noxious stimuli. Together, these data reveal new details about this spinoparabrachial pathway and its key role in the sensation of ongoing pain.
Multi-Resolution Filters for Massive Spatio-Temporal Data

Spatio-temporal datasets are rapidly growing in size. For example, environmental variables are measured with increasing resolution by increasing numbers of automated sensors mounted on satellites a...
Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference

High-dimensional Bayesian inference problems cast a long-standing challenge in generating samples, especially when the posterior has multiple modes. For a wide class of Bayesian inference problems equipped with the multiscale structure that low-dimensional (coarse-scale) surrogate can approximate the original high-dimensional (fine-scale) problem well, we propose to train a Multiscale Invertible Generative Network (MsIGN) for sample generation. A novel prior conditioning layer is designed to bridge networks at different resolutions, enabling coarse-to-fine multi-stage training. Jeffreys divergence is adopted as the training objective to avoid mode dropping. On two high-dimensional Bayesian inverse problems, MsIGN approximates the posterior accurately and clearly captures multiple modes, showing superior performance compared with previous deep generative network approaches. On the natural image synthesis task, MsIGN achieves the superior performance in bits-per-dimension compared with our baseline models and yields great interpret-ability of its neurons in intermediate layers.
Variational Methods in Fluid Dynamics

Variational principle for incompressible laminar flows is presented; case in which boundary conditions specify velocity on one part of boundary and stresses on remainder of boundary is considered; variational principle is applied to approximate computation of velocity distribution for laminar flow in square and rectangular ducts; approximate and exact solutions are compared.
Seizure Type Classification Using EEG Signals and Machine Learning: Setting a Benchmark

Accurate classification of seizure types plays a crucial role in the treatment and disease management of epileptic patients. Epileptic seizure types not only impact the choice of drugs but also the range of activities a patient can safely engage in. With recent advances being made towards artificial intelligence enabled automatic seizure detection, the next frontier is the automatic classification of seizure types. On that note, in this paper, we explore the application of machine learning algorithms for multiclass seizure type classification. We used the recently released TUH EEG seizure corpus (v1.4.0 and v1.5.2) and conducted a thorough search space exploration to evaluate the performance of a combination of various preprocessing techniques, machine learning algorithms, and corresponding hyperparameters on this task. We show that our algorithms can reach a weighted F1 score of up to 0.901 for seizure-wise cross validation and 0.561 for patient-wise cross validation thereby setting a benchmark for scalp EEG based multi-class seizure type classification.
Existence of semistable sheaves on Hirzebruch surfaces

Abstract Let F e denote the Hirzebruch surface P ( O P 1 ⊕ O P 1 ( e ) ) , and let H be any ample divisor. In this paper, we algorithmically determine when the moduli space of semistable sheaves M F e , H ( r , c 1 , c 2 ) is nonempty. Our algorithm relies on certain stacks of prioritary sheaves. We first solve the existence problem for these stacks and then algorithmically determine the Harder-Narasimhan filtration of the general sheaf in the stack. In particular, semistable sheaves exist if and only if the Harder-Narasimhan filtration has length one. We then study sharp Bogomolov inequalities Δ ≥ δ H ( c 1 / r ) for the discriminants of stable sheaves which take the polarization and slope into account; these inequalities essentially completely describe the characters of stable sheaves. The function δ H ( c 1 / r ) can be computed to arbitrary precision by a limiting procedure. In the case of an anticanonically polarized del Pezzo surface, exceptional bundles are always stable and δ H ( c 1 / r ) is computed by exceptional bundles. More generally, we show that for an arbitrary polarization there are further necessary conditions for the existence of stable sheaves beyond those provided by stable exceptional bundles. We compute δ H ( c 1 / r ) exactly in some of these cases. Finally, solutions to the existence problem have immediate applications to the birational geometry of M F e , H ( v ) .
Bounds for coefficients of the f(q) mock theta function and applications to partition ranks

Abstract We compute effective bounds for α ( n ) , the Fourier coefficients of Ramanujan's mock theta function f ( q ) utilizing a finite algebraic formula due to Bruinier and Schwagenscheidt. We then use these bounds to prove two conjectures of Hou and Jagadeesan on the convexity and maximal multiplicative properties of the even and odd partition rank counting functions.
Contests for shares of an uncertain resource

Abstract The process of allocating rights to resources can be viewed as a contest: parties compete with each other for the right to claim a larger allocation. In some situations, the amount of the resource that is available to allocate may be unknown when parties are competing for shares and perhaps not realized until contestants actually attempt to claim their shares of the resource. For example, fishing quotas may be awarded based on estimated fish populations, but if there are fewer fish than anticipated, those who are last to harvest may not be able to fill their quota. We model contests of this form and test the predictions of the model using a controlled laboratory experiment. The general result, supported by both theory and experimental data, is that participants compete less intensively for shares of the resource when uncertainty regarding the size of the prize is resolved later in the process.
Detecting dirty data using SQL: Rigorous house insurance case

Abstract Proficiency with data analytics is an increasingly important skill within in the accounting profession. However, successful data analysis requires clean source data (i.e., source data without errors) in order to draw reliable conclusions. Although users often assume clean source data, this assumption is frequently incorrect. Therefore, identifying and remediating “dirty data” is a prerequisite to effective data analysis. You, an accountant working at a firm that specializes in data analytics, have been hired by Rigorous House Insurance to analyze the company’s claim insurance data. In addition to investigating specific issues mentioned by the company’s controller, you are tasked with identifying any other data integrity issues that you encounter and providing preventative information system internal control suggestions to the client to mitigate these issues in the future.
Physical Education in the COVID Era: Considerations for Online Program Delivery Using the Comprehensive School Physical Activity Program Framework

In the wake of COVID-19, online physical education (OLPE) has become essential to the sustainability of school physical education programs The purpose of this article is to consider factors that may be influential in efforts to deliver OLPE to students The comprehensive school physical activity program model is used to frame a multicomponent conceptualization of OLPE and its goals and outcomes Central to this framing is the intersectionality of school physical education, the family, and the community This article provides a platform for physical education teacher educators and researchers to advance OLPE in its support of both the educational and public health benefits of high-quality physical education programs
The Pervasiveness of Culture in Conflict

Law faculty and scholars are increasingly cognizant of the role of culture in dispute resolution. This essay offers a beginning roadmap for exploring the cultural context of conflict. It begins by considering how to assess our own cultural profiles, highlighting some useful social science constructs for this purpose. It then discusses how our interactive perception of others’ cultural profiles makes a difference. The essay also explores the tensions between, on one hand, the pervasiveness of culture in conflict and, on the other hand, American legal traditions that appear contrary to the incorporation of culture into dispute resolution processes.
Postharvest handling induces changes in fruit DNA methylation status and is associated with alterations in fruit quality in tomato (Solanum lycopersicum L.)

Abstract Postharvest handling of tomato (Solanum lycopersicum L.), specifically low-temperature storage and early harvest, are used to extend shelf life but often reduce fruit quality. Recent work suggests that DNA methylation dynamics influences fruit ripening through the demethylase SlDML2 gene. However, the influence of postharvest handling on DNA methylation in relation to fruit quality is unclear. This work aimed to clarify these issues by analyzing DNA methylation using methyl-sensitive amplification polymorphism (MSAP), semi-quantitative transcriptional analysis of marker genes for fruit quality (RIN; RIPENING INHIBITOR) and DNA methylation (SlDML2; Solanum lycopersicum L. DNA demethylase 2), and, fruit biochemical quality biomarkers. Multivariate analysis of these data suggested that fruit DNA methylation state was associated with different postharvest handling techniques. Chilled postharvest fruit were distinct in their DNA methylation state and quality characteristics, which implied that these three phenomena i.e., chilling, methylation, and quality are highly connected. In addition, different postharvest handling methods modulated SlDML2 transcript levels but had little effect on the level of RIN transcripts in fruit that reached the Turning stage after early harvest, and cold storage. Although not a comprehensive global assessment, these data collectively helped to advance our interpretation of tomato fruit ripening. In conclusion, our findings revealed that postharvest-induced variation in fruit quality is in relation to DNA methylation. Long-term this work will help better connect physiological changes in tomato fruit to events happening at the molecular level.
Deciphering Complex Mechanisms of Resistance and Loss of Potency through Coupled Molecular Dynamics and Machine Learning

Drug resistance threatens many critical therapeutics through mutations in the drug target. The molecular mechanisms by which combinations of mutations, especially those remote from the active site, alter drug binding to confer resistance are poorly understood and thus difficult to counteract. A machine learning strategy was developed that coupled parallel molecular dynamics simulations with experimental potency to identify specific conserved mechanisms underlying resistance. Physical features were extracted from the simulations, analyzed, and integrated into one consistent and interpretable elastic network model. To rigorously test this strategy, HIV-1 protease variants with diverse mutations were used, with potencies ranging from picomolar to micromolar to the drug darunavir. Feature reduction resulted in a model with four specific features that predicts for both the training and test sets inhibitor binding free energy within 1 kcal/mol of the experimental value over this entire range of potency. These predictive features are physically interpretable, as they vary specifically with affinity and diagonally transverse across the protease homodimer. This physics-based strategy of parallel molecular dynamics and machine learning captures mechanisms by which complex combinations of mutations confer resistance and identify critical features that serve as bellwethers of affinity, which will be critical in future drug design.
Demonstration of the trapped-ion quantum CCD computer architecture

The trapped-ion quantum charge-coupled device (QCCD) proposal1,2 lays out a blueprint for a universal quantum computer that uses mobile ions as qubits. Analogous to a charge-coupled device (CCD) camera, which stores and processes imaging information as movable electrical charges in coupled pixels, a QCCD computer stores quantum information in the internal state of electrically charged ions that are transported between different processing zones using dynamic electric fields. The promise of the QCCD architecture is to maintain the low error rates demonstrated in small trapped-ion experiments3–5 by limiting the quantum interactions to multiple small ion crystals, then physically splitting and rearranging the constituent ions of these crystals into new crystals, where further interactions occur. This approach leverages transport timescales that are fast relative to the coherence times of the qubits, the insensitivity of the qubit states of the ion to the electric fields used for transport, and the low crosstalk afforded by spatially separated crystals. However, engineering a machine capable of executing these operations across multiple interaction zones with low error introduces many difficulties, which have slowed progress in scaling this architecture to larger qubit numbers. Here we use a cryogenic surface trap to integrate all necessary elements of the QCCD architecture—a scalable trap design, parallel interaction zones and fast ion transport—into a programmable trapped-ion quantum computer that has a system performance consistent with the low error rates achieved in the individual ion crystals. We apply this approach to realize a teleported CNOT gate using mid-circuit measurement6, negligible crosstalk error and a quantum volume7 of 26 = 64. These results demonstrate that the QCCD architecture provides a viable path towards high-performance quantum computers. The quantum charge-coupled device architecture is demonstrated, with its various elements integrated into a programmable trapped-ion quantum computer and performing simple quantum operations with state-of-the-art levels of error.
Targeted Manipulation of Abundant and Rare Taxa in the Daphnia magna Microbiota with Antibiotics Impacts Host Fitness Differentially.

ABSTRACT Host-associated microbes contribute to host fitness, but it is unclear whether these contributions are from rare keystone taxa, numerically abundant taxa, or interactions among community members. Experimental perturbation of the microbiota can highlight functionally important taxa; however, this approach is primarily applied in systems with complex communities where the perturbation affects hundreds of taxa, making it difficult to pinpoint contributions of key community members. Here, we use the ecological model organism Daphnia magna to examine the importance of rare and abundant taxa by perturbing its relatively simple microbiota with targeted antibiotics. We used sublethal antibiotic doses to target either rare or abundant members across two temperatures and then measured key host life history metrics and shifts in microbial community composition. We find that removal of abundant taxa had greater impacts on host fitness than did removal of rare taxa and that the abundances of nontarget taxa were impacted by antibiotic treatment, suggesting that no rare keystone taxa exist in the Daphnia magna microbiota but that microbe-microbe interactions may play a role in host fitness. We also find that microbial community composition was impacted by antibiotics differently across temperatures, indicating that ecological context shapes within-host microbial responses and effects on host fitness. IMPORTANCE Understanding the contributions of rare and abundant taxa to host fitness is an outstanding question in host microbial ecology. In this study, we use the model zooplankton Daphnia magna and its relatively simple cohort of bacterial taxa to disentangle the roles of distinct taxa in host life history metrics, using a suite of antibiotics to selectively reduce the abundance of functionally important taxa. We also examine how environmental context shapes the importance of these bacterial taxa in host fitness.
Automated analysis of bacterial flow cytometry data with FlowGateNIST

Flow cytometry is commonly used to evaluate the performance of engineered bacteria. With increasing use of high-throughput experimental methods, there is a need for automated analysis methods for flow cytometry data. Here, we describe FlowGateNIST, a Python package for automated analysis of bacterial flow cytometry data. The main components of FlowGateNIST perform automatic gating to differentiate between cells and background events and then between singlet and multiplet events. FlowGateNIST also includes a method for automatic calibration of fluorescence signals using fluorescence calibration beads. FlowGateNIST is open source and freely available with tutorials and example data to facilitate adoption by users with minimal programming experience.
Oxygen isotope systematics of crystalline silicates in a giant cluster IDP: A genetic link to Wild 2 particles and primitive chondrite chondrules

Abstract Anhydrous interplanetary dust particles (IDPs) collected from Earth's stratosphere are the most primitive extraterrestrial materials that likely are the remnants of major building blocks of our solar system. While they probably originated from icy outer solar system comets, this hypothesis needs to be further verified via comparison with Wild 2 (a Jupiter-family comet) particles returned by the Stardust mission. Besides, the origins of their components can be further constrained by comparing with materials in primitive chondrite. Here we investigate the petrology and oxygen isotope systematics of 20 fragments extracted from a giant cluster IDP U2-20GCA. Fifteen are monomineralic or polymineralic fragments composed of olivine and/or pyroxene. Others are plagioclase/glass bearing fragments similar to barred-olivine chondrules, enstatite-rich chondrules, and Al-rich chondrules (ARC). They show a range of Mg# [mol% Mg/(Mg+Fe)] in olivine and pyroxene (99-75) and MnO and Cr2O3 abundances in olivine that resemble Wild 2 particles. Individual fragments have relatively 16O-poor oxygen isotope ratios with δ 18 O and δ 17 O varying from − 6.2 ± 1.0 ‰ to 6.8 ± 1.9 ‰ and − 6.7 ± 2.6 ‰ to 5.3 ± 1.2 ‰ , respectively. Most fragments show Δ 17 O ( = δ 17 O- 0.52 × δ 18 O ) increasing from ∼ − 3 ‰ to ∼ 0 ‰ with decreasing Mg#, similar to those observed in Wild 2 particles and CR chondrite chondrules. Four fragments (including the ARC-like fragment) show oxygen isotope signatures of ordinary (O), R, or CH-CB chondrite chondrules. The similarities among the giant cluster IDP, Wild 2 particles, and primitive chondrite chondrules demonstrate (i) a cometary origin of anhydrous IDPs; (ii) the major source of crystalline silicates in comets is likely to be CR chondrite chondrule-like materials, while minor sources include O, R, or CH-CB chondrite chondrule-like materials. The conclusions support the hypothesis that anhydrous IDPs sampled extremely heterogeneous materials formed in wide solar system regions like Wild 2 particles.
SF-QA: Simple and Fair Evaluation Library for Open-domain Question Answering.

Although open-domain question answering (QA) draws great attention in recent years, it requires large amounts of resources for building the full system and it is often difficult to reproduce previous results due to complex configurations. In this paper, we introduce SF-QA: simple and fair evaluation framework for open-domain QA. SF-QA framework modularizes the pipeline open-domain QA system, which makes the task itself easily accessible and reproducible to research groups without enough computing resources. The proposed evaluation framework is publicly available and anyone can contribute to the code and evaluations.
Inhibition of Secretin/Secretin Receptor Axis Ameliorates NAFLD Phenotypes.

Background & aims Human non-alcoholic fatty liver disease (NAFLD) is characterized at early stages by hepatic steatosis, which may progress to nonalcoholic steatohepatitis (NASH) when the liver displays microvesicular steatosis, lobular inflammation, and pericellular fibrosis. The secretin (SCT)/secretin receptor (SCTR) axis promotes biliary senescence and liver fibrosis in cholestatic models through downregulation of miR-125b signaling. We aim to evaluate the effect of disrupting biliary SCT/SCTR/miR-125b signaling on hepatic steatosis, biliary senescence and liver fibrosis in NAFLD/NASH. Approach & results In vivo, 4 wk male WT, Sct-/- and Sctr-/- mice were fed a control diet (CD) or high-fat diet (HFD) for 16 wks. The expression of SCT/SCTR/miR-125b axis was measured in human NAFLD/NASH liver samples and HFD mouse livers by immunohistochemistry (IHC) and qPCR. Biliary/hepatocyte senescence, ductular reaction and liver angiogenesis were evaluated in mouse liver and human NAFLD/NASH liver samples. miR-125b target lipogenesis genes in hepatocytes were screened and validated by custom RT2 Profiler PCR array and luciferase assay. Biliary SCT/SCTR expression was increased in human NAFLD/NASH samples and in livers of HFD mice, whereas the expression of miR-125b was decreased. Biliary/hepatocyte senescence, ductular reaction, and liver angiogenesis were observed in human NAFLD/NASH samples as well as HFD mice, which were decreased in Sct-/- and Sctr-/- HFD mice. Elovl1 is a lipogenesis gene targeted by miR-125b, and its expression was also decreased in HFD mouse hepatocytes following Sct or Sctr knockout. Bile acid profile in fecal samples have the greatest changes between WT mice and Sct-/- /Sctr-/- mice. Conclusion The biliary SCT/SCTR/miR-125b axis promotes liver steatosis by upregulating lipid biosynthesis gene Elovl1. Targeting the biliary SCT/SCTR/miR-125b axis may be key for ameliorating phenotypes of human NAFLD/NASH.
Supporting Serendipity: Opportunities and Challenges for Human-AI Collaboration in Qualitative Analysis

Qualitative inductive methods are widely used in CSCW and HCI research for their ability to generatively discover deep and contextualized insights, but these inherently manual and human-resource-intensive processes are often infeasible for analyzing large corpora. Researchers have been increasingly interested in ways to apply qualitative methods to "big" data problems, hoping to achieve more generalizable results from larger amounts of data while preserving the depth and richness of qualitative methods. In this paper, we describe a study of qualitative researchers' work practices and their challenges, with an eye towards whether this is an appropriate domain for human-AI collaboration and what successful collaborations might entail. Our findings characterize participants' diverse methodological practices and nuanced collaboration dynamics, and identify areas where they might benefit from AI-based tools. While participants highlight the messiness and uncertainty of qualitative inductive analysis, they still want full agency over the process and believe that AI should not interfere. Our study provides a deep investigation of task delegability in human-AI collaboration in the context of qualitative analysis, and offers directions for the design of AI assistance that honor serendipity, human agency, and ambiguity.
Large-Scale Bundle-Size Pricing: A Theoretical Analysis

Can you sell multiple items by providing only prices for different sizes of bundles rather than the different possible combinations of them? In this paper, we provide a framework for understanding ...
Moduli spaces of Hecke modifications for rational and elliptic curves

We propose definitions of complex manifolds 𝒫M(X,m,n) that could potentially be used to construct the symplectic Khovanov homology of n–stranded links in lens spaces. The manifolds 𝒫M(X,m,n) are defined as moduli spaces of Hecke modifications of rank 2 parabolic bundles over an elliptic curve X. To characterize these spaces, we describe all possible Hecke modifications of all possible rank 2 vector bundles over X, and we use these results to define a canonical open embedding of 𝒫M(X,m,n) into Ms(X,m+n), the moduli space of stable rank 2 parabolic bundles over X with trivial determinant bundle and m+n marked points. We explicitly compute 𝒫M(X,1,n) for n=0,1,2. For comparison, we present analogous results for the case of rational curves, for which a corresponding complex manifold 𝒫M(ℂℙ1,3,n) is isomorphic for n even to a space 𝒴(S2,n) defined by Seidel and Smith that can be used to compute the symplectic Khovanov homology of n–stranded links in S3.
Neural Mechanisms for Executive Control of Speed-Accuracy Tradeoff

Noninvasive studies of speed-accuracy tradeoff (SAT) highlight contributions of medial frontal cortex (MFC), but its role is disputed. Hence, neural spiking was sampled in supplementary eye field (SEF) of MFC and in visuomotor frontal eye field (FEF) and superior colliculus (SC) in macaques performing an SAT task. Like visuomotor structures, SEF neurons signaled proactive urgency and stimulus salience when SAT cues changed. Unlike visuomotor structures, SEF modulation signaled choice and timing errors, which predicted production of corrective saccades and signaled reward prediction error. Simultaneous spiking sampled in SEF and visuomotor structures revealed quantitative but not qualitative modulation across SAT states with stronger correlations following errors. These results contextualize findings using noninvasive measures, complement neurophysiological findings in visuomotor structures, endorse the role of medial frontal cortex as a critic for the actor instantiated in visuomotor structures, and improve our understanding of the distributed neural mechanisms of SAT.
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification

Optimal transport (OT) distances are increasingly used as loss functions for statistical inference, notably in the learning of generative models or supervised learning. Yet, the behavior of minimum Wasserstein estimators is poorly understood, notably in high-dimensional regimes or under model misspecification. In this work we adopt the viewpoint of projection robust (PR) OT, which seeks to maximize the OT cost between two measures by choosing a $k$-dimensional subspace onto which they can be projected. Our first contribution is to establish several fundamental statistical properties of PR Wasserstein distances, complementing and improving previous literature that has been restricted to one-dimensional and well-specified cases. Next, we propose the integral PR Wasserstein (IPRW) distance as an alternative to the PRW distance, by averaging rather than optimizing on subspaces. Our complexity bounds can help explain why both PRW and IPRW distances outperform Wasserstein distances empirically in high-dimensional inference tasks. Finally, we consider parametric inference using the PRW distance. We provide an asymptotic guarantee of two types of minimum PRW estimators and formulate a central limit theorem for max-sliced Wasserstein estimator under model misspecification. To enable our analysis on PRW with projection dimension larger than one, we devise a novel combination of variational analysis and statistical theory.
Adaptive function-on-scalar regression with a smoothing elastic net

Abstract This paper presents a new methodology, called AFSSEN, to simultaneously select significant predictors and produce smooth estimates in a high-dimensional function-on-scalar linear model with sub-Gaussian errors. Outcomes are assumed to lie in a general real separable Hilbert space, H , while parameters lie in a subspace known as a Cameron–Martin space, K , which are closely related to Reproducing Kernel Hilbert Spaces, so that the parameter estimates inherit particular properties, such as smoothness or periodicity, without enforcing such properties on the data. We propose a regularization method in the style of an adaptive Elastic Net penalty that involves mixing two types of functional norms, providing a fine tune control of both the smoothing and variable selection in the estimated model. Asymptotic theory is provided in the form of a functional oracle property, and the paper concludes with a simulation study demonstrating the advantages of using AFSSEN over existing methods in terms of prediction error and variable selection.
Primal-dual incremental gradient method for nonsmooth and convex optimization problems

In this paper, we consider a nonsmooth convex finite-sum problem with a conic constraint. To overcome the challenge of projecting onto the constraint set and computing the full (sub)gradient, we introduce a primal-dual incremental gradient scheme where only a component function and two constraints are used to update each primal-dual sub-iteration in a cyclic order. We demonstrate an asymptotic sublinear rate of convergence in terms of suboptimality and infeasibility which is an improvement over the state-of-the-art incremental gradient schemes in this setting. Numerical results suggest that the proposed scheme compares well with competitive methods.
Betting against betting against beta

Abstract Frazzini and Pedersen’s (2014) Betting Against Beta (BAB) factor is based on the same basic idea as Blacks’(1972) beta-arbitrage, but its astonishing performance has generated academic interest and made it highly influential with practitioners. This performance is driven by non-standard procedures used in its construction that effectively, but non-transparently, equal weight stock returns. For each dollar invested in BAB, the strategy commits on average $1.05 to stocks in the bottom 1% of total market capitalization. BAB earns positive returns after accounting for transaction costs, but earns these by tilting toward profitability and investment. Predictable biases resulting from Frazzini and Pedersen’s non-standard beta estimation procedure drive results presented as evidence supporting BAB’s underlying theory.
A Priori Analysis of a Compressible Flamelet Model using RANS Data for a Dual-Mode Scramjet Combustor

In an effort to make large eddy simulation of hydrocarbon-fueled scramjet combustors more computationally accessible using realistic chemical reaction mechanisms, a compressible flamelet/progress variable (FPV) model was proposed that extends current FPV model formulations to high-speed, compressible flows. Development of this model relied on observations garnered from an a priori analysis of the Reynolds-Averaged Navier-Stokes (RANS) data obtained for the Hypersonic International Flight Research and Experimentation (HI-FiRE) dual-mode scramjet combustor. The RANS data were obtained using a reduced chemical mechanism for the combustion of a JP-7 surrogate and were validated using avail- able experimental data. These RANS data were then post-processed to obtain, in an a priori fashion, the scalar fields corresponding to an FPV-based modeling approach. In the current work, in addition to the proposed compressible flamelet model, a standard incompressible FPV model was also considered. Several candidate progress variables were investigated for their ability to recover static temperature and major and minor product species. The effects of pressure and temperature on the tabulated progress variable source term were characterized, and model coupling terms embedded in the Reynolds- averaged Navier-Stokes equations were studied. Finally, results for the novel compressible flamelet/progress variable model were presented to demonstrate the improvement attained by modeling the effects of pressure and flamelet boundary conditions on the combustion.